diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..dfe0770 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,2 @@ +# Auto detect text files and perform LF normalization +* text=auto diff --git a/MRCG_2DCNN+1DCNN_regression.ipynb b/MRCG_2DCNN+1DCNN_regression.ipynb new file mode 100644 index 0000000..cf698eb --- /dev/null +++ b/MRCG_2DCNN+1DCNN_regression.ipynb @@ -0,0 +1,15226 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import glob\n", + "import os\n", + "os.chdir('/home/user3/anaconda3/VAD_DNN/')\n", + "import librosa\n", + "import librosa.display\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.pyplot import specgram\n", + "import tensorflow as tf\n", + "import MRCG as mrcg\n", + "import scipy.io.wavfile\n", + "import wave\n", + "import time\n", + "\n", + "%matplotlib inline\n", + "plt.style.use('ggplot')\n", + "\n", + "\n", + "plt.rcParams['font.family'] = 'serif'\n", + "plt.rcParams['font.serif'] = 'Ubuntu'\n", + "plt.rcParams['font.monospace'] = 'Ubuntu Mono'\n", + "plt.rcParams['font.size'] = 12\n", + "plt.rcParams['axes.labelsize'] = 11\n", + "plt.rcParams['axes.labelweight'] = 'bold'\n", + "plt.rcParams['axes.titlesize'] = 14\n", + "plt.rcParams['xtick.labelsize'] = 10\n", + "plt.rcParams['ytick.labelsize'] = 10\n", + "plt.rcParams['legend.fontsize'] = 11\n", + "plt.rcParams['figure.titlesize'] = 13 " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50\n" + ] + } + ], + "source": [ + "sample_vad_seg_repo = os.path.join('/home/user3/anaconda3/', 'Data', 'binary_segment')\n", + "samples_vad_seg = glob.glob(os.path.join(sample_vad_seg_repo, '*[npy|npz]'), recursive=True)\n", + "\n", + "samples_vad_seg = sorted(samples_vad_seg) \n", + "print(len(samples_vad_seg))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "36\n" + ] + } + ], + "source": [ + "sample_vad_seg_repo_20 = os.path.join('/home/user3/anaconda3/', 'Data', 'binary_segment', '20도')\n", + "samples_vad_seg_20 = glob.glob(os.path.join(sample_vad_seg_repo_20, '**', '*[npy|npz]'), recursive=True)\n", + "\n", + "samples_vad_seg_20 = sorted(samples_vad_seg_20) \n", + "print(len(samples_vad_seg_20))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "39\n" + ] + } + ], + "source": [ + "sample_vad_seg_repo_40 = os.path.join('/home/user3/anaconda3/', 'Data', 'binary_segment', '40도')\n", + "samples_vad_seg_40 = glob.glob(os.path.join(sample_vad_seg_repo_40, '**', '*[npy|npz]'), recursive=True)\n", + "\n", + "samples_vad_seg_40 = sorted(samples_vad_seg_40) \n", + "print(len(samples_vad_seg_40))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "47\n" + ] + } + ], + "source": [ + "sample_vad_seg_repo_80 = os.path.join('/home/user3/anaconda3/', 'Data', 'binary_segment', '80도')\n", + "samples_vad_seg_80 = glob.glob(os.path.join(sample_vad_seg_repo_80, '**', '*[npy|npz]'), recursive=True)\n", + "\n", + "samples_vad_seg_80 = sorted(samples_vad_seg_80) \n", + "print(len(samples_vad_seg_80))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "51\n" + ] + } + ], + "source": [ + "sample_vad_seg_repo_100 = os.path.join('/home/user3/anaconda3/', 'Data', 'binary_segment', '100도')\n", + "samples_vad_seg_100 = glob.glob(os.path.join(sample_vad_seg_repo_100, '**', '*[npy|npz]'), recursive=True)\n", + "\n", + "samples_vad_seg_100 = sorted(samples_vad_seg_100) \n", + "print(len(samples_vad_seg_100))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "41\n" + ] + } + ], + "source": [ + "sample_vad_seg_repo_140 = os.path.join('/home/user3/anaconda3/', 'Data', 'binary_segment', '140도')\n", + "samples_vad_seg_140 = glob.glob(os.path.join(sample_vad_seg_repo_140, '**', '*[npy|npz]'), recursive=True)\n", + "\n", + "samples_vad_seg_140 = sorted(samples_vad_seg_140) \n", + "print(len(samples_vad_seg_140))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "51\n" + ] + } + ], + "source": [ + "sample_vad_seg_repo_160 = os.path.join('/home/user3/anaconda3/', 'Data', 'binary_segment', '160도')\n", + "samples_vad_seg_160 = glob.glob(os.path.join(sample_vad_seg_repo_160, '**', '*[npy|npz]'), recursive=True)\n", + "\n", + "samples_vad_seg_160 = sorted(samples_vad_seg_160) \n", + "print(len(samples_vad_seg_160))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(0, '/home/user3/anaconda3/Data/binary_segment/01.npy'),\n", + " (1, '/home/user3/anaconda3/Data/binary_segment/02.npy'),\n", + " (2, '/home/user3/anaconda3/Data/binary_segment/03.npy'),\n", + " (3, '/home/user3/anaconda3/Data/binary_segment/04.npy'),\n", + " (4, '/home/user3/anaconda3/Data/binary_segment/05.npy'),\n", + " (5, '/home/user3/anaconda3/Data/binary_segment/06.npy'),\n", + " (6, '/home/user3/anaconda3/Data/binary_segment/07.npy'),\n", + " (7, '/home/user3/anaconda3/Data/binary_segment/08.npy'),\n", + " (8, '/home/user3/anaconda3/Data/binary_segment/09.npy'),\n", + " (9, '/home/user3/anaconda3/Data/binary_segment/10.npy'),\n", + " (10, '/home/user3/anaconda3/Data/binary_segment/11.npy'),\n", + " (11, '/home/user3/anaconda3/Data/binary_segment/12.npy'),\n", 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} + ], + "source": [ + "list(enumerate(samples_vad_seg_160))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50\n" + ] + } + ], + "source": [ + "sample_data_repo = os.path.join('..', 'Data', 'sample_data', 't3_audio')\n", + "samples = glob.glob(os.path.join(sample_data_repo, '**', '*wav'), recursive=True)\n", + "samples = sorted(samples) # sort the samples\n", + "print(len(samples))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "36\n" + ] + } + ], + "source": [ + "sample_data_20 = os.path.join('..', 'Data', 'sample_data', '20도')\n", + "samples_20 = glob.glob(os.path.join(sample_data_20, '**', '*wav'), recursive=True)\n", + "samples_20 = sorted(samples_20) # sort the samples\n", + "print(len(samples_20))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "39\n" + ] + } + ], + "source": [ + "sample_data_40 = os.path.join('..', 'Data', 'sample_data', '40도')\n", + "samples_40 = glob.glob(os.path.join(sample_data_40, '**', '*wav'), recursive=True)\n", + "samples_40 = sorted(samples_40) # sort the samples\n", + "print(len(samples_40))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "47\n" + ] + } + ], + "source": [ + "sample_data_80 = os.path.join('..', 'Data', 'sample_data', '80도')\n", + "samples_80 = glob.glob(os.path.join(sample_data_80, '**', '*wav'), recursive=True)\n", + "samples_80 = sorted(samples_80) # sort the samples\n", + "print(len(samples_80))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "51\n" + ] + } + ], + "source": [ + "sample_data_100 = os.path.join('..', 'Data', 'sample_data', '100도')\n", + "samples_100 = glob.glob(os.path.join(sample_data_100, '**', '*wav'), recursive=True)\n", + "samples_100 = sorted(samples_100) # sort the samples\n", + "print(len(samples_100))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "41\n" + ] + } + ], + "source": [ + "sample_data_140 = os.path.join('..', 'Data', 'sample_data', '140도')\n", + "samples_140 = glob.glob(os.path.join(sample_data_140, '**', '*wav'), recursive=True)\n", + "samples_140 = sorted(samples_140) # sort the samples\n", + "print(len(samples_140))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "51\n" + ] + } + ], + "source": [ + "sample_data_160 = os.path.join('..', 'Data', 'sample_data', '160도')\n", + "samples_160 = glob.glob(os.path.join(sample_data_160, '**', '*wav'), recursive=True)\n", + "samples_160 = sorted(samples_160) # sort the samples\n", + "print(len(samples_160))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "def mrcg_transpose(file_path, sr=44100):\n", + " X, sample_rate = librosa.load(file_path, sr=sr, mono=False)\n", + " mrcg_L = mrcg.mrcg_extract(X[0], sample_rate)\n", + " mrcg_R = mrcg.mrcg_extract(X[1], sample_rate)\n", + " \n", + " return mrcg_L, mrcg_R" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_instances(array_2d, seq_len, hop, label):\n", + "\n", + " row_size, col_size = array_2d.shape[0], array_2d.shape[1]\n", + " rate = len(label)/col_size\n", + " stack_array = [] # 4D tensor that will hold the instances\n", + " label_array = []\n", + "\n", + " j=0\n", + " while j <= (col_size - (seq_len+1)): \n", + " context_frame = array_2d[:, j:(j+seq_len)]\n", + " seg_label = round(label[int(j*rate):int((j+seq_len)*rate)].mean())\n", + " \n", + " stack_array.append(context_frame[:,:,np.newaxis]) # make context_frame to 3d tensor and append \n", + " label_array.append(seg_label)\n", + " \n", + " j = j+hop\n", + " \n", + " return np.stack(stack_array, axis=0), label_array" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_instances_no_label(array_2d, seq_len, hop):\n", + "\n", + " row_size, col_size = array_2d.shape[0], array_2d.shape[1]\n", + " stack_array = [] # 4D tensor that will hold the instances\n", + "\n", + " j=0\n", + " while j <= (col_size - (seq_len+1)): \n", + " context_frame = array_2d[:, j:(j+seq_len)]\n", + " stack_array.append(context_frame[:,:,np.newaxis]) # make context_frame to 3d tensor and append \n", + " \n", + " j = j+hop\n", + " \n", + " return np.stack(stack_array, axis=0)" + ] + }, + { + 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"3.5573649999998906\n", + "get avg\n", + "0.0858290000001034\n", + "fftfilter\n", + "5.3121380000000045\n", + "gamma total\n", + "7.212352999999894\n", + "coch1\n", + "2.264541999999892\n", + "coch2\n", + "3.0135210000000825\n", + "get avg\n", + "0.28768200000013167\n", + "fftfilter\n", + "5.487481000000116\n", + "gamma total\n", + "7.331046000000015\n", + "coch1\n", + "2.551144999999906\n", + "coch2\n", + "3.2142570000000887\n", + "get avg\n", + "0.08440700000005563\n", + "fftfilter\n", + "6.949820000000045\n", + "gamma total\n", + "9.03706299999999\n", + "coch1\n", + "3.0461750000001757\n", + "coch2\n", + "3.3638770000000022\n", + "get avg\n", + "0.03468399999997018\n", + "fftfilter\n", + "6.822838999999931\n", + "gamma total\n", + "8.992236000000048\n", + "coch1\n", + "2.8881750000000466\n", + "coch2\n", + "3.3351419999999052\n", + "get avg\n", + "0.0275770000000648\n", + "fftfilter\n", + "6.193480000000136\n", + "gamma total\n", + "8.273494999999912\n", + "coch1\n", + "2.649842000000035\n", + "coch2\n", + "3.3131180000000313\n", + "get avg\n", + "0.04288700000006429\n", + "fftfilter\n", + "6.227022999999917\n", + "gamma total\n", + "8.43640900000014\n", + "coch1\n", + "2.4188359999998283\n", + "coch2\n", + "3.320425000000114\n", + "get avg\n", + "0.02709499999991749\n", + "fftfilter\n", + "8.18696100000011\n", + "gamma total\n", + "10.682232999999997\n", + "coch1\n", + "3.1186210000000756\n", + "coch2\n", + "3.3856379999999717\n", + "get avg\n", + "0.07155699999998433\n", + "fftfilter\n", + "8.456610000000182\n", + "gamma total\n", + "10.949072999999999\n", + "coch1\n", + "3.1583680000001095\n", + "coch2\n", + "3.375081999999793\n", + "get avg\n", + "0.06975299999999152\n", + "fftfilter\n", + "5.293476000000055\n", + "gamma total\n", + "7.162221000000045\n", + "coch1\n", + "1.8891300000000228\n", + "coch2\n", + "2.6329749999999876\n", + "get avg\n", + "0.4766660000000229\n", + "fftfilter\n", + "5.152252999999973\n", + "gamma total\n", + "7.05858400000011\n", + "coch1\n", + "2.523383999999851\n", + "coch2\n", + "3.2949459999999817\n", + "get avg\n", + "0.02928699999984019\n", + "fftfilter\n", + "5.976605000000063\n", + "gamma total\n", + "8.058043999999882\n", + "coch1\n", + "2.5479100000000017\n", + "coch2\n", + "3.307903000000124\n", + "get avg\n", + "0.02736300000015035\n", + "fftfilter\n", + "6.5507279999999355\n", + "gamma total\n", + "8.603131000000076\n", + "coch1\n", + "2.9212969999998677\n", + "coch2\n", + "3.3447869999999966\n", + "get avg\n", + "0.02786500000001979\n", + "fftfilter\n", + "5.712113000000045\n", + "gamma total\n", + "7.708602999999812\n", + "coch1\n", + "3.388005000000021\n", + "coch2\n", + "3.513881999999967\n", + "get avg\n", + "0.03421000000003005\n", + "fftfilter\n", + "5.516816000000063\n", + "gamma total\n", + "7.510158000000047\n", + "coch1\n", + "2.8003429999998843\n", + "coch2\n", + "3.3350330000000667\n", + "get avg\n", + "0.03862800000001698\n" + ] + } + ], + "source": [ + "mrcg_L_tensor = []\n", + "mrcg_R_tensor = []\n", + "label_list = []\n", + "\n", + "for i in range(0,50):\n", + " deg = np.load(samples_vad_seg[i])\n", + " if('npy' in samples_vad_seg[i].split('/')[-1]):\n", + " label = deg[0]\n", + " else:\n", + " label = deg[\"label\"]\n", + " \n", + " mrcg_L, mrcg_R = mrcg_transpose(samples[i])\n", + " mrcg_L_stack, label_array = generate_instances(mrcg_L, 100, 10, label)\n", + " mrcg_R_stack, label_array = generate_instances(mrcg_R, 100, 10, label)\n", + " \n", + " mrcg_L_tensor.append(mrcg_L_stack)\n", + " mrcg_R_tensor.append(mrcg_R_stack)\n", + " label_list.append(label_array)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fftfilter\n", + "8.882770000000164\n", + "gamma total\n", + "11.395942000000105\n", + "coch1\n", + "3.3650609999999688\n", + "coch2\n", + "3.462344999999914\n", + "get avg\n", + 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"46.45045900000059\n", + "gamma total\n", + "53.874079999999594\n", + "coch1\n", + "4.897305000000415\n", + "coch2\n", + "5.504463000000214\n", + "get avg\n", + "2.4461209999999483\n", + "fftfilter\n", + "45.40147200000047\n", + "gamma total\n", + "52.908206000000064\n", + "coch1\n", + "5.137688999999227\n", + "coch2\n", + "5.463256000000001\n", + "get avg\n", + "2.2676620000002004\n", + "fftfilter\n", + "8.73858299999938\n", + "gamma total\n", + "11.266002000000299\n", + "coch1\n", + "3.1250669999999445\n", + "coch2\n", + "3.4655290000000605\n", + "get avg\n", + "0.07570600000053673\n", + "fftfilter\n", + "8.95382999999947\n", + "gamma total\n", + "11.49341000000004\n", + "coch1\n", + "3.428573000000142\n", + "coch2\n", + "3.4803140000003623\n", + "get avg\n", + "0.07399999999961437\n" + ] + } + ], + "source": [ + "for i in range(0, len(samples_20)):\n", + " deg = np.load(samples_vad_seg_20[i])\n", + " if('npy' in samples_vad_seg_20[i].split('/')[-1]):\n", + " label = deg[0]\n", + " else:\n", + " label = deg[\"label\"]\n", + " \n", + " mrcg_L, mrcg_R = mrcg_transpose(samples_20[i])\n", + " mrcg_L_stack, label_array = generate_instances(mrcg_L, 100, 10, label)\n", + " mrcg_R_stack, label_array = generate_instances(mrcg_R, 100, 10, label)\n", + " \n", + " mrcg_L_tensor.append(mrcg_L_stack)\n", + " mrcg_R_tensor.append(mrcg_R_stack)\n", + " label_list.append(label_array)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fftfilter\n", + "8.702822000000197\n", + "gamma total\n", + "11.219958000000588\n", + "coch1\n", + "3.1009699999995064\n", + "coch2\n", + "3.3940870000005816\n", + "get avg\n", + "0.0760999999993146\n", + "fftfilter\n", + "9.566521000000648\n", + "gamma total\n", + "12.105083999999806\n", + "coch1\n", + "3.025322999999844\n", + "coch2\n", + "3.381908000000294\n", + "get avg\n", + "0.07397799999944255\n", + "fftfilter\n", 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total\n", + "11.10005000000001\n", + "coch1\n", + "3.375463000000309\n", + "coch2\n", + "3.4621769999994285\n", + "get avg\n", + "0.11148499999944761\n", + "fftfilter\n", + "8.194171000000097\n", + "gamma total\n", + "10.870358000000124\n", + "coch1\n", + "3.361761000000115\n", + "coch2\n", + "3.444633000000067\n", + "get avg\n", + "0.08942900000056397\n", + "fftfilter\n", + "44.328943999999865\n", + "gamma total\n", + "51.6289569999999\n", + "coch1\n", + "5.018011000000115\n", + "coch2\n", + "5.20225500000015\n", + "get avg\n", + "2.229298000000199\n", + "fftfilter\n", + "45.517761000000064\n", + "gamma total\n", + "52.83908900000006\n", + "coch1\n", + "4.81291500000043\n", + "coch2\n", + "5.1463639999992665\n", + "get avg\n", + "2.2233369999994466\n" + ] + } + ], + "source": [ + "for i in range(0, len(samples_40)):\n", + " deg = np.load(samples_vad_seg_40[i])\n", + " if('npy' in samples_vad_seg_40[i].split('/')[-1]):\n", + " label = deg[0]\n", + " else:\n", + " label = deg[\"label\"]\n", + " \n", + " mrcg_L, mrcg_R = mrcg_transpose(samples_40[i])\n", + " mrcg_L_stack, label_array = generate_instances(mrcg_L, 100, 10, label)\n", + " mrcg_R_stack, label_array = generate_instances(mrcg_R, 100, 10, label)\n", + " \n", + " mrcg_L_tensor.append(mrcg_L_stack)\n", + " mrcg_R_tensor.append(mrcg_R_stack)\n", + " label_list.append(label_array)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fftfilter\n", + "8.266811999999845\n", + "gamma total\n", + "10.798746999999821\n", + "coch1\n", + "3.4163610000005065\n", + "coch2\n", + "3.5430820000001404\n", + "get avg\n", + "0.0725670000001628\n", + "fftfilter\n", + "8.276598999999806\n", + "gamma total\n", + "10.761947999999393\n", + "coch1\n", + "3.4477740000002086\n", + "coch2\n", + "3.4547899999997753\n", + "get avg\n", + "0.08975199999986216\n", + "fftfilter\n", + "8.861399999999776\n", + "gamma 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"gamma total\n", + "53.23682800000097\n", + "coch1\n", + "5.500189000000319\n", + "coch2\n", + "5.320621999999275\n", + "get avg\n", + "2.332557000001543\n", + "fftfilter\n", + "9.165971000000354\n", + "gamma total\n", + "11.652264000000287\n", + "coch1\n", + "3.434084000000439\n", + "coch2\n", + "3.4695990000000165\n", + "get avg\n", + "0.0743929999989632\n", + "fftfilter\n", + "8.324188000000504\n", + "gamma total\n", + "10.941713000000163\n", + "coch1\n", + "3.351741000000402\n", + "coch2\n", + "3.457311000000118\n", + "get avg\n", + "0.11548100000072736\n", + "fftfilter\n", + "42.668455000000904\n", + "gamma total\n", + "49.90092799999911\n", + "coch1\n", + "4.802610000000641\n", + "coch2\n", + "5.203279999999722\n", + "get avg\n", + "2.401119000000108\n", + "fftfilter\n", + "45.241556999999375\n", + "gamma total\n", + "52.49383200000011\n", + "coch1\n", + "4.899964000000182\n", + "coch2\n", + "5.308442999999897\n", + "get avg\n", + "2.2419669999999314\n" + ] + } + ], + "source": [ + "for i in range(0, len(samples_80)):\n", + " deg = np.load(samples_vad_seg_80[i])\n", + " if('npy' in samples_vad_seg_80[i].split('/')[-1]):\n", + " label = deg[0]\n", + " else:\n", + " label = deg[\"label\"]\n", + " \n", + " mrcg_L, mrcg_R = mrcg_transpose(samples_80[i])\n", + " mrcg_L_stack, label_array = generate_instances(mrcg_L, 100, 10, label)\n", + " mrcg_R_stack, label_array = generate_instances(mrcg_R, 100, 10, label)\n", + " \n", + " mrcg_L_tensor.append(mrcg_L_stack)\n", + " mrcg_R_tensor.append(mrcg_R_stack)\n", + " label_list.append(label_array)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fftfilter\n", + "8.7884439999998\n", + "gamma total\n", + "11.302450000001045\n", + "coch1\n", + "3.4175820000000385\n", + "coch2\n", + "3.498074999999517\n", + "get avg\n", + "0.1096159999997326\n", + "fftfilter\n", + "9.086671000000933\n", + "gamma total\n", + 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"coch2\n", + "3.387157000001025\n", + "get avg\n", + "0.07380199999897741\n", + "fftfilter\n", + "8.186477000002924\n", + "gamma total\n", + "10.826710000001185\n", + "coch1\n", + "3.188596999996662\n", + "coch2\n", + "3.3961530000015046\n", + "get avg\n", + "0.07392999999865424\n", + "fftfilter\n", + "9.03742499999862\n", + "gamma total\n", + "11.809016999999585\n", + "coch1\n", + "3.406637000000046\n", + "coch2\n", + "3.5448949999990873\n", + "get avg\n", + "0.07726700000057463\n", + "fftfilter\n", + "9.124539000000368\n", + "gamma total\n", + "11.639792999998463\n", + "coch1\n", + "3.3263090000000375\n", + "coch2\n", + "3.389801000001171\n", + "get avg\n", + "0.07515500000226893\n", + "fftfilter\n", + "8.897036999998818\n", + "gamma total\n", + "11.441892000002554\n", + "coch1\n", + "3.4006269999990764\n", + "coch2\n", + "3.2719529999994847\n", + "get avg\n", + "0.07409999999799766\n" + ] + } + ], + "source": [ + "for i in range(0, len(samples_100)):\n", + " deg = np.load(samples_vad_seg_100[i])\n", + " if('npy' in samples_vad_seg_100[i].split('/')[-1]):\n", + " label = deg[0]\n", + " else:\n", + " label = deg[\"label\"]\n", + " \n", + " mrcg_L, mrcg_R = mrcg_transpose(samples_100[i])\n", + " mrcg_L_stack, label_array = generate_instances(mrcg_L, 100, 10, label)\n", + " mrcg_R_stack, label_array = generate_instances(mrcg_R, 100, 10, label)\n", + " \n", + " mrcg_L_tensor.append(mrcg_L_stack)\n", + " mrcg_R_tensor.append(mrcg_R_stack)\n", + " label_list.append(label_array)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fftfilter\n", + "8.599471999998059\n", + "gamma total\n", + "11.142283999997744\n", + "coch1\n", + "3.33054600000105\n", + "coch2\n", + "3.4243290000013076\n", + "get avg\n", + "0.07692200000019511\n", + "fftfilter\n", + "8.735700999997789\n", + "gamma total\n", + "11.300631000001886\n", + "coch1\n", + 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"3.254861000001256\n", + "get avg\n", + "0.10457600000154343\n", + "fftfilter\n", + "10.831311000001733\n", + "gamma total\n", + "13.76672900000267\n", + "coch1\n", + "3.304795000000013\n", + "coch2\n", + "3.4417189999985567\n", + "get avg\n", + "0.07648499999777414\n", + "fftfilter\n", + "10.4734040000003\n", + "gamma total\n", + "13.397487000002002\n", + "coch1\n", + "3.4128959999979998\n", + "coch2\n", + "3.4345550000034564\n", + "get avg\n", + "0.1080599999986589\n", + "fftfilter\n", + "11.407972000000882\n", + "gamma total\n", + "14.043961999999738\n", + "coch1\n", + "3.3302390000026207\n", + "coch2\n", + "3.418351000000257\n", + "get avg\n", + "0.13936699999976554\n" + ] + } + ], + "source": [ + "for i in range(0, len(samples_140)):\n", + " deg = np.load(samples_vad_seg_140[i])\n", + " if('npy' in samples_vad_seg_140[i].split('/')[-1]):\n", + " label = deg[0]\n", + " else:\n", + " label = deg[\"label\"]\n", + " \n", + " mrcg_L, mrcg_R = mrcg_transpose(samples_140[i])\n", + " mrcg_L_stack, label_array = generate_instances(mrcg_L, 100, 10, label)\n", + " mrcg_R_stack, label_array = generate_instances(mrcg_R, 100, 10, label)\n", + " \n", + " mrcg_L_tensor.append(mrcg_L_stack)\n", + " mrcg_R_tensor.append(mrcg_R_stack)\n", + " label_list.append(label_array)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fftfilter\n", + "228.69318700000076\n", + "gamma total\n", + "238.34406800000215\n", + "coch1\n", + "12.083802999997715\n", + "coch2\n", + "14.402720000001864\n", + "get avg\n", + "3.7196839999996882\n", + "fftfilter\n", + "229.60996299999897\n", + "gamma total\n", + "238.99216199999864\n", + "coch1\n", + "12.232890000002953\n", + "coch2\n", + "13.926057999997283\n", + "get avg\n", + "3.9750390000008338\n", + "fftfilter\n", + "55.82277500000055\n", + "gamma total\n", + "63.16729000000123\n", + "coch1\n", + "4.840968999997131\n", + "coch2\n", + 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"3.4944280000017898\n", + "get avg\n", + "0.10816199999680975\n", + "fftfilter\n", + "9.482570000000123\n", + "gamma total\n", + "12.04384299999947\n", + "coch1\n", + "2.9923019999987446\n", + "coch2\n", + "3.2569260000018403\n", + "get avg\n", + "0.07424800000080722\n", + "fftfilter\n", + "48.126750999999786\n", + "gamma total\n", + "55.52595800000199\n", + "coch1\n", + "4.778941000000486\n", + "coch2\n", + "4.9926589999995485\n", + "get avg\n", + "2.443093999998382\n", + "fftfilter\n", + "50.6999309999992\n", + "gamma total\n", + "58.272853999998915\n", + "coch1\n", + "5.163315000001603\n", + "coch2\n", + "4.938727000000654\n", + "get avg\n", + "2.440162999999302\n" + ] + } + ], + "source": [ + "for i in range(0, len(samples_160)):\n", + " deg = np.load(samples_vad_seg_160[i])\n", + " if('npy' in samples_vad_seg_160[i].split('/')[-1]):\n", + " label = deg[0]\n", + " else:\n", + " label = deg[\"label\"]\n", + " \n", + " mrcg_L, mrcg_R = mrcg_transpose(samples_160[i])\n", + " mrcg_L_stack, label_array = generate_instances(mrcg_L, 100, 10, label)\n", + " mrcg_R_stack, label_array = generate_instances(mrcg_R, 100, 10, label)\n", + " \n", + " mrcg_L_tensor.append(mrcg_L_stack)\n", + " mrcg_R_tensor.append(mrcg_R_stack)\n", + " label_list.append(label_array)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(19, 768, 100, 1)" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mrcg_L_tensor[0].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "n_20 = 50 + len(samples_20)\n", + "n_40 = n_20 + len(samples_40)\n", + "n_80 = n_40 + len(samples_80)\n", + "n_100 = n_80 + len(samples_100)\n", + "n_140 = n_100 + len(samples_140)\n", + "n_160 = n_140 + len(samples_160)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "total_instances_tensor = []\n", + "\n", + "for i in range(0, n_160):\n", + " concat_tensor = np.concatenate([mrcg_L_tensor[i], mrcg_R_tensor[i]], axis=-1)\n", + " total_instances_tensor.append(concat_tensor)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(19, 768, 100, 2)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total_instances_tensor[0].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "total_label = []\n", + "\n", + "for i in range(0, n_160):\n", + " array_label = np.array(label_list[i])\n", + " total_label.append(array_label)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "vad_label = []\n", + "\n", + "for i in range(0, n_160):\n", + " array_label = np.array(label_list[i])\n", + " vad_label.append(array_label)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(19, 768, 100, 2) (19,)\n", + "(6, 768, 100, 2) (6,)\n", + "(16, 768, 100, 2) (16,)\n", + "(24, 768, 100, 2) (24,)\n", + "(14, 768, 100, 2) (14,)\n", + "(15, 768, 100, 2) (15,)\n", + "(33, 768, 100, 2) (33,)\n", + "(21, 768, 100, 2) (21,)\n", + "(9, 768, 100, 2) (9,)\n", + "(19, 768, 100, 2) (19,)\n", + "(16, 768, 100, 2) (16,)\n", + "(28, 768, 100, 2) (28,)\n", + "(20, 768, 100, 2) (20,)\n", + "(24, 768, 100, 2) (24,)\n", + "(22, 768, 100, 2) (22,)\n", + "(30, 768, 100, 2) (30,)\n", + "(17, 768, 100, 2) (17,)\n", + "(14, 768, 100, 2) (14,)\n", + "(23, 768, 100, 2) (23,)\n", + "(30, 768, 100, 2) (30,)\n", + "(6, 768, 100, 2) (6,)\n", + "(17, 768, 100, 2) (17,)\n", + "(22, 768, 100, 2) (22,)\n", + "(23, 768, 100, 2) (23,)\n", + "(18, 768, 100, 2) (18,)\n", + "(10, 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len(total_label[i])):\n", + " if(total_label[i][j] == 1):\n", + " total_label[i][j] = -30\n", + " \n", + "for i in range(25, 38):\n", + " for j in range(0, len(total_label[i])):\n", + " if(total_label[i][j] == 1):\n", + " total_label[i][j] = 30\n", + " \n", + "for i in range(38, 50):\n", + " for j in range(0, len(total_label[i])):\n", + " if(total_label[i][j] == 1):\n", + " total_label[i][j] = 90\n", + " \n", + "for i in range(50, n_20):\n", + " for j in range(0, len(total_label[i])):\n", + " if(total_label[i][j] == 1):\n", + " total_label[i][j] = -70\n", + "\n", + "for i in range(n_20, n_40):\n", + " for j in range(0, len(total_label[i])):\n", + " if(total_label[i][j] == 1):\n", + " total_label[i][j] = -50\n", + " \n", + "for i in range(n_40, n_80):\n", + " for j in range(0, len(total_label[i])):\n", + " if(total_label[i][j] == 1):\n", + " total_label[i][j] = -10\n", + " \n", + "for i in range(n_80, n_100):\n", + " for j in range(0, len(total_label[i])):\n", + " if(total_label[i][j] == 1):\n", + " total_label[i][j] = 10\n", + " \n", + "for i in range(n_100, n_140):\n", + " for j in range(0, len(total_label[i])):\n", + " if(total_label[i][j] == 1):\n", + " total_label[i][j] = 50\n", + " \n", + "for i in range(n_140, n_160):\n", + " for j in range(0, len(total_label[i])):\n", + " if(total_label[i][j] == 1):\n", + " total_label[i][j] = 70\n", + " \n", + "total_label" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 23, 8, 258, 138, 199, 145, 234, 211, 253, 101, 135, 284, 148,\n", + " 48, 12, 75, 179, 209, 61, 90, 243, 1, 229, 27, 36, 142,\n", + " 60, 103, 302, 121, 92, 210, 24, 15, 49, 2, 65, 6, 17,\n", + " 95, 165, 259, 98, 257, 190, 177, 37, 197, 255, 120, 64, 13,\n", + " 187, 227, 290, 285, 141, 281, 132, 168, 62, 251, 207, 223, 174,\n", + " 205, 216, 96, 119, 144, 20, 117, 171, 252, 249, 175, 86, 154,\n", + " 156, 296, 304, 184, 137, 200, 222, 300, 192, 272, 16, 176, 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114, 233, 246, 100, 221, 267,\n", + " 183, 273, 212, 268, 220, 196, 188, 55, 291, 53, 194, 260, 201,\n", + " 203, 265, 295, 231, 271, 167, 266, 97, 162, 22, 159, 308, 19,\n", + " 245, 110, 93])" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.seed(19)\n", + "idx = np.random.permutation(n_160)\n", + "idx" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "X = []\n", + "y = []\n", + "vad = []\n", + "\n", + "for i in range(0, n_160):\n", + " X.append(total_instances_tensor[idx[i]])\n", + " y.append(total_label[idx[i]])\n", + " vad.append(vad_label[idx[i]])" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "23 (23,) (23,)\n", + "9 (9,) (9,)\n", + "191 (191,) (191,)\n", + "191 (191,) (191,)\n", + "191 (191,) (191,)\n", + "30 (30,) (30,)\n", + "191 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(30,)\n", + "30 (30,) (30,)\n", + "30 (30,) (30,)\n", + "30 (30,) (30,)\n", + "30 (30,) (30,)\n" + ] + } + ], + "source": [ + "for i in range(0, n_160):\n", + " print(X[i].shape[0], y[i].shape, vad[i].shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((27149, 768, 100, 2), (27149,), (27149,))" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_idx = round(n_160 * 0.8)\n", + "X_train = np.concatenate(X[:train_idx], axis=0)\n", + "y_train = np.concatenate(y[:train_idx], axis=0)\n", + "vad_train = np.concatenate(vad[:train_idx], axis=0)\n", + "\n", + "X_train.shape, y_train.shape, vad_train.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((5834, 768, 100, 2), (5834,), (5834,))" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_val = np.concatenate(X[train_idx: ], axis=0)\n", + "y_val = np.concatenate(y[train_idx: ], axis=0)\n", + "vad_val = np.concatenate(vad[train_idx: ], axis=0)\n", + "\n", + "X_val.shape, y_val.shape, vad_val.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape_max_pool_3 : [None, 15, 10, 64]\n", + "shape_conv_4 : [None, 1, 10, 1024]\n", + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input_1 (InputLayer) (None, 768, 100, 2) 0 \n", + "__________________________________________________________________________________________________\n", + "conv2d_1 (Conv2D) (None, 766, 98, 16) 304 input_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "max_pooling2d_1 (MaxPooling2D) (None, 383, 49, 16) 0 conv2d_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_2 (Conv2D) (None, 381, 47, 32) 4640 max_pooling2d_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "max_pooling2d_2 (MaxPooling2D) (None, 190, 23, 32) 0 conv2d_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_3 (Conv2D) (None, 94, 21, 64) 18496 max_pooling2d_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_4 (Conv2D) (None, 46, 20, 64) 24640 conv2d_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "max_pool_3 (MaxPooling2D) (None, 15, 10, 64) 0 conv2d_4[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_5 (Conv2D) (None, 1, 10, 1024) 984064 max_pool_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape_1 (Reshape) (None, 10, 1024) 0 conv2d_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1 (Conv1D) (None, 8, 512) 1573376 reshape_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "flatten_1 (Flatten) (None, 4096) 0 conv1d_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_1 (Dense) (None, 32) 131104 flatten_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_1 (Dropout) (None, 32) 0 dense_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "vad_output (Dense) (None, 1) 33 dropout_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "reg_output (Dense) (None, 1) 33 dropout_1[0][0] \n", + "==================================================================================================\n", + "Total params: 2,736,690\n", + "Trainable params: 2,736,690\n", + "Non-trainable params: 0\n", + "__________________________________________________________________________________________________\n" + ] + } + ], + "source": [ + "from keras.layers import Conv2D, MaxPooling2D, Input, Flatten, Dropout, Dense, Reshape, Conv1D\n", + "from keras.models import Model\n", + "\n", + "input_spectrogram = Input(shape=(X_train.shape[1], X_train.shape[2], X_train.shape[3]))\n", + "\n", + "conv_1 = Conv2D(16, (3, 3), activation='relu')(input_spectrogram)\n", + "max_pool_1 = MaxPooling2D((2, 2))(conv_1)\n", + "\n", + "conv_2 = Conv2D(32, (3, 3), activation='relu')(max_pool_1)\n", + "max_pool_2 = MaxPooling2D((2, 2))(conv_2)\n", + "\n", + "conv_3 = Conv2D(64, (3, 3), strides=(2,1), activation='relu')(max_pool_2)\n", + "conv_3_1 = Conv2D(64, (3, 2), strides=(2,1), activation='relu')(conv_3)\n", + "max_pool_3 = MaxPooling2D((3, 2), name='max_pool_3')(conv_3_1)\n", + "\n", + "shape_max_pool_3 = max_pool_3.get_shape().as_list() # (None, height, width, channel)\n", + "print(\"shape_max_pool_3 : \", shape_max_pool_3)\n", + "# reshaped = layers.Reshape((-1, shape_list[1]*shape_list[3]))(max_pool_3)\n", + "\n", + "conv_4 = Conv2D(1024, (shape_max_pool_3[1], 1), padding='valid', activation='relu')(max_pool_3)\n", + "shape_conv_4 = conv_4.get_shape().as_list()\n", + "print(\"shape_conv_4 : \", shape_conv_4)\n", + "\n", + "reshaped = Reshape((shape_conv_4[2], shape_conv_4[3]))(conv_4) # reshape to (timesteps, features) explicitly \n", + "\n", + "conv_5 = Conv1D(512, kernel_size=3, activation='relu')(reshaped)\n", + "\n", + "flatten = Flatten()(conv_5)\n", + "# flatten_drop = Dropout(0.3)(flatten)\n", + "\n", + "fc1 = Dense(32, activation='relu')(flatten)\n", + "fc1_drop = Dropout(0.1)(fc1)\n", + "\n", + "vad_out = Dense(1, activation='sigmoid', name='vad_output')(fc1_drop)\n", + "dense_out = Dense(1, activation='linear', name='reg_output')(fc1_drop)\n", + "\n", + "\n", + "model = Model(inputs=input_spectrogram, outputs=[vad_out, dense_out])\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "from keras.utils import multi_gpu_model\n", + "\n", + "model = multi_gpu_model(model, gpus=8, cpu_relocation=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 27149 samples, validate on 5834 samples\n", + "Epoch 1/150\n", + "27149/27149 [==============================] - 69s 3ms/step - loss: 225.1290 - vad_output_loss: 0.5838 - reg_output_loss: 224.5451 - vad_output_acc: 0.8896 - reg_output_mean_squared_error: 224.5451 - val_loss: 149.2317 - val_vad_output_loss: 0.1290 - val_reg_output_loss: 149.1028 - val_vad_output_acc: 0.9604 - val_reg_output_mean_squared_error: 149.1028\n", + "Epoch 2/150\n", + "27149/27149 [==============================] - 61s 2ms/step - loss: 96.8235 - vad_output_loss: 0.1830 - reg_output_loss: 96.6405 - vad_output_acc: 0.9499 - reg_output_mean_squared_error: 96.6405 - val_loss: 148.7728 - val_vad_output_loss: 0.1998 - val_reg_output_loss: 148.5730 - val_vad_output_acc: 0.9326 - val_reg_output_mean_squared_error: 148.5730\n", + "Epoch 3/150\n", + "27149/27149 [==============================] - 62s 2ms/step - loss: 66.7972 - vad_output_loss: 0.1536 - reg_output_loss: 66.6436 - vad_output_acc: 0.9555 - reg_output_mean_squared_error: 66.6436 - val_loss: 109.6062 - val_vad_output_loss: 0.0861 - val_reg_output_loss: 109.5201 - val_vad_output_acc: 0.9599 - val_reg_output_mean_squared_error: 109.5201\n", + "Epoch 4/150\n", + "27149/27149 [==============================] - 61s 2ms/step - loss: 53.7835 - vad_output_loss: 0.1272 - reg_output_loss: 53.6564 - vad_output_acc: 0.9608 - reg_output_mean_squared_error: 53.6564 - val_loss: 68.6012 - val_vad_output_loss: 0.0667 - val_reg_output_loss: 68.5345 - val_vad_output_acc: 0.9729 - val_reg_output_mean_squared_error: 68.5345\n", + "Epoch 5/150\n", + "27149/27149 [==============================] - 57s 2ms/step - loss: 44.7087 - vad_output_loss: 0.1009 - reg_output_loss: 44.6077 - vad_output_acc: 0.9691 - reg_output_mean_squared_error: 44.6077 - val_loss: 96.1731 - val_vad_output_loss: 0.0596 - val_reg_output_loss: 96.1135 - val_vad_output_acc: 0.9791 - val_reg_output_mean_squared_error: 96.1135\n", + "Epoch 6/150\n", + "27149/27149 [==============================] - 59s 2ms/step - loss: 37.7225 - vad_output_loss: 0.0921 - reg_output_loss: 37.6303 - vad_output_acc: 0.9737 - reg_output_mean_squared_error: 37.6303 - val_loss: 125.3840 - val_vad_output_loss: 0.0863 - val_reg_output_loss: 125.2978 - val_vad_output_acc: 0.9697 - val_reg_output_mean_squared_error: 125.2978\n", + "Epoch 7/150\n", + "27149/27149 [==============================] - 56s 2ms/step - loss: 33.1883 - vad_output_loss: 0.0701 - reg_output_loss: 33.1182 - vad_output_acc: 0.9857 - reg_output_mean_squared_error: 33.1182 - val_loss: 79.6881 - val_vad_output_loss: 0.0636 - val_reg_output_loss: 79.6245 - val_vad_output_acc: 0.9859 - val_reg_output_mean_squared_error: 79.6245\n", + "Epoch 8/150\n", + "27149/27149 [==============================] - 57s 2ms/step - loss: 27.6923 - vad_output_loss: 0.0537 - reg_output_loss: 27.6386 - vad_output_acc: 0.9889 - reg_output_mean_squared_error: 27.6386 - val_loss: 74.0047 - val_vad_output_loss: 0.0441 - val_reg_output_loss: 73.9605 - val_vad_output_acc: 0.9902 - val_reg_output_mean_squared_error: 73.9605\n", + "Epoch 9/150\n", + "27149/27149 [==============================] - 64s 2ms/step - loss: 24.0492 - vad_output_loss: 0.0461 - reg_output_loss: 24.0030 - vad_output_acc: 0.9909 - reg_output_mean_squared_error: 24.0030 - val_loss: 81.6231 - val_vad_output_loss: 0.0681 - val_reg_output_loss: 81.5550 - val_vad_output_acc: 0.9866 - val_reg_output_mean_squared_error: 81.5550\n", + "Epoch 10/150\n", + "27149/27149 [==============================] - 232s 9ms/step - loss: 22.4092 - vad_output_loss: 0.0425 - reg_output_loss: 22.3666 - vad_output_acc: 0.9916 - reg_output_mean_squared_error: 22.3666 - val_loss: 60.3856 - val_vad_output_loss: 0.0553 - val_reg_output_loss: 60.3303 - val_vad_output_acc: 0.9877 - val_reg_output_mean_squared_error: 60.3303\n", + "Epoch 11/150\n", + " 128/27149 [..............................] - ETA: 12:49 - loss: 27.3947 - vad_output_loss: 0.0125 - reg_output_loss: 27.3822 - vad_output_acc: 1.0000 - reg_output_mean_squared_error: 27.3822" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/user3/.conda/envs/hun/lib/python3.6/site-packages/keras/callbacks.py:122: UserWarning: Method on_batch_end() is slow compared to the batch update (0.210288). Check your callbacks.\n", + " % delta_t_median)\n", + "/home/user3/.conda/envs/hun/lib/python3.6/site-packages/keras/callbacks.py:122: UserWarning: Method on_batch_end() is slow compared to the batch update (0.105738). Check your callbacks.\n", + " % delta_t_median)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "27149/27149 [==============================] - 60s 2ms/step - loss: 20.7227 - vad_output_loss: 0.0367 - reg_output_loss: 20.6859 - vad_output_acc: 0.9924 - reg_output_mean_squared_error: 20.6859 - val_loss: 87.8907 - val_vad_output_loss: 0.1531 - val_reg_output_loss: 87.7375 - val_vad_output_acc: 0.9565 - val_reg_output_mean_squared_error: 87.7375\n", + "Epoch 12/150\n", + "27149/27149 [==============================] - 56s 2ms/step - loss: 21.1416 - vad_output_loss: 0.0372 - reg_output_loss: 21.1043 - vad_output_acc: 0.9925 - reg_output_mean_squared_error: 21.1043 - val_loss: 82.5176 - val_vad_output_loss: 0.0463 - val_reg_output_loss: 82.4713 - val_vad_output_acc: 0.9895 - val_reg_output_mean_squared_error: 82.4713\n", + "Epoch 13/150\n", + "27149/27149 [==============================] - 58s 2ms/step - loss: 17.9259 - vad_output_loss: 0.0295 - reg_output_loss: 17.8964 - vad_output_acc: 0.9942 - reg_output_mean_squared_error: 17.8964 - val_loss: 69.6499 - val_vad_output_loss: 0.0502 - val_reg_output_loss: 69.5997 - val_vad_output_acc: 0.9878 - val_reg_output_mean_squared_error: 69.5997\n", + "Epoch 14/150\n", + "27149/27149 [==============================] - 63s 2ms/step - loss: 18.1366 - vad_output_loss: 0.0256 - reg_output_loss: 18.1110 - vad_output_acc: 0.9947 - reg_output_mean_squared_error: 18.1110 - val_loss: 52.3426 - val_vad_output_loss: 0.0386 - val_reg_output_loss: 52.3041 - val_vad_output_acc: 0.9907 - val_reg_output_mean_squared_error: 52.3041\n", + "Epoch 15/150\n", + "27149/27149 [==============================] - 167s 6ms/step - loss: 16.5951 - vad_output_loss: 0.0232 - reg_output_loss: 16.5720 - vad_output_acc: 0.9955 - reg_output_mean_squared_error: 16.5720 - val_loss: 68.4733 - val_vad_output_loss: 0.0546 - val_reg_output_loss: 68.4187 - val_vad_output_acc: 0.9885 - val_reg_output_mean_squared_error: 68.4187\n", + "Epoch 16/150\n", + " 6080/27149 [=====>........................] - ETA: 3:29 - loss: 15.8839 - vad_output_loss: 0.0181 - reg_output_loss: 15.8658 - vad_output_acc: 0.9965 - reg_output_mean_squared_error: 15.8658" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "KeyboardInterrupt\n", + "\n" + ] + } + ], + "source": [ + "from keras.callbacks import EarlyStopping, ReduceLROnPlateau\n", + "\n", + "model.compile(optimizer ='rmsprop', loss={'vad_output' : 'binary_crossentropy', 'reg_output' : 'mse'},\n", + " metrics ={'vad_output' : 'acc', 'reg_output' : 'mse'})\n", + "\n", + "callbacks_list = [EarlyStopping(monitor='loss', patience=20),\n", + " ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10)]\n", + "\n", + "history = model.fit(X_train, [vad_train, y_train],\n", + " epochs=150, batch_size=64, \n", + " callbacks=callbacks_list,\n", + " validation_data=(X_val, [vad_val, y_val]),\n", + " shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [], + "source": [ + "def predict(model, file_path):\n", + " mrcg_L, mrcg_R = mrcg_transpose(file_path, sr=44100)\n", + " mrcg_L_stack = generate_instances_no_label(mrcg_L, 100, 10)\n", + " mrcg_R_stack = generate_instances_no_label(mrcg_R, 100, 10)\n", + " X = np.concatenate([mrcg_L_stack, mrcg_R_stack], axis=-1)\n", + " vad_pred, pred = model.predict(X)\n", + " vad_pred = np.where(vad_pred >= 0.5, 1, 0)\n", + " return vad_pred, pred" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(63, 63)" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "val_list = idx[train_idx:]\n", + "edge_list = []\n", + "\n", + "for i in range(0, len(val_list)):\n", + " val_idx = val_list[i]\n", + " \n", + " if((val_idx >= 0) & (val_idx < 12)):\n", + " edge_list.append(0)\n", + " elif((val_idx >= 12) & (val_idx < 25)):\n", + " edge_list.append(60)\n", + " elif((val_idx >= 25) & (val_idx < 38)):\n", + " edge_list.append(120)\n", + " elif((val_idx >= 38) & (val_idx < 50)):\n", + " edge_list.append(180)\n", + " elif((val_idx >= 50) & (val_idx < n_20)):\n", + " edge_list.append(20)\n", + " elif((val_idx >= n_20) & (val_idx < n_40)):\n", + " edge_list.append(40)\n", + " elif((val_idx >= n_40) & (val_idx < n_80)):\n", + " edge_list.append(80)\n", + " elif((val_idx >= n_80) & (val_idx < n_100)):\n", + " edge_list.append(100)\n", + " elif((val_idx >= n_100) & (val_idx < n_140)):\n", + " edge_list.append(140)\n", + " elif((val_idx >= n_140) & (val_idx < n_160)):\n", + " edge_list.append(160)\n", + "\n", + "len(val_list), len(edge_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fftfilter\n", + "8.580853000006755\n", + "gamma total\n", + "11.197305999994569\n", + "coch1\n", + "3.387203000005684\n", + "coch2\n", + "3.4984629999962635\n", + "get avg\n", + "0.09682099999918137\n", + "fftfilter\n", + "8.440988000002108\n", + "gamma total\n", + "11.071584999997867\n", + "coch1\n", + "3.394295999998576\n", + "coch2\n", + "3.5107410000055097\n", + "get avg\n", + "0.07675699999526842\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 7.908970074861755 \n", + "\n", + "\n", + "fftfilter\n", + "44.398293999998714\n", + "gamma total\n", + "51.95260500000586\n", + "coch1\n", + "4.844717999993009\n", + "coch2\n", + "5.183794000004127\n", + "get avg\n", + "2.2275700000027427\n", + "fftfilter\n", + "45.0371540000051\n", + "gamma total\n", + "52.59095399999933\n", + "coch1\n", + "4.931824999999662\n", + "coch2\n", + "5.416956000000937\n", + "get avg\n", + "2.2448850000000675\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 18.81810154656713 \n", + "\n", + "\n", + "fftfilter\n", + "9.022891000000527\n", + "gamma total\n", + "11.696755000004487\n", + "coch1\n", + "3.4158800000004703\n", + "coch2\n", + "3.482059999994817\n", + "get avg\n", + "0.09583899999415735\n", + "fftfilter\n", + "9.16808999999921\n", + "gamma total\n", + "11.735764999997627\n", + "coch1\n", + "3.419405000000552\n", + "coch2\n", + "3.5206360000011045\n", + "get avg\n", + "0.07669399999576854\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 2.4513030312479973 \n", + "\n", + "\n", + "fftfilter\n", + "9.208508000003349\n", + "gamma total\n", + "11.923297000001185\n", + "coch1\n", + "3.40919600000052\n", + "coch2\n", + "3.505422999995062\n", + "get avg\n", + "0.10132699999667238\n", + "fftfilter\n", + "9.72697099999641\n", + "gamma total\n", + "12.349135999997088\n", + "coch1\n", + "3.4104980000047362\n", + "coch2\n", + "3.5253049999955692\n", + "get avg\n", + "0.07418200000392972\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 8.082416108035225 \n", + "\n", + "\n", + "fftfilter\n", + "51.206529000002774\n", + "gamma total\n", + "58.92454100000032\n", + "coch1\n", + "4.937967999998364\n", + "coch2\n", + "5.370935999999347\n", + "get avg\n", + "2.443335999996634\n", + "fftfilter\n", + "47.50954600000114\n", + "gamma total\n", + "55.28283600000577\n", + "coch1\n", + "4.590264999998908\n", + "coch2\n", + "5.399073000000499\n", + "get avg\n", + "2.45349300000089\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 26.069718601867255 \n", + "\n", + "\n", + "fftfilter\n", + "9.724172000002\n", + "gamma total\n", + "12.449483999997028\n", + "coch1\n", + "3.336529000000155\n", + "coch2\n", + "3.473471999997855\n", + "get avg\n", + "0.08025199999974575\n", + "fftfilter\n", + "9.490081999996619\n", + "gamma total\n", + "12.237373000003572\n", + "coch1\n", + "3.402386999994633\n", + "coch2\n", + "3.5275390000024345\n", + "get avg\n", + "0.07456499999534572\n" + ] + }, + { + "data": { + "image/png": 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Lvffey9NPPw1AHMc1rqh8yjq8GIbhG4H/PPmwDrgQOBd4HHhmcvt7oih6qpzHlSRJs8NZZ53Fpz71Kd773veyd+9eUqkUv/M7v8NTT8386BBUKkGGYfgZYA9wF/CHURS99RTeHu/YsaMidR3Q1dVFX19fRY8xl9m+lWPbVo5tW1m2b+WcSduOjIzQ2Fj7uVzTWSqVYmJi4rht1dPTAxCcbD8VGV4Mw7AbuBn4c6AbaAvD8KowDKfvimWSJEkVVJGerjAM/wDYH0XRn4VhuBL4d8DVwDnAtVEU9R/jPbcAtwBEUXRZLpcre12HOpBaVRm2b+XYtpVj21aW7Vs5Z9K2u3btoq6urswVzU7j4+PMnz//qO2ZTAam0NNVqdC1Dnh1FEU7j9j+t8CDURR98SS7cHhxhrN9K8e2rRzbtrJs38o5k7YdHh6mqampzBXNLgdC7fHaqmbDi2EYLgWKBwJXGIbpydsAaAEGy31MSZJ0+mbTGYKVUo42qsScrguAFw95/KkwDB8FHgVeAu6swDElSdJpyGQyjI+P17qMaW98fPzAMOJpK/uK9FEUfQv41iGPP1zuY0iSpPJIp9MUCgWGh4cJgpOOkM1JB8LWmV5M28sASZI0x9XX19e6hGmtXPMRXZFekiSpCgxdkiRJVWDokiRJqgJDlyRJUhUYuiRJkqrA0CVJklQFhi5JkqQqMHRJkiRVgaFLkiSpCgxdkiRJVWDokiRJqgJDlyRJUhUYuiRJkqrA0CVJklQFhi5JkqQqMHRJkiRVgaFLkiSpCgxdkiRJVWDokiRJqgJDlyRJUhUYuiRJkqrA0CVJklQFhi5JkqQqMHRJkiRVgaFLkiSpCgxdkiRJVWDokiRJqgJDlyRJUhUYuiRJkqrA0CVJklQFhi5JkqQqMHRJkiRVgaFLkiSpCgxdkiRJVWDokiRJqgJDlyRJUhUYuiRJkqrA0CVJklQFhi5JkqQqMHRJkiRVgaFLkiSpCgxdkiRJVWDokiRJqgJDlyRJUhUYuiRJkqogVe4dhmF4H1APTADfBP4ncAfQAzwN3BJFUa7cx5UkSZrOKtHTVQdcF0XRNVEUfRr4CPCjKIquAMaBt1fgmJIkSdNaJUJXO3BNGIZdk49vpNTjxeTtTRU4piRJ0rRW9uFF4M+BNwBfCsPwPwFdQP/kc/2Tj48ShuEtwC0AURTR1XXMl5VNKpWq+DHmMtu3cmzbyrFtK8v2rRzbtrLK1b5lD11RFP01QBiGdwGfBPZQ6v3aPnnbd5z33Q7cPvkw7us75svKpquri0ofYy6zfSvHtq0c27aybN/KsW0r62Tt29PTM6X9lHV4MQzDQ0NcKzAIfI9SzxfA6ycfS5IkzSnl7ul6ZRiGnwNywCjwHmAncEcYho8AzwB/X+ZjSpIkTXtlDV1RFN0PXHqMp95YzuNIkiTNNC6OKkmSVAWGLkmSpCowdEmSJFWBoUuSJKkKDF2SJElVYOiSJEmqAkOXJElSFRi6JEmSqsDQJUmSVAWGLkmSpCowdEmSJFWBoUuSJKkKDF2SJElVYOiSJEmqAkOXJElSFRi6JEmSqsDQJUmSVAWGLkmSpCowdEmSJFWBoUuSJKkKDF2SJElVYOiSJEmqAkOXJElSFRi6JEmSqsDQJUmSVAWGLkmSpCowdEmSJFWBoUuSJKkKDF2SJElVYOiSJEmqAkOXJElSFRi6JEmSqsDQJUmSVAWGLkmSpCowdEmSJFWBoUuSJKkKDF2SJElVYOiSJEmqAkOXJElSFRi6JEmSqsDQJUmSVAWGLkmSpCowdEmSJFWBoUuSJKkKDF2SJElVYOiSJEmqAkOXJElSFaTKubMwDJcDtwONQD1wSxRFj4dhOAw8MfmyT0RRdHc5jytJkjTdlTV0AS8B742i6IUwDH8T+FgYhr8K/CyKomvKfCxJkqQZo6yhK4qiMeCFyYftlEJYN5AJw/CVwKNRFI2X85iSJEkzQRDHcdl3Gobh1cCXgRuAPPBrwCXANcBNURRtPMZ7bgFuAYii6LJcLlf2ug6VSqWYmJio6DHmMtu3cmzbyrFtK8v2rRzbtrJO1r6ZTAYgONl+yh66wjC8BPhH4JejKFp/xHN/CKSiKPovJ9lNvGPHjrLWdaSuri76+voqeoy5zPatHNu2cmzbyrJ9K8e2rayTtW9PTw9MIXSV9ezFMAzTwFeAXzkQuCa3HdAKDJbzmJIkSTNBuSfSXwAsB/4iDEOACeCbk5PpC8CLwH8t8zElSZKmvXJPpH8caD7GU58t53EkSZJmGhdHlSRJqgJDlyRJUhUYuiRJkqrA0CVJklQFhi5JkqQqMHRJkiRVgaFLkiSpCgxdkiRJVWDokiRJqgJDlyRJUhUYuiRJkqrA0CVJklQFhi5JkqQqMHRJkipubLTIvj3jTOTjWpci1Uyq1gVIkmavYiHmhefGef6ZMYrFQQAydQGNTQkamhI0Tv538H5jgmQqqHHVUmUYuiRJFbG3b4KfPTLC/sEiPUvSnH1eB7teGmBkuMjoSJHBfQV2bc9TLB7+vrr6gIbGBI3Nk4HskPv19aUBmmIxJo6hWIS4GFOMIS5OPo7jye1QjGPiIpOvjSe3wbzOJE3NyRq0iuYyQ5ckqazy+ZjnfjbKphdy1DcEXHFNEwsWpenqaqG1Y/yw18ZxzPhYzMhQkZHhIiMjRUaHSrf9ewvs3JonrsCIZCIBZ59fz1nn1ZFI2rOm6jB0SZLK5qXteZ56bISx0ZgVZ2c474IGUunjh5ogCKhvCKhvSNCRPfr5uBgzNhaXAtlQkfGxIkEAQSIgEUCQKAWoIAhKt0fcTwTBIa+BRCIgjuH5Z8ZYv26M7VtyXHh5I51Zfx2q8vwpkySdsbHRIuseH2XntjwtbQkuf0UT87rO/FdMkAhoaCwNN3YeI5Sdrste0cTinXmeemyUh+4dYunKDKsvrCdT5/llqhxDlyTptMVxzJYXczzz5CjFApx3QT2rzp0ZQ3bzF6bpfF2Knz89xovrx3lpe541FzewaFmaIJj+9WvmMXRJkk7L0GCBJx8dYW9vgc7uFBde3kBzy8yanJ5KBZx/UQOLl2V48pERnnh4hK2bSp/FifYqN0OXJOmUHLoMRDIZcNEVDSxZkZnRvUOt7UmuuamZzRtyPPvUKPd9ez/nnD9zeu00Mxi6JElTduQyEGsuaaC+YXbMgwoSAcvPrmPB4jTrnhjluafG2LbZifYqH3+KJGmWGR0psmt7nl0780xMxNTXJ6irL50hWFefoL4hoK4+QV1DQCYTTKmH6shlIK68ton5PekqfJrqq28onQiwa0fpTMyDE+0vqieTmR0BU7Vh6JKkGS6OYwb2FnhpR55dOyYY7C8A0NhcClgD+wqMjRUpTBz93iBRWoy0fjKElQLay8GsviFgZLjI00+MTnkZiNlifk+azu5W1q8bY+PPSxPt117SQM9SJ9rr9Bi6JGkGmpiI6ds1wa4deXbtyDM+FkMAHZ1JVl9Yz/xFaZpbEoeFg4l8zNhYkfHRyduxmPHRImNjRcZGSwuU7u0tkM8dvRppS1uCy1/ZxLzOufVrI5UKWHNxA4uXpfnZo6M8/uPSRPsLLpv6RPtiISafL/03kY/J5w65n49pbkmSXZAikTDIzXZz6/8eSZrBxkaLB0NW764JigVIpSC7MM38njTdC1PUnWCdqVQ6oDmdpLnlxMcpFmLGx2PGRkvBrFiMWbAoPadDQdu8FNfc1MymF3I8NznRfuXZdaTSQSlQ5V4OUYfdz8cUCyfff6YuYNHSNIuXZ2ibl7QnbZYydEnSNBXHMYP9BXbtmOCl7XkG9pV+ezc0JVi2MlMa/sqmyn52XSL58oKkelmQCFhxzuRE+8dHeeG50iWNEklIpwNS6YB0OiCdCWhoShy8f3D7kY8zAckU7O0tsG1Tjs0bcmx8PkdzS4JFyzMsXpahscl/g9nE0CVJ00gcx+ztLbBja46XduQZGykN9c3rTHLeBfXM70nT0pawJ6SGGhoTXHFNE/lckUQyIHmGoXfBogQLFqXJ54rs2Jpn2+Yc658aY/1TY3RkkyxelqFnSZq0k/hnPEOXJE0Dg/0Ftm/JsX1zjtGRmGQSsgvSzF+TYn5Pmrp6f+FON+UOQelMgmWr6li2qo6R4QLbNufZtinHzx4dZd3jo8zvKQ0/di8of++mqsPQJUk1MjpSPBi0BvtLF3LOLkix+sIM8xelSaX8xTpXNTYlOef8JGevrmNgb4Ftm3Ns35Jn57Y86czk/K9lGdo7nf81kxi6JKmK8rmYndtybN+cp293aQ2HeZ1J1l7aQM8Se7R0uCAIaO9M0d6Z4vyLY3pfmmDbphxbNubY9EKOpuYEi5dnqLs4X+tSNQWGLkmqsGIhZvfkL8tdO/IUi9DUnOCcNfUsXpamaYZdr1C1kUgEzO8pnal6ILxv25xn/box1q/bTNf8FMtWZVjQk3b4cZoydElSBcRxzN6+Ats359ixNU8+F5OpC1i2KsOiZRnaOxwW0ulLZwKWrqxj6co6RkeK7Nmd4rmn9vHYQyNk6gKWrsiwdFXGi3ZPM4YuSSqj4aECW148fEL8gsVpFi3LkJ3vApgqv4bGBBdf3sGipQV275pg84ZxNqwf54Xnxu39mmYMXZJUJnt2T/CTHwwxUYDs/BTnXZBhwaL0nLhkjmovSATMX5hm/sI0oyNFtm7MseXF8Zd7v1ZmWLqysr1fE/m4dL3PWXIR9HIzdElSGezakefRh4ZpbEpw1auaaGxyWEe109BYmjN49uo6dr80weYXSz1fLzx7SO/XGVxloFiMGd5fZHCgwP6BQum2v8jIcBGAZasyrL6wgXTGPzgOZeiSpDO0fXOOJx4eobU9yVXXNZ3wUjxSNQWHTL4/0Pu1ebL3q64+YMmKE/d+xXHM6Ej8crAaKLC/v8DQ/iLF4uQxAmhqSdDWkWTJigzjY0U2bciVLhB+aQMLF3uB8AMMXZJ0Bja9MM5Tj43SmU1yxbXNpB1K1DR1VO/Xhpd7v7ILUixdmSFTlygFq4ECg/0F9g8WmDhkNYr6xoDWtiTZhWla25K0tCVobk0etSr/khUZnnxklMceGmF+T4oLLmv0slIYuiTptD3/7BjP/WyM+T0pLru6iaSLmWoGOFHv1wHpdEBLe4LFyzK0tCVpaUvS2paY8ir87R0prn11Mxt/Ps76dWN8/18HOe+CBlaclSGYwyeTGLok6RTFccyzPxtjw3PjLFqa5uKrGj0rUTPSob1ffbsniGNoaUtS3xCc8ZBgIhGw6rx6Fi5J89Rjozz9xCjbNuW46IoG2ubNzfgxNz+1JJ2muBjzs8dG2fJijuVnZVh7aYPzVTTjBYmA7IJ0Rfbd2JTkymub2LE1z7rHR/nBPUOsPKeOc9bWz7lLXRm6JGmKioWYJx4eYcfWPGetruO8C+oNXNIUBEHAoqUZsgtSPPvkGBvWj7NjW54LL2uge2Flwt505Kw2SZqCiYmYnzw4zI6tec6/qJ7VF9rDJZ2qTCbBRVc08oobm0km4eEHhnnsR8OMjxVrXVpV2NMlSSeRzxV5+AfD7NtT4MLLG1i2qq7WJUkzWmc2xate08KG58Z5/pkxendOsPqiepauzMzqP2bs6ZKkExgfK/LQ94fp31vgsqsbDVxSmSSTAeesqee617bQ2p7gZ4+O8tD3h9g/WKh1aRVTtZ6uMAw/CLwNyAPviqJoXbWOLUmnY2S4yI/vH2J0pMiV1zTNqbknUrU0tya5+oZmtm7M8cyTY9z/nf2cvbqOs1bXH7X+10xXldAVhuFZwH8ELgWuAP4KeFU1ji1Jp2NosMCP7h9iIh9z9XXNdGSdjSFVShAELF1Zx/yeNE8/McrPnx5n26Y8519cz4JFs2dF+2p9i9wAfCeKogngR2EYrg7DMBNFUa5Kxz9M9LVnGS80E9fi4HNEwFbbt0LK0bYBRa64oI2zzmvzYszHMLBvgh/fPwzAK25onrNrCknVVlef4NKrm1iyIs/TT4zy6A9H6OxOsebiBtrmzfzrmVbrm6QL6D/k8QDQCew8sCEMw1uAWwCiKKKrq6tixaQSSSYK/qLR3FQkgEQDzz8DG58fZOXZLZyzupWu+XWz5q/J05VKpZjINfGj+3adgvFAAAAgAElEQVSSySR57Rt7aGvP1LqsWSOVSlX0u30um21t29UF554fs/7pQZ74yR5+cM9+zl7dyqVXddDQWP0/gsrVvtWqfA9w9iGPWye3HRRF0e3A7ZMP476+vooV8+/+/Tl0dXVRyWPMdbZv5Zxp2+7ZsZvf+n4f72oZYeGC89iwfpCfPzNIS1uCpSvrWLwsTWaOXrB5bLiB7/3rThoaE1x9fSP5iUH8MS4fvxcqZ7a2bXYhXP9vmvn50+M8/+wgLz4/yNnn17Pi7Lqqzvc6Wfv29PRMaT/VCl3fB34nDMPfAy4HnqvV0KI017XP7yBV3E3f6ACvv7KRNZc0sGNLji0v5nj6iVGefXKUBYvTLF2Zoas7Net7v+JiTN/uCbZtyrF9az+tbUmuelUTdfVzM3hK000mk2DtJQ0sX5Xh6Z+O8uyTY2zekOP8i2befK+qhK4oip4Pw/DLwMOUzl58ZzWOK+loyWSKrvx+euPSzLB0OmDZqjqWrapjsL/AlhfH2bY5z44teRqbEixZmWHJ8gwNjbMrhOwfKLBtU45tm3OMjcak0wHnnt/K8rMD0pmZ8yUuzRXNrUmuelUzu3fmefqnpfleXd0p1lzSQGv7zJjvVbWB0SiKPgd8rlrHk3R82XiU3YWjlz9obU+y9tJGVl8U89K2PFtezLH+qTHWrxuje0GKZavq6F6YmrEXdx4fK7JjS56tm3IM7CsQBNC9MMWaSzLM70kzf352Vg7RSLNJ98I0XfNTbN6QY/26Me6/ez9LV2Q474L6ad9D7Sk50hyUTU3wRNx63OeTyYBFyzIsWpZheKjAlhdzbN2Y45EHh6mrD1iyPMOSlRmaW6b/X5eFQsyuHXm2bcqxe+cEcQxt85KsuaSBRUvT0/5LWtLREomAFWfXsWhZmp+vG2PTCzl2bM1xzuR8r8Q0Xd/L0CXNQd11AfsKzeTyE2TSJ/4aaGpOsvrCBs5dW8/unRNseXGcDevHeeG5cRoaA9KZBOlMQCpdGqpMp4PJxy/fT6cnHx9yv5K9ZXEc07+nwNZNOXZszZPPxdTVB6w8t47FyzIzZihC0ollMgnWXtrIsrPqeOanozxzYL7XxQ3M75l+c1INXdIclG2pg37o691HT092Su9JJAIWLEqzYFGasdEi2zbl2D9QIJ+PyedjRodiBifvT+RPvr9k6uWQlqkLSNclyGRK9zN1AZlMYvL2wPOl157oS3RkuFTXtk05hoeKJJKwcHGaxcszZLtTBDN0WFTSibUcMd/rkQeH6ZpfWt9rOv2RZeiS5qBsWyP0Q+/uqYeuQ9U3JDhrdf1xn4+LMRMTpQCWzzEZxGLyufio+/lcTC5XZGiwULo/HhMfZ/XXIID0gWCWKQWxukyCdF1A/94Ce3ZPANDZneKs1XUsXJIh7eKv0pxxrPleN7yuhebW6RG8DF3SHJTNzoPNeXr3DFZk/0FicigxAzSd2nvjuNRTlssVyY2XQlguF5MbLx4MZQe2jQwV6R8vhbWGpgTnXlDP4mUZGpucpyXNVQfney1Ns3NbftoELjB0SXNS18Iugng7u/eP1bqUowRBKaylM0mamqf2njiOp93cDUm1lalLsGxVXa3LOIx/DkpzULqllXm5IXpHCrUupSwMXJJmAkOXNAcFQUC2MERv3q8ASaoWv3GlOaorkaM39mLOklQthi5pjupOx/Qlmyge71RBSVJZGbqkOSrbmGQikWLf0PSbTC9Js5GhS5qjsq0NAPS+tKfGlUjS3GDokuao7s4WAHr7BmpciSTNDYYuaY7KdncCsLt/uMaVSNLcYOiS5qjG7ixN+RF2D0/hQomSpDNm6JLmqKCunmx+kN5xz16UpGowdElzWDYepW/Cq4FJUjUYuqQ5LJucoDdoqHUZkjQnGLqkOSxbn2AkWcdQbnZcg1GSpjNDlzSHZZtLlwHavXeoxpVI0uxn6JLmsGx7IwC9u/fWuBJJmv0MXdIcNj87D4DevftrXIkkzX6GLmkOa+vuJF3Ms3vQ6y9KUqUZuqQ5LGjvpGusn97RiVqXIkmznqFLmsOCVIpsYZjenF8FklRpftNKc1w2GKc3rqt1GZI06xm6pDmuOx3Tn2wgVyjWuhRJmtUMXdIcl21MAtA75IWvJamSDF3SHJdtLV0GqHfPQI0rkaTZzdAlzXHdHS0A7O41dElSJRm6pDmus3seibhIb/9wrUuRpFnN0CXNcamubuaND9I7lKt1KZI0qxm6pLmupY3seD+943GtK5GkWc3QJc1xQRCQZYzeQqrWpUjSrGbokkQ2OUFf0EChaG+XJFWKoUsS2bqAQpBg35jXYJSkSjF0SSLbnAagd/94jSuRpNnL0CWJ7nlNAOze3V/jSiRp9jJ0SSLb1Q5A777BGlciSbOXoUsSDV1ZmvPD7B4cq3UpkjRrGbokQUcX2bF+ekcKta5EkmYtQ5ckgoZGsvlBevN+JUhSpfgNKwmAbJCjN84Qx67VJUmVYOiSBEB3JmYsSDOUK9a6FEmalQxdkgDINiQB6B3O17gSSZqdDF2SAMi21gOwq3+kxpVI0uxUtivchmH4NuB9QBJ4EXh7FEX5MAx/E/gDYDswEUXR9eU6pqTyyXa2whD07RmAlR21LkeSZp1y9nQ9ArwqiqIrgPnA6ya31wGfiqLoGgOXNH21dXWSKeTYvW+41qVI0qxUttAVRdHPoyjKhWEYAK3ArsmnuoFlYRiuLtexJJVf0NlFdmwfvcO5WpciSbNS2YYXD/FZ4Ikoin4y+fg+4Drgb8MwfD6Korcf601hGN4C3AIQRRFdXV0VKO1lqVSq4seYy2zfyqlU28ZtrWTHH2ZPrnXO/tv5c1tZtm/l2LaVVa72DU5nTZ4wDH8d+NARm38JeA9wHvDWKIomjnhPElgPvC6KohdOcoh4x44dp1zXqejq6qKvr6+ix5jLbN/KqWTb/sXnv8rDnWv4ytsvrMj+pzt/bivL9q0c27ayTta+PT09AMHJ9nNaPV1RFN0B3HHotjAMbwCuB248NHCFYZiOoihPaW5XChg6nWNKqrxscoLBIMP4RJG6lCc3S1I5lXN48WZgEfD9MAwBvhlF0aeBe8IwbKI0f+z/jaLopTIeU1IZZetLf6j1juRZ3FpX42okaXYpW+iKouj9wPuPsf36ch1DUmV1N2egALuHDF2SVG6OH0g6qKutEYDefc4CkKRyM3RJOqgrO49EXKB3z/5alyJJs46hS9JByY4uOsYH6d0/VutSJGnWMXRJellHlu6xfewemTj5ayVJp8TQJellre1kxwfozfvVIEnl5jerpIOCRIKuYJw9xTSF4qkvnCxJOj5Dl6TDZDNFikGCvaMOMUpSORm6JB2muyEJQO9wvsaVSNLsYuiSdJhsawMAu4fGa1yJJM0uhi5Jh8l2tACwe69rdUlSORm6JB2mvquL1twQvftGal2KJM0qhi5Jh+voIjveT+9QrtaVSNKsYuiSdLh5WbJj++gdd8kISSonQ5ekwzU2kc3vp7eQIo4NXpJULoYuSYcJgoBscoJxkuwfL9S6HEmaNQxdko7SVR8AsHvYBVIlqVwMXZKO0t2cAVwgVZLKydAl6SjZ9kYAdu8fq3ElkjR7GLokHaWlo4P6wji9LpAqSWVj6JJ0lERnV2nZiEF7uiSpXAxdko7WkSU71s/uESfSS1K5GLokHW1eJ13j/fTm/YqQpHLxG1XSUYJMHdl4lP1xirGJYq3LkaRZwdAl6ZiyqVLYctkISSoPQ5ekY8o2JgFDlySVi6FL0jF1t9YDsNvQJUllYeiSdEzzOlpJFgvs7h+pdSmSNCsYuiQdU7IzS+f4AH2GLkkqC0OXpGMK5nWRHd/H7qFcrUuRpFnB0CXp2DqypVXpx+NaVyJJs4KhS9Kxtc8jOz7A3kKSiaLBS5LOlKFL0jEFiSRdyTxFAvaMeAajJJ0pQ5ek48rWBQD0DXsNRkk6U4YuScfV3ZQGXKtLksrB0CXpuLLtTQCewShJZWDoknRcmc5O2nP76R1wrS5JOlOGLknHFXRk6Rrbx+6B0VqXIkkznqFL0vF1dJEd66dvtFDrSiRpxjN0STq+eVmy4/vozQXEsWt1SdKZMHRJOr7mFrL5/eRIMDBub5cknQlDl6TjCoKAbLoIQK/LRkjSGTF0STqhbGMKcK0uSTpThi5JJ9TdWge4Kr0knSlDl6QTamqfR8PEGLv3j9e6FEma0Qxdkk4o6OwiO7aP3S6QKklnxNAl6YSCjizZ8X56vRSQJJ0RQ5ekE5tclb53zHW6JOlMpMq1ozAMlwOPA89MbnpPFEVPhWF4HfBZIAD+NIqifyzXMSVVweSq9EPFBCP5Ao3pZK0rkqQZqWyhC6gDvhNF0VsPbAjDMAF8AfhFoB/4aRiG/xJF0WAZjyupgoK6eroZA0pnMC5tN3RJ0uko5/BiN9AWhuFVYRge+FZeBQxEUbRlMmg9DlxVxmNKqoJsadUI1+qSpDNQzp6u7cC9wK3AOWEYXgt0UerhOqB/cttRwjC8BbgFIIoiurqO+bKySaVSFT/GXGb7Vk4t2rZnXhMAo0HdrP539ee2smzfyrFtK6tc7XtaoSsMw18HPnTE5l+Koui/Tz7/t8BbgAeA9kNe0w70HWufURTdDtw++TDu6zvmy8qmq6uLSh9jLrN9K6cWbdvcVEeqWODFXfvo60tX9djV5M9tZdm+lWPbVtbJ2renp2dK+zmt0BVF0R3AHYduC8MwPXkbAC3AIPACpSHHpZR6uS4BHj6dY0qqnURHF53b++nd31LrUiRpxirnnK5PhWH4KPAo8BJwZxRFRUpDhl8H7gM+5iR6aQbqyJId20fv4FitK5GkGatsc7qiKPrwcbbfB1xeruNIqr5gXhfZsS38bKRQ61IkacZycVRJJ9fRRXa8n715yBdcJFWSToehS9LJtXeSHe8nJmDPiMtGSNLpMHRJOqkglSKbnACg19AlSafF0CVpSrKNpTWPe4cnalyJJM1Mhi5JU5JtaQBclV6STpehS9KUpDs7mZcbZPeQoUuSToehS9LUzOsiO7qP3sHRWlciSTOSoUvSlAQdXXSN99M3lKt1KZI0Ixm6JE1NR5busX30jscUY9fqkqRTZeiSNDUdXWTH9pGPAwbGXJlekk6VoUvS1DS3kc3vBzyDUZJOh6FL0pQEiQTZutL9XkOXJJ0yQ5ekKetqzgCGLkk6HYYuSVPWPK+NxsKYoUuSToOhS9LUzcuSHd3nAqmSdBoMXZKmrrN0BmPv/rFaVyJJM46hS9KUBfOyZMf2sXvEi15L0qkydEmauo4s2fF+RgoBwznX6pKkU2HokjR1kwukgmcwStKpMnRJmrKgoZFsXLrgde+wQ4ySdCoMXZJOSbYxBbgqvSSdKkOXpFPS1tpIujjh8KIknSJDl6RTkuzI0pUbpHfE0CVJp8LQJenUdHSRHd1D7/5crSuRpBnF0CXp1MwrncG4e8jQJUmnwtAl6ZQEnVmyY/3sy8XkC8ValyNJM4ahS9KpmffyWl19rkwvSVNm6JJ0auZ10TXeD7hAqiSdilStC5A0swTpNNlU6RJAlV6rq1CMGcoV2D9eYHD8GLeHPNfdlOb6Fa1ctKCJZCKoaF2Spr9iHLNh7xhndzbUupSDDF2STllncx1BHE+ppyuOY3KFUngazhcZyRUZnrw/nCswnCuyP1dgcHziqFA1nCsSH2e/6URAS12S1rokzXVJHtsxxP2bBpnXkOK65a3csKKV5fPqy/vBJc0I6/tG+eKju3hh7xh/+fqV9LRmal0SYOiSdBrSHR3MmxjmiZ0NJILgkBBVZDhfCksjk7fDuQKF4yWnSXXJlwNUS12S7qb0YY9b61IvP84kaa1PUpcMCIKXe7TyhSKPbB/i+xsH+eZze7nr2b2smFfHjSvbeNWyVtob/LqTZrve4Txf+WnvwT/A3vcLC1nQkq51WQf5LSTplAXzuljZu51H082s7xujLhnQlEnSlEnQlE7SXp9kUUuGpkyCxnTisOeaMpOP0wkaJ2/rUmc+vTSdTPCKpa28YmkrA2MT/GDzIN9/cZAvPbabLz++m0sXNnHDyjauXNxMJul0Vmk2GZ8o8k/P7OX/PLOHOIZ/v6aTN6/ppCE9vf5fN3RJOnUdWW6998uMfurvaG5vJTXN5lC11ad4/bkdvP7cDrb0j/P9jQPcv3GQRx/cQVM6wTXLSsOP52UbDustkzSzxHHM/ZsGueOnvewZmeCVS1v4jUuyzG+eHsOJRzJ0STplQUcXqbhI28hego62WpdzQkvb6/iNS7p5+0VZnto1wvc3DnDfxgG+80I/C5rT3LCijetXtLKgZXp+SUs6tvV9o3zpsV2s7xtjVUc9H3llD+d3N9a6rBMydEk6dR3Z0u2ePli8ora1TFEyEXDxwiYuXtjEu68o8OOtQ3z/xQH+8ak+vvpUH+dnG3jTRQWu6E6QsPdLmrb6RvJ85Yle7ts0yLz6JO//hQXcsLJtRvx/a+iSdOo6ugCI9/Uy/b/mjtaYTnLjyjZuXNlG73Ce+zcOcu/GAf7ku89z3fJW3n/1wmk3ZCrNdUfO23rLmk7evKaDxnSy1qVNmaFL0qlrbYdkEvb21rqSM5ZtSvOWtaUv73/dNMZfP7SZkXyB/3zNorJM8Jd0ZuI45oFNg/zdDJm3dSKGLkmnLEgkob2T+GePUpxF11+8uaEBkq3cvn0FfxQ9wu+n1tMYFGpd1qyxv6GB4uhorcuYlWZr2/682MyXCsv5edzCymCID6U2cf5L++FfYarfPMEb/gNB/fRYINXQJem0BGsuIf7xfcT3/UutSymbkSDgtXFMY9cF/I+z3szH9y/ivz5zB60TI7UubVYYCQKIT7Jom07LbGvbPZkW/n7pa7i/+wLac/v57S1f54bdPyVBfNwFk48neO3NME1CVxBPz3+keMeOHRU9QFdXF319fRU9xlxm+1aObVs5h7bto9uH+PQPttPdlOaPblpCV+P0WWBxpvJnt3JmS9sWijHf+vk+/uHJXgpFeOPqjmkxb+tk7dvT0wOcfIqrExYk6RguX9TMH9ywhD0jE/ze3ZvZuT9X65KkWe3FvWPc+p3NfOmx3azpbuQv37CCX7s4W/PAVU6GLkk6jrXzG/nkLy5ldCLmd+/ezKZ9Y7UuSZp1xiaKfPnx3Xz425voG8nzkVf28F+vXzwjJ8qfjKFLkk7grM56/turl5IMAn7/u1t4rnf2TVaWauWx7UO8759f5K5n9/LqVe385etXcu3y1ll7pQhDlySdxJK2Oj71mmW01iX5+Pe28NOdw7UuSZrR9o1O8KcPbucT920jk0zw3169lPdetYDmutkzlHgshi5JmoLu5jSfevUyeloz/PF9W3loy2CtS5JmnGIcc/cL/fz2P7/Ij7cO8asXdvHnv7R82l++p1wMXZI0Re0NKT75i0s5q6OBP31wB9/d0F/rkqQZY+vAOB+7Zwt/+fBLrGiv4/P/djm/ckEX6eTciSJlW6crDMO/BtZMPlwCfDuKoneHYfiHwK8Ae4BtURT9h3IdU5KqrTmT5I9uWsJ/e2A7t/34JYZzRd64uqPWZUlnbHBsAoKA1jIP8eUKRe58eg//5+k91KcSvO8XFnDTyrZZO2/rRMoWuqIoejdAGIYNwKPApyafqgM+EEXRd8p1LEmqpfpUgv9y3SL+7KGd/M3juxnKFfjVC7vm5C8RzWyFYsxPdw7znRf6eWT7EMUY5jWkWNZex7K2TOm2vZ4lbZnTuizWU7uG+auHd7Fjf47rlrfyW5d1014/d9dlr8QnvwX4VhRFGycfdwPnhmH4fBRFL1bgeJJUdelkgo+8soe/Sr9EtG4Pw7kC77x8PgmDl2aAPSN5vrdhgHs29LN7eIK2+iRvWt1BW32Szf3jbO7P8a/P95MrlBZQD4CFLWmWtdextL1uMozVsbA5Q/IYF4ffP17gb5/YzXc3DDC/Oc0f3riESxY2VflTTj9lXZE+DMMAeA54VRRFuya3/RJwFfBm4BtRFP3+cd57C6XARhRFl+VylV2IMJVKMTExUdFjzGW2b+XYtpVzOm0bxzF/+eAmvvr4dl57Xpbf/8WzSc2hOSqnwp/dyplK2xaKMY9s6ef/rtvJD1/cSyGGK5a288a1C7hmZcdRc6sKxZjtA2Ns6Btm454RNuwZZkPfCNsHRilORodMMmB5RyMru5pY2dnIqs4m9o3m+IsfbGL/WJ63XrqY/3jVEupn+AKnJ2vfTCYDU1iR/rRCVxiGvw586IjNv0RpLtcfRFH0S8d4TzOwDVgQRdHJVhj0MkAznO1bObZt5Zxu28ZxzP9+eg//8GQfVy5u5j9f00PG4HUUf3Yr50Rtu2ckz/deHOCeF17u1bppZRuvOaudhS2nvgDp+ESRbYO5yR6xl//bO/pyKDm7s57fvmoBK+bVn/Znmk7KdRmg0xpejKLoDuCOI7eHYfhO4LtHbEtHUZQHmoBxIH86x5Sk6SoIAsK1XTSlk9z+6C5++5sbuX5FK9ctb2VxW12ty9McVIxfnqv1k22luVoXLWjkNy/p5srFLaSTpz8MXpdKsKqjnlUdhweqwfECW/rHGZsocsnCpmMOO8515Z7TdQHwDwcehGHYCtwbhmGRUgJ8VxRFhTIfU5KmhX977jzmN6f55vp93Pn0HqJ1e1jVUc/1K1q5dlkr8xrm7gRiVcfe0Qm+u6Gfe14YYPdwnra60lyt0+3VOhWtdUnWzp8b622drrLO6SojhxdnONu3cmzbyiln2+4dneDBzYPct3GQDXvHSARw4YImrl/eylVLmmfVRXynyp/dyigUYzaNpvjfj23mJ9uGKMRw4YJGXntWO1edYa+WSmo6vChJOrGOhhS/fF4Hv3xeB9sGxrl/UymA/fmPdpL5ScAvLG7huhWtXLywiZTDMDoNm/aNcd/GQR7YNMie0Qla65L88nmlXq2e1tl3sejZwNAlSRW2uK2Ot12U5Vcv7OK53lHu3zTIg5sHeWDzIK11Sa5d1sJ1K9o4p7Petb50QntG8vxgsgd1475xkgFc2tPMBy5azOrW4pxa3X0mMnRJUpUEQcDq7kZWdzfyjsvm88TOIe7bOMg9Gwb41s/7WdCc5roVrVy3vI1F9lRo0mi+yI+37ue+jQP8bNcIxbh0duAtl8/nmmUttNWnHLqdIQxdklQD6WTAlYtbuHJxCyP5Aj/asp/7Ng0SPbWHrz21h7M66vmFJc1csaiZZe119oDNMYVizJMvDXP/xkF+tHU/44WY+c1p3rKmk+tWtLK41bNiZyJDlyTVWGM6yU2r2rlpVfvB4aMfbNrP3z/Zx98/2Ud3U5orFjdz5aJm1nQ3OjF6lorjmI37xrlv4wAPbBpk31iBpkyC61e0cf2KVlZnGwzfM5yhS5Kmkc7GNG9a3cmbVneyZyTPYzuG+cm2Ie55oZ9vrd9HQyrBJT1NXLmomcsWNZf94sQ6uYlizF3P7uV7G/pJJxM0pRM0ZRI0ppOH3TZnkjSmEzQdvE3QNPncoYvn9o3kuX/jIPdtHGDLQI5UAi7raeb6Fa1cvqjZhXZnEUOXJE1TnY1pXnNWO685q53xiSI/e2mEn2zfzyPbh3loy34SAZzX1cAVi5q5cnEzi1ozZe0JKRRj+scmKBShuzldtv3OZM/2jvD/PbyLzQPjXDi/kYZ0guF8kT0jE2zJ5xjJFRjOFw9eJud4UomApkyChlSCXUN5YuDcrgb+nyvm88plrYbpWcrQJUkzQN3/3969x0hVn2Ec/57dWdgbC7ssy2WBQhWxNUK5e0FZNbVBLU2b5m2L1qSiWGibpq1tUm0b0zRWWxs1waaiTRFNbH5tjIpabaI16QWRa9UqKvVShGVZ7sy4y86tf5yzMqyLwO7MOczM80kms/PbYfblzeHsw5kz74lVMGd8PXPG15PJZvnvvm7W74jz0vtxHtzSyYNbOhk7rIo5rf55YJ9uqT3uKIojqQz7u1Ls7075911p9nX1fn10/WB3mt7s0Da5gSUzW2ioLs9fG/GeNA9t6eSZtw7QXBvj5gWtzBs/rN/nZrNZulNZEsk0H/RkSARBLNGT5oNkhkRPhkQy/eF92+QG2iYPL/jwUoleef7rEREpYhWex5SRNUwZWcPiaaPoTCTZsCPO+h1xnn7zAE9s3U/dkApmja2nqTZ2bKDqSpFIZvp5TRhRHaOxJkZzbYwpI6tprInRWB2jM5Hk8a372LgzwZKZLbRNbiibc4uy2Sz/eO8wD2zs4NCRNIvObmTxtFHUVB3/LT/P86ip8vznaEC75FDoEhEpcqPqqlh4ViMLz2qkK5lhy64E69+Ps2FnnK5khqaaGCOqY0wYPpTpY2r9MFUToym4b6yJ0TC0koqPCVJtk4ezYt0u7l7bzgvvHmL53NGMri/tIzMd8R5+91IHm9oTnNFUzc8umfCR6w2KnAqFLhGRElJTVcH5E4Zx/oRh9F7mLR9HpSaOGMrtl0/kL28e4KEtnXz7yXdYPK2ZRWc3ldyFjVOZLE+8vo9HXtlDhedx/awWrjirseT+nhI+hS4RkRKV77cAKzyPK6c2Mm9CPfet72DV5k7+/t4hvjVvbMkcAXpjTxf3rtvFeweOMG98PTfMHs2oOn2IQPJDoUtERE5Jc20VN1/cytrth1m5voObnnmXRWc38bVpzVTHinO8QSLnRPmm2hg/vriV8yb0f6K8yEApdImIyCnzPI8LJjYwbUwdqzd38tjr+1i7/TDL5o5hxti6qMs7adlsln9tP8z9G3ZzsDvFVVMbWTy9mdoqjWyQ/FPoEhGRAasfUsnyeWNYMKmBFet2cevz22mb1MCSWaf/eImOeA8r13ewYWeCTzYO5ZYFrUwZWRN1WVLCTu9/ESIiUhTOGV3LPVdO4k+v7uXR1/aysT2/4yWOpDLsTiTZ35XC8yDmeVRWeMQq/PvKiqNrlRUeMXEeXRgAAAa7SURBVI9jv+8dPcctncnyxNZ9PPLyHjwPrpvZwlVTdaK8FJ5Cl4iI5MWQygqunj6Kiz7RcHS8xDsHWTZ3DGNOMPizJ52hM5FidyJJR7yH3fFk8LV/f6A7Pej6Kjw+HBjbk84yp7WeG+foRHkJj0KXiIjkVd/xEt95yh8vsXBaHVvbE+xOJNkdT9IR3O9OJNnXlTrmNSo9f/5YS30Vs1vrGV1fRUtdFU01/q+tVCZLOgPpbJZ0Jus/zvauZ4P1o49TwePer89tqWXu+PqyGfIqpweFLhERybv+xkus2tyZ833/U5At9VXMGFtHSxCqcsOV3u6TUqPQJSIiBdM7XmJze4JkZQ21dNNSV0VzbZVClZQdhS4RESkoz/OYOa6e5uZm9uzZE3U5IpEpzil2IiIiIkVGoUtEREQkBApdIiIiIiFQ6BIREREJgUKXiIiISAgUukRERERCoNAlIiIiEgKFLhEREZEQKHSJiIiIhEChS0RERCQECl0iIiIiIVDoEhEREQmBQpeIiIhICBS6REREREKg0CUiIiISAoUuERERkRAodImIiIiEQKFLREREJAQKXSIiIiIhUOgSERERCYFCl4iIiEgIFLpEREREQqDQJSIiIhIChS4RERGRECh0iYiIiIRAoUtEREQkBLGB/kEzawWeBO5wzv0xWFsA/AbwgF/nrP8KuASIA9c453YMtnARERGRYjKgI11mNh94CkjlrFUA9wNfwg9Yt5lZg5ldBkxzzs0BVgK/HHTVIiIiIkVmoEe6XgJmAb/PWTsDOOic+x+AmW0C5gFtwJrgOWuAO/t7QTNbCiwFcM4xbty4AZZ28sL4GeVM/S0c9bZw1NvCUn8LR70trHz0d0BHupxzPc65dJ/lZuBAzuMDwdqH6865ODDiOK+50jk32zk3G//tyYLezGxjGD+nXG/qr3pbjDf1Vv0t1pt6e1r094ROeKTLzK4Fvt9n+Qrn3M4+a3s5NlCNAPbkrptZHccGMxEREZGycMLQ5ZxbDaw+idfaBgw3s4n4wWoGsA7IAD8E7gU+Dzw34GpFREREilTeRkY45zL452Q9CrwA3OKcO+Scew542cw2ADcCN+frZw7SyqgLKHHqb+Got4Wj3haW+ls46m1h5aW/XjabzcfriIiIiMjH0HBUERERkRAodImIiIiEYMAT6YuZmX0PuBpIAjc4516NuKSSYWYvANX4g3PXOOfuiLai4hcMHr4NmO6cW2hmw/E/3DIO+A+w1DnXE2WNxayf/k4CNgGvBU9Z5px7Jar6ilXQx5VALf4+YSnwarB2DrATuNY5dzCqGotVf711zm0yswSwOXjaz51zf42oxKJmZlOBVUAWSABfxf+dNuj9btkd6TKzM4FvAOfhj8L4bbQVlZyhwALn3HwFrsELAsE/gakcnQNzE7A2uMrDEeCaiMoresfp71Dg2WAbnq/ANWC7gOXOufnACuAW4OtAd7Dtvgj8IML6itlHemtmQ4GXc7ZbBa6B2wZ81jl3AfBv4DrytN8tu9CFf4miZ51zKefcWuBTZjYk6qJKyAhgvpk1R11IKQg+FXwpcE/O8qUce5WHy8Kuq1Qcp78t+ONv5plZZTSVFT/nXLdzblvwcAR+UNC2mwfH6W0LMMTMLgwCmAyQcy7tnIsH/ylrBdrJ07ZbjqGr7+T8g8DIiGopRXfjz2PbYGZfiLqYUuCc6+qzlLsN9175QQaon/7uAJ4HfgRsMbN+r6IhJ8fMzge+CfwCbbt51ae3XcDD+KOZXjezyVHWVuzM7DPAm8C5wNPkadstx3O69gJTch43BGuSB865+wDM7DH8HcHj0VZUknqv8rCDo1d+kDxxzr1NcI1YM1sFfBl4IMqaipWZzcA/N2aRc67dzHKvXKJtdxD69jZYviv43q3AEuAnkRRXApxzW4AzzWw5/vlzednvluORrr8BnzOzmJmdB2zVScj5YWa5Ib4BOBRVLSXuOfyjiQBXoas85JWZVQX3HjAMbccDEvTxIeArzrk3gmVtu3nQX297t9uA9r+DELyt2Gsb/lGtvGy7ZXekyzn3lpn9Af8SRUng+ohLKiUXmtldQA/+oe5lEddTqu4EVpvZevxP2D0ccT2l5nYzW4B/Yv2LwJ8jrqdYnQtMAlaYGfif/rocuCjYdtvxT6yXU9dfb9eY2WIgDbwN/DSy6orfF83sJvy+ZoHv4vd00PtdTaQXERERCUE5vr0oIiIiEjqFLhEREZEQKHSJiIiIhEChS0RERCQECl0iIiIiIVDoEhEREQmBQpeIiIhICBS6RERERELwfw2zGVcQ3uOGAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 6.932327890749818 \n", + "\n", + "\n", + "fftfilter\n", + "48.78733600000123\n", + "gamma total\n", + "56.30950300000404\n", + "coch1\n", + "4.869880999998713\n", + "coch2\n", + "5.255903999997827\n", + "get avg\n", + "2.2296990000031656\n", + "fftfilter\n", + "44.417749000000185\n", + "gamma total\n", + "51.99644599999738\n", + "coch1\n", + "4.946609000005992\n", + "coch2\n", + "5.295834999997169\n", + "get avg\n", + "2.3441380000003846\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 2.049097518140041 \n", + "\n", + "\n", + "fftfilter\n", + "9.239594999999099\n", + "gamma total\n", + "11.816694000001007\n", + "coch1\n", + "3.4039110000012442\n", + "coch2\n", + "3.503117000000202\n", + "get avg\n", + "0.07445100000040838\n", + "fftfilter\n", + "10.535848999999871\n", + "gamma total\n", + "13.132939000002807\n", + "coch1\n", + "3.392109000000346\n", + "coch2\n", + "3.5162969999946654\n", + "get avg\n", + "0.07364500000403496\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 1.858501544975532 \n", + "\n", + "\n", + "fftfilter\n", + "9.257249000002048\n", + "gamma total\n", + "11.833840999999666\n", + "coch1\n", + "3.3653249999988475\n", + "coch2\n", + "3.4810030000007828\n", + "get avg\n", + "0.07510199999524048\n", + "fftfilter\n", + "9.2804049999977\n", + "gamma total\n", + "11.83969700000307\n", + "coch1\n", + "3.4134789999952773\n", + "coch2\n", + "3.4813219999996363\n", + "get avg\n", + "0.0760840000002645\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 33.75854159797711 \n", + "\n", + "\n", + "fftfilter\n", + "8.80833100000018\n", + "gamma total\n", + "11.476406000001589\n", + "coch1\n", + "3.394808999997622\n", + "coch2\n", + "3.510561000002781\n", + "get avg\n", + "0.07592000000295229\n", + "fftfilter\n", + "9.036376999996719\n", + "gamma total\n", + "11.746940000004543\n", + "coch1\n", + "3.386140999995405\n", + "coch2\n", + "3.506092000003264\n", + "get avg\n", + "0.07330500000534812\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 9.417486795039785 \n", + "\n", + "\n", + "fftfilter\n", + "9.457461000005424\n", + "gamma total\n", + "12.237295999999333\n", + "coch1\n", + "3.4145600000047125\n", + "coch2\n", + "3.5267909999965923\n", + "get avg\n", + "0.08002599999599624\n", + "fftfilter\n", + "10.08555499999784\n", + "gamma total\n", + "12.724136999997427\n", + "coch1\n", + "3.421728000001167\n", + "coch2\n", + "3.5185070000006817\n", + "get avg\n", + "0.07434700000158045\n" + ] + }, + { + "data": { + "image/png": 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oLTfx+t4RWhol8JqN1NwSkQTffq2Lxr7gjN+vlGKyJZAWFtv46MbKS74/EAjQ0dHBsWPHCAaD3HfffXz1q19lcHCQj3zkI9x9991EIhGefvppGhoaePHFFxkeHubtb387N910Ew0NDfzyl7/kmWeeIT8/nz//8z+/aB+RSISPfvSjPPDAA9x2223nZbc8Hg+rV6+e8ecXQgiReTrG1llcmvw2EVNlMik2X+dk3x4/b7w6gq7r1C3KnPFlE8l0JYDNZuPee+/lueeeA8BgiB9Ws9nM7t27eemll7jqqqvYsWMHw8PDnDlzhj179vDe976XsrIyrFYr5eXlF2339OnTRKNRbrvtNgDsdnvqPpQQQoiUazoVwuE0UFGVWTkRk0mxeauT8ioTB/cFaDolSwbNRGb9q07D5TJRl5PoBa8ny5zFYjHuvvtu/uZv/ua8x3ft2kUsFrvkNjVNm3Dx0bGgTgghRO4Y7I/h64lRv8aWkQtPG02KTVudvPain0P7A+g6LMygjFw2kKv3LEQiEbq6uojFYnzve9/jmmuuueg11113HT/96U9pa2sDOLdK+fbt23niiSdoaWkBmDBgW7JkCZqm8dRTTwGcm14sLy/n2LFj5z0mhBAiuzWdCmEwQu3C9BbQX4rRqNh4rZPKGhOHDwQ4c3zmZT5zkQRdsxCNRvnABz7AmjVr6Orq4jOf+cxFr7nttttwu93cfvvtrF69mvvuuw+ArVu3cs8995wriN+zZw/V1dXnvddisfD444/z7W9/m1WrVrFjxw4ikQj3338/jzzyCGvWrOGJJ55IyWcVQgiRPJFwfJ3FmjoLliSus5gIRqNi4xYnVfPMHHkjyOkGCbymSk1WTJ5ment7+3kPjIyM4HA4EraD2U4vtrS0cPPNN5/LOGWaRB+v6SorKzuX1ROJJcc2eeTYJpcc38k1nghx+PUA192UR1HJ9Ct/0nFsNU3n9VdGaG+JsOIKG0vrbSndfypd7vi6XC6Ay84JZ21NlxBCCJELzltncQYBV7oYDIp1VztQaoSGQ0F0HZatyt3AKxGy5183w9TW1mZslksIIUT28HZHGR7SWLs5fbMTM2UwKNZd5UAZRjh+OIim6SxfnZk3AmQCCbqEEEKINGo6GcZiVbjqzOkeyowog2LtJgdKBTh5NN5Nf+WV0uJoIhJ0CSGEEGkSGNHobI+wZLkVozF7s0PKoFizyY5ScOpYiNJyExXV2RlEJlNm3yIhhBBC5LCzp0Ogw/wlmdsmYqqUUqxeb8fhNHD0YABdy8gb9dJKgi4hhBAiDTRNp/lMmEqXCYcz/essJoLRqFi5xsbQgEazrNN4EQm6hBBCiDTo7ogSCubeOobV88wUlxk5fjhINCLZrvEk6EqQZ555hh//+Mc8/PDD/PKXvwTivbxuvfVWVq1axac+9SkikQj33HMPq1evZtu2bekdsBBCiLRqaQxjtSkqqnOrvFopxaq1dkJBnVPSOPU8ufUvnUaHDh1iYGCAhx566Nxjjz32GFdeeSVPP/00kUiEX//61zQ1NfHaa69hNOZGKlkIIcT0hYIaXe0RFi23YjBkbwH9ZIpLTbjqzJw+HmL+Yit2h+R4QDJdSdXU1HRuPUaz2UxjYyMbNmzAZrNhNstdHUIIMVe1NoXR9cxeZ3G2Vl5pAx0aDskawWOyNtN1+MAIg/2xGb9fKTXhItMABUVGVq+/fJO673//+zz22GNEo1Hsdjs7duzg/vvvZ9WqVXzsYx9D0zQ+8YlP8OlPf5q7774bTdP48Y9/zFNPPcX69ev53ve+N+PxCyGEyE66rtPcGKa41Eh+Qe7OejicRhYus3K6IcTCpdGs6rafLJLpmqFjx47x6KOP4vF42LdvH1u2bJnwdd/85jc5fPgw9957LwB33nknhw8floBLCCHmqH5fjOFBLaezXGOWrrRhsSqOHgxOmuiYS7I27JxKJupSZrvg9UsvvcSNN97IggULAKiurmZgYGBWYxJCCJH7WhrDGIzgqsv9oMtsUSxbZePwgQBd7VGqauZ2aY1kumbIYDAQi818elMIIcTcE43qtDWHcdWaMZtzr4B+IvMXW3DmxxumanO8YaoEXTN03XXXsXPnTo4cOQIgaVMhhBCX1dkaIRqB2oW51ZvrUgwGRf0aO/4hjbOn53bD1IROL7rd7vcCfzv6qxW4ElgOHACOjj5+j8fjOZTI/abDkiVL+Kd/+ic+/vGP4/P5MJlM/NVf/RWHDmX9RxNCCJEkLY1hHHkGSstzt4B+IpUuE6UVJo4fDjJvvhmzZW7mfFSyMjRut/sRoBd4CnjQ4/F8YBpv19vb2897YGRkBIdjdnVc4822pivTJfp4TVdZWRlerzdt+89lcmyTR45tcs314zsyHOO5Xw+x/Aoby+ptCd12Nhzbgb4ou58ZZvEKK/Vr7OkezrRc7vi6XC6Ay84XJyXUdLvdFcDtwL8AFUCh2+2+yu12z63QXggh0iSm6Rzs9NMxNLenczJJS1P836J2Qe4X0E+ksNjEvAVmGk+EGBmemzXRScl0ud3uLwBDHo/nn91u9yLgfcAWYBlwncfj6Z/gPXcBdwF4PJ4N4fD5XxRdXV1YrXNnDny2QqEQlZWVadt/rmcS00mObfLkwrFt6Qvwq6Nd/PZYN73+MHazgS/esoJrF5Wke2g5cXxnStN0nvj+WYpKLNx8qyvh28+WY+sfjvLkD89St8DJtrdXpXs4U3a542uxWGAKma5kBV2HgZs8Hk/HBY//F7DH4/F8+zKbuGh60e/343Q6EzbGbDlBZyrRx2u6siHVna3k2CZPth7bYFTjpeYhfn+qn6M9AQwKNricXDe/gF809NHYF+SjGyp51/LitI4zW49vIvR0RnjleT8btjiS0ioim45tw6EAJ4+G2Lojj+Ky7OhclajpxYR/WrfbXQdoYwGX2+02ezyeiNvtVkA+MDjTbeu6jlJz4xbb2ZA7KYXIfbquc6I3yLOn+3mhaYhAVMOVb+ZDa8u5cWEBpY54P6SravP5+p52vvVaFx3DYf7XugqMObjWX6ZraQxjtigq53ifKoAlK2w0nwlz5I0A1+7Im1PX9WSEmFcAZ8b9/k9ut/sG4hHgK8ATM9moxWIhFAphsyW2+DDX6LqO3+/Hbs+uIkUhxNQMBKPsahzk2dP9NA+EsRoV187P522Li6gvt190AbOZDHz2+hq+c6Cbpxv66B6O8IlrXdhMc/PusXQIhzU6WiPMX2zBaJw7AcZkTGbF8tU23nwtQEdrBFft3KlxS9rdi7N00fQiQDAYJBaLJSQqtlqthEKhWW8nk4z9W9psNozG9N6zkE2p7mwjxzZ5MvXYxjSd1zv8PHt6gH1tQ0Q1WFZq46YlRWydn4/DPLX/vz/d4OM7B7pZVGzj77fNo9ie2qmdTD2+ydZ0MsShAwGuvzmPwuLkHPNsO7a6pvP8M0PEorDtHfkZH4xm7PRiMiUyy5VtJ6gQYu7pGArz7OkB/nBmgN5AlAKrkXctK+Zti4uoK5r+jUW3riihIs/Mo3vaeeB/mviHbbUz2o6YnubGMAVFxqQFXNlIjTZM3bvbT9PJEItXzI1ZLDkDhBAig+h6PKv15FEfh7tGMChYV+3kYxsr2ViTh3mWGYGr5uXz5Zvm8/CuFj7zzFk+fX0Na6rSd9NNrhvsjzHQF2P1Oin5uFBFtZnyKhMnj4aoXWjBYs39Ke/c/4RCCJEljnsD/P1zLXzxD610DYX54Joyvn3bYj5/Yy1b6vJnHXCNWVJq42u3LKDMYeaLO1t49vRFXXxEgjQ3hjEYoGa+FNBPpH6NnUhU58SRYLqHkhKS6RJCiDRr7g/xg4M97G0dptBq5GMbK3j7kiLMxuT9XVzuNPOVm+t45IU2Hnulk86hCHeuKZtTd5IlmxbTaW0KU1VjnhNZnJkoKDJSt9BC06kwC5ZaycvP7R7qEnQJIUSadA9H+PGhHnY1DmI1GrjzyjJuXVGC3ZyaC7TTYuQfbqzlf7/ayX8f6aVrOMJ9W6qSGuzNJZ3tESJhndqFc+fuvJlYvtpGW3OYYweDbNqa21PdEnQJIWZE+/6/o7+yK93DSKgupSAFd3T3m508WXM9/1O1GYXOuzv28r62FyjYPQJAKhdIUcBfAJU11/EDbqbn0Jt8puFH5EcDCd9Xqo5vpmhedS+2vFpKHr6LGMn93Nl8bM3A4rp3cSJ6G91/9zlKB04kdPuGrzyOKihK6DZnSoIuIcSM6Edeh/Iq1Kr16R5KwtjtdgKBxAcbY0Z0I7+MVfMLzUUYA9sN3fyRsZWyhUZYuC1p+70cBdwBVMZO8K8FS/jM5r/hH8wNVKvE1tkk+/hmkqBuo0e/giUcx7DtHUnfX7Yf20W6gWY9QMOaj3Kt2kVCZ7ktmXOHrgRdQohp07UY9Peibr4Nw/s+nO7hJEx+WRmhJLSSCcc0fnuin/8+0stQJMY1dfnceWUZ8wpXJnxfs3E9UN49wj/ubuPTbORz19ewssKRsO0n6/hmorZjQXgzSN07N2HIvzrp+8v2Y2sAVjSGeePVETo238m8HF0UXCbuhRDTN9gPsRiUlKd7JBktpuk8e7qfe355hu8c6GZxsZWv3zKfT19Xw7zCzPnre7yVFQ6+9vb55FuM/MNzLbzQNOOV2+YsXddpOROmtNyIM8cLwxNp3gIzhcVGjh0KEItm51Tp5UjQJYSYPl/8L2pVLEHXRHRd5+XmIe77dSOPvdJJsd3EQztq+eKOOpaWZn6/pup8C199+3yWltr4+ovteA57iWm5eRFMBp83hn9Yo3ZhZgbWmUopRf1aG8ERnTMncmvFmDEyvSiEmD5fT/xnaVl6x5Fhxpbr+ckhLyd7g8wrsPCZ62u4el72LepbYDXy0I5a/vWVTn540MszJ/u5dUUJNy0pnPKyQ3NVS2MYowmqa6U313SVVZipdJk4dSxI3SILVltu5YYk6BJCTJs+mulCMl0AtA2G2XnmreV6yhwm7r26ihsXFmI0ZFewNZ7ZaOAT11SzdX4+vzgWX7fxx296uXlJIbeuKKHcKUHFhaIRnfaWMDV1Fkym7P23T6f6NXZ2/W6IUw0hVq3N/MzwdEjQJYSYPl8PWG3gyO2eOpcyEomx5+wQz50eoMEbwKBgfbWTj2ysYHNNXs70ulJKcdW8fK6al8/J3gC/PNbH08fj/11Tl89tK0uyYso0VdpbwsSiSG+uWcgrMFJeZaKzNUL9GlvWZYkvRYIuIcS06X1eKCnPqS/DqdB0nUNdIzx3eoCXW4YIx3TmFVj48Lpyti0spMSe21+pS0vtfHKrnT/1l/Or4308c6qfPWeHqC+3896VJWyqycvqzF4itDSGycs3UFwqU7CzUeky090RYGhAo6Aod45lbn9DCCGSw+eF4rlTz9U5FOa50enDnpEoTrOB7YsK2bGokKWlufWX+FSUO838r/UV/NEVpTx7eoCnG3x8ZXcb1flm3rOihO2LCrGZciPTNx3DQzF83hgrr5x750SiVbrMHNofoKs9IkGXEGKO8/WgahemexRJFYhovNQ8yHNnBjjSHUABa6qd/Om6Cq6al4d1DgYVF3KYjbxnRQnvWlbMyy1DPHXMx3/u6+JHB3t4+9Ji3rW8OOezf+O1NIZRipztMZVKdoeBwmIjXe0Rltbb0j2chJk7/28QQiSEHonE+3TlYKZL13UOd43w3JkBXmoeJBjVceWb+eCaMm5cVEiZQwrHJ2I0KLbOL+DaunwaegI81eDjySO9PHWsl+sXFPDeFSUsKM6dC+dENE2npTFMRbUJm10C8kSodJk5cSRIKKjlzF2MEnQJIaanb/TOxRxrjHqqN8hf/mo/rQNB7CYD180vYMeiQlaU22WqaIqUUqyscLCywkHHUJinG3w8e3qAnWcGWVPl4N5tVspzZ6boPD2dUUJBWdw6kSpdJk4cge6OSM70PJOgSwgxPaNBlyrJnUxX22CYL/6hBYfFxP1bqtlSlz8na5ISqTrfwl2bqvjAleX8z6l+ftXg476fHeZL22tZUpp7Wa+WxjAWq6LSJdnQRCksNmKzKzrbojkTdMm3ihBiWvTe0caoOTK92DsS4cGdzSjgX25fzY1ztAg8WfKtRt6/qpRH37GAQpvrhnjJAAAgAElEQVSJB//QQvNAbnUbDwU1OtsjzFtgwTDH795MJKXiQWxPV4RYLDdWRJBvFiHE9JybXsz+oGs4FOOLO1sZDGl8/sZaaoul31SylDrM/MvtqzEp+MJzLXQNh9M9pIRpOxtG16BOphYTrtJlJhaF3u5ouoeSEBJ0CSGmx+eFvAKUJbvT/aGoxsPPt9I2FOZzN9Tk5JRXpplXZOeLO+oIxzQ+/1wLvkD2X0h1Xae5MUxRiZH8whwtWEujskoTRiN0tUfSPZSEkKBLCDEtuq8n64voY5rO1/a00dAT4BPXVnNl1dztrJ9q84usfOHGWvqDMb7wXDODoVi6hzQrA30xhgY0KaBPEqNRUVZlorM9gq5n/xSjBF1CiOnp82b11KKu6/z73k72tfm5e1Ml19YVpHtIc86yMjufu6GGjqEID/2hhZFI9gZeLY1hDEaoqZOgK1mqXGaCIzqD/dl7noyRoEsIMT2+HlQWF9F/740enjszwAeuKOMdy4rTPZw568oqJw9c5+K0L8g/Pt9GKKqle0jTFovqtJ2NUD3PjNkiBfTJMnZHaFd79k9HS9AlhJgyPTACgREozc7pxV8c8/Gzoz7esbSIP7qiNN3DmfM2z8vn/i3VHOka4Wt72olq2TV91NkWIRLRpYA+yaw2A0Ulxpyo65KgSwgxdb7ROxezMNP1hzMDfOdAN9fU5fOxjZXS8DRD3LCwkLs3VbKvbZhvvtRBLIsCr+bGMHangdIKaXmZbJU1Zvp9MYKB7MuIjidBlxBi6nzxHl0qywrpX2sb5l9f6eDKSgefuKYao/RSyijvWFbMn64tZ/fZQb71WldWFEyP+DW8XVHqFlokgE+BqnNTjNmd7ZLwXAgxZXrfaGPULCqkb+gJ8NUX2lhYbOWzN9RgNsrfmpnojlWl+MMxnjzqw2E28OF1Feke0iV1tMT7jM1bIB3oUyG/0IDdoehqjzB/cfa2q5GgSwgxdb1eMBigsCTdI5mS5v4QX9rVQqnDxOdvrMVhlj5KmexDa8sZiWj87KgPpyXeyT5TdXdEyS804HDKOZUKY93pmxvDxKI6RlN2ZhflTz4hxNT19UBRCcqY+ReaHn+EB3e2YDYovri9liKb/I2Z6ZRS3LWpkusXFPD9N3r4zYm+dA9pQtGITq83SkW1ZLlSqbLGjBYDbxZ3p5dvISHElOk+b1YU0Q8Gozy4s4VgVOPLN9VRmSd3l2ULg1L89ZZqAhGN/9zXhcNsYNvCwnQP6zze7ii6BhXVcglNpdJyEyZT/K7RbF1YXDJdQoip8/VkfBF9IKLx0K5Wuv0RPrdtHguKZXmfbGMyKB64zsUVlQ6++XIHe1uG0j2k83R3RDCZoKRMgq5UMhoV5VVmurK4O70EXUKIKdE1Dfp6M7qIPhLT+eoLbZz2BfnUVherKhzpHpKYIYvRwN/dUMPiEhuP7GnnYKc/3UMC4isadHdEKKs0Y5C7YFOu0mUmFNQZ6MvO7vQSdAkhpmZ4AKKRjF13UdN1/vWVDl7v8PPxzVVcNS8/3UMSs+QwG/nCjbXU5Fv48vOtHPcG0j0khgc1AiO6TC2mSYXLBCp7W0dI0CWEmJrRxqgqAzNduq7znf3d7G4a5ENry7lpSVG6hyQSJN9q5MEd8RshHvpDC019wbSOp7sjfrGXIvr0sFoNlJQa6WzLzmJ6CbqEEFMz2hiV4szKdOm6zk8OeXn6eB+3rijmjvrsaGchpq7EbuKhHbVYjQa+sLOFwWD6LrhjrSLsDrl8pkuly8xgf4zASPZ1p5ezRggxJfrYEkAZNL0YjGp8/cV2fnKol+2LCvjz9RXSHTxHVeZZ+Ptt8+gPxvhVmlpJSKuIzFBZk73d6SXoEkJMTZ8XzBbIy4xaqY6hMA/87iwvNQ/xp2vLue/qagwScOW0RSU2rpqXx6+P9xGIpD7Lca5VRJXUc6VTXr4BR55Bgi4hRA7r7YGS8ozIJO1vG+aTv2vCF4jwhRtruWNVaUaMSyTfHatKGQ5rPHOqP+X77u6IYJRWEWmnlKLKZcbbFSUaya7WERJ0CSGmRO/zpr1dhKbreA55+dKuViqcZh59xwLWVjvTOiaRWsvL7KyusPOLYz4isdRdcMdaRZRXmjEYJcBPt0qXCU2Dnq7synZJ0CWEmBpfT1rvXByJxPin3W388E0v1y8o4Ks3z5dO83PUHatK6Q1Eeb5pIGX7lFYRmaWk3ITJDF3t2XUXo5w9QojL0qNRGOhL252LrQMhvry7jY6hMB/dUMG7lxfLdOIctq7aycJiKz876mP7osKU1PJJq4jMYjAoKqrf6k6fLd8HkukSQlxefy/oelqmF19pGeJTvzvLcCjGl3bUceuKkqz5ghXJoZTijvpS2gbD7G0dTsk+uzulVUSmqXKZCYd0+nuzpzu9nD1CiMs71xg1dZmumKbzgzd6+MruNuYVWvjndy5gdaUs6yPirqnLpyrPzJNHepO+Dl80ouPrkVYRmaa82oRS0JlFdzFK0CWEuCy9b6xHV2oyXcOhGA/vauW/j/TytsWFfPmmOsoccsETbzEaFLfXl3CyN8ihrpGk7svbHUWTVhEZx2IxUFJuyqrWEQk/g9xu9y7ABkSBp4H/DXwPcAFHgLs8Hk840fsVQiTRuW70yQ+6mvqCfGV3G96RCPdsruTtS4pkOlFMaPuiQn78ppcnj/RyZVXy7mKVVhGZq9Jl4ugbQUb8MRxOY7qHc1nJyHRZgRs8Hs9Wj8fzVeBTwMsej2cTEAI+mIR9CiGSyecFRx7KZk/qbnY3DfLA/5wlFNP5x7fN55alUjAvJmcxGnjPihLe6BzhVG9y1mSUVhGZrdI12p0+S9ZiTEbQVQRsdbvdY38Sbyee8WL0544k7FMIkUS6ryepU4sxTee7B7p59MV2FpXY+Od3LGBFeXIDPJEbbllahMNs4GdHe5OyfWkVkdny8o3k5Ruypq4rGWfRvwC3Av/H7Xb/NVAGjLUO7h/9/SJut/su4C4Aj8dDWVlypzFMJlPS9zGXyfFNnnQc297BfgxVNRQnYb99IxEe+m0D+1sHeN+V1dx3/ULMxvSUm8p5m1zJOL5lwPvWBPjha60EjE5qixMbrHe2xNd5XF5fQV5+5tYVzuVzd8ESOHqwn4KCEiyW5Hx3JOr4Jjzo8ng8/wngdrufAh4Geolnv9pGf3oned+3gG+N/qp7vRO+LGHKyspI9j7mMjm+yZOOYxvr6UQtWJLw/fb4I3z2mbP0B2P89ZZqti8qZKDPl9B9TIect8mVrOP7tjobPz2g+M5Lp/jLq6oTuu3G08PkFxgIhgYIhhK66YSay+duQXH8RoeGI524apPTMPlyx9flck1pOwkNCd1u9/ggrgAYBJ4jnvkCePfo70KILKGHguAfgiS0i/jBwR4GQjG+cnMd2xcVJnz7Ym4otpvYsbiQnWcG6R1J3DSTtIrIDsWlRswWRVdb5k8xJjrTda3b7f4GEAYCwD1AB/A9t9u9DzgK/CDB+xRCJNNoj65E37nY3B/i+cZB3ruyhKWlUr8lZuf2lSU8c6qfpxv6+LP1FQnZ5rlWEVLPldEMBkVltYmujiiapmMwZO4NDwk9kzwez/PA+gmeem8i9yOESKHRdhGJboz6ozd7sJkM3FFfktDtirmpKt/C1roCfnuyn/evKiXPOvv2AdIqIntU1phpPRuhrzdGaXnm/ntJc1QhxCXpYz26Enj34sneAC+3DHPbyhIKbJn7BSmyy/tWlRCMavz2ZN+stzXWKqKs0iStIrJAeZUZZSDjG6VK0CWEuDSfF5SCotKEbfIHB73kW428Z2VxwrYpxMJiGxtcTp5u6CMU1Wa1reGh0VYRVVLPlQ3MZkVpuSnj67ok6BJCXFpfDxQWo0yJyUgd7hrhjQ4/d9SX4DBnfgdpkV3uqC9lIBTjuTMDs9pOd0f84i1F9NmjymVmeEjDP5S5C2BL0CWEuCTd501YEb2u6/zgYA8ldhPvXCZZLpF49RV2lpfZ+flRHzFt5gthd3dEySsw4HDKZTJbVNbE/zDM5EapcjYJIS7N501YEf3+dj/HegK4V5diNcnXj0g8pRR3rCqh2x9hz9nBGW1DWkVkJ4fTSH6hga72zF0SSL71hBCT0nU9Pr2YgCJ6Tdf54cEeqvLMvG1xUQJGJ8TENtXkUVto4cmjvvg5PE1jrSIqpVVE1ql0mfH1RAmHZ1fTlywSdAkhJjc8BOFwQoKul5uHONMX4o+vKMMsd4OJJDIoxfvqSznbH2J/u3/a75dWEdmrymVG16GnIzOzXRJ0CSEm15eYHl0xTeeHb3qpLbRw/YKCRIxMiEu6fkEB5Q4TTx6Z3kLYuq7T3RmVVhFZqqjUiMWqMrZ1hARdQojJnetGP7uga1fjAG2DYe5cU44xg7tFi9xhMihuqy/haE+Ao90jU37f8JBGwK9Jq4gspZSi0mWmqyOCNosbKZJFgi4hxKTONUYtnfn0YiSm8ZNDXpaU2Lh6Xl6CRibE5d20uIgCq5GfHZ16tktaRWS/SpeJaAR8PZk3xShBlxBicj4vmEyQN/PFqJ85NUC3P8oH15ajlGS5ROpYTQbevbyYfW1+mvqCU3qPtIrIfuWVZgwGMvIuRjmrhBCT8/VAcRnKMLOvimBUw3PYy+oKO2urHAkenBCX985lxdhMip8f9V32tdIqIjeYzIqyShNd7ZEZ3b2aTBJ0CSEmpfd5YRZF9L8+3kd/MMYH10iWS6RHvtXI25cUsfvsIF3D4Uu+dqxVRIW0ish6lS4z/mGN4aHMah0hQZcQYnK+HtQMu9EPh2P87GgvG1xOVlZIlkukz3tWlmBQ8Itjl852SauI3FHpimcrM20tRgm6hBAT0rUY9PtmnOn6xTEfw2GND65JTDd7IWaqzGFm28JCfn96gP7gxHU+51pFVJgwSquIrGd3GCgoMmZc6wgJuoQQE+vvA02bUWPU/mCUXzb4uLYun0UltiQMTojpuX1lCZGYzq+P9034/LlWEVLPlTOqakz4emOEQ5kzxShBlxBiYr6ZN0Z98kgv4ZjOn6xJzELZQszWvEIrV9fm8esTfYxEYhc9L60ico+rzsIV6+yoDOoNKEGXEGJCet9oY9RpZrq8IxF+e6KfGxcWMq/AmoSRCTEz76svxR/WeOZU/0XPSauI3JNfYGTBUitmswRdQohMN9YYdZqF9J5Dvejo/PEVkuUSmWVZmZ0rKx384lgfkdhbU07R6GirCOlCL5JMgi4hxMR8XrA7UA7nlN/SMRTm96f7efvSYiry5AImMs8dq0rxBaL8oXHw3GO90ipCpIgEXUKICemjjVGn40dvejEZFP/fqtIkjUqI2VlT5WB+oZU/nBk499i5VhHlEnSJ5JKgSwgxMd/0GqM29QV5oWmQW5cXU2yXi5fITEopNs3Lo8EbYDgcQ9d1ujqkVYRIDQm6hBAT6/OiplFE/6M3vTjMBm6vlyyXyGwbXE40HQ52+qVVhEgpCbqEEBfRwyEYGphypuu4N8De1mFuqy8h32pM8uiEmJ3lZXacFgMH2v3SKkKklARdQoiL9fXGf16ipqvPG6XfF+/u/cODPRRajdy6vCQVoxNiVowGxdoq52jQJa0iROpI4YUQ4mLnGqNOHnS9/uoI0YhOxUYjBztH+MiGCuxmuXCJ7LDe5WRv8zC93VEWLpV+ciI1JOgSQlxE9401Rp14ejEU0vAPxfscPf/aEKUOE7csLUrV8ISYtfWuPKpVH7ourSJE6siZJoS4WN+lG6P298aXUTFYwBWwsmKdHYtRslwie5TYTayy2YlFdGkVIVJGviWFEBfzeaGgCGWeuLjY542iFOw1DmJTBurCMj0jsouu61RjpU0PEYxlzoLIIrdJ0CWEuMjlGqP29cZQdjg4NIKpBBpPhAiF5MIlsod/SMMYUbRoIQ52+tM9HDFHSNAlhLiYzwulE9dzaZpOvy9KSzREXaGFazbnEYvBqWOhFA9SiJkbaxXRa4ywv12CLpEaEnQJIc6j6zr4vKhJMl1DAzFiUTgZDHB1bT6FhSbmzTfTdCpEYESyXSI7dHdGycs3sMRl50C7P37eC5FkEnQJIc434odQACZpF9E3WkTfpUVYVeEAYPkqG7oOJ48GUzZMIWYqGtXp7Y5SUW1mg8uJLxClqV8ytSL5JOgSQpyvb6xH18TTi329UTSjjl/FWF5mB8CRZ2T+IgvNZ8L4h2MpG6oQM9HbHUXT4q0i1lU7AWSKUaSEBF1CiPON9eiaZHqxzxuj3xBlUbHtvGaoS+ttKAOcOCzZLpHZujsiGI1QUm6i1GFmYbGVA+3D6R6WmAMk6BJCnEcf7UY/USF9KKThH9ZoDAWpr7Cf95zNbmDhUiutZyMMDUi2S2QmTdPpbItQVmnCaFQAbHDlcawngD8s561ILgm6hBDn83nBaISCizvMjzVF7dDC1I/Wc423ZIUVkxkaDkm2S2SmtuYIwYDO/MVv9ZZb73Ki6UjrCJF0EnQJIc7n64GiUpTBePFT3ig6Ol6i1JfbL3reYjWweLmNzrYI/b3RVIxWiCnTdZ3Tx4LkFxrOW/pnRZkdp9kgdV0i6SToEkKcR+/zXvLOxYBJo6rATKFt4qVTFi2zYrEqGqS2S2SYrvYoQ4MaS1bYUEqde9xoUKypdvK6tI4QSSZBlxDifD4vqvjiei5d0+nvjdIaDV1UzzWeyaxYstJKT2cUb3ckmSMVYsp0XefUsSB2pwFX3cXLW21wOekNRDkrrSNyxpHuEb75cjt9gczJukvQJYQ4R9c06OuF0oszXYMDGrEYtMXC1JdfXM813oLFVmx2RcObQckciIzg64nR1xtjyXIrBoO66HlpHZF7XmoeYs/ZIRzmzAl1MmckQoj0G+yHWBQmyHT1jdZodeuRS2a6AIwmxdJ6G329Mbo7MuevTDF3nWoIYrEqahdaJnxeWkfkFl3X2dc2zJoqB1ZT5oQ6mTMSIUT6+SZvjNrXGyVq0LHYFRXOi6dnLlS3yILDaaDhkGS7RHoN9MWD/0XLrBhNF2e5xqyvdnKsJ8BIRFpHZLvmgTBdwxE21eSneyjnkaBLCPGWvtHGqBMU0vd5Y3TrYVZVOM4rQp6MwaBYvtrGYH+Mjhap7RLpc7ohiMkEC5ZMnOUas8GVR0yHgx0jKRqZSJZ9rfGM5cYaZ5pHcj4JuoQQ5+i9o41RLwi6xpqitkbDl51aHK+mzkx+gYGGw0E0TbJdIvX8wzHaWiLMX2LFbLn0JW95uR2H2cB+mWLMeq+2DbOkxEap4/JZ+VSSoEsI8ZY+L1ht4Mg77+Gxpqjd+sRNUSejDIrlV9jwD2m0NoUTOlQhpuJ0QwiDircyuRyTQbGmyskBaR2R1fqDUU54A2yal3f5F6eYBF1CiHN0nxeKyy6aPhxrihowx6gtvPQUzYWqaswUlRg5cSRILCYXMpE6oaBGS2OYeQss2OxTu9xtrJHWEdnutbZhdGBzTeYFXRN3N5wht9u9APgW4ABswF0ej+eA2+32A6+Pvuwhj8fzTCL3K4RIEF8PTFBE398bY1DFWFbhwDCFeq7xlFKsuMLGK8/7aT4dZuEUMg5CJMKZEyE0DRavmPo5N9Y64kC7nwXFtmQNTSTRvrZhSh0mFhZn3ndNQoMuoBP4uMfjOeV2u/8M+Jzb7f4T4E2Px7M1wfsSQiRanxc1b8F5D+maTl9vlNbYpZuiXkpZpYnSChMnjgapXWTBdIk7yIRIhEhYp+lUiOpaM3n5Fy9pNZmx1hH7O/y8b1VpEkcokiEc03ijw8+NCwundMNPqiV0etHj8QQ9Hs+p0V+LiAdhFYDF7XZf63a7My/sFEIAoEciMNAHxecX0Y81Re3WI6yaRj3XeGPZrnBIp/GkTNuI5Dt7OkQ0El+EfbrWVzs51j0irSOy0OGuEYJRnU0ZOLUIic90AeB2u7cAfwHcCESAHwB3A993u907PB5P4wTvuQu4C8Dj8VBWNvHab4liMpmSvo+5TI5v8iTr2EY72+gF8ucvxD5u+97OAWCIfmOUzUtrMBtn9rdaWRmcPdXOmeNB1m+uxmqdevYhVeS8Ta5UHd9oVKPp5FlctXaWLKua9vu315t58qiPM34j25Zkx/kg527cm2/2Yzcb2LaqLqFNURN1fBMedLnd7nXAfwHv8Xg8HaMPf2P0uQeBjwB/f+H7PB7Pt4jXgwHoXq830UM7T1lZGcnex1wmxzd5knVs9dMnARg22/CP237LWT9hpVFdbGGgzzerfSxabqD1rMa+l9pZccXMpiqTSc7b5ErV8W06FSIQiLF2sWFG+6sy6zjMBnY1dLC6KAkDTAI5d+Nd6F847eXKSgdD/T6GErjtyx1fl8s1pe0kdHrR7Xabge8Df+TxeI6Pe2xMATCYyH0KIRJD94316Dq/kL7XG6VDm15/rskUFptw1Zo5cyJEKKjNentCXEjTdE43hCgqMVJaMbO8grSOyE6NfSG8I1E2Z2CriDGJznRdASwA/s3tdgNEgadHi+ljwBngHxK8TyFEIowFXeNqukIhjcCwTpcWYWtFYpbTWL7aRntrhJPHQqxel3nZLpHdOlojjPg16tdObeWEyWxwOXm5ZYiz/SG5izFL7GsbRgEbM7SeCxIcdHk8ngPARJ/20UTuRwiRBD4v5BWgrG8VHo81RfUSZnlZYgKkvAIjtQssnD0VYtEyKw6ntAsUiaHrOqeOhXDmG6iqmV0n8vUuaR2RbV5tHWZZmY0iW1LK1RNCvu2EEADofd6Llv/p6403Rc0rMmI3J+7rYtmq+EXs5NFgwrYpRE9nlMH+GEtWWGfdLqDUYWZBUbx1hMh8vSMRTvmCGXvX4hgJuoQQcb6ei9pF9PZE8elRVlQmdhrQ4TQwf7GFlsYww0NyW75IjFMNIWx2xbz501s1YTLrXdI6Ilvsb48HxxJ0CSGyg8+LGldEr2s6fb4YndNcb3GqltbbMBjgxGHJdonZ6/NG6e2Osmi5FYMxMU0xN7jyiOlwsHMkIdsTyfNq6zAVTjPzizK7HagEXUII9MAIBPznTS8ODmjoMejRI9SXJ77g3WozsHCZlbbmCC2N0jBVzM6phhBmi2L+osRddFeU23GYDRxoH07YNkXihaIaBzv9bJqXl5Fd6MeToEsIES+ih/PaRfT1RgFQTihMUmHq0pU2yipNvPFqgEP7R9BkQWwxA0ODMTrbIixcasFkTtxFN946wsF+aR2R0Q52+gnH9Ixc4PpCEnQJIaAv3i5Cjct0+bxRgmgsrExeut5kVlx1vZPFy600nQrz0q5hggHp3yWm59SxIAYjLFia+HN1gyuP3pEozQPhhG9bJMa+tmHsJsOMlylLJQm6hBBvNUYtfivT1dMTpUtLTj3XeAaDon6tnfVbHAz2xdj9zBA+bzSp+xS5Y8Sv0XY2wvxFFqzWxF/SxlpH7Jcpxoyk6Tr72vysdzkxJ6iWL5kk6BJCxKcXlQGKSgAIhzTCIzpdeiQhneinoqbOwta35WM0Kl76wzBNp0IypSMu68zx+I0Yi5Ynp5fWWOuIA+3SOiITnfYF6QtEM/6uxTESdAkh4kFXUQnKGF+Eum+0KWrQGqPCObsmk9NRUGTkupvzKK80cWh/gIP7AsSkzktMIhTSaD4Tpma+OalNdte7nBzrkdYRmejV1mEMCjZI0CWEyBa6r+e8Oxd93igaOtXllpTfDWSxGNi81cnSeistjWFe2jlMYETqvMTFmk6GiMVgyYrkdozf4MojqsGb0joi4+xrG2ZFmZ0CqzHdQ5kSCbqEENB3fo+uru4IPj3Kyqr0rI2oDIoVV9jZeK2D4cF4nZe3W+q8xFuiEZ3Gk2EqXSbyC5N7wR1rHSF1XZmlxx+hsS+UNVOLIEGXEHOeruvx6cXRTJeu6Qz1aXQnqSnqdFTPs7D1pnzMFsUru4Y5czwodV4CgOYzISJhnaUrk78uorSOyEyvtcWD4M3zJOgSQmSLoQGIRs7duTg4oIEGA8YotYWJWU5lNvILjFx3Uz6VLjNH3gjy+isjRKNy4ZvLtJjO6eMhSsuNFJelZnHj9dI6IuO82jpMdb6ZmoL0f09NlQRdQsx1vvN7dI01RS0qMWHIkO7OZrNi47UOll9ho605wovPDTEyLEXNc1Vbc5hgQGdJCrJcY6R1RGYJRDTe7BphU03md6EfT4IuIea6C7rRd3VHCOgxllSn7oI2FUopltXb2Hy9k4BfZ/fvh+nujKR7WCLFdF3n1LEQBUUGyqtSk+UCKHPE1/WT1hGZ4Y1OP1FNz6p6LpCgS4g5T+8bC7rimS5vT5RuPcKqyszs7lxZbea6m/Ow2RV7n/dz8pjUec0lnW0Rhoc0lqy0pTzDsUFaR2SMfa3DOC2GtNedTpcEXULMdb4eMFsgr4BwSEMLQq+KsKg4szJd4znzjGx9Wz6uWjMNbwbZ/9II0YgEXrluLMvlcBqonpe6/nFj1ruc0joiA8Q0ndfahtlQnYfJkD1TiyBBlxDC54XiMpRS55qiWgsNGb+khsmkWL/FQf0aGx1tEV7eNSyBV47raI3Q74uxeIUVQxoutivLHdhNBpliTLOTvUEGQjE2ZdFdi2Mk6BJijhvfGLW7O4Km69RWZcfdQEopFq+wselaJwN9Mfa96EeTDvY5ydsd4fW9IxQWG6ldmJ7z02RQrKl2sL99WKa002hfW7wL/fpqZ7qHMm0SdAkx1/neaoza0RnBR5RVVdlVJ1FVY2bNJjveriivvzoiF8Qc098b5dUX/DicBq66wYkxjVnYDa48vCNRWqR1RNrsax1mVYWDvCzpQj+eBF1CzGF6NAoDfVBShq7pBAc1evQwy8vS04l+NmoXWll5pY325ghHXg9I4JUjhgZivLLbj9Vq4Oob8rBa03vZktYR6dU1HObsQHZ1oR9Pgi4h5rIBH+galJQzOKChdAUOsJuz86th8Qori5ZZaTwZ5uSxULqHI2bJP7C4/1UAACAASURBVBzj5V3DGAxw9TYndkf6z0tpHZFer7ZmXxf68dJ/Bgsh0me0R5cqLsPrjfe8qqhMXe+jRFNKUb/Wxrz5Zo4fCnL2tARe2SowovHKLj+aBlu25eHMy5yppA0uJ0eldURa7GsbZl6Bher87Kg7vZAEXULMYfpoN3pKy2lpDxPQYyxzZd/U4nhKKdZsdlBRbeLN/QE6WqX2JtuEQhqvPD9MKKRx9fXOpC9oPV1jrSMOSeuIlPKHYxzpHsnaqUWQoEuIuW2sG31xGQO+WLwpapY1G5yIwaDYcI2T4hIjB14ewdstneuzRSSis/d5PyPDGpuvc1JUmnmZ17HWEftlijGlXu/wE9Wyd2oRJOgSYm7z9YDDSURZMYQVQatGoS3zLnIzYTIpNl/nxJFnYN8ePwN90XQPSVxGLKrz6gvDDPbH2Hitk7KK1DdAnYqx1hEHpHVESu1rHSbfaszKG33GSNAlxBym93mhpJxe79gi15k1jTNbltE73kxmxd7dfvyySHbG0mI6r73kx9cTY91VDipdmRlwjdngyqNnJMqZPqkbTIWYprO/fZgNLifGLOtCP54EXULMZb4eKC7jbFsITddZVGNN94gSzu6IB16aBq887ycY0NI9JHEBXdN5fe8I3R1Rrtxop2Z+5hdJX1Obj9mgeO7MQLqHMic0eAMMhTU2Z3E9F0jQJcTc5vOiSsvp6YnGm6JWZ38910TyC4xcdb2TUEBj724/kbBMCWUKXdd5c3+A9pYIK9fYmL84OwL/PKuRLbX5PN84QDgmgXyyvdo6jMkA61zZ14V+PAm6hJij9FAQ/EPoRWXE/DBojFLhzOwpndkoLjWx8VonQwPx5YJislxQ2um6zrGDQZrPhFmy0sqSFZm7yPpEdiwuZDisnesdJZJnX9swqyscOMz/r707j44zq+80/lSpVFptWba8ybK8u92Lm97d+04TnMlChryEQDpka0JmkhkggZM0E5gMISSEQ+ZMlpNOBjhNkoF7MkwCoaGD3d30AvTmXtyL90W2vMq2JGst1TJ/SG6MceOt6i2V6vmc4yPXlareX99+VfX1fe97b2VPgTB0SdVq4s7F/uYOagoJGlqSJBKVO1fibMyZX8sV1zVy5FCWDd8fopA3eJXTttdH2b55lMXL06xaXVmBC2D13EZmN6ZYt91LjKW0rz9Dd3+mIje4PpWhS6pWx8bX6NrDPAA65k/+eTTF0LE4zaVXNnBg7xgvP+92QeWyc+somzaOsGBRLZdd1VCRgb8mmeDOZS28uH+Qw4MuS1Iqz3aPjyRW8vpcJxi6pCpVmBjp2jvUxHAhxyUdlXsb9rlaurKO5RfX0bUjw+ZXRspdTtXZsyvDKxuGmbsgxRXXNVZk4DrhrqUtFIBHdzraVSrP7D3OopY65jZX/j8MDV1StTp6GBIJRoZSHE1k6ZxRGROYi2XV6no6l6TZ+tooO7d4239c9u/N8NIzQ7TNTXH1DU0kK/j2f4C5zWkun9vI+u195B01LbqB0RyvHR6eEpcWwdAlVa+jPWRaF1CbTZJshmQFjzacj0QiweprGpi7IMUrLwzT3eV2QaW2b88QG743REtrDdfe1ERNzdQ45+5a1sKBgTFeOzRc7lKmnOf3DZAvVPYq9CczdElVqnD0MHvbrwFg9uypsQr9uUomE1x9fRMzZ9fwwtNDdO9xL71SOdqTZf1D+2malmTNrU2kaqdG4AK4YeE0GmuTrNveW+5Sppxnuwdoqa9hxazKu9HidAxdUrU61sOu1svIFwqsWlQ987lOVZNKcN3NTUybluSRh/az8fkheo9mnWBfBIVCgSOHszz75CBPrR+goSnF9bc1k66bWh89dakktyyazlNdxxkac9eDYsnmC2zYN8g17c1TZiS+Ov95K1W5QqEARw/Tl57HAFmWt80od0llVZtOsua2Zra9nmf39gF2bcswrSXJwiVpOhalqaufWiGh1PL5Avv3jrFj8yi9R3PUphMsv7iOa29oZ2DwWLnLK4m7l7Xw8LZentx9nHuWV/fvU7G8dmiIwbH8lLm0CIYuqToNHqeQGaMm0cxYXZbaKTK35kLUNyS5/Z457Nt3iH1dY+zZmeG1F0d4/aUR5rSn6FxSx5z5qYqf+F1KY2MFunaM35gwPFSgqTnJ6qsa6FiSJpVKUN9Qw8BguassjRWz6ulsSbNue5+hq0ie6R6gNpngivmVvQr9yQxdUjU62sPh6UtIkaRlpqM4J0unkyxeXsfi5XUc78uxZ2eGvbszHOweJF2XoGNRmoVL0kyfUdkrYxfT0GCenVtH6doxSnYMZs6u4bKr6pk7P0WiSkJqIpHgrmUtfGHDYfb0jbKwpbruBi62QqHAs3sHuHxeI/WpqfMeZeiSqtHRw2yZex0ASzqmxgTVUpjWUsMlVzSw6vJ6Dh/Ismdnhp3bRtmxZZSW1hoWLkmzoLN2ys1ROlu9R7Ps2DzKvj3jC4POX1jLspV1zJhVnR8tty9u4cEXDrN+ex/vu2pOucupaHv7MxwYGONnL55Z7lKKqjp/M6QqVzjWQ0/rReQKeS6tokVRz1cymWBuey1z22sZHc2zb/cYXTvHF/h87cVh5rbXsnBJmtnzpv7lx0KhwMF9WbZvHuHo4RypFCxZWceSFXU0NlVn+DxhRkOKaxY08+jOPt57xWxSU/xcKKUT+1leMwVWoT+ZoUuqRkcOk2u6isHaLI1pL5Odi7q65HjIWFlH37Ece3Zl6N6dYf/eMerqE3QsTrNoWZqm5qnVr9lsgb27MuzYPMrgQJ6GxgSXXFFP59I6aqfQ8g8X6u5lLTy9d4AN+wa4rmNaucupWM/sHWBpax2zm2rLXUpRGbqkKjTYO0jDtAZy07PlLqWitbTW0NLawCWX13Nw//jk+x2bR9mxeZTOpWlWXlpPfUNlj/5kxwps2zTCrm0ZxjIFZsys4aobGpnfUTvlR/XOx9XtzbTW17Bue5+h6zxtPDjIpp5hfvnK2eUupegMXVIVej0z/mbWXiWbXJdasibB/I408zvSjAzn2fraCLu3Z9izK8PSlXUsW1VHOl154Wt4KM8zTwzS35tj3oJall5Ux8y2moreK7HUapIJbl/Swtc2HaV3OMuMBj9mz0W+UOALGw7R1pjiJ1e2lrucoqu8dwFJF6w7tYB8ocDqxc7nKrb6hiSrr27kjrXTmN9Ry7bXR3nk346z9fURstnKWXC171iOJ9cdZ2ggx5pbm7j25iZmzU4ZuM7CXctayBXgsV1ugn2uvrOzn+1HR/mlK2ZTN4XuWjwhtggeRdEHgfcAY8BvhBBeievYkn6gkM8xVN9OgQwzm6fWfInJpKm5hquub2L5qhybNg6z6eURdm4ZZeWl9XQuTU/qS3MH94/x/HcHqa1NcNNd01we4xwtbKnjorYG1m3v42dWzTSonqXRbJ4vvXSY5TPruXXx9HKXUxKxhK4oipYDvwJcBVwL/DVwaxzHlia7QqHAWL5AJltgNJcnkyswms0zOvE1kxtvH80WyOTyNO8fY2x4iHQqQV1NknRNgrpUkrqJr+maifZUgtpk4kfe8LNHj9KQbmGkpr9M/8XVZfqMGq67pZkjh7NsenmYjc8Ps33zKKsuq6e9s3bSfSDv2jbKKxuGmdZSw3W3NNHQOPVGG+Jw97IW/urpA2w9MsLKNkeUz8a/bjrKkaEsH76xfcps+3OquEa67gAeDiFkge9FUXRxFEXpEEImpuP/kPCV1xnNNVM5A/2VJ8Ee+/c0soUCWSADjCVgFMiQIAtkKZClQG7i6/jfTzznB+1wmBQJUiSoIUEqkaAG3mh7o50EtUAaSFMgDdQWoDaRpK2mmaZpY+Xqhqo0a3aKG+9s5tD+8fC14ftDbNuUZNXqBubML/9lu0KhwOsvjbB98yhz5qe4+oaptSl13G5eNI2/f+4g67b3GbrOwrHhLP/31aOs6Wjm0rmN5S6nZOIKXW3Ayduv9wGzgP0nGqIoug+4DyCEQFtbW8mKSSVryOZ8M1G8EiSoT0AykaSGJAmSJBNJIFmyD9zxTZtz5At5ChTIJfLkC3kGcv1Ed6wo6e9ZJUqlUiXvk9mz4ZLVBXZsHeCFp4/wzBODzJ1fz9U3zGLu/PJ8OGezeR5fd5Dd20dZtbqFNTe3leTyZxz9O5ncsbKPJ7Yf4SP3XEx9bWkv0VZ6335+/TbG8gX+650X0dY6+UJqsfo3rtB1BFhx0uPpE21vCCE8ADww8bDQ09NTsmJ+7udX0tbWRimPUe3s37NXKBTI5yGXK5DLQj5XIJc78fjkv49/bW5uZnhkkJoaqKlJjH9NJd74e7ImQU1q/HvJJD820Pn/6IfFed62zIRb72mia0eGLa+N8NBXu5nbnmLV6oZY51CNjozfodh7NMclV9SzdCUcPXrkzE88D9X2vnDLgnq+9XqOf3txF7cvaSnpsSq5b3f3jvL1Vw+wdmUrjblBenom3wadZ+rf9vb2s3qduELXo8B/jqLo94FrgE3lurQoTTaJROKNAMVZrODQ1tZCT4+XBqeCZE2CxSvq6FiSZueWUbZtGuE7Dx9nwaJaLrqsvuQLrB7vz/HM44OMjOS55qZG5ne4hEgxXTqngXnNtazb3lfy0FXJvrjhEA21Sd61unJH6s5WLDMkQwhbgS8ATwN/AfxWHMeVpEqQSiVYcUk9d/3kdJatqmP/3jEefeg4zzw5wIHuMfL54s+Q7Dk0xlPrBshmC9x4R7OBqwRObIK98eAQBwccZzidF/YPsmH/INFls5heN/Xvko1tyYgQwueAz8V1PEmqNOm6JJe8pYElK+rYuXWUvbsyHOwepK4+wcLFaRYuTdM87cI/mPbszPDSc0M0NSdZc2sTjU1T/8OuXO5c2sI/vdTD+h19/OLlU2+F9QuRy48vhDqvuXZKLoR6Oi6VK0mTTEPjePhatbqeQ/uzdO0YZfvmUbZtGmVmWw2dS8dXvz/XuwsLhQJbXh1hy6ujtM1Jcc1NjdRW4Er5laStsZYr5jexfnsf77qsjZpJvD5b3Nbv6GN37ygfubmd2prqOA8NXZI0SSWTCeYtqGXeglpGhvPs3ZWha2eGF58ZZuOGYRZ0plm4JE3rrDNvzZPLFXj52SH27h5j4eI0l1/TQLLGABCHu5e18Jkn97Hx4BBXzG8qdzmTwvBYnn966TCr2hq4sbN69qg0dElSBahvSLL84nqWrarjaE+OPTszdO/O0LUjQ/P0JJ1L0nQsTlNX/6MjBpnRPM89NciRwzkuWl3Piovryr4uWDVZ09HMtHSSddt7DV0TvvraEY6N5Pj92+ZU1blo6JKkCpJIJJg1O8Ws2Skuu7KB7q4Me3ZmeO2lEV5/eYS57bV0Lk0ze16KZDLB4ECOpx8fZHgwz5XXN9KxyAnzcautSXLrkhb+fWsvA6M5mqtgwviP0zM0xr+8fpSbF03joipbONbQJUkVKlWbYNGyOhYtq+N4X46unRn27spwoHuMuvoE7Z1pundnKBTg+tubmTXbt/xyuXtpC9/YfIzHd/eztkomjb+Zf3zpMPkC3HtF9d1YUB0z1yRpipvWUsOlVzTw1p+ezjU3NdLSWsPOraOkahPcfLeBq9yWzqxnSWsd67b3lbuUstpxdIRHd/TzUxe1Mre5+kZd/S2UpCkkmUwwv2P87sbMaJ5UbaIkW/ro3N29rIW/e+4QO4+NsKS1vtzlxK5QKPD5DYdorqvhnZfNKnc5ZeFIlyRNUem6pIFrErl1cQupZIL1VTra9Wz3ABsPDvHu1W00p6tzXpuhS5KkGEyvq2FNRzOP7epnLFf8XQYms2y+wBdfOMyC6WnetmJGucspG0OXJEkxuXtZC8dHczzbfbzcpcTq4a29dPdn+OUrZ5Oq4tFXQ5ckSTF5y7wmZjWmqmpC/WAmx5c39nDZ3EauW9Bc7nLKytAlSVJMapIJ7lzSwgv7BzkyNFbucmLxz68e4fhojl+9qroWQj0dQ5ckSTG6a1kL+QI8uqO/3KWU3MGBDF/bdIzbl0xn2czqu2PzVIYuSZJiNH9amsvmNLBuRy+FwtSeUP+lFw+TTMB7q3Ah1NMxdEmSFLO7ls1g//ExXj88XO5SSmZzzzBP7D7Oz148k7bG2nKXMykYuiRJitmNndNoSCWn7IT6QqHA558/xIz6Gt5xycxylzNpGLokSYpZfSrJzYum8VRXP0NjuXKXU3Tf3XOcTT3DvOcts2msrc6FUE/H0CVJUhncvWwGI9kCT+2eWmt2jeXyPPjCYRa11HHX0pZylzOpGLokSSqDi9rq6Zie5uFtveUupage2tLLgYEx3nfVbGqqeCHU0zF0SZJUBolEgrevnMHWIyNsPTI1JtT3j+b4yis9XDm/iavaq3sh1NMxdEmSVCZ3LGmhPpXgm1sqf7RraCzHp76zl+GxPO+70iUiTsfQJUlSmTSla7h9SQtP7O6nf7RyJ9QPZnJ84pE9bOkZ5ndvamdxqwuhno6hS5KkMnr7ihlkcgXWb6/M0a6B0Rwff2QP246M8Hu3LOCmRdPLXdKkZeiSJKmMFrfWc8nsBr61tZd8ha1Q3z+a4w8f6WLnsRE+eusCblg4rdwlTWqGLkmSymztylYODIzxwr7Bcpdy1vpHsvzh+i66ejP8/q0drOkwcJ2JoUuSpDK7fuE0WutreGjLsXKXclZ6R7J8bP0euvsz3H97B9cs8E7Fs2HokiSpzGprEtyzYgbP7xvk4ECm3OX8WMeGs3xsXRf7j2f42O0dXDm/qdwlVQxDlyRJk8Dbls8gkWBSLx9xZGiM+9d1cXhwjD+8o4O3zDNwnQtDlyRJk8CsxlrWdExj3fZeRrP5cpfzI3omAteRoSwfv2Mhq+cauM6VoUuSpEli7coZHM/keXJ3f7lL+SGHBsa4/9td9I3k+O93LuSSOY3lLqkiGbokSZokVs9tZGFLmm9unTyXGA8OZLh/3W6OZ8YD16rZDeUuqWIZuiRJmiQSiQRvX9E6afZj3H88wx98u4uhsTx/dGcnK9sMXBfC0CVJ0iRyx9Lp1KeSZV8+ors/w/3f7mI0V+B/3NXJ8llu7XOhDF2SJE0ijbU13LFkOk/sOk7/SLYsNeztG+X+dV1k8wU+eddCls40cBWDoUuSpElm7cpWxvIF1m3vi/3YXb2j/MG6LgqFAp98a6ebVxeRoUuSpEmmc0Ydl81p4Jtbe8nl49uPcdexEe5f10UykeCP7+6ks6UutmNXA0OXJEmT0NqVrRwaHOOF/fHsx7jj6AgfW9dFbTLBp+7upMPAVXSGLkmSJqE1C6fR2pCKZUL94cExPv7IHupTSf74rZ20T0+X/JjVyNAlSdIklEomeNvyFjbsG2T/8dLtxziWK/BnT3STyRX4xJ0LmT/NwFUqhi5Jkiape5bPIJmAb5VwsdQvbDjIliMj/M4N87ykWGKGLkmSJqlZjbVcv7B0+zF+Z2cf39jSy8+sauWmzulFf339MEOXJEmT2NqVrQxk8jxR5P0Yu3pH+aunD3DJ7AbuvXJOUV9bp2fokiRpErt0TgOdLWke2tJLoVCc5SOGxnL8yePdNNQm+b1bFpBKJoryuvrxDF2SJE1iiUSCt69sZfvREbYcGbng1ysUCvyv7x/gwECGj9y8gJkNqSJUqbNh6JIkaZK7fcl0Goq0H+PXNh3ju13HufeK2Vw6t7EI1elsGbokSZrkGmtruGPpdJ7cfZy+C9iP8dVDQ3zxhUPcsLCZn714ZhEr1Nko2phiFEXvAX4bqAF2AO8NIYxFUfQ+4ONAN5ANIdxerGNKklQt3r6ylYe29PLt7X2889JZ5/z8Y8NZPvNEN/Oaa/nt6+eTSDiPK27FHOl6Frg1hHAtMBf4iYn2OuDTIYSbDVySJJ2fzpY6Vs9t5OGtx855P8ZcvsBnnuxmcCzPR29ZQFO6pkRV6scpWugKIWwJIWSiKEoA04GDE9+aAyyKoujiYh1LkqRq9PaVMzg0mOX5fQPn9LwvvXiYVw8N85/WzGNxa32JqtOZlOKWhc8CL4QQnpl4/BhwG/DFKIq2hhDee7onRVF0H3AfQAiBtra2EpT2A6lUquTHqGb2b+nYt6Vj35aW/XvhfrJ1Jp/f0MO6XYOsvWLJG+0/rm8f29bD/3v9KO9YPY93XrssrlKnlGKdu4nzWfMjiqJ7gQ+d0rwW+ACwCnh3CCF7ynNqgM3AT4QQtp3hEIV9+/adc13noq2tjZ6enpIeo5rZv6Vj35aOfVta9m9xfHljD//n5R7+5qeWvrEx9Zv1bXd/hg9/cxcdLWn+5K2d1NZ4/9z5ONO5297eDnDGSXLnNdIVQngQePDktiiK7gBuB+48OXBFUVQbQhhjfG5XCji3MVFJkvSGe5bPIGzs4Ztbj/FrV899058byeb508e7SdUk+OgtCwxck0Ax/w+8A1gAPBpF0ZNRFH10ov3bURQ9CzwB/HEI4UARjylJUlWZ2ZDihs5prN/R96b7MRYKBf7m6QN09Y3yoRvnM7upNuYqdTpFm9MVQvgd4HdO0357sY4hSZJg7YpWntx9nMd39fPW5TN+5Pvf2trLY7v6efflbVzV3lyGCnU6jjVKklRhLpnTwKKWOh7acuxH9mPc0jPM3z9/iKvbm4guO/f1vFQ6hi5JkirM+H6MM9hxbJTNPT/Yj7F/JMufPdHNzIYaPnhjO0kXQJ1UDF2SJFWg25e00Fj7g/0Yc/kCn/3ufo6N5PjoLR1Mq3MB1MnG0CVJUgVqqE1yx9IWnuo6zrGhDF95pYcX9w/y/mvnsnyWC6BORoYuSZIq1NoVM8jmC3zy37fylY1HuHNpC29d1lLusvQmDF2SJFWojpY6Lp/byPd3H2NJax2/ee1cN7KexEqxDZAkSYrJu1a3MVo4ygevn0NdyrGUyczQJUlSBbtsbiOff3enWyxVACOxJElSDAxdkiRJMTB0SZIkxcDQJUmSFANDlyRJUgwMXZIkSTEwdEmSJMXA0CVJkhQDQ5ckSVIMDF2SJEkxMHRJkiTFwNAlSZIUA0OXJElSDAxdkiRJMTB0SZIkxcDQJUmSFANDlyRJUgwMXZIkSTEwdEmSJMXA0CVJkhQDQ5ckSVIMDF2SJEkxMHRJkiTFwNAlSZIUA0OXJElSDAxdkiRJMTB0SZIkxcDQJUmSFANDlyRJUgwMXZIkSTEwdEmSJMXA0CVJkhQDQ5ckSVIMDF2SJEkxMHRJkiTFwNAlSZIUA0OXJElSDAxdkiRJMTB0SZIkxSBVrBeKomgxsAF4baLpAyGEjVEU3QZ8FkgAnwkhfLlYx5QkSaoURQtdQB3wcAjh3ScaoihKAn8H3A30Ai9GUfRQCKG/iMeVJEma9Ip5eXEO0BJF0Zooimom2pYBfSGEromgtQFYU8RjSpIkVYRijnR1A48AHwFWRlF0C9DG+AjXCb0TbT8iiqL7gPsAQgi0tZ32x4omlUqV/BjVzP4tHfu2dOzb0rJ/S8e+La1i9e95ha4oiu4FPnRK89oQwp9PfP+LwDuBx4EZJ/3MDKDndK8ZQngAeGDiYaGn57Q/VjRtbW2U+hjVzP4tHfu2dOzb0rJ/S8e+La0z9W97e/tZvc55ha4QwoPAgye3RVFUO/E1AUwD+oFtjF9y7GR8lOtK4OnzOaYkSVIlK+acrk9HUfQc8BxwAPjnEEKe8UuGXwUeA+53Er0kSapGRZvTFUL48Ju0PwZcU6zjSJIkVSIXR5UkSYqBoUuSJCkGhi5JkqQYGLokSZJiYOiSJEmKgaFLkiQpBoYuSZKkGBi6JEmSYmDokiRJioGhS5IkKQaGLkmSpBgYuiRJkmJg6JIkSYqBoUuSJCkGhi5JkqQYGLokSZJiYOiSJEmKgaFLkiQpBoYuSZKkGBi6JEmSYmDokiRJioGhS5IkKQaGLkmSpBgYuiRJkmJg6JIkSYqBoUuSJCkGhi5JkqQYGLokSZJiYOiSJEmKgaFLkiQpBoYuSZKkGBi6JEmSYmDokiRJioGhS5IkKQaGLkmSpBgYuiRJkmJg6JIkSYqBoUuSJCkGhi5JkqQYGLokSZJiYOiSJEmKgaFLkiQpBoYuSZKkGBi6JEmSYmDokiRJioGhS5IkKQapYr1QFEV/C1w68XAh8K0QwvujKPoE8C7gCLA3hPALxTqmJElSpSha6AohvB8giqIG4Dng0xPfqgP+awjh4WIdS5IkqdKU4vLifcA3Qgg7Jx7PAS6KomhpCY4lSZJUERKFQqFoLxZFUQLYBNwaQjg40bYWWAP8R+BrIYQ/eJPn3sd4YCOEcHUmkylaXaeTSqXIZrMlPUY1s39Lx74tHfu2tOzf0rFvS+tM/ZtOpwESZ3qd8wpdURTdC3zolOa1jM/l+ngIYe1pntMM7AXmhRBGznCIwr59+865rnPR1tZGT09PSY9Rzezf0rFvS8e+LS37t3Ts29I6U/+2t7fDWYSu85rTFUJ4EHjw1PYoin4dWHdKW20IYQxoAkaBsfM5piRJUiUr2kT6CauBfzzxIIqi6cAjURTlGU+AvxFCyBX5mJIkSZNeUUNXCOHnT3ncD1xTzGNIkiRVIhdHlSRJioGhS5IkKQaGLkmSpBgYuiRJkmJg6JIkSYqBoUuSJCkGhi5JkqQYGLokSZJiYOiSJEmKgaFLkiQpBoYuSZKkGBi6JEmSYmDokiRJioGhS5IkKQaGLkmSpBgYuiRJkmJg6JIkSYqBoUuSJCkGhi5JkqQYGLokSZJiYOiSJEmKgaFLkiQpBoYuSZKkGBi6JEmSYmDokiRJioGhS5IkKQaGLkmSpBgYuiRJkmJg6JIkSYqBoUuSJCkGhi5JkqQYGLokSZJiYOiSJEmKgaFLkiQpBoYuSZKkGBi6JEmSYmDokiRJioGhS5IkKQaGLkmSpBgYuiRJkmJg6JIkSYqBoUuSJCkGhi5JkqQYGLokSZJiYOiSJEmKgaFLkiQpBoYuSZKkGKTOCnX0XAAABQ1JREFU94lRFC0A/g340xDClyfabgM+CySAz5zU/mfAHcAA8N4QQveFFi5JklRJzmukK4qim4FvANmT2pLA3wE/x3jA+lQURdOjKLoLuDyEcC3wAPAnF1y1JElShTnfka5ngKuB/31S2zKgL4TQBRBF0QZgDXA78PWJn/k68Oene8Eoiu4D7gMIIdDe3n6epZ29OI5Rzezf0rFvS8e+LS37t3Ts29IqRv+e10hXCCETQsid0twG9J70uHei7Y32EMIAMONNXvOBEMI1IYRrGL88WdI/URQ9H8dxqvWP/WvfVuIf+9b+rdQ/9u2k6N8zOuNIVxRF9wIfOqV5bQhh3yltR/jhQDUD6Dm5PYqiJn44mEmSJFWFM4auEMKDwINn8VrbgJYoijoZD1ZXAk8DeeD3gL8CfgpYf97VSpIkVaiiLRkRQsgzPifrq8BjwP0hhP4Qwnrg5SiKngPeD/xBsY55gR4odwFTnP1bOvZt6di3pWX/lo59W1pF6d9EoVAoxutIkiTpx3BxVEmSpBgYuiRJkmJw3ivSV7Ioij4IvAcYA34jhPBKmUuaMqIoegyoZ3zh3K+HEP60vBVVvomFhz8FvCWE8PYoiloYv7mlHXgVuC+EkClnjZXsNP27GNgAvDbxIx8IIWwsV32VaqIfHwAaGX9PuA94ZaLtUmAfcG8Ioa9cNVaq0/VtCGFDFEWDwAsTP/ZHIYR/L1OJFS2KoouALwIFYBD4BcY/0y74fbfqRrqiKFoO/ApwPeNLYfx1eSuacuqA20IINxu4LtxEIHgKuIgfrAPzu8D3JnZ5GAXeW6byKt6b9G8d8PDEOXyzgeu8HQB+K4RwM/CXwP3ALwEjE+fu94EPl7G+SvYjfRtFUR3w8knnrYHr/G0D3hpCuBF4CfhVivS+W3Whi/Etih4OIWRDCN8DLo6iKF3uoqaQGcDNURS1lbuQqWDiruA7gf95UvOd/PAuD3fFXddU8Sb9O4fx5W/WRFFUU57KKl8IYSSEsG3i4QzGg4LnbhG8Sd/OAdJRFN00EcB0nkIIuRDCwMQ/yhYA+ynSuVuNoevUlfP7gFllqmUq+gvG12N7Loqinyl3MVNBCGH4lKaTz+ETOz/oPJ2mf7uBR4CPAC9GUXTaXTR0dqIougH4TeCTeO4W1Sl9Owz8A+NLM70eRdGSctZW6aIougLYAqwGHqJI5241zuk6Aqw46fH0iTYVQQjhbwGiKPoXxt8I/rW8FU1JJ3Z56OYHOz+oSEIIO5jYIzaKoi8C7wT+vpw1Vaooiq5kfG7MT4cQ9kdRdPLOJZ67F+DUvp1o/tzE9z4B/BrwsbIUNwWEEF4ElkdR9FuMz58ryvtuNY50PQq8LYqiVBRF1wObnIRcHFEUnRzipwP95aplilvP+GgiwH/AXR6KKoqi2omvCWAansfnZaIfvwS8K4SweaLZc7cITte3J87bCb7/XoCJy4onbGN8VKso527VjXSFELZGUfQFxrcoGgN+vcwlTSU3RVH0OSDD+FD3B8pcz1T158CDURQ9y/gddv9Q5nqmmk9HUXQb4xPrvw/8c5nrqVSrgcXAX0ZRBON3f90D3DJx7u5nfGK9zt3p+vbrURT9IpADdgD/rWzVVb53RFH0u4z3awH4L4z36QW/77oivSRJUgyq8fKiJElS7AxdkiRJMTB0SZIkxcDQJUmSFANDlyRJUgwMXZIkSTEwdEmSJMXA0CVJkhSD/w+dyya3O2SrcwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 38.37912830273434 \n", + "\n", + "\n", + "fftfilter\n", + "50.224897000000055\n", + "gamma total\n", + "57.75485199999821\n", + "coch1\n", + "4.871060999998008\n", + "coch2\n", + "5.379006000002846\n", + "get avg\n", + "2.228320000001986\n", + "fftfilter\n", + "45.52066399999603\n", + "gamma total\n", + "53.015919999998005\n", + "coch1\n", + "4.89737900000182\n", + "coch2\n", + "5.420028000000457\n", + "get avg\n", + "2.439769000004162\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 1.5814806078758752 \n", + "\n", + "\n", + "fftfilter\n", + "43.860171000000264\n", + "gamma total\n", + "51.37669099999766\n", + "coch1\n", + "4.992442000002484\n", + "coch2\n", + "5.38906699999643\n", + "get avg\n", + "2.439723999996204\n", + "fftfilter\n", + "49.69287699999404\n", + "gamma total\n", + "57.38890199999878\n", + "coch1\n", + "4.907660999997461\n", + "coch2\n", + "5.396678999997675\n", + "get avg\n", + "2.221396000000823\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 4.934319551176528 \n", + "\n", + "\n", + "fftfilter\n", + "9.818782999995165\n", + "gamma total\n", + "12.51412999999593\n", + "coch1\n", + "3.4080180000019027\n", + "coch2\n", + "3.5500210000027437\n", + "get avg\n", + "0.07977100000425708\n", + "fftfilter\n", + "9.70272699999623\n", + "gamma total\n", + "12.337246999995841\n", + "coch1\n", + "3.3642260000051465\n", + "coch2\n", + "3.4624709999989136\n", + "get avg\n", + "0.07314800000312971\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 46.5332387061058 \n", + "\n", + "\n", + "fftfilter\n", + "8.830746999999974\n", + "gamma total\n", + "11.525219000002835\n", + "coch1\n", + "3.415181999997003\n", + "coch2\n", + "3.494029000001319\n", + "get avg\n", + "0.07439700000395533\n", + "fftfilter\n", + "8.398228000005474\n", + "gamma total\n", + "11.0013489999983\n", + "coch1\n", + "3.3856660000019474\n", + "coch2\n", + "3.4842229999994743\n", + "get avg\n", + "0.07476799999858486\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 33.71413136468306 \n", + "\n", + "\n", + "fftfilter\n", + "9.190284000003885\n", + "gamma total\n", + "11.843677999997453\n", + "coch1\n", + "3.402490000000398\n", + "coch2\n", + "3.534505999996327\n", + "get avg\n", + "0.10637000000133412\n", + "fftfilter\n", + "10.39215599999443\n", + "gamma total\n", + "13.122723000000406\n", + "coch1\n", + "3.3428209999983665\n", + "coch2\n", + "3.415924000000814\n", + "get avg\n", + "0.07524899999407353\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 34.80331321020226 \n", + "\n", + "\n", + "fftfilter\n", + "46.88682400000107\n", + "gamma total\n", + "54.51547999999457\n", + "coch1\n", + "4.816753000006429\n", + "coch2\n", + "5.49156899999798\n", + "get avg\n", + "2.215424999994866\n", + "fftfilter\n", + "45.46794200000295\n", + "gamma total\n", + "52.91678599999432\n", + "coch1\n", + "4.899534000003769\n", + "coch2\n", + "5.399242999999842\n", + "get avg\n", + "2.3087390000000596\n" + ] + }, + { + "data": { + "image/png": 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Zp512GgcffDAABxxwANu2bZvUc0wXR7okSdKM0dDQQKUy9inO2cTQJUmSZozXvva13HzzzTzwwAMAY14zNhtM6vRiCOHNwJ/UbjYBLwMOB+4GHqy1XxBjvG8yzytJkvYNhx56KJdddhkXXnghHR0dFItF/uiP/oj77pv90SGZqgQZQvgssBm4HvhEjPGd43h4un79+inp14DW1lba29un9ByzhbWosxYZ61BnLeqsRd2+VIuuri7mzBnfWq4BU7GmayYbqVZtbW0AyWiPn5LpxRDCcuBc4PPAcmBhCOHEEMLM3bFMkiRpCk3JSFcI4a+AHTHGvwshHAK8FTgJOAx4bYxx6zCPWQOsAYgxHt/X1zfp/Rpsf0vne2Mt6qxFxjrUWYs6a1G3L9Viw4YNNDU1TXc3ZoXe3l5WrFgxpL2xsRHGMNI1VaHrfuANMcZn92j/KnBrjPGaUZ7C6cUcWYs6a5GxDnXWos5a1O1Ltejs7GTu3LkTeuy+FD7HYqRaTdv0YgjhIKA6ELhCCKXazwSYD2yf7HNKkqSJ25e+IThVJqNGU7Gm62jg8UG3Lwsh3AXcBTwHXDcF55QkSRPQ2NhIb2/vdHdjxuvt7R2YRpywSd+RPsZ4I3DjoNsfnexzSJKkyVEqlahUKnR2dpIko86Q7aapqWm/CGxpmlIoFJ73xbS9DJAkSfu55ubmCT1uX1rblgd3pJckScqBoUuSJCkHhi5JkqQcGLokSZJyYOiSJEnKgaFLkiQpB4YuSZKkHBi6JEmScmDokiRJyoGhS5IkKQeGLkmSpBwYuiRJknJg6JIkScqBoUuSJCkHhi5JkqQcGLokSZJyYOiSJEnKgaFLkiQpB4YuSZKkHBi6JEmScmDokiRJyoGhS5IkKQeGLkmSpBwYuiRJknJg6JIkScqBoUuSJCkHhi5JkqQcGLokSZJyYOiSJEnKgaFLkiQpB4YuSZKkHBi6JEmScmDokiRJyoGhS5IkKQeGLkmSpBwYuiRJknJg6JIkScqBoUuSJCkHhi5JkqQcGLokSZJyYOiSJEnKgaFLkiQpB4YuSZKkHBi6JEmScmDokiRJykFxsp8whHAL0AyUgRuALwHXAm3AA8CaGGPfZJ9XkiRpJpuKka4m4JQY48kxxs8AHwNujzG+AugF3jMF55QkSZrRpiJ0LQJODiG01m6fTjbiRe3nGVNwTkmSpBlt0qcXgc8D5wD/EEL4Y6AV2Fq7b2vt9hAhhDXAGoAYI62twx42aYrF4pSfY7awFnXWImMd6qxFnbWosxYZ6zA+kx66YoxfBgghXA98GthMNvq1rvazfYTHXQVcVbuZtrcPe9ikaW1tZarPMVtYizprkbEOddaizlrUWYuMdci0tbWN6bhJnV4MIQwOcQuA7cBPyEa+AM6u3ZYkSdqvTPZI12tCCFcAfUA3cAHwLHBtCOFO4EHgnyf5nJIkSTPepIauGON/AscNc9ebJ/M8kiRJs42bo0qSJOXA0CVJkpQDQ5ckSVIODF2SJEk5MHRJkiTlwNAlSZKUA0OXJElSDgxdkiRJOTB0SZIk5cDQJUmSlANDlyRJUg4MXZIkSTkwdEmSJOXA0CVJkpQDQ5ckSVIODF2SJEk5MHRJkiTlwNAlSZKUA0OXJElSDgxdkiRJOTB0SZIk5cDQJUmSlANDlyRJUg4MXZIkSTkwdEmSJOXA0CVJkpQDQ5ckSVIODF2SJEk5MHRJkiTlwNAlSZKUA0OXJElSDgxdkiRJOTB0SZIk5cDQJUmSlANDlyRJUg4MXZIkSTkwdEmSJOXA0CVJkpQDQ5ckSVIODF2SJEk5MHRJkiTlwNAlSZKUA0OXJElSDgxdkiRJOTB0SZIk5cDQJUmSlANDlyRJUg6Kk/lkIYSDgauAOUAzsCbGeHcIoRO4p3bYJ2OMN03meSVJkma6SQ1dwHPAhTHGR0MIvwdcGkJ4F/CrGOPJk3wuSZKkWWNSQ1eMsQd4tHZzEVkIWw40hhBeA9wVY+ydzHNKkiTNBkmappP+pCGEk4CvAKcB/cB7gWOBk4EzYoxPDPOYNcAagBjj8X19fZPer8GKxSLlcnlKzzFbWIs6a5GxDnXWos5a1FmLjHXINDY2AiSjHTfpoSuEcCzwDeC3Y4wP73HfJ4BijPEvRnmadP369ZParz21trbS3t4+peeYLaxFnbXIWIc6a1FnLeqsRcY6ZNra2mAMoWtSv70YQigBXwPePhC4am0DFgDbJ/OckiRJs8FkL6Q/GjgY+GIIAaAM3FBbTF8BHgf+5ySfU5Ikacab7IX0dwPzhrnr8sk8jyRJ0mzj5qiSJEk5MHRJkiTlwNClMUmrKeuf7mMqthiRJGl/YOjSmDy3vp9f/KyLjvbKdHdFkqRZydClMdnakYWtvt7qNPdEkqTZydClMdm2JQtd5f5p7ogkSbOUoUujStN0UOhyTZckSRNh6NKourtS+nqzsNVv6JIkaUIMXRrVti31i5k60iVJ0sRM9mWAtA/a2lEhSaBYShzpkiRpggxdGtW2LRXmL2igUnWkS5KkiXJ6UXuVpilbOyosXFKk5EiXJEkTZujSXnV3pfT3pSxaXKBYShzpkiRpggxd2quBRfQLlxQc6ZIk6XkwdGmvBhbRL1iYhS5HuiRJmhhDl/ZqYBF9oZj47UVJkp4HQ5dGNHgRPUCpMaFShmrV4CVJ0ngZujSizh1V+vtSFi8tANk+XeC2EZIkTYShSyPqaM8W0S9Zlo10FWu7uhm6JEkaP0OXRtTRXqHUmDBvfvY2KTVmI12u65IkafwMXRpRR3uZJa0FkiQLWwPTi/3909krSZJmJ0OXhtXbU6VzR5XFrfUrRZVc0yVJ0oQZujSsLZsrACwZFLrqI12GLkmSxsvQpWF1tJdpaIBFSwq72hzpkiRp4gxdGlbHpjILFxcoFJJdbY50SZI0cYYuDVGppGzbUtltahGgUEhoaHCkS5KkiTB0aYitHRWqVVjcWhhyX9HrL0qSNCGGLg2xZWBT1D1GuiBb1+X0oiRJ42fo0hDbt1ZomZPQ1Dz07eFIlyRJE2Po0hBdnVXmzBs6tQjZrvT9fYYuSZLGy9ClIbo6q8yZO/xbw5EuSZImxtCVo75Kdbq7MKpKJaW3Jx0xdLmmS5KkiTF05eS+DZ28Mz7Chp19092VveruyoJhiyNdkiRNKkNXTu5Z30m5mrJ2U/d0d2WvunZmoWvkkS4olyGtGrwkSRoPQ1dOHt7cA8ATW3qnuSd719W599A1sCt9uWzokiRpPAxdOahUUx7dnI1wPbGlZ5p7s3fdXVWSBmhuToa9v7TrUkB59kqSpNnP0JWDp7b10lNOmd/YwBNbeknTmTtK1NVZpWVOA0nD8KGr6EWvJUmaEENXDh5uz0a5Tj9kIdt7K2zuLk9zj0bWtXPk7SJg8EiXoUuSpPEwdOXg4fZuFjYVeNWB8wF4omPmruvq7qoyZ87IbwtHuiRJmhhDVw4ebu/h8GUtHLy4CZi567oq5WyPrpG2i4BBI13uSi9J0rgYuqbYjt4K67b3cfjSFuaUChwwv8TjM/QbjF1de//mIjjSJUnSRBm6ptgjtW8tHtbaDMBLFjfP2JGu7lG2iwDXdEmSNFGGrin2cHs3DQkcunQgdDXx3M5+uvorufelp7z3yxAN7NG1t+nFhgIkDY50SZI0XoauKfZwew8HLWxiTqkAwCGLs/D1ZM5TjA9t6uYd3/w1X/r5c/SOEL66Oqs0NEBzy/DbRQAkSeL1FyVJmgBD1xTq6q/w6/buXVOLkI10Qf4709/97E5S4PuPbOXD33+SxzqGTnF2D+zRlYwcusDrL0qSNBGGrin0L79sp6u/yhmHLNrVtqSlyIKmAo/nvK7roU3dvGRxE58840B6+qtc+qOneHbH7hff7uqs7nVqcYAjXZIkjV9uoSuE8OEQwl0hhNtDCKvzOu90ebi9mxsf3sJZhy3iiGUtu9qTJOEli5t4cGMX/ZV8gkulmvJwew9HtLZwzAvmctkbX0xDA3zu1vW79aGrc+8bow4oGrokSRq3XEJXCOFQ4PeBVwEfAf4+j/NOl/5Kyhf/+1mWzinynpcvG3L/b710Met39PMPv9iQS3+e3tZLT7m6K/wtn1fiohMP4NGOHr5270Ygu4B1X286ptBVcnpRkqRxK+Z0ntOAH8YYy8DtIYQjQwiNMca+0R44FeI319JbmcdUxYYqCYso8f5TFu9aQD/YSQfN561HLeFbD3awckkzbzh00TDPsruuzgqPPdTLS49qprllfFl57aZs24ojB424nXTQfM46bBHfeWgLpxyxheXl0b+5OGDrlg46O+dy7b8+Pa5+zAYJT0/Z+2I2sQ511qLOWtRZi8xsqMPZr1/AkuULp7sbQH6hqxXYOuj2NmAp8OxAQwhhDbAGIMZIa2vrlHWm2FCgXNn7YvHnoylp5BXVMmcec/CIx1x8xlKe3vkAX75rAy9/yQqOXDF/xGPL/VVu+/EzdGzuY2sHnHXuC2lqHhrmBmzp6mNLVz+HtM4F4MlfbGbpnBJHvfiA3RbJf+wNS7jtqZ9z8yObec+hBwA7aHvhYlpbW0Z45sxT2+4kLRzEHKauhpIkTYbFixdOaaYYj7xC12bgpYNuL6i17RJjvAq4qnYzbW9vn7LOvPV3D6O1tZWpOsfab/2CR/tewoZnnqHQ3DzicX/8ymX84Xe38/WfP8mHTjpg2GPSNOWeO7ro2NzPYauaeHRtL9/79lOcdOq8XbvDD9ZfqfKR7z/Jhp39fPnNK1ncUuTeZ7by0qVNbN68ecjxL5hX4tlt3Wx4NsvEff07aG/v3Ovr6+jayONNKf/3/a/c63Gz0VS+L2YT61BnLeqsRZ21yMyWOkx1H9va2sZ0XF4L6f8DeFMIoRhCeBXw0HRNLeaheX4jJA30bRgacgab31Rg1fI5PLipa8Rjnnykj3W/6efw1c0cvrqF406aw7YtFe6+Y/hg9G8PbOapbX30V1P+7f52tnaXeW5nP0eMMHq1Ym6JZ3f07tqjq6l59NGr3v4KTSMPtEmSpGHkErpijI8AXwHuAD4PXJjHeadL86JsWq9n49ZRjoSjlrfw7I5+OrrLQ+6rVFIe/FU3yw8o8tKjsv29DnhRI4ce2cSGdWV6unff5PTJLT1cd/9mTj14AW9YuYgfPrqV/3xyO8Bu36AcbPm8Eht29NLZWaFl7uh7dKV9vfSmCU1FU5ckSeOR1/QiMcYrgCvyOt90am5dAI9D95adLB7l2FXL5wDw4MYuTn7xgt3u29pRoVqBF69s2i0MvfCgRh55sJfnnunn4JdmYaxSTfn//vs55jUV+MAJK+ivVPmPJ7Zx7b2bKDYkrFwy/DTn8rklKtWUHTvGtl0EHZvobSjR3JjbW0eSpH2Cm6NOgebatyR6t42+6/whi5tpLiY8uHHoFGNHezb6tbh191GleQsamDu/gWfX9e9qu+nRrTzW0cOaE1awoKnA0jklzj58MeVqysolzTQWhv+jXjGvBEB31xhDV/tGeguNNDU1jn6sJEnaxdA1BZpbipBWh0z/DafQkHBEawsPbOze1Xb92s188b+fpWNTmXnzG2hq2v2PKUkSDnhRic0by/T1Vunur/KN+9pZtbyF1xxU/xbkW49ayoKmAse8YM6I518+t0SRhGr/2LaLSDs20ttQoqmladRjJUlSnXNEUyBpSGiqdNLTN7YtFY5aPoev/6qdnb0VymnKv/6ynf5qysrmFtoOHH5E6YAXlnh0bS8b1pe5fed2tvZU+PNTlu82DTm/qcCXfvsQmosjh6llc4vMJxtJG9NI1+ZN9BYW09Qy8rcyJUnSUIauKdKc9NJTHdsU3FHLW0iBXz7exaM93fRWUhZTpNwPS1qH/yNauKRAc0vC00/18q3nOjjxRfM4fJhvKM5t3PuC91KhgbbmJijDnDljm17sa1xN0zCbvkqSpJE5vThFmktlegrzSMv9ox572NIWVjY00fGrCr/6dRfHt81lRZKttVqybPhwMzDF2L6hTKWS8t5hLjc0Vgc0Z1OFc+aNcXqx0EhTwY1RJUkaD0PXFGlqaaC3cTF0jL4hW1OxgSOas3VXq9O5vO/ly1jZ1Ex/svfF7YteUCBJE964YhEHLpz3l0MuAAAX7UlEQVT4GqslhSIVUhqbRg9SlfZN9CcFmvYyZSlJkobyk3OKtMxvpL9xHpVNG8d0/DJK9KdVWpMSLd0FllFifdpHdS8XtXqir4eutMLB/c2k6cSvfjWPAjvSyl7PBZCW++nbuQPAkS5JksbJ0DVFdm2QumnbqMf29VYp9jXwYNJFqRkeuLebYrmB9ZU+ntgy8rYTdzyzk4cauujZlrLxuaGbq45VqZKwI63Q3jXKVGhHO721ac+9Lc6XJElD+ck5RZqWZls39GzZ+3UMATraKwD84WkrOGJVC507sq0mNqR9rB3hEkH9lSp3retk6YFF5sxt4KFfdU98tKs3ZScVNnaOEro2b6S3kIUupxclSRofPzmnSEtt09Ge7aNvkLp5Y5mGBli8tMiBL2mkuSWhUICGFli7qXvYx/zyuS66y1VOOmg+RxzdzPatVdb9Zmho2rxx6OWCBuvvT6n0p+xIK2zcuffQlW7eSG9D9o1MpxclSRofQ9cUaW7JQslYNkjtaC+zaGmBQiGhUEh4+YlzWH1cC0esmMODm4Yfwbr96R20FBt42Qvm0HZQiQWLCjx0fw+VSv3YHdsr/OyWnTx8f8+I5+7uzPo3tpGuTfQWa6HLkS5JksbFT84pUmpMaEjL9PTtvcTl/pRtWyosXVbfj2vZihIHHdLEUcta2NJdZsMeI1CVasrPn9nJK144j1Ihu0j1Ucc0091Z5dG19YD18H09kNYvJzScrlroamhiTNOLffOXAtDoSJckSeNi6JoiSZJkG6SmTaSVyojHbdlcJk1hybKhm6AeuSzb7PTBPaYY127qZntvhVcdOG9X27IXlHjhi0s88mAv27dW2Lq5zLPP9NPUnLBze5W+vuFH3AZC19y5DUPC3Z7SzRvpXdwKONIlSdJ4+ck5hZpKFXqbFsHWjhGP2bypDAksWTo0dB20qImlLUX+7f7NdPZlwS1NU77/yBZKDQnHtc3b7fhVx7ZQaky49+ddrP1VD6XGhJedkO3/tWXz8MGvu7NKsZiweF5x1DVdbN5Ib22kyzVdkiSNj6FrCrXMaaCncTFs3jDiMR3tFRYuKlAsDQ0xDUnCR1/TxnM7+/jC7c9STVP+6Z5N3PqbHbx11RJaSrv/8TU1NXD0cS1s21KhfWOZlx7VROvyIkkCW0aYYuzqrDJvfonl80ts7i5THmGzrrRSgS3t9M5fDLhlhCRJ4+Un5xRqmtdEb/Niqps2kqbpkP+2bSmzpb3M0mWFYe9P05Sjlrfw+8cu545ndnLpj57i22s7OPOli3jH6qXDHv+CFxVpO7DE3PkNvHhlI4UiLFjUwJb28rDHd3VWmDe/yPK5JaoptHf2DXscWzdDtUrv3IXZazN0SZI0Ll7wego1L55DZV0//V/7MqWvfn63+/pK87jzlX9NY1LgJdf8FdW+kTdRPQv49ZHv5L84ltduuIfzbvkG6dUpI+3KdQxQTYok3y5TBRYd/l6eOeBkyue/l4a0vraru2kxO1/9WRbf90OWbfgZvPx8nvvkn7Js62Mj9qVvzgLYAk1FpxclSRoPQ9cUyvbq6qf3zHfT2FDfJLWaJtzbdwK91SW8qum/aX7TWXt9ngT4H2nCq8uPc8JKKBz6jlGPHzwOtbi8jN/0t7DzrPNY2LBjVx9+2fdKkmqBI1+2kE1dJ0MnbHzVmSSlEdagNTfTu3gFrOugseBIlyRJ42HomkJNLVkw6XvVb9HwgtKu9sce7KH9vh6OeUULSw/Ze+Aa0AK8eoL9WNpZgX/fwbZVb2DxS7MLYz9yfzcdD/Ty8hPncMAJ7yLZuImGbzzMpkOPo+GYZSM+V9+9myg2QLHBkS5JksbD4Yop1LJrg9TdJwI3rO9n8dICBx3SlE8/5jTQ1JzQsTlbTL/x2X5+/UAvLzq4xIEHZ5udFhsSlrYUR92rq7dcpclRLkmSxs2Rrik0MNI1eFf6tJqyfWuFgw5pzK0fSZKwpLVIx6YyD93XzSNre5m/oIGjj5uz23HL55VG3Taip1yl0UX0kiSNm5+eU6hYTGiZk7B9W32PrM6dVSoVWLi4kGtfFrcW6O5KeeTBXg48uJGTXz9/yDYVy+eW2DDaSFcldY8uSZImwJGuKbZ4aXG3jUm3bc3+f8GifEPXC15Y4tmn+znk8CbaDhx+lG35vBIdT5Tpr6SURghWveWqe3RJkjQBfnpOsUVLC3R3VndNMW7fWiFpgPkL8g1dc+cVOPn180cMXAAr5pZIgfaukUe7eiup20VIkjQBhq4ptrh2eZ8ttUXs27ZUmL+ggYYZOEW3fF72Dcu9LabvcyG9JEkT4qfnFFu4uEDSAFtrU4zbt1Zyn1ocq+Vzs9C1twtf91aqjnRJkjQBhq4pVigkLFxUYMvmMj3dVXp7UhYunplL6VrnlGhI2Os3GHvLqRujSpI0AX565mDx0gJbOyps2zI9i+jHqtCQ0DqntNfpxd5y1esuSpI0AX565mDx0iKVCjzzZB8ACxfN3LIvn7f30NXjlhGSJE3IzP3034csXpqNbD27rp+WuQ2UGmdu2ZfP3fsGqY50SZI0MX565qBlbgONTQlpFRbO0KnFASvmlujoLtNfqQ65r5qm9FVSml1IL0nSuBm6cpAkya7Rrpm6nmvA8nnZXl2bOstD7uuvZNeQdMsISZLGz0/PnAzs15X35X/Ga8Xckffq6i1no19OL0qSNH5+eubkgANLLF1eZEnrzA5de9sgtXdgpMvpRUmSxm1mbhi1D5o3v8CrT5s33d0Y1ZKWIoVk+A1SB0a63KdLkqTx89NTuyk0JLSO8A3GnrIjXZIkTZShS0OsmFtiw7DTi7U1XY50SZI0bn56aojl80o8u6OPNE13a68vpHekS5Kk8TJ0aYhDlzSzvbfCc3tMMQ4spG/224uSJI2bn54a4shlLQCs3dS9W/uukS6nFyVJGjc/PTXEQYuamFtqYO2mrt3a+9wyQpKkCTN0aYiGJOGIZS2OdEmSNIn89NSwjljWwtPb+tjRW9nV1uNCekmSJszQpWENrOt6aNBoV285pSGBYoOhS5Kk8TJ0aViHLW2hkLDbuq7eSpXGQgNJYuiSJGm8DF0aVlOxgUOWNO+2rqu3nNLs1KIkSRMyaddeDCG8G7gIKACPA++JMfaHEH4P+CtgHVCOMZ46WefU1DpyWQs/eGQr/ZUqpUIDvZUqTe7RJUnShEzmJ+idwOtijK8AVgBn1tqbgMtijCcbuGaXI5e10FdJeayjF8hGupoKjnRJkjQRkxa6Yoy/jjH2hRASYAGwoXbXcuDFIYQjJ+tcyseRy+YA9XVdfY50SZI0YZM2vTjI5cA9Mcaf127fApwCfDWE8EiM8T3DPSiEsAZYAxBjpLW1dQq6VlcsFqf8HLPFSLVoBdoWPM1TO1NaW1upJuuZ11zYp+vm+yJjHeqsRZ21qLMWGeswPsmeFzUeixDC+4CP7NF8FnABcATwzhhjeY/HFICHgTNjjI+Ocop0/fr14+7XeLS2ttLe3j6l55gt9laLz/zXOh7r6OGqN6/ko99/koXNBf7ytANz7mF+fF9krEOdtaizFnXWImMdMm1tbQCjrr+Z0EhXjPFa4NrBbSGE04BTgdMHB64QQinG2E+2tqsI7JzIOTU9Vi5p5mdP7WBHb6W2ZURpurskSdKsNJnTi+cCLwT+I4QAcEOM8TPAj0IIc8nWj/2vGONzk3hOTbFDlzQD8FhHT7aQ3i0jJEmakEkLXTHGDwEfGqb91Mk6h/J3yODQVanS7EJ6SZImxE9Q7dWCpgIr5pXqI11uGSFJ0oQYujSqlUuaeayjxy0jJEl6HvwE1ahWLmnmuZ39VFNoKviWkSRpIvwE1agGFtMDLqSXJGmCDF0a1crdQpdvGUmSJsJPUI1qfm0xPUCjC+klSZoQQ5fGZGC0y5EuSZImxk9QjclA6HKfLkmSJsZPUI3J0SvmkABLW6biGumSJO37/ATVmBze2sJXf+dQFjX7lpEkaSIc6dKYGbgkSZo4Q5ckSVIODF2SJEk5MHRJkiTlwNAlSZKUA0OXJElSDgxdkiRJOTB0SZIk5cDQJUmSlANDlyRJUg4MXZIkSTkwdEmSJOXA0CVJkpQDQ5ckSVIODF2SJEk5MHRJkiTlwNAlSZKUA0OXJElSDgxdkiRJOTB0SZIk5cDQJUmSlANDlyRJUg4MXZIkSTkwdEmSJOXA0CVJkpQDQ5ckSVIODF2SJEk5MHRJkiTlwNAlSZKUA0OXJElSDgxdkiRJOTB0SZIk5cDQJUmSlANDlyRJUg4MXZIkSTkwdEmSJOXA0CVJkpSD4mQ9UQjhYOBu4MFa0wUxxvtCCKcAlwMJ8Lcxxm9M1jklSZJmi0kLXUAT8MMY4zsHGkIIDcDVwOuBrcC9IYTvxRi3T+J5JUmSZrzJnF5cDiwMIZwYQijU2lYC22KMT9WC1t3AiZN4TkmSpFlhMke61gE3A5cAh4UQXgu0ko1wDdhaaxsihLAGWAMQY6S1ddjDJk2xWJzyc8wW1qLOWmSsQ521qLMWddYiYx3GZ0KhK4TwPuAjezSfFWP8XO3+rwJvA34KLBp0zCKgfbjnjDFeBVxVu5m2tw972KRpbW1lqs8xW1iLOmuRsQ511qLOWtRZi4x1yLS1tY3puAmFrhjjtcC1g9tCCKXazwSYD2wHHiWbcjyIbJTrWOCOiZxTkiRpNpvMNV2XhRDuAu4CngOuizFWyaYMvwXcAlzqInpJkrQ/mrQ1XTHGj47QfgtwwmSdR5IkaTZyc1RJkqQcGLokSZJyYOiSJEnKgaFLkiQpB4YuSZKkHBi6JEmScmDokiRJyoGhS5IkKQeGLkmSpBwYuiRJknJg6JIkScqBoUuSJCkHhi5JkqQcGLokSZJyYOiSJEnKgaFLkiQpB4YuSZKkHBi6JEmScmDokiRJyoGhS5IkKQeGLkmSpBwYuiRJknJg6JIkScqBoUuSJCkHhi5JkqQcGLokSZJyYOiSJEnKgaFLkiQpB4YuSZKkHBi6JEmScmDokiRJyoGhS5IkKQeGLkmSpBwYuiRJknJg6JIkScqBoUuSJCkHhi5JkqQcGLokSZJyYOiSJEnKgaFLkiQpB4YuSZKkHBi6JEmScmDokiRJyoGhS5IkKQeGLkmSpBwYuiRJknJQnKwnCiF8GVhVu3kg8IMY4/khhE8Abwc2A8/EGN8xWeeUJEmaLSYtdMUYzwcIIbQAdwGX1e5qAi6OMf5wss4lSZI020zF9OIa4MYY4xO128uBw0MIh0zBuSRJkmaFJE3TSXuyEEICPAS8Lsa4odZ2FnAi8DvAd2OMfz7CY9eQBTZijMf39fVNWr+GUywWKZfLU3qO2cJa1FmLjHWosxZ11qLOWmSsQ6axsREgGe24CYWuEML7gI/s0XwW2Vquv4oxnjXMY+YBzwAviDH2jHKKdP369ePu13i0trbS3t4+peeYLaxFnbXIWIc6a1FnLeqsRcY6ZNra2mAMoWtCa7pijNcC1+7ZHkI4D/jxHm2lGGM/MBfoBfonck5JkqTZbNIW0tccDfzLwI0QwgLg5hBClSwBfjDGWJnkc0qSJM14kxq6Yoy/u8ft7cAJk3kOSZKk2cjNUSVJknJg6JIkScqBoUuSJCkHhi5JkqQcGLokSZJyYOiSJEnKgaFLkiQpB4YuSZKkHBi6JEmScmDokiRJyoGhS5IkKQeGLkmSpBwYuiRJknJg6JIkScqBoUuSJCkHhi5JkqQcGLokSZJyYOiSJEnKgaFLkiQpB4YuSZKkHBi6JEmScmDokiRJyoGhS5IkKQeGLkmSpBwYuiRJknJg6JIkScqBoUuSJCkHhi5JkqQcGLokSZJyYOiSJEnKgaFLkiQpB4YuSZKkHBi6JEmScmDokiRJyoGhS5IkKQeGLkmSpBwYuiRJknJg6JIkScqBoUuSJCkHhi5JkqQcGLokSZJyYOiSJEnKgaFLkiQpB4YuSZKkHBi6JEmScmDokiRJyoGhS5IkKQfFiT4whPBC4N+Bz8QYv1FrOwW4HEiAvx3U/lngNGAn8J4Y47rn23FJkqTZZEIjXSGEk4EbgfKgtgbgauCtZAHrb0IIC0IIZwAvizG+ArgK+N/Pu9eSJEmzzERHun4OHA/8w6C2lcC2GONTACGEu4ETgVOBG2rH3AB8brgnDCGsAdYAxBhpa2ubYNfGLo9zzBbWos5aZKxDnbWosxZ11iJjHcZuQiNdMca+GGNlj+ZWYOug21trbbvaY4w7gUUjPOdVMcYTYownkE1PTul/IYRf5HGe2fCftbAW1sFaWAtrYR2e93+jGnWkK4TwPuAjezSfFWNcv0fbZnYPVIuA9sHtIYS57B7MJEmS9gujhq4Y47XAtWN4rkeBhSGEg8iC1bHAHUAV+BPg/wDnAD+ZcG8lSZJmqUnbMiLGWCVbk/Ut4Bbg0hjj9hjjT4BfhRDuAs4H/nyyzvk8XTXdHZhBrEWdtchYhzprUWct6qxFxjqMQ5Km6XT3QZIkaZ/n5qiSJEk5MHRJkiTlYMI70s9mIYQPA+8G+oEPxhjvn+Yu5SaEcDDZHPwcoBlYE2O8O4TQCdxTO+yTMcabpqmLuQoh3EJWhzLZPnJfIvviSBvwAFl9+qatgzkIIbyZ7MsuAE3Ay4DDgbuBB2vtF8QY75uG7uWmtsHz3wDHxBh/K4SwkGHeCyNdeWNfMUwd3g1cBBSAx8muKtIfQvg94K+AdUA5xnjqNHV5ygxTi4MZ5vdiX39PwLC1+DKwqnb3gcAPYoznhxA+AbydbOeCZ2KM75iWDs9Q+13oCiEcCvw+cBzwCuDvgddNa6fy9RxwYYzx0dpfmpeGEN4F/CrGePL0dm1aNAGnxBh7AUIInwJujzFeVvtL5T3AP05nB6dajPE7wHdg1yW7riOryw9jjO+czr7lpfaBchvZ78fAfjsfY4/3Qgjhq2RX3ng92be07w0hfC/GuH0auj3pRqjDncDraoHzFuBMsn+gNAGXxRi/PB19nWoj1GLI78Wgq7Hsk+8JGL4WMcbza/e1AHcBl9UObwIujjH+cBq6OuPtj9OLp5H90pRjjLcDR4YQGqe7U3mJMfbEGB+t3VxE9ku0HGgMIbwmhNA0fb2bFouAk0MIrbXbp7P7FRTOmJZeTYMQwnLgXODzZO+JhSGEE0MIhent2dSrffv6dOALg5qHey/suvJG7UN14Mob+4Th6hBj/HUtcCXAAmBD7a7lwItDCEfm39OpN8J7Yrjfi336PQEj1mLAGuDGGOMTtdvLgcNDCIfk1b/ZZH8MXXvunL8NWDpNfZk2IYSTgD8EPg10A/9MtqXH2hDCS6azbzn7PNn+cXfVptkGvz8Grqqwv7gA+L+1Ub91wM3AJWT/ch/2ShL7khhj9x5Nw70XRrryxj5jmDoMuBy4J8b489rtW4Au4KshhH/Oo295G6YWw/1e7PPvCRj+fVEL4heSvTcG/D+yz9TvhhD+JqfuzRr73fQi2TzzSwfdXlBr22+EEI4Fvgr8dozx2VrzFbX7PgF8APiLaelczgamRkII15MF0IErKKyjflWF/cXvAm8AiDE+Tu06qbUptbcB10xbz6bHcO+Fka68sU+rTbsfCOyaVosx/hfwXyGE/w08HEI4dNAo+j5phN+Ln7IfvidqXgk8FmMcGP0kxvg94HshhL8FngkhfDLG2DNtPZxh9seRrv8A3hRCKIYQXgU8tK8vlB4shFACvga8Pcb48KC2AQuAfWYtwt6EEAb/o2Pgdf+EbOQL4Gz2kyso1K4kUR0I4QPvidq/ZOezn7wn9jDce2HXlTdCCAuoX3ljnxVCOA04FXhXjLE8qH3g740msn/A78y/d/ka4fdiv3tPDPIm4MeDGwa9L+YCvWRfWFPNfjfSFWN8JITwFbJfin7gvGnuUt6OBg4GvhhCgNq39mqL6Stk3076n9PWu3y9JoRwBdBHNsV6AfAscG0I4U6ybyjtk9Mmwzia7M9+wGW1b2QlwH+TLa7f33yOPd4LMcZqCGHgyhsN1K68MZ2dzMG5wAuB/6j9nXFDjPEzwI9q19NtAP5XjPG5aexjXob8Xuyn74kBRwP/MnCjFjpvDiFUyWr0wRhjZbo6NxO5I70kSVIO9sfpRUmSpNwZuiRJknJg6JIkScqBoUuSJCkHhi5JkqQcGLokSZJyYOiSJEnKgaFLkiQpB/8/jC18iinREt4AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 13.421304968384204 \n", + "\n", + "\n", + "fftfilter\n", + "8.910849000007147\n", + "gamma total\n", + "11.594681999995373\n", + "coch1\n", + "3.4084970000039903\n", + "coch2\n", + "3.4989109999951324\n", + "get avg\n", + "0.07457199999771547\n", + "fftfilter\n", + "8.439260999999533\n", + "gamma total\n", + "11.094794999997248\n", + "coch1\n", + "3.4027270000005956\n", + "coch2\n", + "3.5186070000054315\n", + "get avg\n", + "0.07411000000138301\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 18.790323984502336 \n", + "\n", + "\n", + "fftfilter\n", + "47.08459800000128\n", + "gamma total\n", + "54.58950300000288\n", + "coch1\n", + "4.923326999996789\n", + "coch2\n", + "5.48364600000059\n", + "get avg\n", + "2.3378889999949024\n", + "fftfilter\n", + "46.24703299999965\n", + "gamma total\n", + "53.76535100000183\n", + "coch1\n", + "4.891428000002634\n", + "coch2\n", + "5.38919299999543\n", + "get avg\n", + "2.221315999995568\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.4899698779836332 \n", + "\n", + "\n", + "fftfilter\n", + "6.366683000000194\n", + "gamma total\n", + "8.549095000002126\n", + "coch1\n", + "3.1669519999995828\n", + "coch2\n", + "3.374934999999823\n", + "get avg\n", + "0.03089400000317255\n", + "fftfilter\n", + "6.110306000002311\n", + "gamma total\n", + "8.271109000001161\n", + "coch1\n", + "3.3158819999953266\n", + "coch2\n", + "3.362068000002182\n", + "get avg\n", + "0.028207999996084254\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 70.14553601443724 \n", + "\n", + "\n", + "fftfilter\n", + "8.222340000000258\n", + "gamma total\n", + "10.814556999997876\n", + "coch1\n", + "3.3878830000030575\n", + "coch2\n", + "3.5139969999945606\n", + "get avg\n", + "0.07401899999968009\n", + "fftfilter\n", + "8.872960000000603\n", + "gamma total\n", + "11.562932000000728\n", + "coch1\n", + "3.336431999996421\n", + "coch2\n", + "3.449052000003576\n", + "get avg\n", + "0.07439200000226265\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 34.11326947275381 \n", + "\n", + "\n", + "fftfilter\n", + "45.31545100000221\n", + "gamma total\n", + "52.76616500000091\n", + "coch1\n", + "4.875470999999379\n", + "coch2\n", + "5.408546999999089\n", + "get avg\n", + "2.426519999993616\n", + "fftfilter\n", + "45.61760900000081\n", + "gamma total\n", + "53.07194100000197\n", + "coch1\n", + "4.881586999996216\n", + "coch2\n", + "5.370241000004171\n", + "get avg\n", + "2.227152000006754\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 3.830604273135696 \n", + "\n", + "\n", + "fftfilter\n", + "8.540548999997554\n", + "gamma total\n", + "11.200673999999708\n", + "coch1\n", + "3.398464999998396\n", + "coch2\n", + "3.492401000003156\n", + "get avg\n", + "0.07544100000086473\n", + "fftfilter\n", + "8.756419999997888\n", + "gamma total\n", + "11.582289000005403\n", + "coch1\n", + "3.4004209999984596\n", + "coch2\n", + "3.4969829999972717\n", + "get avg\n", + "0.10468199999741046\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 47.55854422855711 \n", + "\n", + "\n", + "fftfilter\n", + "48.37513800000306\n", + "gamma total\n", + "55.876054000000295\n", + "coch1\n", + "4.903504000001703\n", + "coch2\n", + "5.264148999995086\n", + "get avg\n", + "2.463953999998921\n", + "fftfilter\n", + "46.78483899999992\n", + "gamma total\n", + "54.47504200000549\n", + "coch1\n", + "4.845753999994486\n", + "coch2\n", + "5.386101000003691\n", + "get avg\n", + "2.2142769999991287\n" + ] + }, + { + "data": { + "image/png": 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D8847j1WrVgGwYsUKtm7dmuk52sWeLkmSNGsUCgVqtakPcR5IDF2SJGnWeN3rXsctt9zCQw89BDDlOWMHgkyHF+M4fivwO42b3cBJwLHAfcDDjeOXhRB+muV5JUlSZzj66KP5zGc+w+WXX84LL7xAqVTit3/7t/npTw/86BDNVIKM4/hzwGbgRuATIYR37MfTk3Xr1s1Iu5r6+voYGBiY0XMcKKzFOGuRsg7jrMU4azGuk2qxc+dO5s/fv7lcTTMxp2s221Ot+vv7AaJ9PX9GhhfjOF4OXAz8KbAcWBzH8ZlxHM/eFcskSZJm0Iz0dMVx/AfA9hDCn8Rx/HLgl4HXAMcArwshbJnkOZcAlwCEEE4bHR3NvF0TzbV0vjfWYpy1SFmHcdZinLUY10m12LhxI93d3e1uxgFhZGSEQw455CXHu7q6YAo9XTMVuh4E3hhCWL/b8S8Dt4UQvriPl3B4MUfWYpy1SFmHcdZinLUY10m1GBwcpLe3d1rP7aTwORV7qlXbhhfjOF4J1JuBK47jcuNnBCwEtmV9TkmSNH2ddIXgTMmiRjMxp+tE4KkJtz8Tx/E9wD3ABuCGGTinJEmahq6uLkZGRtrdjFlvZGSkOYw4bZmvSB9C+BbwrQm3P5L1OSRJUjbK5TK1Wo3BwUGiaJ8jZLvo7u6eE4EtSRKKxWLLm2m7DZAkSXPcvHnzpvW8TprblgdXpJckScqBoUuSJCkHhi5JkqQcGLokSZJyYOiSJEnKgaFLkiQpB4YuSZKkHBi6JEmScmDokiRJyoGhS5IkKQeGLkmSpBwYuiRJknJg6JIkScqBoUuSJCkHhi5JkqQcGLokSZJyYOiSJEnKgaFLkiQpB4YuSZKkHBi6JEmScmDokiRJyoGhS5IkKQeGLkmSpBwYuiRJknJg6JIkScqBoUuSJCkHhi5JkqQcGLokSZJyYOiSJEnKgaFLkiQpB4YuSZKkHBi6JEmScmDokiRJyoGhS5IkKQeGLkmSpBwYuiRJknJg6JIkScqBoUuSJCkHhi5JkqQcGLokSZJyYOiSJEnKgaFLkiQpB4YuSZKkHBi6JEmScmDokiRJykEp6xeM4/hWYB5QBW4C/hK4HugHHgIuCSGMZn1eSZKk2Wwmerq6gXNCCGeHED4LfBS4M4RwBjACvHsGzilJkjSrzUToWgKcHcdxX+P2+aQ9XjR+XjAD55QkSZrVMh9eBP4UuAj46ziO/yvQB2xp3Lelcfsl4ji+BLgEIIRAX9+kD8tMqVSa8XMcKKzFOGuRsg7jrMU4azHOWqSsw/7JPHSFEP4KII7jG4FPA5tJe7/WNn4O7OF51wLXNm4mAwOTPiwzfX19zPQ5DhTWYpy1SFmHcdZinLUYZy1S1iHV398/pcdlOrwYx/HEELcI2AZ8n7TnC+DCxm1JkqQ5JeuertfGcXw1MAoMAZcB64Hr4zi+G3gY+LuMzylJkjTrZRq6Qgj/Bpw6yV1vzfI8kiRJBxoXR5UkScqBoUuSJCkHhi5JkqQcGLokSZJyYOiSJEnKgaFLkiQpB4YuSZKkHBi6JEmScmDokiRJyoGhS5IkKQeGLkmSpBwYuiRJknJg6JIkScqBoUuSJCkHhi5JkqQcGLokSZJyYOiSJEnKgaFLkiQpB4YuSZKkHBi6JEmScmDokiRJyoGhS5IkKQeGLkmSpBwYuiRJknJg6JIkScqBoUuSJCkHhi5JkqQcGLokSZJyYOiSJEnKgaFLkiQpB4YuSZKkHBi6JEmScmDokiRJyoGhS5IkKQeGLkmSpBwYuiRJknJg6JIkScqBoUuSJCkHhi5JkqQcGLokSZJyYOiSJEnKgaFLkiQpB4YuSZKkHBi6JEmScmDokiRJyoGhS5IkKQeGLkmSpByUsnyxOI5XAdcC84F5wCUhhPviOB4E7m887JMhhJuzPK8kSdJsl2noAjYAl4cQnojj+DeBj8dx/E7gJyGEszM+lyRJ0gEj09AVQhgGnmjcXEIawpYDXXEcvxa4J4QwkuU5JUmSDgRRkiSZv2gcx68BvgScB1SA9wCnAGcDF4QQnp7kOZcAlwCEEE4bHR3NvF0TlUolqtXqjJ7jQGEtxlmLlHUYZy3GWYtx1iJlHVJdXV0A0b4el3noiuP4FOCrwH8IITy2232fAEohhP++j5dJ1q1bl2m7dtfX18fAwMCMnuNAYS3GWYuUdRhnLcZZi3HWImUdUv39/TCF0JXp1YtxHJeBvwV+tRm4GseaFgHbsjynJEnSgSDrifQnAquAL8RxDFAFbmpMpq8BTwH/I+NzSpIkzXpZT6S/D1gwyV1XZXkeSZKkA42Lo0qSJOXA0CVJkpQDQ5ckSVIODF2SJEk5MHRJkiTlwNAlSZKUA0OXJElSDgxdkiRJOTB0SZIk5cDQJUmSlANDlyRJUg4MXZIkSTkwdEmSJOXA0CVJkpQDQ5ckSVIODF2SJEk5MHRJkiTlwNAlSZKUA0OXJElSDgxdkiRJOTB0SZIk5cDQJUmSlANDlyRJUg4MXZIkSTkwdEmSJOXA0CVJkpQDQ1cLkiTh3/9tBxvWVtrdFEmSNMsZulowOpKwaUOVFwaq7W6KJEma5QxdLRgarANQrSRtbokkSZrtDF0t2LmzEbqqhi5JkrR3hq4WjPV0GbokSdI+GLpaMNTs6XIevSRJ2gdDVwvGhhed0yVJkvbB0NWCocE0bDm8KEmS9sXQ1YLm8GLN0CVJkvbB0DVN1UpCZTSB6MAZXqzXD4x2SpLUiQxd09Ts5epdUKBaTVenn81e2FTl//v6Vra+6EKukiS1g6FrmnY2lotYuLgIQG2WZ5lnnhqhXoNnnhxtd1MkSZqTDF3T1OzpWrQ4LeFsnkxfqyZseC5d12LtM6Ozuq2SJHUqQ9c0DQ3WiQowf0Ha0zWbg8zG9RWqVTjmhG6qVVi/xt4uSZLyZuiapp076/T0FCiXI2B2T6Zf+0yF7nkRrzh+Hr0LCzzzlKFLkqS8GbqmaWiwTk9vgVIpvT1be7pGR+s8v77CYSu7KBQiVr68ixcHamzfVmP71hr33jHImp8PtruZkiR1vFK7G3CgGtpZZ9khZUpjPV1tbtAerF9ToV6Hw44sA3DEqi4e/ckw994xyI5tdZIECoWtnPILXW1uqSRJnc3QNQ31WsLwUEJPb0SpNLuHF9c+W6F3YYHFB6Vzz7rnFTj0sDLrn6uw8uVdVCoJz68fJknKRFHU5tZKktS5HF6chqGh9MrFnvmF8Z6uWTi8ODxUZ/PzVQ5buWugOvmM+Zz//yzk5DPmc0h/mdHROtu31sfu37K5yrNPjbSjyZIkdSx7uqZhqLFGVzqna/aGrvWNZSL6j9h16LDcFVHuSnu+lvalP1/YVGXRkvTPD/94iM2banTPK3BIfznHFkuS1LlyC11xHH8IeBdQAd4fQngwr3NnbWhnGrDmzy9QbE6kn4XDi+vXjLJgUWFsAdfJ9PQWmN9b5IWBKqte0Z32jm2qQQQ/vnsn57x5Id3z7BCVJKlVuXybxnF8NPCfgF8APgz8RR7nnSnNhVHnzS8QRRGlElRn2Yr0I8NpeOo/Yu89VVEUcciKHjZvqpIkCRvWpr1jp545n8powk/uGZr1WxxJknQgyKun6zzguyGEKnBnHMfHxXHcFUJoy4JR4WuPMFJbwHSjxPyomzJ1+JPPUSsUKL7sQ9RmWU9Xc2hxxeH7vipx+Yp5PP3EDoZ2JqxfU2HBwgL9K8sMDc3jkR8P8/dfWUed+j5fpxNErJn256KTWIdx1mKctRhnLVIHQh0ufMMiDl6+uN3NAPILXX3Algm3twJLgfXNA3EcXwJcAhBCoK+vb8YaUyoUqdamf6XeDmpsqA9zcblM5aH76Vo5TKG4eL/bnCQJ9931AitX9bLs0HnTbs9k7rltLYuXlHnZUcv3eVViuVgFBtj+YhebN23jpNMOYtmypSxdmnDjvffTF5XpmvW/VpIkvdRBB+3/9/NMySt0bQZeMeH2osaxMSGEa4FrGzeTgYGBGWvML//KMfT19THdc3z1pwPc+ZOtjFzxBxR+++0U6qPsHBze79db+8woP7l3J89v3MEZr+2dVlsmMzJcZ/26IV5xXDebN2/e5+MPPngppTLcf/dmkgQWH1wdey8/qm3hLV0DvPPXzs+sfbNZK5+LTmIdxlmLcdZinLVIHSh1mOk29vf3T+lxeYWufwV+O47j3wNOBx5t19BiFpbNT8s2sLPK8kKREpX9nkhfrSY8/OOh9HU2VKjXEgrFbNbJ2rC2AslLr1rck0Ih4qClJTZtqNK7oMCiJeNT/WpRgYyaJUnSnJbLRPoQwuPAl4C7gD8FLs/jvDNlWW86Of35wQoUS5SSyn4vGfHkoyMMDyUcdWy6CfULA9nNxN+wtkLvggILF0/9f+/SZWmQXHHE+JpeSZI0QpepS5KkVuW2ZEQI4Wrg6rzON5OWN0LXpsEKFAsUk8p+bQM0tLPOE48Os+KIMsecMI+nHx9h4/oqfYe0viZWrZaw+fkqR7ysa79WmD+kv8yTj41w+JHjvWO1Wo0kKlB0xQhJklrm1+k0LJ1fJqIZukqUktH96ul6/OFhSOD4k+dRKkccvKzE8+uy2bzxxYEqtRosO3T/AtyiJUXecvHiXdb0qlfTKxaLBT8mkiS1ym/TaSgXIw7uKfH8YBUKRUr10SnP6UrqCeufq7Di8DLze9OAc0h/mR3b6wzuqLXctk0bq0QR9C1vvROz1ui+c06XJEmtM3RN07LecqOnKw1d9TrU6/sOXi++UGN0JNlle53lK9KA9Pz61ud1bdpQ5aC+4tiekK2oVtMQWCyYuiRJapWha5qW9ZbS0FUoUqqnm0NPpbdr47oKUQTLVoz3RC1YWKR3QYHn17c2xDgyXGfri7X9Hlrck7qhS5KkzBi6pmlZb5mBnRXqxSLF+jAwta2ANq6tcPCyEl1du5Z++YoSA89XW9o4e2Bj2oDlh2RzfUSt8YaKzqSXJKllfptO0/LeMtU6bOlaSKk2tZ6uwR01tm+rc0j/S0PRiiO6qNfgsZ8OT7tNmzZUKXdFLD5ozxtc74+x4UWXjJAkqWWGrmlqrtW1qXvJeOjaRy/VxnVpz9Gh/S8d/lu6rMSqo7t46mcjY5tO748kSdi0scKyQ0pEGQ0H1muN0GVPlyRJLfPbdJrG1uoqL6JYaw4v7it0VViwqEDvwsl7oo5/VQ+LlhR54Ec72Tk4tQ2md2yv8exTIzx43xDDQwnLDs1u6bVqzTldkiRlxdA1TX29jSsOuxZRqqXb+exteLEymi5aesgkvVxNxWLE6WfNJ6kn/PjunftsQ72e8MPvbefHdw/x7NOjHLS0uNfX31/NdbpKhi5JklqW24r0nWZ+uciCrgKbSgspDa8BoLaXnq7nN1RIEvYZinoXFnnZMd08/sgI1Uqy16Uftr1Yo1qBk07vYeXLujIbVmyq1ZoT6bOZIyZJ0lxmT1cLlvWWGSgtoFRtDC/uZSrW+jUVuudFHLx03wHm4GUlSODFzXu/HPKFzenw3/IV5cwDF0C11lyR3p4uSZJaZehqwfLeMpuKvRQr6VBgZQ89XdVqwvPrKxx62NTC0UFLSxDtexPsFweq9MyP6Jk/M/8bm8OLBSfSS5LUMr9NW7CsEboKtVGiAtT2MKfr+fUVajXoP2Jq863K5YhFi4u8MLD3bYFeGKhyUN/MjRDXGhPpS4YuSZJa5rdpC5b1lhiKygwmJUqlaI9XL65/rkJXd7qx9VQtXVbkxc3VPW4tNLSzzvBQwsFLZy50jV296JwuSZJaZuhqwdhaXcX5lEqTX71YqyVsXJcOLRb2Y27UwX0lalXYtmXy3q7m0ONBfTMXiJqBz3W6JElqnd+mLRhbq6vYS6kcTboN0KYNVWpVWDHFocWm5rDhC5smn9f14kCVYhEWLZm50GVPlyRJ2TF0tWC8p2vBLsOLO7bXWPP0KJs3VXnumVHKXRF9y/dvGLBnfoGe3sIe53W9MFBjydLSfvWe7a9avXH1YtGrFyVJapXrdLVgcXeRKEnYWuxheTmiMpqGrgd+tJMXJ4Slw1ft39Bi08F9RQY2VkmShGjC/ofVasK2LTWOemV3629iL+q1xvBiyY+JJEmt8tu0BVEUUabmtymIAAAcz0lEQVROlQKlUsTQzjo7B+u8OFDj5cd003doiaHB+rRXiT+4r8TaZyoM7qgzOpLwwqYqy1eUGR1NSJL0/pk0vk6XHaKSJLXK0NWiUpRQoZDO6aokrFszCsCqV3TRu6C1uVBLG1c73v79HYyOpL1Oj/xkmK7utNfroCkstNqKseHFknO6JElqlaGrRSXqaegqpcN+a5+psOTgYsuBC2DBogKLlhQpFOCVJ3ax7NAS65+r8MyToyxcXKSre2Z7oGrNqxcNXZIktczQ1aIyCZWo2dOVLvFw/KvmZfLaURRxzpsX7nLsqGOLHHVsNq+/L7WaPV2SJGXFyTotKkcJ1Sid09XUf0RXG1uUnbGeLpeMkCSpZYauFpWjhEpUpNjoM1y6rDhjeyHmbXx40Q5RSZJa1RnpoI3KJFSjEqVCGlD6V3ZGLxc4kV6SpCwZulpUiqBSKHHwQXDoYWX6V05veYjZyJ4uSZKy47dpi0qFhEqhxPyehDPO7m13czLVmEdvT5ckSRmwp6tF5QgqhSLUJt8j8UBWS5xIL0lSVgxdLSpHUI1KUJ98j8QDWbU5vDiD+ztKkjRXGLpaVC5AtVCEaueFrnqSUKzXdtn3UZIkTY+hq0XlQkSl0Jk9XbU6FJPOe1+SJLWDoatFpQJUohLUOi+c1BIokrS7GZIkdQRDV4tKUQf3dCWJPV2SJGXE0NWirmKUzunqyKsXoZjY0yVJUhYMXS0qFRpXLzYXteog6fBi570vSZLawdDVonKxQKVQJKlW2t2UzKU9XYYuSZKyYOhqUbkQkUQFah3Z0xU5kV6SpIwYulpULqZrWFU6cJ2uqlcvSpKUGUNXi0qFtISdGLrqOKdLkqSsGLpaVC6lJaxWOy+cVB1elCQpM4auFo0NL3bg4qh1XDJCkqSsGLpaVCoVAah24PBiLYkoRoYuSZKyYOhqUbmYlnC01nnhpEZEsd2NkCSpQxi6WlRu9HR14kT6NHR1XpiUJKkdDF0tKjV6uir1zgsnVRxelCQpK4auFjV7uqoduDhq3Z4uSZIyU8rqheI4fhdwBVAEngLeHUKoxHH8m8AfAGuBagjh3KzOORuMDS86p0uSJO1Flj1ddwOvDyGcARwCvKVxvBv4TAjh7E4LXADlcppbqx04vFijQGNFDEmS1KLMQlcI4WchhNE4jiNgEbCxcddy4Mg4jo/L6lyzSXNx1I7s6Yqc0yVJUlYyG16c4Crg/hDCjxq3bwXOAb4cx/HjIYR3T/akOI4vAS4BCCHQ19c3A00bVyqVMjnHcK0EbKRQ6prxNs+UPdWiRoFyqXjAvq/pyOpzcaCzDuOsxThrMc5apKzD/plW6Irj+NeBD+92+JeAy4AjgHc0D4YQfgj8MI7jPwIei+P46BDCE7u/ZgjhWuDaxs1kYGBgOk2bsr6+PrI4x7YdQwDsGBrO5PXaYU+1qEURUb1+wL6v6cjqc3Ggsw7jrMU4azHOWqSsQ6q/v39Kj5tW6AohXA9cP/FYHMfnAecC54cQqhOOl0MIFdK5XSVgx3TOOVuVy42rFztwFC6d09WBb0ySpDbIcnjxYuAw4F/jOAa4KYTwWeB7cRz3ks4f+18hhA0ZnrPtuppXL3biRPqoQDFyJr0kSVnILHSFED4AfGCS4+dmdY7ZqLnhdbXzlulKQ5cruUmSlAm/UltUKqShq9KJocslIyRJyoyhq0WFKKJUr1HpvNFFagWHFyVJyoqhKwOlpPNCV1KvObwoSVKG/ErNQCmpdtzVi0m1Rj0q2tMlSVJGDF0ZKCd1KklnhZNaNV31o1jorPclSVK7GLoyUE5qVDstdNVqgKFLkqSsGLoyUO7AOV21ij1dkiRlydCVgVJSp5J0Vilr1UZPl3O6JEnKRGclhTYpUadCZ4WTsdDl5YuSJGXCb9QMlKlR7bTQVbOnS5KkLBm6MlDuyOHFxpwul6SXJCkTnZUU2qRMQiXqrFLWGptJFgud9b4kSWoXv1EzUIrqHTu8WHJOlyRJmfAbNQNl6lQotrsZmRpfp8uPiCRJWfAbNQMloNJhpazV0uHFgj1dkiRlwm/UDJSjOtWOm9Pl8KIkSVnyGzUD5SihEnXo8KKhS5KkTPiNmoEydF5PVz3d18g5XZIkZcNv1Ax0dk9XZ70vSZLaxdCVgXKUUIuK1JPO2fW6Vm+s01XqrKUwJElqF0NXBkqFNJhUah0UuhpXLxaLpTa3RJKkzmDoykApSsNWtd45oas6Frr8iEiSlAW/UTNQblSx0kGhq9botSs4p0uSpEwYujJQjjpweLExp6tUMnRJkpQFQ1cGmj1dnTS8OD6R3tAlSVIWDF0ZGBte7KiersY6XQ4vSpKUCUNXBppXL442Jp93guacrmLJqxclScqCoSsDzdBVrdTa3JLs1JJm6PIjIklSFvxGzUC5saxCpVptc0uyMz6ny54uSZKyYOjKQLkx7alS7aCeruacLifSS5KUCUNXBsZ7ujopdKU/nUgvSVI2DF0ZKDfndHVS6GrM6Sq5Ir0kSZnwGzUDzWBS6aSJ9M3hRfe7liQpE4auDDSHF6v1Dloyog6FpEYUmbokScqCoSsDpVJzTlcHha4koZh0zmKvkiS1m6ErA2MT6TtpcdQEiknnvB9JktrN0JWBrsayCh119aKhS5KkTBm6MlBuDi920t6LSUIRQ5ckSVlxufEMFIoloqROB3V0UUsie7okScqQPV0ZiIpFyvUalXon9XRBkc55P5IktZuhKwvFIqWkSrWjhhdxeFGSpAwZurJQKFKuVzvr6kVwyQhJkjJk6MpCsRG6Omp4MXJ4UZKkDBm6sjA2p6vdDclOLYGCoUuSpMwYurJQLKVzujqpp4uIknO6JEnKjKErC4UipU7r6cIPhyRJWfJ7NQsdOqerFHXO+5Ekqd0yWxw1juNVwH3Aw41Dl4UQfhrH8TnAVUAEfD6E8NWszjlrFIuUkyqVDsooNZxIL0lSlrJckb4b+G4I4R3NA3EcF4DrgDcAW4AH4jj+dghhW4bnbb/GkhE7O2p4sUDJ0CVJUmayHF5cDiyO4/jMOI6LjWNHAVtDCM82gtZ9wJkZnnN2KDbmdHVQRqkRUYra3QpJkjpHlj1da4FbgCuBY+I4fh3QR9rD1bSlcewl4ji+BLgEIIRAX9+kD8tMqVTK7Bz1croifT0qzHi7Z8JktahHEeVidEC+n1Zk+bk4kFmHcdZinLUYZy1S1mH/TCt0xXH868CHdzv8SyGEP27c/2Xg7cAPgCUTHrMEGJjsNUMI1wLXNm4mAwOTPiwzfX19ZHWOZOcOyvUaI7Uks9fM02S1qBIR1WsH5PtpRZafiwOZdRhnLcZZi3HWImUdUv39/VN63LRCVwjheuD6icfiOC43fkbAQmAb8ATpkONK0l6uU4C7pnPOWa25DVDSOeNxNQoUo1q7myFJUsfIck7XZ+I4vge4B9gA3BBCqJMOGX4duBX4eMdNogcoljoudNWJKHbO25Ekqe0ym9MVQvjIHo7fCpye1XlmpWIhXZG+g0JXNSoYuiRJypCLo2YgKjT2XqRzUkrN0CVJUqYMXRkpUadKRJJ0xroRNQoUDF2SJGXG0JWRMnUSImqdkbmoRQVKkalLkqSsGLoyUiZdjr7SIamrHhUo+umQJCkzfq1mZCx0dcCm10m95kR6SZIyZujKSHOfwkqtAzZgrNUbPV2mLkmSsmLoyki5EbqqndDTVa1QLZQoOKdLkqTMGLoyUuqg4cV6PV2JvmRPlyRJmTF0ZaSvvhOAn7840uaWtK5eSUOXw4uSJGXH0JWRY0ee56D6ED985sDf5aharQKGLkmSsmToykixWOCsynPcu3aQwdEDe6PoWtWeLkmSsmboykqhyNnDz1CpJ9z13I52t6Yl9ZqhS5KkrBm6slIscszIJpb3lvnhz7MZYkyShL+4awPX3r2Bp18cJkkSHnp+J1ffsY6/uXdjJueYzPjwoh8PSZKyUmp3AzpGsUhUr/G6IxfyT4+8wLbhKovmtVbeTYNVvvvEFgC+9bMtLO4usnUk7YUqRPDOk5cxr5R9MKpV0ysx7emSJCk7dmVkpViCWpXXrVpEPYHbn93O1uEqj28eYqQ6vQVTH9mUXhH5qQuO4P2nL+f45fO54hcO5XfO7qeewBObh7N8B2PG5nS5D5AkSZmxpysrhQJUK6xa0s3hi7r4q7s38pd3p0OAFx93ML956vL9fslHNg3RUypwwvL5nHRoLxcemx7f1ujtenTTEKsPmZ/ZW2iq1ZtzugxdkiRlxW/VrBRLUK0SRRHvO/0QfvGYJfzWqct5ZV8Ptz2zjSTZ/0VTH940xCuX9bxkmG9Rd5HDF3Xx6MDOrFq/i3pzeNGeLkmSMmNPV1aKRWj0EJ2yopdTVvQCsKCrwP/+9w088cIwr1jaM+WX2zFS49ktI5y9cuGk979yWQ93rdlOkiREGW/XU63VgCIlQ5ckSZnxWzUrxSLUXro+16sPX0gxgjue3b5fL/fowBAJcNzyyYPaK/t62D5aZ+320em0dq9qjfdRMHRJkpQZv1UzEhXGe7omWthd5KRDe7nj2e37NcT4yKYhihEcs4fesVcuS48/umloeg3ei3qtefWiHw9JkrLit2pW9tDTBXDWyoVs2FHh6f3Yl/Hh53fy8oPn0b2HJSEOW9TFgq7CjISuaiN0lYrFzF9bkqS5ytCVlcKeQ9eZhy+gsB9DjJVancc3D3P8sj3PAStEEcf29fDoQPahq1ZPQ1eh6DpdkiRlxdCVlVJpj6Fr8bwSq5fP5/YpDjE++cIIlXrCccv3vhzEK5f1sGbrKDtGst3rsdbs6SrZ0yVJUlYMXVkpFCad09V01sqFrNs+ypqt+574/nBjUdTj9tLTBelkeoDHMu7tqo7N6TJ0SZKUFUNXVhor0u/Jqw9fAMCP9rEZ9o7RGt95fAtHLulmyT62EXrF0h6KEfzx7ev43Zuf4bp7Nk579fuJmhPpC/Z0SZKUGUNXVgpFqO058CydX+aog7v50do9h64kSfjCv69nYLDCfznz0H2esqdc4HdedxjnrFpEPUn45mMvcs+6vYe6qajWm8OLfjwkScqK36pZKRb32tMF8OrDFvKzgSG2DE/+uG//bAt3rtnBe161jGP7praQ6muOWMh/fvWh/NEbj2R+ucAD6wf3u+m7q9ebK9Lb0yVJUlYMXVkpTr5O10RnHL6ABLhnkt6u57aO8Df3Pc/p/b289biD9//0hYiTDp3PA+sHp7Xl0ES1Wvr8gqFLkqTMGLqyUixCvb7XwPPyg7pZ2lPi7klC1w+e2UY9SfjtX1hBYZrb+rzq0F6eH6yybntlWs9vqtbT91AquUuUJElZMXRlpXml3x6WjQCIoogzDl/AA+sHGd1t/te9awc5ZmkPB/VMP+g093tsdYixObxYcE6XJEmZ8Vs1K8VGWNpL6AI447AFDFcTHty4c+zYlqEqT7wwzGmH9bbUhEMXdnHogjL3txi67OmSJCl7hq6sNDeH3se8rpMOnU93Mdpl6Yh7G1ccnt6/oOVmnLKil59u3EmlNv15XfVG6CoauiRJyoyhKyvNnq59hK6uYoFT+nu5/dntDI6mj7133SAH9ZR42UHdLTfjlBW9DFfr/KyFBVNrSXMivR8PSZKy4rdqVsbmdO192QiAXzmhj+0jNcKDm6nWEx5YP8hp/b1E05xAP9GJh86nELHfQ4wDOyt84pY1vDBUpbm+atnQJUlSZvxWzUpzeYW9LJDadPTSeVxw1GK++dgL/OtTWxms1DMZWgSYXy7yyr4e7tvP0HXns9u5f/0gtz69dXx40f2uJUnKjKErK8Wp93QBvPvkZZQLBf7y7g2UCnDyir1vbr0/TjtsAU++MMymwakvHfHIpnQ48o5nt1NtTAcrFkxdkiRlxdCVlWbo2secrqaDekr8yuqlVOtw/LL5zC9ntxDpa1cuBNIANRVJkvDwpiFKhYjHNw+zod5NlCTTXi9MkiS9lJenZaUxpyv59v8l6V00padcmET8JHolb9j8GPX/+/3MmnII8LLoJG6//0kuevCf9/n4n3Ut5sWh43lrYR3/TD8/qi2hGLW+cbYkSRpn6MpIdOjhJAsWktx925SfUwJ+n28B0NrGPS/1msMG+Ycj38CmO26jb3TbXh97//JT4OjjOe/ef+Qnr3g7Ty9YQXcytWFSSZI0NYaujEQrX07x6r9vdzPGnL1tlH+46Sl+dOnnueiVe9/L8ecPvMiCxzdx5Oe/wFkPbebpHw9Q7OrKqaWSJM0NzunqUIct6mLVkm5un8K8rh+v28Zxy3ooRBGvXZkOjXrloiRJ2TJ0dbCzVi7kkU1DbN6556sYtw5XefbFIY5bll49ediiLo5c0u2Vi5IkZczQ1cGaVzHeuWbX3q56kpA0Vp1vLhVx/PKesft/7cSlvOGoJTm1UpKkucE5XR3s8MXdHLmkmx/8fDsXHjs+r+v/3L+JW57ayntetYxnt47QVYw4+uB5Y/eftXIRZ61sR4slSepc9nR1uNevWsRjA0Ns2D4KwHC1zncf30KllvDnd23gm4++yHGHLHTLH0mSZpjftB3u9UemE+N/8Ey6bMTtz2xjqFrnf5x3OB8+awWHLChz/jF97WyiJElzQmbDi3Ec/xVwQuPmEcB3QgiXxnH8CeBXgc3AcyGEX8vqnNq35QvKHL+sh397ehu/csJSvvfkVg5b1MXxy3qIls/nnJctpq+vj4GBgXY3VZKkjpZZ6AohXAoQx3EPcA/wmcZd3cAHQwjfzepc2j+vX7WIv7x7Iz98ZjuPbBriN09ZRuQWP5Ik5WomhhcvAb4VQni6cXs5cGwcxy+fgXNpCl575CKKEfzFXRsoRnDeyxe3u0mSJM05mV69GMdxBFwOvH7C4X8EzgS+EcfxN0II/20Pz72ENLARQqCvb2bnGZVKpRk/x2zRB/zCqgFuf/pFzjlqKUcffugu98+lWuyLtUhZh3HWYpy1GGctUtZh/0TN9Zr2RxzHvw58eLfDv0Q6l+sPQgi/NMlzFgDPAYeGEIb3cYpk3bp1+92u/THX5jHd/sw2PnfbOv7gvMM5tX/BLvfNtVrsjbVIWYdx1mKctRhnLVLWIdXf3w+wz3k70+rpCiFcD1y/+/E4jt8H/Mtux8ohhArQC4wAe14eXTPmrJUL+dNfWsXLDpq37wdLkqTMZb046onA2K7PcRwvAm6J47hOmgDfH0KoZXxOTUEURQYuSZLaKNPQFUL4ld1ubwNOz/IckiRJByIXR5UkScqBoUuSJCkHhi5JkqQcGLokSZJyYOiSJEnKgaFLkiQpB4YuSZKkHBi6JEmScmDokiRJyoGhS5IkKQeGLkmSpBwYuiRJknJg6JIkScqBoUuSJCkHhi5JkqQcGLokSZJyYOiSJEnKgaFLkiQpB4YuSZKkHBi6JEmScmDokiRJyoGhS5IkKQeGLkmSpBwYuiRJknJg6JIkScqBoUuSJCkHhi5JkqQcGLokSZJyYOiSJEnKgaFLkiQpB4YuSZKkHBi6JEmScmDokiRJyoGhS5IkKQeGLkmSpBwYuiRJknJg6JIkScqBoUuSJCkHhi5JkqQcGLokSZJyYOiSJEnKgaFLkiQpB4YuSZKkHBi6JEmScmDokiRJyoGhS5IkKQeGLkmSpByUpvvEOI4PA74JfDaE8NXGsXOAq4AI+PyE458DzgN2AO8OIaxtteGSJEkHkmn1dMVxfDbwLaA64VgBuA74ZdKA9YdxHC+K4/gC4KQQwhnAtcAftdxqSZKkA8x0e7p+BJwG/PWEY0cBW0MIzwLEcXwfcCZwLnBT4zE3AX882QvGcXwJcAlACIH+/v5pNm3q8jjHgcJajLMWKeswzlqMsxbjrEXKOkzdtHq6QgijIYTabof7gC0Tbm9pHBs7HkLYASzZw2teG0I4PYRwOunw5Iz+F8fxvXmc50D4z1pYC+tgLayFtbAOLf+3T/vs6Yrj+NeBD+92+JdCCOt2O7aZXQPVEmBg4vE4jnvZNZhJkiTNCfsMXSGE64Hrp/BaTwCL4zheSRqsTgHuAurA7wB/DlwEfH/arZUkSTpAZbZkRAihTjon6+vArcDHQwjbQgjfB34Sx/E9wKXAf8vqnC26tt0NmEWsxThrkbIO46zFOGsxzlqkrMN+iJIkaXcbJEmSOp6Lo0qSJOXA0CVJkpSDaa9IfyCL4/hDwLuACvD+EMKDbW5SbuI4XkU6Bj8fmAdcEkK4L47jQeD+xsM+GUK4uU1NzFUcx7eS1qFKuo7cX5JeONIPPERan9G2NTAHcRy/lfRiF4Bu4CTgWOA+4OHG8ctCCD9tQ/Ny01jg+Q+Bk0MIvxjH8WIm+SzsaeeNTjFJHd4FXAEUgadIdxWpxHH8m8AfAGuBagjh3DY1ecZMUotVTPJ70emfCZi0Fn8FnNC4+wjgOyGES+M4/gTwq6QrFzwXQvi1tjR4lppzoSuO46OB/wScCpwB/AXw+rY2Kl8bgMtDCE80/tL8eBzH7wR+EkI4u71Na4tu4JwQwghAHMefAu4MIXym8ZfKu4G/aWcDZ1oI4Z+Bf4axLbtuIK3Ld0MI72hn2/LS+EK5nfT3o7nezkfZ7bMQx/GXSXfeeAPpVdoPxHH87RDCtjY0O3N7qMPdwOsbgfNW4C2k/0DpBj4TQvirdrR1pu2hFi/5vZiwG0tHfiZg8lqEEC5t3NcD3AN8pvHwbuCDIYTvtqGps95cHF48j/SXphpCuBM4Lo7jrnY3Ki8hhOEQwhONm0tIf4mWA11xHL82juPu9rWuLZYAZ8dx3Ne4fT677qBwQVta1QZxHC8HLgb+lPQzsTiO4zPjOC62t2Uzr3H19fnAn004PNlnYWznjcaXanPnjY4wWR1CCD9rBK4IWARsbNy1HDgyjuPj8m/pzNvDZ2Ky34uO/kzAHmvRdAnwrRDC043by4Fj4zh+eV7tO5DMxdC1+8r5W4GlbWpL28Rx/BrgPwOfBoaAvyNd0uOROI5f1s625exPSdePu6cxzDbx89HcVWGuuAz4fxu9fmuBW4ArSf/lPulOEp0khDC026HJPgt72nmjY0xSh6argPtDCD9q3L4V2Al8OY7jv8ujbXmbpBaT/V50/GcCJv9cNIL45aSfjaZ/JP1O/UYcx3+YU/MOGHNueJF0nPkVE24vahybM+I4PgX4MvAfQgjrG4evbtz3CeC9wH9vS+Ny1hwaieP4RtIA2txBYS3juyrMFb8CvBEghPAUjX1SG0Nqbwe+2LaWtcdkn4U97bzR0RrD7kcAY8NqIYQfAj+M4/iPgMfiOD56Qi96R9rD78UPmIOfiYZXA0+GEJq9n4QQvg18O47jzwPPxXH8yRDCcNtaOMvMxZ6ufwXeHMdxKY7jXwAe7fSJ0hPFcVwG/hb41RDCYxOONS0COmYuwt7EcTzxHx3N9/190p4vgAuZIzsoNHaSqDdDePMz0fiX7ELmyGdiN5N9FsZ23ojjeBHjO290rDiOzwPOBd4ZQqhOON78e6Ob9B/wO/JvXb728Hsx5z4TE7wZ+JeJByZ8LnqBEdIL1tQw53q6QgiPx3H8JdJfigrwvjY3KW8nAquAL8RxDI2r9hqT6WukVyf9j7a1Ll+vjeP4amCUdIj1MmA9cH0cx3eTXqHUkcMmkziR9P9902caV2RFwL+TTq6fa/6Y3T4LIYR6HMfNnTcKNHbeaGcjc3AxcBjwr42/M24KIXwW+F5jP90C8L9CCBva2Ma8vOT3Yo5+JppOBP6+eaMROm+J47hOWqP3hxBq7WrcbOSK9JIkSTmYi8OLkiRJuTN0SZIk5cDQJUmSlANDlyRJUg4MXZIkSTkwdEmSJOXA0CVJkpQDQ5ckSVIO/n/JgDLxgEcUAAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 13.234124074438832 \n", + "\n", + "\n", + "fftfilter\n", + "3.9532669999971404\n", + "gamma total\n", + "4.864966000001004\n", + "coch1\n", + "1.7410880000024918\n", + "coch2\n", + "2.3517199999987497\n", + "get avg\n", + "0.3663629999937257\n", + "fftfilter\n", + "4.069133999997575\n", + "gamma total\n", + "4.9826929999981076\n", + "coch1\n", + "2.3928589999995893\n", + "coch2\n", + "2.550627999997232\n", + "get avg\n", + "0.3711929999990389\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 12.79202 \n", + "\n", + "\n", + "fftfilter\n", + "9.04049600000144\n", + "gamma total\n", + "11.770454999998037\n", + "coch1\n", + "3.4107740000035847\n", + "coch2\n", + "3.538774000000558\n", + "get avg\n", + "0.0754640000013751\n", + "fftfilter\n", + "8.858844000002136\n", + "gamma total\n", + "11.494322999998985\n", + "coch1\n", + "3.4071269999985816\n", + "coch2\n", + "3.465574000001652\n", + "get avg\n", + "0.07422099999530474\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 12.84585432328182 \n", + "\n", + "\n", + "fftfilter\n", + "43.864746999999625\n", + "gamma total\n", + "51.525831999999355\n", + "coch1\n", + "4.895763000000443\n", + "coch2\n", + "5.506294999999227\n", + "get avg\n", + "2.235450000000128\n", + "fftfilter\n", + "42.14117800000531\n", + "gamma total\n", + "49.728548999999475\n", + "coch1\n", + "4.92463900000439\n", + "coch2\n", + "5.323397000000114\n", + "get avg\n", + "2.434255000000121\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 13.10979289660297 \n", + "\n", + "\n", + "fftfilter\n", + "8.949266000003263\n", + "gamma total\n", + "11.535636000000522\n", + "coch1\n", + "3.398771999993187\n", + "coch2\n", + "3.5066380000062054\n", + "get avg\n", + "0.07361600000149338\n", + "fftfilter\n", + "9.552931000005628\n", + "gamma total\n", + "12.151763999994728\n", + "coch1\n", + "3.4190579999994952\n", + "coch2\n", + "3.4614290000026813\n", + "get avg\n", + "0.07430699999531498\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 65.97353123392098 \n", + "\n", + "\n", + "fftfilter\n", + "41.54898500000127\n", + "gamma total\n", + "49.11708099999669\n", + "coch1\n", + "4.904146000000765\n", + "coch2\n", + "5.417113000003155\n", + "get avg\n", + "2.22631900000124\n", + "fftfilter\n", + "41.18495499999699\n", + "gamma total\n", + "48.71922700000141\n", + "coch1\n", + "4.9167989999987185\n", + "coch2\n", + "5.4048449999972945\n", + "get avg\n", + "2.2286430000021937\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 2.405772144386651 \n", + "\n", + "\n", + "fftfilter\n", + "42.09998099999939\n", + "gamma total\n", + "49.65741800000251\n", + "coch1\n", + "4.8960189999997965\n", + "coch2\n", + "5.346607999999833\n", + "get avg\n", + "2.267927000000782\n", + "fftfilter\n", + "40.22336599999835\n", + "gamma total\n", + "47.721363999997266\n", + "coch1\n", + "4.913412000001699\n", + "coch2\n", + "5.51687800000218\n", + "get avg\n", + "2.2224159999968833\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.6496783557343118 \n", + "\n", + "\n", + "fftfilter\n", + "39.77152599999681\n", + "gamma total\n", + "47.29894200000126\n", + "coch1\n", + "4.909209999997984\n", + "coch2\n", + "5.484934000000067\n", + "get avg\n", + "2.2439610000001267\n", + "fftfilter\n", + "39.749909999998636\n", + "gamma total\n", + "47.19686299999739\n", + "coch1\n", + "4.888483000002452\n", + "coch2\n", + "5.364157999996678\n", + "get avg\n", + "2.3817920000001322\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.9389817357014779 \n", + "\n", + "\n", + "fftfilter\n", + "8.94173800000135\n", + "gamma total\n", + "11.513823000001139\n", + "coch1\n", + "3.4235459999981686\n", + "coch2\n", + "3.544051999997464\n", + "get avg\n", + "0.07611299999553012\n", + "fftfilter\n", + "8.552747999994608\n", + "gamma total\n", + "11.200360000002547\n", + "coch1\n", + "3.4049139999988256\n", + "coch2\n", + "3.465447000002314\n", + "get avg\n", + "0.07499899999675108\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 2.471770002419271 \n", + "\n", + "\n", + "fftfilter\n", + "8.06489000000147\n", + "gamma total\n", + "10.652662000000419\n", + "coch1\n", + "3.3771820000038133\n", + "coch2\n", + "3.486834999996063\n", + "get avg\n", + "0.07606899999518646\n", + "fftfilter\n", + "8.581707000004826\n", + "gamma total\n", + "11.15073300000222\n", + "coch1\n", + "3.3751379999957862\n", + "coch2\n", + "3.483172000000195\n", + "get avg\n", + "0.07409900000493508\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 6.363059778043615 \n", + "\n", + "\n", + "fftfilter\n", + "8.55059600000095\n", + "gamma total\n", + "11.152274000000034\n", + "coch1\n", + "3.4146159999945667\n", + "coch2\n", + "3.537145000002056\n", + "get avg\n", + "0.08086300000286428\n", + "fftfilter\n", + "9.121160000002419\n", + "gamma total\n", + "11.85532499999681\n", + "coch1\n", + "3.414358000001812\n", + "coch2\n", + "3.50996799999848\n", + "get avg\n", + "0.07471600000280887\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 6.4664219847321585 \n", + "\n", + "\n", + "fftfilter\n", + "42.32815199999459\n", + "gamma total\n", + "49.96843100000115\n", + "coch1\n", + "4.940168000000995\n", + "coch2\n", + "5.278577999997651\n", + "get avg\n", + "2.228275000001304\n", + "fftfilter\n", + "39.65903799999796\n", + "gamma total\n", + "47.38958899999852\n", + "coch1\n", + "4.906124000001\n", + "coch2\n", + "5.188538999995217\n", + "get avg\n", + "2.2801970000000438\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.592433541406736 \n", + "\n", + "\n", + "fftfilter\n", + "8.540392999995674\n", + "gamma total\n", + "11.133435000003374\n", + "coch1\n", + "3.409812999998394\n", + "coch2\n", + "3.5347079999992275\n", + "get avg\n", + "0.08126500000071246\n", + "fftfilter\n", + "9.254786999998032\n", + "gamma total\n", + "11.908541999997396\n", + "coch1\n", + "3.4151050000000396\n", + "coch2\n", + "3.535645999996632\n", + "get avg\n", + "0.07484000000113156\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 13.361620441455468 \n", + "\n", + "\n", + "fftfilter\n", + "8.400010999997903\n", + "gamma total\n", + "10.994099000003189\n", + "coch1\n", + "3.393200999998953\n", + "coch2\n", + "3.5106940000041504\n", + "get avg\n", + "0.07465200000297045\n", + "fftfilter\n", + "8.26357999999891\n", + "gamma total\n", + "10.835916999996698\n", + "coch1\n", + "3.3972260000009555\n", + "coch2\n", + "3.506442000005336\n", + "get avg\n", + "0.07480299999588169\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 24.784283626099857 \n", + "\n", + "\n", + "fftfilter\n", + "43.0912110000063\n", + "gamma total\n", + "50.707525999998325\n", + "coch1\n", + "4.7946609999999055\n", + "coch2\n", + "5.364985000000161\n", + "get avg\n", + "2.441697000002023\n", + "fftfilter\n", + "42.11155499999586\n", + "gamma total\n", + "49.62659800000256\n", + "coch1\n", + "4.960300999999163\n", + "coch2\n", + "5.198151000004145\n", + "get avg\n", + "2.2364379999999073\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 3.9924350625935108 \n", + "\n", + "\n", + "fftfilter\n", + "9.074186999998346\n", + "gamma total\n", + "11.628852000001643\n", + "coch1\n", + "3.391474999996717\n", + "coch2\n", + "3.519658999997773\n", + "get avg\n", + "0.07512599999608938\n", + "fftfilter\n", + "8.56906799999706\n", + "gamma total\n", + "11.153551000003063\n", + "coch1\n", + "3.397807000001194\n", + "coch2\n", + "3.533636000000115\n", + "get avg\n", + "0.0746110000036424\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 4.455051880075131 \n", + "\n", + "\n", + "fftfilter\n", + "8.788088000001153\n", + "gamma total\n", + "11.343405999999959\n", + "coch1\n", + "3.4011570000002393\n", + "coch2\n", + "3.526732999998785\n", + "get avg\n", + "0.07484800000383984\n", + "fftfilter\n", + "8.563861999995424\n", + "gamma total\n", + "11.146532999999181\n", + "coch1\n", + "3.3950499999991735\n", + "coch2\n", + "3.5247030000027735\n", + "get avg\n", + "0.07593500000075437\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 1.0124875432709295 \n", + "\n", + "\n", + "fftfilter\n", + "40.364255999993475\n", + "gamma total\n", + "47.80941199999506\n", + "coch1\n", + "4.928920000005746\n", + "coch2\n", + "5.313461999998253\n", + "get avg\n", + "2.285979000000225\n", + "fftfilter\n", + "39.51413200000388\n", + "gamma total\n", + "46.960777000000235\n", + "coch1\n", + "4.981582000000344\n", + "coch2\n", + "5.304613000000245\n", + "get avg\n", + "2.292220000002999\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 13.086176019031983 \n", + "\n", + "\n", + "fftfilter\n", + "8.341466999998374\n", + "gamma total\n", + "10.888513000005332\n", + "coch1\n", + "3.3890009999959148\n", + "coch2\n", + "3.5071140000000014\n", + "get avg\n", + "0.07616099999722792\n", + "fftfilter\n", + "8.162570000000414\n", + "gamma total\n", + "10.707561000002897\n", + "coch1\n", + "3.398019000000204\n", + "coch2\n", + "3.511714000000211\n", + "get avg\n", + "0.07490099999995437\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 40.05592579274511 \n", + "\n", + "\n", + "fftfilter\n", + "44.707797000002756\n", + "gamma total\n", + "52.29570099999546\n", + "coch1\n", + "4.9870000000009895\n", + "coch2\n", + "5.310563999999431\n", + "get avg\n", + "2.2325400000045192\n", + "fftfilter\n", + "38.8797029999987\n", + "gamma total\n", + "46.35716899999534\n", + "coch1\n", + "4.921565000004193\n", + "coch2\n", + "5.392136999995273\n", + "get avg\n", + "2.419635999998718\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 13.661154639050304 \n", + "\n", + "\n", + "fftfilter\n", + "8.347359999999753\n", + "gamma total\n", + "11.079121000002488\n", + "coch1\n", + "3.4118230000021867\n", + "coch2\n", + "3.55347299999994\n", + "get avg\n", + "0.08284100000309991\n", + "fftfilter\n", + "8.453335999998671\n", + "gamma total\n", + "11.067423000000417\n", + "coch1\n", + "3.406579000002239\n", + "coch2\n", + "3.5230179999998654\n", + "get avg\n", + "0.07669299999543\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 12.434303168406684 \n", + "\n", + "\n", + "fftfilter\n", + "9.024469000003592\n", + "gamma total\n", + "11.576012000004994\n", + "coch1\n", + "3.3690309999947203\n", + "coch2\n", + "3.485087000000931\n", + "get avg\n", + "0.07556599999952596\n", + "fftfilter\n", + "8.257587000000058\n", + "gamma total\n", + "10.827903999997943\n", + "coch1\n", + "3.380888000006962\n", + "coch2\n", + "3.5136159999965457\n", + "get avg\n", + "0.07552299999952083\n" + ] + }, + { + "data": { + "image/png": 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KK3jooYd47rnnAIjjeJh7VD1VvbwYhuG7gD+sbNYBZwOnA08Cz1fab4qi6NlqnleSJI0NCxcu5Etf+hIf+9jH2LVrF5lMht///d/n2WdHf3QIapUgwzD8CrATuBf40yiKPnAcL487Oztr0q8D2tvb6erqquk5xjPrWzvWtnasbW1Z39o5mdr29vbS2Dj8c7lGskwmQ7FYPGKtOjo6AIKjHacmlxfDMJwKXA/8FTAVmBCG4cVhGI7cFcskSZJqqCYjXWEY/ldgXxRFfxmG4QLgPcAlwGnAFVEU7TnMa5YBywCiKDp/YGCg6v0a6kBqVW1Y39qxtrVjbWvL+tbOydR2+/bt1NXVVblHY1M+n2fatGmvac/lcnAMI121Cl0rgLdEUbTtkPZvAI9EUfT1oxzCy4ujnPWtHWtbO9a2tqxv7ZxMbffv309TU1OVezS2HAi1R6rVsF1eDMNwDlA6ELjCMMxWHgOgBeiu9jklSdKJG0ufEKyVatSoFnO6lgDrhmx/KQzD5cBy4CXgnhqcU5IknYBcLkc+nx/ubox4+Xz+wGXEE1b1FemjKPoh8MMh25+u9jkkSVJ1ZLNZBgcH2b9/P0Fw1Ctk49KBsHWyN9P2NkCSJI1z9fX1w92FEa1a8xFdkV6SJCkBhi5JkqQEGLokSZISYOiSJElKgKFLkiQpAYYuSZKkBBi6JEmSEmDokiRJSoChS5IkKQGGLkmSpAQYuiRJkhJg6JIkSUqAoUuSJCkBhi5JkqQEGLokSZISYOiSJElKgKFLkiQpAYYuSZKkBBi6JEmSEmDokiRJSoChS5IkKQGGLkmSpAQYuiRJkhJg6JIkSUqAoUuSJCkBhi5JkqQEGLokSZISYOiSJElKgKFLkiQpAYYuSZKkBBi6JEmSEmDokiRJSoChS5IkKQGGLkmSpAQYuiRJkhJg6JIkSUqAoUuSJCkBhi5JkqQEGLokSZISYOiSJElKgKFLkiQpAYYuSZKkBBi6JEmSEmDokiRJSoChS5IkKQGZah8wDMOfAvVAEbgf+N/A3UAH8BywLIqigWqfV5IkaSSrxUhXHXBlFEWXR1H0ZeAzwC+iKLoQyAO/WYNzSpIkjWi1CF1twOVhGLZXtpdSHvGi8nh1Dc4pSZI0olX98iLwV8B1wN+GYfgHQDuwp7JvT2X7NcIwXAYsA4iiiPb2wz6tajKZTM3PMZ5Z39qxtrVjbWvL+taOta2tatW36qEriqK/AQjD8F7gC8BOyqNfWyuPXUd43R3AHZXNuKvrsE+rmvb2dmp9jvHM+taOta0da1tb1rd2rG1tHa2+HR0dx3Scql5eDMNwaIhrBbqBBymPfAFcW9mWJEkaV6o90nVZGIa3AQNAH3ATsA24OwzDx4HngW9V+ZySJEkjXlVDVxRFPwPOO8yud1XzPJIkSaONi6NKkiQlwNAlSZKUAEOXJElSAgxdkiRJCTB0SZIkJcDQJUmSlABDlyRJUgIMXZIkSQkwdEmSJCXA0CVJkpQAQ5ckSVICDF2SJEkJMHRJkiQlwNAlSZKUAEOXJElSAgxdkiRJCTB0SZIkJcDQJUmSlABDlyRJUgIMXZIkSQkwdEmSJCXA0CVJkpQAQ5ckSVICDF2SJEkJMHRJkiQlwNAlSZKUAEOXJElSAgxdkiRJCTB0SZIkJcDQJUmSlABDlyRJUgIMXZIkSQkwdEmSJCXA0CVJkpQAQ5ckSVICDF2SJEkJMHRJkiQlwNAlSZKUAEOXJElSAgxdkiRJCTB0SZIkJcDQJUmSlABDlyRJUgIMXZIkSQkwdEmSJCXA0CVJkpQAQ5ckSVICMtU8WBiG84A7gEagHlgWRdGTYRjuB56qPO3zURQ9UM3zSpIkjXRVDV3AS8DHoihaE4bhbwOfC8PwN4BfRVF0eZXPJUmSNGpUNXRFUdQPrKlstlEOYVOBXBiGlwHLoyjKV/OckiRJo0EQx3HVDxqG4SXAXcCbgQLwIeBc4HLg6iiK1h/mNcuAZQBRFJ0/MDBQ9X4NlclkKBaLNT3HeGZ9a8fa1o61rS3rWzvWtraOVt9cLgcQHO04VQ9dYRieC3wHeGcURasP2fenQCaKov98lMPEnZ2dVe3Xodrb2+nq6qrpOcYz61s71rZ2rG1tWd/asba1dbT6dnR0wDGErqp+ejEMwyzwTeD9BwJXpe2AVqC7mueUJEkaDao9kX4JMA/4WhiGAEXg/spk+kFgHfBfqnxOSZKkEa/aE+mfBJoPs+vWap5HkiRptHFxVEmSpAQYuiRJkhJg6JIkSUqAoUuSJCkBhi5JkqQEGLokSZISYOiSJElKgKFLkiQpAYYuSZKkBBi6JEmSEmDokqQRaCBfYnAwHu5uSKqiat/wWpJ0EgqFmBVP9LJlYwGAbC6grj6grj5FfeXx4HbDK9u5uoAgCIa595Jej6FLkkaIXV1FnvplL729JRacVke2LiDfVyLfH9PfX2L3rhL5vgKDg699bRBwMIwNDWUTJ2eYOj1DkDKQScPN0CVJw6xUinnx+X5eeD5PY2OKy5Y2M6n9yL+ei4VyCMv3x+T7S+T7Xr3d3xezd3eBfD6GOE9jU4q5p+SYvSBHXZ2zSqThYuiSpGG0v2eQp37Zy+6dg8yal2XxeY1ks68/KpXJBjRn0zS3vP6xS6WYl7YW2PBinpW/6mf1in465mSZt7COiZP99S8lzb91kjQM4jhmy4YCK57shQDOu6SRmXNyVT1HKhXQMTtHx+wc3XsG2bAmz5aNA2zZUGDCxDTzTy3vS2e89CglwdAlSZRD0O6deeJSXPP5TwMDJZ5d3kfn5gKTpqQ59+ImGptqe9mvtS3N2Rc0cuY5DWzZMMCGNXme/vc+nnu6nzkLcsw7JUdjc7qmfZDGO0OXJAEb1w7w7BObqW8ImDUvx6x5OVpaqx9CunYUeeqx/eT7Ys5YUs/CM+oSneSezQbMP7WOeQtz7NxRZMOaAdatzrN2VZ6pMzLMW1jH1BkZPwkp1YChS9K4VxiIWb2in8lT6shkB1m7Ks+alXkmTk4za16OmXOyZHMnNxJVKpXPsWZlnqbmFJdf3UTbMM6rCoKA9mlZ2qdl6estsWldno1rB/j3f9tPY1OKeQtzzJ6fI+fEe6lqDF2Sxr01q/oZyMdcetUUSPXQ31di68YBNq8f4Nkn+njuqT6mz8oye16OKdOOf/mFnn2DPPmLXvbuHmTOghyL3tBA5iiT5ZPU0Jji9MUNnHpmPdu2FtiwJs/zz/SzakU/M+fkmLcwR9sk/7mQTpZ/iySNa737S6x7Ic/MuVnap9bT1dVDfUOKU86oZ8HpdezdPcjm9QNs3VSgc1Ph4OXH2fNyNB/l8mMcx2xeP8CKJ/tIpQMuuKyRGbOqO1m+mlLpgJlzcsyc8+qJ95vXDzCxPc2C0+qYMTPrml/SCTJ0SRrXVj/bBzGcsaThNfuCIKBtUoa2SRnOekPM9s4CWzYMvOry4+z5OTpmv/by40C+xDPL+3hpS4H2aRnecFEjDY2j51LdwYn3ZzewecMA61/I88TPe2loSjH/1Bxz5teRzRm+pONh6JI0bu3ZVWTLxgILz6g76qcH0+lXll8YevnxV8v7WPFUHzNmZpk1P8eUqRm6Xi7y9GO95PMxZ51THjEbrRPTs7mABafVMX9hjpc6C6x7Ic/zT5fX/JozP8f80+po8lOP0jExdEkal+I45vln+snVBSw8s/64Xnuky49bNxWoqw/I98c0t6S46IomJkwcG79mg1TAjFk5ZszKsWdXkfUv5NmwdoD1Lw4wfWaW+afVMXlKetSGS524fLHEypf7eHZ7L796aT9b9w0wsyXH/In1zG2rY/7EOua21dGUM5yPjd8GknScdmwrsnNHkcXnNZzwZbLDXX7curFAY1OK05fUkxmji462Tcpw7hsznHlOiQ1r8mxYM8BLWwu0tqVZcHodM2dnSaXH5s8uKAzGvLizj19t7+XZl/azqqufYikmHcBp7Q1cPqeVrfsGeHRTNz9eUzr4uqlNGeZNrGdeWx3zJtYxr62e6c1Z0uNojqChS9K4UyrFPP90H00t5XsSVsPQy4/jRX1DijOWlD/1uGXjAOteyPP0Y72sfCZg3sI65i70Xo9jwWApZt3ufp59qZdfbe/l+R295AdjAmDBpHquO30iZ09v5MwpjTRkX/nzjuOYnX1FNuzOs2FPng27+9mwJ8/yrT2U4vJz6tIBc9rqmNdWx/xKIJvbVkdz3dgcFTN0SRp3Nq0boGdfiQsvbyJV+b/suK+X/d//NqVdO4e5d6NPAMwGZsXQVTeZ9QPzWL1iCi+u2M/MdCfzsxtpSfXQ09hIqbd3uLs7JlWztnEMm0r1PFts5tnBFlYUm+mlHIJmp/r4tUwPS3I9nJXpoaU4CFspfwGlQ441qfJ13pC2geaAzaV6Ng42sKFUz4Y9DfxyVwM/iV+JJFOCAaal8kxOFZgUlL8Ofp8qMDEocKwDycE17yaoO74pBLVi6JI0rhQK5UVKJ01JM62j/CswLg1SuuPP6VnxxDD3bvRrr3z1NHawfs41bJ1xGZsHZ9O+81mmb3+c5v2dNPd2kiv0DHdXR51ikCKfztGXriOfytGfPvBVx0AqQylIEQcBJYJXPcYE5X0ElA5ulx/jIHXweYNBig3NM3i2bSHduWYApvd1cdnu5SzZs4ZFe9YyceDVf27xCfwcWWBB5WvocXbnWtnQPJ2NTTPY2DyDHfUTWZmbwK66yRRTr44rQVxiQmE/k/J7mZTvZtJAN5Pye5mc38vEgW4m58vbzcU+0le+DQxdkpS8NSvLC6EuOqfh4KTv+Pv/ACueoOXGz7D/vMuHuYdjQytwDnBmvsSmdQXWrzmbrslLDu7P1QU0t6Robk1VHtM0t6RoaAzG3GT8fLFEz8AgPQOHf+wrlOgvlugvxpXHEvliib7K9oHvi6UTiTjHZ1JDmvOmNbKk8jW16TTg0pqfF14J7Bcc0h7HMd35QXb1DbKzr8Cu3iI7+4rs6mtjV990unqLvNBXpDs/+Jpj5tIB/yvdSHsSP8AxMHRJGjf6eisLoc7JHrwFT7z8EeIf/V+CK66h4a3X07vTy4vVVNeQ4tRFGRaeVU99XRubNnbR0z1IT3eJfd2DbNtSpDDwSphIp6GpJU1LayWItaZoaU3T2JwiPYIm5/cMDPJk536688VyeMoPVoLUkFCVL39feJ2wlAqgIZOiPpOiLpOiIRtQl07RWp9hSqW9IROU9x18Toq6dEB9try/PpNi2uRJdHfvIUX5Ax6poHzZNwggFQQEle0D37/qeQeeQzmkjLTQGwBtjWnaGmEBr11P74DCYIldfcWDoWxnb5FdfUUm1GeT6+xRGLokjRurDiyEenb5UkO8ZT2lu74Kp5xB8IEbR9w/NmNJEAS0tGaZNqP8NVS+v0RPd4mefYPs6y7R0z3Irq4iWzcVhrweGptStLSlmTEzy/SZ2WG5lVLPwCA/WLWb+1btYn/hlRlMDZkULXUpmnJpmnNpZrXW0ZxL0VKXrrSlaMmVv2+pK28359I0ZFOkqvC+a29voivoO+njjGbZdIppzTmmNY/cD7MYuiSNC3t2FdmyocApZ9TR2JQm3r+P0l9/ERqaSP3eHxFkR87/DY83dfUp6upTTJ766n+SisW4PCq2r3RwdGz3ziIvbSmQSsO0jiwz52SZOiNb81GwnoFB7l+1i/tX7WZ/ocTFs5p5z1mTmdGSpSmXJjOOlj3QiTN0SRrzDiyEms0FnHpmHfFgeeI8e3aS+swXCdomDXcXdRiZzIF10F5pi+OY3V2DbN00QOfmAts2F8hkYcbMHB1zs7RPzRz8RGo19OQHuW91OWz1Fkq8cXYz71/czoJJI2NitkYXQ5ekMe/gQqjnNpDNpSj937vg+acJPvz7BKecMdzd03EIgoBJUzJMmpJh0bkxXTuKdG4ssG3rAJs3DJCrC+iYnaVjTo5J7Se+Qv6hYeuS2c28f0k78ycatnTiDF2SxrRSKeb5Z/poak4xd2GO0mM/I37gHwmuegepK64Z7u7pJKRSAVOnZ5k6PcuSwQZ2bCvQuanApvUDbFgzQENjQMecHDPnZGltO7YAti8/yH2rdvGD1YYtVZ+hS9KYtmndAD3dJS6yDQ4VAAAbO0lEQVS4rJFgyzpKd98Op55F8P6PDHfXVEXp9Cv3hiwWYl7aWmDrpgHWrc6zdlWeppYUM+fkmDk3S3PLa1c7PxC27l+1m75iiUtmt/DrSyYzz7ClKjJ0SRqzikMXQm3tpfTfvwhNraR+77MEGSfOj1WZbMCseTlmzcsxkC+xbUv5ZuQvPNfPC8/1M2FimjkLcsyen6O3WOL7K8sjW33FEpfOaeH9iw1bqg1Dl6Qxa82q8kKoZy7OEd/xeejeQ+qzXyJonTjcXVNCcnUp5p5Sx9xT6ujvK9G5aYAtGws8+0QfzzzTy/JiDyuK+3mjYUsJMHRJGpP6ekusXV1ZCPWhu4lXP0vwOzcTzDt1uLumYVJXH8AUWNvTxzO797O40MRFQQuXNbVwxvQGZreO3PWdNDYYuiSNSQcWQj2t8CTxg/cT/No7SV26dLi7pWHQ2T3Awxu7+bcN3WzpHiAdwBtnt3D1ohaaBzKsfq6fZ5/s48WV/Zx6Zj2zF+RG1Or3GjsMXZLGnL27ywuhLpjeS8O3/wrOOJvgfb8z3N1Sgl7eX+CRjd3828Zu1u7KEwCLpjZw3RnTuHR2C631r/zz1z4tQ9eOIqtXvBK+Fp5ZzxzDl6rM0CVpTInjmOef7iebjTnln/4MJkwktewWgvRrP7GmsWVvf5Gfb9rHwxu6ef7l8i1xFk6q53fPm8rlc1uY3Hj4D08EQcCUaeWFVbt2FHlhRT8rnuxjjeFLVWbokjSm7NhWpGtHkbN2/gvZfS+T+uxXCFpah7tbqpHewiC/3NzDv23o5umX9lOKYVZrjt84u50r5rbScRzztIaGr52VkS/Dl6rJ0CVpzDiwEGpjvI85T/8DwQ2fJJizYLi7pSrLF0s80dnDwxv28URnDwODMVObMlx/5iSumNfKvLa6k7p5eRAEtE/LMvlA+HpuSPg6o545pxi+dGIMXZLGjM3rywuhnvervyN9zTtJXfSm4e6STlIcx+zuH2TL3jyb9w7wws4+HtvcQ1+xRFt9mrcsbONNc1s5vb3+pILW4Rw2fD3Vx5pVhi+dGEOXpDGhWIhZ/XQPE/esZdqUEsF7PjzcXdJxGCzFbO8psLk7z5a9A2zpHmDL3jxbuwfYXygdfF5LLsVlc1u4Ym4rS6Y1kq7iza2P5ED4ap+WpWtHoXzZ8ak+Vq3oo6EhRbYuIJsNyOYCcrkU2Vxw8Cs35Ptsrvy8at6QW6NLYqErDMNPAh8ECsBHoyhakdS5JY19a57eTb6Y5vyXf0z6k58hSDlxfiTKF0ts7R5g8958OVhVwlXnvgLFUnzweRMbMsxuzXHl/FZmtdYxa0KOWa05JjVkqj6idTzap2ZpX1oOX1s3FhjIxxQKMX37S+zdE1MYiBksvv4xMlkqIS1FLheQyQakM5DJBKQzAZkM5cd0ZTtbvs1Ref/Q55W3h7MeI1EcxwzkY/r7SvT3xUydMbzvmaESCV1hGC4Efgc4D7gQ+J+A4/6SqqK3O8/aNYPM6FrOpN/+EEFTy3B3SUOs7urjB6t388LO9by0L3+wPRXA9OYssybUccHMZma15pg1oY5ZrTmaciM7NLdPzdI+9fCfhiwNloPYwEA5hA39KreVKBRe2R7oKVEsQrEYMzh49NB2qFQastlu4jgmCOBAvgiC8n+Cg99DQOXx4PfBq17T1JKifWqG9mlZGptSJ1KamhoslsNUX19Mvq908Pv+vhL9vSX6+8vtpVcGR7nmXa3lhXFHgKRGut4M/DiKoiLwizAMzwzDMBdF0UBC53+V6LsryQ82Ex/9qTpBAZutb41Y29eqI0tDKsuscycRzJo33N0RUCzF/GLTPu5btYsXdvbTlE1x6fzJLJ0fM2tCjtmtdcxoyZJNj7x/2E9WKh1Qlw6oO8E7CsVxzOBgOWAUi+UQVn4sbxeL5X2DQ77PZuvp6+sjjg8cA4ghJh7yfeXxVd/HB59fKkHX9iJbNxaAPhqbUrRPy9A+NcPkqRnqG2r/Z1UsxvTsHaR77yC9+8sjVUMDVWHgtb/90hmob0hR35BiUnuK+oZsZTugviFFJjsyAhckF7ragT1DtvcCk4FtBxrCMFwGLAOIooj29vaadSaTSlMcHDl/CJJOTpFBHin1891tE/jwxn7ed04HdZnj/wcik8nU9HfPeNDdX+T+FS9xzzOd7OgZYHZbPZ+6agFvP3MarY11FIvHOYyjY5LJZKpS2ziO2bu7QOeWXrZt6eOlrX1sWlceH2mbmGPGrAZmzGpgekcDdfUnPhoZxzH79xXZtXOAXTvz7O4qP3bvKRx8ThBAQ2OaxqYMEyfX0dicoakpTUNThqamDI1NGRqbM2SzQc0vH1brd0NwIOXWUiVQnRpF0R9WtncAs15npCvu7OysaZ/a29vp6uqq6TnGM+tbO9b28DbtzfONJ3fwROd+pjZl+fAbpnD53Jbj+mVsbU/c1u4B7l+1i4fW7SU/GHP2tEbeecYkzp/ZRKryZ2B9a6dWtY1LMXv3DNK1o0jX9iK7uooHL39OmJguj4JNyzC5PXPEEaViMWbf3kG691S+Kt8XX8lXNDanaJ2QprUtTWtbita2NI2NKYIR8qGDo9W3o6MDKldsX09SI13/Cvx+GIZ/DFwArBquS4uSxqY5E+r4kzfP5ult+7nryR38xaOd3L+6nt89bxpnTGlIpA+7+8orok9rznLBzOZEzjmc4jjmV9t7uW/lLpZ37ieTCnjTvFbeecZE5k88wWtrGlGCVEDbpAxtkzIsPKM8X23PrkoI21Fk/Yt51q7OEwTQNilN+7QMLRPS7N9XOhiy9ve8MsEqk4GWtjQz5+QqAStN64T0iLoEWEuJhK4oil4Mw/Au4DHKn168IYnzShp/3jCjib98+zz+df1evvVMF599YCOXzWnhw2+YwvSWY1+d/FgVBks8vrWHh9bt5YnO8oroAFcvmMANF0ylMTuyJ4SfiIHBEg9v6Oa+VbvZuCfPhLo0v75kMm8/dSJtDa5ENJal0gGTpmSYNCXDaYvK88l27SyPgu3cUWTNyvzBeWVNzeURq1nzKgFrQoqGptSI+SThcEjk8uIJ8PLiKGd9a8faHru+Qol7V+7kH5/fxWAM154+kf+waDLNdYcPQsda2ziOWbc7z4Nr9/Dwhm72DZSY1JDhzfNbuXL+BB7Z2M09z+1kalOWT13WwentyYy01dqeviL/9OJu/unFPeztH2RuWx3vPGMib5rXSu4YJsT73q2dkVLbQiGmt2eQpuaxNXo12i4vSlLiGrIpPnD2FK5Z2MbfP9PF91fu4sG1e3j/knbeftpEMsc5X2RPX5GfbejmwXV72bgnTzYV8MbZzSxdMIFzpjcdXKhzbtsUzp3RxG0/7+SPHtjIB5a0895FkxNZyLMW1u/u575Vu3l4QzfFUsyFM5t45xmTWDKtcVyPWui1stmACRONFkdiZSSNeZMbs3zikhlce/pE7npqB19/Ygc/emE3v3XuVC6e1fy6waEwGLN8aw8PrtvLE509lGI4vb2emy6axuVzW2k+wnpSZ01t5K/eMZ///fh2/v5XXTy1bT83XzqDac3Vv8RZC4XBmF9u3sePXtjN8y/3UZcOuGbhBK49fRIzj+Mm0pJeYeiSNG4smFTP55fO5onO8mT7//HwVhZPbeB3zpvGwsmvTPw+ePlw3d7y5cP8IJMayjdUXrpgArMm1B3T+ZpyaT59WQfndzTxN49v5+YfbeD3LpzGlfMn1OpHPGk7ewv8eM0eHnhxD7v7B5nenOW3z53CW05pO+JlWUnHxtAlaVwJgoALZjZz7owmHlizh2//qotP//MGrprXym9dUs+/rd511MuHx+uq+RM4c0oDt/18G3/582080bmfGy+cNmJWXY/jmGe39/KjF/bw2JZ9xDGc19HEO06byHkdryz5IOnkGLokjUvpVMDbT5vIlfNb+X/P7eL7K3fx0w1PA3Da5Hp+78JpXDG3tWqjO9Oac/z3X5vDPc/t5DvPdrHy5V4+eWkHZ01trMrxT0RvYZB/XdfNP724m817B2jJpXjnGZN4+6ltNfmkpzTeGbokjWuN2TQfesMU3nZqG8/viVnQHDP7GC8fHq90KuD9S9rLy1o82snn/mUT71s0mfcvaT/uSf0nY9OePD96YTf/ur6b/mKJhZPq+cQbp3P53NYTWslf0rExdEkSMKUpy3vnJvOx+9PbG7jtHfP4+vIdRCt28vS2/Xzqsg5m1HB0qViKeawyMX7Fjj6yqYDL57bwjtMmctoYWdJCGukMXZI0DBqzaT5xyQzO72jir//9JW7+0QaWXTCVpQsmVHUZhl19RR54cQ8/XrOHXX1FpjZl+PAbpvCWUybQWu8/AVKS/BsnScPosrmtnNbewF/9Yhv/3y9f4onO/dx00XRajmEuWSmO6ckPsrt/kN19Rfb0F9ndV/nqH2RXb4GVL/cxGMN5M5r42EXTOa/jxD8QIOnkGLokaZhNacry+aWz+f7KXXzrmZdZ1dXHTRdOp7kuVQlRgwcDVfnxlZA1eJibiuTSARMbMrTVZ7jujEm87dS2ml66lHRsDF2SNAKkUwHvWTSZs6c3ceujnXzhZ1tetT8VwIS6NG0NGSbWZ5jTVsekhgxt9WkmVtraGjJMbEjTkBnf97eTRipDlySNIAsn13PbO+bxRGcPDZnUwUDVUpf2sqA0yhm6JGmEqc+kuGxO63B3Q1KVuSCLJElSAgxdkiRJCTB0SZIkJcDQJUmSlABDlyRJUgIMXZIkSQkwdEmSJCXA0CVJkpQAQ5ckSVICDF2SJEkJMHRJkiQlwNAlSZKUAEOXJElSAgxdkiRJCTB0SZIkJcDQJUmSlABDlyRJUgIMXZIkSQkwdEmSJCXA0CVJkpQAQ5ckSVICDF2SJEkJMHRJkiQlwNAlSZKUAEOXJElSAgxdkiRJCTB0SZIkJcDQJUmSlABDlyRJUgIMXZIkSQkwdEmSJCXA0CVJkpQAQ5ckSVICMtU6UBiGHwQ+DqSBdcBvRlFUCMPwt4H/CmwFilEUXVWtc0qSJI0W1Rzpehx4UxRFFwLTgLdV2uuAL0VRdLmBS5IkjVdVC11RFL0QRdFAGIYB0Apsr+yaCswNw/DMap1LkiRptAniOK7qAcMw/EtgQhRFH6lsXwFcCVwHvBhF0W8e4XXLgGUAURSdPzAwUNV+HSqTyVAsFmt6jvHM+taOta0da1tb1rd2rG1tHa2+uVwOIDjacU4odIVh+GHgU4c0vwO4CTgD+EAURcVDXpMGVgNvi6JozVFOEXd2dh53v45He3s7XV1dNT3HeGZ9a8fa1o61rS3rWzvWtraOVt+Ojg44htB1QhPpoyi6G7h7aFsYhm8GrgKWDg1cYRhmoygqUJ7blQF6TuSckiRJo1nVPr0IXA/MBP41DEOA+6Mo+jLwkzAMmyjPH/vvURS9VMVzSpIkjQpVC11RFH0C+MRh2q+q1jkkSZJGKxdHlSRJSoChS5IkKQGGLkmSpAQYuiRJkhJg6JIkSUqAoUuSJCkBhi5JkqQEGLokSZISYOiSJElKgKFLkiQpAYYuSZKkBBi6JEmSEmDokiRJSoChS5IkKQGGLkmSpAQYuiRJkhJg6JIkSUqAoUuSJCkBhi5JkqQEGLokSZISYOiSJElKgKFLkiQpAYYuSZKkBBi6JEmSEmDokiRJSoChS5IkKQGGLkmSpAQYuiRJkhJg6JIkSUqAoUuSJCkBhi5JkqQEGLokSZISYOiSJElKgKFLkiQpAYYuSZKkBBi6JEmSEmDokiRJSoChS5IkKQGGLkmSpAQYuiRJkhJg6JIkSUqAoUuSJCkBhi5JkqQEGLokSZISYOiSJElKgKFLkiQpAYYuSZKkBGSqdaAwDOcBTwLPV5puiqLo2TAMrwRuBQLgz6Mo+k61zilJkjRaVC10AXXAj6Mo+sCBhjAMU8CdwK8Be4CnwzD8URRF3VU8ryRJ0ohXzcuLU4EJYRheHIZhutJ2CrA3iqJNlaD1JHBxFc8pSZI0KlRzpGsr8BBwC3BaGIZXAO2UR7gO2FNpe40wDJcBywCiKKK9/bBPq5pMJlPzc4xn1rd2rG3tWNvasr61Y21rq1r1PaHQFYbhh4FPHdL8jiiK/qKy/xvA+4CHgbYhz2kDug53zCiK7gDuqGzGXV2HfVrVtLe3U+tzjGfWt3asbe1Y29qyvrVjbWvraPXt6Og4puOcUOiKouhu4O6hbWEYZiuPAdACdANrKF9ynEN5lOtc4LETOackSdJoVs05XV8Kw3A5sBx4CbgniqIS5UuG3wN+CnzOSfSSJGk8qtqcriiKPn2E9p8CF1TrPJIkSaORi6NKkiQlwNAlSZKUAEOXJElSAgxdkiRJCTB0SZIkJcDQJUmSlABDlyRJUgIMXZIkSQkwdEmSJCXA0CVJkpQAQ5ckSVICDF2SJEkJMHRJkiQlwNAlSZKUAEOXJElSAgxdkiRJCTB0SZIkJcDQJUmSlABDlyRJUgIMXZIkSQkwdEmSJCXA0CVJkpQAQ5ckSVICDF2SJEkJMHRJkiQlwNAlSZKUAEOXJElSAgxdkiRJCTB0SZIkJcDQJUmSlABDlyRJUgIMXZIkSQkwdEmSJCXA0CVJkpQAQ5ckSVICDF2SJEkJMHRJkiQlwNAlSZKUAEOXJElSAgxdkiRJCTB0SZIkJcDQJUmSlABDlyRJUgIMXZIkSQkwdEmSJCXA0CVJkpSATLUOFIbh3wCLKpuzgX+OoujGMAz/FHg/sBPYEkXRr1frnJIkSaNF1UJXFEU3AoRh2AAsB75U2VUH3BxF0Y+rdS5JkqTRphaXF5cBP4yiaH1leypwehiGC2pwLkmSpFEhiOO4agcLwzAAVgFviqJoe6XtHcDFwHuB+6Io+k9HeO0yyoGNKIrOHxgYqFq/DieTyVAsFmt6jvHM+taOta0da1tb1rd2rG1tHa2+uVwOIDjacU4odIVh+GHgU4c0v4PyXK7/GkXROw7zmmZgCzA9iqL+o5wi7uzsPO5+HY/29na6urpqeo7xzPrWjrWtHWtbW9a3dqxtbR2tvh0dHXAMoeuE5nRFUXQ3cPeh7WEY3gD8yyFt2SiKCkATkAcKJ3JOSZKk0axqE+krlgB/f2AjDMNW4KEwDEuUE+BHoygarPI5JUmSRryqhq4oiv7DIdvdwAXVPIckSdJo5OKokiRJCTB0SZIkJcDQJUmSlABDlyRJUgIMXZIkSQkwdEmSJCXA0CVJkpQAQ5ckSVICDF2SJEkJMHRJkiQlwNAlSZKUAEOXJElSAgxdkiRJCTB0SZIkJcDQJUmSlABDlyRJUgIMXZIkSQkwdEmSJCXA0CVJkpQAQ5ckSVICDF2SJEkJMHRJkiQlwNAlSZKUAEOXJElSAgxdkiRJCTB0SZIkJcDQJUmSlABDlyRJUgIMXZIkSQkwdEmSJCXA0CVJkpQAQ5ckSVICDF2SJEkJMHRJkiQlwNAlSZKUAEOXJElSAgxdkiRJCTB0SZIkJcDQJUmSlABDlyRJUgIMXZIkSQkwdEmSJCXA0CVJkpQAQ5ckSVICDF2SJEkJMHRJkiQlwNAlSZKUgMyJvjAMw5nAD4AvR1H0nUrblcCtQAD8+ZD2rwBvBnqA34yiaOvJdlySJGk0OaGRrjAMLwd+CBSHtKWAO4H3UA5YXwzDsDUMw6uBs6MouhC4A/gfJ91rSZKkUeZER7r+HTgf+NshbacAe6Mo2gQQhuGTwMXAVcD9lefcD/zF4Q4YhuEyYBlAFEV0dHScYNeOXRLnGM+sb+1Y29qxtrVlfWvH2tZWNep7QiNdURQNRFE0eEhzO7BnyPaeStvB9iiKeoC2IxzzjiiKLoii6ALKlydr+hWG4RNJnGe8fllfazsav6yt9R2tX9Z2RNT3qI460hWG4YeBTx3S/I4oijoPadvJqwNVG9A1tD0MwyZeHcwkSZLGhaOGriiK7gbuPoZjrQEmhGE4h3KwOhd4DCgBfwj8NXAd8OAJ91aSJGmUqtqSEVEUlSjPyfoe8FPgc1EUdUdR9CDwqzAMlwM3Av+pWuc8SXcMdwfGOOtbO9a2dqxtbVnf2rG2tVWV+gZxHFfjOJIkSXodLo4qSZKUAEOXJElSAk54RfrRLAzDTwIfBArAR6MoWjHMXRozwjD8KVBPeeHc+6Mo+vLw9mj0qyw8/EXgnCiK3h6G4QTKH27pAJ4DlkVRNDCcfRzNDlPfecCTwPOVp9wURdGzw9W/0apSxzuARsq/E5YBKypti4BO4MNRFO0drj6OVoerbRRFT4ZhuB94qvK0z0dR9MAwdXFUC8PwdOAbQAzsB36d8r9pJ/17d9yNdIVhuBD4HeCNlJfC+J/D26Mxpw64Moqiyw1cJ68SCB4FTueVdWA+A/yicpeHPPCbw9S9Ue8I9a0Dflx5D19u4DphLwEfi6LocuBrwOeADwH9lffuL4FPD2P/RrPX1DYMwzrgV0PetwauE7cGeEsURZcCzwC/S5V+74670EX5FkU/jqKoGEXRL4AzwzDMDXenxpA24PIwDNuHuyNjQeVTwUuBrw5pXsqr7/JwddL9GiuOUN+plJe/uTgMw/Tw9Gz0i6KoP4qiNZXNNspBwfduFRyhtlOBXBiGl1UCmE5QFEWDURT1VP6nbCawjSq9d8dj6Dp05fy9wORh6stY9FeU12NbHobhu4a7M2NBFEV9hzQNfQ8fuPODTtBh6rsVeAi4BXg6DMPD3kVDxyYMw0uA3wO+gO/dqjqktn3AtygvzbQyDMP5w9m30S4MwzcALwBLgB9RpffueJzTtRM4dch2a6VNVRBF0d8AhGF4L+VfBN8f3h6NSQfu8rCVV+78oCqJomgdlXvEhmH4DeB9wNeHs0+jVRiG51KeG/POKIq2hWE49M4lvndPwqG1rTTfVtn3p8BHgP88LJ0bA6IoehpYGIbhxyjPn6vK793xONL1r8BbwzDMhGH4RmCVk5CrIwzDoSG+Fegerr6McQ9SHk0EuBbv8lBVYRhmK48B0ILv4xNSqeM3gfdHUbS60ux7twoOV9sD79sKf/+ehMplxQPWUB7Vqsp7d9yNdEVR9GIYhndRvkVRAbhhmLs0llwWhuFtwADloe6bhrk/Y9VfAHeHYfg45U/YfWuY+zPWfCkMwyspT6z/JXDPMPdntFoCzAO+FoYhlD/9dQ1wReW9u43yxHodv8PV9v4wDH8DGATWAf9l2Ho3+l0fhuFnKNc1Bv6Ack1P+veuK9JLkiQlYDxeXpQkSUqcoUuSJCkBhi5JkvT/t1vHAgAAAACD/K1nsasoYiBdAAAD6QIAGEgXAMBAugAABtIFADAIhrrlFIx87p4AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 6.513341490233809 \n", + "\n", + "\n", + "fftfilter\n", + "8.924363000005542\n", + "gamma total\n", + "11.514063999995415\n", + "coch1\n", + "3.40080900000612\n", + "coch2\n", + "3.531960000000254\n", + "get avg\n", + "0.07429899999988265\n", + "fftfilter\n", + "8.626735999998346\n", + "gamma total\n", + "11.21455799999967\n", + "coch1\n", + "3.4163269999990007\n", + "coch2\n", + "3.5239569999976084\n", + "get avg\n", + "0.07344200000079582\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 3.5341273995985616 \n", + "\n", + "\n", + "fftfilter\n", + "8.527994000003673\n", + "gamma total\n", + "11.083161999995355\n", + "coch1\n", + "3.387288999998418\n", + "coch2\n", + "3.535157000005711\n", + "get avg\n", + "0.07855500000005122\n", + "fftfilter\n", + "8.340773000003537\n", + "gamma total\n", + "10.8892130000022\n", + "coch1\n", + "3.4083109999992303\n", + "coch2\n", + "3.5082629999960773\n", + "get avg\n", + "0.07569800000055693\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 5.653459086348473 \n", + "\n", + "\n", + "fftfilter\n", + "41.845221999996284\n", + "gamma total\n", + "49.32803899999999\n", + "coch1\n", + "4.901194999998552\n", + "coch2\n", + "5.374391999997897\n", + "get avg\n", + "2.220749000000069\n", + "fftfilter\n", + "41.03381700000318\n", + "gamma total\n", + "48.45992299999489\n", + "coch1\n", + "4.951755000001867\n", + "coch2\n", + "5.327301000004809\n", + "get avg\n", + "2.364986000000499\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 18.466763736466078 \n", + "\n", + "\n", + "fftfilter\n", + "40.43985700000485\n", + "gamma total\n", + "47.96853199999896\n", + "coch1\n", + "4.9693330000009155\n", + "coch2\n", + "5.444737999998324\n", + "get avg\n", + "2.2150059999985388\n", + "fftfilter\n", + "39.60746800000197\n", + "gamma total\n", + "47.18509500000073\n", + "coch1\n", + "4.96003099999507\n", + "coch2\n", + "5.311140000005253\n", + "get avg\n", + "2.2206940000032773\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 2.675852935344887 \n", + "\n", + "\n", + "fftfilter\n", + "154.13489699999627\n", + "gamma total\n", + "163.01615499999753\n", + "coch1\n", + "10.468391000002157\n", + "coch2\n", + "11.795680999995966\n", + "get avg\n", + "3.783565000005183\n", + "fftfilter\n", + "180.5488039999982\n", + "gamma total\n", + "189.73701299999811\n", + "coch1\n", + "11.502432000001136\n", + "coch2\n", + "12.257173999998486\n", + "get avg\n", + "3.7647959999958402\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 6.481194936474579 \n", + "\n", + "\n", + "fftfilter\n", + "7.989241000002949\n", + "gamma total\n", + "10.551289000002726\n", + "coch1\n", + "3.396264999995765\n", + "coch2\n", + "3.5234960000016144\n", + "get avg\n", + "0.07441600000311155\n", + "fftfilter\n", + "7.402436999997008\n", + "gamma total\n", + "9.9619029999958\n", + "coch1\n", + "3.3987790000028326\n", + "coch2\n", + "3.5323740000021644\n", + "get avg\n", + "0.07486700000299606\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 1.8737443877333049 \n", + "\n", + "\n", + "fftfilter\n", + "7.43981299999723\n", + "gamma total\n", + "10.062505000001693\n", + "coch1\n", + "3.3902349999989383\n", + "coch2\n", + "3.5303640000056475\n", + "get avg\n", + "0.07584399999905145\n", + "fftfilter\n", + "7.385979000006046\n", + "gamma total\n", + "9.939970999999787\n", + "coch1\n", + "3.3954870000015944\n", + "coch2\n", + "3.53465299999516\n", + "get avg\n", + "0.0743780000047991\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 19.87907003443891 \n", + "\n", + "\n", + "fftfilter\n", + "34.939800000000105\n", + "gamma total\n", + "42.34923600000184\n", + "coch1\n", + "4.927277999995567\n", + "coch2\n", + "5.386403000004066\n", + "get avg\n", + "2.4212009999973816\n", + "fftfilter\n", + "38.27644000000146\n", + "gamma total\n", + "46.040108000001055\n", + "coch1\n", + "4.9033219999982975\n", + "coch2\n", + "5.359686000003421\n", + "get avg\n", + "2.27216700000281\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 1.5059453570913994 \n", + "\n", + "\n", + "fftfilter\n", + "7.633922000000894\n", + "gamma total\n", + "10.177309999999125\n", + "coch1\n", + "3.3914660000009462\n", + "coch2\n", + "3.4952100000009523\n", + "get avg\n", + "0.07464600000093924\n", + "fftfilter\n", + "8.395821999998589\n", + "gamma total\n", + "10.948121999994328\n", + "coch1\n", + "3.371725000004517\n", + "coch2\n", + "3.458954999994603\n", + "get avg\n", + "0.07528699999966193\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 3.292826978341749 \n", + "\n", + "\n", + "fftfilter\n", + "7.47689900000114\n", + "gamma total\n", + "10.029058000000077\n", + "coch1\n", + "3.391218000004301\n", + "coch2\n", + "3.5075379999980214\n", + "get avg\n", + "0.07423199999902863\n", + "fftfilter\n", + "7.401127999997698\n", + "gamma total\n", + "9.965761999999813\n", + "coch1\n", + "3.392609000002267\n", + "coch2\n", + "3.49416399999609\n", + "get avg\n", + "0.07594499999686377\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 6.013442915240921 \n", + "\n", + "\n", + "fftfilter\n", + "7.450009000000136\n", + "gamma total\n", + "10.024549999994633\n", + "coch1\n", + "3.3918240000057267\n", + "coch2\n", + "3.5055779999966035\n", + "get avg\n", + "0.07743800000025658\n", + "fftfilter\n", + "7.3510909999968135\n", + "gamma total\n", + "9.92250100000092\n", + "coch1\n", + "3.4029069999960484\n", + "coch2\n", + "3.508889000004274\n", + "get avg\n", + "0.07427399999869522\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 46.829114103970134 \n", + "\n", + "\n", + "fftfilter\n", + "5.209236999995483\n", + "gamma total\n", + "7.382253999996465\n", + "coch1\n", + "3.211326000004192\n", + "coch2\n", + "3.371823999994376\n", + "get avg\n", + "0.029245999998238403\n", + "fftfilter\n", + "5.032511999997951\n", + "gamma total\n", + "7.179897000001802\n", + "coch1\n", + "3.0665359999984503\n", + "coch2\n", + "3.3615550000031362\n", + "get avg\n", + "0.029956999998830725\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 68.5563644281125 \n", + "\n", + "\n", + "fftfilter\n", + "7.344251999995322\n", + "gamma total\n", + "9.890919999998005\n", + "coch1\n", + "3.3904099999999744\n", + "coch2\n", + "3.4979630000016186\n", + "get avg\n", + "0.07553800000459887\n", + "fftfilter\n", + "7.312851999995473\n", + "gamma total\n", + "9.864086000001407\n", + "coch1\n", + "3.3914799999984098\n", + "coch2\n", + "3.489462999998068\n", + "get avg\n", + "0.07484599999588681\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 11.8811914389887 \n", + "\n", + "\n", + "fftfilter\n", + "7.2873210000034305\n", + "gamma total\n", + "9.83666799999628\n", + "coch1\n", + "3.394958000004408\n", + "coch2\n", + "3.506502999996883\n", + "get avg\n", + "0.07582399999955669\n", + "fftfilter\n", + "7.353580000002694\n", + "gamma total\n", + "9.915279000000737\n", + "coch1\n", + "3.3846019999982673\n", + "coch2\n", + "3.5024950000006356\n", + "get avg\n", + "0.0746999999973923\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 11.515744500840672 \n", + "\n", + "\n", + "fftfilter\n", + "7.310416999993322\n", + "gamma total\n", + "9.849690000002738\n", + "coch1\n", + "3.3882850000009057\n", + "coch2\n", + "3.4852850000024773\n", + "get avg\n", + "0.07385100000101374\n", + "fftfilter\n", + "7.681834000002709\n", + "gamma total\n", + "10.244075999995403\n", + "coch1\n", + "3.391739000006055\n", + "coch2\n", + "3.5298709999988205\n", + "get avg\n", + "0.07547300000442192\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 81.71094133803093 \n", + "\n", + "\n", + "fftfilter\n", + "7.398765999998432\n", + "gamma total\n", + "9.98879300000408\n", + "coch1\n", + "3.3866029999990133\n", + "coch2\n", + "3.493532000000414\n", + "get avg\n", + "0.07398100000136765\n", + "fftfilter\n", + "7.9265269999959855\n", + "gamma total\n", + "10.48687599999539\n", + "coch1\n", + "3.413107000000309\n", + "coch2\n", + "3.5043770000047516\n", + "get avg\n", + "0.07494299999962095\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 32.9507912397006 \n", + "\n", + "\n", + "fftfilter\n", + "7.488753999998153\n", + "gamma total\n", + "10.05024499999854\n", + "coch1\n", + "3.4163360000020475\n", + "coch2\n", + "3.543893999994907\n", + "get avg\n", + "0.07266399999934947\n", + "fftfilter\n", + "7.491584999996121\n", + "gamma total\n", + "10.036338000005344\n", + "coch1\n", + "3.400922000000719\n", + "coch2\n", + "3.5296180000004824\n", + "get avg\n", + "0.07386299999780022\n" + ] + }, + { + "data": { + "image/png": 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yOpx//vl86Utf4uMf/zgjIyMEAgF++7d/mx07Vn50MOVKkJZlfQUYBh4E/sC27Q+ewbc7yWSyLHXNicfjDA0NlfUa1UztWz5q2/JR25aX2rd8zqVtZ2ZmqKmp/Fyu5SwQCJDL5U7aVolEAsCc6jxlGV60LKsNuBX4M6ANaLAs60rLspbvjmUiIiIiZVSWni7Lsv4rMGnb9p9alnUe8IvA24DNwNW2bY8t8T23A7cD2LZ9eSaTKXldC82lVikPtW/5qG3LR21bXmrf8jmXth0cHCQcDpe4otUpnU7T3t5+wvFQKASn0dNVrtC1E7jRtu3+Rcf/FnjCtu2vnuIUGl5c4dS+5aO2LR+1bXmpfcvnXNp2enqa2traEle0usyF2pO1VcWGFy3LWg8U5gKXZVnB4qMBYsBEqa8pIiIiZ281rRAsl1K0UTnmdF0EHFjw/EuWZT0HPAcMAA+U4ZoiIiJyFkKhEOl0utJlLHvpdHpuGPGslXxHetu2vwd8b8Hzz5b6GiIiIlIawWCQfD7P9PQ0xpxyhKwqzYWtc72Ztm4DJCIiUuUikUilS1jWSjUfUTvSi4iIiHhAoUtERETEAwpdIiIiIh5Q6BIRERHxgEKXiIiIiAcUukREREQ8oNAlIiIi4gGFLhEREREPKHSJiIiIeEChS0RERMQDCl0iIiIiHlDoEhEREfGAQpeIiIiIBxS6RERERDyg0CUiIiLiAYUuEREREQ8odImIiIh4QKFLRERExAMKXSIiIiIeUOgSERER8YBCl4iIiIgHFLpEREREPKDQJSIiIuIBhS4RERERDyh0iYiIiHhAoUtERETEAwpdIiIiIh5Q6BIRERHxgEKXiIiIiAcUukREREQ8oNAlIiIi4gGFLhEREREPKHSJiIiIeEChS0RERMQDCl0iIiIiHlDoEhEREfGAQpeIiIiIBxS6RERERDyg0CUiIiLiAYUuEREREQ8odImIiIh4QKFLRERExAMKXSIiIiIeUOgSERER8YBCl4iIiIgHAqU+oWVZjwARIAd8F/hL4H4gAewCbrdtO1Pq64qIiIgsZ+Xo6QoD19q2fZVt218GPgf8xLbtNwNp4ENluKaIiIjIslaO0NUIXGVZVrz4/HrcHi+KjzeU4ZoiIiIiy1rJhxeBPwPeA/y1ZVm/A8SBseJrY8XnJ7As63bgdgDbtonHl3xbyQQCgbJfo5qpfctHbVs+atvyUvuWj9q2vErVviUPXbZt/xWAZVkPAl8EhnF7v/qKj0Mn+b57gXuLT52hoSXfVjLxeJxyX6OaqX3LR21bPmrb8lL7lo/atrxO1b6JROK0zlPS4UXLshaGuHpgAvgRbs8XwC3F5yIiIiJVpdQ9Xe+wLOtuIAPMAncA/cD9lmU9C7wC/H2JrykiIiKy7JU0dNm2/Shw2RIvvbeU1xERERFZabQ5qoiIiIgHFLpEREREPKDQJSIiIuIBhS4RERERDyh0iYiIiHhAoUtERETEAwpdIiIiIh5Q6BIRERHxgEKXiIiIiAcUukREREQ8oNAlIiIi4gGFLhEREREPKHSJiIiIeEChS0RERMQDCl0iIiIiHlDoEhEREfGAQpeIiIiIBxS6RERERDyg0CUiIiLiAYUuEREREQ8odImIiIh4QKFLRERExAMKXSIiIiIeUOgSERER8YBCl4iIiIgHFLpEREREPKDQJSIiIuIBhS4RERERDyh0iYiIiHhAoUtERETEAwpdIiIiIh5Q6BIRERHxgEKXiIiIiAcUukREREQ8oNAlIiIi4gGFLhEREREPKHSJiIiIeEChS0RERMQDCl0iIiIiHlDoEhEREfGAQpeIiIiIBxS6RERERDyg0CUiIiLiAYUuEREREQ8odImIiIh4QKFLRERExAMKXSIiIiIeCJTyZJZldQP3AjVABLjdtu0XLMuaBl4svu0Pbdt+qJTXFREREVnuShq6gAHg47Ztv2ZZ1m8AX7As61eBl23bvqrE1xIRERFZMUoaumzbTgGvFZ824oawNiBkWdY7gOds206X8poiIiIiK4FxHKfkJ7Us623A14B3Alngw8ClwFXADbZtH1zie24HbgewbfvyTCZT8roWCgQC5HK5sl6jmql9y0dtWz5q2/JS+5aP2ra8TtW+oVAIwJzqPCUPXZZlXQp8E/gF27b3LnrtD4CAbdv/5RSncZLJZEnrWiwejzM0NFTWa1QztW/5qG3LR21bXmrf8lHbltep2jeRSMBphK6Srl60LCsIfB345bnAVTw2px6YKOU1RURERFaCUk+kvwjoBv7CsiyAHPDd4mT6PHAA+L9LfE0RERGRZa/UE+lfAOqWeOmuUl5HREREZKXR5qgiIiIiHlDoEhEREfGAQpeIiIiIBxS6RERERDyg0CUiIiLiAYUuEREREQ8odImIiIh4QKFLRERExAMKXSIiIiIeUOgSERER8YBCl4iIiIgHFLpEREREPKDQJSIiIuIBhS4RERERDyh0iYiIiHhAoUtERETEAwpdIiIiIh5Q6BIRERHxgEKXiIiIiAcUukREREQ8oNAlIiIi4gGFLhEREREPKHSJiIiIeEChS0RERMQDCl0iIiIiHlDoEhEREfGAQpeIiIiIBxS6RERERDyg0CUiIiLiAYUukRXEKTgUCk6lyxARkbMQqHQBInJqmXSBntcyHNyXplCYIN4WoK0jQFtHkGiN/u0kIrISKHSJLGMzU3kOvJrm0IEM+Ty0dQRoaq7h0MFJBvqywCx19T7aOoK0dQRojgfw+02lyxYRkSUodIksQ2MjOfbvSZM8ksUYWLs+xHlbwtQ3+onH42zaZpiaLHC0P8vR/hw9+9Ic2JvGH6DYCxakrSNITW1pesEcxyE16zA1mWd6osDUZJ6pyQI+PzQ1B2hq8dPQHCAYVOATETkZhS6RZcJxHI7259i/N83w0RyBIGzcEmbDpvAJQ4jGGGL1fmL1fjZugVzWYehozg1hAzkGk7PM94KtKfaCtZ66FyyXc5guBqqpiQLTk3kmJwpMT+XJ515/nz8AdTE/uazDYF9q/nhdvY+mlgCNzX6aWvzEGvz4fApiIiKg0CVScfm8Q19vhv1700xNFIhEDVsvibD+vPBp9xwFgoY1nUHWdAZxHOf4XrDX0hx4NY3fD/H2AG1rgjS3BkinCsVwVQxZk3lSM8dP0o/W+qiL+WhpDVFX76cu5qM25icSNRjj1pZJFxgbyTM2kmd0OMdAX5bDBzMA+P3Q0OynqTlAY4ufppbAcd97Tu2Wc8hkHLIZh0ymQCjko67eV5GQVyg4TIzlGT6WY+RYnkjUsPGCSMl6GkVkdVDoEqmQbKZAz/4MB19Nk0451Df4uPTKGhLrg+cUHE7oBcs5DM/1gvXP9YK9LhCAuno/La0B6mJ+6up91MX81Nb58AdOXUco7KOtw51XBm6P3cx0gdHhPGPDOUaH8+4CgL3u+8MRc1xvWH2jn3ye+fCUSbtByn3ukE3PhavCgpDlUMifWIs/AI1Nfhpb3CHPxuZAWRYaFPIOY6N5ho/mGD6WY3QoR67YE1hT62Owv0Dv/gxru0Ocf2GYupi/5DWIyMqj0CXisZnpAgdfTdN7IE0+5/Y+XXJlmNb2QEl6gBYLBAztiSDtCbcXbHrS7ZmK1BjqYn7CkdL0PM0xxlBb56e2zs/arhDghpSJsTyjxd6wseF8cSHAG/P5IBgyhMKGYMg9byhkCBafh0KvP6ZmHcZG3JB34NU0TsE9RyRq5kNYU3OAhmY/gdMIkwvlcg5jwzmGj7m9WaPDufnQV1fvo7MrREubu5AhWuNjdqbA/j0peg9kONyTIbEuyKYLI9Q3KnyJVDOFLhEPFPIOE+N5DuxNkzzsho3E+iAbt4RpaPLuj6Exxh0mrPf2L3+f3w0+jS0BNmwKA68PS05O5AkEFoYo33zI8vs5o0C4ttsNefm8w8SoG/LmetsGjrjtbgzEGnw0FhcANLUEqKv3HXedXNZhZMjtxRo+lmNsJD8f4uob/XSd93rICkdO7EmL1vjYflkNm7ZGOLA3zcHX0iQPZWnvDLD5wgiNLcv7V6/jOMxMFRgfyzM5nic169DSGqA9ESAY0pCpyNla3n/yRZYxx3HIZh3Ssw7pVIFUyiE9WyCdckil3Mf0rHs8m3HnSvkDsGFTmA2bw1U/32fxsGQp+f2GpniApngAcENeOlWYn3c2OpwneTjDoQPu+wMBaGwO0NJaYCA5zfhYHhw3oDU2+zlvc5iW1gDNcf8ZhY5wxMeFF0fZeEGYg/vSHHw1w+N9U7SuCbBpa4SW1sr/Cs5l3X8QTIwt+G98wcIJA8GA4dCBDMZAS1uAjs4g7Z3aI07kTFX+T7zIMuY4DscG3Tk782GqGLLSKYdC4cTv8fkgHPURiRhqY36aWw2RqI9I1NCxNqieggoJR3y0J3y0J16fezY1WWBsuDjkOZLnwKuT1Df62HShG7Ka4oEzHopcSijsY8v2KOdtidD7Wpr9e9M89fAUza1+Nm+NEC/T0PJCc3Pt3GBVmA9YM9Ov/xAHg4b6Rh/rN4Sob3Tn28Xq/fj8zA8J9/dl2fHCLDtemKWx2e8u4FgbpC7mK/tnEFnpFLpEljC3fcOru1KMjbiTd0JhQzhiCEd81NW7w0rhiBuo5o5HIj4CwTMbEpPKWLjgYN0Gd1gyHo8zNDRUtmsGg4bzL4zQvSnMoQMZ9u9J8fSj0zQ2+9m0NUJ74tzDVzbrMDtdYHamwMy0uzp1fCzP5Fh+frI/QG3MR0OT+9nnAla05uTz++Z6Di+8OMrkhDtcO9CXZc+OFHt2pKiN+VjTGaSjM0hji19/BkSWoNAlssBc2Nq7M8X4aJ5orY83XRFlbXdIO71LyQQChvM2h+naGOJIT4bXdqd59olpYg0+Nm2NkFgbxCyxgtVx3KHqmQWhyg1Yrx+bG8qev1YQ6hv8rO1e0HvVcOaLCRaK1fuJbXWD4uxMgcGkG8AO7E2zf0+acOT1LUxa2nSXBJE5Cl0iLB22Ln6zG7a0uaeUi99v6NoYZt2GEH2Hsrz2SooXfjLD3piP7vPDFPKvh6nZ6QIzM4XjNqkFd55gTY2PaK2Pphb3LgTRWt/8sVKvTl0sWuPW2n1+mGymwGC/u1fbkd4MvfszBILQ1uEGsIaGJcbjRaqIQpdUNcdxGEy6w4jjo3lqFLakAnw+w7ruEGvXB+nvy7LvlRS7XnT3UwuGDNEad1PaeHvADVS1PqI17mMwVN5QdSaCIR9ru0Ks7QqRzzsMDboBbKAvS/JQll0v9rDxghDdG8OntQecyGqj0CVVSWFLliPjMyTWhehYG2RmukA47COwQu9n6fe/vj/cmy53GB7K0/tanldemuXA3jSbtkZYvyGET0OPUkUUuqSqzIWtvTtTTIzlqanzcclbonR2KWzJ8jG3wexqYXyGeFuAC7auYe8rA+zZMcuO52d5bU+aLdvC+vMnVcOz0GVZ1qeB24As8Fu2be/06toijuMw0Jfl1V3pBWGrhs6uc7vljoicmZa2AG+/vo5jAzn27Ejx0k9n2bc7zZbtERLrgstmqFSkHDwJXZZlnQ/8B+Ay4M3A/wSu8eLaUt0Wh61ahS2RijPG0NYRpHVNgIG+LHt3ugsI9r3i44KLoiXZOkNkOfKqp+udwL/btp0DfmJZ1oWWZYVs2854dP3j2N/aTTpfh3Pqt8pZMhxeFu0bMUGivojClsgyZIyhY22INZ1Bkofc8PXsE+6+ZVsuipTtfqSyvKVmC3znOwdI5cMlOd8tP1dPc1tDSc51rrwKXXFgbMHzcaAF6J87YFnW7cDtALZtE4/Hy1ZMwOcnl9cf5NXOwTCC4cKmIW79lStXTdgKBAJl/fNRzdS25fVG7dvaChdd6rB/7yQvPTvCM49O094R4bK3trAmEfW40pVnNfzsFgoOe3dN8PzTQ4ScFlLMEuDctxlpamo457YpVft6FbqGgU0LntcXj82zbfte4N7iU6ecu0L/4i9tLvvO09VuObRvajbNr/zv/TSM9TMycn5Fayml5dC2q5XatrxOp32bWuGad9Vy6ECGfa+k+Ld/7qN1TYALti//G4VX0kr/2R0fzfHyc7OMjeSprcvyDzNj3N42xNU3vq0k5z/XtjlV+yYSidMf2ZnWAAAgAElEQVQ6j1c/wT8GftuyrN8DrgD2VGpoUapHJBomnp0imdWGjCIrid9v2LDJ3TS297U0+3anefyHU7R3Brhge5T6xtWzsrPa5bIOe3emOLgvTTBkuPTKGvbsf5kJ6ujubKl0eSXnyZ13bdveB3wNeAb4M+DjXlxXJGFmSeZDlS5DRM5CIGDYeEGEn7ulni3bIwwfzfHoQ5PseyWF4yyHWaNyLgb6svz4+xMceDXNug0h3vnuGGu7Q/SOzhLKZ+noXlvpEkvOs75a27bvBu726noiAImIw6PpBgq5HL6AhiZEVqJA0LB5W4Tu80PseGGWPTtSjA7nuPTKGoIhT/oOpIRmZwrseGGGwb4csQYfl99QS3P89d/PvTOwLj1MoOaiClZZHvpplVUt0RBlJhBlPJmsdCkico5CYR+XvbWGbZdGOdqf47EfTDExlq90WXKaCgWH/XtT/PjfJhgayHHhxRGuuSl2XOAC6HVqWe+bqVCV5aV/+suq1tneBCMFkocHaVq/vtLliMg5MsZw3uYwjU1+nv/JNI//cJI3XV7Dug2aRnAuUrMF+o9kMQYam/3UN/pLuuJ7dDjHy8/NMDFWoD0RYPtlUWpqT5ybNzqdYSxQQ/cq/d+p0CWrWmL9GtidpG9wjG2VLkZESqa5NcA1N8V4/qlpXvrpDKPDObZdGsWvezmetlzOYeBIliO9GY4N5li4uaLPBw1Nfhqb/TQ0B2hq9lMb853xvmnZTIHdL6fo3Z8hEjVc8Y4a1nSe/M4DvUeOAtDdUnPWn2s5U+iSVa2tOUagkCM5qcWyIqtNOOLjrdfVsWdHiv170oyP5rn87bXU1GrmzMk4BYdjR3P09WTo78uSz0G0xnD+BWHWdofw+2BsJM/oSJ6xkRyHDmbI73N/fwaC0NgUoLHFDWONzQEiUbNkgHIch+ThLLtenCWddtiwKcQFF0VPeQP33r5hIEx3Z2s5Pn7FKXTJqub3GToKU/Sl9UtYZDXy+QxbL47S1OLnpWdmeOyhSS5/Ww2ta4KVLm3ZcByHibE8R3qy9B3KkE45BILQuT7E2u4QzXH/ccGpps5Pojgbwyk4TE4UGBvJMTaSZ2wkz/69aZziTjzhiJkPYO6jn2zWYcfzsxwbyNHQ5OctV0dpbD69uNEzMktjOk3D+gtK3QzLgkKXrHqJQI5kphanUMD4FL5EVqOOtSFi9X6ee3Kapx+dZstFETZdGK7q2wjNzhTo681wpCfD5EQB44O2jgBru0K0J4KnNRRrfIb6RneO1/rz3GP5vBvixoq9YWPDeQaTqde/x4DfD9svi9K9MYQ5g7lhPSkfXekhTKz+jD/vSqDQJateoi7I84Va8iNDBOJtlS5HRMqkrt7PVTfGePnZGfbuSDE2nOOSK2sIVdG2EtmsQ//hDEd6swwfzQHQ1OLnosujJNYFCYXPvS38fkNTS4CmlgAQnr/ueLE3LJNxOG9zmEj0zK6VLzgcpoaf982ec43LlUKXrHqJeB25KT9DvUdYo9AlsqoFAoZL31pDU0uGXS/N8vhDU1zxjhoamlbfX3eO45DNOmTTDjOT0+zeMc1AMkshD7V1PrZsj9DZFaS2rvw7+AeDhnh7kHj72Q/rJifSZE2A7prV2zu5+n4KRRZJdLZBzzB9/cOsqXQxIlJ2xhg2bA7T0Ozn+aemeeJHU8t6WwnHcchl3ZV+mYxDJuMGqUzGIZtxyKQL81+7z4uvZZ0FKw4nCYYM6ze487Qam/0rbmi1Z2AUgO54bYUrKR+FLln11rY3AcMkR2e4vNLFiIhnmuPFbSV+MsNLP51hZCjH9su831Yil3OYnS4wM1Ngdrpw3Ncz0wUyaYc3uqtRIAjBkI9QyBAMGRpqX//affTRvqaRQHi6pHtrea03OYLPgXVrV++IhEKXrHoNET81hQx9M7rxtUi1CUd8vPXaWvbuSPFacVuJK95R2m0lshmH2Rk3QM0FqfnnM26oWsj4IFrjo6bWR1tHkHDEEAq/HqBCIUNw/rk5rSAVj9cwNLSyd3HvGU3ROTNJsHN7pUspG4UuWfWMMfM3vnYcZ8V1uYvIufH5DBdeHKWxxc9LP3W3lVjX7f4+cBxe/6/A/LHC/LHF73Hc1wruKr7ZmQK57KLr+V8PVQ1NQWpqfURr3efRGt9J97aqdr0pP5tnj0FTS6VLKRuFLqkKiahhd7oJJsehvrHS5YhIBXSsDRFr8PPi0zP0HkhjjPuPMvfR7YHyLXp+4uvu/n8mAGGfoaU14AaqmteDVSisUHWmpjN5jhLhRv/sqm47hS6pCp0NER6fDZDuO0JEoUukatXF/Fx9Y6zSZcgih8bSAHTVre7tPVb3pxMpSqxpxjE++o8MVLoUERFZ5ODRSQC646s7ECt0SVVIdLhzBJJDExWuREREFusdGKUmN0tr5+re2EehS6pCot7dnyc5oRtfi4gsNz1jabqn+jGJdZUupawUuqQq1AT9NDkpkhn9yIuILCeO49Cb9rN+5ijE2ytdTlnpbyCpGolgjj5/Pc7syt7LRkRkNTk6nWWWAF3+FMZf/lsWVZJCl1SNzrog/dE4DBypdCkiIlLUW1y52L3KVy6CQpdUkUQ8xkSojokjCl0iIstFz7A7+tDVVl/hSspPoUuqxtwKxv6BkQpXIiIic3oGx2mfHSba2VnpUspOoUuqRmdjBIC+Uc3pEhFZLnrGM3RN92M61la6lLJT6JKq0V4bwucUSOrG1yIiy0ImX6A/46drehDa1dMlsmoE/YZ2X4akE8XJar8uEZFKOzyeoYChyzeDCYYqXU7ZKXRJVUlEDcmaVhhMVroUEZGq1zOaAqA7Vh23glbokqqSaKqhPxonn9QKRhGRSusZTRHKZ1jT1lTpUjyh0CVVpbO9kbQ/xEhSN74WEam03mOTrJsexL/Kb/8zR6FLqkpnUw2gG1+LiCwHPeNZuqtk5SIodEmVmb/x9aQm0ouIVNLYbI7xvI+uqX5Yo9Alsuo0RwOEyZPMBnAK+UqXIyJStXqKt//pMjOYmtoKV+MNhS6pKj5j6AjmSUZaYGiw0uWIiFStnjF35eL6+mCFK/GOQpdUnUQsRDIah36tYBQRqZSe0TRNmQka17RVuhTPKHRJ1elsrWcw2kymT6FLRKRSeoen3flcVTKJHhS6pAolmmspGD9HB4crXYqISFXKFxwOTebomhrAdFTHdhGg0CVVqLO4grFvbLbClYiIVKe+yQw5x9A1rZ4ukVUtEStuGzHr4DhOhasREak+vaPFlYv5CYg1Vrga7yh0SdWJhf3U+/Ikgw0wNlLpckREqk7PWBq/U2BtYxhjTKXL8YxCl1SlRNTQH22F/sOVLkVEpOr0jqVIpIYIdXRWuhRPKXRJVUo01ZCsieModImIeK5nJEX3RF/V7EQ/R6FLqlKipY6RcAOz/clKlyIiUlWmM3mOzebpmu6vqpWLoNAlVWp+BePQZIUrERGpLr1zt/+ZGqiqlYug0CVVam4FY/9ktsKViIhUl/l7LmaGobm1wtV4S6FLqlJHLITBIWlqcaYmKl2OiEjV6BlNU1vI0NJcj/FVVwyprk8rUhQO+IgHHZI1ugejiIiXesfSdM0M4quyoUVQ6JIqlqh3b3ytFYwiIt4oOA69Yym6xg5BlU2iBwiU6kSWZd0GfALwAweAD9m2nbUs6zeA/wr0ATnbtq8r1TVFzkVncy2PDLbiJF+odCkiIlXh2HSW2ZxD93Q/puPCSpfjuVL2dD0LXGPb9puBduDm4vEw8CXbtq9S4JLlJFEfZiYQZezo0UqXIiJSFXqKt/9ZPz0AHesrXI33Sha6bNt+1bbtjGVZBqgHBosvtQFdlmVVX6SVZW1u24jkWKrClYiIVIe57SLWzx6D1jUVrsZ7JRteXOAu4EXbtn9afP4IcC3wt5Zl7bNt+0NLfZNlWbcDtwPYtk08Hi9Daa8LBAJlv0Y1Wwntuy1YBxwhmQ1wbW0NvmhNpUs6LSuhbVcqtW15qX3LZ6W0bf/sEB2Faera24mvWTmhq1Tte1ahy7KsXwM+s+jwu4E7gHXAB+cO2rb9OPC4ZVn/HdhrWdb5tm2/tvictm3fC9xbfOoMDQ2dTWmnLR6PU+5rVLOV0L6BgkPAuCsYh195GdN1fqVLOi0roW1XKrVteal9y2eltO3ewQnWT/WTb+tYEfXOOVX7JhKJ0zrPWYUu27bvB+5feMyyrHcC1wHX27adW3A8aNt2FnduVwCYOptripSa32foiPpIRltx+g+vmNAlIrISpXMF+iczvGO0B3Nh9a1chNIOL94KdAI/tiwL4Lu2bX8Z+IFlWbW488f+m23bAyW8psg5STTVkDzWCv2HKl2KiMiqdng8Q8GBrskkJC6qdDkVUbLQZdv2J4FPLnH8ulJdQ6TUOhvCPB+Nk+t/klClixERWcV6iouWuqYHMFW4MSpoc1SpcolYiJzPz9DQeKVLERFZ1XrG0oTJ054agXaFLpGqkyhuG9E34+DkdPNrEZFy6R1Nsy4/gb85jgmHK11ORSh0SVXrjLmhqz/SAkf7K1yNiMjq5DgOPWNpuqYGqvL2P3MUuqSqNUT81PjRja9FRMpoLJVnIp2ne3h/1c7nAoUuqXLGGDrrQ/PbRoiISOn1FHei75o4op4ukWqWaIiQrGtXT5eISJn0jBZXLk4NYBS6RKpXoj7EULCe1EBfpUsREVmVesfSNPuyxHIz6ukSqWaJWAjHGAYmUjiFQqXLERFZdXrG0nTlxqC+EVNbV+lyKkahS6peZ3HbiP5AIwwfrXA1IiKrS67gcHg8Q9dUf1X3coFClwgdsSAAfTWtMKB5XSIipZScyJArOHQdfa2q53OBQpcINUE/TREfyWhcKxhFREpsfuXiaA9U8XYRoNAlAkBnfZhkbA0kFbpEREqpdyyNH4fOmWPq6ap0ASLLQWd9mP5oK46GF0VESqpnNMXaQIagk1dPV6ULEFkOEvVBJvwRJo8ew3GcSpcjIrJqzK9cjNZAQ3Oly6kohS4R3G0jAPqpgYmxClcjIrI6TKXzDM3kWD/RBx3rMMZUuqSKUugSwd0gFSBZ0wqaTC8iUhK94+4k+u5j+6r6notzFLpEgPbaED6DVjCKiJRQz2hx5eLgvqrfowsUukQACPoN7bVBdwWjQpeISEn0jqWJBRyaMxOYNQpdCl0iRYn6EMlYB45ufC0iUhI9YynW+9MYqPqVi6DQJTIvUR+iP9hAQaFLROScFRyH3rEM3dlRCAQh3lbpkipOoUukqDMWIm0CjKTyODPTlS5HRGRFOzqVJZUrsH7iCKxZi/H5K11SxSl0iRTN3/g6Gte8LhGRczR3+5/uwVe1crFIoUukaOG2EVrBKCJybnrG3Llc65K7tXKxSKFLpKg5GiDsNyRr20DzukREzknvWJo1EYjkM+rpKlLoEinyGUOiPkRf41r1dImInKOe0TRd/pT7RD1dgEKXyHESsZA7p0s3vhYROWvpXIH+yQzdmWHw+aAtUemSlgWFLpEFErEQg75assNDOJl0pcsREVmRDo2ncYD140egtQMTDFa6pGVBoUtkgUR9iAKGo+EmGOirdDkiIitSb3HlYtfAXm2KuoBCl8gCnfMrGHUPRhGRs9UzmibsN7T37dEk+gUUukQWSMTmQleb5nWJiJylnrE0XbUGXz4HuufiPIUukQViYT/1YT/J5vXq6RIROQuO47ihyzcLgEkodM0JVLoAkeUmEQvRP9UOBx+tdCkiUgZDM1mOTmXxGYPfB35j8Bnw+Qx+Y/AbTv6ar/iaAWNMpT/KsjSayjOZztPlG3IPrNHw4hyFLpFFEvUhXhxqhMEkTj6P8et+YSKrxcMHxvlfPx0gk3fO+VwBnyFeE6CtNkhrbZC2umDxa/dYvCaI31d9waxn1N2bq2vsEDTHMZFohStaPhS6RBbpjIV4mBCz+Kk91q9/pYmsAtl8ga8+f5Tv7xtje3sN79/aDEDBgXzBIe845AtQcBzyjvt40teKx9I5x+01m87yQv80o7O5467pM9ASDSwKZO7jXDgL+lffLJ+5ey6u79+r+VyLKHSJLJKod/eTSUbjbOo/otAlssIdm87y5cf72Dec4tYLm/nwJa1l6YHK5AsMTec4Ou0GsWPFx6NTWXYNzvDYbI7Cog62pmiApoifWNhPXcidU1oXcp/Hwn5iIT91Yd/rX4f8y773rHc0TUtNgFhyP+bqmypdzrKi0CWyyNwKxv6aOOf3H8Zc+tYKVyQiZ+vF/mnuejJJLu/wu1d38rb1sbJdK+T3kagPkShuPbNYruAwPJPl2KJgNjabYzJT4Nh0mqlMnqlM/oRwtlBt0Dcf0ubC2Nr4JJfEA2xuiVR8rlnveJquGgOZtG7/s4hCl8giHbEQBkg2d+nG1yIrVMFxeGDnMP/48hDrG8J8/prO+X34KiXgM7TXhWive+M6Co7DTLbAZNoNYJPp4n+ZPFPpAhOZPFMLjvVPZnj6yCT/mHfoiAW5rruBazfU0xHz/vPmCg6Hx9Nc2pIBwCh0HUehS2SRcMBHa22AZGMnTv8PKl2OiJyhqXSeP/tJkmf7prmmu57/dOUaIoGVM3fKZwx1xaHE0xWNNfIvL/XwyMEJvrljiG/sGGJLPMp1G+q5qque+rA3C4L6JjLkCtCVKq5cVOg6jkKXyBISsRDJaffG106hgPGtnF/YItXswEiKLz/ex9BMltuvaOfdmxsrPtzmhdpwgBs2NnLDxkaGZrI8dnCCRw5O8FfPDvLXzw9yWaKO6zbU8+bOOkIlnLzvOA7HpnPsG5ll31CKlwdnAOga64W6ekysvmTXWg0UukSWkKgP8chgLU46BaPD0NJa6ZJE5BR+tH+Mv3x2kFjIz3/7uS4uaK3OrQriNUF+cVsLt25tpmcszSMHJ3i0Z4KfHpmiNujj7etjXLehga1tUXxnGEjHUzn2Dad4bTjFvuFZ9g2nGE/nAXf4dENTmF/c2szah/bonotLUOgSWUIiFmLG8TEerKO5/7BCl8gylskXuO+5QR56bZyL2mv43FUJGiP6680Yw4amCBuaIvzaJa3sGJzhkYPjPN47wQ/2j9NWG+Ca7gau21DPuobwCd8/my2wf+T1cLVvOMXR6ax7bmBdQ4jLO+vY1BJhU0uE7sYwQb8Px3Eo/N0RzOVv9/gTL3/6qRRZwsIbXzcNHMZsv6zCFYnIUo5OZfnS433sH0nx/q3N3HZxebaDWOn8PsMlHbVc0lHLf8wVePrwJI8enODbrwzzwK5hNjZHuLa7npDf8OpwiteGZzkykZlfRdlWG2RTS4R3b25kU0uU85rD1ARPMk9schymJzWfawkKXSJLmL/xddM6tmkFo8iy9EJyij99Mknegf98TSdXrivfdhCrSSTg47oNDVy3oYHR2RyP907wyMFx/uaFowA0hP2c3xLhHevrOb/Yi9VwJj2Hxd+ZWrl4IoUukSW01gYJ+AzJlm6c5POVLkdEFig4DvbOYb758hDrG8P87tWdJ90bS95YUzTAL1zQzC9c0Ez/ZAafcXu1zmXxgdN/yP1Cc7pOoNAlsgS/z9ARC9Kfb4e9hytdjogUTabz3P1UkueT01zXXc/Hr1xDeAVtB7GclWxfr/4jEI5CU7w051tFFLpETiIRC5GcaoCpSZzJcUysodIliVQtx3F4PjnNXz07yMhslv/45nZu3lQd20GsNE7/YVjTqf83S1DoEjmJzvoQz/eFyGPw9R8GhS6Rinh5YJp/+NkQe4ZmWVMX5I9v7GJLvDq3g1gR+o9gLnxTpatYlkoWuizL6gZeAF4pHrrDtu0dlmVdC9yFu8L0T2zb/maprilSTolYiJxjOBZpoqP/CGbz9kqXJFJV9g7N8vc/O8bLAzO0RAPc8ZZ2bjivkaBfPSjLlTM7A2PDWrl4EqXs6QoD/27b9gfnDliW5QPuA34OGANesizrX23bnijhdUXKYm5ibrIhQUdfD042U+GKXE4mvWxqWW3UtuV1uu17YDTNP+4c5bn+GRrCPn7zkhZu3hhzd1IvZHEKHhS7wiybn90jPQAYTaJfUilDVxvQYFnWlcBztm3ngY3AuG3bhwAsy3oBuBLQDe1k2essTirtbzsf58cP4vz4XytcketopQtYxdS25XWq9j1S08Y3u2/kqbaLqc3OcNvhR3l335NE/90NE8paJ7fsfnY71le6gmWplKGrD3gYuBPYbFnW1UAct4drzljx2Aksy7oduB3Atm3i8fKueggEAmW/RjVbDe3b4jjUhg4yfOk7qbt4TaXLmefz+SkU8pUuY1VS25bXydq3Pxfg6xNNPDxTR9g43FY3yvtj49Rt2AJs8b7QFWg5/ez6mlqIbL1oVU2kL9XfaWcVuizL+jXgM4sOv9u27f9RfP1vgQ8AjwGNC97TCAwtdU7btu8F7i0+dYaGlnxbycTjccp9jWq2Wto3EQvSk/Exe8O7K13KvNXStsuR2ra8Frfv0EwWe8cwP9w/ht9n+IULm3j/1mbqixtxzlaq0BVouf3sTg8PV7qEkjpV+yYSidM6z1mFLtu27wfuX3jMsqxg8dEAMWACeA13yHE9bi/XpcAzZ3NNkUpIxELsPjZT6TJEVpWx2RwP7Brm+/vGcHB416ZGfml7nOaoFtTL6lbKn/AvFVcqGuBp4AHbtgvFYcNvAz7gC5pELytJoj7EYz0TpHMFbcAoco4mUlnuf/Eo/7J3lGzB4frzGvjl7XHa6oKVLk3EEyULXbZtf/Ykxx8BrijVdUS8lIiFcID+yQzdTZFKlyNV4th0lp8emaIh4qe1NkhbbZDGiH9FzpFxHIfXRlI8dWiSf39tHzOZPFd31/MrF8XnbywvUi3UlyvyBub+UkgqdIkH+iczPLBrmB8fGCfvHP9ayG+I1wRpqw3QVhecD2Nzj83RAH7f8ghluYLDrqMzPHN4kqePTDE8k8Nn4KrzWnj/lpj+LEnVUugSeQMdMXfYIzmRrXAlspodGkvzT7uGeaJ3goDPcPOmRt69pYlc3uHodJZj0zmOTmeLX2d55sgU46njV6r5DbTUBGmrc4PZXBjrjIXoagpTE/SX9TOkcgVeSE7xzOEpnk1OMZ0pEPIbLu2o5UMXx7giUct5a9csq8neIl5T6BJ5AzVBP03RAK8cm2H/SC2NET+NkeXToyAr22vDKf5p1xBPH54iEjC894Jm3nthM00LJpSfrFconStwbD6IHR/KftY/w8hsjoWdZWvqgnQ1htnQFKa7KcKGxjBtdUF85zBkOZ7K8WzfFE8fnuJnA9Nk8g6xkI8r19Zx5doYl3TUEtFcSJF5Cl0ip3BeU5jnk9M8n5wG3JUi9RE/TZEAjdEATRE3mDVFAzRGAjRFi88jAWqCvhU5D6cajaVyDPRPUE++7L1Cu4/O8E+7hnk+OU1tyMcvX9TCLVuaqQ+f/nXDAR9rG8KsbQgv+Xo27zA0k+XIeIaDYyl6RtMcHE3z0yNT82EsGvAtCGJhNjRF6GoMv2FQGpjM8MyRKZ4+PMmeoVkKDrTWBLjp/EauXFvHtrYa/aNE5CQUukRO4c6rO+kdSzM6m2N0NsdYKsfobJ7RlPv8yHiasVSO3BLbZYf8phjEAsRrAlx/XgOXJWrPqXdBzl0mX+DASJpXh2d5dWiWV4dTDE65Q8g+A+c1RdjeXsPWtihbW2uInUEYOhnHcXh5cAZ75zA7B2eoD/v58CWtvHtzY1lCXtBv6IiF6IiFePPauvnj6VyB3rE0PWNpDo66YezRngn+bZ/7A2yANbEg3Y2R+TDWEA7wQr87dNgzlgagqzHML21v4a1rY2xoCusfFyKnQaFL5BQiAR9b4tE3fI/jOExlCm4wSy0KZ8Vjrxyd4clDk6xrCPG+C5u5trueoF9DL+XmOA79k9n5gLV3KEXPWGo+JLfUBNjcEuXmTY1sSbTwUu8xdh2d4Xt7R3lw9wgGN2Bsa4uyra2GbW01NJ7BflKO4/Bc3zT2ziFeHU7RHA3wkcvbuOn8xooMvYUDPjbHo2xe8DPtOO7csZ7RuTCWpmcsxdOHJ+d7xQxwYWuU37ysjbesraMjppWHImdKoUukBIwxxMJ+YmE/61l6uCdXcHi8Z4IHd49wz9MD/P3PhrhlSxM3b2qkLlTe4azlxHEc/v/27j+2rvK+4/j7xs61Hf9MbJLgEJZCSAgZtBA6ygilhY2pdMtKuz1UW4e0tQ2j/3Rj7f5oNw1NU9dundpK3aSyVq2gk7an1do1MA1UWPdHBdtoSoHwI0nTFYgDxA6Jf8T29Y+7P84hcYxDEufe5+ba75d0dXUf2+c8+erk+OPnPOc5rwxPsHtgjN39o7w4WKK5sUB7MatfW/7eXmygrWnJCe2ns1ba0PgUewZGeb5/lN39Y+wZGGWolCWs5sYC67tb2Hbpiix4dDfTvez4GlE9PT1szp+hUZqaZk//GLtePcquV4/y/Z8e4YHd2VPN1nQUTwhh57W+cZ2pqekyj704xLd2DfCz18ZZ2bqUO39pFTdd1HnOhe1CocCqtiKr2opcs7b9WPvoxDQvHBmn/+hEFjab/ZUhnQ3/B0mJNC4p8O6LOnnXWzp44uWjfOeZAe574iDfenqAm9d3su3SFXP+8q6UcrlM39AEO/uG2TswxopljazpKOavpjOaT3QmhsenslGmgTH25JfyBsezO++KDQXWdjZx6Og0Q6VphsanmJwun3RbxYZCHsYaaC8uyd+zUHbo6CS7B0bpG8ouExaACzubuGZtOxvzgLW2s+m05xsVG5awedUyNq9aBmSh+aeHshD2zKtH+eHPh3ho7xEAVrYu5RdXZSFs03nL2N0/yrd3DfDSYIk1HUU+fu35vHNdB411NtepZWk2yruRNx/plXR6DF1SYoVCdhv9lee3su/QGN999hD3P/8a9z//Gtf/Qgfv27SCi1ZUZh2j8clpnnrlKD/qG2Zn3wgv5/OWllWcs9cAAArxSURBVLc0MjR+4jy0jqaG4yGsPX/vLLK6rXjaYWFiqsz/HR5jd/9YfjlvjL6hUvbvBi7oLPL2NW1s6GlmQ3cLF3Y1nbDtcrnM+FSZofEphktTDI1PMVSaYnh8mqH88/CM977B0rGw1l7MLpvddHEXG7qbWd/dXNG5Uo1LClkA6Wnh/Zd1MzVd5ueHx4+NhD2+f4RH9h1/4Ma6riY+ubWXa9e2O7FcEgCFcvnkf1XWULmvr6+qOzjXHg660FjfM3NwZILvPXeIh/YeYWxymreuXsatl3XzttXL3jBB+c1qWy6X2T9UYmd+t+WuV44yMV2m2FDgilXLuKq3jS29raxuLzI1nc3jeelIif1D4+wfLB17HZ6xBlRDAVa1FWeMih1/jU1MH7tMuHtglH2HxpnIR6qWNzewoaeFS7qb2dDTwvoVzbSe45dRz+a4LZfLvDRY4tmDo6xoaWRLb6uTy2fxvFA91ra6TvOB16f8D+9Il3QOOK91KR/esorbLu/hwT2H2fH8a9z9yIus62rifZtWcP2bXJoam5zmyZdH2Nk3ws4DI8fuwrugo8h7NnSxpbeNy1a2UJw1j6hhyYy722g74WvDpakTQtj+wRJ9gyV+fGBkzst/xYYC61c0896Ny9mQh6yeZY2LKnQUCtml0rUnWcJBkgxd0jmkrdjABzZ3s+3S5fxXPun+i48e4L6fHGTbpcu5eX0X5XKZF4+M56NZw+x6dZTJ6TJNDQWuWN3KrZtWcFVvK6va5n93WVux4diltJmmpsscHJnIgthQiWJDYc7LhJKkNzJ0SeegpQ1L+JWLu7jxok529o3wnWcP8fWdB/mXpwboaH6Bl4eytZIu6Cjy3g1dXNXbxuaVLVW/K65hSYHV7UVWtxfZUtU9SdLCY+iSzmFLCgWuXtPG1Wva2DMwyo7nXqPc0Mitm7q46vw2VrZV725HSVJlGbqkOnFJdwt3XdfihFlJqlPn1gp9kiRJC5ShS5IkKQFDlyRJUgKGLkmSpAQMXZIkSQkYuiRJkhIwdEmSJCVg6JIkSUrA0CVJkpSAoUuSJCkBQ5ckSVIChi5JkqQEDF2SJEkJGLokSZISMHRJkiQlYOiSJElKwNAlSZKUgKFLkiQpAUOXJElSAoYuSZKkBAxdkiRJCRi6JEmSEjB0SZIkJWDokiRJSsDQJUmSlIChS5IkKQFDlyRJUgKGLkmSpAQMXZIkSQkYuiRJkhIwdEmSJCXQWKkNhRC+AmzOP64F/iPGeEcI4W7gNmAAeCnG+MFK7VOSJKleVCx0xRjvAAghtACPA5/Nv9QE/FGM8cFK7UuSJKneVOPy4nbggRjjz/LPK4GNIYSLqrAvSZKkulAol8sV21gIoQA8B7wzxvhK3nYLcA3wAeB7McZPneRnt5MFNmKMW0qlUsX6NZfGxkYmJyeruo/FzPpWj7WtHmtbXda3eqxtdZ2qvsViEaBwqu3MK3SFEG4H7prVfAvZXK6/iDHeMsfPtAEvAatjjGOn2EW5r6/vjPt1Jnp6eujv76/qPhYz61s91rZ6rG11Wd/qsbbVdar69vb2wmmErnnN6Yox3gvcO7s9hPAR4Puz2pbGGCeAVmAcmJjPPiVJkupZxSbS5y4H/un1DyGEDuCREMI0WQL8aIxxqsL7lCRJOudVNHTFGH971udB4OpK7kOSJKkeuTiqJElSAoYuSZKkBAxdkiRJCRi6JEmSEjB0SZIkJWDokiRJSsDQJUmSlIChS5IkKQFDlyRJUgKGLkmSpAQMXZIkSQkYuiRJkhIwdEmSJCVg6JIkSUrA0CVJkpSAoUuSJCkBQ5ckSVIChi5JkqQEDF2SJEkJGLokSZISMHRJkiQlYOiSJElKwNAlSZKUgKFLkiQpAUOXJElSAoYuSZKkBAxdkiRJCRi6JEmSEjB0SZIkJWDokiRJSsDQJUmSlIChS5IkKQFDlyRJUgKGLkmSpAQMXZIkSQkYuiRJkhIwdEmSJCVg6JIkSUrA0CVJkpSAoUuSJCkBQ5ckSVIChi5JkqQEDF2SJEkJGLokSZISMHRJkiQlYOiSJElKwNAlSZKUQON8fzCEsAa4H/hcjPGf87YbgL8DCsDfzmj/G+DdwDDwoRjj/rPtuCRJUj2Z10hXCGEr8AAwOaNtCfCPwPvJAtZnQggdIYSbgCtijG8H7gH++qx7LUmSVGfmO9L1P8AW4Gsz2i4GjsQYXwAIIewErgHeBezIv2cH8Pm5NhhC2A5sB4gx0tvbO8+unb4U+1jMrG/1WNvqsbbVZX2rx9pWVyXqO6+RrhhjKcY4Nau5Bzg84/PhvO1Ye4xxGOg6yTbviTFeHWO8muzyZFVfIYQfpdjPYn1ZX2tbjy9ra33r9WVtz4n6ntIpR7pCCLcDd81qviXG2DerbYATA1UX0D+zPYTQyonBTJIkaVE4ZeiKMd4L3Hsa29oLdIYQLiQLVlcC/w1MA58E/h74DeDhefdWkiSpTlVsyYgY4zTZnKx/BX4AfDrGOBhjfBh4MoTwOHAH8KlK7fMs3VPrDixw1rd6rG31WNvqsr7VY22rqyL1LZTL5UpsR5IkSW/CxVElSZISMHRJkiQlMO8V6etZCOGPgd8FJoCPxhifrnGXFowQwg+AZrKFc3fEGD9X2x7Vv3zh4c8Ab40xvieE0El2c0svsAvYHmMs1bKP9WyO+q4DdgLP5N9yZ4zxqVr1r17ldbwHWEZ2TtgOPJ23bQb6gNtjjEdq1cd6NVdtY4w7QwgjwI/zb/vLGONDNepiXQshbAS+AZSBEeCDZL/Tzvq8u+hGukII64HfB95BthTGP9S2RwtOE3BDjHGrgevs5YHgh8BGjq8D8wng0fwpD+PAh2rUvbp3kvo2AQ/mx/BWA9e8vQx8LMa4Ffgy8Gng94Cx/Nh9DPiTGvavnr2htiGEJuDJGcetgWv+9gK/GmP8ZeAnwB9QofPuogtdZI8oejDGOBljfBTYFEIo1rpTC0gXsDWE0FPrjiwE+V3BNwJfmtF8Iyc+5eGm1P1aKE5S35Vky99cE0JoqE3P6l+McSzGuDf/2EUWFDx2K+AktV0JFEMI1+UBTPMUY5yKMQ7nf5StAQ5QoWN3MYau2SvnHwG6a9SXheiLZOuxPR5C+M1ad2YhiDGOzmqaeQy//uQHzdMc9d0PPAL8KfBECGHOp2jo9IQQrgX+EPgrPHYralZtR4Fvki3N9GwI4S217Fu9CyG8DdgNXA78OxU6dhfjnK4B4JIZnzvyNlVAjPErACGE75KdCP6ttj1akF5/ysN+jj/5QRUSY9xH/ozYEMI3gN8CvlrLPtWrEMKVZHNjtsUYD4QQZj65xGP3LMyubd78hfxrdwMfBv6sJp1bAGKMTwDrQwgfI5s/V5Hz7mIc6fpP4NdCCI0hhHcAzzkJuTJCCDNDfAcwWKu+LHAPk40mAvw6PuWhokIIS/P3AtCOx/G85HW8D7gtxvh83uyxWwFz1fb14zbn+fcs5JcVX7eXbFSrIsfuohvpijHuCSF8newRRRPAR2rcpYXkuhDCF4AS2VD3nTXuz0L1eeDeEML/kt1h980a92eh+WwI4QayifWPAd+ucX/q1eXAOuDLIQTI7v66Gbg+P3YPkE2s15mbq7Y7Qgi/A0wB+4A/r1nv6t+tIYRPkNW1DHycrKZnfd51RXpJkqQEFuPlRUmSpOQMXZIkSQkYuiRJkhIwdEmSJCVg6JIkSUrA0CVJkpSAoUuSJCkBQ5ckSVIC/w+aCDjaAuBpWAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 4.59104679842884 \n", + "\n", + "\n", + "fftfilter\n", + "7.8440780000019\n", + "gamma total\n", + "10.411673000002338\n", + "coch1\n", + "3.4123879999970086\n", + "coch2\n", + "3.4891700000007404\n", + "get avg\n", + "0.07425299999886192\n", + "fftfilter\n", + "7.9030299999940326\n", + "gamma total\n", + "10.488826999993762\n", + "coch1\n", + "3.401335000002291\n", + "coch2\n", + "3.516795000003185\n", + "get avg\n", + "0.07577400000445778\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 10.896442985653168 \n", + "\n", + "\n", + "total_rmse: 17.065871642071187\n" + ] + } + ], + "source": [ + "rmse_list = []\n", + "k = 0\n", + "\n", + "for i in val_list:\n", + " if(i < 50):\n", + " vad_pred, pred = predict(model, samples[i])\n", + " elif((i >= 50) & (i < n_20)):\n", + " vad_pred, pred = predict(model, samples_20[i - 50])\n", + " elif((i >= n_20) & (i < n_40)):\n", + " vad_pred, pred = predict(model, samples_40[i - n_20])\n", + " elif((i >= n_40) & (i < n_80)):\n", + " vad_pred, pred = predict(model, samples_80[i - n_40])\n", + " elif((i >= n_80) & (i < n_100)):\n", + " vad_pred, pred = predict(model, samples_100[i - n_80])\n", + " elif((i >= n_100) & (i < n_140)):\n", + " vad_pred, pred = predict(model, samples_140[i - n_100])\n", + " elif((i >= n_140) & (i < n_160)):\n", + " vad_pred, pred = predict(model, samples_160[i - n_140])\n", + " error = total_label[i].reshape(-1, 1) - pred\n", + " plt.figure(figsize=(10, 8))\n", + " plt.title('%d degree' %(edge_list[k]))\n", + " plt.plot(range(0, len(total_label[i])), total_label[i], label='actual')\n", + " plt.plot(range(0, len(pred)), pred, label='predict')\n", + " plt.plot(range(0, len(error)), error, label='diff')\n", + " plt.ylim(-100, 100)\n", + " plt.legend()\n", + " plt.show()\n", + " \n", + " for j in range(0, len(error)):\n", + " if((vad_label[i][j] == 0) & (vad_pred[j] != 0) | ((vad_label[i][j] != 0) & (vad_pred[j] == 0))):\n", + " error[j] = 180\n", + " \n", + " rmse = np.sqrt((error ** 2).mean())\n", + " rmse_list.append(rmse)\n", + " print('RMSE:', rmse, '\\n\\n')\n", + " k = k + 1\n", + " \n", + "print('total_rmse:', np.array(rmse_list).mean())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Classification" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "total_label = []\n", + "\n", + "for i in range(0, n_160):\n", + " array_label = np.array(label_list[i])\n", + " total_label.append(array_label)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vad_label = total_label.copy" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1.]),\n", + " array([1., 1., 1., 1., 1., 1.]),\n", + " array([0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1.]),\n", + " array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 0., 0.]),\n", + " array([1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.]),\n", + " array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0.]),\n", + " array([0., 0., 0., 0., 0., 0., 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3., 3., 0.], dtype=float32),\n", + " array([3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\n", + " 3., 3., 3., 3., 3.], dtype=float32),\n", + " array([3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 0., 0.],\n", + " dtype=float32),\n", + " array([0., 0., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\n", + " 3., 3., 3., 3.], dtype=float32),\n", + " array([0., 0., 0., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\n", + " 3., 3., 3., 3., 3., 3., 3., 3.], dtype=float32),\n", + " array([3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\n", + " 3., 3., 3., 3., 3., 3.], dtype=float32),\n", + " array([3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\n", + " 3., 3., 3., 3., 3., 3.], dtype=float32),\n", + " array([3., 3., 3., 3., 3., 3., 3., 3., 3.], dtype=float32),\n", + " array([3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\n", + " 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.], dtype=float32),\n", + " array([3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\n", + " 3., 3., 3.], dtype=float32),\n", + " array([3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.],\n", + " dtype=float32),\n", + " array([3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\n", + " 3., 3., 3., 3.], dtype=float32),\n", + " array([3., 3., 3., 3., 3., 3., 3., 3., 3., 0., 0., 0., 0., 0., 0., 0.],\n", + " dtype=float32),\n", + " array([4., 4., 4., 4., 4., 4., 4., 4., 4., 0., 0., 0., 0.]),\n", + " array([4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4.,\n", + " 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 0.]),\n", + " array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 4.,\n", + " 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4.,\n", + " 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 0., 0.]),\n", + " array([0., 0., 0., 0., 0., 0., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4.,\n", + " 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 0., 0.]),\n", + " array([0., 0., 0., 4., 4., 4., 4., 4., 4., 4., 0., 0., 0., 0., 0., 0., 0.,\n", + " 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 0., 0., 0.]),\n", + " array([0., 0., 0., 0., 0., 0., 0., 0., 4., 4., 4., 4., 4., 4., 4.]),\n", + " array([0., 0., 0., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4.,\n", + " 0., 0., 0.]),\n", + " array([4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4.,\n", + " 4., 0.]),\n", + " array([0., 0., 0., 0., 0., 4., 4., 4., 4., 4., 4., 4., 4., 4., 0., 0., 0.,\n", + " 0., 0., 4., 4., 4., 4., 4., 4., 4., 4., 4.]),\n", + " array([0., 0., 4., 4., 4., 4., 4., 4., 4., 4., 4., 0., 0., 0., 0.]),\n", + " array([0., 0., 0., 0., 0., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 0., 0.,\n", + " 0., 0., 0.]),\n", + " array([0., 0., 0., 0., 0., 0., 4., 4., 4., 4., 4., 4., 4., 4., 0., 0., 0.,\n", + " 0.])]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "for i in range(0, 12):\n", + " for j in range(0, len(total_label[i])):\n", + " if(total_label[i][j] == 1):\n", + " total_label[i][j] = 1\n", + "\n", + "for i in range(12, 25):\n", + " for j in range(0, len(total_label[i])):\n", + " if(total_label[i][j] == 1):\n", + " total_label[i][j] = 4\n", + " \n", + "for i in range(25, 38):\n", + " for j in range(0, len(total_label[i])):\n", + " if(total_label[i][j] == 1):\n", + " total_label[i][j] = 7\n", + " \n", + "for i in range(38, 50):\n", + " for j in range(0, len(total_label[i])):\n", + " if(total_label[i][j] == 1):\n", + " total_label[i][j] = 10\n", + " \n", + "for i in range(50, n_20):\n", + " for j in range(0, len(total_label[i])):\n", + " if(total_label[i][j] == 1):\n", + " total_label[i][j] = 2\n", + "\n", + "for i in range(n_20, n_40):\n", + " for j in range(0, len(total_label[i])):\n", + " if(total_label[i][j] == 1):\n", + " total_label[i][j] = 3\n", + " \n", + "for i in range(n_40, n_80):\n", + " for j in range(0, len(total_label[i])):\n", + " if(total_label[i][j] == 1):\n", + " total_label[i][j] = 5\n", + " \n", + "for i in range(n_80, n_100):\n", + " for j in range(0, len(total_label[i])):\n", + " if(total_label[i][j] == 1):\n", + " total_label[i][j] = 6\n", + " \n", + "for i in range(n_100, n_140):\n", + " for j in range(0, len(total_label[i])):\n", + " if(total_label[i][j] == 1):\n", + " total_label[i][j] = 8\n", + " \n", + "for i in range(n_140, n_160):\n", + " for j in range(0, len(total_label[i])):\n", + " if(total_label[i][j] == 1):\n", + " total_label[i][j] = 9\n", + " \n", + "total_label" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "X = []\n", + "y = []\n", + "vad = []\n", + "\n", + "for i in range(0, n_160):\n", + " X.append(total_instances_tensor[idx[i]])\n", + " y.append(total_label[idx[i]])\n", + " vad.append(vad_label[idx[i]])" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((781, 768, 100, 2), (781,))" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_idx = round(n_160 * 0.8)\n", + "X_train = np.concatenate(X[:train_idx], axis=0)\n", + "y_train = np.concatenate(y[:train_idx], axis=0)\n", + "vad_train = np.concatenate(vad[:train_idx], axis=0)\n", + "\n", + "X_train.shape, y_train.shape, vad_train.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((242, 768, 100, 2), (242,))" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_val = np.concatenate(X[train_idx: ], axis=0)\n", + "y_val = np.concatenate(y[train_idx: ], axis=0)\n", + "vad_val = np.concatenate(vad[train_idx: ], axis=0)\n", + "\n", + "X_val.shape, y_val.shape, vad_val.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "from keras.utils import np_utils\n", + "\n", + "y_train = np_utils.to_categorical(y_train, 11)\n", + "y_val = np_utils.to_categorical(y_val, 11)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape_max_pool_3 : [None, 15, 10, 64]\n", + "shape_conv_4 : [None, 1, 10, 1024]\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "input_2 (InputLayer) (None, 768, 100, 2) 0 \n", + "_________________________________________________________________\n", + "conv2d_6 (Conv2D) (None, 766, 98, 16) 304 \n", + "_________________________________________________________________\n", + "max_pooling2d_3 (MaxPooling2 (None, 383, 49, 16) 0 \n", + "_________________________________________________________________\n", + "conv2d_7 (Conv2D) (None, 381, 47, 32) 4640 \n", + "_________________________________________________________________\n", + "max_pooling2d_4 (MaxPooling2 (None, 190, 23, 32) 0 \n", + "_________________________________________________________________\n", + "conv2d_8 (Conv2D) (None, 94, 21, 64) 18496 \n", + "_________________________________________________________________\n", + "conv2d_9 (Conv2D) (None, 46, 20, 64) 24640 \n", + "_________________________________________________________________\n", + "max_pool_3 (MaxPooling2D) (None, 15, 10, 64) 0 \n", + "_________________________________________________________________\n", + "conv2d_10 (Conv2D) (None, 1, 10, 1024) 984064 \n", + "_________________________________________________________________\n", + "reshape_2 (Reshape) (None, 10, 1024) 0 \n", + "_________________________________________________________________\n", + "conv1d_2 (Conv1D) (None, 8, 512) 1573376 \n", + "_________________________________________________________________\n", + "flatten_2 (Flatten) (None, 4096) 0 \n", + "_________________________________________________________________\n", + "dense_3 (Dense) (None, 32) 131104 \n", + "_________________________________________________________________\n", + "dropout_2 (Dropout) (None, 32) 0 \n", + "_________________________________________________________________\n", + "dense_4 (Dense) (None, 5) 165 \n", + "=================================================================\n", + "Total params: 2,736,789\n", + "Trainable params: 2,736,789\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "from keras.layers import Conv2D, MaxPooling2D, Input, Flatten, Dropout, Dense, Reshape, Conv1D\n", + "from keras.models import Model\n", + "\n", + "input_spectrogram = Input(shape=(X_train.shape[1], X_train.shape[2], X_train.shape[3]))\n", + "\n", + "conv_1 = Conv2D(16, (3, 3), activation='relu')(input_spectrogram)\n", + "max_pool_1 = MaxPooling2D((2, 2))(conv_1)\n", + "\n", + "conv_2 = Conv2D(32, (3, 3), activation='relu')(max_pool_1)\n", + "max_pool_2 = MaxPooling2D((2, 2))(conv_2)\n", + "\n", + "conv_3 = Conv2D(64, (3, 3), strides=(2,1), activation='relu')(max_pool_2)\n", + "conv_3_1 = Conv2D(64, (3, 2), strides=(2,1), activation='relu')(conv_3)\n", + "max_pool_3 = MaxPooling2D((3, 2), name='max_pool_3')(conv_3_1)\n", + "\n", + "shape_max_pool_3 = max_pool_3.get_shape().as_list() # (None, height, width, channel)\n", + "print(\"shape_max_pool_3 : \", shape_max_pool_3)\n", + "# reshaped = layers.Reshape((-1, shape_list[1]*shape_list[3]))(max_pool_3)\n", + "\n", + "conv_4 = Conv2D(1024, (shape_max_pool_3[1], 1), padding='valid', activation='relu')(max_pool_3)\n", + "shape_conv_4 = conv_4.get_shape().as_list()\n", + "print(\"shape_conv_4 : \", shape_conv_4)\n", + "\n", + "reshaped = Reshape((shape_conv_4[2], shape_conv_4[3]))(conv_4) # reshape to (timesteps, features) explicitly \n", + "\n", + "conv_5 = Conv1D(512, kernel_size=3, activation='relu')(reshaped)\n", + "\n", + "flatten = Flatten()(conv_5)\n", + "# flatten_drop = Dropout(0.3)(flatten)\n", + "\n", + "fc1 = Dense(32, activation='relu')(flatten)\n", + "fc1_drop = Dropout(0.1)(fc1)\n", + "\n", + "vad_out = Dense(1, activation='sigmoid')(fc1_drop)\n", + "dense_out = Dense(11, activation='softmax')(fc1_drop)\n", + "\n", + "model_clf = Model(inputs=input_spectrogram, outputs=[vad_out, dense_out])\n", + "model_clf.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "from keras.utils import multi_gpu_model\n", + "\n", + "model_clf = multi_gpu_model(model_clf, gpus=8, cpu_relocation=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 781 samples, validate on 242 samples\n", + "Epoch 1/150\n", + "781/781 [==============================] - 3s 4ms/step - loss: 1.7509 - acc: 0.2420 - val_loss: 1.6114 - val_acc: 0.2397\n", + "Epoch 2/150\n", + "781/781 [==============================] - 2s 2ms/step - loss: 1.6027 - acc: 0.2471 - val_loss: 1.5803 - val_acc: 0.3636\n", + "Epoch 3/150\n", + "781/781 [==============================] - 2s 2ms/step - loss: 1.5583 - acc: 0.3099 - val_loss: 1.4678 - val_acc: 0.3884\n", + "Epoch 4/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 1.5215 - acc: 0.3009 - val_loss: 1.5108 - val_acc: 0.2851\n", + "Epoch 5/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 1.4936 - acc: 0.2881 - val_loss: 1.4104 - val_acc: 0.4215\n", + "Epoch 6/150\n", + "781/781 [==============================] - 2s 2ms/step - loss: 1.4577 - acc: 0.3367 - val_loss: 1.5430 - val_acc: 0.3182\n", + "Epoch 7/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 1.4810 - acc: 0.3496 - val_loss: 1.4030 - val_acc: 0.3182\n", + "Epoch 8/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 1.3852 - acc: 0.3470 - val_loss: 1.4359 - val_acc: 0.3967\n", + "Epoch 9/150\n", + "781/781 [==============================] - 2s 2ms/step - loss: 1.3465 - acc: 0.3816 - val_loss: 1.3040 - val_acc: 0.3058\n", + "Epoch 10/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 1.2668 - acc: 0.3278 - val_loss: 1.2587 - val_acc: 0.4132\n", + "Epoch 11/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 1.3861 - acc: 0.3419 - val_loss: 1.3279 - val_acc: 0.3471\n", + "Epoch 12/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 1.3277 - acc: 0.3572 - val_loss: 1.4892 - val_acc: 0.3182\n", + "Epoch 13/150\n", + "781/781 [==============================] - 2s 2ms/step - loss: 1.3033 - acc: 0.3841 - val_loss: 0.9978 - val_acc: 0.5909\n", + "Epoch 14/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 1.0392 - acc: 0.4750 - val_loss: 0.9405 - val_acc: 0.5372\n", + "Epoch 15/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.9331 - acc: 0.6312 - val_loss: 0.8654 - val_acc: 0.6777\n", + "Epoch 16/150\n", + "781/781 [==============================] - 2s 2ms/step - loss: 0.8766 - acc: 0.6082 - val_loss: 1.1370 - val_acc: 0.4793\n", + "Epoch 17/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 1.0320 - acc: 0.5455 - val_loss: 0.8344 - val_acc: 0.6612\n", + "Epoch 18/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.8254 - acc: 0.6863 - val_loss: 1.0306 - val_acc: 0.5826\n", + "Epoch 19/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.7862 - acc: 0.6338 - val_loss: 0.8901 - val_acc: 0.6157\n", + "Epoch 20/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.7014 - acc: 0.6978 - val_loss: 1.0493 - val_acc: 0.6488\n", + "Epoch 21/150\n", + "781/781 [==============================] - 2s 2ms/step - loss: 0.5914 - acc: 0.7542 - val_loss: 0.9477 - val_acc: 0.7149\n", + "Epoch 22/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.4203 - acc: 0.8143 - val_loss: 0.7598 - val_acc: 0.7686\n", + "Epoch 23/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.4179 - acc: 0.8156 - val_loss: 0.7412 - val_acc: 0.7562\n", + "Epoch 24/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.5596 - acc: 0.7785 - val_loss: 0.9602 - val_acc: 0.6240\n", + "Epoch 25/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.6656 - acc: 0.7465 - val_loss: 1.0311 - val_acc: 0.6653\n", + "Epoch 26/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.6559 - acc: 0.7375 - val_loss: 0.9394 - val_acc: 0.5992\n", + "Epoch 27/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.6931 - acc: 0.7119 - val_loss: 0.7541 - val_acc: 0.7273\n", + "Epoch 28/150\n", + "781/781 [==============================] - 2s 2ms/step - loss: 0.4142 - acc: 0.8323 - val_loss: 0.6935 - val_acc: 0.7190\n", + "Epoch 29/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.3364 - acc: 0.8656 - val_loss: 0.5765 - val_acc: 0.7851\n", + "Epoch 30/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.2649 - acc: 0.8912 - val_loss: 0.9512 - val_acc: 0.7438\n", + "Epoch 31/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.2908 - acc: 0.8988 - val_loss: 0.8880 - val_acc: 0.6983\n", + "Epoch 32/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.3526 - acc: 0.8579 - val_loss: 0.7634 - val_acc: 0.7521\n", + "Epoch 33/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.2895 - acc: 0.8950 - val_loss: 0.7199 - val_acc: 0.8140\n", + "Epoch 34/150\n", + "781/781 [==============================] - 2s 2ms/step - loss: 0.1652 - acc: 0.9501 - val_loss: 1.0318 - val_acc: 0.8017\n", + "Epoch 35/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.1517 - acc: 0.9411 - val_loss: 1.1672 - val_acc: 0.8017\n", + "Epoch 36/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.1135 - acc: 0.9616 - val_loss: 1.0269 - val_acc: 0.8264\n", + "Epoch 37/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.1066 - acc: 0.9552 - val_loss: 1.3315 - val_acc: 0.7851\n", + "Epoch 38/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0880 - acc: 0.9706 - val_loss: 1.4180 - val_acc: 0.7975\n", + "Epoch 39/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0649 - acc: 0.9821 - val_loss: 1.2148 - val_acc: 0.8099\n", + "Epoch 40/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0508 - acc: 0.9846 - val_loss: 1.2327 - val_acc: 0.8099\n", + "Epoch 41/150\n", + "781/781 [==============================] - 2s 2ms/step - loss: 0.0573 - acc: 0.9795 - val_loss: 1.2991 - val_acc: 0.7975\n", + "Epoch 42/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0470 - acc: 0.9821 - val_loss: 1.3415 - val_acc: 0.7934\n", + "Epoch 43/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0469 - acc: 0.9898 - val_loss: 1.4146 - val_acc: 0.7851\n", + "Epoch 44/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0390 - acc: 0.9923 - val_loss: 1.4410 - val_acc: 0.7851\n", + "Epoch 45/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0455 - acc: 0.9885 - val_loss: 1.4831 - val_acc: 0.7810\n", + "Epoch 46/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0443 - acc: 0.9898 - val_loss: 1.4711 - val_acc: 0.7851\n", + "Epoch 47/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0397 - acc: 0.9885 - val_loss: 1.4795 - val_acc: 0.7851\n", + "Epoch 48/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0364 - acc: 0.9936 - val_loss: 1.5046 - val_acc: 0.7769\n", + "Epoch 49/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0379 - acc: 0.9923 - val_loss: 1.4896 - val_acc: 0.7769\n", + "Epoch 50/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0315 - acc: 0.9885 - val_loss: 1.4949 - val_acc: 0.7810\n", + "Epoch 51/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0272 - acc: 0.9949 - val_loss: 1.4996 - val_acc: 0.7810\n", + "Epoch 52/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0410 - acc: 0.9910 - val_loss: 1.5006 - val_acc: 0.7769\n", + "Epoch 53/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0325 - acc: 0.9910 - val_loss: 1.5041 - val_acc: 0.7769\n", + "Epoch 54/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0299 - acc: 0.9910 - val_loss: 1.5018 - val_acc: 0.7769\n", + "Epoch 55/150\n", + "781/781 [==============================] - 2s 2ms/step - loss: 0.0361 - acc: 0.9936 - val_loss: 1.4983 - val_acc: 0.7769\n", + "Epoch 56/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0336 - acc: 0.9923 - val_loss: 1.4949 - val_acc: 0.7769\n", + "Epoch 57/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0354 - acc: 0.9872 - val_loss: 1.4986 - val_acc: 0.7810\n", + "Epoch 58/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0364 - acc: 0.9898 - val_loss: 1.4947 - val_acc: 0.7810\n", + "Epoch 59/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0288 - acc: 0.9936 - val_loss: 1.4866 - val_acc: 0.7851\n", + "Epoch 60/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0313 - acc: 0.9923 - val_loss: 1.4858 - val_acc: 0.7851\n", + "Epoch 61/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0334 - acc: 0.9923 - val_loss: 1.4855 - val_acc: 0.7851\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 62/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0343 - acc: 0.9923 - val_loss: 1.4855 - val_acc: 0.7851\n", + "Epoch 63/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0290 - acc: 0.9936 - val_loss: 1.4861 - val_acc: 0.7851\n", + "Epoch 64/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0300 - acc: 0.9936 - val_loss: 1.4866 - val_acc: 0.7851\n", + "Epoch 65/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0375 - acc: 0.9898 - val_loss: 1.4859 - val_acc: 0.7851\n", + "Epoch 66/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0415 - acc: 0.9872 - val_loss: 1.4863 - val_acc: 0.7851\n", + "Epoch 67/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0366 - acc: 0.9885 - val_loss: 1.4873 - val_acc: 0.7851\n", + "Epoch 68/150\n", + "781/781 [==============================] - 2s 2ms/step - loss: 0.0354 - acc: 0.9910 - val_loss: 1.4877 - val_acc: 0.7851\n", + "Epoch 69/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0331 - acc: 0.9936 - val_loss: 1.4883 - val_acc: 0.7851\n", + "Epoch 70/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0360 - acc: 0.9910 - val_loss: 1.4884 - val_acc: 0.7851\n", + "Epoch 71/150\n", + "781/781 [==============================] - 1s 2ms/step - loss: 0.0317 - acc: 0.9898 - val_loss: 1.4883 - val_acc: 0.7851\n" + ] + } + ], + "source": [ + "from keras.callbacks import EarlyStopping, ReduceLROnPlateau\n", + "\n", + "model_clf.compile(optimizer ='adam', loss=['binary_crossentropy', 'categorical_crossentropy'], metrics =['acc', 'acc'])\n", + "\n", + "callbacks_list = [EarlyStopping(monitor='loss', patience=20),\n", + " ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10)]\n", + "\n", + "history = model_clf.fit(X_train, [vad_train, y_train],\n", + " epochs=150, batch_size=64, \n", + " callbacks=callbacks_list,\n", + " validation_data=(X_val, [vad_val, y_val]),\n", + " shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "def predict(model, file_path):\n", + " mrcg_L, mrcg_R = mrcg_transpose(file_path, sr=44100)\n", + " mrcg_L_stack = generate_instances_no_label(mrcg_L, 100, 10)\n", + " mrcg_R_stack = generate_instances_no_label(mrcg_R, 100, 10)\n", + " X = np.concatenate([mrcg_L_stack, mrcg_R_stack], axis=-1)\n", + " vad_pred, pred = np.argmax(model.predict(X), axis=1)\n", + " return pred" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "val_list = idx[train_idx:]\n", + "edge_list = []\n", + "\n", + "for i in range(0, len(val_list)):\n", + " val_idx = val_list[i]\n", + " \n", + " if((val_idx >= 0) & (val_idx < 12)):\n", + " edge_list.append(0)\n", + " elif((val_idx >= 12) & (val_idx < 25)):\n", + " edge_list.append(60)\n", + " elif((val_idx >= 25) & (val_idx < 38)):\n", + " edge_list.append(120)\n", + " elif((val_idx >= 38) & (val_idx < 50)):\n", + " edge_list.append(180)\n", + " elif((val_idx >= 50) & (val_idx < n_20)):\n", + " edge_list.append(20)\n", + " elif((val_idx >= n_20) & (val_idx < n_40)):\n", + " edge_list.append(40)\n", + " elif((val_idx >= n_40) & (val_idx < n_80)):\n", + " edge_list.append(80)\n", + " elif((val_idx >= n_80) & (val_idx < n_100)):\n", + " edge_list.append(100)\n", + " elif((val_idx >= n_100) & (val_idx < n_140)):\n", + " edge_list.append(140)\n", + " elif((val_idx >= n_140) & (val_idx < n_160)):\n", + " edge_list.append(160)\n", + "\n", + "len(val_list), len(edge_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fftfilter\n", + "5.306520999999975\n", + "gamma total\n", + "7.129069000000072\n", + "coch1\n", + "2.61152200000015\n", + "coch2\n", + "3.201068999999734\n", + "get avg\n", + "0.024499999999989086\n", + "fftfilter\n", + "5.572591000000102\n", + "gamma total\n", + "7.40264900000011\n", + "coch1\n", + "2.603411999999935\n", + "coch2\n", + "3.2137149999998655\n", + "get avg\n", + "0.026143000000047323\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 63.63961030678928 \n", + "\n", + "\n", + "fftfilter\n", + "5.118500999999924\n", + "gamma total\n", + "6.937108999999964\n", + "coch1\n", + "2.669006999999965\n", + "coch2\n", + "3.2135840000000826\n", + "get avg\n", + "0.03901499999983571\n", + "fftfilter\n", + "5.491958999999952\n", + "gamma total\n", + "7.4416229999997086\n", + "coch1\n", + "2.6081240000003163\n", + "coch2\n", + "3.2135879999996178\n", + "get avg\n", + "0.03945999999996275\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 90.0 \n", + "\n", + "\n", + "fftfilter\n", + "8.879953000000114\n", + "gamma total\n", + "11.326923999999963\n", + "coch1\n", + "3.305765000000065\n", + "coch2\n", + "3.3962689999998474\n", + "get avg\n", + "0.07385900000008405\n", + "fftfilter\n", + "9.324395000000095\n", + "gamma total\n", + "11.769283000000087\n", + "coch1\n", + "3.302926999999727\n", + "coch2\n", + "3.4350170000002436\n", + "get avg\n", + "0.07605299999977433\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 98.5900603509299 \n", + "\n", + "\n", + "fftfilter\n", + "6.954228999999941\n", + "gamma total\n", + "9.156911000000036\n", + "coch1\n", + "3.2340800000001764\n", + "coch2\n", + "3.278111000000081\n", + "get avg\n", + "0.04506400000036592\n", + "fftfilter\n", + "6.853837999999996\n", + "gamma total\n", + "9.070024999999987\n", + "coch1\n", + "3.2323309999997036\n", + "coch2\n", + "3.287017999999989\n", + "get avg\n", + "0.029201000000284694\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 131.07943462579544 \n", + "\n", + "\n", + "fftfilter\n", + "7.786166000000321\n", + "gamma total\n", + "10.177752999999939\n", + "coch1\n", + "3.2530980000001364\n", + "coch2\n", + "3.3409389999997074\n", + "get avg\n", + "0.06469300000026124\n", + "fftfilter\n", + "8.268200999999863\n", + "gamma total\n", + "10.647249999999985\n", + "coch1\n", + "3.25076799999988\n", + "coch2\n", + "3.297618000000057\n", + "get avg\n", + "0.06406700000025012\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 124.70765814495917 \n", + "\n", + "\n", + "fftfilter\n", + "6.697357999999895\n", + "gamma total\n", + "8.94318099999964\n", + "coch1\n", + "3.175199000000248\n", + "coch2\n", + "3.2817879999997785\n", + "get avg\n", + "0.06051000000024942\n", + "fftfilter\n", + "7.472609999999804\n", + "gamma total\n", + "9.744950999999674\n", + "coch1\n", + "3.2412630000003446\n", + "coch2\n", + "3.2971489999999903\n", + "get avg\n", + "0.06098100000008344\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 37.53259453027346 \n", + "\n", + "\n", + "fftfilter\n", + "5.770130999999765\n", + "gamma total\n", + "7.805637999999817\n", + "coch1\n", + "3.0181250000000546\n", + "coch2\n", + "3.2574150000000373\n", + "get avg\n", + "0.027255000000423024\n", + "fftfilter\n", + "5.852687999999944\n", + "gamma total\n", + "7.996147999999721\n", + "coch1\n", + "3.220252000000073\n", + "coch2\n", + "3.2383859999999913\n", + "get avg\n", + "0.027496000000155618\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 40.24922359499622 \n", + "\n", + "\n", + "fftfilter\n", + "13.579717999999957\n", + "gamma total\n", + "17.006701999999677\n", + "coch1\n", + "3.4623350000001665\n", + "coch2\n", + "3.641395999999986\n", + "get avg\n", + "0.1997220000002926\n", + "fftfilter\n", + "12.205481999999847\n", + "gamma total\n", + "15.61785400000008\n", + "coch1\n", + "3.436894999999822\n", + "coch2\n", + "3.611711000000014\n", + "get avg\n", + "0.15530500000022585\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 26.25569846962102 \n", + "\n", + "\n", + "fftfilter\n", + "7.7166879999999765\n", + "gamma total\n", + "10.179589999999735\n", + "coch1\n", + "3.2534260000002178\n", + "coch2\n", + "3.3186319999999796\n", + "get avg\n", + "0.07114799999999377\n", + "fftfilter\n", + "7.811688000000231\n", + "gamma total\n", + "10.25675799999999\n", + "coch1\n", + "3.280062000000271\n", + "coch2\n", + "3.388586999999916\n", + "get avg\n", + "0.06972700000005716\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 76.06388292556649 \n", + "\n", + "\n", + "fftfilter\n", + "5.342942000000221\n", + "gamma total\n", + "7.170924000000014\n", + "coch1\n", + "2.5846690000003036\n", + "coch2\n", + "3.1655000000000655\n", + "get avg\n", + "0.02302400000007765\n", + "fftfilter\n", + "4.779935000000023\n", + "gamma total\n", + "6.608342999999877\n", + "coch1\n", + "2.6802240000001802\n", + "coch2\n", + "3.1813379999998688\n", + "get avg\n", + "0.022830000000340078\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.0 \n", + "\n", + "\n", + "total_rmse: 68.8118162948931\n" + ] + } + ], + "source": [ + "rmse_list = []\n", + "k = 0\n", + "\n", + "for i in val_list:\n", + " if(i < 50):\n", + " vad_pred, pred = predict(model, samples[i])\n", + " elif((i >= 50) & (i < n_20)):\n", + " vad_pred, pred = predict(model, samples_20[i - 50])\n", + " elif((i >= n_20) & (i < n_40)):\n", + " vad_pred, pred = predict(model, samples_40[i - n_20])\n", + " elif((i >= n_40) & (i < n_80)):\n", + " vad_pred, pred = predict(model, samples_80[i - n_40])\n", + " elif((i >= n_80) & (i < n_100)):\n", + " vad_pred, pred = predict(model, samples_100[i - n_80])\n", + " elif((i >= n_100) & (i < n_140)):\n", + " vad_pred, pred = predict(model, samples_140[i - n_100])\n", + " elif((i >= n_140) & (i < n_160)):\n", + " vad_pred, pred = predict(model, samples_160[i - n_140])\n", + " error = total_label[i].reshape(-1, 1) - pred\n", + " plt.figure(figsize=(10, 8))\n", + " plt.title('%d degree' %(edge_list[k]))\n", + " plt.plot(range(0, len(total_label[i])), total_label[i], label='actual')\n", + " plt.plot(range(0, len(pred)), pred, label='predict')\n", + " plt.plot(range(0, len(error)), error, label='diff')\n", + " plt.ylim(-5, 5)\n", + " plt.legend()\n", + " plt.show()\n", + " \n", + " for j in range(0, len(error)):\n", + " if((vad_label[i][j] == 0) & (vad_pred[j] != 0) | ((vad_label[i][j] != 0) & (vad_pred[j] == 0))):\n", + " error[j] = 180\n", + " else:\n", + " abs_error = abs(error[j])\n", + " error[j] = 20 * abs_error\n", + " \n", + " rmse = np.sqrt((error ** 2).mean())\n", + " rmse_list.append(rmse)\n", + " print('RMSE:', rmse, '\\n\\n')\n", + " k = k + 1\n", + " \n", + "print('total_rmse:', np.array(rmse_list).mean())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:hun] *", + "language": "python", + "name": "conda-env-hun-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Predict_SSL_STFT_Siamese_MultiHead.ipynb b/Predict_SSL_STFT_Siamese_MultiHead.ipynb new file mode 100644 index 0000000..41f1953 --- /dev/null +++ b/Predict_SSL_STFT_Siamese_MultiHead.ipynb @@ -0,0 +1,682 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
- last read : 2019. 06. 3
\n", + "\n", + "\n", + "## Sound Source Location (방위각 추정) and VAD as Multi-task learning\n", + "- Based on STFT magnitude and phase. \n", + "- 2 Models: Convolutional GRU & 2D-CNN then 2D-CNN\n", + "\n", + "### Model 시험 함수의 Inputs 형태 : \n", + "- `predict_utterances(model_path, X, test_idx)` \n", + " - model_path : 테스트할 모델의 path\n", + " - X : ndarray. 오디오 샘플(파일)들. 가령 50개의 오디오 파일이 있으면 X 의 길이는 50\n", + " - 각 오디오 샘플은 1.16 초 길이의 ndarray 형태의 instance 들로 구성. \n", + " - 각 instance는 (512, 100, 4)의 shape를 갖음. 각 instance는 11.6 msec 간격. \n", + " - 512 : height of STFT.\n", + " - 100 : number of frames. Abut 1.16 sec duration\n", + " - 4 : Four channels (left mag. left phase, right mag, right phase)\n", + " - X[7] : 8번째 오디오 파일. \n", + " - X[7].shape == (17, 512, 100, 4) 이라면 : 8번째 오디오 파일이 17개의 instance로 구성.\n", + "- test_idx : numpy vector. Indices of X to consider. \n", + "\n", + "\n", + "### Model 시험 함수의 출력 : \n", + "- test_idx 길이 만큼의 sample 들 내의 instance들에 대한 예측 " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'tf_utils'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhome\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Google_Sync'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Dev_Exercise'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'utils'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtf_utils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'tf_utils'" + ] + } + ], + "source": [ + "# Common imports\n", + "import numpy as np\n", + "import pandas as pd\n", + "import os, sys, glob \n", + "import tensorflow as tf\n", + "\n", + "import librosa\n", + "import librosa.display\n", + "\n", + "# To plot pretty figures\n", + "# import matplotlib\n", + "# import matplotlib.pyplot as plt\n", + "# %matplotlib inline\n", + "# plt.style.use('ggplot')\n", + "# plt.rcParams['axes.labelsize'] = 14\n", + "# plt.rcParams['xtick.labelsize'] = 12\n", + "# plt.rcParams['ytick.labelsize'] = 12\n", + "\n", + "def reset_graph(seed=42):\n", + " tf.reset_default_graph() \n", + " tf.set_random_seed(seed)\n", + " np.random.seed(seed)\n", + " \n", + "def reset_keras_session(seed=42):\n", + " tf.keras.backend.clear_session()\n", + " tf.set_random_seed(seed)\n", + " np.random.seed(seed)\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\") # To rid of warnings \n", + "\n", + "if sys.platform == 'win32': # if windows \n", + " home = os.path.join('D:', os.sep, 'hblee') # d:\\hblee\n", + " data_repo = os.path.join('D:', os.sep, 'Data_Repo_Win') # d:\\Data_Repo_Win\n", + "elif sys.platform == \"linux\" or sys.platform == \"linux2\" : # if linux \n", + " home = os.path.expanduser(\"~\") # home = os.getenv(\"HOME\")\n", + " data_repo = os.path.join(home, 'Data_Repo')\n", + " \n", + "sys.path.append(os.path.join(home, 'Google_Sync', 'Dev_Exercise', 'utils'))\n", + "from tf_utils import *\n", + " \n", + "from tensorflow import keras \n", + "keras.__version__, tf.VERSION" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "'''\n", + "samples : audio samples(files). 50 of them \n", + "samples_vad_seg : samples segmented as to voice region (1) and non-voice region (0) \n", + "\n", + "samples and samples_vad_seg should be aligned. \n", + "'''\n", + "\n", + "sample_data_repo = os.path.join('..', 'Data', 'sample_data', 't3_audio')\n", + "samples = glob.glob(os.path.join(sample_data_repo, '**', '*wav'), recursive=True)\n", + "samples = sorted(samples) # sort the samples\n", + "\n", + "sample_vad_seg_repo = os.path.join('..', 'Data', 'binary_segment') # 적절하게 변경 필요 \n", + "samples_vad_seg = glob.glob(os.path.join(sample_vad_seg_repo, '**', '*[npy|npz]'), recursive=True)\n", + "samples_vad_seg = sorted(samples_vad_seg) \n", + "\n", + "# Checking \n", + "print('samples: ', len(samples), samples[25])\n", + "print('samples segmented: ', len(samples_vad_seg), samples_vad_seg[25]) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# data set 만들기" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def mag_phase(file_path, sr=44100, n_fft=1024, hop_length=512, db=False, n_mels=50) :\n", + " \"\"\"\n", + " stft의 magnitude와 phase 리턴\n", + " \"\"\"\n", + " audio, sr = librosa.load(file_path, sr=sr, mono=False) # 원래의 sr, stereo\n", + " DL = librosa.stft(audio[0], n_fft=n_fft, hop_length=hop_length)\n", + " DL_mag, DL_phase = librosa.magphase(DL)\n", + " \n", + " DR = librosa.stft(audio[1], n_fft=n_fft, hop_length=hop_length)\n", + " DR_mag, DR_phase = librosa.magphase(DR)\n", + " \n", + " if db :\n", + " DL_mag = librosa.core.amplitude_to_db(DL_mag)\n", + " DR_mag = librosa.core.amplitude_to_db(DR_mag)\n", + " \n", + " # rescale the right magnitudes w.r.t left channel magnitude \n", + " avg = DL_mag.mean() \n", + " stdv = DL_mag.std()\n", + " DL_mag = (DL_mag - avg)/stdv\n", + " DR_mag = (DR_mag - avg)/stdv\n", + " \n", + " # return( (DL_mag, np.angle(DL_phase)), (DR_mag, np.angle(DR_phase)) )\n", + " return( (DL_mag[1:, :], np.angle(DL_phase)[1:, :]), (DR_mag[1:, :], np.angle(DR_phase)[1:, :]) )" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def generatio_tensor_instances(array_2d, dest_path, seq_len, hop, label):\n", + " \"\"\"\n", + " array_2d : spectrogram.\n", + " seq_len : number of frames in a instance\n", + " label : 0 and 1's. The same length as original numpy vector \n", + " \"\"\"\n", + " row_size, col_size = array_2d.shape[0], array_2d.shape[1]\n", + " ratio = len(label)/col_size # ratio : how many data points per frame \n", + " stack_array = [] # 4D tensor that will hold the instances\n", + " label_array = []\n", + "\n", + " j=0\n", + " while j <= (col_size - (seq_len+1)): \n", + " context_frame = array_2d[:, j:(j+seq_len)]\n", + " # seg_label = round( label[int(j*ratio):int((j+seq_len)*ratio)].mean() ) \n", + " threshold = 0.5 # if greater than the threshold, then speech \n", + " seg_label = 1 if label[int(j*ratio):int((j+seq_len)*ratio)].mean() > threshold else 0\n", + "\n", + "# # store the instances\n", + "# dest_path_ext = ''.join([dest_path, '_', str(j)])\n", + "# os.makedirs(os.path.dirname(dest_path_ext), exist_ok=True)\n", + "\n", + "# np.savez(dest_path_ext, spectrogram = context_frame,\n", + "# label=seg_label)\n", + " \n", + " stack_array.append(context_frame[:,:,np.newaxis]) # make context_frame to 3d tensor & append \n", + " label_array.append(seg_label)\n", + " \n", + " j = j+hop\n", + " \n", + " return np.stack(stack_array, axis=0), label_array" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50 50 50\n" + ] + }, + { + "data": { + "text/plain": [ + "((15, 512, 100, 1), (15, 512, 100, 1), (15,))" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "no_samples = len(samples) \n", + "\n", + "mag_L_instances = [] # elements are ndarrays\n", + "mag_R_instances = []\n", + "phase_L_instances = []\n", + "phase_R_instances = []\n", + "label_instances = [] # elements are lists\n", + "\n", + "for i in range(0, no_samples):\n", + " voice_noise_label = np.load(samples_vad_seg[i])\n", + " if('npy' in samples_vad_seg[i].split('/')[-1]):\n", + " label = voice_noise_label[0] # use the left channel label. this take care of 0 degree problem\n", + " else: # npz file\n", + " label = voice_noise_label[\"label\"] \n", + " (mag_L, phase_L), (mag_R, phase_R) = mag_phase(samples[i], db=True)\n", + " \n", + " # generate instances with 1.16 sec duration (100 frames), at every 0.116 sec apart (10 hops)\n", + " voice_dest_path = os.path.join(\"mag\", \"Left\", str(i))\n", + " mag_L_instances_sub, _ = generatio_tensor_instances(mag_L, voice_dest_path, 100, 10, label)\n", + " \n", + " voice_dest_path = os.path.join(\"mag\", \"Right\", str(i))\n", + " mag_R_instances_sub, _ = generatio_tensor_instances(mag_R, voice_dest_path, 100, 10, label)\n", + " \n", + " voice_dest_path = os.path.join(\"phase\", \"Left\", str(i))\n", + " phase_L_instances_sub, _ = generatio_tensor_instances(phase_L, voice_dest_path, 100, 10, label)\n", + " \n", + " voice_dest_path = os.path.join(\"phase\", \"Right\", str(i))\n", + " phase_R_instances_sub, label_sub = generatio_tensor_instances(phase_R, voice_dest_path, 100, 10, label)\n", + " \n", + " mag_L_instances.append(mag_L_instances_sub)\n", + " mag_R_instances.append(mag_R_instances_sub)\n", + " phase_L_instances.append(phase_L_instances_sub)\n", + " phase_R_instances.append(phase_R_instances_sub)\n", + " \n", + " label_instances.append(np.array(label_sub))\n", + " \n", + "\n", + "print(len(mag_L_instances), len(phase_R_instances), len(label_instances))\n", + "\n", + "mag_L_instances[0].shape, phase_R_instances[0].shape, label_instances[0].shape\n", + "# the first sample produced 15 instances. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(50, (15, 512, 100, 4))" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stacked_instances = []\n", + "\n", + "for i in range(0, no_samples):\n", + " concat_tensor = np.concatenate([mag_L_instances[i], phase_L_instances[i], \n", + " mag_R_instances[i], phase_R_instances[i]], axis = -1)\n", + " stacked_instances.append(concat_tensor)\n", + " \n", + "len(stacked_instances), stacked_instances[0].shape # L, R magnitudes and phases are stacked." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### noise와 voice 방향에 따라 labeling\n", + "- noise : 0 \n", + "- 0도 : 1 \n", + "- 60도 : 2 \n", + "- 120도 : 3 \n", + "- 180도 : 4 " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import copy\n", + "vad_label_instances = copy.deepcopy(label_instances)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(12,25):\n", + " for j in range(0, len(label_instances[i])):\n", + " if(label_instances[i][j] == 1):\n", + " label_instances[i][j] = 2\n", + " \n", + "for i in range(25,38):\n", + " for j in range(0, len(label_instances[i])):\n", + " if(label_instances[i][j] == 1):\n", + " label_instances[i][j] = 3\n", + " \n", + "for i in range(38,50):\n", + " for j in range(0, len(label_instances[i])):\n", + " if(label_instances[i][j] == 1):\n", + " label_instances[i][j] = 4\n", + " \n", + "# label_instances" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have 822 instances. And we have labeled them into 5 classes. Let's see how those labels are distributed." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Note that `stacked_instances` indices has : 0~11(Class-1), 12~24(Class-2), 25~37(Class-3), 38~49(Class-4) and Class-0 is assigned to the noise \n" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(50,) (50,) (50,)\n" + ] + } + ], + "source": [ + "X = np.array(stacked_instances) # transform the list to ndarray\n", + "\n", + "y = np.array(label_instances)\n", + "\n", + "y_vad = np.array(vad_label_instances)\n", + "\n", + "print(X.shape, y.shape, y_vad.shape )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "def predict_utterances(model_path, samples_instances, samples_indices) :\n", + " \"\"\"\n", + " samples_instances : ndarray holding 'samples' number of sample representation in ndarrays. \n", + " Each sample has the shape: (instances_in_sample, 512, 100, 4)\n", + " samples_indices : indices of the samples to consider in 'samples_instances'\n", + " \"\"\"\n", + " labels_pred = [] \n", + " \n", + " model = keras.models.load_model(model_path)\n", + " \n", + " X = samples_instances[[samples_indices]]\n", + " \n", + " for i, sample in enumerate(X) : # for the instances in each utterance sample \n", + " x_L = sample[:, :, :, :2]\n", + " x_R = sample[:, :, :, 2:]\n", + " \n", + " labels_pred.append( np.argmax(model.predict([x_L, x_R]), axis=1) )\n", + " \n", + " return np.array(labels_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "models = sorted(glob.glob(os.path.join('.', 'models', '*.h5')))" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n", + " 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n", + " 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idx = np.arange(0, no_samples)\n", + "# idx = np.random.permutation(no_samples)\n", + "# test_idx = idx[-10:]\n", + "test_idx = idx\n", + "test_idx" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "predictions=predict_utterances(models[0], X, test_idx) # test against the first model " + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(25,) (25,)\n" + ] + }, + { + "data": { + "text/plain": [ + "[(3, 4),\n", + " (3, 4),\n", + " (3, 3),\n", + " (3, 3),\n", + " (3, 3),\n", + " (3, 3),\n", + " (3, 3),\n", + " (3, 3),\n", + " (3, 3),\n", + " (3, 3),\n", + " (3, 3),\n", + " (3, 0),\n", + " (3, 0),\n", + " (3, 0),\n", + " (3, 3),\n", + " (3, 3),\n", + " (3, 3),\n", + " (3, 3),\n", + " (3, 3),\n", + " (3, 3),\n", + " (3, 3),\n", + " (3, 3),\n", + " (3, 3),\n", + " (3, 3),\n", + " (3, 0)]" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample_index = 33\n", + "print(y[test_idx][sample_index].shape, predictions[sample_index].shape)\n", + "list(zip(y[test_idx][sample_index], predictions[sample_index]))" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "def class_instances_count(sample_instances_labels) : \n", + " \"\"\"\n", + " sample_instances_labels : class labels for the instances\n", + " returns a list where elements are : (class_label, count)\n", + " \"\"\"\n", + " # sample_instances_labels : class labels for the instances \n", + " import operator\n", + " unique, counts = np.unique(sample_instances_labels, return_counts=True)\n", + " dict_temp = dict(zip(unique, counts))\n", + " return sorted(dict_temp.items(), key=operator.itemgetter(1), reverse=True) " + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(3, 19), (0, 4), (4, 2)]" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class_instances_count(predictions[sample_index])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ensemble of models\n", + "- For each model, do (instance) predictions for all the sample files" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [], + "source": [ + "ensemble_pred = predict_utterances(models[0], X, test_idx)\n", + "\n", + "for model in models[1: ] :\n", + " predictions=predict_utterances(model, X, test_idx)\n", + " for sample_ind in range(0, len(test_idx)):\n", + " ensemble_pred[sample_ind] = np.concatenate([ensemble_pred[sample_ind], predictions[sample_ind]], \n", + " axis=-1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "0~11(Class-1), 12~24(Class-2), 25~37(Class-3), 38~49(Class-4) and Class-0 is assigned to the noise" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(0, 1),\n", + " (1, 1),\n", + " (2, 1),\n", + " (3, 1),\n", + " (4, 1),\n", + " (5, 1),\n", + " (6, 1),\n", + " (7, 1),\n", + " (8, 1),\n", + " (9, 1),\n", + " (10, 1),\n", + " (11, 1),\n", + " (12, 2),\n", + " (13, 2),\n", + " (14, 2),\n", + " (15, 2),\n", + " (16, 2),\n", + " (17, 2),\n", + " (18, 2),\n", + " (19, 2),\n", + " (20, 2),\n", + " (21, 2),\n", + " (22, 2),\n", + " (23, 2),\n", + " (24, 2),\n", + " (25, 3),\n", + " (26, 3),\n", + " (27, 3),\n", + " (28, 3),\n", + " (29, 3),\n", + " (30, 3),\n", + " (31, 3),\n", + " (32, 3),\n", + " (33, 3),\n", + " (34, 3),\n", + " (35, 3),\n", + " (36, 3),\n", + " (37, 3),\n", + " (38, 4),\n", + " (39, 4),\n", + " (40, 4),\n", + " (41, 4),\n", + " (42, 4),\n", + " (43, 4),\n", + " (44, 4),\n", + " (45, 4),\n", + " (46, 4),\n", + " (47, 4),\n", + " (48, 4),\n", + " (49, 4)]" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ensemble_predictions = []\n", + "\n", + "for instances_labels in ensemble_pred :\n", + " for class_label, class_count in class_instances_count(instances_labels) :\n", + " if class_label != 0 :\n", + " ensemble_predictions.append(class_label)\n", + " break\n", + " else :\n", + " continue\n", + " \n", + "\n", + "list(zip(range(0, 50), ensemble_predictions)) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:tf.kr]", + "language": "python", + "name": "conda-env-tf.kr-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Predict_save_SSL_STFT_Siamese_results.ipynb b/Predict_save_SSL_STFT_Siamese_results.ipynb new file mode 100644 index 0000000..1bd239f --- /dev/null +++ b/Predict_save_SSL_STFT_Siamese_results.ipynb @@ -0,0 +1,2281 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Predict" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('2.2.4-tf', '1.13.1')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Common imports\n", + "import numpy as np\n", + "import pandas as pd\n", + "import os, sys, glob \n", + "import tensorflow as tf\n", + "\n", + "import librosa\n", + "import librosa.display\n", + "\n", + "# To plot pretty figures\n", + "# import matplotlib\n", + "# import matplotlib.pyplot as plt\n", + "# %matplotlib inline\n", + "# plt.style.use('ggplot')\n", + "# plt.rcParams['axes.labelsize'] = 14\n", + "# plt.rcParams['xtick.labelsize'] = 12\n", + "# plt.rcParams['ytick.labelsize'] = 12\n", + "\n", + "def reset_graph(seed=42):\n", + " tf.reset_default_graph() \n", + " tf.set_random_seed(seed)\n", + " np.random.seed(seed)\n", + " \n", + "def reset_keras_session(seed=42):\n", + " tf.keras.backend.clear_session()\n", + " tf.set_random_seed(seed)\n", + " np.random.seed(seed)\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\") # To rid of warnings \n", + "\n", + "if sys.platform == 'win32': # if windows \n", + " home = os.path.join('D:', os.sep, 'hblee') # d:\\hblee\n", + " data_repo = os.path.join('D:', os.sep, 'Data_Repo_Win') # d:\\Data_Repo_Win\n", + "elif sys.platform == \"linux\" or sys.platform == \"linux2\" : # if linux \n", + " home = os.path.expanduser(\"~\") # home = os.getenv(\"HOME\")\n", + " data_repo = os.path.join(home, 'Data_Repo')\n", + " \n", + "#sys.path.append(os.path.join(home, 'Google_Sync', 'Dev_Exercise', 'utils'))\n", + "#from tf_utils import *\n", + " \n", + "from tensorflow import keras \n", + "keras.__version__, tf.VERSION" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "samples: 315 /home/user4/Downloads/audio/100도/output31.wav\n", + "samples segmented: 315 /home/user4/Downloads/binary_segment/100도/output31.npz\n" + ] + } + ], + "source": [ + "'''\n", + "samples : audio samples(files). 50 of them \n", + "samples_vad_seg : samples segmented as to voice region (1) and non-voice region (0) \n", + "\n", + "samples and samples_vad_seg should be aligned. \n", + "'''\n", + "\n", + "sample_data_repo = os.path.join(home, 'Downloads', 'audio')\n", + "samples = glob.glob(os.path.join(sample_data_repo, '**', '*wav'), recursive=True)\n", + "samples = sorted(samples) # sort the samples\n", + "\n", + "sample_vad_seg_repo = os.path.join(home, 'Downloads', 'binary_segment') # 적절하게 변경 필요 \n", + "samples_vad_seg = glob.glob(os.path.join(sample_vad_seg_repo, '**', '*[npy|npz]'), recursive=True)\n", + "samples_vad_seg = sorted(samples_vad_seg) \n", + "\n", + "# Checking \n", + "print('samples: ', len(samples), samples[25])\n", + "print('samples segmented: ', len(samples_vad_seg), samples_vad_seg[25]) " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def mag_phase(file_path, sr=48000, n_fft=1024, hop_length=512, db=False, n_mels=50) :\n", + " \"\"\"\n", + " stft의 magnitude와 phase 리턴\n", + " \"\"\"\n", + " audio, sr = librosa.load(file_path, sr=sr, mono=False) # 원래의 sr, stereo\n", + " DL = librosa.stft(audio[0], n_fft=n_fft, hop_length=hop_length)\n", + " DL_mag, DL_phase = librosa.magphase(DL)\n", + " \n", + " DR = librosa.stft(audio[1], n_fft=n_fft, hop_length=hop_length)\n", + " DR_mag, DR_phase = librosa.magphase(DR)\n", + " \n", + " if db :\n", + " DL_mag = librosa.core.amplitude_to_db(DL_mag)\n", + " DR_mag = librosa.core.amplitude_to_db(DR_mag)\n", + " \n", + " # rescale the right magnitudes w.r.t left channel magnitude \n", + " avg = DL_mag.mean() \n", + " stdv = DL_mag.std()\n", + " DL_mag = (DL_mag - avg)/stdv\n", + " DR_mag = (DR_mag - avg)/stdv\n", + " \n", + " # return( (DL_mag, np.angle(DL_phase)), (DR_mag, np.angle(DR_phase)) )\n", + " return( (DL_mag[1:, :], np.angle(DL_phase)[1:, :]), (DR_mag[1:, :], np.angle(DR_phase)[1:, :]) )" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def generatio_tensor_instances(array_2d, dest_path, seq_len, hop, label):\n", + " \"\"\"\n", + " array_2d : spectrogram.\n", + " seq_len : number of frames in a instance\n", + " label : 0 and 1's. The same length as original numpy vector \n", + " \"\"\"\n", + " row_size, col_size = array_2d.shape[0], array_2d.shape[1]\n", + " ratio = len(label)/col_size # ratio : how many data points per frame \n", + " stack_array = [] # 4D tensor that will hold the instances\n", + " label_array = []\n", + "\n", + " j=0\n", + " while j <= (col_size - (seq_len+1)): \n", + " context_frame = array_2d[:, j:(j+seq_len)]\n", + " # seg_label = round( label[int(j*ratio):int((j+seq_len)*ratio)].mean() ) \n", + " threshold = 0.5 # if greater than the threshold, then speech \n", + " seg_label = 1 if label[int(j*ratio):int((j+seq_len)*ratio)].mean() > threshold else 0\n", + "\n", + "# # store the instances\n", + "# dest_path_ext = ''.join([dest_path, '_', str(j)])\n", + "# os.makedirs(os.path.dirname(dest_path_ext), exist_ok=True)\n", + "\n", + "# np.savez(dest_path_ext, spectrogram = context_frame,\n", + "# label=seg_label)\n", + " \n", + " stack_array.append(context_frame[:,:,np.newaxis]) # make context_frame to 3d tensor & append \n", + " label_array.append(seg_label)\n", + " \n", + " j = j+hop\n", + " \n", + " return np.stack(stack_array, axis=0), label_array" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "315 315 315\n" + ] + }, + { + "data": { + "text/plain": [ + "((28, 512, 100, 1), (28, 512, 100, 1), (28,))" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "no_samples = len(samples) \n", + "\n", + "mag_L_instances = [] # elements are ndarrays\n", + "mag_R_instances = []\n", + "phase_L_instances = []\n", + "phase_R_instances = []\n", + "label_instances = [] # elements are lists\n", + "\n", + "for i in range(0, no_samples):\n", + " voice_noise_label = np.load(samples_vad_seg[i])\n", + " if('npy' in samples_vad_seg[i].split('/')[-1]):\n", + " label = voice_noise_label[0] # use the left channel label. this take care of 0 degree problem\n", + " else: # npz file\n", + " label = voice_noise_label[\"label\"] \n", + " (mag_L, phase_L), (mag_R, phase_R) = mag_phase(samples[i], db=True)\n", + " \n", + " # generate instances with 1.16 sec duration (100 frames), at every 0.116 sec apart (10 hops)\n", + " voice_dest_path = os.path.join(\"mag\", \"Left\", str(i))\n", + " mag_L_instances_sub, _ = generatio_tensor_instances(mag_L, voice_dest_path, 100, 10, label)\n", + " \n", + " voice_dest_path = os.path.join(\"mag\", \"Right\", str(i))\n", + " mag_R_instances_sub, _ = generatio_tensor_instances(mag_R, voice_dest_path, 100, 10, label)\n", + " \n", + " voice_dest_path = os.path.join(\"phase\", \"Left\", str(i))\n", + " phase_L_instances_sub, _ = generatio_tensor_instances(phase_L, voice_dest_path, 100, 10, label)\n", + " \n", + " voice_dest_path = os.path.join(\"phase\", \"Right\", str(i))\n", + " phase_R_instances_sub, label_sub = generatio_tensor_instances(phase_R, voice_dest_path, 100, 10, label)\n", + " \n", + " mag_L_instances.append(mag_L_instances_sub)\n", + " mag_R_instances.append(mag_R_instances_sub)\n", + " phase_L_instances.append(phase_L_instances_sub)\n", + " phase_R_instances.append(phase_R_instances_sub)\n", + " \n", + " label_instances.append(np.array(label_sub))\n", + " \n", + "\n", + "print(len(mag_L_instances), len(phase_R_instances), len(label_instances))\n", + "\n", + "mag_L_instances[0].shape, phase_R_instances[0].shape, label_instances[0].shape\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(315, (28, 512, 100, 4))" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stacked_instances = []\n", + "\n", + "for i in range(0, no_samples):\n", + " concat_tensor = np.concatenate([mag_L_instances[i], phase_L_instances[i], \n", + " mag_R_instances[i], phase_R_instances[i]], axis = -1)\n", + " stacked_instances.append(concat_tensor)\n", + " \n", + "len(stacked_instances), stacked_instances[0].shape # L, R magnitudes and phases are stacked." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "import copy\n", + "vad_label_instances = copy.deepcopy(label_instances)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(315,) (315,) (315,)\n" + ] + } + ], + "source": [ + "X = np.array(stacked_instances) # transform the list to ndarray\n", + "\n", + "y = np.array(label_instances)\n", + "\n", + "y_vad = np.array(vad_label_instances)\n", + "\n", + "print(X.shape, y.shape, y_vad.shape )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def predict_utterances(model_path, samples_instances, samples_indices) :\n", + " \"\"\"\n", + " samples_instances : ndarray holding 'samples' number of sample representation in ndarrays. \n", + " Each sample has the shape: (instances_in_sample, 512, 100, 4)\n", + " samples_indices : indices of the samples to consider in 'samples_instances'\n", + " \"\"\"\n", + " labels_pred = [] \n", + " \n", + " model = keras.models.load_model(model_path)\n", + " \n", + " X = samples_instances[[samples_indices]]\n", + " \n", + " for i, sample in enumerate(X) : # for the instances in each utterance sample \n", + " x_L = sample[:, :, :, :2]\n", + " x_R = sample[:, :, :, 2:]\n", + " \n", + " labels_pred.append( np.argmax(model.predict([x_L, x_R]), axis=1) )\n", + " \n", + " return np.array(labels_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "models = sorted(glob.glob(os.path.join('.', '*.h5')))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['./Best_SSL_STFT_Siamese_2DConv_1DConv.h5',\n", + " './Best_SSL_STFT_Siamese_2DConv_RNN.h5']" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "models" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n", + " 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n", + " 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n", + " 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n", + " 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,\n", + " 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,\n", + " 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,\n", + " 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103,\n", + " 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n", + " 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n", + " 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n", + " 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n", + " 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n", + " 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n", + " 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,\n", + " 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207,\n", + " 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220,\n", + " 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,\n", + " 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246,\n", + " 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259,\n", + " 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272,\n", + " 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285,\n", + " 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298,\n", + " 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311,\n", + " 312, 313, 314])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idx = np.arange(0, no_samples)\n", + "# idx = np.random.permutation(no_samples)\n", + "# test_idx = idx[-10:]\n", + "test_idx = idx\n", + "test_idx" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /home/user4/.conda/envs/tf.kr/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Colocations handled automatically by placer.\n", + "WARNING:tensorflow:From /home/user4/.conda/envs/tf.kr/lib/python3.7/site-packages/tensorflow/python/keras/layers/core.py:143: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n", + "WARNING:tensorflow:From /home/user4/.conda/envs/tf.kr/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.cast instead.\n" + ] + } + ], + "source": [ + "predictions=predict_utterances(models[0], X, test_idx) # test against the first model " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(28,) (28,)\n" + ] + }, + { + "data": { + "text/plain": [ + "[(0, 0),\n", + " (0, 0),\n", + " (0, 0),\n", + " (0, 0),\n", + " (0, 0),\n", + " (1, 0),\n", + " (1, 0),\n", + " (1, 0),\n", + " (1, 0),\n", + " (1, 0),\n", + " (1, 0),\n", + " (1, 0),\n", + " (1, 0),\n", + " (1, 0),\n", + " (1, 0),\n", + " (1, 0),\n", + " (1, 0),\n", + " (1, 0),\n", + " (1, 0),\n", + " (1, 0),\n", + " (1, 0),\n", + " (1, 0),\n", + " (1, 0),\n", + " (1, 0),\n", + " (0, 0),\n", + " (0, 0),\n", + " (0, 0),\n", + " (0, 0)]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample_index = 33\n", + "print(y[test_idx][sample_index].shape, predictions[sample_index].shape)\n", + "list(zip(y[test_idx][sample_index], predictions[sample_index]))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "def class_instances_count(sample_instances_labels) : \n", + " \"\"\"\n", + " sample_instances_labels : class labels for the instances\n", + " returns a list where elements are : (class_label, count)\n", + " \"\"\"\n", + " # sample_instances_labels : class labels for the instances \n", + " import operator\n", + " unique, counts = np.unique(sample_instances_labels, return_counts=True)\n", + " dict_temp = dict(zip(unique, counts))\n", + " return sorted(dict_temp.items(), key=operator.itemgetter(1), reverse=True) " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(0, 28)]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class_instances_count(predictions[sample_index])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ensemble " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "ensemble_pred = predict_utterances(models[0], X, test_idx)\n", + "\n", + "for model in models[1: ] :\n", + " predictions=predict_utterances(model, X, test_idx)\n", + " for sample_ind in range(0, len(test_idx)):\n", + " ensemble_pred[sample_ind] = np.concatenate([ensemble_pred[sample_ind], predictions[sample_ind]], \n", + " axis=-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": true + }, + "outputs": [ + 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ensemble_predictions)) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# making .json file" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "from collections import OrderedDict" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + "\t\"track3_results\": [\n", + "\t\t{\n", + "\t\t\t\"id\": 1,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 2,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 3,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 4,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 5,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 6,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + 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"\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 194,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 195,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 196,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 197,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 198,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 199,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 200,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 201,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 202,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 203,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 204,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 205,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 206,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 207,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 208,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 209,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 210,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 211,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 212,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 213,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 214,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 215,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 216,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 217,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + 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"\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 231,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 232,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 233,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 234,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 235,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 236,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 237,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 238,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 239,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 240,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 241,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 242,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 243,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 244,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 245,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 246,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 247,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 248,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 249,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 250,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 251,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 252,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 253,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 254,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 255,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 256,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 257,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 258,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 259,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 260,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 261,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 262,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 263,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 264,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 265,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 266,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 267,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 268,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 269,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 270,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 271,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 272,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 273,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 274,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 275,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 276,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 277,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 278,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 279,\n", + "\t\t\t\"angle\": 20\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 280,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 281,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 282,\n", + "\t\t\t\"angle\": 140\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 283,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 284,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 285,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 286,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 287,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 288,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 289,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 290,\n", + "\t\t\t\"angle\": 20\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 291,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 292,\n", + "\t\t\t\"angle\": 20\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 293,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 294,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 295,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 296,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 297,\n", + "\t\t\t\"angle\": 20\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 298,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 299,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 300,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 301,\n", + "\t\t\t\"angle\": 20\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 302,\n", + "\t\t\t\"angle\": 160\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 303,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 304,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 305,\n", + "\t\t\t\"angle\": 20\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 306,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 307,\n", + "\t\t\t\"angle\": 20\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 308,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 309,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 310,\n", + "\t\t\t\"angle\": 20\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 311,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 312,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 313,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 314,\n", + "\t\t\t\"angle\": -1\n", + "\t\t},\n", + "\t\t{\n", + "\t\t\t\"id\": 315,\n", + "\t\t\t\"angle\": -1\n", + "\t\t}\n", + "\t]\n", + "}\n" + ] + } + ], + "source": [ + "file_data = OrderedDict()\n", + "file_data['track3_results'] = []\n", + "ids = []\n", + "for i in range(no_samples) :\n", + " if (ensemble_predictions[i] == 0) :\n", + " ids.append({\"id\":i+1, \"angle\":-1})\n", + " \n", + " elif (ensemble_predictions[i] == 1) :\n", + " ids.append({\"id\":i+1, \"angle\":0})\n", + " \n", + " elif (ensemble_predictions[i] == 2) :\n", + " ids.append({\"id\":i+1, \"angle\":20})\n", + " \n", + " elif (ensemble_predictions[i] == 3) :\n", + " ids.append({\"id\":i+1, \"angle\":40})\n", + " \n", + " elif (ensemble_predictions[i] == 4) :\n", + " ids.append({\"id\":i+1, \"angle\":60})\n", + " \n", + " elif (ensemble_predictions[i] == 5):\n", + " ids.append({\"id\":i+1, \"angle\":80})\n", + " \n", + " elif (ensemble_predictions[i] == 6) :\n", + " ids.append({\"id\":i+1, \"angle\":100})\n", + " \n", + " elif (ensemble_predictions[i] == 7) :\n", + " ids.append({\"id\":i+1, \"angle\":120})\n", + " \n", + " elif (ensemble_predictions[i] == 8) :\n", + " ids.append({\"id\":i+1, \"angle\":140})\n", + " \n", + " elif (ensemble_predictions[i] == 9) :\n", + " ids.append({\"id\":i+1, \"angle\":160})\n", + " \n", + " elif (ensemble_predictions[i] == 10) :\n", + " ids.append({\"id\":i+1, \"angle\":180})\n", + " \n", + " \n", + "for i in range(no_samples) :\n", + " file_data['track3_results'].append(ids[i])\n", + "\n", + " \n", + "# check json file\n", + "# print(json.dumps(file_data, ensure_ascii=False, indent='\\t'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# saving json file" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "with open('results.json', 'w', encoding='utf-8') as make_file :\n", + " json.dump(file_data, make_file, ensure_ascii=False, indent='\\t')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:tf.kr]", + "language": "python", + "name": "conda-env-tf.kr-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/README.md b/README.md new file mode 100644 index 0000000..4ad9400 --- /dev/null +++ b/README.md @@ -0,0 +1,2 @@ +# AI-grand_challenge + AI-grand_challenge diff --git a/SSL_MRCG_(ConvRNN_n_2D1D-CNN).ipynb b/SSL_MRCG_(ConvRNN_n_2D1D-CNN).ipynb new file mode 100644 index 0000000..40b6b13 --- /dev/null +++ b/SSL_MRCG_(ConvRNN_n_2D1D-CNN).ipynb @@ -0,0 +1,1970 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
- last edit : 2019. 05. 20
\n", + "\n", + "## SSL (방위각 추정)\n", + "- Based on MRCG\n", + "- Using 2DCNN plus 1DCNN\n", + "\n", + "### Inputs : \n", + "- Input : MRCG of Left and Right Channels " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('2.1.6-tf', '1.12.0')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Common imports\n", + "import numpy as np\n", + "import pandas as pd\n", + "import os, sys, glob \n", + "import tensorflow as tf\n", + "\n", + "import librosa\n", + "import librosa.display\n", + "\n", + "# To plot pretty figures\n", + "# import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "# plt.style.use('ggplot')\n", + "plt.rcParams['axes.labelsize'] = 14\n", + "plt.rcParams['xtick.labelsize'] = 12\n", + "plt.rcParams['ytick.labelsize'] = 12\n", + "\n", + "def reset_graph(seed=42):\n", + " tf.reset_default_graph() \n", + " tf.set_random_seed(seed)\n", + " np.random.seed(seed)\n", + " \n", + "def reset_keras_session(seed=42):\n", + " tf.keras.backend.clear_session()\n", + " tf.set_random_seed(seed)\n", + " np.random.seed(seed)\n", + "\n", + "# import warnings\n", + "# warnings.filterwarnings(\"ignore\") # To rid of warnings \n", + "\n", + "os_sep = os.sep \n", + "\n", + "if sys.platform == 'win32': # if windows \n", + " home = os.path.join('D:', os.sep, 'hblee') # d:\\hblee\n", + " data_repo = os.path.join('D:', os.sep, 'Data_Repo_Win') # d:\\Data_Repo_Win\n", + "elif sys.platform == \"linux\" or sys.platform == \"linux2\" : # if linux \n", + " home = os.path.expanduser(\"~\") # home = os.getenv(\"HOME\")\n", + " data_repo = os.path.join(home, 'Data_Repo')\n", + " \n", + "sys.path.append(os.path.join(home, 'Google_Sync', 'Dev_Exercise', 'utils'))\n", + "# from tf_utils import *\n", + " \n", + "import MRCG as mrcg\n", + "import scipy.io.wavfile\n", + "import wave\n", + "import time\n", + "\n", + "from tensorflow import keras \n", + "keras.__version__, tf.VERSION" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def show_model_graph(model, to_file=\"./temp_model_plot.png\", show_shapes=True):\n", + " from IPython.display import Image\n", + " from tensorflow.keras.utils import plot_model\n", + " \n", + " plot_model(model, to_file, show_shapes=show_shapes)\n", + " return Image(to_file) " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50\n", + "50\n" + ] + } + ], + "source": [ + "sample_vad_seg_repo = os.path.join('..', 'Data', 'binary_segment') # 적절하게 변경 필요 \n", + "samples_vad_seg = glob.glob(os.path.join(sample_vad_seg_repo, '**', '*[npy|npz]'), recursive=True)\n", + "\n", + "samples_vad_seg = sorted(samples_vad_seg) \n", + "print(len(samples_vad_seg))\n", + "# list(enumerate(samples_vad_seg))\n", + "\n", + "sample_data_repo = os.path.join('..', 'Data', 'sample_data', 't3_audio')\n", + "samples = glob.glob(os.path.join(sample_data_repo, '**', '*wav'), recursive=True)\n", + "samples = sorted(samples) # sort the samples\n", + "print(len(samples))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('..\\\\Data\\\\binary_segment\\\\01.npy',\n", + " '..\\\\Data\\\\sample_data\\\\t3_audio\\\\t3_audio_0001.wav'),\n", + " ('..\\\\Data\\\\binary_segment\\\\08.npy',\n", + " '..\\\\Data\\\\sample_data\\\\t3_audio\\\\t3_audio_0008.wav'),\n", + " ('..\\\\Data\\\\binary_segment\\\\15.npy',\n", + " '..\\\\Data\\\\sample_data\\\\t3_audio\\\\t3_audio_0015.wav'),\n", + " ('..\\\\Data\\\\binary_segment\\\\22.npy',\n", + " '..\\\\Data\\\\sample_data\\\\t3_audio\\\\t3_audio_0022.wav'),\n", + " ('..\\\\Data\\\\binary_segment\\\\29.npy',\n", + " '..\\\\Data\\\\sample_data\\\\t3_audio\\\\t3_audio_0029.wav'),\n", + " ('..\\\\Data\\\\binary_segment\\\\36.npy',\n", + " '..\\\\Data\\\\sample_data\\\\t3_audio\\\\t3_audio_0036.wav'),\n", + " ('..\\\\Data\\\\binary_segment\\\\sample_data_43.npz',\n", + " '..\\\\Data\\\\sample_data\\\\t3_audio\\\\t3_audio_0043.wav'),\n", + " ('..\\\\Data\\\\binary_segment\\\\sample_data_50.npz',\n", + " '..\\\\Data\\\\sample_data\\\\t3_audio\\\\t3_audio_0050.wav')]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check if the \"samples_vad_seg\" and \"samples\" are aligned \n", + "list(zip(samples_vad_seg, samples))[::7]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Note ; `samples_vad_seg` 와 `samples` 가 align 되어야 함\n", + "\n", + "처음 12개 샘플 (0도)을 [0|1]* 로 레이블링한 것은 왼쪽 것을 사용해야 함. 오른쪽 채널을 모두 0 으로 하였기에." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2, 126155)\n", + "1.0 92610\n", + "0.0 33545\n", + "dtype: int64\n", + "\n", + "0.0 126155\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "# testing for a sample in 0-degree set\n", + "audio = np.load(samples_vad_seg[0])\n", + "print(audio.shape)\n", + "\n", + "print(pd.Series(audio[0]).value_counts())\n", + "print()\n", + "print(pd.Series(audio[1]).value_counts())" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def mrcg_generation(file_path, sr=44100):\n", + " \"\"\"\n", + " file_path : path to the audio file \n", + " \"\"\"\n", + " X, sample_rate = librosa.load(file_path, sr=sr, mono=False)\n", + " mrcg_L = mrcg.mrcg_extract(X[0], sample_rate)\n", + " mrcg_R = mrcg.mrcg_extract(X[1], sample_rate)\n", + " \n", + " return mrcg_L, mrcg_R\n", + "\n", + "def generate_instances(array_2d, seq_len, hop, label):\n", + " \"\"\"\n", + " array_2d : spectrogram or mrcg like (contrains frames generated from the audio file)\n", + " seq_len : number of frames in an instance\n", + " hop : jump between the instances \n", + " label : 0 and 1's. Has same length as original numpy vector from the audio \n", + " \"\"\"\n", + "\n", + " row_size, col_size = array_2d.shape[0], array_2d.shape[1]\n", + " ratio = len(label)/col_size # audio_samples per frame \n", + " stack_array = [] # 4D tensor that will hold the instances\n", + " label_array = []\n", + "\n", + " j=0\n", + " while j <= (col_size - (seq_len+1)): \n", + " context_frame = array_2d[:, j:(j+seq_len)]\n", + " seg_label = round(label[int(j*ratio):int((j+seq_len)*ratio)].mean()) \n", + " \n", + " stack_array.append(context_frame[:,:,np.newaxis]) # build context_frame to 3d tensor & append \n", + " label_array.append(seg_label)\n", + " \n", + " j = j+hop\n", + " \n", + " return np.stack(stack_array, axis=0), label_array # Return 4D tensor " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "\n", + "# samples_vad_seg : list with paths to a file that segments audio file/sample to noise and signal \n", + "# samples : list with paths to original audio files\n", + "\n", + "mrcg_L_tensor = [] # holds 1sec mcrg instances generated from the left, grouped with audio samples \n", + "mrcg_R_tensor = [] # holds 1sec mcrg instances generated from the right, grouped with audio samples \n", + "label_list = [] \n", + "\n", + "for i in range(0, 50): # 'samples_vad_seg' and 'samples' are aligned\n", + " print(\"%d, \"%(i), end='')\n", + " sn_label = np.load(samples_vad_seg[i])\n", + " if('npy' in samples_vad_seg[i].split('/')[-1]):\n", + " label = sn_label[0] # use the left channel label. this take care of 0 degree problem\n", + " else: # npz file\n", + " label = sn_label[\"label\"] \n", + " # label : holds 0/1 train that segments the audio sample to signal and noise\n", + " \n", + " # generate instance tensors with 1sec duration (100 frames), at every 0.1 sec apart (10 hops)\n", + " mrcg_L, mrcg_R = mrcg_generation(samples[i])\n", + " mrcg_L_stack, _ = generate_instances(mrcg_L, 100, 10, label)\n", + " mrcg_R_stack, label_array = generate_instances(mrcg_R, 100, 10, label)\n", + " \n", + " mrcg_L_tensor.append(mrcg_L_stack)\n", + " mrcg_R_tensor.append(mrcg_R_stack)\n", + " label_list.append(label_array)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have left, right, and label 1sec instances of mrcg represenations grouped in audio samples. \n", + " \n", + "#### Saving and restoring :" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# np.savez('mcrg_representation.npz', mrcg_L_tensor=mrcg_L_tensor, mrcg_R_tensor=mrcg_R_tensor, \n", + "# label_list=label_list)\n", + "\n", + "mrcg_L_tensor = np.load('mcrg_representation.npz')['mrcg_L_tensor']\n", + "mrcg_R_tensor = np.load('mcrg_representation.npz')['mrcg_R_tensor']\n", + "label_list = np.load('mcrg_representation.npz')['label_list']" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50 50 50\n" + ] + }, + { + "data": { + "text/plain": [ + "((14, 768, 100, 1),\n", + " (14, 768, 100, 1),\n", + " [0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(len(mrcg_L_tensor), len(mrcg_R_tensor), len(label_list))\n", + "\n", + "'''\n", + "checking what 18'th audio sample's mrcg representation looks like\n", + "- the audio sample generated 14 instances to each left and right channels\n", + "- Out of 14 instances, 3 are labeled Noise, and 11 as Signal(Voice)\n", + "'''\n", + "sample_index = 17\n", + "mrcg_L_tensor[sample_index].shape, mrcg_R_tensor[sample_index].shape, label_list[sample_index]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(50, (14, 768, 100, 2))" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Now stack the left and right instaces along the last dimension so that 'no_channels' becomes 2\n", + "total_instances_tensor = []\n", + "\n", + "for i in range(0, 50):\n", + " concat_tensor = np.concatenate([mrcg_L_tensor[i], mrcg_R_tensor[i]], axis=-1)\n", + " total_instances_tensor.append(concat_tensor)\n", + "\n", + "len(total_instances_tensor), total_instances_tensor[sample_index].shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`total_instances_tensor` has 50 elements, where each element holds instances produced from the corresponding audio" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "total_label = []\n", + "\n", + "for i in range(0, 50):\n", + " array_label = np.array(label_list[i]) # Convert to ndarrays\n", + " total_label.append(array_label)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7424703021349098" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Test: the ratio of instances with 0 or 1 label. 74% are labeled 1 (voice)\n", + "ave=[]\n", + "for arr in total_label:\n", + " ave.append(np.mean(arr))\n", + "\n", + "np.mean(ave)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(19, 768, 100, 2) (19,)\n", + "(15, 768, 100, 2) (15,)\n", + "(16, 768, 100, 2) (16,)\n", + "(30, 768, 100, 2) (30,)\n", + "(6, 768, 100, 2) (6,)\n", + "(10, 768, 100, 2) (10,)\n", + "(23, 768, 100, 2) (23,)\n", + "(14, 768, 100, 2) (14,)\n", + "(47, 768, 100, 2) (47,)\n", + "(19, 768, 100, 2) (19,)\n" + ] + } + ], + "source": [ + "# check\n", + "for i in range(0, 50, 5):\n", + " print(total_instances_tensor[i].shape, total_label[i].shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "각각의 음성 샘플에 대해 알맞게 데이터가 형성" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1023" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# the total number of instances generated:\n", + "val = 0\n", + "for i in range(0, 50):\n", + " val = val + total_instances_tensor[i].shape[0]\n", + " \n", + "val" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### noise와 voice 방향에 따라 labeling\n", + "- noise : 0 \n", + "- 0도 : 1 \n", + "- 60도 : 2 \n", + "- 120도 : 3 \n", + "- 180도 : 4 " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1.]),\n", + " array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0.]),\n", + " array([1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0.]),\n", + " array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 2., 2., 2., 2., 2., 2.,\n", + " 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 0., 0.]),\n", + " array([2., 2., 2., 2., 2., 2.]),\n", + " array([3., 3., 3., 3., 3., 3., 3., 3., 3., 0.], dtype=float32),\n", + " array([3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\n", + " 3., 3., 3., 3., 3., 3.], dtype=float32),\n", + " array([3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.],\n", + " dtype=float32),\n", + " array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 4.,\n", + " 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4.,\n", + " 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 0., 0.]),\n", + " array([4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4.,\n", + " 4., 0.])]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "for i in range(12,25):\n", + " for j in range(0, len(total_label[i])):\n", + " if(total_label[i][j] == 1):\n", + " total_label[i][j] = 2 # 60 degree to label 2\n", + " \n", + "for i in range(25,38):\n", + " for j in range(0, len(total_label[i])):\n", + " if(total_label[i][j] == 1):\n", + " total_label[i][j] = 3 # 120 degree to label 3\n", + " \n", + "for i in range(38,50):\n", + " for j in range(0, len(total_label[i])):\n", + " if(total_label[i][j] == 1):\n", + " total_label[i][j] = 4\n", + " \n", + "total_label[::5] # Checking ..." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(19, 768, 100, 2) (19,)\n", + "(16, 768, 100, 2) (16,)\n", + "(6, 768, 100, 2) (6,)\n", + "(23, 768, 100, 2) (23,)\n", + "(47, 768, 100, 2) (47,)\n" + ] + } + ], + "source": [ + "# Check again ...\n", + "for i in range(0, 50, 10):\n", + " print(total_instances_tensor[i].shape, total_label[i].shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have 1023 instances. And we have labeled them into 5 classes. Let's see how those labels are distributed." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0 276\n", + "1.0 155\n", + "2.0 165\n", + "3.0 234\n", + "4.0 193\n", + "dtype: int64" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "instances_labels = np.array([])\n", + "for audio_clip in total_label:\n", + " instances_labels = np.hstack([instances_labels, audio_clip])\n", + " \n", + "pd.Series(instances_labels).value_counts().sort_index() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Class-0 Noise instance has the greatest proportion\n", + "\n", + "### Now we have `total_instances_tensor & total_label`\n", + "- `total_instances_tensor` : List. 50 elements. Each element has instances of the audio \n", + "- `total_label` ; List. 50 elements \n", + "\n", + "## Construct `train and validation set` split.\n", + "- Out of instances generated from the 50 audio samples, we will take instances from 40 audio files for training set, and the remaining to the validation set. Try to mix them evenly.\n", + "- Note that `total_label` indices has : 0~11(Class-1), 12~24(Class-2), 23~37(Class-3), 38~49(Class-4) and Class-0 is assigned to the noise " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([24, 23, 35, 30, 26, 20, 18, 43, 37, 48, 13, 36, 14, 1, 12, 6, 49,\n", + " 0, 45, 27, 8, 3, 28, 42, 25, 17, 38, 39, 21, 4, 32, 11, 9, 7,\n", + " 16, 15, 41, 44, 33, 5, 47, 22, 2, 31, 10, 19, 40, 34, 46, 29]),\n", + " array([47, 22, 2, 31, 10, 19, 40, 34, 46, 29]))" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Convert to ndarrays \n", + "total_instances_tensors = np.array(total_instances_tensor)\n", + "total_label_tensors = np.array(total_label)\n", + "\n", + "# randomly choose indices to be split to training and validation set\n", + "np.random.seed(19)\n", + "idx = np.random.permutation(50)\n", + "idx, idx[-10:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "validation set \n", + "- 0도 - 2개 \n", + "- 60도 - 2개 \n", + "- 120도 - 3개 \n", + "- 180도 - 3개 " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(18, 768, 100, 2) (18,)\n", + "(24, 768, 100, 2) (24,)\n", + "(9, 768, 100, 2) (9,)\n", + "(9, 768, 100, 2) (9,)\n", + "(15, 768, 100, 2) (15,)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\user\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", + " \"\"\"Entry point for launching an IPython kernel.\n", + "C:\\Users\\user\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", + " \n" + ] + } + ], + "source": [ + "X = total_instances_tensors[[idx]] # Shuffle the data using fancy indexing\n", + "y = total_label_tensors[[idx]]\n", + "\n", + "# Test \n", + "for i in range(0, 50, 10):\n", + " print(X[i].shape, y[i].shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(781, 768, 100, 2) (781,)\n", + "(242, 768, 100, 2) (242,)\n" + ] + } + ], + "source": [ + "# Split to Train and Val set \n", + "X_train = np.concatenate(X[:40], axis=0)\n", + "y_train = np.concatenate(y[:40], axis=0)\n", + "\n", + "X_val = np.concatenate(X[40: ], axis=0)\n", + "y_val = np.concatenate(y[40: ], axis=0)\n", + "\n", + "print(X_train.shape, y_train.shape)\n", + "print(X_val.shape, y_val.shape)\n", + "\n", + "# Change numeric to categoricals\n", + "y_train = keras.utils.to_categorical(y_train, 5)\n", + "y_val = keras.utils.to_categorical(y_val, 5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 모델1. 2D CNN + Bidirectional GRU" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.layers import Bidirectional\n", + "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Input, Flatten, Dropout\n", + "from tensorflow.keras import layers, models" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "input_1 (InputLayer) (None, 768, 100, 2) 0 \n", + "_________________________________________________________________\n", + "conv2d (Conv2D) (None, 768, 100, 16) 304 \n", + "_________________________________________________________________\n", + "max_pooling2d (MaxPooling2D) (None, 256, 50, 16) 0 \n", + "_________________________________________________________________\n", + "conv2d_1 (Conv2D) (None, 256, 50, 32) 4640 \n", + "_________________________________________________________________\n", + "max_pooling2d_1 (MaxPooling2 (None, 85, 25, 32) 0 \n", + "_________________________________________________________________\n", + "conv2d_2 (Conv2D) (None, 85, 25, 64) 18496 \n", + "_________________________________________________________________\n", + "max_pooling2d_2 (MaxPooling2 (None, 28, 12, 64) 0 \n", + "_________________________________________________________________\n", + "conv2d_3 (Conv2D) (None, 28, 12, 128) 73856 \n", + "_________________________________________________________________\n", + "max_pooling2d_3 (MaxPooling2 (None, 9, 6, 128) 0 \n", + "_________________________________________________________________\n", + "conv2d_4 (Conv2D) (None, 1, 6, 512) 590336 \n", + "_________________________________________________________________\n", + "reshape (Reshape) (None, 6, 512) 0 \n", + "_________________________________________________________________\n", + "bidirectional (Bidirectional (None, 512) 1181184 \n", + "_________________________________________________________________\n", + "dense (Dense) (None, 32) 16416 \n", + "_________________________________________________________________\n", + "dropout (Dropout) (None, 32) 0 \n", + "_________________________________________________________________\n", + "dense_1 (Dense) (None, 5) 165 \n", + "=================================================================\n", + "Total params: 1,885,397\n", + "Trainable params: 1,885,397\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "if 'model' in locals():\n", + " del model\n", + "\n", + "input_spectrogram = Input(shape=(X_train.shape[1], X_train.shape[2], X_train.shape[3])) # 768x100x2\n", + "\n", + "conv_1 = Conv2D(16, (3, 3), activation='relu', padding='same')(input_spectrogram)\n", + "conv_1_pool = MaxPooling2D((3, 2))(conv_1)\n", + "\n", + "conv_2 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv_1_pool)\n", + "conv_2_pool = MaxPooling2D((3, 2))(conv_2)\n", + "\n", + "conv_3 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv_2_pool)\n", + "conv_3_pool = MaxPooling2D((3, 2))(conv_3)\n", + "\n", + "conv_4 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv_3_pool)\n", + "conv_4_pool = MaxPooling2D((3, 2))(conv_4)\n", + "\n", + "shape_conv_4_pool = conv_4_pool.get_shape().as_list() # (None, height, width, channel)\n", + "conv_5 = Conv2D(512, (shape_conv_4_pool[1], 1), padding='valid', activation='relu')(conv_4_pool)\n", + "shape_conv_5 = conv_5.get_shape().as_list()\n", + "\n", + "reshaped = layers.Reshape((shape_conv_5[2], shape_conv_5[3]))(conv_5) # reshape to (timesteps, features) explicitly \n", + "bgru = Bidirectional(layers.GRU(units=256))(reshaped)\n", + "\n", + "fc1 = layers.Dense(32, activation='relu')(bgru)\n", + "fc1_drop = Dropout(0.5)(fc1)\n", + "\n", + "dense_out = layers.Dense(5, activation='softmax')(fc1_drop)\n", + "\n", + "model = models.Model(inputs=input_spectrogram, outputs=dense_out)\n", + "model.summary() " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "show_model_graph(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 781 samples, validate on 242 samples\n", + "Epoch 1/150\n", + "781/781 [==============================] - 8s 10ms/step - loss: 2.4442 - acc: 0.2458 - val_loss: 1.6270 - val_acc: 0.2314\n", + "Epoch 2/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.6237 - acc: 0.1729 - val_loss: 1.6109 - val_acc: 0.2314\n", + "Epoch 3/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.6110 - acc: 0.1754 - val_loss: 1.6097 - val_acc: 0.1694\n", + "Epoch 4/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.6120 - acc: 0.2330 - val_loss: 1.6103 - val_acc: 0.2397\n", + "Epoch 5/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.6064 - acc: 0.2369 - val_loss: 1.6114 - val_acc: 0.2397\n", + "Epoch 6/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.6068 - acc: 0.2791 - val_loss: 1.6131 - val_acc: 0.2397\n", + "Epoch 7/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.6084 - acc: 0.2791 - val_loss: 1.6105 - val_acc: 0.2397\n", + "Epoch 8/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.6042 - acc: 0.2791 - val_loss: 1.6101 - val_acc: 0.2397\n", + "Epoch 9/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.6015 - acc: 0.2702 - val_loss: 1.6111 - val_acc: 0.2397\n", + "Epoch 10/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.6016 - acc: 0.2535 - val_loss: 1.6152 - val_acc: 0.2397\n", + "Epoch 11/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.6026 - acc: 0.2356 - val_loss: 1.6092 - val_acc: 0.2397\n", + "Epoch 12/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.5917 - acc: 0.2612 - val_loss: 1.6031 - val_acc: 0.2397\n", + "Epoch 13/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.5890 - acc: 0.2574 - val_loss: 1.6034 - val_acc: 0.2521\n", + "Epoch 14/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.5787 - acc: 0.2586 - val_loss: 1.6001 - val_acc: 0.2397\n", + "Epoch 15/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.5680 - acc: 0.2548 - val_loss: 1.5713 - val_acc: 0.2521\n", + "Epoch 16/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.5466 - acc: 0.3175 - val_loss: 1.5434 - val_acc: 0.4339\n", + "Epoch 17/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.5479 - acc: 0.3047 - val_loss: 1.4918 - val_acc: 0.3471\n", + "Epoch 18/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.4647 - acc: 0.3150 - val_loss: 1.9200 - val_acc: 0.3140\n", + "Epoch 19/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.6224 - acc: 0.3060 - val_loss: 1.4953 - val_acc: 0.2397\n", + "Epoch 20/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.5231 - acc: 0.2766 - val_loss: 1.5000 - val_acc: 0.3554\n", + "Epoch 21/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.5245 - acc: 0.3047 - val_loss: 1.5451 - val_acc: 0.1198\n", + "Epoch 22/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.4924 - acc: 0.2958 - val_loss: 1.4618 - val_acc: 0.3471\n", + "Epoch 23/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.3629 - acc: 0.3777 - val_loss: 1.3649 - val_acc: 0.3926\n", + "Epoch 24/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.3639 - acc: 0.3611 - val_loss: 1.2844 - val_acc: 0.3430\n", + "Epoch 25/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.3012 - acc: 0.3572 - val_loss: 1.3402 - val_acc: 0.3554\n", + "Epoch 26/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.3407 - acc: 0.3867 - val_loss: 1.3142 - val_acc: 0.4174\n", + "Epoch 27/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.2288 - acc: 0.4251 - val_loss: 1.1002 - val_acc: 0.5248\n", + "Epoch 28/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.1181 - acc: 0.4930 - val_loss: 1.0663 - val_acc: 0.4959\n", + "Epoch 29/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.0924 - acc: 0.4827 - val_loss: 1.0404 - val_acc: 0.5455\n", + "Epoch 30/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.9984 - acc: 0.5480 - val_loss: 0.9081 - val_acc: 0.5372\n", + "Epoch 31/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.8987 - acc: 0.5659 - val_loss: 0.7802 - val_acc: 0.6983\n", + "Epoch 32/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.9032 - acc: 0.5762 - val_loss: 0.8206 - val_acc: 0.6612\n", + "Epoch 33/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.8695 - acc: 0.5941 - val_loss: 0.7827 - val_acc: 0.6860\n", + "Epoch 34/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.8055 - acc: 0.6069 - val_loss: 0.7686 - val_acc: 0.7107\n", + "Epoch 35/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.7325 - acc: 0.6633 - val_loss: 0.6482 - val_acc: 0.7727\n", + "Epoch 36/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.7160 - acc: 0.6556 - val_loss: 0.7473 - val_acc: 0.7066\n", + "Epoch 37/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.7280 - acc: 0.6709 - val_loss: 0.9142 - val_acc: 0.6653\n", + "Epoch 38/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.6226 - acc: 0.7567 - val_loss: 0.7516 - val_acc: 0.7190\n", + "Epoch 39/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.5680 - acc: 0.7554 - val_loss: 0.5825 - val_acc: 0.7893\n", + "Epoch 40/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.4797 - acc: 0.7862 - val_loss: 0.6329 - val_acc: 0.7273\n", + "Epoch 41/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.4810 - acc: 0.7926 - val_loss: 0.7724 - val_acc: 0.7107\n", + "Epoch 42/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.4866 - acc: 0.7887 - val_loss: 0.7193 - val_acc: 0.7810\n", + "Epoch 43/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.4889 - acc: 0.7990 - val_loss: 0.8055 - val_acc: 0.7727\n", + "Epoch 44/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.4368 - acc: 0.8105 - val_loss: 0.6303 - val_acc: 0.7686\n", + "Epoch 45/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.4248 - acc: 0.8003 - val_loss: 0.9705 - val_acc: 0.7521\n", + "Epoch 46/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.4243 - acc: 0.8195 - val_loss: 0.5509 - val_acc: 0.8182\n", + "Epoch 47/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.5038 - acc: 0.7746 - val_loss: 1.4727 - val_acc: 0.6983\n", + "Epoch 48/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.7491 - acc: 0.7375 - val_loss: 0.5059 - val_acc: 0.8471\n", + "Epoch 49/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.4957 - acc: 0.7836 - val_loss: 0.7874 - val_acc: 0.8017\n", + "Epoch 50/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.7039 - acc: 0.7478 - val_loss: 0.7799 - val_acc: 0.7314\n", + "Epoch 51/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.5709 - acc: 0.7465 - val_loss: 0.5716 - val_acc: 0.7934\n", + "Epoch 52/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.4070 - acc: 0.8092 - val_loss: 0.7531 - val_acc: 0.7934\n", + "Epoch 53/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.3386 - acc: 0.8681 - val_loss: 0.5146 - val_acc: 0.8306\n", + "Epoch 54/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.3131 - acc: 0.8745 - val_loss: 0.7345 - val_acc: 0.8058\n", + "Epoch 55/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.2301 - acc: 0.9040 - val_loss: 0.7428 - val_acc: 0.8512\n", + "Epoch 56/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.2097 - acc: 0.9104 - val_loss: 1.0019 - val_acc: 0.7851\n", + "Epoch 57/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.2198 - acc: 0.8976 - val_loss: 0.8252 - val_acc: 0.8140\n", + "Epoch 58/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.2273 - acc: 0.9065 - val_loss: 0.9796 - val_acc: 0.8017\n", + "Epoch 59/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.1862 - acc: 0.9091 - val_loss: 0.7707 - val_acc: 0.8430\n", + "Epoch 60/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.1664 - acc: 0.9168 - val_loss: 1.1966 - val_acc: 0.7851\n", + "Epoch 61/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.1698 - acc: 0.9206 - val_loss: 1.0109 - val_acc: 0.8223\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 62/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.1274 - acc: 0.9437 - val_loss: 1.3082 - val_acc: 0.8017\n", + "Epoch 63/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.1455 - acc: 0.9309 - val_loss: 1.3312 - val_acc: 0.8017\n", + "Epoch 64/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.1125 - acc: 0.9488 - val_loss: 1.3211 - val_acc: 0.8058\n", + "Epoch 65/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.1213 - acc: 0.9462 - val_loss: 1.5457 - val_acc: 0.7975\n", + "Epoch 66/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.1022 - acc: 0.9603 - val_loss: 1.1388 - val_acc: 0.8223\n", + "Epoch 67/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.1026 - acc: 0.9552 - val_loss: 1.7957 - val_acc: 0.7851\n", + "Epoch 68/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0799 - acc: 0.9667 - val_loss: 1.7068 - val_acc: 0.7893\n", + "Epoch 69/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0889 - acc: 0.9590 - val_loss: 1.3465 - val_acc: 0.8182\n", + "Epoch 70/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0969 - acc: 0.9513 - val_loss: 1.6805 - val_acc: 0.8099\n", + "Epoch 71/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0772 - acc: 0.9577 - val_loss: 1.7066 - val_acc: 0.8017\n", + "Epoch 72/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0945 - acc: 0.9488 - val_loss: 1.6915 - val_acc: 0.8017\n", + "Epoch 73/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0854 - acc: 0.9603 - val_loss: 1.7423 - val_acc: 0.8099\n", + "Epoch 74/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0903 - acc: 0.9552 - val_loss: 1.9702 - val_acc: 0.7893\n", + "Epoch 75/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0716 - acc: 0.9654 - val_loss: 2.0655 - val_acc: 0.7769\n", + "Epoch 76/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0732 - acc: 0.9654 - val_loss: 1.6102 - val_acc: 0.8099\n", + "Epoch 77/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0784 - acc: 0.9680 - val_loss: 1.8804 - val_acc: 0.8058\n", + "Epoch 78/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0679 - acc: 0.9757 - val_loss: 2.1746 - val_acc: 0.7769\n", + "Epoch 79/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0811 - acc: 0.9782 - val_loss: 2.0473 - val_acc: 0.7934\n", + "Epoch 80/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0637 - acc: 0.9770 - val_loss: 1.9809 - val_acc: 0.8017\n", + "Epoch 81/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0690 - acc: 0.9782 - val_loss: 1.9330 - val_acc: 0.8099\n", + "Epoch 82/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0674 - acc: 0.9744 - val_loss: 1.9030 - val_acc: 0.8099\n", + "Epoch 83/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0907 - acc: 0.9629 - val_loss: 1.8849 - val_acc: 0.8099\n", + "Epoch 84/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0771 - acc: 0.9744 - val_loss: 1.8964 - val_acc: 0.8099\n", + "Epoch 85/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0565 - acc: 0.9821 - val_loss: 1.9087 - val_acc: 0.8099\n", + "Epoch 86/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0685 - acc: 0.9731 - val_loss: 1.9304 - val_acc: 0.8099\n", + "Epoch 87/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0841 - acc: 0.9680 - val_loss: 1.9565 - val_acc: 0.8099\n", + "Epoch 88/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0854 - acc: 0.9680 - val_loss: 1.9880 - val_acc: 0.8058\n", + "Epoch 89/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0803 - acc: 0.9718 - val_loss: 2.0420 - val_acc: 0.7975\n", + "Epoch 90/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0657 - acc: 0.9795 - val_loss: 2.0515 - val_acc: 0.7975\n", + "Epoch 91/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0631 - acc: 0.9757 - val_loss: 2.0553 - val_acc: 0.7975\n", + "Epoch 92/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0708 - acc: 0.9744 - val_loss: 2.0577 - val_acc: 0.7975\n", + "Epoch 93/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0627 - acc: 0.9795 - val_loss: 2.0660 - val_acc: 0.7975\n", + "Epoch 94/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0793 - acc: 0.9770 - val_loss: 2.0716 - val_acc: 0.8017\n", + "Epoch 95/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0475 - acc: 0.9859 - val_loss: 2.0762 - val_acc: 0.8017\n", + "Epoch 96/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0568 - acc: 0.9859 - val_loss: 2.1050 - val_acc: 0.7893\n", + "Epoch 97/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0697 - acc: 0.9706 - val_loss: 2.1053 - val_acc: 0.7851\n", + "Epoch 98/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0733 - acc: 0.9641 - val_loss: 2.0715 - val_acc: 0.7975\n", + "Epoch 99/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0732 - acc: 0.9706 - val_loss: 2.0455 - val_acc: 0.8017\n", + "Epoch 100/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0623 - acc: 0.9757 - val_loss: 2.0545 - val_acc: 0.7975\n", + "Epoch 101/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0673 - acc: 0.9834 - val_loss: 2.0114 - val_acc: 0.8099\n", + "Epoch 102/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0597 - acc: 0.9757 - val_loss: 2.0102 - val_acc: 0.8058\n", + "Epoch 103/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0790 - acc: 0.9693 - val_loss: 2.0415 - val_acc: 0.8017\n", + "Epoch 104/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0695 - acc: 0.9770 - val_loss: 2.0564 - val_acc: 0.7975\n", + "Epoch 105/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0666 - acc: 0.9808 - val_loss: 2.0625 - val_acc: 0.7975\n", + "Epoch 106/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0598 - acc: 0.9821 - val_loss: 2.0777 - val_acc: 0.7975\n", + "Epoch 107/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0518 - acc: 0.9795 - val_loss: 2.1275 - val_acc: 0.7934\n", + "Epoch 108/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0776 - acc: 0.9718 - val_loss: 2.1318 - val_acc: 0.7934\n", + "Epoch 109/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0608 - acc: 0.9808 - val_loss: 2.1294 - val_acc: 0.7934\n", + "Epoch 110/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0643 - acc: 0.9834 - val_loss: 2.1291 - val_acc: 0.7934\n", + "Epoch 111/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0536 - acc: 0.9834 - val_loss: 2.1291 - val_acc: 0.7934\n", + "Epoch 112/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0707 - acc: 0.9782 - val_loss: 2.1274 - val_acc: 0.7934\n", + "Epoch 113/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0684 - acc: 0.9795 - val_loss: 2.1227 - val_acc: 0.7975\n", + "Epoch 114/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0527 - acc: 0.9846 - val_loss: 2.1189 - val_acc: 0.7975\n", + "Epoch 115/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0829 - acc: 0.9641 - val_loss: 2.1209 - val_acc: 0.7975\n", + "Epoch 116/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0621 - acc: 0.9770 - val_loss: 2.1234 - val_acc: 0.7934\n", + "Epoch 117/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0611 - acc: 0.9770 - val_loss: 2.1223 - val_acc: 0.7934\n", + "Epoch 118/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0519 - acc: 0.9834 - val_loss: 2.1193 - val_acc: 0.7934\n", + "Epoch 119/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0765 - acc: 0.9770 - val_loss: 2.1156 - val_acc: 0.7934\n", + "Epoch 120/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0696 - acc: 0.9718 - val_loss: 2.1143 - val_acc: 0.7975\n", + "Epoch 121/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0617 - acc: 0.9795 - val_loss: 2.1121 - val_acc: 0.7975\n", + "Epoch 122/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0744 - acc: 0.9731 - val_loss: 2.1092 - val_acc: 0.7975\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 123/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0667 - acc: 0.9770 - val_loss: 2.1081 - val_acc: 0.7975\n", + "Epoch 124/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0840 - acc: 0.9706 - val_loss: 2.1109 - val_acc: 0.7975\n", + "Epoch 125/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0582 - acc: 0.9808 - val_loss: 2.1115 - val_acc: 0.7975\n" + ] + } + ], + "source": [ + "model.compile(optimizer ='adam',loss='categorical_crossentropy', metrics =['acc'])\n", + "\n", + "callbacks_list = [tf.keras.callbacks.EarlyStopping(monitor='loss', patience=30),\n", + " tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss',\n", + " factor=0.1, patience=30)]\n", + "\n", + "history = model.fit(X_train, y_train,\n", + " epochs=150, batch_size=128, \n", + " callbacks=callbacks_list,\n", + " validation_data=(X_val, y_val),\n", + " shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy : 0.7975206611570248\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[51, 2, 0, 0, 5],\n", + " [ 4, 13, 0, 0, 0],\n", + " [21, 0, 25, 0, 0],\n", + " [13, 0, 0, 52, 0],\n", + " [ 4, 0, 0, 0, 52]], dtype=int64)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sklearn.metrics \n", + "# from sklearn.metrics import confusion_matrix, classification_report\n", + "\n", + "y_val_pred = model.predict(X_val)\n", + "cm = sklearn.metrics.confusion_matrix(np.argmax(y_val, axis=1), \n", + " np.argmax(y_val_pred, axis=1))\n", + "acc = sklearn.metrics.accuracy_score(np.argmax(y_val, axis=1),\n", + " np.argmax(y_val_pred, axis=1))\n", + "print(\"Accuracy : \", acc)\n", + "model.save('SSL_MRCG_2DConv_RNN.h5') \n", + "cm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 모델2. 2D CNN + 1D CNN\n", + "- 처음에는 2D CNN을 써서 `주파수-시간` 2차원 도메인. 2D CNN 뒤에 1D CNN을 stack.\n", + "- 앞서 만든 데이터셋 활용 : `X_train, y_train, X_val, y_val` " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X_train : (781, 768, 100, 2)\n", + "y_train : (781, 5)\n", + "X_val : (242, 768, 100, 2)\n", + "y_val : (242, 5)\n" + ] + } + ], + "source": [ + "for data_set in [[X_train, 'X_train'], [y_train, 'y_train'], \n", + " [X_val,'X_val'], [y_val, 'y_val']]:\n", + " print(data_set[1], \": \", data_set[0].shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Version-1" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "input_2 (InputLayer) (None, 768, 100, 2) 0 \n", + "_________________________________________________________________\n", + "conv2d_5 (Conv2D) (None, 766, 98, 16) 304 \n", + "_________________________________________________________________\n", + "max_pooling2d_4 (MaxPooling2 (None, 255, 49, 16) 0 \n", + "_________________________________________________________________\n", + "conv2d_6 (Conv2D) (None, 253, 47, 64) 9280 \n", + "_________________________________________________________________\n", + "max_pooling2d_5 (MaxPooling2 (None, 84, 23, 64) 0 \n", + "_________________________________________________________________\n", + "conv2d_7 (Conv2D) (None, 84, 23, 128) 73856 \n", + "_________________________________________________________________\n", + "max_pooling2d_6 (MaxPooling2 (None, 21, 11, 128) 0 \n", + "_________________________________________________________________\n", + "conv2d_8 (Conv2D) (None, 21, 11, 256) 295168 \n", + "_________________________________________________________________\n", + "max_pooling2d_7 (MaxPooling2 (None, 5, 5, 256) 0 \n", + "_________________________________________________________________\n", + "conv2d_9 (Conv2D) (None, 1, 5, 512) 655872 \n", + "_________________________________________________________________\n", + "reshape_1 (Reshape) (None, 5, 512) 0 \n", + "_________________________________________________________________\n", + "conv1d (Conv1D) (None, 3, 512) 786944 \n", + "_________________________________________________________________\n", + "flatten (Flatten) (None, 1536) 0 \n", + "_________________________________________________________________\n", + "dropout_1 (Dropout) (None, 1536) 0 \n", + "_________________________________________________________________\n", + "dense_2 (Dense) (None, 64) 98368 \n", + "_________________________________________________________________\n", + "dropout_2 (Dropout) (None, 64) 0 \n", + "_________________________________________________________________\n", + "dense_3 (Dense) (None, 5) 325 \n", + "=================================================================\n", + "Total params: 1,920,117\n", + "Trainable params: 1,920,117\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "from tensorflow.keras.layers import Conv1D, MaxPooling1D, Input, Flatten, Dropout\n", + "\n", + "if 'model' in locals():\n", + " del model\n", + " \n", + "input_spectrogram = Input(shape=(X_train.shape[1], X_train.shape[2], X_train.shape[3]))\n", + "\n", + "conv_1 = Conv2D(16, (3, 3), activation='relu', padding='valid')(input_spectrogram)\n", + "conv_1_pool = MaxPooling2D((3, 2))(conv_1)\n", + "\n", + "conv_2 = Conv2D(64, (3, 3), activation='relu', padding='valid')(conv_1_pool)\n", + "conv_2_pool = MaxPooling2D((3, 2))(conv_2)\n", + "\n", + "conv_3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv_2_pool)\n", + "conv_3_pool = MaxPooling2D((4, 2))(conv_3)\n", + "\n", + "conv_4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv_3_pool)\n", + "conv_4_pool = MaxPooling2D((4, 2))(conv_4)\n", + "shape_conv_4_pool = conv_4_pool.get_shape().as_list() # (None, height, width, channel)\n", + "\n", + "conv_5 = Conv2D(512, (shape_conv_4_pool[1], 1), padding='valid', activation='relu')(conv_4_pool)\n", + "shape_conv_5 = conv_5.get_shape().as_list()\n", + "\n", + "reshaped = layers.Reshape((shape_conv_5[2], shape_conv_5[3]))(conv_5) # reshape to (timesteps, features) explicitly \n", + "\n", + "conv_6 = Conv1D(512, kernel_size=3, activation='relu')(reshaped)\n", + "flatten = layers.Flatten()(conv_6)\n", + "flatten_drop = Dropout(0.3)(flatten)\n", + "\n", + "fc1 = layers.Dense(64, activation='relu')(flatten_drop)\n", + "fc1_drop = Dropout(0.3)(fc1)\n", + "\n", + "dense_out = layers.Dense(5, activation='softmax')(fc1_drop)\n", + "\n", + "model = models.Model(inputs=input_spectrogram, outputs=dense_out)\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 781 samples, validate on 242 samples\n", + "Epoch 1/150\n", + "781/781 [==============================] - 5s 6ms/step - loss: 1.8627 - acc: 0.1908 - val_loss: 1.6192 - val_acc: 0.0702\n", + "Epoch 2/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.6156 - acc: 0.2074 - val_loss: 1.6067 - val_acc: 0.2397\n", + "Epoch 3/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.6023 - acc: 0.2817 - val_loss: 1.5888 - val_acc: 0.2603\n", + "Epoch 4/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.5781 - acc: 0.2945 - val_loss: 1.4863 - val_acc: 0.4008\n", + "Epoch 5/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.5412 - acc: 0.3124 - val_loss: 1.4713 - val_acc: 0.4050\n", + "Epoch 6/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.5222 - acc: 0.2919 - val_loss: 1.5012 - val_acc: 0.3512\n", + "Epoch 7/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.5096 - acc: 0.3316 - val_loss: 1.5992 - val_acc: 0.2562\n", + "Epoch 8/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.4856 - acc: 0.2753 - val_loss: 1.4591 - val_acc: 0.3678\n", + "Epoch 9/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.4258 - acc: 0.3239 - val_loss: 1.5439 - val_acc: 0.1446\n", + "Epoch 10/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.4191 - acc: 0.3303 - val_loss: 1.4673 - val_acc: 0.2314\n", + "Epoch 11/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.3680 - acc: 0.3470 - val_loss: 1.3455 - val_acc: 0.2893\n", + "Epoch 12/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.2785 - acc: 0.3611 - val_loss: 1.1729 - val_acc: 0.3843\n", + "Epoch 13/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.1486 - acc: 0.4763 - val_loss: 1.2368 - val_acc: 0.3802\n", + "Epoch 14/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.1720 - acc: 0.4328 - val_loss: 1.0488 - val_acc: 0.4711\n", + "Epoch 15/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.0066 - acc: 0.5608 - val_loss: 1.4138 - val_acc: 0.3182\n", + "Epoch 16/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.3917 - acc: 0.3329 - val_loss: 1.3982 - val_acc: 0.2686\n", + "Epoch 17/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.2317 - acc: 0.4174 - val_loss: 1.2843 - val_acc: 0.3802\n", + "Epoch 18/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.0610 - acc: 0.5544 - val_loss: 1.1900 - val_acc: 0.5579\n", + "Epoch 19/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.9472 - acc: 0.5928 - val_loss: 1.0458 - val_acc: 0.5124\n", + "Epoch 20/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.8611 - acc: 0.6172 - val_loss: 0.8920 - val_acc: 0.5661\n", + "Epoch 21/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.7354 - acc: 0.6735 - val_loss: 0.8006 - val_acc: 0.7025\n", + "Epoch 22/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.6775 - acc: 0.7119 - val_loss: 1.1127 - val_acc: 0.5331\n", + "Epoch 23/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.6996 - acc: 0.7247 - val_loss: 1.0769 - val_acc: 0.6736\n", + "Epoch 24/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.6353 - acc: 0.7516 - val_loss: 0.7163 - val_acc: 0.7438\n", + "Epoch 25/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.6728 - acc: 0.7196 - val_loss: 0.9830 - val_acc: 0.5950\n", + "Epoch 26/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.6935 - acc: 0.7234 - val_loss: 0.6844 - val_acc: 0.7479\n", + "Epoch 27/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.5316 - acc: 0.7695 - val_loss: 0.8206 - val_acc: 0.7025\n", + "Epoch 28/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.5920 - acc: 0.8067 - val_loss: 0.7461 - val_acc: 0.7231\n", + "Epoch 29/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.5311 - acc: 0.8131 - val_loss: 1.6412 - val_acc: 0.5702\n", + "Epoch 30/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.5686 - acc: 0.7913 - val_loss: 0.7301 - val_acc: 0.7355\n", + "Epoch 31/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.4116 - acc: 0.8476 - val_loss: 0.6678 - val_acc: 0.8017\n", + "Epoch 32/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.3120 - acc: 0.8796 - val_loss: 0.6255 - val_acc: 0.7810\n", + "Epoch 33/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.1910 - acc: 0.9334 - val_loss: 0.8740 - val_acc: 0.8017\n", + "Epoch 34/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.1410 - acc: 0.9501 - val_loss: 1.0953 - val_acc: 0.7975\n", + "Epoch 35/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.1047 - acc: 0.9616 - val_loss: 0.7670 - val_acc: 0.8306\n", + "Epoch 36/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.1265 - acc: 0.9565 - val_loss: 0.6848 - val_acc: 0.8099\n", + "Epoch 37/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.2030 - acc: 0.9232 - val_loss: 0.8914 - val_acc: 0.7810\n", + "Epoch 38/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.1310 - acc: 0.9616 - val_loss: 0.9194 - val_acc: 0.7769\n", + "Epoch 39/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.1372 - acc: 0.9590 - val_loss: 1.6132 - val_acc: 0.6983\n", + "Epoch 40/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.1741 - acc: 0.9373 - val_loss: 0.9467 - val_acc: 0.7397\n", + "Epoch 41/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.1066 - acc: 0.9654 - val_loss: 0.9768 - val_acc: 0.7686\n", + "Epoch 42/150\n", + "781/781 [==============================] - 3s 3ms/step - loss: 0.1200 - acc: 0.9603 - val_loss: 1.1025 - val_acc: 0.7810\n", + "Epoch 43/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0423 - acc: 0.9885 - val_loss: 1.1153 - val_acc: 0.7893\n", + "Epoch 44/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0434 - acc: 0.9898 - val_loss: 1.1018 - val_acc: 0.7934\n", + "Epoch 45/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0290 - acc: 0.9923 - val_loss: 1.0728 - val_acc: 0.7893\n", + "Epoch 46/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0430 - acc: 0.9898 - val_loss: 1.0893 - val_acc: 0.7934\n", + "Epoch 47/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0296 - acc: 0.9923 - val_loss: 1.1235 - val_acc: 0.7893\n", + "Epoch 48/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0314 - acc: 0.9898 - val_loss: 1.1701 - val_acc: 0.7893\n", + "Epoch 49/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0311 - acc: 0.9923 - val_loss: 1.1720 - val_acc: 0.7893\n", + "Epoch 50/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0280 - acc: 0.9910 - val_loss: 1.2221 - val_acc: 0.7810\n", + "Epoch 51/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0248 - acc: 0.9949 - val_loss: 1.2365 - val_acc: 0.7769\n", + "Epoch 52/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0213 - acc: 0.9974 - val_loss: 1.2553 - val_acc: 0.7810\n", + "Epoch 53/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0219 - acc: 0.9962 - val_loss: 1.2487 - val_acc: 0.7810\n", + "Epoch 54/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0262 - acc: 0.9923 - val_loss: 1.2481 - val_acc: 0.7810\n", + "Epoch 55/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0197 - acc: 0.9962 - val_loss: 1.2470 - val_acc: 0.7769\n", + "Epoch 56/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0253 - acc: 0.9923 - val_loss: 1.2397 - val_acc: 0.7769\n", + "Epoch 57/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0220 - acc: 0.9949 - val_loss: 1.2294 - val_acc: 0.7769\n", + "Epoch 58/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0234 - acc: 0.9974 - val_loss: 1.2239 - val_acc: 0.7769\n", + "Epoch 59/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0229 - acc: 0.9962 - val_loss: 1.2204 - val_acc: 0.7851\n", + "Epoch 60/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0198 - acc: 0.9962 - val_loss: 1.2170 - val_acc: 0.7851\n", + "Epoch 61/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0192 - acc: 0.9962 - val_loss: 1.2174 - val_acc: 0.7851\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 62/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0188 - acc: 0.9962 - val_loss: 1.2233 - val_acc: 0.7810\n", + "Epoch 63/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0290 - acc: 0.9910 - val_loss: 1.2235 - val_acc: 0.7810\n", + "Epoch 64/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0250 - acc: 0.9936 - val_loss: 1.2234 - val_acc: 0.7810\n", + "Epoch 65/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0213 - acc: 0.9962 - val_loss: 1.2236 - val_acc: 0.7810\n", + "Epoch 66/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0232 - acc: 0.9949 - val_loss: 1.2239 - val_acc: 0.7810\n", + "Epoch 67/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0152 - acc: 0.9987 - val_loss: 1.2241 - val_acc: 0.7810\n", + "Epoch 68/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0222 - acc: 0.9949 - val_loss: 1.2244 - val_acc: 0.7810\n", + "Epoch 69/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0184 - acc: 0.9962 - val_loss: 1.2247 - val_acc: 0.7810\n", + "Epoch 70/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0158 - acc: 0.9974 - val_loss: 1.2252 - val_acc: 0.7810\n", + "Epoch 71/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0188 - acc: 0.9987 - val_loss: 1.2251 - val_acc: 0.7810\n", + "Epoch 72/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0190 - acc: 0.9936 - val_loss: 1.2253 - val_acc: 0.7810\n", + "Epoch 73/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0211 - acc: 0.9949 - val_loss: 1.2253 - val_acc: 0.7810\n", + "Epoch 74/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0250 - acc: 0.9949 - val_loss: 1.2253 - val_acc: 0.7810\n", + "Epoch 75/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0230 - acc: 0.9949 - val_loss: 1.2254 - val_acc: 0.7810\n", + "Epoch 76/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0196 - acc: 0.9936 - val_loss: 1.2254 - val_acc: 0.7810\n", + "Epoch 77/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0233 - acc: 0.9923 - val_loss: 1.2254 - val_acc: 0.7810\n", + "Epoch 78/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0182 - acc: 0.9987 - val_loss: 1.2254 - val_acc: 0.7810\n", + "Epoch 79/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0192 - acc: 0.9974 - val_loss: 1.2254 - val_acc: 0.7810\n", + "Epoch 80/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0215 - acc: 0.9949 - val_loss: 1.2254 - val_acc: 0.7810\n", + "Epoch 81/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0253 - acc: 0.9923 - val_loss: 1.2255 - val_acc: 0.7810\n", + "Epoch 82/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0201 - acc: 0.9974 - val_loss: 1.2255 - val_acc: 0.7810\n", + "Epoch 83/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0191 - acc: 0.9987 - val_loss: 1.2255 - val_acc: 0.7810\n", + "Epoch 84/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0218 - acc: 0.9936 - val_loss: 1.2255 - val_acc: 0.7810\n", + "Epoch 85/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0169 - acc: 0.9974 - val_loss: 1.2255 - val_acc: 0.7810\n", + "Epoch 86/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0219 - acc: 0.9962 - val_loss: 1.2255 - val_acc: 0.7810\n", + "Epoch 87/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0242 - acc: 0.9923 - val_loss: 1.2255 - val_acc: 0.7810\n" + ] + } + ], + "source": [ + "model.compile(optimizer ='adam',loss='categorical_crossentropy', metrics =['acc'])\n", + "\n", + "callbacks_list = [keras.callbacks.EarlyStopping(monitor='loss', patience=20),\n", + " keras.callbacks.ReduceLROnPlateau(monitor='val_loss',\n", + " factor=0.1, patience=10)]\n", + "\n", + "history = model.fit(X_train, y_train,\n", + " epochs=150, batch_size=64, \n", + " callbacks=callbacks_list,\n", + " validation_data=(X_val, y_val),\n", + " shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy : 0.78099173553719\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[48, 2, 0, 4, 4],\n", + " [ 4, 13, 0, 0, 0],\n", + " [23, 0, 22, 1, 0],\n", + " [14, 0, 0, 51, 0],\n", + " [ 0, 0, 0, 1, 55]], dtype=int64)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sklearn.metrics \n", + "# from sklearn.metrics import confusion_matrix, classification_report\n", + "\n", + "y_val_pred = model.predict(X_val)\n", + "cm = sklearn.metrics.confusion_matrix(np.argmax(y_val, axis=1), \n", + " np.argmax(y_val_pred, axis=1))\n", + "acc = sklearn.metrics.accuracy_score(np.argmax(y_val, axis=1),\n", + " np.argmax(y_val_pred, axis=1))\n", + "print(\"Accuracy : \", acc)\n", + "model.save('SSL_MRCG_2DConv_1DConv.h5') \n", + "cm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Version-2" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape_max_pool_3 : [None, 15, 10, 64]\n", + "shape_conv_4 : [None, 1, 10, 1024]\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "input_1 (InputLayer) (None, 768, 100, 2) 0 \n", + "_________________________________________________________________\n", + "conv2d (Conv2D) (None, 766, 98, 16) 304 \n", + "_________________________________________________________________\n", + "max_pooling2d (MaxPooling2D) (None, 383, 49, 16) 0 \n", + "_________________________________________________________________\n", + "conv2d_1 (Conv2D) (None, 381, 47, 32) 4640 \n", + "_________________________________________________________________\n", + "max_pooling2d_1 (MaxPooling2 (None, 190, 23, 32) 0 \n", + "_________________________________________________________________\n", + "conv2d_2 (Conv2D) (None, 94, 21, 64) 18496 \n", + "_________________________________________________________________\n", + "conv2d_3 (Conv2D) (None, 46, 20, 64) 24640 \n", + "_________________________________________________________________\n", + "max_pool_3 (MaxPooling2D) (None, 15, 10, 64) 0 \n", + "_________________________________________________________________\n", + "conv2d_4 (Conv2D) (None, 1, 10, 1024) 984064 \n", + "_________________________________________________________________\n", + "reshape (Reshape) (None, 10, 1024) 0 \n", + "_________________________________________________________________\n", + "conv1d (Conv1D) (None, 8, 512) 1573376 \n", + "_________________________________________________________________\n", + "flatten (Flatten) (None, 4096) 0 \n", + "_________________________________________________________________\n", + "dense (Dense) (None, 32) 131104 \n", + "_________________________________________________________________\n", + "dropout (Dropout) (None, 32) 0 \n", + "_________________________________________________________________\n", + "dense_1 (Dense) (None, 5) 165 \n", + "=================================================================\n", + "Total params: 2,736,789\n", + "Trainable params: 2,736,789\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "reset_keras_session(100)\n", + "\n", + "if 'model' in locals():\n", + " del model\n", + " \n", + "input_spectrogram = Input(shape=(X_train.shape[1], X_train.shape[2], X_train.shape[3]))\n", + "\n", + "conv_1 = Conv2D(16, (3, 3), activation='relu')(input_spectrogram)\n", + "max_pool_1 = MaxPooling2D((2, 2))(conv_1)\n", + "\n", + "conv_2 = Conv2D(32, (3, 3), activation='relu')(max_pool_1)\n", + "max_pool_2 = MaxPooling2D((2, 2))(conv_2)\n", + "\n", + "conv_3 = Conv2D(64, (3, 3), strides=(2,1), activation='relu')(max_pool_2)\n", + "conv_3_1 = Conv2D(64, (3, 2), strides=(2,1), activation='relu')(conv_3)\n", + "max_pool_3 = MaxPooling2D((3, 2), name='max_pool_3')(conv_3_1)\n", + "\n", + "shape_max_pool_3 = max_pool_3.get_shape().as_list() # (None, height, width, channel)\n", + "print(\"shape_max_pool_3 : \", shape_max_pool_3)\n", + "# reshaped = layers.Reshape((-1, shape_list[1]*shape_list[3]))(max_pool_3)\n", + "\n", + "conv_4 = Conv2D(1024, (shape_max_pool_3[1], 1), padding='valid', activation='relu')(max_pool_3)\n", + "shape_conv_4 = conv_4.get_shape().as_list()\n", + "print(\"shape_conv_4 : \", shape_conv_4)\n", + "\n", + "reshaped = layers.Reshape((shape_conv_4[2], shape_conv_4[3]))(conv_4) # reshape to (timesteps, features) explicitly \n", + "\n", + "conv_5 = Conv1D(512, kernel_size=3, activation='relu')(reshaped)\n", + "\n", + "flatten = layers.Flatten()(conv_5)\n", + "# flatten_drop = Dropout(0.3)(flatten)\n", + "\n", + "fc1 = layers.Dense(32, activation='relu')(flatten)\n", + "fc1_drop = Dropout(0.1)(fc1)\n", + "dense_out = layers.Dense(5, activation='softmax')(fc1_drop)\n", + "\n", + "model = models.Model(inputs=input_spectrogram, outputs=dense_out)\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 781 samples, validate on 242 samples\n", + "Epoch 1/150\n", + "781/781 [==============================] - 4s 5ms/step - loss: 1.6582 - acc: 0.2023 - val_loss: 1.6416 - val_acc: 0.2397\n", + "Epoch 2/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.6149 - acc: 0.2561 - val_loss: 1.6208 - val_acc: 0.2397\n", + "Epoch 3/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.6034 - acc: 0.2804 - val_loss: 1.6599 - val_acc: 0.2397\n", + "Epoch 4/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.6028 - acc: 0.2702 - val_loss: 1.6192 - val_acc: 0.2397\n", + "Epoch 5/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.5803 - acc: 0.2778 - val_loss: 1.6122 - val_acc: 0.2397\n", + "Epoch 6/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.5447 - acc: 0.2907 - val_loss: 1.5811 - val_acc: 0.2397\n", + "Epoch 7/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.4996 - acc: 0.2996 - val_loss: 1.5370 - val_acc: 0.2769\n", + "Epoch 8/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.4388 - acc: 0.3560 - val_loss: 1.5151 - val_acc: 0.2686\n", + "Epoch 9/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.4807 - acc: 0.3047 - val_loss: 1.5357 - val_acc: 0.2025\n", + "Epoch 10/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.4186 - acc: 0.3713 - val_loss: 1.4479 - val_acc: 0.2686\n", + "Epoch 11/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.3251 - acc: 0.3739 - val_loss: 1.4169 - val_acc: 0.2769\n", + "Epoch 12/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.3250 - acc: 0.3572 - val_loss: 1.4078 - val_acc: 0.2521\n", + "Epoch 13/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.2351 - acc: 0.4110 - val_loss: 1.3854 - val_acc: 0.2727\n", + "Epoch 14/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.1897 - acc: 0.4136 - val_loss: 1.3413 - val_acc: 0.2851\n", + "Epoch 15/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.2120 - acc: 0.4200 - val_loss: 1.3406 - val_acc: 0.3719\n", + "Epoch 16/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.1663 - acc: 0.4699 - val_loss: 1.4276 - val_acc: 0.2562\n", + "Epoch 17/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.1031 - acc: 0.5122 - val_loss: 1.3372 - val_acc: 0.3471\n", + "Epoch 18/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 1.0104 - acc: 0.5698 - val_loss: 1.1651 - val_acc: 0.5413\n", + "Epoch 19/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.9019 - acc: 0.6082 - val_loss: 1.1978 - val_acc: 0.4793\n", + "Epoch 20/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.7916 - acc: 0.6581 - val_loss: 1.4117 - val_acc: 0.4339\n", + "Epoch 21/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.7327 - acc: 0.6620 - val_loss: 1.7775 - val_acc: 0.2893\n", + "Epoch 22/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.8356 - acc: 0.6018 - val_loss: 1.2626 - val_acc: 0.3967\n", + "Epoch 23/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.7703 - acc: 0.6594 - val_loss: 1.1338 - val_acc: 0.4050\n", + "Epoch 24/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.7452 - acc: 0.6658 - val_loss: 0.9563 - val_acc: 0.5785\n", + "Epoch 25/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.5832 - acc: 0.7746 - val_loss: 1.1631 - val_acc: 0.6364\n", + "Epoch 26/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.5591 - acc: 0.7862 - val_loss: 1.0294 - val_acc: 0.7066\n", + "Epoch 27/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.4463 - acc: 0.8220 - val_loss: 1.1896 - val_acc: 0.6694\n", + "Epoch 28/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.5832 - acc: 0.7900 - val_loss: 1.0657 - val_acc: 0.6364\n", + "Epoch 29/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.5295 - acc: 0.7721 - val_loss: 0.7717 - val_acc: 0.7107\n", + "Epoch 30/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.3643 - acc: 0.8604 - val_loss: 1.1368 - val_acc: 0.6694\n", + "Epoch 31/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.3065 - acc: 0.8745 - val_loss: 1.0086 - val_acc: 0.6281\n", + "Epoch 32/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.3052 - acc: 0.8694 - val_loss: 0.8558 - val_acc: 0.7231\n", + "Epoch 33/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.2635 - acc: 0.8963 - val_loss: 1.1982 - val_acc: 0.6570\n", + "Epoch 34/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.2276 - acc: 0.8988 - val_loss: 1.5228 - val_acc: 0.6488\n", + "Epoch 35/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.2967 - acc: 0.8822 - val_loss: 0.7645 - val_acc: 0.7727\n", + "Epoch 36/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.1980 - acc: 0.9283 - val_loss: 1.0783 - val_acc: 0.6488\n", + "Epoch 37/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.1615 - acc: 0.9347 - val_loss: 1.3431 - val_acc: 0.6860\n", + "Epoch 38/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.1432 - acc: 0.9488 - val_loss: 1.9602 - val_acc: 0.5744\n", + "Epoch 39/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.1428 - acc: 0.9373 - val_loss: 1.6288 - val_acc: 0.5909\n", + "Epoch 40/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.1723 - acc: 0.9334 - val_loss: 1.7206 - val_acc: 0.5992\n", + "Epoch 41/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.1324 - acc: 0.9539 - val_loss: 1.3986 - val_acc: 0.7562\n", + "Epoch 42/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.1236 - acc: 0.9552 - val_loss: 0.9453 - val_acc: 0.7355\n", + "Epoch 43/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.1026 - acc: 0.9590 - val_loss: 1.7326 - val_acc: 0.7107\n", + "Epoch 44/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.1162 - acc: 0.9577 - val_loss: 3.3009 - val_acc: 0.5909\n", + "Epoch 45/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.1396 - acc: 0.9539 - val_loss: 1.6922 - val_acc: 0.6983\n", + "Epoch 46/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.1324 - acc: 0.9577 - val_loss: 1.4523 - val_acc: 0.7479\n", + "Epoch 47/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0821 - acc: 0.9680 - val_loss: 1.3521 - val_acc: 0.7727\n", + "Epoch 48/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0638 - acc: 0.9782 - val_loss: 1.2620 - val_acc: 0.7727\n", + "Epoch 49/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0893 - acc: 0.9680 - val_loss: 1.2438 - val_acc: 0.7727\n", + "Epoch 50/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0795 - acc: 0.9629 - val_loss: 1.2943 - val_acc: 0.7727\n", + "Epoch 51/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0654 - acc: 0.9757 - val_loss: 1.3800 - val_acc: 0.7686\n", + "Epoch 52/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0620 - acc: 0.9744 - val_loss: 1.3651 - val_acc: 0.7769\n", + "Epoch 53/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0706 - acc: 0.9706 - val_loss: 1.3691 - val_acc: 0.7769\n", + "Epoch 54/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0601 - acc: 0.9744 - val_loss: 1.3794 - val_acc: 0.7686\n", + "Epoch 55/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0684 - acc: 0.9706 - val_loss: 1.4025 - val_acc: 0.7686\n", + "Epoch 56/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0622 - acc: 0.9757 - val_loss: 1.4095 - val_acc: 0.7686\n", + "Epoch 57/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0643 - acc: 0.9706 - val_loss: 1.4124 - val_acc: 0.7645\n", + "Epoch 58/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0601 - acc: 0.9757 - val_loss: 1.4143 - val_acc: 0.7645\n", + "Epoch 59/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0563 - acc: 0.9770 - val_loss: 1.4197 - val_acc: 0.7645\n", + "Epoch 60/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0771 - acc: 0.9693 - val_loss: 1.4258 - val_acc: 0.7645\n", + "Epoch 61/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0543 - acc: 0.9782 - val_loss: 1.4250 - val_acc: 0.7645\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 62/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0535 - acc: 0.9821 - val_loss: 1.4253 - val_acc: 0.7645\n", + "Epoch 63/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0699 - acc: 0.9718 - val_loss: 1.4210 - val_acc: 0.7645\n", + "Epoch 64/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0530 - acc: 0.9770 - val_loss: 1.4187 - val_acc: 0.7645\n", + "Epoch 65/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0640 - acc: 0.9693 - val_loss: 1.4172 - val_acc: 0.7645\n", + "Epoch 66/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0540 - acc: 0.9795 - val_loss: 1.4172 - val_acc: 0.7645\n", + "Epoch 67/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0737 - acc: 0.9693 - val_loss: 1.4170 - val_acc: 0.7645\n", + "Epoch 68/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0657 - acc: 0.9731 - val_loss: 1.4168 - val_acc: 0.7645\n", + "Epoch 69/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0693 - acc: 0.9680 - val_loss: 1.4171 - val_acc: 0.7645\n", + "Epoch 70/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0696 - acc: 0.9706 - val_loss: 1.4179 - val_acc: 0.7645\n", + "Epoch 71/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0735 - acc: 0.9680 - val_loss: 1.4182 - val_acc: 0.7686\n", + "Epoch 72/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0547 - acc: 0.9782 - val_loss: 1.4187 - val_acc: 0.7686\n", + "Epoch 73/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0662 - acc: 0.9731 - val_loss: 1.4191 - val_acc: 0.7686\n", + "Epoch 74/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0497 - acc: 0.9821 - val_loss: 1.4200 - val_acc: 0.7686\n", + "Epoch 75/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0702 - acc: 0.9744 - val_loss: 1.4205 - val_acc: 0.7686\n", + "Epoch 76/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0699 - acc: 0.9706 - val_loss: 1.4206 - val_acc: 0.7686\n", + "Epoch 77/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0621 - acc: 0.9706 - val_loss: 1.4206 - val_acc: 0.7686\n", + "Epoch 78/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0636 - acc: 0.9718 - val_loss: 1.4206 - val_acc: 0.7686\n", + "Epoch 79/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0637 - acc: 0.9770 - val_loss: 1.4206 - val_acc: 0.7686\n", + "Epoch 80/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0637 - acc: 0.9744 - val_loss: 1.4206 - val_acc: 0.7686\n", + "Epoch 81/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0617 - acc: 0.9782 - val_loss: 1.4206 - val_acc: 0.7686\n", + "Epoch 82/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0614 - acc: 0.9731 - val_loss: 1.4206 - val_acc: 0.7686\n", + "Epoch 83/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0588 - acc: 0.9757 - val_loss: 1.4206 - val_acc: 0.7686\n", + "Epoch 84/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0592 - acc: 0.9744 - val_loss: 1.4206 - val_acc: 0.7686\n", + "Epoch 85/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0682 - acc: 0.9680 - val_loss: 1.4207 - val_acc: 0.7686\n", + "Epoch 86/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0497 - acc: 0.9834 - val_loss: 1.4207 - val_acc: 0.7686\n", + "Epoch 87/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0513 - acc: 0.9770 - val_loss: 1.4207 - val_acc: 0.7686\n", + "Epoch 88/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0579 - acc: 0.9731 - val_loss: 1.4207 - val_acc: 0.7686\n", + "Epoch 89/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0719 - acc: 0.9667 - val_loss: 1.4207 - val_acc: 0.7686\n", + "Epoch 90/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0720 - acc: 0.9693 - val_loss: 1.4207 - val_acc: 0.7686\n", + "Epoch 91/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0642 - acc: 0.9718 - val_loss: 1.4207 - val_acc: 0.7686\n", + "Epoch 92/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0528 - acc: 0.9757 - val_loss: 1.4207 - val_acc: 0.7686\n", + "Epoch 93/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0741 - acc: 0.9680 - val_loss: 1.4207 - val_acc: 0.7686\n", + "Epoch 94/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0754 - acc: 0.9641 - val_loss: 1.4207 - val_acc: 0.7686\n", + "Epoch 95/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0691 - acc: 0.9693 - val_loss: 1.4207 - val_acc: 0.7686\n", + "Epoch 96/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0882 - acc: 0.9616 - val_loss: 1.4207 - val_acc: 0.7686\n", + "Epoch 97/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0640 - acc: 0.9718 - val_loss: 1.4207 - val_acc: 0.7686\n", + "Epoch 98/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0721 - acc: 0.9667 - val_loss: 1.4207 - val_acc: 0.7686\n", + "Epoch 99/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0700 - acc: 0.9770 - val_loss: 1.4207 - val_acc: 0.7686\n", + "Epoch 100/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0662 - acc: 0.9680 - val_loss: 1.4207 - val_acc: 0.7686\n", + "Epoch 101/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0704 - acc: 0.9718 - val_loss: 1.4207 - val_acc: 0.7686\n", + "Epoch 102/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0684 - acc: 0.9641 - val_loss: 1.4207 - val_acc: 0.7686\n", + "Epoch 103/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0606 - acc: 0.9795 - val_loss: 1.4207 - val_acc: 0.7686\n", + "Epoch 104/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0611 - acc: 0.9770 - val_loss: 1.4207 - val_acc: 0.7686\n", + "Epoch 105/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0541 - acc: 0.9795 - val_loss: 1.4207 - val_acc: 0.7686\n", + "Epoch 106/150\n", + "781/781 [==============================] - 2s 3ms/step - loss: 0.0557 - acc: 0.9770 - val_loss: 1.4207 - val_acc: 0.7686\n" + ] + } + ], + "source": [ + "model.compile(optimizer ='adam',loss='categorical_crossentropy', metrics =['acc'])\n", + "\n", + "callbacks_list = [keras.callbacks.EarlyStopping(monitor='loss', patience=20),\n", + " keras.callbacks.ReduceLROnPlateau(monitor='val_loss',\n", + " factor=0.1, patience=10)]\n", + "\n", + "history = model.fit(X_train, y_train,\n", + " epochs=150, batch_size=64, \n", + " callbacks=callbacks_list,\n", + " validation_data=(X_val, y_val),\n", + " shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy : 0.8057851239669421\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[50, 3, 0, 1, 4],\n", + " [ 3, 14, 0, 0, 0],\n", + " [17, 0, 26, 3, 0],\n", + " [14, 0, 0, 51, 0],\n", + " [ 2, 0, 0, 0, 54]], dtype=int64)" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sklearn.metrics \n", + "# from sklearn.metrics import confusion_matrix, classification_report\n", + "\n", + "y_val_pred = model.predict(X_val)\n", + "cm = sklearn.metrics.confusion_matrix(np.argmax(y_val, axis=1), \n", + " np.argmax(y_val_pred, axis=1))\n", + "acc = sklearn.metrics.accuracy_score(np.argmax(y_val, axis=1),\n", + " np.argmax(y_val_pred, axis=1))\n", + "print(\"Accuracy : \", acc)\n", + "model.save('SSL_MRCG_2DConv_1DConv.h5') \n", + "cm" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tensorflow", + "language": "python", + "name": "tensorflow" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/SSL_STFT_(ConvRNN_n_2D1D-CNN).ipynb b/SSL_STFT_(ConvRNN_n_2D1D-CNN).ipynb new file mode 100644 index 0000000..151180c --- /dev/null +++ b/SSL_STFT_(ConvRNN_n_2D1D-CNN).ipynb @@ -0,0 +1,3370 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
- last read : 2019. 05. 18
\n", + "\n", + "## SSL (방위각 추정)\n", + "- Based on Spectrogram and Phase\n", + "- 2 Models: Convolutional GRU & 2D-CNN then 2D-CNN\n", + "\n", + "### Inputs : \n", + "- Input : Magnitude and Phase from two stereo channels : So there are 4 feature maps\n", + "- Magnitude, Phase : Basic Magnitude and Phase with STFT. \n", + "- STFT : n_fft=1024, hop = 512 with sr=44100\n", + "- Stack 4 feature maps. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('2.2.4-tf', '1.13.1')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Common imports\n", + "import numpy as np\n", + "import pandas as pd\n", + "import os, sys, glob \n", + "import tensorflow as tf\n", + "\n", + "import librosa\n", + "import librosa.display\n", + "\n", + "# To plot pretty figures\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "# plt.style.use('ggplot')\n", + "plt.rcParams['axes.labelsize'] = 14\n", + "plt.rcParams['xtick.labelsize'] = 12\n", + "plt.rcParams['ytick.labelsize'] = 12\n", + "\n", + "def reset_graph(seed=42):\n", + " tf.reset_default_graph() \n", + " tf.set_random_seed(seed)\n", + " np.random.seed(seed)\n", + " \n", + "def reset_keras_session(seed=42):\n", + " tf.keras.backend.clear_session()\n", + " tf.set_random_seed(seed)\n", + " np.random.seed(seed)\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\") # To rid of warnings \n", + "\n", + "os_sep = os.sep \n", + "\n", + "if sys.platform == 'win32': # if windows \n", + " home = os.path.join('D:', os.sep, 'hblee') # d:\\hblee\n", + " data_repo = os.path.join('D:', os.sep, 'Data_Repo_Win') # d:\\Data_Repo_Win\n", + "elif sys.platform == \"linux\" or sys.platform == \"linux2\" : # if linux \n", + " home = os.path.expanduser(\"~\") # home = os.getenv(\"HOME\")\n", + " data_repo = os.path.join(home, 'Data_Repo')\n", + " \n", + "#sys.path.append(os.path.join(home, 'Google_Sync', 'Dev_Exercise', 'utils'))\n", + " \n", + "from tensorflow import keras \n", + "keras.__version__, tf.VERSION" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "samples: 315 /home/user4/Downloads/audio/100도/output31.wav\n", + "samples segmented: 315 /home/user4/Downloads/binary_segment/100도/output31.npz\n" + ] + } + ], + "source": [ + "\n", + "sample_data_repo = os.path.join(home, 'Downloads', 'audio')\n", + "samples = glob.glob(os.path.join(sample_data_repo, '**', '*wav'), recursive=True)\n", + "samples = sorted(samples) # sort the samples\n", + "\n", + "sample_vad_seg_repo = os.path.join(home, 'Downloads', 'binary_segment') # 적절하게 변경 필요 \n", + "samples_vad_seg = glob.glob(os.path.join(sample_vad_seg_repo, '**', '*[npy|npz]'), recursive=True)\n", + "samples_vad_seg = sorted(samples_vad_seg) \n", + "\n", + "# Checking \n", + "print('samples: ', len(samples), samples[25])\n", + "print('samples segmented: ', len(samples_vad_seg), samples_vad_seg[25]) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# train data set 만들기" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def mag_phase(file_path, sr=48000, n_fft=1024, hop_length=512, db=False, n_mels=50) :\n", + " \"\"\"\n", + " stft의 magnitude와 phase 리턴. 첫번째 bin 제거 \n", + " input : file path to an audio sample. Assumed stereo. \n", + " \"\"\"\n", + " audio, sr = librosa.load(file_path, sr=sr, mono=False) # 원래의 sr, stereo\n", + " DL = librosa.stft(audio[0], n_fft=n_fft, hop_length=hop_length)\n", + " DL_mag, DL_phase = librosa.magphase(DL)\n", + " \n", + " DR = librosa.stft(audio[1], n_fft=n_fft, hop_length=hop_length)\n", + " DR_mag, DR_phase = librosa.magphase(DR)\n", + " \n", + " if db :\n", + " DL_mag = librosa.core.amplitude_to_db(DL_mag)\n", + " DR_mag = librosa.core.amplitude_to_db(DR_mag)\n", + " \n", + " # rescale the right magnitudes w.r.t left channel magnitude \n", + " avg = DL_mag.mean() \n", + " stdv = DL_mag.std()\n", + " DL_mag = (DL_mag - avg)/stdv\n", + " DR_mag = (DR_mag - avg)/stdv\n", + " \n", + " # return( (DL_mag, np.angle(DL_phase)), (DR_mag, np.angle(DR_phase)) )\n", + " return( (DL_mag[1:, :], np.angle(DL_phase)[1:, :]), (DR_mag[1:, :], np.angle(DR_phase)[1:, :]) )" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def generatio_tensor_instances(array_2d, dest_path, seq_len, hop, label):\n", + " \"\"\"\n", + " array_2d : spectrogram.\n", + " seq_len : number of frames in a instance\n", + " label : 0 and 1's. The same length as original numpy vector \n", + " \"\"\"\n", + " row_size, col_size = array_2d.shape[0], array_2d.shape[1]\n", + " ratio = len(label)/col_size # ratio : how many data points per frame \n", + " stack_array = [] # 4D tensor that will hold the instances\n", + " label_array = []\n", + "\n", + " j=0\n", + " while j <= (col_size - (seq_len+1)): \n", + " context_frame = array_2d[:, j:(j+seq_len)]\n", + " seg_label = round( label[int(j*ratio):int((j+seq_len)*ratio)].mean() ) # 이 것 바꿀 필요 \n", + " \n", + " dest_path_ext = ''.join([dest_path, '_', str(j)])\n", + " os.makedirs(os.path.dirname(dest_path_ext), exist_ok=True)\n", + "\n", + " np.savez(dest_path_ext, spectrogram = context_frame,\n", + " label=seg_label)\n", + " \n", + " stack_array.append(context_frame[:,:,np.newaxis]) # make context_frame to 3d tensor & append \n", + " label_array.append(seg_label)\n", + " \n", + " j = j+hop\n", + " \n", + " return np.stack(stack_array, axis=0), label_array" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 각각의 샘플에 대해 mag_phase() 함수와 generate_instances() 함수를 사용하고 마지막 차원에 대해 concatenate하여 4d tensor를 만들어준다." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "315 315 315\n" + ] + }, + { + "data": { + "text/plain": [ + "((28, 512, 100, 1), (28, 512, 100, 1), (28,))" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "no_samples = len(samples) \n", + "\n", + "mag_L_instances = [] # elements are ndarrays\n", + "mag_R_instances = []\n", + "phase_L_instances = []\n", + "phase_R_instances = []\n", + "label_instances = [] # elements are lists\n", + "\n", + "for i in range(0, no_samples):\n", + " voice_noise_label = np.load(samples_vad_seg[i])\n", + " if('npy' in samples_vad_seg[i].split('/')[-1]):\n", + " label = voice_noise_label[0] # use the left channel label. this take care of 0 degree problem\n", + " else: # npz file\n", + " label = voice_noise_label[\"label\"] \n", + " (mag_L, phase_L), (mag_R, phase_R) = mag_phase(samples[i], db=True)\n", + " \n", + " # generate instances with 1.16 sec duration (100 frames), at every 0.116 sec apart (10 hops)\n", + " voice_dest_path = os.path.join(\"mag\", \"Left\", str(i))\n", + " mag_L_instances_sub, _ = generatio_tensor_instances(mag_L, voice_dest_path, 100, 10, label)\n", + " \n", + " voice_dest_path = os.path.join(\"mag\", \"Right\", str(i))\n", + " mag_R_instances_sub, _ = generatio_tensor_instances(mag_R, voice_dest_path, 100, 10, label)\n", + " \n", + " voice_dest_path = os.path.join(\"phase\", \"Left\", str(i))\n", + " phase_L_instances_sub, _ = generatio_tensor_instances(phase_L, voice_dest_path, 100, 10, label)\n", + " \n", + " voice_dest_path = os.path.join(\"phase\", \"Right\", str(i))\n", + " phase_R_instances_sub, label_sub = generatio_tensor_instances(phase_R, voice_dest_path, 100, 10, label)\n", + " \n", + " mag_L_instances.append(mag_L_instances_sub)\n", + " mag_R_instances.append(mag_R_instances_sub)\n", + " phase_L_instances.append(phase_L_instances_sub)\n", + " phase_R_instances.append(phase_R_instances_sub)\n", + " \n", + " label_instances.append(np.array(label_sub))\n", + " \n", + "\n", + "print(len(mag_L_instances), len(phase_R_instances), len(label_instances))\n", + "\n", + "mag_L_instances[0].shape, phase_R_instances[0].shape, label_instances[0].shape\n", + "# the first sample produced 15 instances. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(315, (28, 512, 100, 4))" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stacked_instances = []\n", + "\n", + "for i in range(0, no_samples):\n", + " concat_tensor = np.concatenate([mag_L_instances[i], phase_L_instances[i], \n", + " mag_R_instances[i], phase_R_instances[i]], axis = -1)\n", + " stacked_instances.append(concat_tensor)\n", + " \n", + "len(stacked_instances), stacked_instances[0].shape # L, R magnitudes and phases are stacked." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Numer of the instances generated : : 30862\n", + "Percentage of voice instances: 0.4461591195302443\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(743, 512, 100, 4) (743,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(179, 512, 100, 4) (179,)\n", + "(179, 512, 100, 4) (179,)\n", + "(179, 512, 100, 4) (179,)\n", + "(743, 512, 100, 4) (743,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(743, 512, 100, 4) (743,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(179, 512, 100, 4) (179,)\n", + "(743, 512, 100, 4) (743,)\n", + "(17, 512, 100, 4) (17,)\n", + "(13, 512, 100, 4) (13,)\n", + "(14, 512, 100, 4) (14,)\n", + "(27, 512, 100, 4) (27,)\n", + "(5, 512, 100, 4) (5,)\n", + "(9, 512, 100, 4) (9,)\n", + "(21, 512, 100, 4) (21,)\n", + "(12, 512, 100, 4) (12,)\n", + "(43, 512, 100, 4) (43,)\n", + "(17, 512, 100, 4) (17,)\n" + ] + } + ], + "source": [ + "# the total number of instances generated:\n", + "total = 0\n", + "for i in range(0, no_samples):\n", + " total = total + stacked_instances[i].shape[0]\n", + "print(\"Numer of the instances generated : : \",total) \n", + "\n", + "# the ratio of instances with 0 or 1 label. 76% of the instances are labeled 1 (voice)\n", + "ave=[]\n", + "for sample in label_instances:\n", + " ave.append(np.mean(sample))\n", + "print(\"Percentage of voice instances: \", np.mean(ave) )\n", + "\n", + "# Check the shapes \n", + "for i in range(0, no_samples, 5):\n", + " print(stacked_instances[i].shape, label_instances[i].shape)\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "각각의 음성 샘플에 대해 알맞게 데이터가 형성" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To this point, the `label_instances` showed the labels for `voice (1) and non_voice (0)` instances. Now transform the `label_instances` to show the **class labels of the voice directions.**\n", + "\n", + "#### noise와 voice 방향에 따라 labeling\n", + "- noise : 0 \n", + "- 0도 : 1 \n", + "- 20도 : 2\n", + "- 40도 : 3\n", + "- 60도 : 4\n", + "- 80도 : 5\n", + "- 100도 : 6\n", + "- 120도 : 7\n", + "- 140도 : 8\n", + "- 160도 : 9\n", + "- 180도 :10 " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(143,179): #20도\n", + " for j in range(0, len(label_instances[i])):\n", + " if(label_instances[i][j] == 1):\n", + " label_instances[i][j] = 2\n", + " \n", + "for i in range(179,218): #40도\n", + " for j in range(0, len(label_instances[i])):\n", + " if(label_instances[i][j] == 1):\n", + " label_instances[i][j] = 3\n", + " \n", + "for i in range(277,290): #60도\n", + " for j in range(0, len(label_instances[i])):\n", + " if(label_instances[i][j] == 1):\n", + " label_instances[i][j] = 4\n", + " \n", + "for i in range(218,265): #80도\n", + " for j in range(0, len(label_instances[i])):\n", + " if(label_instances[i][j] == 1):\n", + " label_instances[i][j] = 5\n", + " \n", + "for i in range(0,51): #100도\n", + " for j in range(0, len(label_instances[i])):\n", + " if(label_instances[i][j] == 1):\n", + " label_instances[i][j] = 6\n", + " \n", + "for i in range(290,303): #120도\n", + " for j in range(0, len(label_instances[i])):\n", + " if(label_instances[i][j] == 1):\n", + " label_instances[i][j] = 7\n", + " \n", + "for i in range(51,92): #140도\n", + " for j in range(0, len(label_instances[i])):\n", + " if(label_instances[i][j] == 1):\n", + " label_instances[i][j] = 8\n", + "\n", + "for i in range(92,143): #160도\n", + " for j in range(0, len(label_instances[i])):\n", + " if(label_instances[i][j] == 1):\n", + " label_instances[i][j] = 9\n", + " \n", + "for i in range(303,no_samples): #180도\n", + " for j in range(0, len(label_instances[i])):\n", + " if(label_instances[i][j] == 1):\n", + " label_instances[i][j] = 10\n", + "# 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4., 4.]),\n", + " array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 4.,\n", + " 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 0.]),\n", + " array([4., 4., 4., 4., 4.]),\n", + " array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]),\n", + " array([0., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4.,\n", + " 4., 4., 0.]),\n", + " array([4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 0.,\n", + " 0., 0., 0., 0.]),\n", + " array([0., 0., 0., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 0.]),\n", + " array([7., 7., 7., 7., 7., 7., 7., 7., 7.]),\n", + " array([0., 7., 7., 7., 7., 7., 0., 0., 0., 0., 0., 0., 7., 7., 7., 7., 7.,\n", + " 7., 7., 7., 7.]),\n", + " array([0., 0., 0., 0., 0., 7., 7., 7., 7., 7., 7., 7., 7.]),\n", + " array([0., 0., 0., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 0.,\n", + " 0., 0.], dtype=float32),\n", + " array([0., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7.,\n", + " 7., 7., 7., 7., 7., 7.], dtype=float32),\n", + " array([0., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7.,\n", + " 7., 7., 0., 0.], dtype=float32),\n", + " array([0., 0., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7.,\n", + " 7., 7., 7., 7.], dtype=float32),\n", + " array([7., 7., 7., 7., 7., 7., 7., 7.], dtype=float32),\n", + " array([7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7.,\n", + " 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7.], dtype=float32),\n", + " array([7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7.,\n", + " 7.], dtype=float32),\n", + " array([7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7.], dtype=float32),\n", + " array([7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7.,\n", + " 7., 7.], dtype=float32),\n", + " array([7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 7.],\n", + " dtype=float32),\n", + " array([10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10.],\n", + " dtype=float32),\n", + " array([10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10.,\n", + " 10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10.],\n", + " dtype=float32),\n", + " array([ 0., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10.,\n", + " 10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0.], dtype=float32),\n", + " array([ 0., 0., 0., 0., 0., 0., 10., 10., 10., 10., 10., 10., 10.,\n", + " 10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10.,\n", + " 10., 0.]),\n", + " array([ 0., 0., 0., 10., 10., 10., 10., 10., 10., 10., 0., 0., 0.,\n", + " 0., 0., 0., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10.,\n", + " 10., 10., 0., 0., 0.]),\n", + " array([ 0., 0., 0., 0., 0., 0., 0., 0., 10., 10., 10., 10., 10.]),\n", + " array([ 0., 0., 0., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10.,\n", + " 10., 10., 10., 0., 0., 0.]),\n", + " array([10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10.,\n", + " 10., 10., 10., 10.]),\n", + " array([ 0., 0., 0., 0., 10., 10., 10., 10., 10., 10., 10., 10., 0.,\n", + " 0., 0., 0., 0., 0., 10., 10., 10., 10., 10., 10., 10., 10.]),\n", + " array([ 0., 0., 10., 10., 10., 10., 10., 10., 10., 10., 10., 0., 0.]),\n", + " array([ 0., 0., 0., 0., 0., 10., 10., 10., 10., 10., 10., 10., 10.,\n", + " 10., 0., 0., 0., 0.]),\n", + " array([ 0., 0., 0., 0., 0., 0., 10., 10., 10., 10., 10., 10., 10.,\n", + " 0., 0., 0.])]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "label_instances" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have 822 instances. And we have labeled them into 5 classes. Let's see how those labels are distributed." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0 23999\n", + "1.0 153\n", + "2.0 655\n", + "3.0 1278\n", + "4.0 139\n", + "5.0 1715\n", + "6.0 910\n", + "7.0 203\n", + "8.0 733\n", + "9.0 901\n", + "10.0 176\n", + "dtype: int64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "instances_labels = np.array([])\n", + "for audio_clip in label_instances:\n", + " instances_labels = np.hstack([instances_labels, audio_clip])\n", + " \n", + "pd.Series(instances_labels).value_counts().sort_index() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Class-0 Noise instances have the largest number\n", + "\n", + "### Now we have `total_instances_tensor & total_label`\n", + "- `total_instances_tensor` : list. 50 elements. Each element has instances of the audio \n", + "- `total_label` ; list. 50 elements \n", + "\n", + "## Construct `train and validation set` split.\n", + "- Out of 50 audio samples, we will take 40 samples for training set, and the remaining to the validation set. Try to mix them evenly.\n", + "- Note that `total_label` indices has : 0~11(Class-1), 12~24(Class-2), 23~37(Class-3), 38~49(Class-4) and Class-0 is assigned to the noise \n", + "- 샘플을 랜덤하게 섞어주고 처음부터 40개는 train data set으로 마지막 10개는 validation data set으로 사용" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(743, 512, 100, 4) (743,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(179, 512, 100, 4) (179,)\n", + "(20, 512, 100, 4) (20,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(13, 512, 100, 4) (13,)\n", + "(28, 512, 100, 4) (28,)\n", + "(12, 512, 100, 4) (12,)\n", + "(28, 512, 100, 4) (28,)\n", + "(31, 512, 100, 4) (31,)\n", + "(179, 512, 100, 4) (179,)\n", + "(743, 512, 100, 4) (743,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(21, 512, 100, 4) (21,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(18, 512, 100, 4) (18,)\n", + "(21, 512, 100, 4) (21,)\n", + "(28, 512, 100, 4) (28,)\n", + "(19, 512, 100, 4) (19,)\n", + "\n", + "(23053, 512, 100, 4) (23053,)\n", + "(7809, 512, 100, 4) (7809,)\n" + ] + } + ], + "source": [ + "import copy\n", + "vad_label_instances = copy.deepcopy(label_instances)\n", + "\n", + "# transform the list to ndarray\n", + "total_instances_tensors = np.array(stacked_instances) \n", + "total_label_tensors = np.array(label_instances)\n", + "total_vad_label_tensors = np.array(vad_label_instances)\n", + "\n", + "# randomly choose indices to be split to training and validation set\n", + "np.random.seed(77) # 19, 7, 5, 113, 34\n", + "\n", + "idx = np.random.permutation(no_samples)\n", + "\n", + "'''\n", + "c1 = 0\n", + "c2 = 0\n", + "c3 = 0\n", + "c4 = 0\n", + "\n", + "for i in idx[-10:]:\n", + " if 0 <= i <= 11:\n", + " c1 = c1 + 1\n", + " elif 12 <= i <= 24 :\n", + " c2 = c2 + 1\n", + " elif 25 <= i <= 37 :\n", + " c3 = c3 + 1\n", + " elif 38 <= i :\n", + " c4 = c4 + 1\n", + "\n", + "print(\"Valid set distr.: Class-1: %d, Class-2: %d, Class-3: %d, Class-4: %d\\n\" % (c1, c2, c3, c4))\n", + "'''\n", + "\n", + "# Shuffle \n", + "X = total_instances_tensors[[idx]] # Shuffle the data using fancy indexing\n", + "y = total_label_tensors[[idx]]\n", + "y_vad = total_vad_label_tensors[[idx]]\n", + "\n", + "# Test \n", + "for i in range(0, no_samples, 10):\n", + " print(X[i].shape, y[i].shape)\n", + " \n", + "# Split \n", + "X_train = np.concatenate(X[:221], axis=0)\n", + "y_train = np.concatenate(y[:221], axis=0)\n", + "y_train_vad = np.concatenate(y_vad[:40], axis=0)\n", + "\n", + "X_val = np.concatenate(X[221: ], axis=0)\n", + "y_val = np.concatenate(y[221: ], axis=0)\n", + "y_val_vad = np.concatenate(y_vad[221: ], axis=0)\n", + "\n", + "print()\n", + "print(X_train.shape, y_train.shape)\n", + "print(X_val.shape, y_val.shape)\n", + "\n", + "# Convert scalar y's to One-Hot. 'y_val_vad' is not needed because it is binary classification \n", + "y_train = keras.utils.to_categorical(y_train, 11) # 5-Classes classification \n", + "y_val = keras.utils.to_categorical(y_val, 11)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 모델1. 2D CNN + Bidirectional GRU" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.layers import TimeDistributed, Bidirectional\n", + "from tensorflow.keras.layers import Conv2D, Conv1D, MaxPooling2D, MaxPooling1D, Input, Flatten, Dropout\n", + "from tensorflow.keras import layers, models\n", + "from sklearn.metrics import confusion_matrix, classification_report" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /home/user4/.conda/envs/tf.kr/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Colocations handled automatically by placer.\n", + "WARNING:tensorflow:From /home/user4/.conda/envs/tf.kr/lib/python3.7/site-packages/tensorflow/python/keras/layers/core.py:143: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "input_1 (InputLayer) (None, 512, 100, 4) 0 \n", + "_________________________________________________________________\n", + "conv2d (Conv2D) (None, 512, 100, 64) 2368 \n", + "_________________________________________________________________\n", + "max_pooling2d (MaxPooling2D) (None, 170, 50, 64) 0 \n", + "_________________________________________________________________\n", + "conv2d_1 (Conv2D) (None, 170, 50, 64) 36928 \n", + "_________________________________________________________________\n", + "max_pooling2d_1 (MaxPooling2 (None, 56, 25, 64) 0 \n", + "_________________________________________________________________\n", + "conv2d_2 (Conv2D) (None, 56, 25, 64) 36928 \n", + "_________________________________________________________________\n", + "max_pooling2d_2 (MaxPooling2 (None, 18, 12, 64) 0 \n", + "_________________________________________________________________\n", + "conv2d_3 (Conv2D) (None, 18, 12, 64) 36928 \n", + "_________________________________________________________________\n", + "max_pooling2d_3 (MaxPooling2 (None, 6, 6, 64) 0 \n", + "_________________________________________________________________\n", + "conv2d_4 (Conv2D) (None, 1, 6, 128) 49280 \n", + "_________________________________________________________________\n", + "reshape (Reshape) (None, 6, 128) 0 \n", + "_________________________________________________________________\n", + "bidirectional (Bidirectional (None, 256) 197376 \n", + "_________________________________________________________________\n", + "dense (Dense) (None, 32) 8224 \n", + "_________________________________________________________________\n", + "dropout (Dropout) (None, 32) 0 \n", + "_________________________________________________________________\n", + "dense_1 (Dense) (None, 11) 363 \n", + "=================================================================\n", + "Total params: 368,395\n", + "Trainable params: 368,395\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "# reset_keras_session(100)\n", + "\n", + "if 'model' in locals():\n", + " del model\n", + " \n", + "input_spectrogram = Input(shape=(X_train.shape[1], X_train.shape[2], X_train.shape[3]))\n", + "\n", + "conv_1 = Conv2D(64, (3, 3), activation='relu', padding='same')(input_spectrogram)\n", + "conv_1_pool = MaxPooling2D((3, 2))(conv_1)\n", + "\n", + "conv_2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv_1_pool)\n", + "conv_2_pool = MaxPooling2D((3, 2))(conv_2)\n", + "\n", + "conv_3 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv_2_pool)\n", + "conv_3_pool = MaxPooling2D((3, 2))(conv_3)\n", + "\n", + "conv_4 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv_3_pool)\n", + "conv_4_pool = MaxPooling2D((3, 2))(conv_4)\n", + "\n", + "shape_conv_4_pool = conv_4_pool.get_shape().as_list() # (None, height, width, channel)\n", + "conv_5 = Conv2D(128, (shape_conv_4_pool[1], 1), padding='valid', activation='relu')(conv_4_pool)\n", + "# 앞의 conv_5 필터 수를 256 으로 늘리면 학습이 잘 진행되지 않음 \n", + "\n", + "shape_conv_5 = conv_5.get_shape().as_list()\n", + "reshaped = layers.Reshape((shape_conv_5[2], shape_conv_5[3]))(conv_5) # reshape to (timesteps, features) explicitly \n", + "bgru = Bidirectional(layers.GRU(units=128))(reshaped) # GRU units 의 수를 늘리면? \n", + "\n", + "fc1 = layers.Dense(32, activation='relu')(bgru)\n", + "fc1_drop = Dropout(0.5)(fc1)\n", + "\n", + "dense_out = layers.Dense(11, activation='softmax')(fc1_drop)\n", + "\n", + "model = models.Model(inputs=input_spectrogram, outputs=dense_out)\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#show_model_graph(model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 23053 samples, validate on 7809 samples\n", + "WARNING:tensorflow:From /home/user4/.conda/envs/tf.kr/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.cast instead.\n", + "Epoch 1/100\n", + "23053/23053 [==============================] - 93s 4ms/sample - loss: 1.2889 - acc: 0.7566 - val_loss: 1.1853 - val_acc: 0.7336\n", + "Epoch 2/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 1.1123 - acc: 0.7925 - val_loss: 1.1751 - val_acc: 0.7336\n", + "Epoch 3/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 1.0734 - acc: 0.7925 - val_loss: 1.1746 - val_acc: 0.7336\n", + "Epoch 4/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 1.0430 - acc: 0.7925 - val_loss: 1.1783 - val_acc: 0.7336\n", + "Epoch 5/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 1.0277 - acc: 0.7925 - val_loss: 1.1758 - val_acc: 0.7336\n", + "Epoch 6/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 1.0116 - acc: 0.7925 - val_loss: 1.1635 - val_acc: 0.7336\n", + "Epoch 7/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 1.0029 - acc: 0.7925 - val_loss: 1.1664 - val_acc: 0.7336\n", + "Epoch 8/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 0.9916 - acc: 0.7925 - val_loss: 1.1605 - val_acc: 0.7336\n", + "Epoch 9/100\n", + " 7584/23053 [========>.....................] - ETA: 54s - loss: 0.7597 - acc: 0.8449" + ] + } + ], + "source": [ + "model.compile(optimizer ='adam',loss='categorical_crossentropy', metrics =['acc'])\n", + "\n", + "callbacks_list = [keras.callbacks.EarlyStopping(monitor='acc', patience=50),\n", + " keras.callbacks.ReduceLROnPlateau(monitor='val_loss',\n", + " factor=0.1, patience=50)]\n", + "\n", + "history = model.fit(X_train, y_train,\n", + " epochs=100, batch_size=32, \n", + " callbacks=callbacks_list,\n", + " validation_data=(X_val, y_val),\n", + " shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "import sklearn.metrics \n", + "y_val_pred = model.predict(X_val)\n", + "cm = sklearn.metrics.confusion_matrix(np.argmax(y_val, axis=1), \n", + " np.argmax(y_val_pred, axis=1))\n", + "acc = sklearn.metrics.accuracy_score(np.argmax(y_val, axis=1),\n", + " np.argmax(y_val_pred, axis=1))\n", + "print(\"Accuracy : \", acc)\n", + "cm\n", + "\n", + "model.save('SSL_STFT_2DConv_RNN.h5') " + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy : 0.826530612244898\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[31, 1, 3, 5, 5],\n", + " [ 1, 12, 0, 0, 0],\n", + " [11, 2, 25, 0, 0],\n", + " [ 1, 0, 0, 52, 0],\n", + " [ 5, 0, 0, 0, 42]], dtype=int64)" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# You can restore the best saved model and compare :\n", + "best_model = keras.models.load_model(os.path.join('best_models_archive', 'SSL_2DConv_RNN_STFT.h5'))\n", + "y_val_pred = best_model.predict(X_val)\n", + "cm = sklearn.metrics.confusion_matrix(np.argmax(y_val, axis=1), \n", + " np.argmax(y_val_pred, axis=1))\n", + "acc = sklearn.metrics.accuracy_score(np.argmax(y_val, axis=1),\n", + " np.argmax(y_val_pred, axis=1))\n", + "print(\"Accuracy : \", acc)\n", + "cm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Best Model의 Validation Set에 대한 Confusion Matrix 분석\n", + "- 특히 Class_0 (Noise)에 대한 판단에 오류가 많다. " + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 45\n", + "1 13\n", + "2 38\n", + "3 53\n", + "4 47\n", + "dtype: int64" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 실제 ground truth 의 class 분포 \n", + "pd.Series(np.argmax(y_val, axis=1)).value_counts().sort_index() " + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 59\n", + "1 28\n", + "2 14\n", + "3 39\n", + "4 56\n", + "dtype: int64" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 예측의 class 분포\n", + "pd.Series(np.argmax(y_val_pred, axis=1)).value_counts().sort_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- class-0 (Noise)가 실제로 45개 그 중 35개만 맞춤. 특히 ground-true가 class-2 (60도) 인데, 이를 class-0 (noise)라 틀리게 분류한 것이 11개 \n", + "- class-0 noise를 class-3 또는 class-4라 잘 못 분류한 것이 10개" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 모델2. 2D CNN + 1D CNN\n", + "- 처음에는 2D CNN을 써서 `주파수-시간` 2차원 도메인. 2D CNN 뒤에 1D CNN을 stack.\n", + "- 앞서 만든 데이터셋 활용 : `X_train, y_train, X_val, y_val` " + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X_train : (626, 512, 100, 4)\n", + "y_train : (626, 5)\n", + "X_val : (196, 512, 100, 4)\n", + "y_val : (196, 5)\n" + ] + } + ], + "source": [ + "for data_set in [[X_train, 'X_train'], [y_train, 'y_train'], \n", + " [X_val,'X_val'], [y_val, 'y_val']]:\n", + " print(data_set[1], \": \", data_set[0].shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "input_13 (InputLayer) (None, 512, 100, 4) 0 \n", + "_________________________________________________________________\n", + "conv2d_60 (Conv2D) (None, 510, 98, 32) 1184 \n", + "_________________________________________________________________\n", + "max_pooling2d_48 (MaxPooling (None, 170, 49, 32) 0 \n", + "_________________________________________________________________\n", + "conv2d_61 (Conv2D) (None, 168, 47, 64) 18496 \n", + "_________________________________________________________________\n", + "max_pooling2d_49 (MaxPooling (None, 56, 23, 64) 0 \n", + "_________________________________________________________________\n", + "conv2d_62 (Conv2D) (None, 56, 23, 128) 73856 \n", + "_________________________________________________________________\n", + "max_pooling2d_50 (MaxPooling (None, 18, 11, 128) 0 \n", + "_________________________________________________________________\n", + "conv2d_63 (Conv2D) (None, 18, 11, 256) 295168 \n", + "_________________________________________________________________\n", + "max_pooling2d_51 (MaxPooling (None, 6, 5, 256) 0 \n", + "_________________________________________________________________\n", + "conv2d_64 (Conv2D) (None, 1, 5, 512) 786944 \n", + "_________________________________________________________________\n", + "reshape_12 (Reshape) (None, 5, 512) 0 \n", + "_________________________________________________________________\n", + "conv1d_5 (Conv1D) (None, 3, 1024) 1573888 \n", + "_________________________________________________________________\n", + "flatten_4 (Flatten) (None, 3072) 0 \n", + "_________________________________________________________________\n", + "dense_22 (Dense) (None, 64) 196672 \n", + "_________________________________________________________________\n", + "dropout_11 (Dropout) (None, 64) 0 \n", + "_________________________________________________________________\n", + "dense_23 (Dense) (None, 5) 325 \n", + "=================================================================\n", + "Total params: 2,946,533\n", + "Trainable params: 2,946,533\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "if 'model' in locals():\n", + " del model\n", + " \n", + "input_spectrogram = Input(shape=(X_train.shape[1], X_train.shape[2], X_train.shape[3]))\n", + "\n", + "conv_1 = Conv2D(32, (3, 3), activation='relu', padding='valid')(input_spectrogram)\n", + "conv_1_pool = MaxPooling2D((3, 2))(conv_1)\n", + "\n", + "conv_2 = Conv2D(64, (3, 3), activation='relu', padding='valid')(conv_1_pool)\n", + "conv_2_pool = MaxPooling2D((3, 2))(conv_2)\n", + "\n", + "conv_3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv_2_pool)\n", + "conv_3_pool = MaxPooling2D((3, 2))(conv_3)\n", + "\n", + "conv_4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv_3_pool)\n", + "conv_4_pool = MaxPooling2D((3, 2))(conv_4)\n", + "\n", + "shape_conv_4_pool = conv_4_pool.get_shape().as_list() # (None, height, width, channel)\n", + "conv_5 = Conv2D(512, (shape_conv_4_pool[1], 1), padding='valid', activation='relu')(conv_4_pool)\n", + "\n", + "shape_conv_5 = conv_5.get_shape().as_list()\n", + "reshaped = layers.Reshape((shape_conv_5[2], shape_conv_5[3]))(conv_5) # reshape to (timesteps, features) explicitly \n", + "\n", + "conv_6 = Conv1D(1024, kernel_size=3, activation='relu')(reshaped)\n", + "flatten = layers.Flatten()(conv_6)\n", + "\n", + "fc1 = layers.Dense(64, activation='relu')(flatten)\n", + "fc1_drop = Dropout(0.1)(fc1)\n", + "\n", + "dense_out = layers.Dense(5, activation='softmax')(fc1_drop)\n", + "\n", + "model = models.Model(inputs=input_spectrogram, outputs=dense_out)\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 626 samples, validate on 196 samples\n", + "Epoch 1/150\n", + "626/626 [==============================] - 5s 9ms/step - loss: 1.9043 - acc: 0.1853 - val_loss: 1.6162 - val_acc: 0.2296\n", + "Epoch 2/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 1.6138 - acc: 0.2412 - val_loss: 1.5509 - val_acc: 0.3571\n", + "Epoch 3/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 1.6051 - acc: 0.2236 - val_loss: 1.5535 - val_acc: 0.3571\n", + "Epoch 4/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 1.5914 - acc: 0.2332 - val_loss: 1.4584 - val_acc: 0.3622\n", + "Epoch 5/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 1.5777 - acc: 0.2460 - val_loss: 1.5800 - val_acc: 0.2296\n", + "Epoch 6/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 1.6255 - acc: 0.2188 - val_loss: 1.6074 - val_acc: 0.2296\n", + "Epoch 7/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 1.6451 - acc: 0.2173 - val_loss: 1.5798 - val_acc: 0.2959\n", + "Epoch 8/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 1.6361 - acc: 0.2236 - val_loss: 1.5996 - val_acc: 0.2296\n", + "Epoch 9/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 1.6319 - acc: 0.2125 - val_loss: 1.5677 - val_acc: 0.2296\n", + "Epoch 10/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 1.6000 - acc: 0.2300 - val_loss: 1.5346 - val_acc: 0.2296\n", + "Epoch 11/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 1.5518 - acc: 0.2188 - val_loss: 1.5277 - val_acc: 0.2959\n", + "Epoch 12/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 1.5119 - acc: 0.2572 - val_loss: 1.4045 - val_acc: 0.3673\n", + "Epoch 13/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 1.4840 - acc: 0.2444 - val_loss: 1.4329 - val_acc: 0.2296\n", + "Epoch 14/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 1.4206 - acc: 0.2812 - val_loss: 1.3303 - val_acc: 0.3673\n", + "Epoch 15/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 1.4754 - acc: 0.2380 - val_loss: 1.4269 - val_acc: 0.3418\n", + "Epoch 16/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 1.4070 - acc: 0.2572 - val_loss: 1.3444 - val_acc: 0.3673\n", + "Epoch 17/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 1.4424 - acc: 0.2173 - val_loss: 1.4119 - val_acc: 0.4847\n", + "Epoch 18/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 1.3587 - acc: 0.3419 - val_loss: 1.2711 - val_acc: 0.4286\n", + "Epoch 19/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 1.2727 - acc: 0.3466 - val_loss: 1.0751 - val_acc: 0.5510\n", + "Epoch 20/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 1.1998 - acc: 0.3834 - val_loss: 1.0747 - val_acc: 0.5459\n", + "Epoch 21/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 1.2545 - acc: 0.4105 - val_loss: 1.2027 - val_acc: 0.5000\n", + "Epoch 22/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 1.3652 - acc: 0.3514 - val_loss: 1.2264 - val_acc: 0.5612\n", + "Epoch 23/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 1.2195 - acc: 0.4585 - val_loss: 1.0694 - val_acc: 0.5816\n", + "Epoch 24/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 1.2210 - acc: 0.3994 - val_loss: 1.1830 - val_acc: 0.4643\n", + "Epoch 25/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 1.2360 - acc: 0.4073 - val_loss: 1.1299 - val_acc: 0.5918\n", + "Epoch 26/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 1.1751 - acc: 0.4089 - val_loss: 1.0935 - val_acc: 0.4643\n", + "Epoch 27/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 1.0403 - acc: 0.5176 - val_loss: 0.8942 - val_acc: 0.6122\n", + "Epoch 28/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.9374 - acc: 0.5655 - val_loss: 0.9121 - val_acc: 0.5969\n", + "Epoch 29/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.9690 - acc: 0.4776 - val_loss: 0.8243 - val_acc: 0.6173\n", + "Epoch 30/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.8971 - acc: 0.5559 - val_loss: 1.0870 - val_acc: 0.7143\n", + "Epoch 31/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 1.0224 - acc: 0.5240 - val_loss: 0.8490 - val_acc: 0.6684\n", + "Epoch 32/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.9144 - acc: 0.5863 - val_loss: 1.0446 - val_acc: 0.5306\n", + "Epoch 33/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.9436 - acc: 0.5591 - val_loss: 0.9048 - val_acc: 0.5816\n", + "Epoch 34/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.7960 - acc: 0.6358 - val_loss: 0.7195 - val_acc: 0.6480\n", + "Epoch 35/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.7224 - acc: 0.6406 - val_loss: 0.7078 - val_acc: 0.6429\n", + "Epoch 36/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.6945 - acc: 0.6534 - val_loss: 0.6130 - val_acc: 0.7398\n", + "Epoch 37/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.6138 - acc: 0.7077 - val_loss: 0.6411 - val_acc: 0.7398\n", + "Epoch 38/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.6120 - acc: 0.7141 - val_loss: 0.6844 - val_acc: 0.6224\n", + "Epoch 39/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.5641 - acc: 0.7380 - val_loss: 0.5928 - val_acc: 0.7500\n", + "Epoch 40/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.6299 - acc: 0.6933 - val_loss: 0.6653 - val_acc: 0.7194\n", + "Epoch 41/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.4687 - acc: 0.8051 - val_loss: 0.5616 - val_acc: 0.7245\n", + "Epoch 42/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.3243 - acc: 0.8834 - val_loss: 0.8395 - val_acc: 0.6531\n", + "Epoch 43/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.3781 - acc: 0.8546 - val_loss: 0.6364 - val_acc: 0.7908\n", + "Epoch 44/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.6739 - acc: 0.7700 - val_loss: 1.0621 - val_acc: 0.6071\n", + "Epoch 45/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.7391 - acc: 0.6981 - val_loss: 0.6460 - val_acc: 0.7347\n", + "Epoch 46/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.4642 - acc: 0.8291 - val_loss: 1.0345 - val_acc: 0.6684\n", + "Epoch 47/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.3786 - acc: 0.8802 - val_loss: 1.1972 - val_acc: 0.5816\n", + "Epoch 48/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.2815 - acc: 0.9137 - val_loss: 0.8839 - val_acc: 0.7296\n", + "Epoch 49/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.3514 - acc: 0.8770 - val_loss: 0.6836 - val_acc: 0.7347\n", + "Epoch 50/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.2606 - acc: 0.8978 - val_loss: 0.4156 - val_acc: 0.8265\n", + "Epoch 51/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.1950 - acc: 0.9393 - val_loss: 0.3864 - val_acc: 0.8724\n", + "Epoch 52/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.2513 - acc: 0.8946 - val_loss: 0.6559 - val_acc: 0.6837\n", + "Epoch 53/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.2400 - acc: 0.9297 - val_loss: 1.4592 - val_acc: 0.6735\n", + "Epoch 54/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.2839 - acc: 0.9169 - val_loss: 1.0491 - val_acc: 0.7041\n", + "Epoch 55/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.1958 - acc: 0.9297 - val_loss: 0.5871 - val_acc: 0.7806\n", + "Epoch 56/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.2016 - acc: 0.9185 - val_loss: 0.6110 - val_acc: 0.7653\n", + "Epoch 57/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.1570 - acc: 0.9377 - val_loss: 0.6808 - val_acc: 0.7500\n", + "Epoch 58/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0988 - acc: 0.9728 - val_loss: 0.7876 - val_acc: 0.7449\n", + "Epoch 59/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0628 - acc: 0.9808 - val_loss: 1.3038 - val_acc: 0.7449\n", + "Epoch 60/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0520 - acc: 0.9824 - val_loss: 0.6594 - val_acc: 0.8112\n", + "Epoch 61/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0442 - acc: 0.9840 - val_loss: 1.2047 - val_acc: 0.7449\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 62/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0579 - acc: 0.9824 - val_loss: 0.7344 - val_acc: 0.8061\n", + "Epoch 63/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0400 - acc: 0.9872 - val_loss: 1.2280 - val_acc: 0.7194\n", + "Epoch 64/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0180 - acc: 0.9952 - val_loss: 1.3101 - val_acc: 0.7500\n", + "Epoch 65/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0165 - acc: 0.9984 - val_loss: 1.2408 - val_acc: 0.7500\n", + "Epoch 66/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0174 - acc: 0.9968 - val_loss: 1.3353 - val_acc: 0.7398\n", + "Epoch 67/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0303 - acc: 0.9904 - val_loss: 1.1344 - val_acc: 0.7296\n", + "Epoch 68/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0564 - acc: 0.9712 - val_loss: 1.1362 - val_acc: 0.7704\n", + "Epoch 69/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0467 - acc: 0.9824 - val_loss: 1.3238 - val_acc: 0.7449\n", + "Epoch 70/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0212 - acc: 0.9952 - val_loss: 1.4089 - val_acc: 0.7398\n", + "Epoch 71/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0046 - acc: 0.9984 - val_loss: 1.5882 - val_acc: 0.7347\n", + "Epoch 72/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0029 - acc: 1.0000 - val_loss: 1.6349 - val_acc: 0.7347\n", + "Epoch 73/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0023 - acc: 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[==============================] - 3s 4ms/step - loss: 7.2457e-04 - acc: 1.0000 - val_loss: 1.7646 - val_acc: 0.7704\n", + "Epoch 81/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 8.4384e-04 - acc: 1.0000 - val_loss: 1.8408 - val_acc: 0.7500\n", + "Epoch 82/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0026 - acc: 1.0000 - val_loss: 1.6702 - val_acc: 0.7704\n", + "Epoch 83/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0014 - acc: 1.0000 - val_loss: 1.8511 - val_acc: 0.7449\n", + "Epoch 84/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 6.7391e-04 - acc: 1.0000 - val_loss: 1.9200 - val_acc: 0.7653\n", + "Epoch 85/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 8.7888e-04 - acc: 1.0000 - val_loss: 2.0785 - val_acc: 0.7296\n", + "Epoch 86/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0031 - acc: 0.9984 - val_loss: 1.7602 - val_acc: 0.7602\n", + "Epoch 87/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0108 - acc: 0.9952 - val_loss: 1.8444 - val_acc: 0.7500\n", + "Epoch 88/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0080 - acc: 0.9952 - val_loss: 1.3874 - val_acc: 0.8112\n", + "Epoch 89/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0189 - acc: 0.9936 - val_loss: 1.9928 - val_acc: 0.6480\n", + "Epoch 90/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0262 - acc: 0.9904 - val_loss: 1.4002 - val_acc: 0.7959\n", + "Epoch 91/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0166 - acc: 0.9968 - val_loss: 1.2444 - val_acc: 0.8061\n", + "Epoch 92/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0852 - acc: 0.9792 - val_loss: 1.3017 - val_acc: 0.7755\n", + "Epoch 93/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.1866 - acc: 0.9569 - val_loss: 1.3537 - val_acc: 0.7857\n", + "Epoch 94/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.8109 - acc: 0.7923 - val_loss: 1.3971 - val_acc: 0.4337\n", + "Epoch 95/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.3157 - acc: 0.9329 - val_loss: 1.0201 - val_acc: 0.7806\n", + "Epoch 96/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.3575 - acc: 0.9153 - val_loss: 1.2411 - val_acc: 0.6429\n", + "Epoch 97/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.7536 - acc: 0.7700 - val_loss: 1.3372 - val_acc: 0.6327\n", + "Epoch 98/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.6185 - acc: 0.7859 - val_loss: 0.6423 - val_acc: 0.7143\n", + "Epoch 99/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.3432 - acc: 0.8770 - val_loss: 0.5008 - val_acc: 0.8061\n", + "Epoch 100/150\n", + "626/626 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0.8112\n", + "Epoch 107/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0221 - acc: 0.9968 - val_loss: 0.8317 - val_acc: 0.8112\n", + "Epoch 108/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0213 - acc: 0.9984 - val_loss: 0.8529 - val_acc: 0.8061\n", + "Epoch 109/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0187 - acc: 0.9984 - val_loss: 0.8748 - val_acc: 0.8061\n", + "Epoch 110/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0151 - acc: 1.0000 - val_loss: 0.9010 - val_acc: 0.8010\n", + "Epoch 111/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0113 - acc: 1.0000 - val_loss: 0.9181 - val_acc: 0.8010\n", + "Epoch 112/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0096 - acc: 1.0000 - val_loss: 0.9337 - val_acc: 0.8112\n", + "Epoch 113/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0076 - acc: 1.0000 - val_loss: 0.9478 - val_acc: 0.8112\n", + "Epoch 114/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0080 - acc: 0.9984 - val_loss: 0.9703 - val_acc: 0.8112\n", + "Epoch 115/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0074 - acc: 1.0000 - val_loss: 0.9912 - val_acc: 0.8112\n", + "Epoch 116/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0055 - acc: 1.0000 - val_loss: 1.0098 - val_acc: 0.8112\n", + "Epoch 117/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0089 - acc: 0.9984 - val_loss: 1.0257 - val_acc: 0.8061\n", + "Epoch 118/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0052 - acc: 1.0000 - val_loss: 1.0413 - val_acc: 0.8061\n", + "Epoch 119/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0052 - acc: 1.0000 - val_loss: 1.0780 - val_acc: 0.8010\n", + "Epoch 120/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0044 - acc: 1.0000 - val_loss: 1.1004 - val_acc: 0.8010\n", + "Epoch 121/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0053 - acc: 0.9984 - val_loss: 1.1087 - val_acc: 0.8010\n", + "Epoch 122/150\n", + "626/626 [==============================] - 3s 4ms/step - loss: 0.0071 - acc: 0.9968 - val_loss: 1.1271 - val_acc: 0.8010\n" + ] + } + ], + "source": [ + "model.compile(optimizer ='adam',loss='categorical_crossentropy', metrics =['acc'])\n", + "\n", + "callbacks_list = [keras.callbacks.EarlyStopping(monitor='acc', patience=50),\n", + " keras.callbacks.ReduceLROnPlateau(monitor='val_loss',\n", + " factor=0.1, patience=50)]\n", + "\n", + "history = model.fit(X_train, y_train,\n", + " epochs=150, batch_size=32, \n", + " callbacks=callbacks_list,\n", + " validation_data=(X_val, y_val),\n", + " shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy : 0.8010204081632653\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[29, 9, 2, 2, 3],\n", + " [ 0, 10, 0, 3, 0],\n", + " [10, 0, 28, 0, 0],\n", + " [ 9, 0, 0, 44, 0],\n", + " [ 1, 0, 0, 0, 46]], dtype=int64)" + ] + }, + "execution_count": 137, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sklearn.metrics \n", + "y_val_pred = model.predict(X_val)\n", + "cm = sklearn.metrics.confusion_matrix(np.argmax(y_val, axis=1), \n", + " np.argmax(y_val_pred, axis=1))\n", + "acc = sklearn.metrics.accuracy_score(np.argmax(y_val, axis=1),\n", + " np.argmax(y_val_pred, axis=1))\n", + "print(\"Accuracy : \", acc)\n", + "model.save('SSL_STFT_2DConv_1DConv.h5') \n", + "\n", + "cm" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:tf.kr]", + "language": "python", + "name": "conda-env-tf.kr-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/SSL_STFT_Siamese_MultiHead.ipynb b/SSL_STFT_Siamese_MultiHead.ipynb new file mode 100644 index 0000000..73d1139 --- /dev/null +++ b/SSL_STFT_Siamese_MultiHead.ipynb @@ -0,0 +1,3651 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
- last read : 2019. 06. 2
\n", + "\n", + "\n", + "## Sound Source Location (방위각 추정) and VAD as Multi-task learning\n", + "- Based on STFT magnitude and phase. \n", + "- 2 Models: \n", + " - 2D CNN for feature extraction -> GRU \n", + " - 2D CNN for feature extraction ->1D CNN\n", + "\n", + "### Inputs : \n", + "- Input : Magnitude and Phase from two stereo channels : So there are 4 feature maps. The first freq bin is not used.\n", + "- Magnitude, Phase : Basic Magnitude and Phase with STFT. \n", + "- STFT : n_fft=1024, hop = 512 with sr=44100. So each frame is 23.2 msec duration. Hop is 11.6 msec. With 100 consecutive frames with the hop, the duration is `1024/44100 + 99 * (512/44100) = 1.1726 sec `\n", + "- Stack 4 feature maps. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "('2.2.4-tf', '1.13.1')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Common imports\n", + "import numpy as np\n", + "import pandas as pd\n", + "import os, sys, glob \n", + "import tensorflow as tf\n", + "\n", + "import librosa\n", + "import librosa.display\n", + "\n", + "# To plot pretty figures\n", + "# import matplotlib\n", + "# import matplotlib.pyplot as plt\n", + "# %matplotlib inline\n", + "# plt.style.use('ggplot')\n", + "# plt.rcParams['axes.labelsize'] = 14\n", + "# plt.rcParams['xtick.labelsize'] = 12\n", + "# plt.rcParams['ytick.labelsize'] = 12\n", + "\n", + "def reset_graph(seed=42):\n", + " tf.reset_default_graph() \n", + " tf.set_random_seed(seed)\n", + " np.random.seed(seed)\n", + " \n", + "def reset_keras_session(seed=42):\n", + " tf.keras.backend.clear_session()\n", + " tf.set_random_seed(seed)\n", + " np.random.seed(seed)\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\") # To rid of warnings \n", + "\n", + "if sys.platform == 'win32': # if windows \n", + " home = os.path.join('D:', os.sep, 'hblee') # d:\\hblee\n", + " data_repo = os.path.join('D:', os.sep, 'Data_Repo_Win') # d:\\Data_Repo_Win\n", + "elif sys.platform == \"linux\" or sys.platform == \"linux2\" : # if linux \n", + " home = os.path.expanduser(\"~\") # home = os.getenv(\"HOME\")\n", + " data_repo = os.path.join(home, 'Data_Repo')\n", + " \n", + "sys.path.append(os.path.join(home, 'Google_Sync', 'Dev_Exercise', 'utils'))\n", + "# from tf_utils import *\n", + " \n", + "from tensorflow import keras \n", + "keras.__version__, tf.VERSION" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "'''\n", + "samples : audio samples(files). 50 of them \n", + "samples_vad_seg : samples segmented as to voice region (1) and non-voice region (0) \n", + "\n", + "samples and samples_vad_seg should be aligned. \n", + "'''\n", + "\n", + "sample_data_repo1= os.path.join(home, 'Downloads','t3_audio')\n", + "sample_data_repo2= os.path.join(home, 'Downloads','audio','20도')\n", + "sample_data_repo3= os.path.join(home, 'Downloads','audio','40도')\n", + "sample_data_repo4= os.path.join(home, 'Downloads','audio','80도')\n", + "sample_data_repo5= os.path.join(home, 'Downloads','audio','100도')\n", + "sample_data_repo6= os.path.join(home, 'Downloads','audio','140도')\n", + "sample_data_repo7= os.path.join(home, 'Downloads','audio','160도')\n", + "\n", + "samples1 = glob.glob(os.path.join(sample_data_repo1, '**', '*wav'), recursive=True)\n", + "samples2 = glob.glob(os.path.join(sample_data_repo2, '**', '*wav'), recursive=True)\n", + "samples3 = glob.glob(os.path.join(sample_data_repo3, '**', '*wav'), recursive=True)\n", + "samples4 = glob.glob(os.path.join(sample_data_repo4, '**', '*wav'), recursive=True)\n", + "samples5 = glob.glob(os.path.join(sample_data_repo5, '**', '*wav'), recursive=True)\n", + "samples6 = glob.glob(os.path.join(sample_data_repo6, '**', '*wav'), recursive=True)\n", + "samples7 = glob.glob(os.path.join(sample_data_repo7, '**', '*wav'), recursive=True)\n", + "\n", + "\n", + "samples1 = sorted(samples1)\n", + "samples2 = sorted(samples2)\n", + "samples3 = sorted(samples3)\n", + "samples4 = sorted(samples4)\n", + "samples5 = sorted(samples5)\n", + "samples6 = sorted(samples6)\n", + "samples7 = sorted(samples7)\n", + "\n", + "\n", + "\n", + "sample_vad_seg_repo1 = os.path.join(home, 'Downloads','t3_audio_label') \n", + "sample_vad_seg_repo2 = os.path.join(home, 'Downloads','binary_segment','20도') # 적절하게 변경 필요 \n", + "sample_vad_seg_repo3 = os.path.join(home, 'Downloads','binary_segment','40도')\n", + "sample_vad_seg_repo4 = os.path.join(home, 'Downloads','binary_segment','80도')\n", + "sample_vad_seg_repo5 = os.path.join(home, 'Downloads','binary_segment','100도')\n", + "sample_vad_seg_repo6 = os.path.join(home, 'Downloads','binary_segment','140도')\n", + "sample_vad_seg_repo7 = os.path.join(home, 'Downloads','binary_segment','160도')\n", + "\n", + "\n", + "samples_vad_seg1 = glob.glob(os.path.join(sample_vad_seg_repo1, '**', '*[npy|npz]'), recursive=True)\n", + "samples_vad_seg2 = glob.glob(os.path.join(sample_vad_seg_repo2, '**', '*[npy|npz]'), recursive=True)\n", + "samples_vad_seg3 = glob.glob(os.path.join(sample_vad_seg_repo3, '**', '*[npy|npz]'), recursive=True)\n", + "samples_vad_seg4 = glob.glob(os.path.join(sample_vad_seg_repo4, '**', '*[npy|npz]'), recursive=True)\n", + "samples_vad_seg5 = glob.glob(os.path.join(sample_vad_seg_repo5, '**', '*[npy|npz]'), recursive=True)\n", + "samples_vad_seg6 = glob.glob(os.path.join(sample_vad_seg_repo6, '**', '*[npy|npz]'), recursive=True)\n", + "samples_vad_seg7 = glob.glob(os.path.join(sample_vad_seg_repo7, '**', '*[npy|npz]'), recursive=True)\n", + "\n", + "\n", + "samples_vad_seg1 = sorted(samples_vad_seg1)\n", + "samples_vad_seg2 = sorted(samples_vad_seg2)\n", + "samples_vad_seg3 = sorted(samples_vad_seg3)\n", + "samples_vad_seg4 = sorted(samples_vad_seg4)\n", + "samples_vad_seg5 = sorted(samples_vad_seg5)\n", + "samples_vad_seg6 = sorted(samples_vad_seg6)\n", + "samples_vad_seg7 = sorted(samples_vad_seg7)\n", + "\n", + "\n", + "\n", + "# Checking \n", + "#print('samples: ', len(samples), samples[25])\n", + "#print('samples segmented: ', len(samples_vad_seg), samples_vad_seg[25]) " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "samples = []\n", + "samples.append(samples1)\n", + "samples.append(samples2)\n", + "samples.append(samples3)\n", + "samples.append(samples4)\n", + "samples.append(samples5)\n", + "samples.append(samples6)\n", + "samples.append(samples7)\n", + "\n", + "samples_vad_seg = []\n", + "samples_vad_seg.append(samples_vad_seg1)\n", + "samples_vad_seg.append(samples_vad_seg2)\n", + "samples_vad_seg.append(samples_vad_seg3)\n", + "samples_vad_seg.append(samples_vad_seg4)\n", + "samples_vad_seg.append(samples_vad_seg5)\n", + "samples_vad_seg.append(samples_vad_seg6)\n", + "samples_vad_seg.append(samples_vad_seg7)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "samples = list(np.concatenate(samples))\n", + "samples_vad_seg = list(np.concatenate(samples_vad_seg))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "no_20 = 50 + len(samples2)\n", + "no_40 = no_20 + len(samples3)\n", + "no_80 = no_40 + len(samples4)\n", + "no_100 = no_80 + len(samples5)\n", + "no_140 = no_100 + len(samples6)\n", + "no_160 = no_140 + len(samples7)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# train data set 만들기" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "def mag_phase(file_path, sr=48000, n_fft=1024, hop_length=512, db=False, n_mels=50) :\n", + " \"\"\"\n", + " stft의 magnitude와 phase 리턴. 첫번째 bin 제거 \n", + " input : file path to an audio sample. Assumed stereo. \n", + " \"\"\"\n", + " audio, sr = librosa.load(file_path, sr=sr, mono=False) # 원래의 sr, stereo\n", + " DL = librosa.stft(audio[0], n_fft=n_fft, hop_length=hop_length)\n", + " DL_mag, DL_phase = librosa.magphase(DL)\n", + " \n", + " DR = librosa.stft(audio[1], n_fft=n_fft, hop_length=hop_length)\n", + " DR_mag, DR_phase = librosa.magphase(DR)\n", + " \n", + " if db :\n", + " DL_mag = librosa.core.amplitude_to_db(DL_mag)\n", + " DR_mag = librosa.core.amplitude_to_db(DR_mag)\n", + " \n", + " # rescale the right magnitudes w.r.t left channel magnitude \n", + " avg = DL_mag.mean() \n", + " stdv = DL_mag.std()\n", + " DL_mag = (DL_mag - avg)/stdv\n", + " DR_mag = (DR_mag - avg)/stdv\n", + " \n", + " # return( (DL_mag, np.angle(DL_phase)), (DR_mag, np.angle(DR_phase)) )\n", + " return( (DL_mag[1:, :], np.angle(DL_phase)[1:, :]), (DR_mag[1:, :], np.angle(DR_phase)[1:, :]) )" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "def generatio_tensor_instances(array_2d, dest_path, seq_len, hop, label):\n", + " \"\"\"\n", + " array_2d : ndarray. STFT magnitude or phase.\n", + " dest_path : file path\n", + " seq_len : number of frames in an instance.\n", + " label : segmented labels. 0 and 1's. The same length as original wav file of the audio sample. \n", + " \"\"\"\n", + " row_size, col_size = array_2d.shape[0], array_2d.shape[1]\n", + " ratio = len(label)/col_size # ratio : how many data points per frame \n", + " stack_array = [] # 4D tensor that will hold the instances\n", + " label_array = []\n", + "\n", + " j=0\n", + " while j <= (col_size - (seq_len+1)): \n", + " context_frame = array_2d[:, j:(j+seq_len)]\n", + " # seg_label = round( label[int(j*ratio):int((j+seq_len)*ratio)].mean() ) \n", + " threshold = 0.5 # if greater than the threshold, then speech \n", + " seg_label = 1 if label[int(j*ratio):int((j+seq_len)*ratio)].mean() > threshold else 0\n", + "\n", + "# # store the instances\n", + "# dest_path_ext = ''.join([dest_path, '_', str(j)])\n", + "# os.makedirs(os.path.dirname(dest_path_ext), exist_ok=True)\n", + "\n", + "# np.savez(dest_path_ext, spectrogram = context_frame,\n", + "# label=seg_label)\n", + " \n", + " stack_array.append(context_frame[:,:,np.newaxis]) # make context_frame to 3d tensor & append \n", + " label_array.append(seg_label)\n", + " \n", + " j = j+hop\n", + " \n", + " return np.stack(stack_array, axis=0), label_array" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "315 315 315\n" + ] + }, + { + "data": { + "text/plain": [ + "((17, 512, 100, 1), (17, 512, 100, 1), (17,))" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "no_samples = len(samples) \n", + "\n", + "mag_L_instances = [] # elements are ndarrays\n", + "mag_R_instances = []\n", + "phase_L_instances = []\n", + "phase_R_instances = []\n", + "label_instances = [] # elements are lists\n", + "\n", + "for i in range(0, no_samples):\n", + " voice_noise_label = np.load(samples_vad_seg[i])\n", + " if('npy' in samples_vad_seg[i].split('/')[-1]):\n", + " label = voice_noise_label[0] # use the left channel label. this take care of 0 degree problem\n", + " else: # npz file\n", + " label = voice_noise_label[\"label\"] \n", + " (mag_L, phase_L), (mag_R, phase_R) = mag_phase(samples[i], db=True)\n", + " \n", + " # generate instances with 1.16 sec duration (100 frames), at every 0.116 sec apart (10 hops)\n", + " voice_dest_path = os.path.join(\"mag\", \"Left\", str(i))\n", + " mag_L_instances_sub, _ = generatio_tensor_instances(mag_L, voice_dest_path, 100, 10, label)\n", + " \n", + " voice_dest_path = os.path.join(\"mag\", \"Right\", str(i))\n", + " mag_R_instances_sub, _ = generatio_tensor_instances(mag_R, voice_dest_path, 100, 10, label)\n", + " \n", + " voice_dest_path = os.path.join(\"phase\", \"Left\", str(i))\n", + " phase_L_instances_sub, _ = generatio_tensor_instances(phase_L, voice_dest_path, 100, 10, label)\n", + " \n", + " voice_dest_path = os.path.join(\"phase\", \"Right\", str(i))\n", + " phase_R_instances_sub, label_sub = generatio_tensor_instances(phase_R, voice_dest_path, 100, 10, label)\n", + " \n", + " mag_L_instances.append(mag_L_instances_sub)\n", + " mag_R_instances.append(mag_R_instances_sub)\n", + " phase_L_instances.append(phase_L_instances_sub)\n", + " phase_R_instances.append(phase_R_instances_sub)\n", + " \n", + " label_instances.append(np.array(label_sub))\n", + " \n", + "\n", + "print(len(mag_L_instances), len(phase_R_instances), len(label_instances))\n", + "\n", + "mag_L_instances[0].shape, phase_R_instances[0].shape, label_instances[0].shape\n", + "# the first sample produced 15 instances. " + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(315, (17, 512, 100, 4))" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stacked_instances = []\n", + "\n", + "for i in range(0, no_samples):\n", + " concat_tensor = np.concatenate([mag_L_instances[i], phase_L_instances[i], \n", + " mag_R_instances[i], phase_R_instances[i]], axis = -1)\n", + " stacked_instances.append(concat_tensor)\n", + " \n", + "len(stacked_instances), stacked_instances[0].shape # L, R magnitudes and phases are stacked." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`stacked_instances` has 50 samples, where each sample holds ndarray instances produced from the corresponding audio sample\n", + "\n", + "#### Checking :" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Numer of the instances generated : : 30862\n", + "Percentage of voice instances: 0.4615719270952825\n", + "(17, 512, 100, 4) (17,)\n", + "(13, 512, 100, 4) (13,)\n", + "(14, 512, 100, 4) (14,)\n", + "(27, 512, 100, 4) (27,)\n", + "(5, 512, 100, 4) (5,)\n", + "(9, 512, 100, 4) (9,)\n", + "(21, 512, 100, 4) (21,)\n", + "(12, 512, 100, 4) (12,)\n", + "(43, 512, 100, 4) (43,)\n", + "(17, 512, 100, 4) (17,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(743, 512, 100, 4) (743,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(743, 512, 100, 4) (743,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(743, 512, 100, 4) (743,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(743, 512, 100, 4) (743,)\n", + "(743, 512, 100, 4) (743,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n" + ] + } + ], + "source": [ + "# the total number of instances generated:\n", + "total = 0\n", + "for i in range(0, no_samples):\n", + " total = total + stacked_instances[i].shape[0]\n", + "print(\"Numer of the instances generated : : \",total) \n", + "\n", + "# the ratio of instances with 0 or 1 label. 76% of the instances are labeled 1 (voice)\n", + "ave=[]\n", + "for sample in label_instances:\n", + " ave.append(np.mean(sample))\n", + "print(\"Percentage of voice instances: \", np.mean(ave) )\n", + "\n", + "# Check the shapes \n", + "for i in range(0, no_samples, 5):\n", + " print(stacked_instances[i].shape, label_instances[i].shape)\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To this point, the `label_instances` showed the labels for `voice (1) and non_voice (0)` instances. Now transform the `label_instances` to show the **class labels of the voice directions.**\n", + "\n", + "#### noise와 voice 방향에 따라 labeling\n", + "- noise : 0 \n", + "- 0도 : 1 , -90 \n", + "- 20도 : 2 , -70\n", + "- 40도 : 3 , -50\n", + "- 60도 : 4 , -30\n", + "- 80도 : 5 , -10\n", + "- 100도 : 6 , 10\n", + "- 120도 : 7 , 30\n", + "- 140도 : 8 , 50\n", + "- 160도 : 9 , 70\n", + "- 180도 :10 , 90" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "import copy\n", + "vad_label_instances = copy.deepcopy(label_instances)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(0,12): #0\n", + " for j in range(0, len(label_instances[i])):\n", + " if(label_instances[i][j] == 1):\n", + " label_instances[i][j] = 1\n", + " \n", + "for i in range(12,25): #60도\n", + " for j in range(0, len(label_instances[i])):\n", + " if(label_instances[i][j] == 1):\n", + " label_instances[i][j] = 4\n", + " \n", + "for i in range(25,38): #120도\n", + " for j in range(0, len(label_instances[i])):\n", + " if(label_instances[i][j] == 1):\n", + " label_instances[i][j] = 7\n", + " \n", + "for i in range(38,50): #180도\n", + " for j in range(0, len(label_instances[i])):\n", + " if(label_instances[i][j] == 1):\n", + " label_instances[i][j] = 10\n", + " \n", + "for i in range(50,no_20): #20도\n", + " for j in range(0, len(label_instances[i])):\n", + " if(label_instances[i][j] == 1):\n", + " label_instances[i][j] = 2\n", + " \n", + "for i in range(no_20,no_40): #40도\n", + " for j in range(0, len(label_instances[i])):\n", + " if(label_instances[i][j] == 1):\n", + " label_instances[i][j] = 3\n", + " \n", + "for i in range(no_40,no_80): #80도\n", + " for j in range(0, len(label_instances[i])):\n", + " if(label_instances[i][j] == 1):\n", + " label_instances[i][j] = 5\n", + " \n", + "for i in range(no_80,no_100): #100도\n", + " for j in range(0, len(label_instances[i])):\n", + " if(label_instances[i][j] == 1):\n", + " label_instances[i][j] = 6\n", + " \n", + "for i in range(no_100,no_140): #140도\n", + " for j in range(0, len(label_instances[i])):\n", + " if(label_instances[i][j] == 1):\n", + " label_instances[i][j] = 8\n", + "\n", + "for i in range(no_140,no_160): #160도\n", + " for j in range(0, len(label_instances[i])):\n", + " if(label_instances[i][j] == 1):\n", + " label_instances[i][j] = 9\n", + " \n", + "\n", + "# label_instances" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have 822 instances. And we have labeled them into 5 classes. Let's see how those labels are distributed." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0 25449\n", + "1.0 143\n", + "2.0 655\n", + "3.0 698\n", + "4.0 150\n", + "5.0 830\n", + "6.0 910\n", + "7.0 215\n", + "8.0 733\n", + "9.0 901\n", + "10.0 178\n", + "dtype: int64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "instances_labels = np.array([])\n", + "for audio_clip in label_instances:\n", + " instances_labels = np.hstack([instances_labels, audio_clip])\n", + " \n", + "pd.Series(instances_labels).value_counts().sort_index() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### `non-voice` instances (Class-0) have the largest percentage\n", + "\n", + "### Now we have `stacked_instances & label_instances`\n", + "- `stacked_instances` : list of 50 ndarrays. Each ndarray has instances of the audio sample\n", + "- `label_instances` ; list of 50 ndarrays denoting sample's class labels\n", + "\n", + "## Construct `train and validation set` split.\n", + "- Out of 50 audio samples, we will take 40 samples for training set, and the remaining to the validation set. Try to mix them evenly.\n", + "- Note that `stacked_instances` indices has : 0~11(Class-1), 12~24(Class-2), 23~37(Class-3), 38~49(Class-4) and Class-0 is assigned to the non_voice " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "total_instances_tensors = np.load('total_instances_tensors.npy', allow_pickle=True)\n", + "total_label_tensors = np.load('total_label_tensors.npy', allow_pickle=True)\n", + "total_vad_label_tensors = np.load('total_vad_label_tensors.npy', allow_pickle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(21, 512, 100, 4) (21,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(5, 512, 100, 4) (5,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(179, 512, 100, 4) (179,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(28, 512, 100, 4) (28,)\n", + "(13, 512, 100, 4) (13,)\n", + "(28, 512, 100, 4) (28,)\n", + "(13, 512, 100, 4) (13,)\n", + "(28, 512, 100, 4) (28,)\n", + "(17, 512, 100, 4) (17,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "(179, 512, 100, 4) (179,)\n", + "(28, 512, 100, 4) (28,)\n", + "\n", + "(25400, 512, 100, 4) (25400,)\n", + "(5462, 512, 100, 4) (5462,)\n" + ] + } + ], + "source": [ + "# transform the list to ndarray\n", + "#total_instances_tensors = np.array(stacked_instances) \n", + "#total_label_tensors = np.array(label_instances)\n", + "#total_vad_label_tensors = np.array(vad_label_instances)\n", + "\n", + "# randomly choose indices to be split to training and validation set\n", + "np.random.seed(19) # 19, 7, 5, 113, 34\n", + "\n", + "no_samples = len(samples) \n", + "idx = np.random.permutation(no_samples)\n", + "\n", + "'''\n", + "c1 = 0\n", + "c2 = 0\n", + "c3 = 0\n", + "c4 = 0\n", + "\n", + "for i in idx[-10:]:\n", + " if 0 <= i <= 11:\n", + " c1 = c1 + 1\n", + " elif 12 <= i <= 24 :\n", + " c2 = c2 + 1\n", + " elif 25 <= i <= 37 :\n", + " c3 = c3 + 1\n", + " elif 38 <= i :\n", + " c4 = c4 + 1\n", + "\n", + "print(\"Valid set distr.: Class-1: %d, Class-2: %d, Class-3: %d, Class-4: %d\\n\" % (c1, c2, c3, c4))\n", + "'''\n", + "\n", + "# Shuffle \n", + "X = total_instances_tensors[[idx]] # Shuffle the data using fancy indexing\n", + "y = total_label_tensors[[idx]]\n", + "y_vad = total_vad_label_tensors[[idx]]\n", + "\n", + "train_index = round(no_samples * 0.8) \n", + "\n", + "# Test \n", + "for i in range(0, no_samples, 10):\n", + " print(X[i].shape, y[i].shape)\n", + " \n", + "# Split \n", + "X_train = np.concatenate(X[:train_index], axis=0)\n", + "y_train = np.concatenate(y[:train_index], axis=0)\n", + "vad_train = np.concatenate(y_vad[:train_index], axis=0)\n", + "\n", + "X_val = np.concatenate(X[train_index: ], axis=0)\n", + "y_val = np.concatenate(y[train_index: ], axis=0)\n", + "vad_val= np.concatenate(y_vad[train_index: ], axis=0)\n", + "\n", + "print()\n", + "print(X_train.shape, y_train.shape)\n", + "print(X_val.shape, y_val.shape)\n", + "\n", + "# Convert scalar y's to One-Hot. 'y_val_vad' is not needed because it is binary classification \n", + "y_train = keras.utils.to_categorical(y_train, 11) # 5-Classes classification \n", + "y_val = keras.utils.to_categorical(y_val, 11)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# 모델1. (2D CNN + Bidirectional GRU) based Network 2output" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.layers import TimeDistributed, Bidirectional\n", + "from tensorflow.keras.layers import Conv2D, Conv1D, MaxPooling2D, MaxPooling1D, Input, Flatten, Dropout\n", + "from tensorflow.keras import layers, models\n", + "from sklearn.metrics import confusion_matrix, classification_report" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input_1 (InputLayer) (None, 512, 100, 4) 0 \n", + "__________________________________________________________________________________________________\n", + "conv2d (Conv2D) (None, 512, 100, 64) 2368 input_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "max_pooling2d (MaxPooling2D) (None, 170, 50, 64) 0 conv2d[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_1 (Conv2D) (None, 170, 50, 64) 36928 max_pooling2d[0][0] \n", + "__________________________________________________________________________________________________\n", + "max_pooling2d_1 (MaxPooling2D) (None, 56, 25, 64) 0 conv2d_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_2 (Conv2D) (None, 56, 25, 64) 36928 max_pooling2d_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "max_pooling2d_2 (MaxPooling2D) (None, 18, 12, 64) 0 conv2d_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_3 (Conv2D) (None, 18, 12, 64) 36928 max_pooling2d_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "max_pooling2d_3 (MaxPooling2D) (None, 6, 6, 64) 0 conv2d_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_4 (Conv2D) (None, 1, 6, 128) 49280 max_pooling2d_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape (Reshape) (None, 6, 128) 0 conv2d_4[0][0] \n", + "__________________________________________________________________________________________________\n", + "bidirectional (Bidirectional) (None, 256) 197376 reshape[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense (Dense) (None, 32) 8224 bidirectional[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout (Dropout) (None, 32) 0 dense[0][0] \n", + "__________________________________________________________________________________________________\n", + "vad_out (Dense) (None, 1) 33 dropout[0][0] \n", + "__________________________________________________________________________________________________\n", + "class_out (Dense) (None, 11) 363 dropout[0][0] \n", + "==================================================================================================\n", + "Total params: 368,428\n", + "Trainable params: 368,428\n", + "Non-trainable params: 0\n", + "__________________________________________________________________________________________________\n" + ] + } + ], + "source": [ + "reset_keras_session(100)\n", + "\n", + "if 'model' in locals():\n", + " del model\n", + " \n", + "input_spectrogram = Input(shape=(X_train.shape[1], X_train.shape[2], X_train.shape[3]))\n", + "\n", + "conv_1 = Conv2D(64, (3, 3), activation='relu', padding='same')(input_spectrogram)\n", + "conv_1_pool = MaxPooling2D((3, 2))(conv_1)\n", + "\n", + "conv_2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv_1_pool)\n", + "conv_2_pool = MaxPooling2D((3, 2))(conv_2)\n", + "\n", + "conv_3 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv_2_pool)\n", + "conv_3_pool = MaxPooling2D((3, 2))(conv_3)\n", + "\n", + "conv_4 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv_3_pool)\n", + "conv_4_pool = MaxPooling2D((3, 2))(conv_4)\n", + "\n", + "shape_conv_4_pool = conv_4_pool.get_shape().as_list() # (None, height, width, channel)\n", + "conv_5 = Conv2D(128, (shape_conv_4_pool[1], 1), padding='valid', activation='relu')(conv_4_pool)\n", + "# 앞의 conv_5 필터 수를 256 으로 늘리면 학습이 잘 진행되지 않음 \n", + "\n", + "shape_conv_5 = conv_5.get_shape().as_list()\n", + "reshaped = layers.Reshape((shape_conv_5[2], shape_conv_5[3]))(conv_5) # reshape to (timesteps, features) explicitly \n", + "bgru = Bidirectional(layers.GRU(units=128))(reshaped) # GRU units 의 수를 늘리면? \n", + "\n", + "fc1 = layers.Dense(32, activation='relu')(bgru)\n", + "fc1_drop = Dropout(0.5)(fc1)\n", + "\n", + "vad_out = layers.Dense(1, activation='sigmoid', name='vad_out')(fc1_drop)\n", + "class_out = layers.Dense(11, activation='softmax', name='class_out')(fc1_drop)\n", + "\n", + "\n", + "\n", + "model = models.Model(inputs=input_spectrogram, outputs=[vad_out, class_out])\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 25400 samples, validate on 5462 samples\n", + "Epoch 1/150\n", + "25400/25400 [==============================] - 87s 3ms/sample - loss: 0.7343 - vad_out_loss: 0.1803 - class_out_loss: 0.5540 - vad_out_acc: 0.9297 - class_out_acc: 0.8287 - val_loss: 0.4833 - val_vad_out_loss: 0.0446 - val_class_out_loss: 0.4387 - val_vad_out_acc: 0.9962 - val_class_out_acc: 0.8330\n", + "Epoch 2/150\n", + "25400/25400 [==============================] - 76s 3ms/sample - loss: 0.4412 - vad_out_loss: 0.0550 - class_out_loss: 0.3862 - vad_out_acc: 0.9843 - class_out_acc: 0.8506 - val_loss: 0.3947 - val_vad_out_loss: 0.0124 - val_class_out_loss: 0.3823 - val_vad_out_acc: 0.9962 - val_class_out_acc: 0.8327\n", + "Epoch 3/150\n", + "25400/25400 [==============================] - 77s 3ms/sample - loss: 0.3787 - vad_out_loss: 0.0440 - class_out_loss: 0.3346 - vad_out_acc: 0.9861 - class_out_acc: 0.8645 - val_loss: 0.3322 - val_vad_out_loss: 0.0125 - val_class_out_loss: 0.3197 - val_vad_out_acc: 0.9974 - val_class_out_acc: 0.8662\n", + "Epoch 4/150\n", + "25400/25400 [==============================] - 76s 3ms/sample - loss: 0.3498 - vad_out_loss: 0.0384 - class_out_loss: 0.3114 - vad_out_acc: 0.9873 - class_out_acc: 0.8676 - val_loss: 0.3139 - val_vad_out_loss: 0.0138 - val_class_out_loss: 0.3002 - val_vad_out_acc: 0.9973 - val_class_out_acc: 0.8667\n", + "Epoch 5/150\n", + "25400/25400 [==============================] - 77s 3ms/sample - loss: 0.3311 - vad_out_loss: 0.0381 - class_out_loss: 0.2930 - vad_out_acc: 0.9872 - class_out_acc: 0.8733 - val_loss: 0.2854 - val_vad_out_loss: 0.0148 - val_class_out_loss: 0.2706 - val_vad_out_acc: 0.9960 - val_class_out_acc: 0.8665\n", + "Epoch 6/150\n", + "25400/25400 [==============================] - 77s 3ms/sample - loss: 0.2917 - vad_out_loss: 0.0305 - class_out_loss: 0.2612 - vad_out_acc: 0.9891 - class_out_acc: 0.8841 - val_loss: 0.2439 - val_vad_out_loss: 0.0120 - val_class_out_loss: 0.2319 - val_vad_out_acc: 0.9971 - val_class_out_acc: 0.9006\n", + "Epoch 7/150\n", + "25400/25400 [==============================] - 76s 3ms/sample - loss: 0.2633 - vad_out_loss: 0.0291 - class_out_loss: 0.2342 - vad_out_acc: 0.9893 - class_out_acc: 0.8930 - val_loss: 0.2686 - val_vad_out_loss: 0.0112 - val_class_out_loss: 0.2575 - val_vad_out_acc: 0.9962 - val_class_out_acc: 0.8951\n", + "Epoch 8/150\n", + "25400/25400 [==============================] - 77s 3ms/sample - loss: 0.2502 - vad_out_loss: 0.0289 - class_out_loss: 0.2213 - vad_out_acc: 0.9889 - class_out_acc: 0.8985 - val_loss: 0.2078 - val_vad_out_loss: 0.0098 - val_class_out_loss: 0.1980 - val_vad_out_acc: 0.9973 - val_class_out_acc: 0.9260\n", + "Epoch 9/150\n", + "25400/25400 [==============================] - 77s 3ms/sample - loss: 0.2288 - vad_out_loss: 0.0275 - class_out_loss: 0.2013 - vad_out_acc: 0.9891 - class_out_acc: 0.9040 - val_loss: 0.2000 - val_vad_out_loss: 0.0095 - val_class_out_loss: 0.1905 - val_vad_out_acc: 0.9973 - val_class_out_acc: 0.9253\n", + "Epoch 10/150\n", + "25400/25400 [==============================] - 77s 3ms/sample - loss: 0.2260 - vad_out_loss: 0.0275 - class_out_loss: 0.1985 - vad_out_acc: 0.9901 - class_out_acc: 0.9082 - val_loss: 0.2108 - val_vad_out_loss: 0.0130 - val_class_out_loss: 0.1977 - val_vad_out_acc: 0.9958 - val_class_out_acc: 0.9268\n", + "Epoch 11/150\n", + "25400/25400 [==============================] - 77s 3ms/sample - loss: 0.2169 - vad_out_loss: 0.0269 - class_out_loss: 0.1900 - vad_out_acc: 0.9894 - class_out_acc: 0.9101 - val_loss: 0.2034 - val_vad_out_loss: 0.0101 - val_class_out_loss: 0.1933 - val_vad_out_acc: 0.9974 - val_class_out_acc: 0.9301\n", + "Epoch 12/150\n", + "25400/25400 [==============================] - 77s 3ms/sample - loss: 0.2083 - vad_out_loss: 0.0255 - class_out_loss: 0.1829 - vad_out_acc: 0.9896 - class_out_acc: 0.9150 - val_loss: 0.2552 - val_vad_out_loss: 0.0126 - val_class_out_loss: 0.2427 - val_vad_out_acc: 0.9973 - val_class_out_acc: 0.9244\n", + "Epoch 13/150\n", + "25400/25400 [==============================] - 77s 3ms/sample - loss: 0.1961 - vad_out_loss: 0.0245 - class_out_loss: 0.1716 - vad_out_acc: 0.9900 - class_out_acc: 0.9194 - val_loss: 0.1857 - val_vad_out_loss: 0.0074 - val_class_out_loss: 0.1783 - val_vad_out_acc: 0.9978 - val_class_out_acc: 0.9381\n", + "Epoch 14/150\n", + "25400/25400 [==============================] - 77s 3ms/sample - loss: 0.1891 - vad_out_loss: 0.0261 - class_out_loss: 0.1629 - vad_out_acc: 0.9896 - class_out_acc: 0.9287 - val_loss: 0.2063 - val_vad_out_loss: 0.0090 - val_class_out_loss: 0.1973 - val_vad_out_acc: 0.9978 - val_class_out_acc: 0.9346\n", + "Epoch 15/150\n", + "25400/25400 [==============================] - 77s 3ms/sample - loss: 0.1844 - vad_out_loss: 0.0261 - class_out_loss: 0.1583 - vad_out_acc: 0.9897 - class_out_acc: 0.9330 - val_loss: 0.1795 - val_vad_out_loss: 0.0103 - val_class_out_loss: 0.1692 - val_vad_out_acc: 0.9974 - val_class_out_acc: 0.9475\n", + "Epoch 16/150\n", + "25400/25400 [==============================] - 76s 3ms/sample - loss: 0.1637 - vad_out_loss: 0.0236 - class_out_loss: 0.1401 - vad_out_acc: 0.9894 - class_out_acc: 0.9420 - val_loss: 0.1663 - val_vad_out_loss: 0.0082 - val_class_out_loss: 0.1581 - val_vad_out_acc: 0.9978 - val_class_out_acc: 0.9434\n", + "Epoch 17/150\n", + "25400/25400 [==============================] - 77s 3ms/sample - loss: 0.1576 - vad_out_loss: 0.0249 - class_out_loss: 0.1327 - vad_out_acc: 0.9888 - class_out_acc: 0.9450 - val_loss: 0.1694 - val_vad_out_loss: 0.0093 - val_class_out_loss: 0.1601 - val_vad_out_acc: 0.9974 - val_class_out_acc: 0.9486\n", + "Epoch 18/150\n", + "25400/25400 [==============================] - 78s 3ms/sample - loss: 0.1556 - vad_out_loss: 0.0240 - class_out_loss: 0.1317 - vad_out_acc: 0.9898 - class_out_acc: 0.9489 - val_loss: 0.2284 - val_vad_out_loss: 0.0136 - val_class_out_loss: 0.2147 - val_vad_out_acc: 0.9963 - val_class_out_acc: 0.9449\n", + "Epoch 19/150\n", + "25400/25400 [==============================] - 77s 3ms/sample - loss: 0.1402 - vad_out_loss: 0.0239 - class_out_loss: 0.1163 - vad_out_acc: 0.9894 - class_out_acc: 0.9528 - val_loss: 0.2588 - val_vad_out_loss: 0.0125 - val_class_out_loss: 0.2463 - val_vad_out_acc: 0.9973 - val_class_out_acc: 0.9345\n", + "Epoch 20/150\n", + "25400/25400 [==============================] - 78s 3ms/sample - loss: 0.1311 - vad_out_loss: 0.0226 - class_out_loss: 0.1085 - vad_out_acc: 0.9898 - class_out_acc: 0.9568 - val_loss: 0.2042 - val_vad_out_loss: 0.0098 - val_class_out_loss: 0.1945 - val_vad_out_acc: 0.9976 - val_class_out_acc: 0.9498\n", + "Epoch 21/150\n", + "25400/25400 [==============================] - 78s 3ms/sample - loss: 0.1259 - vad_out_loss: 0.0226 - class_out_loss: 0.1033 - vad_out_acc: 0.9901 - class_out_acc: 0.9586 - val_loss: 0.2330 - val_vad_out_loss: 0.0141 - val_class_out_loss: 0.2190 - val_vad_out_acc: 0.9973 - val_class_out_acc: 0.9476\n", + "Epoch 22/150\n", + "25400/25400 [==============================] - 77s 3ms/sample - loss: 0.1453 - vad_out_loss: 0.0291 - class_out_loss: 0.1162 - vad_out_acc: 0.9890 - class_out_acc: 0.9554 - val_loss: 0.1881 - val_vad_out_loss: 0.0093 - val_class_out_loss: 0.1788 - val_vad_out_acc: 0.9978 - val_class_out_acc: 0.9412\n", + "Epoch 23/150\n", + "25400/25400 [==============================] - 78s 3ms/sample - loss: 0.1073 - vad_out_loss: 0.0203 - class_out_loss: 0.0870 - vad_out_acc: 0.9910 - class_out_acc: 0.9638 - val_loss: 0.2133 - val_vad_out_loss: 0.0163 - val_class_out_loss: 0.1970 - val_vad_out_acc: 0.9960 - val_class_out_acc: 0.9546\n", + "Epoch 24/150\n", + "25400/25400 [==============================] - 78s 3ms/sample - loss: 0.1166 - vad_out_loss: 0.0225 - class_out_loss: 0.0941 - vad_out_acc: 0.9898 - class_out_acc: 0.9622 - val_loss: 0.1835 - val_vad_out_loss: 0.0110 - val_class_out_loss: 0.1725 - val_vad_out_acc: 0.9969 - val_class_out_acc: 0.9572\n", + "Epoch 25/150\n", + "25400/25400 [==============================] - 78s 3ms/sample - loss: 0.1134 - vad_out_loss: 0.0234 - class_out_loss: 0.0901 - vad_out_acc: 0.9897 - class_out_acc: 0.9649 - val_loss: 0.2432 - val_vad_out_loss: 0.0112 - val_class_out_loss: 0.2320 - val_vad_out_acc: 0.9960 - val_class_out_acc: 0.9550\n", + "Epoch 26/150\n", + "25400/25400 [==============================] - 77s 3ms/sample - loss: 0.0996 - vad_out_loss: 0.0207 - class_out_loss: 0.0789 - vad_out_acc: 0.9902 - class_out_acc: 0.9680 - val_loss: 0.2508 - val_vad_out_loss: 0.0085 - val_class_out_loss: 0.2423 - val_vad_out_acc: 0.9974 - val_class_out_acc: 0.9529\n", + "Epoch 27/150\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "25400/25400 [==============================] - 77s 3ms/sample - loss: 0.0904 - vad_out_loss: 0.0198 - class_out_loss: 0.0705 - vad_out_acc: 0.9904 - class_out_acc: 0.9704 - val_loss: 0.2284 - val_vad_out_loss: 0.0090 - val_class_out_loss: 0.2194 - val_vad_out_acc: 0.9978 - val_class_out_acc: 0.9493\n", + "Epoch 28/150\n", + "25400/25400 [==============================] - 79s 3ms/sample - loss: 0.1109 - vad_out_loss: 0.0248 - class_out_loss: 0.0861 - vad_out_acc: 0.9892 - class_out_acc: 0.9680 - val_loss: 0.2604 - val_vad_out_loss: 0.0100 - val_class_out_loss: 0.2504 - val_vad_out_acc: 0.9976 - val_class_out_acc: 0.9544\n", + "Epoch 29/150\n", + "25400/25400 [==============================] - 79s 3ms/sample - loss: 0.1078 - vad_out_loss: 0.0226 - class_out_loss: 0.0851 - vad_out_acc: 0.9896 - class_out_acc: 0.9675 - val_loss: 0.2283 - val_vad_out_loss: 0.0103 - val_class_out_loss: 0.2180 - val_vad_out_acc: 0.9960 - val_class_out_acc: 0.9453\n", + "Epoch 30/150\n", + "25400/25400 [==============================] - 77s 3ms/sample - loss: 0.0981 - vad_out_loss: 0.0213 - class_out_loss: 0.0767 - vad_out_acc: 0.9904 - class_out_acc: 0.9692 - val_loss: 0.2228 - val_vad_out_loss: 0.0085 - val_class_out_loss: 0.2143 - val_vad_out_acc: 0.9974 - val_class_out_acc: 0.9509\n", + "Epoch 31/150\n", + "25400/25400 [==============================] - 77s 3ms/sample - loss: 0.0888 - vad_out_loss: 0.0197 - class_out_loss: 0.0691 - vad_out_acc: 0.9906 - class_out_acc: 0.9720 - val_loss: 0.2586 - val_vad_out_loss: 0.0096 - val_class_out_loss: 0.2490 - val_vad_out_acc: 0.9919 - val_class_out_acc: 0.9526\n", + "Epoch 32/150\n", + "25400/25400 [==============================] - 76s 3ms/sample - loss: 0.0840 - vad_out_loss: 0.0198 - class_out_loss: 0.0641 - vad_out_acc: 0.9915 - class_out_acc: 0.9742 - val_loss: 0.2664 - val_vad_out_loss: 0.0092 - val_class_out_loss: 0.2572 - val_vad_out_acc: 0.9976 - val_class_out_acc: 0.9531\n", + "Epoch 33/150\n", + "25400/25400 [==============================] - 76s 3ms/sample - loss: 0.0995 - vad_out_loss: 0.0227 - class_out_loss: 0.0768 - vad_out_acc: 0.9907 - class_out_acc: 0.9720 - val_loss: 0.2026 - val_vad_out_loss: 0.0121 - val_class_out_loss: 0.1905 - val_vad_out_acc: 0.9918 - val_class_out_acc: 0.9491\n", + "Epoch 34/150\n", + "25400/25400 [==============================] - 74s 3ms/sample - loss: 0.0848 - vad_out_loss: 0.0180 - class_out_loss: 0.0668 - vad_out_acc: 0.9920 - class_out_acc: 0.9735 - val_loss: 0.2245 - val_vad_out_loss: 0.0096 - val_class_out_loss: 0.2149 - val_vad_out_acc: 0.9974 - val_class_out_acc: 0.9504\n", + "Epoch 35/150\n", + "25400/25400 [==============================] - 75s 3ms/sample - loss: 0.0700 - vad_out_loss: 0.0182 - class_out_loss: 0.0518 - vad_out_acc: 0.9916 - class_out_acc: 0.9780 - val_loss: 0.2728 - val_vad_out_loss: 0.0078 - val_class_out_loss: 0.2650 - val_vad_out_acc: 0.9980 - val_class_out_acc: 0.9535\n", + "Epoch 36/150\n", + "25400/25400 [==============================] - 77s 3ms/sample - loss: 0.0814 - vad_out_loss: 0.0191 - class_out_loss: 0.0623 - vad_out_acc: 0.9919 - class_out_acc: 0.9761 - val_loss: 0.2666 - val_vad_out_loss: 0.0088 - val_class_out_loss: 0.2578 - val_vad_out_acc: 0.9974 - val_class_out_acc: 0.9489\n" + ] + } + ], + "source": [ + "from tensorflow.keras.utils import multi_gpu_model\n", + "model = multi_gpu_model(model, gpus=8)\n", + "\n", + "model.compile(optimizer ='adam',loss={'vad_out':'binary_crossentropy','class_out':'categorical_crossentropy'}, \n", + " metrics ={'vad_out':'acc','class_out':'acc'})\n", + "\n", + "callbacks_list = [keras.callbacks.EarlyStopping(monitor='val_loss', patience=20),\n", + " keras.callbacks.ModelCheckpoint(filepath='Best_SSL_STFT_2DConv_RNN_2output.h5', \n", + " monitor='val_loss', save_best_only=True, mode='auto'),\n", + " keras.callbacks.ReduceLROnPlateau(monitor='val_loss',\n", + " factor=0.1, patience=50)]\n", + "\n", + "history = model.fit(X_train, [vad_train, y_train],\n", + " epochs=150, batch_size=64, \n", + " callbacks=callbacks_list,\n", + " validation_data=(X_val, [vad_val, y_val]),\n", + " shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy : 0.9978030025631637\n", + "[[4332 11]\n", + " [ 1 1118]]\n", + "Accuracy : 0.9434273160014647\n", + "[[4338 0 0 0 0 0 0 0 0 5]\n", + " [ 0 74 30 0 20 4 0 0 0 0]\n", + " [ 0 8 194 0 8 0 0 0 0 0]\n", + " [ 43 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 3 0 92 14 0 1 0 0]\n", + " [ 12 0 0 0 65 164 0 6 1 0]\n", + " [ 0 0 0 0 0 0 0 0 0 8]\n", + " [ 0 0 1 0 7 3 0 67 47 0]\n", + " [ 0 0 1 0 1 9 0 11 212 0]\n", + " [ 1 0 0 0 0 0 0 0 0 12]]\n" + ] + } + ], + "source": [ + "import sklearn.metrics \n", + "best_model = keras.models.load_model(os.path.join('.', 'Best_SSL_STFT_2DConv_RNN_2output.h5'))\n", + "vad_pred, pred = best_model.predict(X_val)\n", + "\n", + "cm_vad = sklearn.metrics.confusion_matrix(vad_val, \n", + " np.where(vad_pred>=0.5, 1, 0))\n", + "\n", + "cm_class = sklearn.metrics.confusion_matrix(np.argmax(y_val, axis=1), \n", + " np.argmax(pred, axis=1))\n", + "\n", + "acc_vad = sklearn.metrics.accuracy_score(vad_val, \n", + " np.where(vad_pred>=0.5, 1, 0))\n", + "\n", + "acc_class = sklearn.metrics.accuracy_score(np.argmax(y_val, axis=1),\n", + " np.argmax(pred, axis=1))\n", + "\n", + "print(\"Accuracy : \", acc_vad)\n", + "print(cm_vad)\n", + "print(\"Accuracy : \", acc_class)\n", + "print(cm_class)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 모델2. 2D CNN + 1D CNN 2output" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input_1 (InputLayer) (None, 512, 100, 4) 0 \n", + "__________________________________________________________________________________________________\n", + "conv2d (Conv2D) (None, 510, 98, 32) 1184 input_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "max_pooling2d (MaxPooling2D) (None, 170, 49, 32) 0 conv2d[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_1 (Conv2D) (None, 168, 47, 64) 18496 max_pooling2d[0][0] \n", + "__________________________________________________________________________________________________\n", + "max_pooling2d_1 (MaxPooling2D) (None, 56, 23, 64) 0 conv2d_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_2 (Conv2D) (None, 56, 23, 128) 73856 max_pooling2d_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "max_pooling2d_2 (MaxPooling2D) (None, 18, 11, 128) 0 conv2d_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_3 (Conv2D) (None, 18, 11, 256) 295168 max_pooling2d_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "max_pooling2d_3 (MaxPooling2D) (None, 6, 5, 256) 0 conv2d_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_4 (Conv2D) (None, 1, 5, 512) 786944 max_pooling2d_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape (Reshape) (None, 5, 512) 0 conv2d_4[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d (Conv1D) (None, 3, 1024) 1573888 reshape[0][0] \n", + "__________________________________________________________________________________________________\n", + "flatten (Flatten) (None, 3072) 0 conv1d[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense (Dense) (None, 64) 196672 flatten[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout (Dropout) (None, 64) 0 dense[0][0] \n", + "__________________________________________________________________________________________________\n", + "vad_out (Dense) (None, 1) 65 dropout[0][0] \n", + "__________________________________________________________________________________________________\n", + "class_out (Dense) (None, 11) 715 dropout[0][0] \n", + "==================================================================================================\n", + "Total params: 2,946,988\n", + "Trainable params: 2,946,988\n", + "Non-trainable params: 0\n", + "__________________________________________________________________________________________________\n" + ] + } + ], + "source": [ + "reset_keras_session(100)\n", + "if 'model' in locals():\n", + " del model\n", + " \n", + "input_spectrogram = Input(shape=(X_train.shape[1], X_train.shape[2], X_train.shape[3]))\n", + "\n", + "conv_1 = Conv2D(32, (3, 3), activation='relu', padding='valid')(input_spectrogram)\n", + "conv_1_pool = MaxPooling2D((3, 2))(conv_1)\n", + "\n", + "conv_2 = Conv2D(64, (3, 3), activation='relu', padding='valid')(conv_1_pool)\n", + "conv_2_pool = MaxPooling2D((3, 2))(conv_2)\n", + "\n", + "conv_3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv_2_pool)\n", + "conv_3_pool = MaxPooling2D((3, 2))(conv_3)\n", + "\n", + "conv_4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv_3_pool)\n", + "conv_4_pool = MaxPooling2D((3, 2))(conv_4)\n", + "\n", + "shape_conv_4_pool = conv_4_pool.get_shape().as_list() # (None, height, width, channel)\n", + "conv_5 = Conv2D(512, (shape_conv_4_pool[1], 1), padding='valid', activation='relu')(conv_4_pool)\n", + "\n", + "shape_conv_5 = conv_5.get_shape().as_list()\n", + "reshaped = layers.Reshape((shape_conv_5[2], shape_conv_5[3]))(conv_5) # reshape to (timesteps, features) explicitly \n", + "\n", + "conv_6 = Conv1D(1024, kernel_size=3, activation='relu')(reshaped)\n", + "flatten = layers.Flatten()(conv_6)\n", + "\n", + "fc1 = layers.Dense(64, activation='relu')(flatten)\n", + "fc1_drop = Dropout(0.1)(fc1)\n", + "\n", + "#dense_out = layers.Dense(5, activation='softmax')(fc1_drop)\n", + "vad_out = layers.Dense(1, activation='sigmoid', name='vad_out')(fc1_drop)\n", + "class_out = layers.Dense(11, activation='softmax', name='class_out')(fc1_drop)\n", + "\n", + "model = models.Model(inputs=input_spectrogram, outputs=[vad_out,class_out])\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 25400 samples, validate on 5462 samples\n", + "Epoch 1/150\n", + "25400/25400 [==============================] - 45s 2ms/sample - loss: 0.4759 - vad_out_loss: 0.0766 - class_out_loss: 0.3993 - vad_out_acc: 0.9682 - class_out_acc: 0.8572 - val_loss: 0.3429 - val_vad_out_loss: 0.0168 - val_class_out_loss: 0.3261 - val_vad_out_acc: 0.9956 - val_class_out_acc: 0.8739\n", + "Epoch 2/150\n", + "25400/25400 [==============================] - 44s 2ms/sample - loss: 0.2799 - vad_out_loss: 0.0310 - class_out_loss: 0.2489 - vad_out_acc: 0.9879 - class_out_acc: 0.8965 - val_loss: 0.2590 - val_vad_out_loss: 0.0118 - val_class_out_loss: 0.2472 - val_vad_out_acc: 0.9973 - val_class_out_acc: 0.9039\n", + "Epoch 3/150\n", + "25400/25400 [==============================] - 44s 2ms/sample - loss: 0.1989 - vad_out_loss: 0.0260 - class_out_loss: 0.1730 - vad_out_acc: 0.9891 - class_out_acc: 0.9259 - val_loss: 0.1794 - val_vad_out_loss: 0.0132 - val_class_out_loss: 0.1662 - val_vad_out_acc: 0.9930 - val_class_out_acc: 0.9310\n", + "Epoch 4/150\n", + "25400/25400 [==============================] - 42s 2ms/sample - loss: 0.1707 - vad_out_loss: 0.0248 - class_out_loss: 0.1458 - vad_out_acc: 0.9885 - class_out_acc: 0.9402 - val_loss: 0.1779 - val_vad_out_loss: 0.0159 - val_class_out_loss: 0.1619 - val_vad_out_acc: 0.9938 - val_class_out_acc: 0.9321\n", + "Epoch 5/150\n", + "25400/25400 [==============================] - 44s 2ms/sample - loss: 0.1524 - vad_out_loss: 0.0241 - class_out_loss: 0.1283 - vad_out_acc: 0.9894 - class_out_acc: 0.9483 - val_loss: 0.2010 - val_vad_out_loss: 0.0180 - val_class_out_loss: 0.1830 - val_vad_out_acc: 0.9956 - val_class_out_acc: 0.9222\n", + "Epoch 6/150\n", + "25400/25400 [==============================] - 44s 2ms/sample - loss: 0.1176 - vad_out_loss: 0.0205 - class_out_loss: 0.0972 - vad_out_acc: 0.9900 - class_out_acc: 0.9616 - val_loss: 0.1477 - val_vad_out_loss: 0.0130 - val_class_out_loss: 0.1347 - val_vad_out_acc: 0.9954 - val_class_out_acc: 0.9475\n", + "Epoch 7/150\n", + "25400/25400 [==============================] - 44s 2ms/sample - loss: 0.1000 - vad_out_loss: 0.0196 - class_out_loss: 0.0804 - vad_out_acc: 0.9915 - class_out_acc: 0.9712 - val_loss: 0.1920 - val_vad_out_loss: 0.0223 - val_class_out_loss: 0.1697 - val_vad_out_acc: 0.9886 - val_class_out_acc: 0.9447: 0.9916 - class_out\n", + "Epoch 8/150\n", + "25400/25400 [==============================] - 42s 2ms/sample - loss: 0.0877 - vad_out_loss: 0.0177 - class_out_loss: 0.0700 - vad_out_acc: 0.9918 - class_out_acc: 0.9735 - val_loss: 0.1642 - val_vad_out_loss: 0.0187 - val_class_out_loss: 0.1455 - val_vad_out_acc: 0.9897 - val_class_out_acc: 0.9528\n", + "Epoch 9/150\n", + "25400/25400 [==============================] - 44s 2ms/sample - loss: 0.0729 - vad_out_loss: 0.0173 - class_out_loss: 0.0555 - vad_out_acc: 0.9933 - class_out_acc: 0.9801 - val_loss: 0.1876 - val_vad_out_loss: 0.0202 - val_class_out_loss: 0.1673 - val_vad_out_acc: 0.9925 - val_class_out_acc: 0.9476\n", + "Epoch 10/150\n", + "25400/25400 [==============================] - 44s 2ms/sample - loss: 0.0642 - vad_out_loss: 0.0150 - class_out_loss: 0.0492 - vad_out_acc: 0.9937 - class_out_acc: 0.9822 - val_loss: 0.2414 - val_vad_out_loss: 0.0175 - val_class_out_loss: 0.2238 - val_vad_out_acc: 0.9927 - val_class_out_acc: 0.9431\n", + "Epoch 11/150\n", + "25400/25400 [==============================] - 44s 2ms/sample - loss: 0.0500 - vad_out_loss: 0.0123 - class_out_loss: 0.0377 - vad_out_acc: 0.9946 - class_out_acc: 0.9868 - val_loss: 0.2278 - val_vad_out_loss: 0.0171 - val_class_out_loss: 0.2107 - val_vad_out_acc: 0.9921 - val_class_out_acc: 0.9486\n", + "Epoch 12/150\n", + "25400/25400 [==============================] - 42s 2ms/sample - loss: 0.0495 - vad_out_loss: 0.0112 - class_out_loss: 0.0383 - vad_out_acc: 0.9957 - class_out_acc: 0.9870 - val_loss: 0.2428 - val_vad_out_loss: 0.0106 - val_class_out_loss: 0.2323 - val_vad_out_acc: 0.9956 - val_class_out_acc: 0.9352\n", + "Epoch 13/150\n", + "25400/25400 [==============================] - 44s 2ms/sample - loss: 0.0431 - vad_out_loss: 0.0097 - class_out_loss: 0.0334 - vad_out_acc: 0.9957 - class_out_acc: 0.9890 - val_loss: 0.2105 - val_vad_out_loss: 0.0208 - val_class_out_loss: 0.1897 - val_vad_out_acc: 0.9912 - val_class_out_acc: 0.9491\n", + "Epoch 14/150\n", + "25400/25400 [==============================] - 44s 2ms/sample - loss: 0.0343 - vad_out_loss: 0.0086 - class_out_loss: 0.0257 - vad_out_acc: 0.9965 - class_out_acc: 0.9922 - val_loss: 0.1988 - val_vad_out_loss: 0.0176 - val_class_out_loss: 0.1811 - val_vad_out_acc: 0.9930 - val_class_out_acc: 0.9517\n", + "Epoch 15/150\n", + "25400/25400 [==============================] - 44s 2ms/sample - loss: 0.0313 - vad_out_loss: 0.0070 - class_out_loss: 0.0243 - vad_out_acc: 0.9972 - class_out_acc: 0.9925 - val_loss: 0.4590 - val_vad_out_loss: 0.0296 - val_class_out_loss: 0.4294 - val_vad_out_acc: 0.9918 - val_class_out_acc: 0.9323\n", + "Epoch 16/150\n", + "25400/25400 [==============================] - 44s 2ms/sample - loss: 0.0399 - vad_out_loss: 0.0100 - class_out_loss: 0.0299 - vad_out_acc: 0.9968 - class_out_acc: 0.9912 - val_loss: 0.2512 - val_vad_out_loss: 0.0166 - val_class_out_loss: 0.2346 - val_vad_out_acc: 0.9930 - val_class_out_acc: 0.9431\n", + "Epoch 17/150\n", + "25400/25400 [==============================] - 42s 2ms/sample - loss: 0.0314 - vad_out_loss: 0.0064 - class_out_loss: 0.0250 - vad_out_acc: 0.9976 - class_out_acc: 0.9923 - val_loss: 0.2589 - val_vad_out_loss: 0.0274 - val_class_out_loss: 0.2315 - val_vad_out_acc: 0.9925 - val_class_out_acc: 0.9469\n", + "Epoch 18/150\n", + "25400/25400 [==============================] - 42s 2ms/sample - loss: 0.0273 - vad_out_loss: 0.0059 - class_out_loss: 0.0214 - vad_out_acc: 0.9981 - class_out_acc: 0.9933 - val_loss: 0.3264 - val_vad_out_loss: 0.0225 - val_class_out_loss: 0.3040 - val_vad_out_acc: 0.9963 - val_class_out_acc: 0.9469\n", + "Epoch 19/150\n", + "25400/25400 [==============================] - 44s 2ms/sample - loss: 0.0326 - vad_out_loss: 0.0078 - class_out_loss: 0.0248 - vad_out_acc: 0.9973 - class_out_acc: 0.9922 - val_loss: 0.3349 - val_vad_out_loss: 0.0263 - val_class_out_loss: 0.3086 - val_vad_out_acc: 0.9941 - val_class_out_acc: 0.9475\n", + "Epoch 20/150\n", + "25400/25400 [==============================] - 44s 2ms/sample - loss: 0.0302 - vad_out_loss: 0.0065 - class_out_loss: 0.0237 - vad_out_acc: 0.9980 - class_out_acc: 0.9928 - val_loss: 0.2428 - val_vad_out_loss: 0.0249 - val_class_out_loss: 0.2179 - val_vad_out_acc: 0.9945 - val_class_out_acc: 0.9500\n", + "Epoch 21/150\n", + "25400/25400 [==============================] - 44s 2ms/sample - loss: 0.0206 - vad_out_loss: 0.0040 - class_out_loss: 0.0166 - vad_out_acc: 0.9984 - class_out_acc: 0.9947 - val_loss: 0.2835 - val_vad_out_loss: 0.0374 - val_class_out_loss: 0.2461 - val_vad_out_acc: 0.9943 - val_class_out_acc: 0.9539\n", + "Epoch 22/150\n", + "25400/25400 [==============================] - 42s 2ms/sample - loss: 0.0276 - vad_out_loss: 0.0063 - class_out_loss: 0.0213 - vad_out_acc: 0.9980 - class_out_acc: 0.9939 - val_loss: 0.2827 - val_vad_out_loss: 0.0185 - val_class_out_loss: 0.2642 - val_vad_out_acc: 0.9936 - val_class_out_acc: 0.9513\n", + "Epoch 23/150\n", + "25400/25400 [==============================] - 42s 2ms/sample - loss: 0.0199 - vad_out_loss: 0.0049 - class_out_loss: 0.0151 - vad_out_acc: 0.9985 - class_out_acc: 0.9954 - val_loss: 0.2672 - val_vad_out_loss: 0.0354 - val_class_out_loss: 0.2318 - val_vad_out_acc: 0.9923 - val_class_out_acc: 0.9568\n", + "Epoch 24/150\n", + "25400/25400 [==============================] - 43s 2ms/sample - loss: 0.0241 - vad_out_loss: 0.0066 - class_out_loss: 0.0175 - vad_out_acc: 0.9981 - class_out_acc: 0.9950 - val_loss: 0.2868 - val_vad_out_loss: 0.0359 - val_class_out_loss: 0.2509 - val_vad_out_acc: 0.9910 - val_class_out_acc: 0.9520\n", + "Epoch 25/150\n", + "25400/25400 [==============================] - 44s 2ms/sample - loss: 0.0171 - vad_out_loss: 0.0037 - class_out_loss: 0.0133 - vad_out_acc: 0.9991 - class_out_acc: 0.9961 - val_loss: 0.2495 - val_vad_out_loss: 0.0344 - val_class_out_loss: 0.2151 - val_vad_out_acc: 0.9932 - val_class_out_acc: 0.9590\n", + "Epoch 26/150\n", + "25400/25400 [==============================] - 42s 2ms/sample - loss: 0.0247 - vad_out_loss: 0.0064 - class_out_loss: 0.0183 - vad_out_acc: 0.9980 - class_out_acc: 0.9952 - val_loss: 0.2483 - val_vad_out_loss: 0.0259 - val_class_out_loss: 0.2223 - val_vad_out_acc: 0.9945 - val_class_out_acc: 0.9570\n" + ] + } + ], + "source": [ + "from tensorflow.keras.utils import multi_gpu_model\n", + "model = multi_gpu_model(model, gpus=8)\n", + "\n", + "model.compile(optimizer ='adam',loss={'vad_out':'binary_crossentropy','class_out':'categorical_crossentropy'}, \n", + " metrics ={'vad_out':'acc','class_out':'acc'})\n", + "\n", + "callbacks_list = [keras.callbacks.EarlyStopping(monitor='val_loss', patience=20),\n", + " keras.callbacks.ModelCheckpoint(filepath='Best_SSL_STFT_2DConv_1DCNN_2output.h5', \n", + " monitor='val_loss', save_best_only=True, mode='auto'),\n", + " keras.callbacks.ReduceLROnPlateau(monitor='val_loss',\n", + " factor=0.1, patience=50)]\n", + "\n", + "history = model.fit(X_train, [vad_train, y_train],\n", + " epochs=150, batch_size=64, \n", + " callbacks=callbacks_list,\n", + " validation_data=(X_val, [vad_val, y_val]),\n", + " shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy : 0.995422922006591\n", + "[[4332 11]\n", + " [ 14 1105]]\n", + "Accuracy : 0.9474551446356646\n", + "[[4332 0 0 0 5 0 0 4 0 0 2]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 3 0 68 36 0 4 17 0 0 0 0]\n", + " [ 1 0 38 150 0 15 4 0 1 1 0]\n", + " [ 8 0 0 0 30 0 0 5 0 0 0]\n", + " [ 0 0 1 0 0 84 21 0 4 0 0]\n", + " [ 3 0 0 0 0 45 198 0 0 2 0]\n", + " [ 4 3 0 0 0 0 0 1 0 0 0]\n", + " [ 0 0 0 0 0 5 0 0 111 9 0]\n", + " [ 0 0 0 4 0 0 0 0 37 193 0]\n", + " [ 0 0 0 0 0 0 0 5 0 0 8]]\n" + ] + } + ], + "source": [ + "import sklearn.metrics \n", + "best_model = keras.models.load_model(os.path.join('.', 'Best_SSL_STFT_2DConv_1DCNN_2output.h5'))\n", + "vad_pred, pred = best_model.predict(X_val)\n", + "\n", + "cm_vad = sklearn.metrics.confusion_matrix(vad_val, \n", + " np.where(vad_pred>=0.5, 1, 0))\n", + "\n", + "cm_class = sklearn.metrics.confusion_matrix(np.argmax(y_val, axis=1), \n", + " np.argmax(pred, axis=1))\n", + "\n", + "acc_vad = sklearn.metrics.accuracy_score(vad_val, \n", + " np.where(vad_pred>=0.5, 1, 0))\n", + "\n", + "acc_class = sklearn.metrics.accuracy_score(np.argmax(y_val, axis=1),\n", + " np.argmax(pred, axis=1))\n", + "\n", + "print(\"Accuracy : \", acc_vad)\n", + "print(cm_vad)\n", + "print(\"Accuracy : \", acc_class)\n", + "print(cm_class)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "model = keras.models.load_model(os.path.join('.', 'Best_SSL_STFT_2DConv_1DCNN_2output.h5'))" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "def generatio_tensor_instances_no_label(array_2d, seq_len, hop):\n", + "\n", + " row_size, col_size = array_2d.shape[0], array_2d.shape[1]\n", + " stack_array = [] # 4D tensor that will hold the instances\n", + "\n", + " j=0\n", + " while j <= (col_size - (seq_len+1)): \n", + " context_frame = array_2d[:, j:(j+seq_len)]\n", + " stack_array.append(context_frame[:,:,np.newaxis]) # make context_frame to 3d tensor and append \n", + " \n", + " j = j+hop\n", + " \n", + " return np.stack(stack_array, axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "def predict(model, file_path) :\n", + " (mag_L, phase_L), (mag_R, phase_R) = mag_phase(file_path, db=True)\n", + " mag_L_instances_sub = generatio_tensor_instances_no_label(mag_L, 100, 10)\n", + " mag_R_instances_sub = generatio_tensor_instances_no_label(mag_R, 100, 10)\n", + " phase_L_instances_sub = generatio_tensor_instances_no_label(phase_L, 100, 10)\n", + " phase_R_instances_sub = generatio_tensor_instances_no_label(phase_R, 100, 10)\n", + " X_concat_tensor = np.concatenate([mag_L_instances_sub , phase_L_instances_sub , \n", + " mag_R_instances_sub , phase_R_instances_sub], axis = -1)\n", + " vad_pred, pred = model.predict(X_concat_tensor)\n", + " vad_pred = np.where(vad_pred >= 0.5, 1, 0)\n", + " pred = np.argmax(pred, axis=1)\n", + " return vad_pred, pred" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(63, 63)" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "val_list = idx[train_index:]\n", + "edge_list = []\n", + "\n", + "for i in range(0, len(val_list)):\n", + " val_idx = val_list[i]\n", + " \n", + " if((val_idx >= 0) & (val_idx < 12)):\n", + " edge_list.append(0)\n", + " elif((val_idx >= 12) & (val_idx < 25)):\n", + " edge_list.append(60)\n", + " elif((val_idx >= 25) & (val_idx < 38)):\n", + " edge_list.append(120)\n", + " elif((val_idx >= 38) & (val_idx < 50)):\n", + " edge_list.append(180)\n", + " elif((val_idx >= 50) & (val_idx < no_20)):\n", + " edge_list.append(20)\n", + " elif((val_idx >= no_20) & (val_idx < no_40)):\n", + " edge_list.append(40)\n", + " elif((val_idx >= no_40) & (val_idx < no_80)):\n", + " edge_list.append(80)\n", + " elif((val_idx >= no_80) & (val_idx < no_100)):\n", + " edge_list.append(100)\n", + " elif((val_idx >= no_100) & (val_idx < no_140)):\n", + " edge_list.append(140)\n", + " elif((val_idx >= no_140) & (val_idx < no_160)):\n", + " edge_list.append(160)\n", + "\n", + "len(val_list), len(edge_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([256, 218, 157, 76, 198, 67, 112, 247, 52, 286, 282, 102, 107,\n", + " 126, 122, 88, 169, 85, 195, 44, 106, 215, 305, 63, 32, 309,\n", + " 124, 191, 114, 233, 246, 100, 221, 267, 183, 273, 212, 268, 220,\n", + " 196, 188, 55, 291, 53, 194, 260, 201, 203, 265, 295, 231, 271,\n", + " 167, 266, 97, 162, 22, 159, 308, 19, 245, 110, 93])" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "val_list" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(28,)" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vad_label_instances[256].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(17, 1)" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vad_pred.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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EREQ+ghVXXMHGnqUZHXNK/nxmXXrpdreJxWI0NTURj8dJp9MEAgEcxxlcX1lZSVdXF6FQiLKyMgC8Xi/xeDyjWTNtzIuYMWYq8Hng/CGL/9sYU0j/ocll71snIiIi8h6dnZ2UlpaSm5sLQElJicuJMmPMi5i1dh0w833Lzhrr9xUREZHMG2nmSnaM7qwvIiIiWa+4uJiWlhZSqRTJZJLW1la3I2XEeJ0jJiIiIvKhBQIBwuEwBQUF+Hw+ioqK3I6UEcba7L8zRDAYtN3d3W7HEBER2SX19PSQn5/vdoysNNx3Y4yJWWuDo9lfhyZFREREXKIiJiIiIuISFTERERERl6iIiYiIiLhERUxERETEJSpiIiIiIi5RERMREZEJp7q6mrq6Oqqqqujo6AD6H4Pk9/spLCwklUoRiUTw+/1EIhGX026bbugqIiIiE1ZNTc3g68bGRqLRKOXl5QDU19eTSCTweDxuxRuRipiIiIhMCLW1tTQ0NODz+cjLy8NxHCoqKgiFQvT29tLc3Ex7ezttbW0kk0n6+vpwHIdoNEo4HHY7/rBUxERERGTUOpuf563X+jI65l55Xj57+kHb3SYWi9HU1EQ8HiedThMIBHAcZ3B9ZWUlXV1dhEIhysrKAPB6vcTj8YxmzTQVMREREcl6nZ2dlJaWkpubC0BJSYnLiTJDRUxERERGbaSZK9kxumpSREREsl5xcTEtLS2kUimSySStra1uR8oIzYiJiIhI1gsEAoTDYQoKCvD5fBQVFbkdKSOMtdbtDCMKBoO2u7vb7RgiIiK7pJ6eHvLz892OkZWG+26MMTFrbXA0++vQpIiIiIhLVMREREREXKIiJiIiIuISFTERERERl6iIiYiIiLhERUxERETEJSpiIiIiMuFUV1dTV1dHVVUVHR0dQP9jkPx+P4WFhaRSKSKRCH6/n0gk4nLabdMNXUVERGTCqqmpGXzd2NhINBqlvLwcgPr6ehKJBB6Px614I1IRExERkQmhtraWhoYGfD4feXl5OI5DRUUFoVCI3t5empubaW9vp62tjWQySV9fH47jEI1GCYfDbscfloqYiIiIjNrDv65n1Sv/yuiYvgM+yXEVi7e7TSwWo6mpiXg8TjqdJhAI4DjO4PrKykq6uroIhUKUlZUB4PV6icfjGc2aaSpiIiIikvU6OzspLS0lNzcXgJKSEpcTZYaKmIiIiIzaSDNXsmN01aSIiIhkveLiYlpaWkilUiSTSVpbW92OlBGaERMREZGsFwgECIfDFBQU4PP5KCoqcjtSRhhrrdsZRhQMBm13d7fbMURERHZJPT095Ofnux0jKw333RhjYtba4Gj216FJEREREZeoiImIiIi4REVMRERExCUqYiIiIiIuURETERERcYmKmIiIiIhLVMRERERkwqmurqauro6qqio6OjqA/scg+f1+CgsLSaVSRCIR/H4/kUjE5bTbphu6ioiIyIRVU1Mz+LqxsZFoNEp5eTkA9fX1JBIJPB6PW/FGpCImIiIiE0JtbS0NDQ34fD7y8vJwHIeKigpCoRC9vb00NzfT3t5OW1sbyWSSvr4+HMchGo0SDofdjj8sFTEREREZtd7Wl9j0xrqMjrnbflPZ45QDt7tNLBajqamJeDxOOp0mEAjgOM7g+srKSrq6ugiFQpSVlQHg9XqJx+MZzZppKmIiIiKS9To7OyktLSU3NxeAkpISlxNlhoqYiIiIjNpIM1eyY8b8qkljzDJjzNPGmLgxpntg2QxjzAPGmBcGfu851jlERERk4iouLqalpYVUKkUymaS1tdXtSBkxXrevOM5aWzjkSeSXAA9aa+cBDw78LSIiIjKsQCBAOBymoKCARYsWUVRU5HakjDDW2rF9A2OWAUFr7VtDlj0HHGutfdMYsy/wiLX24G2NEQwGbXd395jmFBERkeH19PSQn5/vdoysNNx3Y4yJDZl82q7xmBGzwJ+MMTFjzOKBZftYa98ceL0C2GcccoiIiIhklfE4Wf8Ya+1yY4wPeMAYs3ToSmutNcZ8YFpuoLQtBpg9e/Y4xBQREREZX2M+I2atXT7wexVwN3A4sHLgkCQDv1cNs1+9tTZorQ3uvffeYx1TREREZNyNaREzxkw1xkx79zXwBWAJcC9wzsBm5wD3jGUOERERkWw01ocm9wHuNsa8+163WWv/aIz5K9BsjPka8Apw+hjnEBEREck6Y1rErLX/AgqGWf42cMJYvreIiIhIthuv+4iJiIiIZEx1dTV1dXVUVVXR0dEB9D8Gye/3U1hYSCqVIhKJ4Pf7iUQiLqfdNj3iSERERCasmpqawdeNjY1Eo1HKy8sBqK+vJ5FI4PF43Io3IhUxERERmRBqa2tpaGjA5/ORl5eH4zhUVFQQCoXo7e2lubmZ9vZ22traSCaT9PX14TgO0WiUcDjsdvxhqYiJiIjIqLW1tbFixYqMjjlr1iwWLVq03W1isRhNTU3E43HS6TSBQADHcQbXV1ZW0tXVRSgUoqysDACv10s8Hs9o1kxTERMREZGs19nZSWlpKbm5uQCUlJS4nCgzVMRERERk1EaauZIdo6smRUREJOsVFxfT0tJCKpUimUzS2trqdqSM0IyYiIiIZL1AIEA4HKagoACfz0dRUZHbkTLCWPuB521nnWAwaLu7u92OISIiskvq6ekhPz/f7RhZabjvxhgTs9YGR7O/Dk2KiIiIuERFTERERMQlKmIiIiIiLlERExEREXGJipiIiIiIS1TERERERFyiIiYiIiITTnV1NXV1dVRVVdHR0QH0PwbJ7/dTWFhIKpUiEong9/uJRCIup9023dBVREREJqyamprB142NjUSjUcrLywGor68nkUjg8XjcijciFTERERGZEGpra2loaMDn85GXl4fjOFRUVBAKhejt7aW5uZn29nba2tpIJpP09fXhOA7RaJRwOOx2/GGpiImIiMioPf/8j0n29WR0zGnefA466Ifb3SYWi9HU1EQ8HiedThMIBHAcZ3B9ZWUlXV1dhEIhysrKAPB6vcTj8YxmzTQVMREREcl6nZ2dlJaWkpubC0BJSYnLiTJDRUxERERGbaSZK9kxumpSREREsl5xcTEtLS2kUimSySStra1uR8oIzYiJiIhI1gsEAoTDYQoKCvD5fBQVFbkdKSOMtdbtDCMKBoO2u7vb7RgiIiK7pJ6eHvLz892OkZWG+26MMTFrbXA0++vQpIiIiIhLVMREREREXKIiJiIiIuISFTERERERl6iIiYiIiLhERUxERETEJSpiIiIiMuFUV1dTV1dHVVUVHR0dQP9jkPx+P4WFhaRSKSKRCH6/n0gk4nLabdMNXUVERGTCqqmpGXzd2NhINBqlvLwcgPr6ehKJBB6Px614I1IRExERkQmhtraWhoYGfD4feXl5OI5DRUUFoVCI3t5empubaW9vp62tjWQySV9fH47jEI1GCYfDbscfloqYiIiIjNoPX3idJX2pjI55qPdj/Hje/tvdJhaL0dTURDweJ51OEwgEcBxncH1lZSVdXV2EQiHKysoA8Hq9xOPxjGbNNBUxERERyXqdnZ2UlpaSm5sLQElJicuJMkNFTEREREZtpJkr2TG6alJERESyXnFxMS0tLaRSKZLJJK2trW5HygjNiImIiEjWCwQChMNhCgoK8Pl8FBUVuR0pI4y11u0MIwoGg7a7u9vtGCIiIruknp4e8vPz3Y6RlYb7bowxMWttcDT769CkiIiIiEtUxERERERcoiImIiIi4hIVMRERERGXqIiJiIiIuERFTERERMQlY1bEjDF5xpiHjTHPGmOeMcZ8Z2B5tTFmuTEmPvBz8lhlEBERkZ1TdXU1dXV1VFVV0dHRAfQ/Bsnv91NYWEgqlSISieD3+4lEIi6n3baxvKFrGvietfZvxphpQMwY88DAuquttXVj+N4iIiKyC6ipqRl83djYSDQapby8HID6+noSiQQej8eteCMasyJmrX0TeHPgddIY0wN8YqzeT0RERHZutbW1NDQ04PP5yMvLw3EcKioqCIVC9Pb20tzcTHt7O21tbSSTSfr6+nAch2g0Sjgcdjv+sMblEUfGmDnAp4G/AEcD3zLGnA100z9r9s545BAREZGP5vLWZ3j2jbUZHfOQ/abzo1P8290mFovR1NREPB4nnU4TCARwHGdwfWVlJV1dXYRCIcrKygDwer3E4/GMZs20MT9Z3xjjBe4CLrTWrgVuAA4ECumfMfvpNvZbbIzpNsZ0r169eqxjioiISBbr7OyktLSU3Nxcpk+fTklJiduRMmJMZ8SMMTn0l7BGa+3vAay1K4esvwn4w3D7WmvrgXrof9bkWOYUERGR0Rlp5kp2zFheNWmAm4Eea+3Phizfd8hmpcCSscogIiIiO4fi4mJaWlpIpVIkk0laW1vdjpQRYzkjdjRwFvC0MebdA7SXAl8xxhQCFlgGnD+GGURERGQnEAgECIfDFBQU4PP5KCoqcjtSRhhrs/+oXzAYtN3d3W7HEBER2SX19PSQn5/vdoysNNx3Y4yJWWuDo9lfd9YXERERcYmKmIiIiIhLVMREREREXKIiJiIiIuISFTERERERl6iIiYiIiLhERUxEREQmnOrqaurq6qiqqqKjowPofwyS3++nsLCQVCpFJBLB7/cTiURcTrtt4/LQbxEREZGxUFNTM/i6sbGRaDRKeXk5APX19SQSCTwej1vxRqQiJiIiIhNCbW0tDQ0N+Hw+8vLycByHiooKQqEQvb29NDc3097eTltbG8lkkr6+PhzHIRqNEg6H3Y4/LBUxERERGb22S2DF05kdc9YCWHTldjeJxWI0NTURj8dJp9MEAgEcxxlcX1lZSVdXF6FQiLKyMgC8Xi/xeHxbQ2YFFTERERHJep2dnZSWlpKbmwtASUmJy4kyQ0VMRERERm+EmSvZMbpqUkRERLJecXExLS0tpFIpkskkra2tbkfKCM2IiYiISNYLBAKEw2EKCgrw+XwUFRW5HSkjjLXW7QwjCgaDtru72+0YIiIiu6Senh7y8/PdjpGVhvtujDExa21wNPvr0KSIiIiIS1TERERERFyiIiYiIiLiEhUxEREREZeoiImIiIi4REVMRERExCUqYiIiIjLhVFdXU1dXR1VVFR0dHUD/Y5D8fj+FhYWkUikikQh+v59IJOJy2m3TDV1FRERkwqqpqRl83djYSDQapby8HID6+noSiQQej8eteCNSERMREZEJoba2loaGBnw+H3l5eTiOQ0VFBaFQiN7eXpqbm2lvb6etrY1kMklfXx+O4xCNRgmHw27HH5aKmIiIiIzaVU9dxdLE0oyOOX/GfC4+/OLtbhOLxWhqaiIej5NOpwkEAjiOM7i+srKSrq4uQqEQZWVlAHi9XuLxeEazZpqKmIiIiGS9zs5OSktLyc3NBaCkpMTlRJmhIiYiIiKjNtLMlewYXTUpIiIiWa+4uJiWlhZSqRTJZJLW1la3I2WEZsREREQk6wUCAcLhMAUFBfh8PoqKityOlBHGWut2hhEFg0Hb3d3tdgwREZFdUk9PD/n5+W7HyErDfTfGmJi1Njia/XVoUkRERMQlOjQpO4eXO6H1O7Ax6XaSsbX3wXDov8EhX4LcGW6nERGRj0hFTCa+pffBHf8Oe8yG+V90O83YsVvglSfgDxfC/RfBgSfAgjI4eBFMmeZ2OhER+RBUxGRii98G93wL9iuEM+/c+WeJrIUV/4Sn74Qlv4cX2mHyx+Cgk/pL2ac+Dzm7u51SRERGSUVMJq4nrof2S+GTx0K4EaZ43U409oyBfQv6f068HF77Cyy5E55pgWdbYMp0yD+l//Dl3M+BR/8TFxHJZvqntEw81sJD/w866yC/BP7tf2DyFLdTjb9Jk+CAo/p/Fl4FLz8CT98FPa0Qb4Spe8Mhp/aXsrwj+rffWW1aD2uXw5rXYM1yWPP6zn++oMhQc46B+Se7nUI+BBUxmVi2buk/P6r7FgicDaFrYJLH7VTu80yGT53Y/7P5anjhT/0zZX//Dfz1Jvh4HvhL+w9fzjqsf2ZtotiShr4VAwXrtYHC9fp7f1KJ9+1kYLep/b9FdnZb0/Dk9XDYGXDyf8PuH3c70biorq7G6/Wydu1aiouLOfHEE+ns7OSCCy6LLtIDAAAgAElEQVQgJyeHJ554gqqqKu6//35OPvlkfvKTn7gdeVgqYjJxpDfB3efDM7+Hoy+EE6sHC0XPs8/Q8FQ7mz36F+//+TQc8Glg4F6BK4GVDwIPupgpU/bt/5laBFOHLtd//rJrmvdOHxc8fTUs64LSG2HuZ92ONG5qamoGXzc2NhKNRikvLwegvr6eRCKBx5O9/4ddRUwmhk3roPlseLGj/9yoYy4EYN26dVxx+6+454AFvHXA8S6HFBFxh/nEFh5cN4vzn3mEExtOgaO+Ccf/cKe7eKe2tpaGhgZ8Ph95eXk4jkNFRQWhUIje3l6am5tpb2+nra2NZDJJX18fjuMQjUYJh8Nuxx+Wiphkv9Q7cFsYXv8rlFzXf0gSuK7+F9w5x8dzc4/nE1te5xsvPMiBH9vb5bAThN0EW7e6nWIYBjy7oZktkdHbsHkDD87cyp+nBYkdfgiLVh3ODzp/xr4vPgin1cO+h2X0/VZccQUbe5ZmdMwp+fOZdeml290mFovR1NREPB4nnU4TCARwHGdwfWVlJV1dXYRCIcrKygDwer3E4/GMZs00FTHJbskV8JvT4O0X4MsNcEgJba338r+spPNTR5HLesIrHiF6bJhZJ4bcTisi4oqvATfe9EuaZ8/grn0+x+N7z+O0F+L88Kbj4bhL4ejvTPjzaTs7OyktLSU3NxeAkpISlxNlhoqYZK/Ey/CbU6FvNXy1mddyPslVTT/nfp/DBj7B5/q6qZycx4lfudDtpCIirrvgvG9w1rp1/PfvbuTuuYdy/cEn8+icAyh/7AnOef7k/nPHZsz9yO8z0syV7Jid+Hp2mdBWPgO3nAQb1sA5rdQ89DdKXniGO/f5HAdsXs5lLz5JU8n5nHiyLtcWEXnX1KlTubzye9w79yBKVz3KC7vN5dLDKyifeSpPXbsY/nZr/y2AJqDi4mJaWlpIpVIkk0laW1vdjpQRmhGT7PPqX+C2L0POVG7Z87vcvqSHf3zqBHxbV7L4Xw9x8RnnM/WkqSOPIyKyi5rzybnc8Mlvc/ddd/KbKWt4cPoRPFV8CKc8G+PSv3+FmeHrwDuxzqkNBAKEw2EKCgrw+XwUFRW5HSkjjJ0AzTgYDNru7m63Y8h4eKEDbi/nr5OP4NqDF/LQx4vIYTML3+rmok+fxKfmHeR2QhGRCednN17DXQfm8dLkA5m95RVO7/k7F33+8FHfBLanp4f8/PwxTjkxDffdGGNi1trgaPbXoUnJHkvuInlrORcd8H3OPPw/eWCPozg89U9++vZr3Hj6f6iEiYh8SN+94EL+9JmFVLz6EEnjpe7QUwn9axXN10T1FAqXuVbEjDELjTHPGWNeNMZc4lYOyRJ/vZmfPvgPTvzcr/jt/sez15YE33/uEe7+4rn825dPdzudiMiEN3XqVK4857vc9vE9ODnxGP/Y/RAuOuxUvtbawJKOZrfj7bJcKWLGGA9wPbAIOAT4ijHmEDeyiMus5c7rL+eU1R5+cthprJk0jbNffYg/Hn4C371AV0OKiGTap4MOt/zbN6l97RkO3fgc9808hrCZwaX/W8e6te+4HW+X49bJ+ocDL1pr/wVgjGkCvgQ861IezrvjOtJZ/AiEnVVq8mQezz8JyyQWvvMY39rXIXjOd3d4nFeX9vDYzW1s2TwGIUVEstze82dw4nln7dCjfM4+5985G7jiVz/l7gMP4pY5J9L55IN8qm/VB7Y994AgL7/zweUTkWerZfbMfdyOMcitIvYJ4LUhf78OHDF0A2PMYmAxwOzZs8c80EMzP816Pjbm7yPvNQnLYRuf5YyVmzmn4ps7vH8q2cc9V9xE7+qD2DK5EGw23i1eRGRsrYlPYnnl/xA4ax6Fx+/Y494uPf97nLd6NbV/+i1t+36aF/f44L3GvjppMms9O8fV6jme7Pp/7K5cNWmMKQMWWmsrB/4+CzjCWvut4bbXVZMynPYbG3j1L1PYNMWH2fhXNk15AbMl+68CFhHJNDvtY+y+diFbPFOY5nmaky45jX1mz8nY+Lpqcts+6lWTbs2ILQfyhvy9/8AykRHFH3mEvzUsJTXlIHZjJZtohFSKrevXuh1NRMQdayexafLvYY9ZJNPH03L5P5m5/72UXHo+u02Z4na6MVFdXY3X62Xt2rUUFxdz4okn0tnZyQUXXEBOTg5PPPEEVVVV3H///Zx88sn85Cc/cTvysNwqYn8F5hlj5tJfwM4AvupSFpkgVr32Kn+88k76Ni9gkmd/NuXeSXrNeraufwtjpjH54/swNc/ndkwRkXG1adMm0i+9w+b0Kszba7EfWwFbD2PlysO49fw7OPCEHI47K+x2zDFTU1Mz+LqxsZFoNEp5eTkA9fX1JBKJHTp3bry5UsSstWljzLeAdsAD3GKtfcaNLJL9Nm3cyL1X1vP2q7NJTz4M63mE9Lo32Jp8i614yJniY9Gl32PefL/bUUVEXPPLSy9k078SpNevYqvphmnPkDPpCzz72EyWPfILjlpcxPwjjhh5oCxWW1tLQ0MDPp+PvLw8HMehoqKCUChEb28vzc3NtLe309bWRjKZpK+vD8dxiEajhMPZWUZde8SRtfZ+4H633l8mhod/ezsvdWxi4+5+2NRD2sbYsn4t1m4gx+Nj9klHcOo557sdU0TEdd+44hrWr1vHTRd+my3JJHZ1Lxsm3Y/dYxobkot46OZeum/7GV/8QTl77v3hjx50Nj/PW6/1ZTA57JXn5bOnb/+m3bFYjKamJuLxOOl0mkAggOM4g+srKyvp6uoiFApRVlYGgNfrJR6PZzRrpulZk5KVno9189gvn2R9ziHkTEqwafLvMJtTbNnUi8fMIGfOfnzzqmsBWJl4kwdvvYRT3mknlw0uJxcRGX9xzzzePObbhI4/i+/cdDNPdz/Ow9f8D5s3vw3L32FSzh2YqbNZs7GYOy7uxDd/Fad8b3FWH7J7v87OTkpLS8nNzQWgpKTE5USZoSImWeWd1au4/4rfsrbvEJj0KdIfu5cta9awdd1qjMklx7sPlddeS+7UqWxJp7ntd5dzwku/5ask6NitiNVT93P7I4iIjKtJW9Icv7YL59Fv8Ye/3M6c03/MguBnWPDbz9B43VW8/fhSNm1ehVmzjkneVzGbj2T5vw7m119rIP/UvfjMqTtWaEaauZIdoyImWWHLli384Wc3sbJnbzbvVoid1IVdv4z02gRgycnxcdQFZ1F0zHEA3P/Ibezz6NWctfV5lpg5PHVElFMXLnb1M4iIuOX1lctoaryM0jV/ZPOtC/nN3qdSUvFfnPkfF8N/wHX/+XXSbybZklzFJtOFnRJj8uZF/P2PXp6/7+cc/93PMzs/ux9wU1xcTEVFBdFolHQ6TWtrK+efP/FPTVERE9c9ee8feOb3K9mw+0Gw+SU2e+5ja3Id1q5j8qS92evIgzjzO1EAlrz0d16+o4ovpjp5m2ncesBiwuU/5tDddnf5U4iIuGf/feZwxncbeezvf2LTff/FWW81sazuYe7zn0u49CL+4+obeOutlTRGoqTXr4UVCTZ67sV692R96iTu+9ky9pjRzpd+8DVyp013++MMKxAIEA6HKSgowOfzUVRU5HakjHDlhq47ajxu6Npw/jXYra49A32XtXVLDqmceUxO97He24ZZmWKLTTDJfBzPPtP59s9vAGDtujXc03Axp65sYQqbaJn+BY4s/y9m7/PBO0CLiOzqbm+5hkC8nnks5zHPoWz8wsUcf0T/IchH2u7m6d+0smnLW8BkcqbsgSdnHuQcwW4bV5MzeeUHxvOfcwRz958zvh9ijJhJlpmzZ2VsvIl6Q9ess3njTKzZOW96l90sWz/2RzatSrC1bzWwGzm5PsK1l7PPfnlsSadpbvkpRy65hbNYwZ9zPs2kRZdwurPQ7eAiIlkrfOqF9H3ha9z6m8v40ht3MvX+c7jr0RMo/Gotxy4q5dhFpfxP7aWsW/IGmzeuYsumFOzxIrvbY9ic/mBJsdaDtTkufJIxsGWL2wneQ0VswHr7MGTXfza7BLMVNr3ZC2xmN8/eLPjqyRwb6r/s+JGn/sDk9iv5ypaneZH9uL3gR4RLd/yB4CIiuyJv7jTOPv9aXnj1fJY0/ZBT+zpYU/8Et+7/ZcrOqqXysisA+Pk3zmNroo+t76ykz/wZk/PBo0OWc9lqd5KnlxiAvd1OMUhFbMDmDb3AJrdj7JImm72Y6t+Xyh/+FwAvv/EC3bddxqnJDlJM4dZ9yznt7CsJ505zOamIyMQzb7afed//PQ88dhfTH/wJZy//X5Ze2cGS4NcpC32T7/zyJl556TnuufwqNm/shU3D/LvQbsHa7HpY9odlbHbdskPniA14Nv7XMR1fhjfZsxsHLSgAYOOmDdx+66V88bVm9qSPe3OPJf+MH3PwAQtcTikisnPYkk7TdOeVFC/9NXmspiOniGmnXMYRh/Vfkb7yjdd4e9WKD+yXNjnMmzdvvOOOCYNh94F7kWXCRz1HTEVMssJd999A/lM3cAiv8NdJ80kc9z1O+uzpbscSEdkpJda+RVvDJZS+dS+T2ErLHos49qwrmbXXJ4bdfriyIf10sn6G/H3pE1i71e0Yu5zEOyvZ+sgN/Nump1jOXvz2oO/yldMvwzNZ/9UUERkrM6bvxZn/8T/En3+SN+6q4Yw197Liuk5+88mv4j/8ix/Y3rN5KutSmX2skVuMMeTuPtXtGIM0IzZg7Y/2ZbpZP6bvIcNL2d34/cxTOOnsK9lrjw///DMREflw7n3gFuY8fh2H2X8Nu77npGbyD8iufz5X//RGvFNzWZtcR/ERAU4sPoLOv/yNCy65gpzJk3ni3l9TVXcD9z/0GCcffzQ/+eF/ArCBHHbf79CM5dCMWIbc88l/x27ZOU5EnFDMJBYcdSpnzj/K7SQiIrusks+fy+Zjy/ldy9VsTr71gfX5k6fx1m57uZBs29Z7cjGeqXz7sv6r6d8Cbr7nYb514bf58pdPYx3wq8YWXnjhn3g8Ht79VMZ4yKZbgGtGTERERLYrW84Rq62tpaGhAZ/PR15eHo7jsGTJEkKhEL29vXz/+9/n4x//OJ/5zGdIJpPcd999LFiwgGg0SjgcHpNMmhETERGRcfPwr+tZ9crwhzA/LN8Bn+S4iu0/LzgWi9HU1EQ8HiedThMIBHAcZ3B9ZWUlXV1dhEIhysr670fp9XqJx+MZzZppKmIiIiKS9To7OyktLSV34NYTJSUlLifKDBUxERERGbWRZq5kx+gp1yIiIpL1iouLaWlpIZVKkUwmaW1tdTtSRmhGTERERLJeIBAgHA5TUFCAz+ejqKjI7UgZoasmRUREZLuy5arJbPRRr5rUoUkRERERl6iIiYiIiLhERUxERETEJSpiIiIiIi5RERMRERFxiYqYiIiIiEtUxERERGTCqa6upq6ujqqqKjo6OoD+xyD5/X4KCwtJpVJEIhH8fj+RSMTltNumG7qKiIjIhFVTUzP4urGxkWg0Snl5OQD19fUkEgk8Ho9b8UakIiYiIiITQm1tLQ0NDfh8PvLy8nAch4qKCkKhEL29vTQ3N9Pe3k5bWxvJZJK+vj4cxyEajRIOh92OPywVMRERERm13taX2PTGuoyOudt+U9njlAO3u00sFqOpqYl4PE46nSYQCOA4zuD6yspKurq6CIVClJWVAeD1eonH4xnNmmkqYiIiIpL1Ojs7KS0tJTc3F4CSkhKXE2WGipiIiIiM2kgzV7JjdNWkiIiIZL3i4mJaWlpIpVIkk0laW1vdjpQRmhETERGRrBcIBAiHwxQUFODz+SgqKnI7UkYYa63bGUYUDAZtd3e32zFERER2ST09PeTn57sdIysN990YY2LW2uBo9tehSRERERGXqIiJiIiIuERFTERERMQlKmIiIiIiLlERExEREXGJipiIiIiIS1TEREREZMKprq6mrq6OqqoqOjo6gP7HIPn9fgoLC0mlUkQiEfx+P5FIxOW026YbuoqIiMiEVVNTM/i6sbGRaDRKeXk5APX19SQSCTwej1vxRqQiJiIiIhNCbW0tDQ0N+Hw+8vLycByHiooKQqEQvb29NDc3097eTltbG8lkkr6+PhzHIRqNEg6H3Y4/LBUxERERGbW2tjZWrFiR0TFnzZrFokWLtrtNLBajqamJeDxOOp0mEAjgOM7g+srKSrq6ugiFQpSVlQHg9XqJx+MZzZppY3KOmDHmJ8aYpcaYfxpj7jbG7DGwfI4xJmWMiQ/83DgW7y8iIiI7l87OTkpLS8nNzWX69OmUlJS4HSkjxmpG7AEgaq1NG2OuAqLAxQPrXrLWFo7R+4qIiMgYGmnmSnbMmMyIWWv/ZK1ND/z5JLD/WLyPiIiI7BqKi4tpaWkhlUqRTCZpbW11O1JGjMc5YucCtw/5e64x5u/AWuAH1trOccggIiIiE1ggECAcDlNQUIDP56OoqMjtSBlhrLUfbkdjOoBZw6y6zFp7z8A2lwFB4DRrrTXGTAG81tq3jTEO0AL4rbVrhxl/MbAYYPbs2c4rr7zyoXKKiIjIR9PT00N+fr7bMbLScN+NMSZmrQ2OZv8PPSNmrT1xe+uNMRVACDjBDrQ9a+1GYOPA65gx5iXgIKB7mPHrgXqAYDD44dqiiIiISBYbq6smFwLfB0qsteuHLN/bGOMZeP1JYB7wr7HIICIiIpLtxuocsV8AU4AHjDEAT1prLwCKgRpjzGZgK3CBtTYxRhlEREREstqYFDFr7ae2sfwu4K6xeE8RERGRiUYP/RYRERFxiYqYiIiIiEtUxERERGTCqa6upq6ujqqqKjo6OoD+xyD5/X4KCwtJpVJEIhH8fj+RSMTltNumh36LiIjIhFVTUzP4urGxkWg0Snl5OQD19fUkEgk8Ho9b8UakIiYiIiITQm1tLQ0NDfh8PvLy8nAch4qKCkKhEL29vTQ3N9Pe3k5bWxvJZJK+vj4cxyEajRIOh92OPywVMRERERm155//Mcm+noyOOc2bz0EH/XC728RiMZqamojH46TTaQKBAI7jDK6vrKykq6uLUChEWVkZAF6vl3g8ntGsmaYiJiIiIlmvs7OT0tJScnNzASgpKXE5UWaoiImIiMiojTRzJTtGV02KiIhI1isuLqalpYVUKkUymaS1tdXtSBmhGTERERHJeoFAgHA4TEFBAT6fj6KiIrcjZYSx1rqdYUTBYNB2d3e7HUNERGSX1NPTQ35+vtsxstJw340xJmatDY5mfx2aFBEREXGJipiIiIiIS1TERERERFyiIiYiIiLiEhUxEREREZeoiImIiIi4REVMREREJpzq6mrq6uqoqqqio6MD6H8Mkt/vp7CwkFQqRSQSwe/3E4lEXE67bbqhq4iIiExYNTU1g68bGxuJRqOUl5cDUF9fTyKRwOPxuBVvRCpiIiIiMiHU1tbS0NCAz+cjLy8Px3GoqKggFArR29tLc3Mz7e3ttLW1kUwm6evrw3EcotEo4XDY7fjDUhETERGRUfvhC6+zpC+V0TEP9X6MH8/bf7vbxGIxmpqaiMfjpNNpAoEAjuMMrq+srKSrq4tQKERZWRkAXq+XeDye0ayZpiImIiIiWa+zs5PS0lJyc3MBKCkpcTlRZqiIiYiIyKiNNHMlO0ZXTYqIiEjWKy4upqWlhVQqRTKZpLW11e1IGaEZMREREcl6gUCAcDhMQUEBPp+PoqIityNlhLHWup1hRMFg0HZ3d7sdQ0REZJfU09NDfn6+2zGy0nDfjTEmZq0NjmZ/HZoUERERcYmKmIiIiIhLVMREREREXKIiJiIiIuISFTERERERl6iIiYiIiLhERUxEREQmnOrqaurq6qiqqqKjowPofwyS3++nsLCQVCpFJBLB7/cTiURcTrttuqGriIiITFg1NTWDrxsbG4lGo5SXlwNQX19PIpHA4/G4FW9EKmIiIiIyIdTW1tLQ0IDP5yMvLw/HcaioqCAUCtHb20tzczPt7e20tbWRTCbp6+vDcRyi0SjhcNjt+MNSERMREZFRu7z1GZ59Y21Gxzxkv+n86BT/dreJxWI0NTURj8dJp9MEAgEcxxlcX1lZSVdXF6FQiLKyMgC8Xi/xeDyjWTNNRUxERESyXmdnJ6WlpeTm5gJQUlLicqLMUBETERGRURtp5kp2jK6aFBERkaxXXFxMS0sLqVSKZDJJa2ur25EyQjNiIiIikvUCgQDhcJiCggJ8Ph9FRUVuR8oIY611O8OIgsGg7e7udjuGiIjILqmnp4f8/Hy3Y2Sl4b4bY0zMWhsczf46NCkiIiLiEhUxEREREZeoiImIiIi4REVMRERExCVjVsSMMdXGmOXGmPjAz8lD1kWNMS8aY54zxpw0VhlEREREstlY377iamtt3dAFxphDgDMAP7Af0GGMOchau2WMs4iIiIhkFTcOTX4JaLLWbrTWvgy8CBzuQg4RERGZoKqrq6mrq6OqqoqOjg6g/zFIfr+fwsJCUqkUkUgEv99PJBJxOe22jfWM2LeMMWcD3cD3rLXvAJ8AnhyyzesDy0RERER2SE1NzeDrxsZGotEo5eXlANTX15NIJPB4PG7FG9FHKmLGmA5g1jCrLgNuAH4M2IHfPwXO3YGxFwOLAWbPnv1RYoqIiMhOoLa2loaGBnw+H3l5eTiOQ0VFBaFQiN7eXpqbm2lvb6etrY1kMklfXx+O4xCNRgmHw27HH9ZHKmLW2hNHs50x5ibgDwN/Lgfyhqzef2DZ+8euB+qh/876HyWniIiIZEjbJbDi6cyOOWsBLLpyu5vEYjGampqIx+Ok02kCgQCO4wyur6yspKuri1AoRFlZGQBer5d4PJ7ZrBk2lldN7jvkz1JgycDre4EzjDFTjDFzgXnAU2OVQ0RERCa+zs5OSktLyc3NZfr06ZSUlLgdKSPG8hyx/zbGFNJ/aHIZcD6AtfYZY0wz8CyQBr6pKyZFREQmiBFmrmTHjNmMmLX2LGvtAmvtYdbaEmvtm0PW1VprD7TWHmytbRurDCL/v737D467rvM4/vp000bidnCasi2lwSpQSTe46e7Gu5nDjHIeirMXL5pj9cwdkQvgqH8wZXZwcchlonHsTPRwbpgbc+gYZoJpBo5IDkOOOAyXOONpgtujkAIVKgUJtN2m7tINdJPP/ZFtrDVp0ma3n03zfPzDd7+f749XP/Ol8+7n++MDALg41NbWqq+vT5lMRqlUSv39/a4j5UWh35oEAABYtmAwqGg0qkAgIJ/Pp5qaGteR8sJYW/zPwYfDYTs6Ouo6BgAAq9L4+LgqKytdxyhK8/WNMWbMWhteyv7MNQkAAOAIhRgAAIAjFGIAAACOUIgBAAA4QiEGAADgCIUYAACAIxRiAABgxWltbVVHR4daWlo0NDQkaXYaJL/fr+rqamUyGcViMfn9fsViMcdpF8YHXQEAwIrV1tY2t9zd3a14PK7GxkZJUmdnp5LJpDwej6t4i6IQAwAAK0J7e7u6urrk8/lUUVGhUCikpqYmRSIRTU5Oqre3V4ODgxoYGFAqlVI6nVYoFFI8Hlc0GnUdf14UYgAAYMl2/2q39if35/WY1264Vnd/5O6zbjM2Nqaenh4lEglls1kFg0GFQqG59ubmZo2MjCgSiaihoUGS5PV6lUgk8po13yjEAABA0RseHlZ9fb3KysokSXV1dY4T5QeFGAAAWLLFRq5wbnhrEgAAFL3a2lr19fUpk8kolUqpv7/fdaS8YEQMAAAUvWAwqGg0qkAgIJ/Pp5qaGteR8sJYa11nWFQ4HLajo6OuYwAAsCqNj4+rsrLSdYyiNF/fGGPGrLXhpezPrUkAAABHKMQAAAAcoRADAABwhEIMAADAEQoxAAAARyjEAAAAHKEQAwAAK05ra6s6OjrU0tKioaEhSbPTIPn9flVXVyuTySgWi8nv9ysWizlOuzA+6AoAAFastra2ueXu7m7F43E1NjZKkjo7O5VMJuXxeFzFWxSFGAAAWBHa29vV1dUln8+niooKhUIhNTU1KRKJaHJyUr29vRocHNTAwIBSqZTS6bRCoZDi8bii0ajr+POiEAMAAEs28e1v653x/Xk9Zmnltdp8zz1n3WZsbEw9PT1KJBLKZrMKBoMKhUJz7c3NzRoZGVEkElFDQ4Mkyev1KpFI5DVrvlGIAQCAojc8PKz6+nqVlZVJkurq6hwnyg8KMQAAsGSLjVzh3PDWJAAAKHq1tbXq6+tTJpNRKpVSf3+/60h5wYgYAAAoesFgUNFoVIFAQD6fTzU1Na4j5YWx1rrOsKhwOGxHR0ddxwAAYFUaHx9XZWWl6xhFab6+McaMWWvDS9mfW5MAAACOUIgBAAA4QiEGAADgCIUYAACAIxRiAAAAjlCIAQAAOEIhBgAAVpzW1lZ1dHSopaVFQ0NDkmanQfL7/aqurlYmk1EsFpPf71csFnOcdmF80BUAAKxYbW1tc8vd3d2Kx+NqbGyUJHV2diqZTMrj8biKtygKMQAAsCK0t7erq6tLPp9PFRUVCoVCampqUiQS0eTkpHp7ezU4OKiBgQGlUiml02mFQiHF43FFo1HX8edFIQYAAJZsuPdFHTmUzusxN1Z49dGbt591m7GxMfX09CiRSCibzSoYDCoUCs21Nzc3a2RkRJFIRA0NDZIkr9erRCKR16z5RiEGAACK3vDwsOrr61VWViZJqqurc5woPyjEAADAki02coVzw1uTAACg6NXW1qqvr0+ZTEapVEr9/f2uI+UFI2IAAKDoBYNBRaNRBQIB+Xw+1dTUuI6UF8Zam/+DGrNH0odyP98nadJaW22M2SZpXNILubZfWmu/vNjxwuGwHR0dzXtOAACwuPHxcVVWVrqOUZTm6xtjzJi1NryU/QsyImatnXtH1BjzXUnHT2v+rbW2uhDnBQAAWEkKemvSGGMk3SzphkKeBwAAYCUq9MP6H5X0prX2pdPWfcAY8xtjzNPGmI8utKMx5nZjzKgxZvTw4cMFjm7kev4AAA5/SURBVAkAAHDhnfeImDFmSNLmeZq+Ya39aW75C5J+clrbG5KutNYeNcaEJPUZY/zW2j+ceRBrbaekTmn2GbHzzQkAAFCszrsQs9Z+4mztxpgSSZ+VNPfZW2vtO5LeyS2PGWN+K2m7JJ7EBwAAq04hb01+QtJ+a+1rp1YYYy4zxnhyyx+UdI2klwuYAQAAoGgVshD7vP70tqQk1Ur6P2NMQtLDkr5srU0WMAMAALgItba2qqOjQy0tLRoaGpI0Ow2S3+9XdXW1MpmMYrGY/H6/YrGY47QLK9hbk9bapnnWPSLpkUKdEwAArC5tbW1zy93d3YrH42psbJQkdXZ2KplMyuPxuIq3KL6sDwAAVoT29nZ1dXXJ5/OpoqJCoVBITU1NikQimpycVG9vrwYHBzUwMKBUKqV0Oq1QKKR4PK5oNLr4CRygEAMAAEv21I879dbv8vt4t+/9H9THm24/6zZjY2Pq6elRIpFQNptVMBhUKDT3PqCam5s1MjKiSCSihoYGSZLX61Uikchr1nyjEAMAAEVveHhY9fX1KisrkyTV1dU5TpQfFGIAAGDJFhu5wrkp9Jf1AQAAlq22tlZ9fX3KZDJKpVLq7+93HSkvGBEDAABFLxgMKhqNKhAIyOfzqaamxnWkvDDWFv/sQeFw2I6O8vF9AABcGB8fV2VlpesYRWm+vjHGjFlrw0vZn1uTAAAAjlCIAQAAOEIhBgAA4AiFGAAAgCMUYgAAAI5QiAEAADhCIQYAAFac1tZWdXR0qKWlRUNDQ5Jmp0Hy+/2qrq5WJpNRLBaT3+9XLBZznHZhfNAVAACsWG1tbXPL3d3disfjamxslCR1dnYqmUzK4/G4ircoCjEAALAitLe3q6urSz6fTxUVFQqFQmpqalIkEtHk5KR6e3s1ODiogYEBpVIppdNphUIhxeNxRaNR1/HnRSEGAACWbLL/t3r392/n9ZjrtrxX7/vbq866zdjYmHp6epRIJJTNZhUMBhUKhebam5ubNTIyokgkooaGBkmS1+tVIpHIa9Z8oxADAABFb3h4WPX19SorK5Mk1dXVOU6UHxRiAABgyRYbucK54a1JAABQ9Gpra9XX16dMJqNUKqX+/n7XkfKCETEAAFD0gsGgotGoAoGAfD6fampqXEfKC2OtdZ1hUeFw2I6OjrqOAQDAqjQ+Pq7KykrXMYrSfH1jjBmz1oaXsj+3JgEAAByhEAMAAHCEQgwAAMARCjEAAABHKMQAAAAcoRADAABwhEIMAACsOK2trero6FBLS4uGhoYkzU6D5Pf7VV1drUwmo1gsJr/fr1gs5jjtwvigKwAAWLHa2trmlru7uxWPx9XY2ChJ6uzsVDKZlMfjcRVvURRiAABgRWhvb1dXV5d8Pp8qKioUCoXU1NSkSCSiyclJ9fb2anBwUAMDA0qlUkqn0wqFQorH44pGo67jz4tCDAAALNnAwIAmJibyeszNmzfrpptuOus2Y2Nj6unpUSKRUDabVTAYVCgUmmtvbm7WyMiIIpGIGhoaJEler1eJRCKvWfONQgwAABS94eFh1dfXq6ysTJJUV1fnOFF+UIgBAIAlW2zkCueGtyYBAEDRq62tVV9fnzKZjFKplPr7+11HygtGxAAAQNELBoOKRqMKBALy+XyqqalxHSkvjLXWdYZFhcNhOzo66joGAACr0vj4uCorK13HKErz9Y0xZsxaG17K/tyaBAAAcIRCDAAAwBEKMQAAAEcoxAAAAByhEAMAAHCEQgwAAMARCjEAALDitLa2qqOjQy0tLRoaGpI0Ow2S3+9XdXW1MpmMYrGY/H6/YrGY47QL44OuAABgxWpra5tb7u7uVjweV2NjoySps7NTyWRSHo/HVbxFLWtEzBjz98aY54wxM8aY8BltcWPMAWPMC8aYT562/lO5dQeMMV9fzvkBAMDq0d7eru3bt+v666/XCy+8IElqamrSww8/rAceeEC9vb2699579cUvflF1dXVKp9MKhULas2eP4+QLW+6I2D5Jn5X0g9NXGmN2SPq8JL+kLZKGjDHbc833S/obSa9J+rUx5jFr7fPLzAEAAC6AF1/8plLp8bwec723Utu333vWbcbGxtTT06NEIqFsNqtgMKhQKDTX3tzcrJGREUUiETU0NEiSvF6vEolEXrPm27IKMWvtuCQZY85s+oykHmvtO5JeMcYckPSRXNsBa+3Luf16cttSiAEAgAUNDw+rvr5eZWVlkqS6ujrHifKjUM+IXSHpl6f9fi23TpIOnbH+LwqUAQAA5NliI1c4N4sWYsaYIUmb52n6hrX2p/mPNHfe2yXdnvuZNsa8UKhznWajpCMX4Dz4U/S7G/S7G/S7G/T7Mjz55JPXTU9PZ891v+np6RKPx3PO+83n8ssvX3P//feX1tXVZbLZrB5++OFLGhoaTh49enTNwYMHp/ft2zd99OjRdaeWJWlmZqZs3759J/Jx/oVMTEyU7Nix49kzVr9/qfsvWohZaz9xzqmk1yVVnPZ7a26dzrL+zPN2Suo8j3OfN2PM6FJnS0f+0O9u0O9u0O9u0O/Ls3fv3oNVVVXnXMju27evsqqqKi8PlFVVVemZZ57Z3NDQsLG8vPxkIBA45vV6T6xbt+6S8vLy41VVVcfWrVu37dRybred+Tr/Qqanpzcu59oq1K3JxyQ9ZIz5nmYf1r9G0q8kGUnXGGM+oNkC7POS/qFAGQAAwEVk9+7dE7t3755YqP2RRx45ePrvEydO/KbgoZZpWYWYMaZe0r9JukzS48aYhLX2k9ba54wxvZp9CD8r6avW2uncPl+TNCjJI+lH1trnlvUnAAAAWKGW+9bko5IeXaCtXVL7POt/JulnyzlvAV3QW6GYQ7+7Qb+7Qb+7Qb87sHHjxsOuMxQ7pjg6Te65NFxg9Lsb9Lsb9Lsb9Lsbmzdv5gWJRVCIAQAAOMJck5qddknS9zX73NoD1trvOI60KhhjDkpKSZqWlOWNpsIxxvxIUkTSW9baqty6DZL2SNom6aCkm621xxY6Bs7NAn3eKuk2Sadu19yTe1wDeWKMqZD0oKRNkqykTmvt97neC2tqamrtK6+88oFsNrtWksrLyw9v2bLlrUOHDm05evToxpKSkqwkbdmy5fUNGzYcd5u2uKz6ETFjjEez0y7dJGmHpC/kpmjChfFxa201RVjB/VjSp85Y93VJP7fWXiPp57nfyJ8f68/7XJL+NXfNV1OEFURW0l3W2h2S/lLSV3N/p3O9F5AxRlu3bn3tuuuue66ysnL8yJEjvrfffvs9knTZZZe9WVVV9XxVVdXzFGF/btUXYpqdeumAtfZla+27kk5NuwRcNKy1/yMpecbqz0jqyi13Sfq7CxrqIrdAn6PArLVvWGufyS2nJI1rdmYXrvcCKi0tPbl+/foTklRSUjJTWlqaeffdd9cV8py7du3a0tLSsunOO+/c0tfXt16SnnjiCe/VV1/tv/baa3ek02lzxx13bL366qv9d9xxx9ZCZlkObk3O/g/KtEtuWEn/bYyxkn7Aw7QX3CZr7Ru55QnN3spB4X3NGPNPkkY1O3LD7bECMcZsk7RT0v+K6/2CmZqaWjc1NVW2fv36dDqd9h45csSXTCbLy8rKTlx55ZWH1q5dO53P8913332/P7X84IMPbti1a9cbX/nKV5KS9NBDD208duxYoqSkeMud4k2G1eB6a+3rxhifpCeNMftzowi4wKy1NlcQo7D+XdI3NfuPkG9K+q6kW50mukgZY7ySHpF0p7X2D8aYuTau98LJZrNrDhw4cNUVV1xxqKSkZGbTpk1vbd269feSdOjQoSteffXViquuuurg+R7/7rvv3rxnz56N5eXlJ7ds2fLuzp07T3zuc5/bFolEjh87dszz+OOPb3j66acvfeKJJy5Np9OeEydOeKqqqnbcddddb9x2221F+Y8eCrGzT8eEArLWvp7771vGmEc1e5uYQuzCedMYc7m19g1jzOWS3nId6GJnrX3z1LIx5j8k/ZfDOBctY8xazRZh3dba/8yt5nrPkzvHX63Y//ZU2Z+3WE1PTV1iPCXZNVPJTTqY/NNRRztjpqemLvEce7H0zD2vfe97TtxXeeWhM9efbnh4uOzRRx/d8Oyzzz5/8uRJVVdX79i5c+fcPJK7du068otf/MIbiUSOf+lLXzomSWVlZTv379///Hn+US8InhGTfq3ctEvGmHWanXbpMceZLnrGmPcaY9afWpZ0o6R9blOtOo9JuiW3fIuknzrMsirkCoBT6sU1n3dmdujrh5LGrbXfO62J672grGbeeec9Zs2amTVr15784+qZuaHImex0iVmzZuZ8z/DUU095P/3pT0+uX79+ZsOGDTM33njj5DJDF4VVPyJmrc0y7ZITmyQ9mrtdUCLpIWvtE24jXbyMMT+R9DFJG40xr0n6F0nfkdRrjPlnSb+TdLO7hBefBfr8Y8aYas3emjwo6Q5nAS9efyXpHyU9a4xJ5NbdI673vJlv5Or48ePel1566UOlpaUZY971SLOfqkgmkxsymcwlkrRu/bqpbduu/l1paenJM/dfzVZ9ISYV/bRLFyVr7cuSAq5zrBbW2i8s0PTXFzTIKrJAn//wggdZZay1I5LMAs1c7wVy6aWXpsPh8NiZ6/P5uYobbrghfeutt2771re+9cbJkyfNk08++b5bbrllxU+hRCEGAACK3vXXX3+ivr4+WVVV5S8vLz/54Q9/+G3XmfLBWMuLIwAAYGF79+49GAgEmDdyHnv37t0YCAS2ne/+PKwPAADgCIUYAACAIxRiAAAAjlCIAQCAxczMzMws9DbqqpXrk/P+NppEIQYAABa37/Dhw5dSjP3RzMyMOXz48KVa5oeZ+XwFAAA4q2w22zwxMfHAxMRElRjEOWVG0r5sNtu8nIPw+QoAAABHqGoBAAAcoRADAABwhEIMAADAEQoxAAAARyjEAAAAHPl/23gpSttkAOYAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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o7u4mmUySz+cJBoM4jjOxvLm5meHhYcLhMA0NDQB4vV6SyWRRsxZbyYuYMWYp8E7gI5Nm/60xpprCqcmtRy0TEREROcLQ0BD19fVUVFQAUFdX53Ki4ih5EbPWvg6sOmre+0v9uSIiIlJ8041cyYnRk/VFRESk7NXW1tLb20sulyOTydDX1+d2pKI4WdeIiYiIiPzOgsEgkUiEqqoqfD4fNTU1bkcqCmNt+T8ZIhQK2ZGREbdjiIiIzEmpVAq/3+92jLI01bExxiSstaGZbK9TkyIiIiIuURETERERcYmKmIiIiIhLVMREREREXKIiJiIiIuISFTERERERl6iIiYiIyKzT2tpKR0cHLS0tDA4OAoXXIAUCAaqrq8nlckSjUQKBANFo1OW0x6YHuoqIiMis1dbWNjHd1dVFLBajsbERgHg8TjqdxuPxuBVvWipiIiIiMiu0t7fT2dmJz+ejsrISx3FoamoiHA4zOjpKT08PAwMD9Pf3k8lkyGazOI5DLBYjEom4HX9KKmIiIiIyY0M9v+KVF7JF3efqSi9v/8OLjrtOIpGgu7ubZDJJPp8nGAziOM7E8ubmZoaHhwmHwzQ0NADg9XpJJpNFzVpsKmIiIiJS9oaGhqivr6eiogKAuro6lxMVh4qYiIiIzNh0I1dyYnTXpIiIiJS92tpaent7yeVyZDIZ+vr63I5UFBoRExERkbIXDAaJRCJUVVXh8/moqalxO1JRGGut2xmmFQqF7MjIiNsxRERE5qRUKoXf73c7Rlma6tgYYxLW2tBMttepSRERERGXqIiJiIiIuERFTERERMQlKmIiIiIiLlERExEREXGJipiIiIiIS1TEREREZNZpbW2lo6ODlpYWBgcHgcJrkAKBANXV1eRyOaLRKIFAgGg06nLaY9MDXUVERGTWamtrm5ju6uoiFovR2NgIQDweJ51O4/F43Io3LRUxERERmRXa29vp7OzE5/NRWVmJ4zg0NTURDocZHR2lp6eHgYEB+vv7yWQyZLNZHMchFosRiUTcjj8lFTERERGZsUf+Jc6ubc8WdZ++8y7gqqbbjrtOIpGgu7ubZDJJPp8nGAziOM7E8ubmZoaHhwmHwzQ0NADg9XpJJpNFzVpsKmIiIiJS9oaGhqivr6eiogKAuro6lxMVh4qYiIiIzNh0I1dyYnTXpIiIiJS92tpaent7yeVyZDIZ+vr63I5UFBoRExERkbIXDAaJRCJUVVXh8/moqalxO1JRGGut2xmmFQqF7MjIiNsxRERE5qRUKoXf73c7Rlma6tgYYxLW2tBMttepSRERERGXqIiJiIiIuERFTERERMQlKmIiIiIiLlERExEREXGJipiIiIiIS1TEREREZNZpbW2lo6ODlpYWBgcHgcJrkAKBANXV1eRyOaLRKIFAgGg06nLaY9MDXUVERGTWamtrm5ju6uoiFovR2NgIQDweJ51O4/F43Io3LRUxERERmRXa29vp7OzE5/NRWVmJ4zg0NTURDocZHR2lp6eHgYEB+vv7yWQyZLNZHMchFosRiUTcjj8lFTERERGZsdG+Zzj44utF3efCs5ey4oYLj7tOIpGgu7ubZDJJPp8nGAziOM7E8ubmZoaHhwmHwzQ0NADg9XpJJpNFzVpsKmIiIiJS9oaGhqivr6eiogKAuro6lxMVh4qYiIiIzNh0I1dyYkp+16QxZqsx5hfGmKQxZmR83kpjzMPGmKfHv59e6hwiIiIye9XW1tLb20sulyOTydDX1+d2pKI4WY+vuMpaWz3pTeR3At+z1q4Hvjf+s4iIiMiUgsEgkUiEqqoqrrvuOmpqatyOVBTGWlvaDzBmKxCy1r4yad5TwDustS8ZY84CHrXWXnysfYRCITsyMlLSnCIiIjK1VCqF3+93O0ZZmurYGGMSkwafjutkjIhZ4LvGmIQx5rbxeWdaa18an34ZOPMk5BAREREpKyfjYv23WWt3GGN8wMPGmC2TF1prrTHmt4blxkvbbQBr1649CTFFRERETq6Sj4hZa3eMf98FbAIuB3aOn5Jk/PuuKbaLW2tD1trQGWecUeqYIiIiIiddSYuYMWapMWbZG9PAu4AngAeBD46v9kHggVLmEBERESlHpT41eSawyRjzxmd93Vr7HWPMT4EeY8wfA9uAPyxxDhEREZGyU9IiZq19FqiaYv6rwNWl/GwRERGRcneyniMmIiIiUjStra10dHTQ0tLC4OAgUHgNUiAQoLq6mlwuRzQaJRAIEI1GXU57bHrFkYiIiMxabW1tE9NdXV3EYjEaGxsBiMfjpNNpPB6PW/GmpSImIiIis0J7ezudnZ34fD4qKytxHIempibC4TCjo6P09PQwMDBAf38/mUyGbDaL4zjEYjEikYjb8aekIiYiIiIz1t/fz8svv1zUfa5Zs4brrrvuuOskEgm6u7tJJpPk83mCwSCO40wsb25uZnh4mHA4TENDAwBer5dkMlnUrMWmIiYiIiJlb2hoiPr6eioqKgCoq6tzOVFxqIiJiIjIjE03ciUnRndNioiISNmrra2lt7eXXC5HJpOhr6/P7UhFoRExERERKXvBYJBIJEJVVRU+n4+amhq3IxWFsfa33rdddkKhkB0ZGXE7hoiIyJyUSqXw+/1uxyhLUx0bY0zCWhuayfY6NSkiIiLiEhUxEREREZeoiImIiIi4REVMRERExCUqYiIiIiIuURETERERcYmKmIiIiMw6ra2tdHR00NLSwuDgIFB4DVIgEKC6uppcLkc0GiUQCBCNRl1Oe2x6oKuIiIjMWm1tbRPTXV1dxGIxGhsbAYjH46TTaTwej1vxpqUiJiIiIrNCe3s7nZ2d+Hw+KisrcRyHpqYmwuEwo6Oj9PT0MDAwQH9/P5lMhmw2i+M4xGIxIpGI2/GnpCImIiIiM/arX32aTDZV1H0u8/q56KK/Ou46iUSC7u5ukskk+XyeYDCI4zgTy5ubmxkeHiYcDtPQ0ACA1+slmUwWNWuxqYiJiIhI2RsaGqK+vp6KigoA6urqXE5UHCpiIiIiMmPTjVzJidFdkyIiIlL2amtr6e3tJZfLkclk6OvrcztSUWhETERERMpeMBgkEolQVVWFz+ejpqbG7UhFYay1bmeYVigUsiMjI27HEBERmZNSqRR+v9/tGGVpqmNjjElYa0Mz2V6nJkVERERcoiImIiIi4hIVMRERERGXqIiJiIiIuERFTERERMQlKmIiIiIiLlERExERkVmntbWVjo4OWlpaGBwcBAqvQQoEAlRXV5PL5YhGowQCAaLRqMtpj00PdBUREZFZq62tbWK6q6uLWCxGY2MjAPF4nHQ6jcfjcSvetFTEREREZFZob2+ns7MTn89HZWUljuPQ1NREOBxmdHSUnp4eBgYG6O/vJ5PJkM1mcRyHWCxGJBJxO/6UVMRERERkxv7q6e08kc0VdZ+Xepfw6fXnHnedRCJBd3c3yWSSfD5PMBjEcZyJ5c3NzQwPDxMOh2loaADA6/WSTCaLmrXYVMRERESk7A0NDVFfX09FRQUAdXV1LicqDhUxERERmbHpRq7kxOiuSRERESl7tbW19Pb2ksvlyGQy9PX1uR2pKDQiJiIiImUvGAwSiUSoqqrC5/NRU1PjdqSiMNZatzNMKxQK2ZGREbdjiIiIzEmpVAq/3+92jLI01bExxiSstaGZbK9TkyIiIiIuURETERERcYmKmIiIiIhLVMREREREXKIiJiIiIuISFTERERERl5SsiBljKo0xjxhjfmmMedIY8/Hx+a3GmB3GmOT41/WlyiAiIiKnptbWVjo6OmhpaWFwcBAovAYpEAhQXV1NLpcjGo0SCASIRqMupz22Uj7QNQ/8ubX2Z8aYZUDCGPPw+LLPW2s7SvjZIiIiMge0tbVNTHd1dRGLxWhsbAQgHo+TTqfxeDxuxZtWyYqYtfYl4KXx6YwxJgWcU6rPExERkVNbe3s7nZ2d+Hw+KisrcRyHpqYmwuEwo6Oj9PT0MDAwQH9/P5lMhmw2i+M4xGIxIpGI2/GndFJecWSMWQe8BfgxcCXwMWPMB4ARCqNmr52MHCIiIvLm/HXfk/zyxb1F3eclZy/nUzcEjrtOIpGgu7ubZDJJPp8nGAziOM7E8ubmZoaHhwmHwzQ0NADg9XpJJpNFzVpsJb9Y3xjjBb4J/C9r7V7gy8CFQDWFEbO/O8Z2txljRowxI7t37y51TBERESljQ0ND1NfXU1FRwfLly6mrq3M7UlGUdETMGLOAQgnrstZ+C8Bau3PS8q8C/zHVttbaOBCHwrsmS5lTREREZma6kSs5MaW8a9IA9wEpa+3nJs0/a9Jq9cATpcogIiIip4ba2lp6e3vJ5XJkMhn6+vrcjlQUpRwRuxJ4P/ALY8wbJ2jvAt5rjKkGLLAV+EgJM4iIiMgpIBgMEolEqKqqwufzUVNT43akojDWlv9Zv1AoZEdGRtyOISIiMielUin8fr/bMcrSVMfGGJOw1oZmsr2erC8iIiLiEhUxEREREZeoiImIiIi4REVMRERExCUqYiIiIiIuURETERERcYmKmIiIiMw6ra2tdHR00NLSwuDgIFB4DVIgEKC6uppcLkc0GiUQCBCNRl1Oe2wn5aXfIiIiIqXQ1tY2Md3V1UUsFqOxsRGAeDxOOp3G4/G4FW9aKmIiIiIyK7S3t9PZ2YnP56OyshLHcWhqaiIcDjM6OkpPTw8DAwP09/eTyWTIZrM4jkMsFiMSibgdf0oqYiIiIjJz/XfCy78o7j7XbITrPnPcVRKJBN3d3SSTSfL5PMFgEMdxJpY3NzczPDxMOBymoaEBAK/XSzKZPNYuy4KKmIiIiJS9oaEh6uvrqaioAKCurs7lRMWhIiYiIiIzN83IlZwY3TUpIiIiZa+2tpbe3l5yuRyZTIa+vj63IxWFRsRERESk7AWDQSKRCFVVVfh8PmpqatyOVBTGWut2hmmFQiE7MjLidgwREZE5KZVK4ff73Y5RlqY6NsaYhLU2NJPtdWpSRERExCUqYiIiIiIuURETERERcYmKmIiIiIhLVMREREREXKIiJiIiIuISFTERERGZdVpbW+no6KClpYXBwUGg8BqkQCBAdXU1uVyOaDRKIBAgGo26nPbY9EBXERERmbXa2tompru6uojFYjQ2NgIQj8dJp9N4PB634k1LRUxERERmhfb2djo7O/H5fFRWVuI4Dk1NTYTDYUZHR+np6WFgYID+/n4ymQzZbBbHcYjFYkQiEbfjT0lFTERERGbssz/5LFvSW4q6zw0rN3DH5Xccd51EIkF3dzfJZJJ8Pk8wGMRxnInlzc3NDA8PEw6HaWhoAMDr9ZJMJouatdhUxERERKTsDQ0NUV9fT0VFBQB1dXUuJyoOFTERERGZselGruTE6K5JERERKXu1tbX09vaSy+XIZDL09fW5HakoNCImIiIiZS8YDBKJRKiqqsLn81FTU+N2pKIw1lq3M0wrFArZkZERt2OIiIjMSalUCr/f73aMsjTVsTHGJKy1oZlsr1OTIiIiMneMHXY7wRF0alJEREROffkDsGcH2MOw6vfAGLcTASpiIiIiciobOwzZnZDdVShf3jPdTnQEFTERERE59VgLuTTsfRHG8rBkJSw/CzwL3U52BBUxERERObUcfB32bIdD+2BBBay8ABYudTvVlFTERERE5NRw+CDsfakwEjZvAaw4D5acXjbXg01FRUxERERmndbWVrxeL3v37qX2bW/jmt+/jKHv9XP7Hf+bBYsWs3nzj2n51F/z0EMPcf3113P33Xe7HXlKKmIi8rs7+Do88/3CHUirL4Z5eiKOyEn1+quw/Sfw+ivF2Z8xsGo9nF0N8xcVZ5+lZC1tsT+DvTsg8xJdDzxM7K5P0vjBPwIgHo+TTqfxeDwuBz02FTEROXHWwi++AQ9/CjIvFuYtXgGVl8PaK6DyCjgnCAuWuJtT5FRiLbz6DLzwODw//vXq06X5LM+iwn/DlW8t/Dc9trY0n3OC2tvb6ezsxOfzUXnO2TiXXEDTBz9A+N3/ndGD8+l54DsMPLqZ/u8OkslkyGazOI5DLBYjEom4HX9KKmIicmJ2JKD/zsK/ws+qhhu+CPtegec3w/M/hqe/W1hv3gI4q6rwR/yNcuY9w93sIrN/FaJ0AAAgAElEQVRJ/iC8lCwUrhd+XPi+b3zka8nphZJU/b7Cf1+nnQsU4TqosTzsfOI3RW/zvfDDL8C7e2DXPFi4lJe/+E8cePpZMMUbAV/k38Cau+467jqJRILu7m6SiRHyr20n+Afvxrm4EhZWwPJzaL7lFoZ/PEI4HKahoQEAr9dLMpksWs5SUBETkZnJ7ITv/TUku2CpD268F6re95vTkdXvK3zfl/7N/zRe+DH85Kuw+R8Ky1ZeOF7Kxv+Vvfqisr6IVuSk2peG7T/9zT9qXvwZ5PcXlp1+Pqx/F6x9a+EfNasvKt2lACvPB/8NhelDOdjxM3htfuGxD7lROJAp3I2IgXkeMJ5CFlPa039Djz1G/f94FxXZrTD/MHXXX1t4Jtj8V2f13xEVMRE5vvwBePzL8NjdhekrPw5v/wQsXg7Ac1ufZk9m9KiNVsOacOHLyWPSW5n3Sgrz6tPw882Ynz0MgPV4wXcRdtVFjJ1xCXZFZeEPu8gcYPa9innlaebtfgpeeQqT3Q6ANfPh9HVQGWHMt4Gx1Rtg8bLfbLgzAzsTJzHpQsy8BexbsgaWwPJYCxzejzl0APK5wigaFMqQZxHMX4KdvwjmLz6hgrRvX/bYC/P7OZTZzaH9r5PzVGCXrOLQvMUczOfJ5/McOLCfffteP2L6N/t9/YhdGWNYsqTihI5AKamIicjUrIWn+mHgLnjtObj4enjX/4ZVF7LpwS4Gt/07Oxdu41eLDmNn+sd2CbAOYPH4jDzwS8j9Ep7vhedL8YuIlDkPcCZw5uJJM7cXvnY9ArvciTXZFy75AvP3LThypgEWwJGvrT4I9iAcovBVROuufAtf/Z9/ScOff5jD+9I8+O0HueWDt5DNZ9l1YDfP7dt6xDTAmB2bmH7DAgsXLQkUN9yboCImIr9tVwq+E4NnHyncDdn4LX6wc4xvdv0Fuxc+x5bFB8kvN5x9yHJ5dhUeyutJ1SJSXIusB+/YgulXLKHLN1ZxY93/4JZ3NLBq9Uqqqy9jkfUwn3ksHs83eRrAYH47ty2v05jGWut2hmmFQiE7MjLidgyRU9++NDz6GfjpP8EiL6kNH+Uft/2cVxb+mi2L93NgnmF1fowL9q/inLHLuOOPPsNSr9ft1CJSYqlUCr/f73aMsjTVsTHGJKy1oZlsrxExEYHDeUj8MzzSzkvZA3xu5bXsWvQsv0r/H7Ir5nHa4TE27juNM/N+7vzg3axYsdLtxCIipwTXipgx5lrgixTOjv+TtfYzbmURmdOe/QF7HvgL7j58Pi96z+HXq0d5bf4vqBgb4+KclzMOredjN36K89etdzupiMgpx5UiZozxAPcC76RwReJPjTEPWmt/6UYekbno9Ree4HP/fjfbFm/lueVZds1/koVjlg37lxDMXkDjH/wZoeor3I4pInJKc2tE7HLg19baZwGMMd3AjYBrRewj/3ANYxx26+NFTqoxc5gdC9PsOMPgsZaL9y/kkn3ruGHjh3jXO26Y0T6ymQxdX/5/qfDuZt688r/WVER+dxcH/5xXd50aVzNZa1h95u+5HWOCW0f1HOCFST9vB946eQVjzG3AbQBr15b+1Qq/qHiJ1+eV150UIqUyD7jggIfavWuprbyZyE1/NONtv/p3H6PCu40V5z7P74UKz/2xZXYXkogUl2fBIRYs3u92jKIYGyuvZxWWbb211saBOBTumiz15/3oQ0+W+iNEZq1/+YcYnnkpVpz7Ahe8ZRRrDXtfOZudWy7m/Etv4ap33+J2RBEpoVQqxfLlumuyFNwqYjuAykk/nzs+T0TKxDe+djf7sptZUbmDyksK77fb+9qZvPBfb2GF7x3c/L6PuZxQROay1tZWvF4ve/fupba2lmuuuYahoSFuv/12FixYwObNm2lpaeGhhx7i+uuv5+6773Y78pTcKmI/BdYbY86nUMDeA7zPpSwiMu7b3/ondm37DisqX+b0815ipYHX965ix5NVeBZVc+ttLUesf/jwYV4ZHuHwgSI/QltEysrh1Us5uCfjdowjHN5/gPz8BXzyz6MAHNyT4V//+V+Ifvx/8b7Ie+Bgnng8zsvPbcPj8UzkN/PmsWDZUjejH8GVImatzRtjPgYMUHh8xdestTo3KOKCH33/QVL/2cWKc15m+eodrK2y5F4/jZefupQDh9bzxx/v+K1ttv/Ho+x+9Ckq7NksXbDChdQicjLZOqAMethn7rmb///+r+NbfQbnnnUOb9n4Fpqbb+f6a65ldO8evvmtb/Hw4PcY6H+YTDZLNpvlirfX8hd/+mfcUvf/AJC3B1XEAKy1DwEPufX5InPZk8mf8KPvfoEV5+zkNN8LrHvLYQ7sX8ruZzeQ2bOWW2//LN5ly47YZtdjP+XFh37GooNnsGzhGazybGB0//McWLaT+Uv1iiORU5md5+fw/AMAPN7/IumXc0Xd/8o1S7jiurOPu87Pfv5zevru5/HB75M/fJj/9q7/TnX1Ruy8w4x5DvHB97+HH44Mc90730V9uA6AMy48j8e/930ADlPIb+bNO+ZnuKFsL9Y/2b4z4Gf+fJ1ekbnBGMsFITh4cDHpFy5kz+6zeM+H7+a0lauOWO/V5C/Zfv8w87Oncdris1nNJYyObefVhVs4771Xsdb/By79BiJyMqVSKZasWQ3A/KVp5i3MF3X/85cuntj/sfy0+xfcfEsDqy4oPEnhxvp6FixbimfJYhauWM6SNauPmAbAmGn36zYVsXG7f30x8zxjbscQOSmsNby+dzk3vK+Ns689/4hle55+jm1f/z4mvZgViytZZfzsMTt5dd4vOeemK7j08ve6lFpEysHb//AityOcUlTExr3/T3vdjiDimtxLu3m289vkX5rH6YvXstJcRNbzKq/YFGuu2UjgnQ1uRxSROa62tpampiZisRj5fJ6+vj4+8pGPuB3rTVMRE5mjDuzJ8Nw/P8j+5w6wYtE6Tpt3Ibn5e3k1/xSrrryA9TfegMdTXg8+FJG5KxgMEolEqKqqwufzUVNT43akojDWlv+rSUKhkB0ZGSnpZ/zn//oqheeNi5z6jJ3H8oXnstCzmAP519l7+HlOC67hvMh1eBYumNE+Xj98mO++spcfpDPkZ8HfERH53b03v5fK9afGKcn5xnD24uLdYJRKpfD7j3zYrTEmYa0NzShP0ZLMcovM6Xjm6c4vmSOsZc+hbSy94DQu+OANzK9YPKPNDoyN8Wg6w6adrzHwyl5yY2OsXOBhmUbORE5pNy+xvH741LiOemGZvc5QRWzcJZ/XNTAiUzlsLT96LcumXa/x7d172JM/zMoFHm5Zczo3+U7nihVLmWfK6w+biBRXKpXC713idoxTkoqYiPwWay2Jvfvo3fUaD+4aZdfBPEs987hu9WncdObp/MHpy1hQZv+qFBGZjVTERAQolK/U6/vZtPM1eneN8sL+gyyaZ7hm1XJu8p3ONauWs8Sj6yhFRIpJRUxkjtuaO8Cmna+xaecov9q3H4+B2tOX8Yl1a7jujNNYPl/Xf4mIlIqK2LgX9x9E933JXLF/bIzBV/eyaecoycw+AK44bSmfuehcwmesYPVC/WkQETkZ9Nd23Dt+uoW9+VPjjhCRmbrMu4SWC8/mRt8Kzini7dwiIqXW2tqK1+tl79691NbWcs011zA0NMTtt9/OggUL2Lx5My0tLTz00ENcf/313H333W5HnpKK2Lj29edyaExjYjI3GAOXn7aUC2f42AoRkXLV1tY2Md3V1UUsFqOxsRGAeDxOOp0u64dTq4iNu2XNSrcjiIiIyHG0t7fT2dmJz+ejsrISx3FoamoiHA4zOjpKT08PAwMD9Pf3k8lkyGazOI5DLBYjEom4HX9KKmIiIiIyY4/8S5xd254t6j59513AVU23HXedRCJBd3c3yWSSfD5PMBjEcZyJ5c3NzQwPDxMOh2loKDwb1Ov1kkwmi5q12FTEREREpOwNDQ1RX19PRUUFAHV1dS4nKg4VMREREZmx6Uau5MTo6YwiIiJS9mpra+nt7SWXy5HJZOjr63M7UlFoRExERETKXjAYJBKJUFVVhc/no6amxu1IRWGsLf9HNoRCITsyMuJ2DBERkTkplUrh9/vdjlGWpjo2xpiEtTY0k+11alJERETEJSpiIiIiIi5RERMRERFxiYqYiIiIiEtUxERERERcoiImIiIi4hIVMREREZl1Wltb6ejooKWlhcHBQaDwGqRAIEB1dTW5XI5oNEogECAajbqc9tj0QFcRERGZtdra2iamu7q6iMViNDY2AhCPx0mn03g8HrfiTUtFTERERGaF9vZ2Ojs78fl8VFZW4jgOTU1NhMNhRkdH6enpYWBggP7+fjKZDNlsFsdxiMViRCIRt+NPSUVMREREZmy07xkOvvh6Ufe58OylrLjhwuOuk0gk6O7uJplMks/nCQaDOI4zsby5uZnh4WHC4TANDQ0AeL1ekslkUbMWm4qYiIiIlL2hoSHq6+upqKgAoK6uzuVExaEiJiIiIjM23ciVnBjdNSkiIiJlr7a2lt7eXnK5HJlMhr6+PrcjFYVGxERERKTsBYNBIpEIVVVV+Hw+ampq3I5UFMZa63aGaYVCITsyMuJ2DBERkTkplUrh9/vdjlGWpjo2xpiEtTY0k+11alJERETEJSpiIiIiIi5RERMRERFxiYqYiIiIiEtUxERERERcoiImIiIi4hIVMREREZl1Wltb6ejooKWlhcHBQaDwGqRAIEB1dTW5XI5oNEogECAajbqc9tj0QFcRERGZtdra2iamu7q6iMViNDY2AhCPx0mn03g8HrfiTUtFTERERGaF9vZ2Ojs78fl8VFZW4jgOTU1NhMNhRkdH6enpYWBggP7+fjKZDNlsFsdxiMViRCIRt+NPSUVMREREZqy/v5+XX365qPtcs2YN11133XHXSSQSdHd3k0wmyefzBINBHMeZWN7c3Mzw8DDhcJiGhgYAvF4vyWSyqFmLrSTXiBlj7jbGbDHG/JcxZpMxZsX4/HXGmJwxJjn+9ZVSfL6IiIicWoaGhqivr6eiooLly5dTV1fndqSiKNWI2MNAzFqbN8Z8FogBd4wve8ZaW12izxUREZESmm7kSk5MSUbErLXftdbmx398HDi3FJ8jIiIic0NtbS29vb3kcjkymQx9fX1uRyqKk3GN2IeAf5/08/nGmP8E9gKftNYOnYQMIiIiMosFg0EikQhVVVX4fD5qamrcjlQUxlr7u21ozCCwZopFf2mtfWB8nb8EQsDN1lprjFkEeK21rxpjHKAXCFhr906x/9uA2wDWrl3rbNu27XfKKSIiIm9OKpXC7/e7HaMsTXVsjDEJa21oJtv/ziNi1tprjrfcGNMEhIGr7Xjbs9YeAA6MTyeMMc8AFwEjU+w/DsQBQqHQ79YWRURERMpYqe6avBb4C6DOWrtv0vwzjDGe8ekLgPXAs6XIICIiIlLuSnWN2D8Ai4CHjTEAj1trbwdqgTZjzCFgDLjdWpsuUQYRERGRslaSImat/b1jzP8m8M1SfKaIiIjIbKOXfouIiIi4REVMRERExCUqYiIiIjLrtLa20tHRQUtLC4ODg0DhNUiBQIDq6mpyuRzRaJRAIEA0GnU57bHppd8iIiIya7W1tU1Md3V1EYvFaGxsBCAej5NOp/F4PG7Fm5aKmIiIiMwK7e3tdHZ24vP5qKysxHEcmpqaCIfDjI6O0tPTw8DAAP39/WQyGbLZLI7jEIvFiEQibsefkoqYiIiIzNivfvVpMtlUUfe5zOvnoov+6rjrJBIJuru7SSaT5PN5gsEgjuNMLG9ubmZ4eJhwOExDQwMAXq+XZDJZ1KzFpiImIiIiZW9oaIj6+noqKioAqKurczlRcaiIiYiIyIxNN3IlJ0Z3TYqIiEjZq62tpbe3l1wuRyaToa+vz+1IRaERMRERESl7wWCQSCRCVVUVPp+PmpoatyMVhbHWup1hWqFQyI6MjLgdQ0REZE5KpVL4/X63Y5SlqY6NMSZhrQ3NZHudmhQRERFxiYqYiIiIiEtUxERERERcoiImIiIi4hIVMRERERGXqIiJiIiIuERFTERERGad1tZWOjo6aGlpYXBwECi8BikQCFBdXU0ulyMajRIIBIhGoy6nPTY90FVERERmrba2tonprq4uYrEYjY2NAMTjcdLpNB6Px61401IRExERkVmhvb2dzs5OfD4flZWVOI5DU1MT4XCY0dFRenp6GBgYoL+/n0wmQzabxXEcYrEYkUjE7fhTUhETERGRGfurp7fzRDZX1H1e6l3Cp9efe9x1EokE3d3dJJNJ8vk8wWAQx3Emljc3NzM8PEw4HKahoQEAr9dLMpksatZiUxETERGRsjc0NER9fT0VFRUA1NXVuZyoOFTEREREZMamG7mSE6O7JkVERKTs1dbW0tvbSy6XI5PJ0NfX53akotCImIiIiJS9YDBIJBKhqqoKn89HTU2N25GKwlhr3c4wrVAoZEdGRtyOISIiMielUin8fr/bMcrSVMfGGJOw1oZmsr1OTYqIiIi4REVMRERExCUqYiIiIiIuURETERERcYmKmIiIiIhLVMREREREXKIiJiIiIrNOa2srHR0dtLS0MDg4CBRegxQIBKiuriaXyxGNRgkEAkSjUZfTHpse6CoiIiKzVltb28R0V1cXsViMxsZGAOLxOOl0Go/H41a8aamIiYiIyKzQ3t5OZ2cnPp+PyspKHMehqamJcDjM6OgoPT09DAwM0N/fTyaTIZvN4jgOsViMSCTidvwpqYiJiIjIjP1135P88sW9Rd3nJWcv51M3BI67TiKRoLu7m2QyST6fJxgM4jjOxPLm5maGh4cJh8M0NDQA4PV6SSaTRc1abCpiIiIiUvaGhoaor6+noqICgLq6OpcTFYeKmIiIiMzYdCNXcmJ016SIiIiUvdraWnp7e8nlcmQyGfr6+tyOVBQaERMREZGyFwwGiUQiVFVV4fP5qKmpcTtSURhrrdsZphUKhezIyIjbMUREROakVCqF3+93O0ZZmurYGGMS1trQTLbXqUkRERERl6iIiYiIiLhERUxERETEJSpiIiIiIi4pWREzxrQaY3YYY5LjX9dPWhYzxvzaGPOUMebdpcogIiIiUs5K/fiKz1trOybPMMZcArwHCABnA4PGmIustYdLnEVERESkrLhxavJGoNtae8Ba+xzwa+ByF3KIiIjILNXa2kpHRwctLS0MDg4ChdcgBQIBqquryeVyRKNRAoEA0WjU5bTHVuoRsY8ZYz4AjAB/bq19DTgHeHzSOtvH54mIiIickLa2tonprq4uYrEYjY2NAMTjcdLpNB6Px61403pTRcwYMwismWLRXwJfBj4N2PHvfwd86AT2fRtwG8DatWvfTEwRERE5BbS3t9PZ2YnP56OyshLHcWhqaiIcDjM6OkpPTw8DAwP09/eTyWTIZrM4jkMsFiMSibgdf0pvqohZa6+ZyXrGmK8C/zH+4w6gctLic8fnHb3vOBCHwpP130xOERERKZL+O+HlXxR3n2s2wnWfOe4qiUSC7u5ukskk+XyeYDCI4zgTy5ubmxkeHiYcDtPQ0ACA1+slmUwWN2uRlfKuybMm/VgPPDE+/SDwHmPMImPM+cB64CelyiEiIiKz39DQEPX19VRUVLB8+XLq6urcjlQUpbxG7G+NMdUUTk1uBT4CYK190hjTA/wSyAN/qjsmRUREZolpRq7kxJRsRMxa+35r7UZr7WXW2jpr7UuTlrVbay+01l5sre0vVQYRERE5NdTW1tLb20sulyOTydDX1+d2pKIo9V2TIiIiIm9aMBgkEolQVVWFz+ejpqbG7UhFYawt/+vgQ6GQHRkZcTuGiIjInJRKpfD7/W7HKEtTHRtjTMJaG5rJ9nrXpIiIiIhLVMREREREXKIiJiIiIuISFTERERERl6iIiYiIiLhERUxERETEJSpiIiIiMuu0trbS0dFBS0sLg4ODQOE1SIFAgOrqanK5HNFolEAgQDQadTntsemBriIiIjJrtbW1TUx3dXURi8VobGwEIB6Pk06n8Xg8bsWbloqYiIiIzArt7e10dnbi8/morKzEcRyampoIh8OMjo7S09PDwMAA/f39ZDIZstksjuMQi8WIRCJux5+SipiIiIjM2Gd/8lm2pLcUdZ8bVm7gjsvvOO46iUSC7u5ukskk+XyeYDCI4zgTy5ubmxkeHiYcDtPQ0ACA1+slmUwWNWuxqYiJiIhI2RsaGqK+vp6KigoA6urqXE5UHCpiIiIiMmPTjVzJidFdkyIiIlL2amtr6e3tJZfLkclk6OvrcztSUWhETERERMpeMBgkEolQVVWFz+ejpqbG7UhFYay1bmeYVigUsiMjI27HEBERmZNSqRR+v9/tGGVpqmNjjElYa0Mz2V6nJkVERERcoiImIiIi4hIVMRERERGXqIiJiIiIuERFTERERMQlKmIiIiIiLlERExERkVmntbWVjo4OWlpaGBwcBAqvQQoEAlRXV5PL5YhGowQCAaLRqMtpj00PdBUREZFZq62tbWK6q6uLWCxGY2MjAPF4nHQ6jcfjcSvetFTEREREZFZob2+ns7MTn89HZWUljuPQ1NREOBxmdHSUnp4eBgYG6O/vJ5PJkM1mcRyHWCxGJBJxO/6UVMRERERkxl7+m7/hQGpLUfe5yL+BNXfdddx1EokE3d3dJJNJ8vk8wWAQx3Emljc3NzM8PEw4HKahoQEAr9dLMpksatZiUxETERGRsjc0NER9fT0VFRUA1NXVuZyoOFTEREREZMamG7mSE6O7JkVERKTs1dbW0tvbSy6XI5PJ0NfX53akotCImIiIiJS9YDBIJBKhqqoKn89HTU2N25GKwvzf9u4/Nu67vuP4651zYmocBcXpJXXjEmga6pyLHZ+zTVqIoGOFopuZwatheKvp3AYBf1SpTnCgepbBEpUMK5qqCa8gXMnFsdLF1CtOFqOqs5HYsMtlTev+CG1oWuI2ycXhrjk3OfuzP3wxIdixE9/5e46fj3/6vc/nvvd95aNvo3c+3x8f55zXGeZUVVXlhoaGvI4BAMCyNDIyotLSUq9j5KSZxsbMhp1zVfPZn0uTAAAAHqEQAwAA8AiFGAAAgEcoxAAAADxCIQYAAOARCjEAAACPUIgBAIAlp7m5WW1tbWpqalJ/f7+kqWWQAoGAKioqlEwmFQ6HFQgEFA6HPU47O17oCgAAlqyWlpbp7c7OTkUiEdXX10uS2tvbFYvF5PP5vIo3JwoxAACwJLS2tqqjo0N+v18lJSUKBoNqaGhQKBTS2NiYuru7deDAAfX19SkejyuRSCgYDCoSiaiurs7r+DOiEAMAAPM20P2yTh5LZPQ315UU6iN3bbnsd4aHh9XV1aVoNKpUKqXKykoFg8Hp/sbGRg0ODioUCqm2tlaSVFhYqGg0mtGsmUYhBgAAct7AwIBqampUUFAgSaqurvY4UWZQiAEAgHmba+YKV4anJgEAQM7buXOnenp6lEwmFY/H1dvb63WkjGBGDAAA5LzKykrV1dWpvLxcfr9f27dv9zpSRphzLvM/arZH0ofSH98nacw5V2FmmySNSHop3fdL59yX5vq9qqoqNzQ0lPGcAABgbiMjIyotLfU6Rk6aaWzMbNg5VzWf/bMyI+acm35G1My+K+nMRd2/cc5VZOO4AAAAS0lWL02amUm6S9Lt2TwOAADAUpTtm/U/Iukt59wrF7V9wMx+bWbPmNlHZtvRzO4zsyEzGzpx4kSWYwIAACy+q54RM7N+SRtm6Pqmc+6n6e3PS/rJRX3HJd3knDtlZkFJPWYWcM79/tIfcc61S2qXpu4Ru9qcAAAAueqqCzHn3Mcv129meZI+I2n6tbfOuXclvZveHjaz30jaIok78QEAwLKTzUuTH5f0onPujQsNZna9mfnS2x+UdIukV7OYAQAAIGdlsxD7nP74sqQk7ZT0f2YWlbRX0pecc7EsZgAAANeg5uZmtbW1qampSf39/ZKmlkEKBAKqqKhQMplUOBxWIBBQOBz2OO3ssvbUpHOuYYa2JyQ9ka1jAgCA5aWlpWV6u7OzU5FIRPX19ZKk9vZ2xWIx+Xw+r+LNiTfrAwCAJaG1tVUdHR3y+/0qKSlRMBhUQ0ODQqGQxsbG1N3drQMHDqivr0/xeFyJRELBYFCRSER1dXVzH8ADFGIAAGDenv5xu97+bWZv7/a//4P6WMN9l/3O8PCwurq6FI1GlUqlVFlZqWBw+nlANTY2anBwUKFQSLW1tZKkwsJCRaPRjGbNNAoxAACQ8wYGBlRTU6OCggJJUnV1tceJMoNCDAAAzNtcM1e4Mtl+sz4AAMCC7dy5Uz09PUomk4rH4+rt7fU6UkYwIwYAAHJeZWWl6urqVF5eLr/fr+3bt3sdKSPMudxfPaiqqsoNDfHyfQAAvDAyMqLS0lKvY+SkmcbGzIadc1Xz2Z9LkwAAAB6hEAMAAPAIhRgAAIBHKMQAAAA8QiEGAADgEQoxAAAAj1CIAQCAJae5uVltbW1qampSf3+/pKllkAKBgCoqKpRMJhUOhxUIBBQOhz1OOzte6AoAAJaslpaW6e3Ozk5FIhHV19dLktrb2xWLxeTz+byKNycKMQAAsCS0traqo6NDfr9fJSUlCgaDamhoUCgU0tjYmLq7u3XgwAH19fUpHo8rkUgoGAwqEomorq7O6/gzohADAADzNtb7G5373TsZ/c1Vxe/V+/7m5st+Z3h4WF1dXYpGo0qlUqqsrFQwGJzub2xs1ODgoEKhkGprayVJhYWFikajGc2aaRRiAAAg5w0MDKimpkYFBQWSpOrqao8TZQaFGAAAmLe5Zq5wZXhqEgAA5LydO3eqp6dHyWRS8Xhcvb29XkfKCGbEAABAzqusrFRdXZ3Ky8vl9/u1fft2ryNlhDnnvM4wp6qqKjc0NOR1DAAAlqWRkRGVlpZ6HSMnzTQ2ZjbsnKuaz0mELAoAAArxSURBVP5cmgQAAPAIhRgAAIBHKMQAAAA8QiEGAADgEQoxAAAAj1CIAQAAeIRCDAAALDnNzc1qa2tTU1OT+vv7JU0tgxQIBFRRUaFkMqlwOKxAIKBwOOxx2tnxQlcAALBktbS0TG93dnYqEomovr5ektTe3q5YLCafz+dVvDlRiAEAgCWhtbVVHR0d8vv9KikpUTAYVENDg0KhkMbGxtTd3a0DBw6or69P8XhciURCwWBQkUhEdXV1XsefEYUYAACYt76+Po2Ojmb0Nzds2KA777zzst8ZHh5WV1eXotGoUqmUKisrFQwGp/sbGxs1ODioUCik2tpaSVJhYaGi0WhGs2YahRgAAMh5AwMDqqmpUUFBgSSpurra40SZQSEGAADmba6ZK1wZnpoEAAA5b+fOnerp6VEymVQ8Hldvb6/XkTKCGTEAAJDzKisrVVdXp/Lycvn9fm3fvt3rSBlhzjmvM8ypqqrKDQ0NeR0DAIBlaWRkRKWlpV7HyEkzjY2ZDTvnquazP5cmAQAAPEIhBgAA4BEKMQAAAI9QiAEAAHiEQgwAAMAjFGIAAAAeoRADAABLTnNzs9ra2tTU1KT+/n5JU8sgBQIBVVRUKJlMKhwOKxAIKBwOe5x2drzQFQAALFktLS3T252dnYpEIqqvr5cktbe3KxaLyefzeRVvTguaETOzvzOz581s0syqLumLmNkRM3vJzD5xUfsn021HzOzrCzk+AABYPlpbW7Vlyxbt2LFDL730kiSpoaFBe/fu1aOPPqru7m49+OCD+sIXvqDq6molEgkFg0Ht2bPH4+SzW+iM2GFJn5H0g4sbzWyrpM9JCkgqltRvZlvS3Y9I+mtJb0j6lZk96Zx7YYE5AADAInj55W8pnhjJ6G+uLizVli0PXvY7w8PD6urqUjQaVSqVUmVlpYLB4HR/Y2OjBgcHFQqFVFtbK0kqLCxUNBrNaNZMW1Ah5pwbkSQzu7Tr05K6nHPvSnrNzI5I+rN03xHn3Kvp/brS36UQAwAAsxoYGFBNTY0KCgokSdXV1R4nyoxs3SN2o6RfXvT5jXSbJB27pP3Ps5QBAABk2FwzV7gycxZiZtYvacMMXd90zv0085Gmj3ufpPvSHxNm9lK2jnWRdZJOLsJx8McYd28w7t5g3L3BuC/AwYMHb5uYmEhd6X4TExN5Pp/vivebyQ033LDikUceya+urk6mUint3bv3utra2vOnTp1acfTo0YnDhw9PnDp1atWFbUmanJwsOHz48NlMHH82o6OjeVu3bn3ukub3z3f/OQsx59zHrziV9Kakkos+b0y36TLtlx63XVL7VRz7qpnZ0HxXS0fmMO7eYNy9wbh7g3FfmEOHDh0tKyu74kL28OHDpWVlZRm5oaysrEzPPvvshtra2nVFRUXny8vLTxcWFp5dtWrVdUVFRWfKyspOr1q1atOF7fRu2zJ1/NlMTEysW8i5la1Lk09KetzMvqepm/VvkfS/kkzSLWb2AU0VYJ+T9PdZygAAAK4hDz300OhDDz00Olv/E088cfTiz2fPnv111kMt0IIKMTOrkfSvkq6X9JSZRZ1zn3DOPW9m3Zq6CT8l6SvOuYn0Pl+VdECST9KPnHPPL+hPAAAAsEQt9KnJfZL2zdLXKql1hvafSfrZQo6bRYt6KRTTGHdvMO7eYNy9wbh7YN26dSe8zpDrWOLoIun70rDIGHdvMO7eYNy9wbh7Y8OGDTwgMQcKMQAAAI+w1qSmll2S9H1N3bf2qHPuOx5HWhbM7KikuKQJSSmeaMoeM/uRpJCkt51zZem2tZL2SNok6aiku5xzp2f7DVyZWca8WdK9ki5crvlG+nYNZIiZlUh6TNJ6SU5Su3Pu+5zv2TU+Pr7ytdde+0AqlVopSUVFRSeKi4vfPnbsWPGpU6fW5eXlpSSpuLj4zbVr157xNm1uWfYzYmbm09SyS3dK2irp8+klmrA4Puacq6AIy7ofS/rkJW1fl/Rz59wtkn6e/ozM+bH+dMwl6V/S53wFRVhWpCQ94JzbKukvJH0l/Xc653sWmZk2btz4xm233fZ8aWnpyMmTJ/3vvPPOeyTp+uuvf6usrOyFsrKyFyjC/tSyL8Q0tfTSEefcq865c5IuLLsEXDOcc/8tKXZJ86cldaS3OyT97aKGusbNMubIMufccefcs+ntuKQRTa3swvmeRfn5+edXr159VpLy8vIm8/Pzk+fOnVuVzWPu3r27uKmpaf39999f3NPTs1qS9u/fX7h58+bArbfeujWRSNiuXbs2bt68ObBr166N2cyyEFyanPoflGWXvOEk/ZeZOUk/4GbaRbfeOXc8vT2qqUs5yL6vmtk/ShrS1MwNl8eyxMw2Sdom6X/E+b5oxsfHV42PjxesXr06kUgkCk+ePOmPxWJFBQUFZ2+66aZjK1eunMjk8R5++OHfXdh+7LHH1u7evfv4l7/85ZgkPf744+tOnz4dzcvL3XInd5NhOdjhnHvTzPySDprZi+lZBCwy55xLF8TIrn+T9C1N/SPkW5K+K+keTxNdo8ysUNITku53zv3ezKb7ON+zJ5VKrThy5MjNN95447G8vLzJ9evXv71x48bfSdKxY8dufP3110tuvvnmo1f7+1/72tc27NmzZ11RUdH54uLic9u2bTv72c9+dlMoFDpz+vRp31NPPbX2mWeeWbN///41iUTCd/bsWV9ZWdnWBx544Pi9996bk//ooRC7/HJMyCLn3Jvp/75tZvs0dZmYQmzxvGVmNzjnjpvZDZLe9jrQtc4599aFbTP7d0n/6WGca5aZrdRUEdbpnPuPdDPne4bcP/J6yYvvjBf8aY/TxPj4debLS60Yj63X0dgfzzq6SZsYH7/Od/rl/Ev3vPW97zn7cOlNxy5tv9jAwEDBvn371j733HMvnD9/XhUVFVu3bds2vY7k7t27T/7iF78oDIVCZ774xS+elqSCgoJtL7744gtX+UddFNwjJv1K6WWXzGyVppZdetLjTNc8M3uvma2+sC3pDkmHvU217Dwp6e709t2SfuphlmUhXQBcUCPO+YyzqamvH0oacc5976Iuzvescpp899332IoVkytWrjz/h+bJ6anIydREnq1YMXm1R3j66acLP/WpT42tXr16cu3atZN33HHH2AJD54RlPyPmnEux7JIn1kval75ckCfpcefcfm8jXbvM7CeSPippnZm9IemfJX1HUreZ/ZOk30q6y7uE155ZxvyjZlahqUuTRyXt8izgtesvJf2DpOfMLJpu+4Y43zNmppmrM2fOFL7yyisfys/PT5qd80lTr6qIxWJrk8nkdZK0avWq8U2bNv82Pz///KX7L2fLvhCTcn7ZpWuSc+5VSeVe51gunHOfn6XrrxY1yDIyy5j/cNGDLDPOuUFJNks353uWrFmzJlFVVTV8aXsmX1dx++23J+65555N3/72t4+fP3/eDh48+L677757yS+hRCEGAABy3o4dO87W1NTEysrKAkVFRec//OEPv+N1pkww53hwBAAAzO7QoUNHy8vLWTdyBocOHVpXXl6+6Wr352Z9AAAAj1CIAQAAeIRCDAAAwCMUYgAAYC6Tk5OTsz2Numylx+Sq340mUYgBAIC5HT5x4sQairE/mJyctBMnTqzRAl/MzOsrAADAZaVSqcbR0dFHR0dHy8QkzgWTkg6nUqnGhfwIr68AAADwCFUtAACARyjEAAAAPEIhBgAA4BEKMQAAAI9QiAEAAHjk/wHKtHrwgiIPKgAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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5PKurq0xOTjI7O8vMzAyVSoW1tTWSJKFUKlEoFNKOv639KmK/zGGnJUMIT48x3rH5sRv48j7lkCRJT8Cdb30rDy3fUNMxn9xyCedec81R91laWmJiYoJyucz6+jq5XI4kSba29/f3s7CwQD6fp6enB4CmpibK5XJNs9banhexEMJpwM8Av3bY6v8WQmhj49TkTY/bJkmS9APm5+fp7u6msbERgK6urpQT1caeF7EY43eBsx+37rV7/b2SJKn2dpq50rHxyfqSJKnudXZ2MjU1RbVapVKpMD09nXakmtiva8QkSZJ+aLlcjkKhQGtrK5lMho6OjrQj1USIsf6fDNHe3h4XFxfTjiFJ0glpeXmZlpaWtGPUpe1+mxDCUoyxfTfHe2pSkiQpJRYxSZKklFjEJEmSUmIRkyRJSolFTJIkKSUWMUmSpJRYxCRJ0oEzODjIyMgIAwMDzM3NARuvQcpms7S1tVGtVikWi2SzWYrFYsppj8wHukqSpANraGhoa3l8fJxSqURvby8Ao6OjrKys0NDQkFa8HVnEJEnSgTA8PMzY2BiZTIbm5maSJKGvr498Ps/q6iqTk5PMzs4yMzNDpVJhbW2NJEkolUoUCoW042/LIiZJknZtfvJr3HvLWk3HPKe5iRe/+uKj7rO0tMTExATlcpn19XVyuRxJkmxt7+/vZ2FhgXw+T09PDwBNTU2Uy+WaZq01i5gkSap78/PzdHd309jYCEBXV1fKiWrDIiZJknZtp5krHRvvmpQkSXWvs7OTqakpqtUqlUqF6enptCPVhDNikiSp7uVyOQqFAq2trWQyGTo6OtKOVBMhxph2hh21t7fHxcXFtGNIknRCWl5epqWlJe0YdWm73yaEsBRjbN/N8Z6alCRJSolFTJIkKSUWMUmSpJRYxCRJklJiEZMkSUqJRUySJCklFjFJknTgDA4OMjIywsDAAHNzc8DGa5Cy2SxtbW1Uq1WKxSLZbJZisZhy2iPzga6SJOnAGhoa2loeHx+nVCrR29sLwOjoKCsrKzQ0NKQVb0cWMUmSdCAMDw8zNjZGJpOhubmZJEno6+sjn8+zurrK5OQks7OzzMzMUKlUWFtbI0kSSqUShUIh7fjbsohJkqRd++wHRrn75m/WdMzMs57NS/uuOOo+S0tLTExMUC6XWV9fJ5fLkSTJ1vb+/n4WFhbI5/P09PQA0NTURLlcrmnWWrOISZKkujc/P093dzeNjY0AdHV1pZyoNixikiRp13aaudKx8a5JSZJU9zo7O5mamqJarVKpVJienk47Uk04IyZJkupeLpejUCjQ2tpKJpOho6Mj7Ug1EWKMaWfYUXt7e1xcXEw7hiRJJ6Tl5WVaWlrSjlGXtvttQghLMcb23RzvqUlJkqSUWMQkSZJSYhGTJElKiUVMkiQpJRYxSZKklFjEJEmSUmIRkyRJB87g4CAjIyMMDAwwNzcHbLwGKZvN0tbWRrVapVgsks1mKRaLKac9Mh/oKkmSDqyhoaGt5fHxcUqlEr29vQCMjo6ysrJCQ0NDWvF2ZBGTJEkHwvDwMGNjY2QyGZqbm0mShL6+PvL5PKurq0xOTjI7O8vMzAyVSoW1tTWSJKFUKlEoFNKOvy2LmCRJ2rXV6W/w8O3fremYT3rGaZzxyh896j5LS0tMTExQLpdZX18nl8uRJMnW9v7+fhYWFsjn8/T09ADQ1NREuVyuadZas4hJkqS6Nz8/T3d3N42NjQB0dXWlnKg2LGKSJGnXdpq50rHZ87smQwg3hRD+OYRQDiEsbq47K4Tw6RDCv2z+e+Ze55AkSQdXZ2cnU1NTVKtVKpUK09PTaUeqif16fMVLY4xth72J/GrgMzHGi4DPbH6WJEnaVi6Xo1Ao0NrayuWXX05HR0fakWoixBj39gtCuAlojzHee9i6G4GXxBjvCCE8HfhcjPG5Rxqjvb09Li4u7mlOSZK0veXlZVpaWtKOUZe2+21CCEuHTT4d1X7MiEXgUyGEpRDCFZvrnhZjvGNz+U7gafuQQ5Ikqa7sx8X6PxljvC2EkAE+HUK44fCNMcYYQvg303Kbpe0KgPPPP38fYkqSJO2vPZ8RizHetvnv3cB1wI8Dd22ekmTz37u3OW40xtgeY2x/6lOfutcxJUmS9t2eFrEQwmkhhNMfWwZ+Fvgy8AngVzZ3+xXg43uZQ5IkqR7t9anJpwHXhRAe+64PxRj/MoTw98BkCOE/AjcDr97jHJIkSXVnT4tYjPGbQOs2678DXLaX3y1JklTv9us5YpIkSTUzODjIyMgIAwMDzM3NARuvQcpms7S1tVGtVikWi2SzWYrFYsppj8xXHEmSpANraGhoa3l8fJxSqURvby8Ao6OjrKys0NDQkFa8HVnEJEnSgTA8PMzY2BiZTIbm5maSJKGvr498Ps/q6iqTk5PMzs4yMzNDpVJhbW2NJEkolUoUCoW042/LIiZJknZtZmaGO++8s6ZjnnvuuVx++eVH3WdpaYmJiQnK5TLr6+vkcjmSJNna3t/fz8LCAvl8np6eHgCampool8s1zVprFjFJklT35ufn6e7uprGxEYCurq6UE9WGRUySJO3aTjNXOjbeNSlJkupeZ2cnU1NTVKtVKpUK09PTaUeqCWfEJElS3cvlchQKBVpbW8lkMnR0dKQdqSZCjP/mfdt1p729PS4uLqYdQ5KkE9Ly8jItLS1px6hL2/02IYSlGGP7bo731KQkSVJKLGKSJEkpsYhJkiSlxCImSZKUEouYJElSSixikiRJKbGISZKkA2dwcJCRkREGBgaYm5sDNl6DlM1maWtro1qtUiwWyWazFIvFlNMemQ90lSRJB9bQ0NDW8vj4OKVSid7eXgBGR0dZWVmhoaEhrXg7sohJkqQDYXh4mLGxMTKZDM3NzSRJQl9fH/l8ntXVVSYnJ5mdnWVmZoZKpcLa2hpJklAqlSgUCmnH35ZFTJIk7drXvvYWKmvLNR3z9KYWLr74zUfdZ2lpiYmJCcrlMuvr6+RyOZIk2dre39/PwsIC+Xyenp4eAJqamiiXyzXNWmsWMUmSVPfm5+fp7u6msbERgK6urpQT1YZFTJIk7dpOM1c6Nt41KUmS6l5nZydTU1NUq1UqlQrT09NpR6oJZ8QkSVLdy+VyFAoFWltbyWQydHR0pB2pJkKMMe0MO2pvb4+Li4tpx5Ak6YS0vLxMS0tL2jHq0na/TQhhKcbYvpvjPTUpSZKUEouYJElSSixikiRJKbGISZIkpcQiJkmSlBKLmCRJUkosYpIk6cAZHBxkZGSEgYEB5ubmgI3XIGWzWdra2qhWqxSLRbLZLMViMeW0R+YDXSVJ0oE1NDS0tTw+Pk6pVKK3txeA0dFRVlZWaGhoSCvejixikiTpQBgeHmZsbIxMJkNzczNJktDX10c+n2d1dZXJyUlmZ2eZmZmhUqmwtrZGkiSUSiUKhULa8bdlEZMkSbv25n+5lS+vVWs65vObfoS3XHTeUfdZWlpiYmKCcrnM+vo6uVyOJEm2tvf397OwsEA+n6enpweApqYmyuVyTbPWmkVMkiTVvfn5ebq7u2lsbASgq6sr5US1YRGTJEm7ttPMlY6Nd01KkqS619nZydTUFNVqlUqlwvT0dNqRasIZMUmSVPdyuRyFQoHW1lYymQwdHR1pR6qJEGNMO8OO2tvb4+LiYtoxJEk6IS0vL9PS0pJ2jLq03W8TQliKMbbv5nhPTUqSJKXEIiZJkpQSi5gkSVJKLGKSJEkpsYhJkiSlxCImSZKUkj0rYiGE5hDCZ0MIXw0hfCWE8Fub6wdDCLeFEMqb/71irzJIkqTj0+DgICMjIwwMDDA3NwdsvAYpm83S1tZGtVqlWCySzWYpFosppz2yvXyg6zrwn2OMXwohnA4shRA+vbntnTHGkT38bkmSdAIYGhraWh4fH6dUKtHb2wvA6OgoKysrNDQ0pBVvR3tWxGKMdwB3bC5XQgjLwDP36vskSdLxbXh4mLGxMTKZDM3NzSRJQl9fH/l8ntXVVSYnJ5mdnWVmZoZKpcLa2hpJklAqlSgUCmnH39a+vOIohHAB8GPA3wIvAt4YQngdsMjGrNl9+5FDkiQ9Mb83/RW+evsDNR3zec84xO++MnvUfZaWlpiYmKBcLrO+vk4ulyNJkq3t/f39LCwskM/n6enpAaCpqYlyuVzTrLW25xfrhxCagI8Bvx1jfAD4E+BHgTY2ZszecYTjrgghLIYQFu+55569jilJkurY/Pw83d3dNDY2cujQIbq6utKOVBN7OiMWQjiFjRI2HmP8XwAxxrsO2/5nwP/e7tgY4ygwChvvmtzLnJIkaXd2mrnSsdnLuyYD8D5gOcb4h4etf/phu3UDX96rDJIk6fjQ2dnJ1NQU1WqVSqXC9PR02pFqYi9nxF4EvBb45xDCYydorwF+OYTQBkTgJuDX9jCDJEk6DuRyOQqFAq2trWQyGTo6OtKOVBMhxvo/69fe3h4XFxfTjiFJ0glpeXmZlpaWtGPUpe1+mxDCUoyxfTfH+2R9SZKklFjEJEmSUmIRkyRJSolFTJIkKSUWMUmSpJRYxCRJklJiEZMkSQfO4OAgIyMjDAwMMDc3B2y8BimbzdLW1ka1WqVYLJLNZikWiymnPbJ9eem3JEnSXhgaGtpaHh8fp1Qq0dvbC8Do6CgrKys0NDSkFW9HFjFJknQgDA8PMzY2RiaTobm5mSRJ6OvrI5/Ps7q6yuTkJLOzs8zMzFCpVFhbWyNJEkqlEoVCIe3427KISZKk3Zu5Gu7859qOee6lcPkfHHWXpaUlJiYmKJfLrK+vk8vlSJJka3t/fz8LCwvk83l6enoAaGpqolwuH2nIumARkyRJdW9+fp7u7m4aGxsB6OrqSjlRbVjEJEnS7u0wc6Vj412TkiSp7nV2djI1NUW1WqVSqTA9PZ12pJpwRkySJNW9XC5HoVCgtbWVTCZDR0dH2pFqIsQY086wo/b29ri4uJh2DEmSTkjLy8u0tLSkHaMubffbhBCWYoztuzneU5OSJEkpsYhJkiSlxCImSZKUEouYJElSSixikiRJKbGISZIkpcQiJkmSDpzBwUFGRkYYGBhgbm4O2HgNUjabpa2tjWq1SrFYJJvNUiwWU057ZD7QVZIkHVhDQ0Nby+Pj45RKJXp7ewEYHR1lZWWFhoaGtOLtyCImSZIOhOHhYcbGxshkMjQ3N5MkCX19feTzeVZXV5mcnGR2dpaZmRkqlQpra2skSUKpVKJQKKQdf1sWMUmStGtv+7u3ccPKDTUd85KzLuGqH7/qqPssLS0xMTFBuVxmfX2dXC5HkiRb2/v7+1lYWCCfz9PT0wNAU1MT5XK5pllrzSImSZLq3vz8PN3d3TQ2NgLQ1dWVcqLasIhJkqRd22nmSsfGuyYlSVLd6+zsZGpqimq1SqVSYXp6Ou1INeGMmCRJqnu5XI5CoUBrayuZTIaOjo60I9VEiDGmnWFH7e3tcXFxMe0YkiSdkJaXl2lpaUk7Rl3a7rcJISzFGNt3c7ynJiVJklJiEZMkSUqJRUySJCklFjFJkqSUWMQkSZJS4uMr9tGbrv5NPv/0f88j8fjov4HIi757I2+/9vfSjiJJ0oFkEdtHy+deyrfvOAPq9yXwxyR+Hz73tGzaMSRJJ6DBwUGampp44IEH6Ozs5GUvexnz8/NceeWVnHLKKVx//fUMDAzwyU9+kle84hW8/e1vTzvytixi++jb8Sw4GV730KcYyn4DHl6DKz6Xdqwf2iVv+zgr321MO4Yk6QQ2NDS0tTw+Pk6pVKK3txeA0dFRVlZWaGio3xmQ4+Mc2QFx33cb+ZHT1xl62x/CbV+C8w72U4GfdmqF9bVA6eo3ph1FknQCGB4e5uKLL+Ynf/InufHGGwHo6+vjox/9KP/jf/wPJicnefOb38xrXvMaurq6WFtbI0kSPvzhD6ec/MicEdsnpavfyCNczjOfXoG7l+GR7x74Inbh9+/lZs5k5exnpR1FkrRP7nzrW3lo+Yaajvnklks495prjrrP0tISExMTlMtl1tfXyeVyJEmytb2/v5+FhQXy+Tw9PT0ANDU1US6Xa5q11pwR2ycrZz+LwEZ54da/31h53q7eflC3zl/5NgDffNJTU04iSTrezc8oK89OAAAgAElEQVTP093dTWNjI4cOHaKrqyvtSDXhjNg+eaysnL/ybbj1IWg8G868MOVUT8zQ7/8hfz70/3HHw09JO4okaZ/sNHOlY+OM2D654+GncFLjRnnh1r/fOC0ZQtqxnrCnNFVZqzyZd14znHYUSdJxrLOzk6mpKarVKpVKhenp6bQj1YQzYvvgndcMs3byj3HmGQ9CdRXuvRFe8Itpx6qJ8xru576Hf4S7Gh9MO4ok6TiWy+UoFAq0traSyWTo6DjY11k/xiK2D+469UF4MHJew/1w29LGygN+of5jnl29k3/mXG46/ZlpR5EkHeeuvfZarr322iNu/8AHPvADn9fW1vY40RPnqcl98FhJeXb1Trh1EQjwjFy6oWrktNtvIJ4EN4cz044iSdKBk1oRCyG8PIRwYwjh6yGEq9PKsR9uDmcST4LT7rgBbluEp14Cpx5KO1ZNvPXd/5Mnn/597n2wKe0okiQdOKkUsRBCA/Ae4HLgecAvhxCel0aW/XDvg02cevr3eesffXDzQv2D/diKxzuncY2HKydx9dX9aUeRJOlASesasR8Hvh5j/CZACGECeBXw1ZTycNm7Psj6HvXShytnct7T7mf2Pb/NRy/4bR55pAE++sd78l37KQAv+PYKF4SzuT0+hc887Wf5qXf9edqxJEk1NvjC5xLu+k7aMWqigchFTzsn7Rhb0ipizwRuOezzrcC/O3yHEMIVwBUA559//p4H+sY95xDX494MfhI8/5Hb+NRTns505iWE+CiwR9+1j2Jo4LtP+lueu3gjXzjl2dx912nAaWnHkiTV2Pr3G3j4kfp9X+OxCHV2dXzd3jUZYxwFRgHa29v3vLXc9Puv2ONv+Dle/Yk/5dT4IJ+78KlccOFFe/x9e+8lsx/mpsZzmRz6Nd6SdhhJ0p5ZXl6m5bwz0o5xXEqrF94GNB/2+bzNdce1mxrP5YJHbjkuShjABWv3cOtJ5/FXn/p42lEkSSeYwcFBRkZGGBgYYG5uDth4DVI2m6WtrY1qtUqxWCSbzVIsFlNOe2RpzYj9PXBRCOFCNgrYLwH/Z0pZ9sVfferj3Hryefzs/denHaVmnnn3fTx6VgPzN3yJn/7ZV6UdR5J0AhoaGtpaHh8fp1Qq0dvbC8Do6CgrKys0NNTvadVUiliMcT2E8EZgFmgA3h9j/EoaWfbL/A1f4tFLn8Uz774v7Sg185yTN64Huz3jdLUkae8NDw8zNjZGJpOhubmZJEno6+sjn8+zurrK5OQks7OzzMzMUKlUWFtbI0kSSqUShUIh7fjbSu0asRjjJ4FPpvX9++2xsvJYeTke/Oob/hPv/Mws337K2WlHkSTtk/nJr3HvLbV9Yv05zU28+NUXH3WfpaUlJiYmKJfLrK+vk8vlSJJka3t/fz8LCwvk83l6enoAaGpqolwu1zRrrdXZvQPHr28/5Wwyj97Fr77hP6Udpaae9b3b+daTm7lvZSXtKJKk49j8/Dzd3d00NjZy6NAhurq60o5UE3V71+Tx5L6VFb715GYurt6UdpSaO3/1O/x944/x5//zPfxfv/XmtONIkvbYTjNXOjbOiO2DP/+f72E1nMn5q8fHw/AO9/S7HwDglkb/pyRJ2judnZ1MTU1RrVapVCpMT0+nHakmnBHbB4+VlKfffX/KSWov/+JX8KcPPMxt55yVdhRJ0nEsl8tRKBRobW0lk8nQ0dGRdqSasIjtg9vOOYtT4sP83It/Lu0oNdfW/kLO//THuKkpk3YUSdJx7tprr+Xaa6894vYPfOADP/B5ba22NxXsBc8n7YObmjKc//1baGt/YdpR9sQFa3fz7YZmyotfTDuKJEkHijNim37jQ+9g/aS96aXfzryIzvsX92TsevDMe1d45Iwn8fZbrue0rx8/D6yVJG143XP+PTet3JV2jJo46dHI+eecm3aMLRaxTX957gt5MOzNM75C/D7Pvv2ePRm7HjznkVM4NT7IZ874ibSjSJL2QE9D4P6TT087Rk2cwiNpR/gBFrFN/y+rEFf3ZOynHDqbtt/83T0Zux5c8Rv/hZ++8avcdtu30o4iSdoDh3g6z+bhtGPURAgh7Qg/wCK26ad++vi7kH4/Pee5z+M5z31e2jEkSXtgeXmZ0w/5Oru94MX6kiRJKbGISZKkA2dwcJCRkREGBgaYm5sDNl6DlM1maWtro1qtUiwWyWazFIvFlNMemacmJUnSgTU0NLS1PD4+TqlUore3F4DR0VFWVlZoaGhIK96OLGKSJOlAGB4eZmxsjEwmQ3NzM0mS0NfXRz6fZ3V1lcnJSWZnZ5mZmaFSqbC2tkaSJJRKJQqFQtrxt2URkyRJu/bZD4xy983frOmYmWc9m5f2XXHUfZaWlpiYmKBcLrO+vk4ulyNJkq3t/f39LCwskM/n6enpAaCpqYlyuVzTrLVmEZMkSXVvfn6e7u5uGhsbAejq6ko5UW1YxCRJ0q7tNHOlY+Ndk5Ikqe51dnYyNTVFtVqlUqkwPT2ddqSacEZMkiTVvVwuR6FQoLW1lUwmQ0dHR9qRaiLEGNPOsKP29va4uHj8vjRbkqR6try8TEtLS9ox6tJ2v00IYSnG2L6b4z01KUmSlBKLmCRJUkosYpIkSSmxiEmSJKXEIiZJkpQSi5gkSVJKLGKSJOnAGRwcZGRkhIGBAebm5oCN1yBls1na2tqoVqsUi0Wy2SzFYjHltEfmA10lSdKBNTQ0tLU8Pj5OqVSit7cXgNHRUVZWVmhoaEgr3o4sYpIk6UAYHh5mbGyMTCZDc3MzSZLQ19dHPp9ndXWVyclJZmdnmZmZoVKpsLa2RpIklEolCoVC2vG3ZRGTJEm7tjr9DR6+/bs1HfNJzziNM175o0fdZ2lpiYmJCcrlMuvr6+RyOZIk2dre39/PwsIC+Xyenp4eAJqamiiXyzXNWmsWMUmSVPfm5+fp7u6msbERgK6urpQT1YZFTJIk7dpOM1c6Nt41KUmS6l5nZydTU1NUq1UqlQrT09NpR6oJZ8QkSVLdy+VyFAoFWltbyWQydHR0pB2pJkKMMe0MO2pvb4+Li4tpx5Ak6YS0vLxMS0tL2jHq0na/TQhhKcbYvpvjPTUpSZKUEouYJElSSixikiRJKbGISZIkpcQiJkmSlBKLmCRJUkosYpIk6cAZHBxkZGSEgYEB5ubmgI3XIGWzWdra2qhWqxSLRbLZLMViMeW0R+YDXSVJ0oE1NDS0tTw+Pk6pVKK3txeA0dFRVlZWaGhoSCvejixikiTpQBgeHmZsbIxMJkNzczNJktDX10c+n2d1dZXJyUlmZ2eZmZmhUqmwtrZGkiSUSiUKhULa8bdlEZMkSbs2MzPDnXfeWdMxzz33XC6//PKj7rO0tMTExATlcpn19XVyuRxJkmxt7+/vZ2FhgXw+T09PDwBNTU2Uy+WaZq21PblGLITw9hDCDSGEfwohXBdCOGNz/QUhhGoIobz533v34vslSdLxZX5+nu7ubhobGzl06BBdXV1pR6qJvZoR+zRQijGuhxDeBpSAqza3fSPG2LZH3ytJkvbQTjNXOjZ7MiMWY/xUjHF98+MXgfP24nskSdKJobOzk6mpKarVKpVKhenp6bQj1cR+XCP2euDDh32+MITwD8ADwJtijPP7kEGSJB1guVyOQqFAa2srmUyGjo6OtCPVRIgx/nAHhjAHnLvNpmtjjB/f3OdaoB34hRhjDCE8GWiKMX4nhJAAU0A2xvjANuNfAVwBcP755yc333zzD5VTkiQ9McvLy7S0tKQdoy5t99uEEJZijO27Of6HnhGLMb7saNtDCH1AHrgsbra9GONDwEOby0shhG8AFwOL24w/CowCtLe3/3BtUZIkqY7t1V2TLwf+C9AVY3zwsPVPDSE0bC4/G7gI+OZeZJAkSap3e3WN2P8DPBn4dAgB4IsxxiuBTmAohPAI8ChwZYxxZY8ySJIk1bU9KWIxxuccYf3HgI/txXdKkiQdNL70W5IkKSUWMUmSpJRYxCRJ0oEzODjIyMgIAwMDzM3NARuvQcpms7S1tVGtVikWi2SzWYrFYsppj8yXfkuSpANraGhoa3l8fJxSqURvby8Ao6OjrKys0NDQkFa8HVnEJEnSgTA8PMzY2BiZTIbm5maSJKGvr498Ps/q6iqTk5PMzs4yMzNDpVJhbW2NJEkolUoUCoW042/LIiZJknbta197C5W15ZqOeXpTCxdf/Oaj7rO0tMTExATlcpn19XVyuRxJkmxt7+/vZ2FhgXw+T09PDwBNTU2Uy+WaZq01i5gkSap78/PzdHd309jYCEBXV1fKiWrDIiZJknZtp5krHRvvmpQkSXWvs7OTqakpqtUqlUqF6enptCPVhDNikiSp7uVyOQqFAq2trWQyGTo6OtKOVBMhxph2hh21t7fHxcXFtGNIknRCWl5epqWlJe0YdWm73yaEsBRjbN/N8Z6alCRJSolFTJIkKSUWMUmSpJRYxCRJklJiEZMkSUqJRUySJCklFjFJknTgDA4OMjIywsDAAHNzc8DGa5Cy2SxtbW1Uq1WKxSLZbJZisZhy2iPzga6SJOnAGhoa2loeHx+nVCrR29sLwOjoKCsrKzQ0NKQVb0cWMUmSdCAMDw8zNjZGJpOhubmZJEno6+sjn8+zurrK5OQks7OzzMzMUKlUWFtbI0kSSqUShUIh7fjbsohJkqRde/O/3MqX16o1HfP5TT/CWy4676j7LC0tMTExQblcZn19nVwuR5IkW9v7+/tZWFggn8/T09MDQFNTE+VyuaZZa80iJkmS6t78/Dzd3d00NjYC0NXVlXKi2rCISZKkXdtp5krHxrsmJUlS3evs7GRqaopqtUqlUmF6ejrtSDXhjJgkSap7uVyOQqFAa2srmUyGjo6OtCPVRIgxpp1hR+3t7XFxcTHtGJIknZCWl5dpaWlJO0Zd2u63CSEsxRjbd3O8pyYlSZJSYhGTJElKiUVMkiQpJRYxSZKklFjEJEmSUmIRkyRJSolFTJIkHTiDg4OMjIwwMDDA3NwcsPEapGw2S1tbG9VqlWKxSDabpVgsppz2yHygqyRJOrCGhoa2lsfHxymVSvT29gIwOjrKysoKDQ0NacXbkUVMkiQdCMPDw4yNjZHJZGhubiZJEvr6+sjn86yurjI5Ocns7CwzMzNUKhXW1tZIkoRSqUShUEg7/rYsYpIkadd+b/orfPX2B2o65vOecYjffWX2qPssLS0xMTFBuVxmfX2dXC5HkiRb2/v7+1lYWCCfz9PT0wNAU1MT5XK5pllrzSImSZLq3vz8PN3d3TQ2NgLQ1dWVcqLasIhJkqRd22nmSsfGuyYlSVLd6+zsZGpqimq1SqVSYXp6Ou1INeGMmCRJqnu5XI5CoUBrayuZTIaOjo60I9VEiDGmnWFH7e3tcXFxMe0YkiSdkJaXl2lpaUk7Rl3a7rcJISzFGNt3c7ynJiVJklJiEZMkSUqJRUySJCklFjFJkqSU7FkRCyEMhhBuCyGUN/97xWHbSiGEr4cQbgwh/B97lUGSJKme7fXjK94ZYxw5fEUI4XnALwFZ4BnAXAjh4hjj9/c4iyRJUl1J49Tkq4CJGONDMcZvAV8HfjyFHJIk6YAaHBxkZGSEgYEB5ubmgI3XIGWzWdra2qhWqxSLRbLZLMViMeW0R7bXM2JvDCG8DlgE/nOM8T7gmcAXD9vn1s11kiRJx2RoaGhreXx8nFKpRG9vLwCjo6OsrKzQ0NCQVrwdPaEiFkKYA87dZtO1wJ8AbwHi5r/vAF5/DGNfAVwBcP755z+RmJIk6TgwPDzM2NgYmUyG5uZmkiShr6+PfD7P6uoqk5OTzM7OMjMzQ6VSYW1tjSRJKJVKFAqFtONv6wkVsRjjy3azXwjhz4D/vfnxNqD5sM3nba57/NijwChsPFn/ieSUJEk1MnM13PnPtR3z3Evh8j846i5LS0tMTExQLpdZX18nl8uRJMnW9v7+fhYWFsjn8/T09ADQ1NREuVyubdYa28u7Jp9+2Mdu4Muby58AfimE8OQQwoXARcDf7VUOSZJ08M3Pz9Pd3U1jYyOHDh2iq6sr7Ug1sZfXiP23EEIbG6cmbwJ+DSDG+JUQwiTwVWAd+E3vmJQk6YDYYeZKx2bPZsRijK+NMV4aY3xBjLErxnjHYduGY4w/GmN8boxxZq8ySJKk40NnZydTU1NUq1UqlQrT09NpR6qJvb5rUpIk6QnL5XIUCgVaW1vJZDJ0dHSkHakmQoz1fx18e3t7XFxcTDuGJEknpOXlZVpaWtKOUZe2+21CCEsxxvbdHO+7JiVJklJiEZMkSUqJRUySJCklFjFJkqSUWMQkSZJSYhGTJElKiUVMkiQdOIODg4yMjDAwMMDc3Byw8RqkbDZLW1sb1WqVYrFINpulWCymnPbIfKCrJEk6sIaGhraWx8fHKZVK9Pb2AjA6OsrKygoNDQ1pxduRRUySJB0Iw8PDjI2NkclkaG5uJkkS+vr6yOfzrK6uMjk5yezsLDMzM1QqFdbW1kiShFKpRKFQSDv+tixikiRp1972d2/jhpUbajrmJWddwlU/ftVR91laWmJiYoJyucz6+jq5XI4kSba29/f3s7CwQD6fp6enB4CmpibK5XJNs9aaRUySJNW9+fl5uru7aWxsBKCrqyvlRLVhEZMkSbu208yVjo13TUqSpLrX2dnJ1NQU1WqVSqXC9PR02pFqwhkxSZJU93K5HIVCgdbWVjKZDB0dHWlHqokQY0w7w47a29vj4uJi2jEkSTohLS8v09LSknaMurTdbxNCWIoxtu/meE9NSpIkpcQiJkmSlBKLmCRJUkosYpIkSSmxiEmSJKXEIiZJkpQSi5gkSTpwBgcHGRkZYWBggLm5OWDjNUjZbJa2tjaq1SrFYpFsNkuxWEw57ZH5QFdJknRgDQ0NbS2Pj49TKpXo7e0FYHR0lJWVFRoaGtKKtyOLmCRJOhCGh4cZGxsjk8nQ3NxMkiT09fWRz+dZXV1lcnKS2dlZZmZmqFQqrK2tkSQJpVKJQqGQdvxtWcQkSdKu3fnWt/LQ8g01HfPJLZdw7jXXHHWfpaUlJiYmKJfLrK+vk8vlSJJka3t/fz8LCwvk83l6enoAaGpqolwu1zRrrVnEJElS3Zufn6e7u5vGxkYAurq6Uk5UGxYxSZK0azvNXOnYeNekJEmqe52dnUxNTVGtVqlUKkxPT6cdqSacEZMkSXUvl8tRKBRobW0lk8nQ0dGRdqSaCDHGtDPsqL29PS4uLqYdQ5KkE9Ly8jItLS1px6hL2/02IYSlGGP7bo731KQkSVJKLGKSJEkpsYhJkiSlxCImSZKUEouYJElSSixikiRJKbGISZKkA2dwcJCRkREGBgaYm5sDNl6DlM1maWtro1qtUiwWyWazFIvFlNMemQ90lSRJB9bQ0NDW8vj4OKVSid7eXgBGR0dZWVmhoaEhrXg7sohJkqQDYXh4mLGxMTKZDM3NzSRJQl9fH/l8ntXVVSYnJ5mdnWVmZoZKpcLa2hpJklAqlSgUCmnH35ZFTJIk7dr85Ne495a1mo55TnMTL371xUfdZ2lpiYmJCcrlMuvr6+RyOZIk2dre39/PwsIC+Xyenp4eAJqamiiXyzXNWmsWMUmSVPfm5+fp7u6msbERgK6urpQT1YZFTJIk7dpOM1c6Nt41KUmS6l5nZydTU1NUq1UqlQrT09NpR6oJZ8QkSVLdy+VyFAoFWltbyWQydHR0pB2pJkKMsfaDhvBh4LmbH88AVmOMbSGEC4Bl4MbNbV+MMV6503jt7e1xcXGx5jklSdLOlpeXaWlpSTtGXdrutwkhLMUY23dz/J7MiMUYt+4RDSG8A7j/sM3fiDG27cX3SpIkHSR7emoyhBCAVwM/vZffI0mSdBDt9cX6LwbuijH+y2HrLgwh/EMI4a9DCC8+0oEhhCtCCIshhMV77rlnj2NKkiTtvx96RiyEMAecu82ma2OMH99c/mXgLw7bdgdwfozxOyGEBJgKIWRjjA88fpAY4ygwChvXiP2wOSVJkurVD13EYowvO9r2EMLJwC8AW4+9jTE+BDy0ubwUQvgGcDHglfiSJOmEs5enJl8G3BBjvPWxFSGEp4YQGjaXnw1cBHxzDzNIkiTVrb0sYr/ED56WBOgE/imEUAY+ClwZY1zZwwySJOk4NDg4yMjICAMDA8zNzQEbr0HKZrO0tbVRrVYpFotks1mKxWLKaY9sz+6ajDH2bbPuY8DH9uo7JUnSiWVoaGhreXx8nFKpRG9vLwCjo6OsrKzQ0NCQVrwd+WR9SZJ0IAwPDzM2NkYmk6G5uZkkSejr6yOfz7O6usrk5CSzs7PMzMxQqVRYW1sjSRJKpRKFQmHnL0iBRUySJO3aZz8wyt031/by7syzns1L+6446j5LS0tMTExQLpdZX18nl8uRJFv3A9Lf38/CwgL5fJ6enh4AmpqaKJfLNc1aaxYxSZJU9+bn5+nu7qaxsRGArq6ulBPVhkVMkiTt2k4zVzo2e/1kfUmSpCess7OTqakpqtUqlUqF6enptCPVhDNikiSp7uVyOQqFAq2trWQyGTo6OtKOVBMhxvp/e1B7e3tcXPTh+5IkpWF5eZmWlpa0Y9Sl7X6bEML/z979Bzdy3nee//aAlhwIODIAMjOBOQnjbSkQ0GGjNfbdVoXnyjG3TIxqa4WjbpVcnMTMmosNvXVBNUvHVVLGuezS1nqj24Prypc6XTaJUSWu6ZOP2PhIwQu7GB7EqtzFyhJHmYIlOR7nx1Crpdmk1dNjDaTp+2OGLkYhZzjjlp6m5/2qcrnZzW5+3G7OfOZpdD/PBUHwvuPsz61JAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAnDif+MQn5IknnpBarSZf+cpXROTaNEiFQkGKxaJcvnxZHn30USkUCvLoo48qTns0XugKAABOrE9+8pPfX37qqafksccekw9/+MMiIvLkk0/Kzs6OxGIxVfFuiiIGAABOhMcff1w+97nPyenTp+XcuXNy/vx5+chHPiK2bcvu7q584QtfkC9/+cvyzDPPyGuvvSae58n58+flsccek0ceeUR1/ENRxAAAwLHtfumbcuXipVCPeVf2Hhn60N+74fc899xz8vnPf17W19fljTfekAceeEDOnz///e0f/ehH5dlnnxXbtuXhhx8WEZFEIiHr6+uhZg0bRQwAAERep9ORcrks8XhcREQefPBBxYnCQREDAADHdrORK9wanpoEAACR94EPfECazaZcvnxZXnvtNfnSl76kOlIoGBEDAACR98ADD8gjjzwipmnK6dOn5f3vf7/qSKHQgiBQneGm3ve+9wVf+9rXVMcAAOCO9MILL8j999+vOkYkHXZuNE17LgiC9x1nf25NAgAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAA4MT5xCc+IU888YTUajX5yle+IiLXpkEqFApSLBbl8uXL8uijj0qhUJBHH31Ucdqj8UJXAABwYn3yk5/8/vJTTz0ljz32mHz4wx8WEZEnn3xSdnZ2JBaLqYp3UxQxAABwIjz++OPyuc99Tk6fPi3nzp2T8+fPy0c+8hGxbVt2d3flC1/4gnz5y1+WZ555Rl577TXxPE/Onz8vjz32mDzyyCOq4x+KIgYAAI7tmWeekVdeeSXUY549e1Y++MEP3vB7nnvuOfn85z8v6+vr8sYbb8gDDzwg58+f//72j370o/Lss8+Kbdvy8MMPi4hIIpGQ9fX1ULOGjSIGAAAir9PpSLlclng8LiIiDz74oOJE4aCIAQCAY7vZyBVuDU9NAgCAyPvABz4gzWZTLl++LK+99pp86UtfUh0pFIyIAQCAyHvggQfkkUceEdM05fTp0/L+979fdaRQaEEQqM5wU+973/uCr33ta6pjAABwR3rhhRfk/vvvVx0jkg47N5qmPRcEwfuOsz+3JgEAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAHDifOITn5AnnnhCarWafOUrXxGRa9MgFQoFKRaLcvnyZXn00UelUCjIo48+qjjt0XihKwAAOLE++clPfn/5qaeekscee0w+/OEPi4jIk08+KTs7OxKLxVTFu6kfaERM07T/VtO0r2uadlXTtPe9Zdtjmqa9rGnaNzRN+4UD63/x+rqXNU375z/IzwcAAHeOxx9/XO677z4ZGxuTb3zjGyIi8pGPfESefvpp+f3f/335whe+IB//+MflV37lV+TBBx8Uz/Pk/PnzsrCwoDj50X7QEbHnReS/EZH/7eBKTdPyIvJLIlIQkayIfEXTtPuub/6siPwDEflrEfkzTdP+OAiCzR8wBwAAeAe8+OKn5DXvhVCPmUzcL/fd9/Ebfs9zzz0nn//852V9fV3eeOMNeeCBB+T8+fPf3/7Rj35Unn32WbFtWx5++GEREUkkErK+vh5q1rD9QEUsCIIXREQ0TXvrpn8oIp8PguB1EfmWpmkvi8h/fn3by0EQ/MX1/T5//XspYgAA4EidTkfK5bLE43EREXnwwQcVJwrH2/UZsfeIyJ8e+Pqvr68TEfmrt6z/L96mDAAAIGQ3G7nCrblpEdM07SsicvaQTb8TBMG/Cz/S93/uPxGRf3L9S0/TtG+8XT/rgIyIbL8DP+ck4twcjXNzY5yfo3Fujsa5Odo7fm7a7fbPvPnmm2+8kz/zrX78x3/81Gc/+9m7H3zwwctvvPGGPP300z/y8MMP97/zne+cunDhwpvPP//8m9/5znfu+ta3viXPP//8FRGRq1evxp9//nn/7cz1yiuvDOTz+Y23rP7J4+5/0yIWBMF/fcupRP5GRM4d+Hr4+jq5wfq3/twnReTJ2/jZt03TtK8dd7b0Ow3n5micmxvj/ByNc3M0zs3RVJybbrd7wTAMpcXYMAz58z//80XERXIAACAASURBVLMPP/xwJp1O903TdBOJhH/XXXf9SDqd3jMMw73rrrtGNE1LGIax/yE268Dy2+LNN9/M/CD/f7xdtyb/WETmNU3713Ltw/r3isj/KyKaiNyradpPybUC9ksi8t+9TRkAAMAPkU9/+tOvfPrTn37lqO1f/OIXLzz//PP373/t+/5/eGeS3b4fqIhpmlYWkf9FRH5MRJY0TVsPguAXgiD4uqZpX5BrH8J/Q0Q+FgTBm9f3+Wci8mURiYnIHwRB8PUf6H8BAADACfWDPjW5KCKLR2x7XEQeP2T9sogs/yA/9230jt4KPWE4N0fj3NwY5+donJujcW6Oxrm5gUwm859UZ7gVWhAEqjMAAIAI63a7F0zT5OGJQ3S73YxpmiO3uz9zTQIAACjCXJNybdolEfmMXPvc2u8HQfAvFUdSRtO0cyLSEJEzIhKIyJNBEHxG07RPiMi0iOwP+f729dvMdxxN0y6IyGsi8qaIvBEEwfs0TUuJyIKIjIjIBRH5R0EQuKoyqqBp2k/LtXOw770iUhORIbkDrx1N0/5ARGwReTUIAuP6ukOvE+3aW7E/IyIlEfFF5CNBEPy5itzvlCPOz++KyIdE5IqIfFNEpoIg2NU0bUREXhCR/dcY/WkQBP/0HQ/9Djni3HxCjvg90jTtMRH5x3Ltz6T/PgiCL7/jod8h3/zmN0e++93vDg4MDLzxMz/zM18XEXnppZfe+/rrr79bROTNN9+MxWKxNw3D2Pze975319e//nXj7rvv/p6ISDwe99773vf+pcr8h7njR8Q0TYvJtWmXPigieRH55etTNN2p3hCR2SAI8iLy90XkYwfOx/8cBEHx+n9+6P8ivYn/6vp52H9k+Z+LyFeDILhXRL56/es7ShAE39i/PkTkvFwrFPufIb0Tr50/EpFffMu6o66TD8q1p8vvlWvvT/y9dyijSn8kf/f8tEXECIJgVEReFJHHDmz75oFr6Ie2hF33R/J3z43IIb9Hb5lS8BdF5H+9/vfaD6VMJrOt6/pLB9fde++9f2EYxqZhGJuDg4Pu4ODg9/8RfNddd72+vy2KJUyEIiZybeqll4Mg+IsgCK6IyP60S3ekIAi29v8lHgTBa3LtX6HvufFekGvXzOeuL39ORB5SmCUKfl6u/cX5bdVBVAmC4P8WkZ23rD7qOvmHItIIrvlTERnSNO3H35mkahx2foIg+PdBEOy/NPRP5dq7Ju84R1w7R/n+lIJBEHxLRA5OKfhDZ3Bw0HvXu971hoiI4zjZWq12plqtZpvNZjIIAvnqV7+a/rmf+7l0LpfLX7p0SXviiScGdF0vVCqVyF5LFLFrJeOt0y5RPETk+u0AS0T+n+ur/pmmaf+fpml/oGnajyoLpl4gIv9e07Tnrs8AISJyJgiCrevLr8i1W7t3sl8SkX974GuunWuOuk74c+jv+g0ReebA1z+ladp/0DRtVdO0/1JVKMUO+z2646+der1+8aGHHnrtu9/9buKZZ56R2dnZi71eb/Oee+4JvvjFLw588YtfDH7rt37rnr29vYTqrIehiOFQmqYlROSLIlINguC7cu1Wyd8TkaKIbInI/6QwnmpjQRA8INduJ31M07QPHNwYXHsU+Y59HFnTtLtE5EER+T+ur+LaOcSdfp3ciKZpvyPXPibx1PVVWyLyE0EQWCLiyLUXhv9nqvIpwu+RiMzNzZ396Z/+6Z/+1V/91bteeumlu0VEJicnR/7wD//wRz/zmc9k2+32qccff/w9Dz744E+VSqWfuHz5sjzyyCPyJ3/yJ3vf+ta33vvGG29ErvfwYf0bT8d0R9I07V1yrYQ9FQTB/ykiEgTBfzyw/X8Xkf9LUTzlgiD4m+v//aqmaYty7TbAf9Q07ceDINi6fkvpVaUh1fqgiPz5/jXDtfO3HHWd8OfQdZqmfUSufVD956+XVQmC4HURef368nOapn1TRO4Tka+pyvlOu8Hv0Tt+7VRf+MtzvUvfi4d5zNw97/br9//EX93oezqdTnxxcTH13HPPvfjSSy/pjzzyyD2WZfkiIlevXpUPfehDP7K+vr73oQ99yJ2amnJFROLxuNXr9TZFRF544YXBy5cvvzuZTL6tc0/eqsg1QwX+TK5Pu3T9X/K/JNemaLojXX9669+IyAtBEPzrA+sPfl6lLCLPv9PZokDTtHs0TUvuL4vIhFw7F38sIr9+/dt+XUT+nZqEkfDLcuC2JNfO33LUdfLHIvJr2jV/X0T2DtzCvGNcf4L9fxCRB4Mg8A+s/7H9D6BrmvZeufZQw1+oSanGDX6P/lhEfknTtLuvTx+4P6XgD52VlZVEqVTaTSQSQSKRkImJid39bVeuXPmRu++++3uapl09sO77g02XL1++6/XXX7/73e9+9+vvdO6bueNHxIIgeINpl/6WnxWRXxWRDU3T1q+v+2259jRpUa7dSrkgIhU18ZQ7IyKL1/qqDIjIfBAELU3T/kxEvqBp2j8WkW+LyD9SmFGZ6+X0H8jfvj7+1Z147Wia9m9F5OdEJKNp2l+LyP8oIv9SDr9OluXaqytelmtPm06944HfYUecn8dE5G4RaV//Hdt/TcUHROSTmqb1ReSqiPzTIAiO+2H2E+eIc/Nzh/0e3WhKwbfLzUau3k7f+973hnq9XubNN98cuHz58o9duXJlT0Suvv7664lUKrUjIvfsf+93v/vdhIicev755/MiEpw7d+7b73rXu97Wc3M77vgiJhL5aZfeUUEQPCvXJmd/K86PiARB8BciYh6y/jty7UnBO1oQBJdEJP2Wdb+qKI5SQRD88hGb/s51cv0W3Mfe3kTRcsT5+TdHfO8X5drHJe4It3Jurn//oVMK/rAZHx/3fuM3fiPzxBNPbPT7fe3ZZ5/N//qv//olEfmRwcHB/3T27FlXDhSxTCazKyJXDcPYVBb6GChiAAAg8sbGxvxyubxjGEYhnU73R0dHL6nOFAbmmgQAADfEXJNHY65JAACAE4oiBgAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAwInjOE62VqudqVar2WazmRQRabVaCV3XC7lcLu95nlapVIZ1XS9UKpVh1XmPwgtdAQDAiVWv1y/uLzcajZTjOFszMzM7IiLz8/MZ13XXBwaiW3eimwwAAOCAubm5swsLC5l0Ot3PZrNXLMvyJycnR2zb3nNdN7a0tJRaXV0dbLVag57nxXzfjxmGkZ+dnd2anp52Vec/DEUMAAAc26NPd8+9+Mpr8TCPed/ZpP+7D5s3nEy80+nEFxcXUxsbG5v9fl+KxWLesix/f7vjONtra2sJ27b3pqamXBGReDxu9Xo95poEAAD4QaysrCRKpdJuMpm8KiIyMTGxqzpTGChiAADg2G42coVbw1OTAAAg8sbHx73l5eUhz/M013VPtdvtIdWZwsCIGAAAiLyxsTG/XC7vGIZRSKfT/dHR0UuqM4VBC4JAdQYAABBh3W73gmma26pzRFG3282Ypjlyu/tzaxIAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAAAnjuM42VqtdqZarWabzWZSRKTVaiV0XS/kcrm853lapVIZ1nW9UKlUhlXnPQovdAUAACdWvV6/uL/caDRSjuNszczM7IiIzM/PZ1zXXR8YiG7diW4yAACAA+bm5s4uLCxk0ul0P5vNXrEsy5+cnByxbXvPdd3Y0tJSanV1dbDVag16nhfzfT9mGEZ+dnZ2a3p62lWd/zAUMQAAcHzNj52TVzfjoR7zdN6Xhz57w8nEO51OfHFxMbWxsbHZ7/elWCzmLcvy97c7jrO9traWsG17b2pqyhURicfjVq/X2ww1a8goYgAAIPJWVlYSpVJpN5lMXhURmZiY2FWdKQwUMQAAcHw3GbnCreGpSQAAEHnj4+Pe8vLykOd5muu6p9rt9pDqTGFgRAwAAETe2NiYXy6XdwzDKKTT6f7o6Ogl1ZnCoAVBoDoDAACIsG63e8E0zW3VOaKo2+1mTNMcud39uTUJAACgCEUMAABAEYoYAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQCAE8dxnGytVjtTrVazzWYzKSLSarUSuq4Xcrlc3vM8rVKpDOu6XqhUKsOq8x6FF7oCAIATq16vX9xfbjQaKcdxtmZmZnZERObn5zOu664PDES37kQ3GQAAwAFzc3NnFxYWMul0up/NZq9YluVPTk6O2La957pubGlpKbW6ujrYarUGPc+L+b4fMwwjPzs7uzU9Pe2qzn8YihgAADi2j699/NzL7svxMI+p/6juf+pnP3XDycQ7nU58cXExtbGxsdnv96VYLOYty/L3tzuOs722tpawbXtvamrKFRGJx+NWr9fbDDNr2ChiAAAg8lZWVhKlUmk3mUxeFRGZmJjYVZ0pDBQxAABwbDcbucKt4alJAAAQeePj497y8vKQ53ma67qn2u32kOpMYWBEDAAARN7Y2JhfLpd3DMMopNPp/ujo6CXVmcKgBUGgOgMAAIiwbrd7wTTNbdU5oqjb7WZM0xy53f25NQkAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAIATx3GcbK1WO1OtVrPNZjMpItJqtRK6rhdyuVze8zytUqkM67peqFQqw6rzHoUXugIAgBOrXq9f3F9uNBopx3G2ZmZmdkRE5ufnM67rrg8MRLfuRDcZAADAAXNzc2cXFhYy6XS6n81mr1iW5U9OTo7Ytr3num5saWkptbq6OthqtQY9z4v5vh8zDCM/Ozu7NT097arOfxiKGAAAOLaLv/07515/6aV4mMe8+957/ey/ePyGk4l3Op344uJiamNjY7Pf70uxWMxbluXvb3ccZ3ttbS1h2/be1NSUKyISj8etXq+3GWbWsFHEAABA5K2srCRKpdJuMpm8KiIyMTGxqzpTGChiAADg2G42coVbw1OTAAAg8sbHx73l5eUhz/M013VPtdvtIdWZwsCIGAAAiLyxsTG/XC7vGIZRSKfT/dHR0UuqM4VBC4JAdQYAABBh3W73gmma26pzRFG3282Ypjlyu/tzaxIAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAAAnjuM42VqtdqZarWabzWZSRKTVaiV0XS/kcrm853lapVIZ1nW9UKlUhlXnPQovdAUAACdWvV6/uL/caDRSjuNszczM7IiIzM/PZ1zXXR8YiG7diW4yAACAA+bm5s4uLCxk0ul0P5vNXrEsy5+cnByxbXvPdd3Y0tJSanV1dbDVag16nhfzfT9mGEZ+dnZ2a3p62lWd/zAUMQAAcGxfbbxwbudvvHiYx0y9J+H//K/df8PJxDudTnxxcTG1sbGx2e/3pVgs5i3L8ve3O46zvba2lrBte29qasoVEYnH41av19sMM2vYKGIAACDyVlZWEqVSaTeZTF4VEZmYmNhVnSkMFDEAAHBsNxu5wq3hqUkAABB54+Pj3vLy8pDneZrruqfa7faQ6kxhYEQMAABE3tjYmF8ul3cMwyik0+n+6OjoJdWZwqAFQaA6AwAAiLBut3vBNM1t1TmiqNvtZkzTHLnd/bk1CQAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAgBPHcZxsrVY7U61Ws81mMyki0mq1ErquF3K5XN7zPK1SqQzrul6oVCrDqvMehRe6AgCAE6ter1/cX240GinHcbZmZmZ2RETm5+czruuuDwxEt+5ENxkAAMABc3NzZxcWFjLpdLqfzWavWJblT05Ojti2vee6bmxpaSm1uro62Gq1Bj3Pi/m+HzMMIz87O7s1PT3tqs5/GIoYAAA4ti//Xv3c9l99Ox7mMTPnftL/hd+s3nAy8U6nE19cXExtbGxs9vt9KRaLecuy/P3tjuNsr62tJWzb3puamnJFROLxuNXr9TbDzBo2ihgAAIi8lZWVRKlU2k0mk1dFRCYmJnZVZwoDRQwAABzbzUaucGt4ahIAAETe+Pi4t7y8POR5nua67ql2uz2kOlMYGBEDAACRNzY25pfL5R3DMArpdLo/Ojp6SXWmMGhBEKjOAAAAIqzb7V4wTXNbdY4o6na7GdM0R253f25NAgAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAA4MRxHCdbq9XOVKvVbLPZTIqItFqthK7rhVwul/c8T6tUKsO6rhcqlcqw6rxH4YWuAADgxKrX6xf3lxuNRspxnK2ZmZkdEZH5+fmM67rrAwPRrTvRTQYAAHDA3Nzc2YWFhUw6ne5ns9krlmX5k5OTI7Zt77muG1taWkqtrq4OtlqtQc/zYr7vxwzDyM/Ozm5NT0+7qvMfhiIGAACObefpF8/1X7kUD/OY7zp7j596+L4bTibe6XTii4uLqY2Njc1+vy/FYjFvWZa/v91xnO21tbWEbdt7U1NTrohIPB63er3eZphZw0YRAwAAkbeyspIolUq7yWTyqojIxMTErupMYaCIAQCAY7vZyBVuDU9NAgCAyBsfH/eWl5eHPM/TXNc91W63h1RnCgMjYgAAIPLGxsb8crm8YxhGIZ1O90dHRy+pzhQGLQgC1RkAAECEdbvdC6ZpbqvOEUXdbjdjmubI7e7PrUkAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAACcOI7jZGu12plqtZptNptJEZFWq5XQdb2Qy+XynudplUplWNf1QqVSGVad9yi80BUAAJxY9Xr94v5yo9FIOY6zNTMzsyMiMj8/n3Fdd31gILp1J7rJAAAADpibmzu7sLCQSafT/Ww2e8WyLH9ycnLEtu0913VjS0tLqdXV1cFWqzXoeV7M9/2YYRj52dnZrenpaVd1/sNQxAAAwLE1m81zr776ajzMY54+fdp/6KGHbjiZeKfTiS8uLqY2NjY2+/2+FIvFvGVZ/v52x3G219bWErZt701NTbkiIvF43Or1epthZg0bRQwAAETeyspKolQq7SaTyasiIhMTE7uqM4WBIgYAAI7tZiNXuDU8NQkAACJvfHzcW15eHvI8T3Nd91S73R5SnSkMjIgBAIDIGxsb88vl8o5hGIV0Ot0fHR29pDpTGLQgCFRnAAAAEdbtdi+YprmtOkcUdbvdjGmaI7e7P7cmAQAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAgCIUMQAAcOI4jpOt1WpnqtVqttlsJkVEWq1WQtf1Qi6Xy3uep1UqlWFd1wuVSmVYdd6j8EJXAABwYtXr9Yv7y41GI+U4ztbMzMyOiMj8/HzGdd31gYHo1p3oJgMAADhgbm7u7MLCQiadTvez2ewVy7L8ycnJEdu291zXjS0tLaVWV1cHW63WoOd5Md/3Y4Zh5GdnZ7emp6dd1fkPQxEDAADHtvnC3LlL3ovxMI95T+I+P3//p284mXin04kvLi6mNjY2Nvv9vhSLxbxlWf7+dsdxttfW1hK2be9NTU25IiLxeNzq9XqbYWYNG0UMAABE3srKSqJUKu0mk8mrIiITExO7qjOFgSIGAACO7WYjV7g1PDUJAAAib3x83FteXh7yPE9zXfdUu90eUp0pDIyIAQCAyBsbG/PL5fKOYRiFdDrdHx0dvaQ6Uxi0IAhUZwAAABHW7XYvmKa5rTpHFHW73YxpmiO3uz+3JgEAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAHDiOI6TrdVqZ6rVarbZbCZFRFqtVkLX9UIul8t7nqdVKpVhXdcLlUplWHXeo/BCVwAAcGLV6/WL+8uNRiPlOM7WzMzMjojI/Px8xnXd9YGB6Nad6CYDAAA4YG5u7uzCwkImnU73s9nsFcuy/MnJyRHbtvdc140tLS2lVldXB1ut1qDneTHf92OGYeRnZ2e3pqenXdX5D0MRAwAAx1Z94S/P9S59Lx7mMXP3vNuv3/8TN5xMvNPpxBcXF1MbGxub/X5fisVi3rIsf3+74zjba2trCdu296amplwRkXg8bvV6vc0ws4aNIgYAACJvZWUlUSqVdpPJ5FURkYmJiV3VmcJAEQMAAMd2s5Er3BqemgQAAJE3Pj7uLS8vD3mep7mue6rdbg+pzhQGRsQAAEDkjY2N+eVyeccwjEI6ne6Pjo5eUp0pDFoQBKozAACACOt2uxdM09xWnSOKut1uxjTNkdvdn1uTAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAOHEcx8nWarUz1Wo122w2kyIirVYroet6IZfL5T3P0yqVyrCu64VKpTKsOu9ReKErAAA4ser1+sX95UajkXIcZ2tmZmZHRGR+fj7juu76wEB06050kwEAABwwNzd3dmFhIZNOp/vZbPaKZVn+5OTkiG3be67rxpaWllKrq6uDrVZr0PO8mO/7McMw8rOzs1vT09Ou6vyHoYgBAIBje/Tp7rkXX3ktHuYx7zub9H/3YfOGk4l3Op344uJiamNjY7Pf70uxWMxbluXvb3ccZ3ttbS1h2/be1NSUKyISj8etXq+3GWbWsFHEAABA5K2srCRKpdJuMpm8KiIyMTGxqzpTGChiAADg2G42coVbw1OTAAAg8sbHx73l5eUhz/M013VPtdvtIdWZwsCIGAAAiLyxsTG/XC7vGIZRSKfT/dHR0UuqM4VBC4JAdQYAABBh3W73gmma26pzRFG3282Ypjlyu/tzaxIAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAAAnjuM42VqtdqZarWabzWZSRKTVaiV0XS/kcrm853lapVIZ1nW9UKlUhlXnPQovdAUAACdWvV6/uL/caDRSjuNszczM7IiIzM/PZ1zXXR8YiG7diW4yAACAA+bm5s4uLCxk0ul0P5vNXrEsy5+cnByxbXvPdd3Y0tJSanV1dbDVag16nhfzfT9mGEZ+dnZ2a3p62lWd/zAUMQAAcHzNj52TVzfjoR7zdN6Xhz57w8nEO51OfHFxMbWxsbHZ7/elWCzmLcvy97c7jrO9traWsG17b2pqyhURicfjVq/X2ww1a8goYgAAIPJWVlYSpVJpN5lMXhURmZiY2FWdKQwUMQAAcHw3GbnCreGpSQAAEHnj4+Pe8vLykOd5muu6p9rt9pDqTGFgRAwAAETe2NiYXy6XdwzDKKTT6f7o6Ogl1ZnCoAVBoDoDAACIsG63e8E0zW3VOaKo2+1mTNMcud39uTUJAACgCEUMAABAEYoYAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQCAE8dxnGytVjtTrVazzWYzKSLSarUSuq4Xcrlc3vM8rVKpDOu6XqhUKsOq8x6FF7oCAIATq16vX9xfbjQaKcdxtmZmZnZERObn5zOu664PDES37kQ3GQAAwAFzc3NnFxYWMul0up/NZq9YluVPTk6O2La957pubGlpKbW6ujrYarUGPc+L+b4fMwwjPzs7uzU9Pe2qzn8YihgAADi2j699/NzL7svxMI+p/6juf+pnP3XDycQ7nU58cXExtbGxsdnv96VYLOYty/L3tzuOs722tpawbXtvamrKFRGJx+NWr9fbDDNr2ChiAAAg8lZWVhKlUmk3mUxeFRGZmJjYVZ0pDBQxAABwbDcbucKt4alJAAAQeePj497y8vKQ53ma67qn2u32kOpMYWBEDAAARN7Y2JhfLpd3DMMopNPp/ujo6CXVmcKgBUGgOgMAAIiwbrd7wTTNbdU5oqjb7WZM0xy53f25NQkAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAIATx3GcbK1WO1OtVrPNZjMpItJqtRK6rhdyuVze8zytUqkM67peqFQqw6rzHoUXugIAgBOrXq9f3F9uNBopx3G2ZmZmdkRE5ufnM67rrg8MRLfuRDcZAADAAXNzc2cXFhYy6XS6n81mr1iW5U9OTo7Ytr3num5saWkptbq6OthqtQY9z4v5vh8zDCM/Ozu7NT097arOfxiKGAAAOLaLv/07515/6aV4mMe8+957/ey/ePyGk4l3Op344uJiamNjY7Pf70uxWMxbluXvb3ccZ3ttbS1h2/be1NSUKyISj8etXq+3GWbWsFHEAABA5K2srCRKpdJuMpm8KiIyMTGxqzpTGChiAADg2G42coVbw1OTAAAg8sbHx73l5eUhz/M013VPtdvtIdWZwsCIGAAAiLyxsTG/XC7vGIZRSKfT/dHR0UuqM4VBC4JAdQYAABBh3W73gmma26pzRFG3282Ypjlyu/tzaxIAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAAAnjuM42VqtdqZarWabzWZSRKTVaiV0XS/kcrm853lapVIZ1nW9UKlUhlXnPQovdAUAACdWvV6/uL/caDRSjuNszczM7IiIzM/PZ1zXXR8YiG7diW4yAACAA+bm5s4uLCxk0ul0P5vNXrEsy5+cnByxbXvPdd3Y0tJSanV1dbDVag16nhfzfT9mGEZ+dnZ2a3p62lWd/zAUMQAAcGxfbbxwbudvvHiYx0y9J+H//K/df8PJxDudTnxxcTG1sbGx2e/3pVgs5i3L8ve3O46zvba2lrBte29qasoVEYnH41av19sMM2vYKGIAACDyVlZWEqVSaTeZTF4VEZmYmNhVnSkMFDEAAHBsNxu5wq3hqUkAABB54+Pj3vLy8pDneZrruqfa7faQ6kxhYEQMAABE3tjYmF8ul3cMwyik0+n+6OjoJdWZwqAFQaA6AwAAiLBut3vBNM1t1TmiqNvtZkzTHLnd/bk1CQAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAgBPHcZxsrVY7Xn5kTwAAIABJREFUU61Ws81mMyki0mq1ErquF3K5XN7zPK1SqQzrul6oVCrDqvMehRe6AgCAE6ter1/cX240GinHcbZmZmZ2RETm5+czruuuDwxEt+5ENxkAAMABc3NzZxcWFjLpdLqfzWavWJblT05Ojti2vee6bmxpaSm1uro62Gq1Bj3Pi/m+HzMMIz87O7s1PT3tqs5/GIoYAAA4ti//Xv3c9l99Ox7mMTPnftL/hd+s3nAy8U6nE19cXExtbGxs9vt9KRaLecuy/P3tjuNsr62tJWzb3puamnJFROLxuNXr9TbDzBo2ihgAAIi8lZWVRKlU2k0mk1dFRCYmJnZVZwoDRQwAABzbzUaucGt4ahIAAETe+Pi4t7y8POR5nua67ql2uz2kOlMYGBEDAACRNzY25pfL5R3DMArpdLo/Ojp6SXWmMGhBEKjOAAAAIqzb7V4wTXNbdY4o6na7GdM0R253f25NAgAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAA4MRxHCdbq9XOVKvVbLPZTIqItFqthK7rhVwul/c8T6tUKsO6rhcqlcqw6rxH4YWuAADgxKrX6xf3lxuNRspxnK2ZmZkdEZH5+fmM67rrAwPRrTvRTQYAAHDA3Nzc2YWFhUw6ne5ns9krlmX5k5OTI7Zt77muG1taWkqtrq4OtlqtQc/zYr7vxwzDyM/Ozm5NT0+7qvMfhiIGAACObefpF8/1X7kUD/OY7zp7j596+L4bTibe6XTii4uLqY2Njc1+vy/FYjFvWZa/v91xnO21tbWEbdt7U1NTrohIPB63er3eZphZw0YRAwAAkbeyspIolUq7yWTyqojIxMTErupMYaCIAQCAY7vZyBVuDU9NAgCAyBsfH/eWl5eHPM/TXNc91W63h1RnCgMjYgAAIPLGxsb8crm8YxhGIZ1O90dHRy+pzhQGLQgC1RkAAECEdbvdC6ZpbqvOEUXdbjdjmubI7e7PrUkAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAACcOI7jZGu12plqtZptNptJEZFWq5XQdb2Qy+XynudplUplWNf1QqVSGVad9yi80BUAAJxY9Xr94v5yo9FIOY6zNTMzsyMiMj8/n3Fdd31gILp1J7rJAAAADpibmzu7sLCQSafT/Ww2e8WyLH9ycnLEtu0913VjS0tLqdXV1cFWqzXoeV7M9/2YYRj52dnZrenpaVd1/sNQxAAAwLE1m81zr776ajzMY54+fdp/6KGHbjiZeKfTiS8uLqY2NjY2+/2+FIvFvGVZ/v52x3G219bWErZt701NTbkiIvF43Or1epthZg0bRQwAAETeyspKolQq7SaTyasiIhMTE7uqM4WBIgYAAI7tZiNXuDU8NQkAACJvfHzcW15eHvI8T3Nd91S73R5SnSkMjIgBAIDIGxsb88vl8o5hGIV0Ot0fHR29pDpTGLQgCFRnAAAAEdbtdi+YprmtOkcUdbvdjGmaI7e7P7cmAQAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAgCIUMQAAcOI4jpOt1WpnqtVqttlsJkVEWq1WQtf1Qi6Xy3uep1UqlWFd1wuVSmVYdd6j8EJXAABwYtXr9Yv7y41GI+U4ztbMzMyOiMj8/HzGdd31gYHo1p3oJgMAADhgbm7u7MLCQiadTvez2ewVy7L8ycnJEdu291zXjS0tLaVWV1cHW63WoOd5Md/3Y4Zh5GdnZ7emp6dd1fkPQxEDAADHtvnC3LlL3ovxMI95T+I+P3//p284mXin04kvLi6mNjY2Nvv9vhSLxbxlWf7+dsdxttfW1hK2be9NTU25IiLxeNzq9XqbYWYNG0UMAABE3srKSqJUKu0mk8mrIiITExO7qjOFgSIGAACO7WYjV7g1PDUJAAAib3x83FteXh7yPE9zXfdUu90eUp0pDIyIAQCAyBsbG/PL5fKOYRiFdDrdHx0dvaQ6Uxi0IAhUZwAAABHW7XYvmKa5rTpHFHW73YxpmiO3uz+3JgEAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAHDiOI6TrdVqZ6rVarbZbCZFRFqtVkLX9UIul8t7nqdVKpVhXdcLlUplWHXeo/BCVwAAcGLV6/WL+8uNRiPlOM7WzMzMjojI/Px8xnXd9YGB6Nad6CYDAAA4YG5u7uzCwkImnU73s9nsFcuy/MnJyRHbtvdc140tLS2lVldXB1ut1qDneTHf92OGYeRnZ2e3pqenXdX5D0MRAwAAx1Z94S/P9S59Lx7mMXP3vNuv3/8TN5xMvNPpxBcXF1MbGxub/X5fisVi3rIsf3+74zjba2trCdu296amplwRkXg8bvV6vc0ws4aNIgYAACJvZWUlUSqVdpPJ5FURkYmJiV3VmcJAEQMAAMd2s5Er3BqemgQAAJE3Pj7uLS8vD3mep7mue6rdbg+pzhQGRsQAAEDkjY2N+eVyeccwjEI6ne6Pjo5eUp0pDFoQBKozAACACOt2uxdM09xWnSOKut1uxjTNkdvdn1uTAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAOHEcx8nWarUz1Wo122w2kyIirVYroet6IZfL5T3P0yqVyrCu64VKpTKsOu9ReKErAAA4ser1+sX95UajkXIcZ2tmZmZHRGR+fj7juu76wEB06050kwEAABwwNzd3dmFhIZNOp/vZbPaKZVn+5OTkiG3be67rxpaWllKrq6uDrVZr0PO8mO/7McMw8rOzs1vT09Ou6vyHoYgBAIBje/Tp7rkXX3ktHuYx7zub9H/3YfOGk4l3Op344uJiamNjY7Pf70uxWMxbluXvb3ccZ3ttbS1h2/be1NSUKyISj8etXq+3GWbWsFHEAABA5K2srCRKpdJuMpm8KiIyMTGxqzpTGChiAADg2G42coVbw1OTAAAg8sbHx73l5eUhz/M013VPtdvtIdWZwsCIGAAAiLyxsTG/XC7vGIZRSKfT/dHR0UuqM4VBC4JAdQYAABBh3W73gmma26pzRFG3282Ypjlyu/tzaxIAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAAAnjuM42VqtdqZarWabzWZSRKTVaiV0XS/kcrm853lapVIZ1nW9UKlUhlXnPQovdAUAACdWvV6/uL/caDRSjuNszczM7IiIzM/PZ1zXXR8YiG7diW4yAACAA+bm5s4uLCxk0ul0P5vNXrEsy5+cnByxbXvPdd3Y0tJSanV1dbDVag16nhfzfT9mGEZ+dnZ2a3p62lWd/zAUMQAAcHzNj52TVzfjoR7zdN6Xhz57w8nEO51OfHFxMbWxsbHZ7/elWCzmLcvy97c7jrO9traWsG17b2pqyhURicfjVq/X2ww1a8goYgAAIPJWVlYSpVJpN5lMXhURmZiY2FWdKQwUMQAAcHw3GbnCreGpSQAAEHnj4+Pe8vLykOd5muu6p9rt9pDqTGFgRAwAAETe2NiYXy6XdwzDKKTT6f7o6Ogl1ZnCoAVBoDoDAACIsG63e8E0zW3VOaKo2+1mTNMcud39uTUJAACgCEUMAABAEYoYAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQCAE8dxnGytVjtTrVazzWYzKSLSarUSuq4Xcrlc3vM8rVKpDOu6XqhUKsOq8x6FF7oCAIATq16vX9xfbjQaKcdxtmZmZnZERObn5zOu664PDES37kQ3GQAAwAFzc3NnFxYWMul0up/NZq9YluVPTk6O2La957pubGlpKbW6ujrYarUGPc+L+b4fMwwjPzs7uzU9Pe2qzn8YihgAADi2j699/NzL7svxMI+p/6juf+pnP3XDycQ7nU58cXExtbGxsdnv96VYLOYty/L3tzuOs722tpawbXtvamrKFRGJx+NWr9fbDDNr2ChiAAAg8lZWVhKlUmk3mUxeFRGZmJjYVZ0pDBQxAABwbDcbucKt4alJAAAQeePj497y8vKQ53ma67qn2u32kOpMYWBEDAAARN7Y2JhfLpd3DMMopNPp/ujo6CXVmcKgBUGgOgMAAIiwbrd7wTTNbdU5oqjb7WZM0xy53f25NQkAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAIATx3GcbK1WO1OtVrPNZjMpItJqtRK6rhdyuVze8zytUqkM67peqFQqw6rzHoUXugIAgBOrXq9f3F9uNBopx3G2ZmZmdkRE5ufnM67rrg8MRLfuRDcZAADAAXNzc2cXFhYy6XS6n81mr1iW5U9OTo7Ytr3num5saWkptbq6OthqtQY9z4v5vh8zDCM/Ozu7NT097arOfxiKGAAAOLaLv/07515/6aV4mMe8+957/ey/ePyGk4l3Op344uJiamNjY7Pf70uxWMxbluXvb3ccZ3ttbS1h2/be1NSUKyISj8etXq+3GWbWsFHEAABA5K2srCRKpdJuMpm8KiIyMTGxqzpTGChiAADg2G42coVbw1OTAAAg8sbHx73l5eUhz/M013VPtdvtIdWZwsCIGAAAiLyxsTG/XC7vGIZRSKfT/dHR0UuqM4VBC4JAdQYAABBh3W73gmma26pzRFG3282Ypjlyu/tzaxIAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAAAnjuM42VqtdqZarWabzWZSRKTVaiV0XS/kcrm853lapVIZ1nW9UKlUhlXnPQovdAUAACdWvV6/uL/caDRSjuNszczM7IiIzM/PZ1zXXR8YiG7diW4yAACAA+bm5s4uLCxk0ul0P5vNXrEsy5+cnByxbXvPdd3Y0tJSanV1dbDVag16nhfzfT9mGEZ+dnZ2a3p62lWd/zAUMQAAcGxfbbxwbudvvHiYx0y9J+H//K/df8PJxDudTnxxcTG1sbGx2e/3pVgs5i3L8ve3O46zvba2lrBte29qasoVEYnH41av19sMM2vYKGIAACDyVlZWEqVSaTeZTF4VEZmYmNhVnSkMFDEAAHBsNxu5wq3hqUkAABB54+Pj3vLy8pDneZrruqfa7faQ6kxhYEQMAABE3tjYmF8ul3cMwyik0+n+6OjoJdWZwqAFQaA6AwAAiLBut3vBNM1t1TmiqNvtZkzTHLnd/bk1CQAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAgBPHcZxsrVY7U61Ws81mMyki0mq1ErquF3K5XN7zPK1SqQzrul6oVCrDqvMehRe6AgCAE6ter1/cX240GinHcbZmZmZ2RETm5+czruuuDwxEt+5ENxkAAMABc3NzZxcWFjLpdLqfzWavWJblT05Ojti2vee6bmxpaSm1uro62Gq1Bj3Pi/m+HzMMIz87O7s1PT3tqs5/GIoYAAA4ti//Xv3c9l99Ox7mMTPnftL/hd+s3nAy8U6nE19cXExtbGxs9vt9KRaLecuy/P3tjuNsr62tJWzb3puamnJFROLxuNXr9TbDzBo2ihgAAIi8lZWVRKlU2k0mk1dFRCYmJnZVZwoDRQwAABzbzUaucGt4ahIAAETe+Pi4t7y8POR5nua67ql2uz2kOlMYGBEDAACRNzY25pfL5R3DMArpdLo/Ojp6SXWmMGhBEKjOAAAAIqzb7V4wTXNbdY4o6na7GdM0R253f25NAgAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAA4MRxHCdbq9XOVKvVbLPZTIqItFqthK7rhVwul/c8T6tUKsO6rhcqlcqw6rxH4YWuAADgxKrX6xf3lxuNRspxnK2ZmZkdEZH5+fmM67rrAwPRrTvRTQYAAHDA3Nzc2YWFhUw6ne5ns9krlmX5k5OTI7Zt77muG1taWkqtrq4OtlqtQc/zYr7vxwzDyM/Ozm5NT0+7qvMfhiIGAACObefpF8/1X7kUD/OY7zp7j596+L4bTibe6XTii4uLqY2Njc1+vy/FYjFvWZa/v91xnO21tbWEbdt7U1NTrohIPB63er3eZphZw0YRAwAAkbeyspIolUq7yWTyqojIxMTErupMYaCIAQCAY7vZyBVuDU9NAgCAyBsfH/eWl5eHPM/TXNc91W63h1RnCgMjYgAAIPLGxsb8crm8YxhGIZ1O90dHRy+pzhQGLQgC1RkAAECEdbvdC6ZpbqvOEUXdbjdjmubI7e7PrUkAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAACcOI7jZGu12plqtZptNptJEZFWq5XQdb2Qy+XynudplUplWNf1QqVSGVad9yi80BUAAJxY9Xr94v5yo9FIOY6zNTMzsyMiMj8/n3Fdd31gILp1J7rJAAAADpibmzu7sLCQSafT/Ww2e8WyLH9ycnLEtu0913VjS0tLqdXV1cFWqzXoeV7M9/2YYRj52dnZrenpaVd1/sNQxAAAwLE1m81zr776ajzMY54+fdp/6KGHbjiZeKfTiS8uLqY2NjY2+/2+FIvFvGVZ/v52x3G219bWErZt701NTbkiIvF43Or1epthZg0bRQwAAETeyspKolQq7SaTyasiIhMTE7uqM4WBIgYAAI7tZiNXuDU8NQkAACJvfHzcW15eHvI8T3Nd91S73R5SnSkMjIgBAIDIGxsb88vl8o5hGIV0Ot0fHR29pDpTGLQgCFRnAAAAEdbtdi+YprmtOkcUdbvdjGmaI7e7P7cmAQAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAgCIUMQAAcOI4jpOt1WpnqtVqttlsJkVEWq1WQtf1Qi6Xy3uep1UqlWFd1wuVSmVYdd6j8EJXAABwYtXr9Yv7y41GI+U4ztbMzMyOiMj8/HzGdd31gYHo1p3oJgMAAP8/e/cTokiYp3n8tcxlBlvJHEO6CjdrNg/Rja2BYZzHkwsuSBxa7POCBwnWuUjIIMzQXpoZGObiZdi7B0Fo0IsSiywi4r0CIVu6+1A7y1YOTZJvJhMVzJRQ7imhDvUnqwh4I+H7OQW8RPAcH37B+774xGAweDWdTnOaph3z+fwHy7LCVqt1Zdv2g5QyuVgsspvN5tzzvPMgCJJhGCYNwyj2+/2bTqcjVef/HIoYAAB4suvfDV6/D36fivKbP0n/PCz+4h+/epn4drtNzWaz7H6/vz4ej6JSqRQtywof113Xvd3tdmnbth/a7bYUQohUKmUdDofrKLNGjSIGAABib71epxuNxn0mk/kohBD1ev1edaYoUMQAAMCTfWtyhe/DrkkAABB7tVotWC6XF0EQJKSUL1ar1YXqTFFgIgYAAGKvWq2GzWbzzjCMkqZpx3K5/F51pigkTqeT6gwAACDGfN9/a5rmreocceT7fs40zasffZ9fkwAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAHh2XNfND4fDl71eLz+fzzNCCOF5XlrX9VKhUCgGQZBwHOdS1/WS4ziXqvN+CQe6AgCAZ2s0Gr17fB6Px1nXdW+63e6dEEJMJpOclPLN2Vl86058kwEAAHxiMBi8mk6nOU3Tjvl8/oNlWWGr1bqybftBSplcLBbZzWZz7nneeRAEyTAMk4ZhFPv9/k2n05Gq838ORQwAADxZ73f/8vrw/t9TUX6z8JM/D0e/+MuvXia+3W5Ts9ksu9/vr4/Ho6hUKkXLssLHddd1b3e7Xdq27Yd2uy2FECKVSlmHw+E6yqxRo4gBAIDYW6/X6UajcZ/JZD4KIUS9Xr9XnSkKFDEAAPBk35pc4fuwaxIAAMRerVYLlsvlRRAECSnli9VqdaE6UxSYiAEAgNirVqths9m8MwyjpGnasVwuv1edKQqJ0+mkOgMAAIgx3/ffmqZ5qzpHHPm+nzNN8+pH3+fXJAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAJ4d13Xzw+HwZa/Xy8/n84wQQniel9Z1vVQoFIpBECQcx7nUdb3kOM6l6rxfwoGuAADg2RqNRu8en8fjcdZ13Ztut3snhBCTySQnpXxzdhbfuhPfZAAAAJ8YDAavptNpTtO0Yz6f/2BZVthqta5s236QUiYXi0V2s9mce553HgRBMgzDpGEYxX6/f9PpdKTq/J9DEQMAAE/2N7/1X//+X/8tFeU3f/4qE/7Tr8yvXia+3W5Ts9ksu9/vr4/Ho6hUKkXLssLHddd1b3e7Xdq27Yd2uy2FECKVSlmHw+E6yqxRo4gBAIDYW6/X6UajcZ/JZD4KIUS9Xr9XnSkKFDEAAPBk35pc4fuwaxIAAMRerVYLlsvlRRAECSnli9VqdaE6UxSYiAEAgNirVqths9m8MwyjpGnasVwuv1edKQqJ0+mkOgMAAIgx3/ffmqZ5qzpHHPm+nzNN8+pH3+fXJAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAJ4d13Xzw+HwZa/Xy8/n84wQQniel9Z1vVQoFIpBECQcx7nUdb3kOM6l6rxfwoGuAADg2RqNRu8en8fjcdZ13Ztut3snhBCTySQnpXxzdhbfuhPfZAAAAJ8YDAavptNpTtO0Yz6f/2BZVthqta5s236QUiYXi0V2s9mce553HgRBMgzDpGEYxX6/f9PpdKTq/J9DEQMAAE83/+vX4k/XqUi/+dNiKH75z1+9THy73aZms1l2v99fH49HUalUipZlhY/rruve7na7tG3bD+12WwohRCqVsg6Hw3WkWSNGEQMAALG3Xq/TjUbjPpPJfBRCiHq9fq86UxQoYgAA4Om+MbnC92HXJAAAiL1arRYsl8uLIAgSUsoXq9XqQnWmKDARAwAAsVetVsNms3lnGEZJ07RjuVx+rzpTFBKn00l1BgAAEGO+7781TfNWdY448n0/Z5rm1Y++z69JAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAPDuu6+aHw+HLXq+Xn8/nGSGE8Dwvret6qVAoFIMgSDiOc6nreslxnEvVeb+EA10BAMCzNRqN3j0+j8fjrOu6N91u904IISaTSU5K+ebsLL51J77JAAAAPjEYDF5Np9OcpmnHfD7/wbKssNVqXdm2/SClTC4Wi+xmszn3PO88CIJkGIZJwzCK/X7/ptPpSNX5P4ciBgAAnuzXu1+//qP8YyrKb+p/oYe/+avffPUy8e12m5rNZtn9fn99PB5FpVIpWpYVPq67rnu72+3Stm0/tNttKYQQqVTKOhwO11FmjRpFDAAAxN56vU43Go37TCbzUQgh6vX6vepMUaCIAQCAJ/vW5Arfh12TAAAg9mq1WrBcLi+CIEhIKV+sVqsL1ZmiwEQMAADEXrVaDZvN5p1hGCVN047lcvm96kxRSJxOJ9UZAABAjPm+/9Y0zVvVOeLI9/2caZpXP/o+vyYBAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAABAEYoYAACAIhQxAADw7Liumx8Ohy97vV5+Pp9nhBDC87y0ruulQqFQDIIg4TjOpa7rJcdxLlXn/RIOdAUAAM/WaDR69/g8Ho+zruvedLvdOyGEmEwmOSnlm7Oz+Nad+CYDAAD4xGAweDWdTnOaph3z+fwHy7LCVqt1Zdv2g5QyuVgsspvN5tzzvPMgCJJhGCYNwyj2+/2bTqcjVef/HIoYAAB4snd/+3ev/+MPf0hF+c0/+9nPwvw//P1XLxPfbrep2WyW3e/318fjUVQqlaJlWeHjuuu6t7vdLm3b9kO73ZZCCJFKpazD4XAdZdaoUcQAAEDsrdfrdKPRuM9kMh+FEKJer9+rzhQFihgAAHiyb02u8H3YNQkAAGKvVqsFy+XyIgiChJTyxWq1ulCdKQpMxAAAQOxVq9Ww2WzeGYZR0jTtWC6X36vOFIXE6XRSnQEAAMSY7/tvTdO8VZ0jjnzfz5mmefWj7/NrEgAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAM+O67r54XD4stfr5efzeUYIITzPS+u6XioUCsUgCBKO41zqul5yHOdSdd4v4UBXAADwbI1Go3ePz+PxOOu67k23270TQojJZJKTUr45O4tv3YlvMgAAgE8MBoNX0+k0p2naMZ/Pf7AsK2y1Wle2bT9IKZOLxSK72WzOPc87D4IgGYZh0jCMYr/fv+l0OlJ1/s+hiAEAgCf73+Pfvb77f0Eqym9m/3M6/K///RdfvUx8u92mZrNZdr/fXx+PR1GpVIqWZYWP667r3u52u7Rt2w/tdlsKIUQqlbIOh8N1lFmjRhEDAACxt16v041G4z6TyXwUQoh6vX6vOlMUKGIAAODJvjW5wvdh1yQAAIi9Wq0WLJfLiyAIElLKF6vV6kJ1pigwEQMAALFXrVbDZrN5ZxhGSdO0Y7lcfq86UxQSp9NJdQYAABBjvu+/NU3zVnWOOPJ9P2ea5tWPvs+vSQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAADw7ruvmh8Phy16vl5/P5xkhhPA8L63reqlQKBSDIEg4jnOp63rJcZxL1Xm/hANdAQDAszUajd49Po/H46zrujfdbvdOCCEmk0lOSvnm7Cy+dSe+yQAAAD4xGAxeTafTnKZpx3w+/8GyrLDVal3Ztv0gpUwuFovsZrM59zzvPAiCZBiGScMwiv1+/6bT6UjV+T+HIgYAAJ7sf/3P0evb//t/UlF+M/f6v4T/7X/0vnqZ+Ha7Tc1ms+x+v78+Ho+iUqkULcsKH9dd173d7XZp27Yf2u22FEKIVCplHQ6H6yizRo0iBgAAYm+9XqcbjcZ9JpP5KIQQ9Xr9XnWmKFBKxgFnAAAgAElEQVTEAADAk31rcoXvw65JAAAQe7VaLVgulxdBECSklC9Wq9WF6kxRYCIGAABir1qths1m884wjJKmacdyufxedaYoJE6nk+oMAAAgxnzff2ua5q3qHHHk+37ONM2rH32fX5MAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAABAEYoYAAB4dlzXzQ+Hw5e9Xi8/n88zQgjheV5a1/VSoVAoBkGQcBznUtf1kuM4l6rzfgkHugIAgGdrNBq9e3wej8dZ13Vvut3unRBCTCaTnJTyzdlZfOtOfJMBAAB8YjAYvJpOpzlN0475fP6DZVlhq9W6sm37QUqZXCwW2c1mc+553nkQBMkwDJOGYRT7/f5Np9ORqvN/DkUMAAA82d1vf//6+K/vU1F+8z+9+kmY/dXPv3qZ+Ha7Tc1ms+x+v78+Ho+iUqkULcsKH9dd173d7XZp27Yf2u22FEKIVCplHQ6H6yizRo0iBgAAYm+9XqcbjcZ9JpP5KIQQ9Xr9XnWmKFDEAADAk31rcoXvw65JAAAQe7VaLVgulxdBECSklC9Wq9WF6kxRYCIGAABir1qths1m884wjJKmacdyufxedaYoJE6nk+oMAAAgxnzff2ua5q3qHHHk+37ONM2rH32fX5MAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAABAEYoYAAB4dlzXzQ+Hw5e9Xi8/n88zQgjheV5a1/VSoVAoBkGQcBznUtf1kuM4l6rzfgkHugIAgGdrNBq9e3wej8dZ13Vvut3unRBCTCaTnJTyzdlZfOtOfJMBAAB8YjAYvJpOpzlN0475fP6DZVlhq9W6sm37QUqZXCwW2c1mc+553nkQBMkwDJOGYRT7/f5Np9ORqvN/DkUMAAA82Xw+f/2nP/0pFeU3f/rTn4a//OUvv3qZ+Ha7Tc1ms+x+v78+Ho+iUqkULcsKH9dd173d7XZp27Yf2u22FEKIVCplHQ6H6yizRo0iBgAAYm+9XqcbjcZ9JpP5KIQQ9Xr9XnWmKFDEAADAk31rcoXvw65JAAAQe7VaLVgulxdBECSklC9Wq9WF6kxRYCIGAABir1qths1m884wjJKmacdyufxedaYoJE6nk+oMAAAgxnzff2ua5q3qHHHk+37ONM2rH32fX5MAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAABAEYoYAAB4dlzXzQ+Hw5e9Xi8/n88zQgjheV5a1/VSoVAoBkGQcBznUtf1kuM4l6rzfgkHugIAgGdrNBq9e3wej8dZ13Vvut3unRBCTCaTnJTyzdlZfOtOfJMBAAB8YjAYvJpOpzlN0475fP6DZVlhq9W6sm37QUqZXCwW2c1mc+553nkQBMkwDJOGYRT7/f5Np9ORqvN/DkUMAAA82fXvBq/fB79PRfnNn6R/HhZ/8Y9fvUx8u92mZrNZdr/fXx+PR1GpVIqWZYWP667r3u52u7Rt2w/tdlsKIUQqlbIOh8N1lFmjRhEDAACxt16v041G4z6TyXwUQoh6vX6vOlMUKGIAAODJvjW5wvdh1yQAAIi9Wq0WLJfLiyAIElLKF6vV6kJ1pigwEQMAALFXrVbDZrN5ZxhGSdO0Y7lcfq86UxQSp9NJdQYAABBjvu+/NU3zVnWOOPJ9P2ea5tWPvs+vSQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAADw7ruvmh8Phy16vl5/P5xkhhPA8L63reqlQKBSDIEg4jnOp63rJcZxL1Xm/hANdAQDAszUajd49Po/H46zrujfdbvdOCCEmk0lOSvnm7Cy+dSe+yQAAAD4xGAxeTafTnKZpx3w+/8GyrLDVal3Ztv0gpUwuFovsZrM59zzvPAiCZBiGScMwiv1+/6bT6UjV+T+HIgYAAJ6s97t/eX14/++pKL9Z+Mmfh6Nf/OVXLxPfbrep2WyW3e/318fjUVQqlaJlWeHjuuu6t7vdLm3b9kO73ZZCCJFKpazD4XAdZdaoUcQAAEDsrdfrdKPRuM9kMh+FEKJer9+rzhQFihgAAHiyb02u8H3YNQkAAGKvVqsFy+XyIgiChJTyxWq1ulCdKQpMxAAAQOxVq9Ww2WzeGYZR0jTtWC6X36vOFIXE6XRSnQEAAMSY7/tvTdO8VZ0jjnzfz5mmefWj7/NrEgAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAM+O67r54XD4stfr5efzeUYIITzPS+u6XioUCsUgCBKO41zqul5yHOdSdd4v4UBXAADwbI1Go3ePz+PxOOu67k23270TQojJZJKTUr45O4tv3YlvMgAAgE8MBoNX0+k0p2naMZ/Pf7AsK2y1Wle2bT9IKZOLxSK72WzOPc87D4IgGYZh0jCMYr/fv+l0OlJ1/s+hiAEAgCf7m9/6r3//r/+WivKbP3+VCf/pV+ZXLxPfbrep2WyW3e/318fjUVQqlaJlWeHjuuu6t7vdLm3b9kO73ZZCCJFKpazD4XAdZdaoUcQAAEDsrdfrdKPRuM9kMh+FEKJer9+rzhQFihgAAHiyb02u8H3YNQkAAGKvVqsFy+XyIgiChJTyxWq1ulCdKQpMxAAAQOxVq9Ww2WzeGYZR0jTtWC6X36vOFIXE6XRSnQEAAMSY7/tvTdO8VZ0jjnzfz5mmefWj7/NrEgAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAM+O67r54XD4stfr5efzeUYIITzPS+u6XioUCsUgCBKO41zqul5yHOdSdd4v4UBXAADwbI1Go3ePz+PxOOu67k23270TQojJZJKTUr45O4tv3YlvMgAAgE8MBoNX0+k0p2naMZ/Pf7AsK2y1Wle2bT9IKZOLxSK72WzOPc87D4IgGYZh0jCMYr/fv+l0OlJ1/s+hiAEAgKeb//Vr8afrVKTf/GkxFL/8569eJr7dblOz2Sy73++vj8ejqFQqRcuywsd113Vvd7td2rbth3a7LYUQIpVKWYfD4TrSrBGjiAEAgNhbr9fpRqNxn8lkPgohRL1ev1edKQoUMQAA8HTfmFzh+7BrEgAAxF6tVguWy+VFEAQJKeWL1Wp1oTpTFJiIAQCA2KtWq2Gz2bwzDKOkadqxXC6/V50pConT6aQ6AwAAiDHf99+apnmrOkcc+b6fM03z6kff59ckAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAnh3XdfPD4fBlr9fLz+fzjBBCeJ6X1nW9VCgUikEQJBzHudR1veQ4zqXqvF/Cga4AAODZGo1G7x6fx+Nx1nXdm263eyeEEJPJJCelfHN2Ft+6E99kAAAAnxgMBq+m02lO07RjPp//YFlW2Gq1rmzbfpBSJheLRXaz2Zx7nnceBEEyDMOkYRjFfr9/0+l0pOr8n0MRAwAAT/br3a9f/1H+MRXlN/W/0MPf/NVvvnqZ+Ha7Tc1ms+x+v78+Ho+iUqkULcsKH9dd173d7XZp27Yf2u22FEKIVCplHQ6H6yizRo0iBgAAYm+9XqcbjcZ9JpP5KIQQ9Xr9XnWmKFDEAADAk31rcoXvw65JAAAQe7VaLVgulxdBECSklC9Wq9WF6kxRYCIGAABir1qths1m884wjJKmacdyufxedaYoJE6nk+oMAAAgxnzff2ua5q3qHHHk+37ONM2rH32fX5MAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAABAEYoYAAB4dlzXzQ+Hw5e9Xi8/n88zQgjheV5a1/VSoVAoBkGQcBznUtf1kuM4l6rzfgkHugIAgGdrNBq9e3wej8dZ13Vvut3unRBCTCaTnJTyzdlZfOtOfJMBAAB8YjAYvJpOpzlN0475fP6DZVlhq9W6sm37QUqZXCwW2c1mc+553nkQBMkwDJOGYRT7/f5Np9ORqvN/DkUMAAA82bu//bvX//GHP6Si/Oaf/exnYf4f/v6rl4lvt9vUbDbL7vf76+PxKCqVStGyrPBx3XXd291ul7Zt+6HdbkshhEilUtbhcLiOMmvUKGIAACD21ut1utFo3GcymY9CCFGv1+9VZ4oCRQwAADzZtyZX+D7smgQAALFXq9WC5XJ5EQRBQkr5YrVaXajOFAUmYgAAIPaq1WrYbDbvDMMoaZp2LJfL71VnikLidDqpzgAAAGLM9/23pmneqs4RR77v50zTvPrR9/k1CQAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAgGfHdd38cDh82ev18vP5PCOEEJ7npXVdLxUKhWIQBAnHcS51XS85jnOpOu+XcKArAAB4tkaj0bvH5/F4nHVd96bb7d4JIcRkMslJKd+cncW37sQ3GQAAwCcGg8Gr6XSa0zTtmM/nP1iWFbZarSvbth+klMnFYpHdbDbnnuedB0GQDMMwaRhGsd/v33Q6Hak6/+dQxAAAwJP97/HvXt/9vyAV5Tez/zkd/tf//ouvXia+3W5Ts9ksu9/vr4/Ho6hUKkXLssLHddd1b3e7Xdq27Yd2uy2FECKVSlmHw+E6yqxRo4gBAIDYW6/X6UajcZ/JZD4KIUS9Xr9XnSkKFDEAAPBk35pc4fuwaxIAAMRerVYLlsvlRRAECSnli9VqdaE6UxSYiAEAgNirVqths9m8MwyjpGnasVwuv1edKQqJ0+mkOgMAAIgx3/ffmqZ5qzpHHPm+nzNN8+pH3+fXJAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAJ4d13Xzw+HwZa/Xy8/n84wQQniel9Z1vVQoFIpBECQcx7nUdb3kOM6l6rxfwoGuAADg2RqNRu8en8fjcdZ13Ztut3snhBCTySQnpXxzdhbfuhPfZAAAAJ8YDAavptNpTtO0Yz6f/2BZVthqta5s236QUiYXi0V2s9mce553HgRBMgzDpGEYxX6/f9PpdKTq/J9DEQMAAE/2v/7n6PXt//0/qSi/mXv9X8L/9j96X71MfLvdpmazWXa/318fj0dRqVSKlmWFj+uu697udru0bdsP7XZbCiFEKpWyDofDdZRZo0YRAwAAsbder9ONRuM+k8l8FEKIer1+rzpTFChiAADgyb41ucL3YdckAACIvVqtFiyXy4sgCBJSyher1epCdaYoMBEDAACxV61Ww2azeWcYRknTtGO5XH6vOlMUEqfTSXUGAAAQY77vvzVN81Z1jjjyfT9nmubVj77Pr0kAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAAA8O67r5ofD4cter5efz+cZIYTwPC+t63qpUCgUgyBIOI5zqet6yXGcS9V5v4QDXQEAwLM1Go3ePT6Px+Os67o33W73TgghJpNJTkr55uwsvnUnvskAAAA+MRgMXk2n05ymacd8Pv/Bsqyw1Wpd2bb9IKVMLhaL7GazOfc87zwIgmQYhknDMIr9fv+m0+lI1fk/hyIGAACe7O63v399/Nf3qSi/+Z9e/STM/urnX71MfLvdpmazWXa/318fj0dRqVSKlmWFj+uu697udru0bdsP7XZbCiFEKpWyDofDdZRZo0YRAwAAsbder9ONRuM+k8l8FEKIer1+rzpTFChiAADgyb41ucL3YdckAACIvVqtFiyXy4sgCBJSyher1epCdaYoMBEDAACxV61Ww2azeWcYRknTtGO5XH6vOlMUEqfTSXUGAAAQY77vvzVN81Z1jjjyfT9nmubVj77Pr0kAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAAA8O67r5ofD4cter5efz+cZIYTwPC+t63qpUCgUgyBIOI5zqet6yXGcS9V5v4QDXQEAwLM1Go3ePT6Px+Os67o33W73TgghJpNJTkr55uwsvnUnvskAAAA+MRgMXk2n05ymacd8Pv/Bsqyw1Wpd2bb9IKVMLhaL7GazOfc87zwIgmQYhknDMIr9fv+m0+lI1fk/hyIGAACebD6fv/7Tn/6UivKbP/3pT8Nf/vKXX71MfLvdpmazWXa/318fj0dRqVSKlmWFj+uu697udru0bdsP7XZbCiFEKpWyDofDdZRZo0YRAwAAsbder9ONRuM+k8l8FEKIer1+rzpTFChiAADgyb41ucL3YdckAACIvVqtFiyXy4sgCBJSyher1epCdaYoMBEDAACxV61Ww2azeWcYRknTtGO5XH6vOlMUEqfTSXUGAAAQY77vvzVN81Z1jjjyfT9nmubVj77Pr0kAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAAA8O67r5ofD4cter5efz+cZIYTwPC+t63qpUCgUgyBIOI5zqet6yXGcS9V5v4QDXQEAwLM1Go3ePT6Px+Os67o33W73TgghJpNJTkr55uwsvnUnvskAAAA+MRgMXk2n05ymacd8Pv/Bsqyw1Wpd2bb9IKVMLhaL7GazOfc87zwIgmQYhknDMIr9fv+m0+lI1fk/hyIGAACe7Pp3g9fvg9+novzmT9I/D4u/+MevXia+3W5Ts9ksu9/vr4/Ho6hUKkXLssLHddd1b3e7Xdq27Yd2uy2FECKVSlmHw+E6yqxRo4gBAIDYW6/X6UajcZ/JZD4KIUS9Xr9XnSkKFDEAAPBk35pc4fuwaxIAAMRerVYLlsvlRRAECSnli9VqdaE6UxSYiAEAgNirVqths9m8MwyjpGnasVwuv1edKQqJ0+mkOgMAAIgx3/ffmqZ5qzpHHPm+nzNN8+pH3+fXJAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAJ4d13Xzw+HwZa/Xy8/n84wQQniel9Z1vVQoFIpBECQcx7nUdb3kOM6l6rxfwoGuAADg2RqNRu8en8fjcdZ13Ztut3snhBCTySQnpXxzdhbfuhPfZAAAAJ8YDAavptNpTtO0Yz6f/2BZVthqta5s236QUiYXi0V2s9mce553HgRBMgzDpGEYxX6/f9PpdKTq/J9DEQMAAE/W+92/vD68//dUlN8s/OTPw9Ev/vKrl4lvt9vUbDbL7vf76+PxKCqVStGyrPBx3XXd291ul7Zt+6HdbkshhEilUtbhcLiOMmvUKGIAACD21ut1utFo3GcymY9CCFGv1+9VZ4oCRQwAADzZtyZX+D7smgQAALFXq9WC5XJ5EQRBQkr5YrVaXajOFAUmYgAAIPaq1WrYbDbvDMMoaZp2LJfL71VnikLidDqpzgAAAGLM9/23pmneqs4RR77v50zTvPrR9/k1CQAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAgGfHdd38cDh82ev18vP5PCOEEJ7npXVdLxUKhWIQBAnHcS51XS85jnOpOu+XcKArAAB4tkaj0bvH5/F4nHVd96bb7d4JIcRkMslJKd+cncW37sQ3GQAAwCcGg8Gr6XSa0zTtmM/nP1iWFbZarSvbth+klMnFYpHdbDbnnuedB0GQDMMwaRhGsd/v33Q6Hak6/+dQxAAAwJP9zW/917//139LRfnNn7/KhP/0K/Orl4lvt9vUbDbL7vf76+PxKCqVStGyrPBx3XXd291ul7Zt+6HdbkshhEilUtbhcLiOMmvUKGIAACD21ut1utFo3GcymY9CCFGv1+9VZ4oCRQwAADzZtyZX+D7smgQAALFXq9WC5XJ5EQRBQkr5YrVaXajOFAUmYgAAIPaq1WrYbDbvDMMoaZp2LJfL71VnikLidDqpzgAAAGLM9/23pmneqs4RR77v50zTvPrR9/k1CQAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAgGfHdd38cDh82ev18vP5PCOEEJ7npXVdLxUKhWIQBAnHcS51XS85jnOpOu+XcKArAAB4tkaj0bvH5/F4nHVd96bb7d4JIcRkMslJKd+cncW37sQ3GQAAwCcGg8Gr6XSa0zTtmM/nP1iWFbZarSvbth+klMnFYpHdbDbnnuedB0GQDMMwaRhGsd/v33Q6Hak6/+dQxAAAwNPN//q1+NN1KtJv/rQYil/+81cvE99ut6nZbJbd7/fXx+NRVCqVomVZ4eO667q3u90ubdv2Q7vdlkIIkUqlrMPhcB1p1ohRxAAAQOyt1+t0o9G4z2QyH4UQol6v36vOFAWKGAAAeLpvTK7wfdg1CQAAYq9WqwXL5fIiCIKElPLFarW6UJ0pCkzEAABA7FWr1bDZbN4ZhlHSNO1YLpffq84UhcTpdFKdAQAAxJjv+29N07xVnSOOfN/PmaZ59aPv82sSAABAEYoYAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAz47ruvnhcPiy1+vl5/N5RgghPM9L67peKhQKxSAIEo7jXOq6XnIc51J13i/hQFcAAPBsjUajd4/P4/E467ruTbfbvRNCiMlkkpNSvjk7i2/diW8yAACATwwGg1fT6TSnadoxn89/sCwrbLVaV7ZtP0gpk4vFIrvZbM49zzsPgiAZhmHSMIxiv9+/6XQ6UnX+z6GIAQCAJ/v17tev/yj/mIrym/pf6OFv/uo3X71MfLvdpmazWXa/318fj0dRqVSKlmWFj+uu697udru0bdsP7XZbCiFEKpWyDofDdZRZo0YRAwAAsbder9ONRuM+k8l8FEKIer1+rzpTFChiAADgyb41ucL3YdckAACIvVqtFiyXy4sgCBJSyher1epCdaYoMBEDAACxV61Ww2azeWcYRknTtGO5XH6vOlMUEqfTSXUGAAAQY77vvzVN81Z1jjjyfT9nmubVj77Pr0kAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAAA8O67r5ofD4cter5efz+cZIYTwPC+t63qpUCgUgyBIOI5zqet6yXGcS9V5v4QDXQEAwLM1Go3ePT6Px+Os67o33W73TgghJpNJTkr55uwsvnUnvskAAAA+MRgMXk2n05ymacd8Pv/Bsqyw1Wpd2bb9IKVMLhaL7GazOfc87zwIgmQYhknDMIr9fv+m0+lI1fk/hyIGAACe7N3f/t3r//jDH1JRfvPPfvazMP8Pf//Vy8S3221qNptl9/v99fF4FJVKpWhZVvi47rru7W63S9u2/dBut6UQQqRSKetwOFxHmTVqFDEAABB76/U63Wg07jOZzEchhKjX6/eqM0WBIgYAAJ7sW5MrfB92TQIAgNir1WrBcrm8CIIgIaV8sVqtLlRnigITMQAAEHvVajVsNpt3hmGUNE07lsvl96ozRSFxOp1UZwAAADHm+/5b0zRvVeeII9/3c6ZpXv3o+/yaBAAAUIQiBgAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAwLPjum5+OBy+7PV6+fl8nhFCCM/z0rqulwqFQjEIgoTjOJe6rpccx7lUnfdLONAVAAA8W6PR6N3j83g8zrque9Ptdu+EEGIymeSklG/OzuJbd+KbDAAA4BODweDVdDrNaZp2zOfzHyzLClut1pVt2w9SyuRischuNptzz/POgyBIhmGYNAyj2O/3bzqdjlSd/3MoYgAA4Mn+9/h3r+/+X5CK8pvZ/5wO/+t//8VXLxPfbrep2WyW3e/318fjUVQqlaJlWeHjuuu6t7vdLm3b9kO73ZZCCJFKpazD4XAdZdaoUcQAAEDsrdfrdKPRuM9kMh+FEKJer9+rzhQFihgAAHiyb02u8H3YNQkAAGKvVqsFy+XyIgiChJTyxWq1ulCdKQpMxAAAQOxVq9Ww2WzeGYZR0jTtWC6X36vOFIXE6XRSnQEAAMSY7/tvTdO8VZ0jjnzfz5mmefWj7/NrEgAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAM+O67r54XD4stfr5efzeUYIITzPS+u6XioUCsUgCBKO41zqul5yHOdSdd4v4UBXAADwbI1Go3ePz+PxOOu67k23270TQojJZJKTUr45O4tv3YlvMgAAgE8MBoNX0+k0p2naMZ/Pf7AsK2y1Wle2bT9IKZOLxSK72WzOPc87D4IgGYZh0jCMYr/fv+l0OlJ1/s+hiAEAgCf7X/9z9Pr2//6fVJTfzL3+L+F/+x+9r14mvt1uU7PZLLvf76+Px6OoVCpFy7LCx3XXdW93u13atu2HdrsthRAilUpZh8PhOsqsUaOIAQCA2Fuv1+lGo3GfyWQ+CiFEvV6/V50pChQxAADwZN+aXOH7sGsSAADEXq1WC5bL5UUQBAkp5YvVanWhOlMUmIgBAIDYq1arYbPZvDMMo6Rp2rFcLr9XnSkKidPppDoDAACIMd/335qmeas6Rxz5vp8zTfPqR9/n1yQAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACeHdd188Ph8GWv18vP5/OMEEJ4npfWdb1UKBSKQRAkHMe51HW95DjOpeq8X8KBrgAA4NkajUbvHp/H43HWdd2bbrd7J4QQk8kkJ6V8c3YW37oT32QAAACfGAwGr6bTaU7TtGM+n/9gWVbYarWubNt+kFImF4tFdrPZnHuedx4EQTIMw6RhGMV+v3/T6XSk6vyfQxEDAABPdvfb378+/uv7VJTf/E+vfhJmf/Xzr14mvt1uU7PZLLvf76+Px6OoVCpFy7LCx3XXdW93u13atu2HdrsthRAilUpZh8PhOsqsUaOIAQCA2Fuv1+lGo3GfyWQ+CiFEvV6/V50pChQxAADwZN+aXOH7sGsSAADEXq1WC5bL5UUQBAkp5YvVanWhOlMUmIgBAIDYq1arYbPZvDMMo6Rp2rFcLr9XnSkKidPppDoDABFS3BsAACAASURBVACIMd/335qmeas6Rxz5vp8zTfPqR9/n1yQAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACeHdd188Ph8GWv18vP5/OMEEJ4npfWdb1UKBSKQRAkHMe51HW95DjOpeq8X8KBrgAA4NkajUbvHp/H43HWdd2bbrd7J4QQk8kkJ6V8c3YW37oT32QAAACfGAwGr6bTaU7TtGM+n/9gWVbYarWubNt+kFImF4tFdrPZnHuedx4EQTIMw6RhGMV+v3/T6XSk6vyfQxEDAABPNp/PX//pT39KRfnNn/70p+Evf/nLr14mvt1uU7PZLLvf76+Px6OoVCpFy7LCx3XXdW93u13atu2HdrsthRAilUpZh8PhOsqsUaOIAQCA2Fuv1+lGo3GfyWQ+CiFEvV6/V50pChQxAADwZN+aXOH7sGsSAADEXq1WC5bL5UUQBAkp5YvVanWhOlMUmIgBAIDYq1arYbPZvDMMo6Rp2rFcLr9XnSkKidPppDoDAACIMd/335qmeas6Rxz5vp8zTfPqR9/n1yQAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACeHdd188Ph8GWv18vP5/OMEEJ4npfWdb1UKBSKQRAkHMe51HW95DjOpeq8X8KBrgAA4NkajUbvHp/H43HWdd2bbrd7J4QQk8kkJ6V8c3YW37oT32QAAACfGAwGr6bTaU7TtGM+n/9gWVbYarWubNt+kFImF4tFdrPZnHuedx4EQTIMw6RhGMV+v3/T6XSk6vyfQxEDAABPdv27wev3we9TUX7zJ+mfh8Vf/ONXLxPfbrep2WyW3e/318fjUVQqlaJlWeHjuuu6t7vdLm3b9kO73ZZCCJFKpazD4XAdZdaoUcQAAEDsrdfrdKPRuM9kMh+FEKJer9+rzhQFihgAAHiyb02u8H3YNQkAAGKvVqsFy+XyIgiChJTyxWq1ulCdKQpMxAAAQOxVq9Ww2WzeGYZR0jTtWC6X36vOFIXE6XRSnQEAAMSY7/tvTdO8VZ0jjnzfz5mmefWj7/NrEgAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAM+O67r54XD4stfr5efzeUYIITzPS+u6XioUCsUgCBKO41zqul5yHOdSdd4v4UBXAADwbI1Go3ePz+PxOOu67k23270TQojJZJKTUr45O4tv3YlvMgAAgE8MBoNX0+k0p2naMZ/Pf7AsK2y1Wle2bT9IKZOLxSK72WzOPc87D4IgGYZh0jCMYr/fv+l0OlJ1/s+hiAEAgCfr/e5fXh/e/3sqym8WfvLn4egXf/nVy8S3221qNptl9/v99fF4FJVKpWhZVvi47rru7W63S9u2/dBut6UQQqRSKetwOFxHmTVqFDEAABB76/U63Wg07jOZzEchhKjX6/eqM0WBIgYAAJ7sW5MrfB92TQIAgNir1WrBcrm8CIIgIaV8sVqtLlRnigITMQAAEHvVajVsNpt3hmGUNE07lsvl96ozRSFxOp1UZwAAADHm+/5b0zRvVeeII9/3c6ZpXv3o+/yaBAAAUIQiBgAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAwLPjum5+OBy+7PV6+fl8nhFCCM/z0rqulwqFQjEIgoTjOJe6rpccx7lUnfdLONAVAAA8W6PR6N3j83g8zrque9Ptdu+EEGIymeSklG/OzuJbd+KbDAAA4BODweDVdDrNaZp2zOfzHyzLClut1pVt2w9SyuRischuNptzz/POgyBIhmGYNAyj2O/3bzqdjlSd/3MoYgAA4Mn+5rf+69//67+lovzmz19lwn/6lfnVy8S3221qNptl9/v99fF4FJVKpWhZVvi47rru7W63S9u2/dBut6UQQqRSKetwOFxHmTVqFDEAABB76/U63Wg07jOZzEchhKjX6/eqM0WBIgYAAJ7sW5MrfB92TQIAgNir1WrBcrm8CIIgIaV8sVqtLlRnigITMQAAEHvVajVsNpt3hmGUNE07lsvl96ozRSFxOp1UZwAAADHm+/5b0zRvVeeII9/3c6ZpXv3o+/yaBAAAUIQiBgAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAwLPjum5+OBy+7PV6+fl8nhFCCM/z0rqulwqFQjEIgoTjOJe6rpccx7lUnfdLONAVAAA8W6PR6N3j83g8zrque9Ptdu+EEGIymeSklG/OzuJbd+KbDAAA4BODweDVdDrNaZp2zOfzHyzLClut1pVt2w9SyuRischuNptzz/POgyBIhmGYNAyj2O/3bzqdjlSd/3MoYgAA4Onmf/1a/Ok6Fek3f1oMxS//+auXiW+329RsNsvu9/vr4/EoKpVK0bKs8HHddd3b3W6Xtm37od1uSyGESKVS1uFwuI40a8QoYgAAIPbW63W60WjcZzKZj0IIUa/X71VnigJFDAAAPN03Jlf4PuyaBAAAsVer1YLlcnkRBEFCSvlitVpdqM4UBSZiAAAg9qrVathsNu8MwyhpmnYsl8vvVWeKQuJ0OqnOAAAAYsz3/bemad6qzhFHvu/nTNO8+tH3+TUJAACgCEUMAABAEYoYAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQCAZ8d13fxwOHzZ6/Xy8/k8I4QQnueldV0vFQqFYhAECcdxLnVdLzmOc6k675dwoCsAAHi2RqPRu8fn8XicdV33ptvt3gkhxGQyyUkp35ydxbfuxDcZAADAJwaDwavpdJrTNO2Yz+c/WJYVtlqtK9u2H6SUycVikd1sNuee550HQZAMwzBpGEax3+/fdDodqTr/51DEAADAk/169+vXf5R/TEX5Tf0v9PA3f/Wbr14mvt1uU7PZLLvf76+Px6OoVCpFy7LCx3XXdW93u13atu2HdrsthRAilUpZh8PhOsqsUaOIAQCA2Fuv1+lGo3GfyWQ+CiFEvV6/V50pChQxAADwZN+aXOH7sGsSAADEXq1WC5bL5UUQBAkp5YvVanWhOlMUmIgBAIDYq1arYbPZvDMMo6Rp2rFcLr9XnSkKidPppDoDAACIMd/335qmeas6Rxz5vp8zTfPqR9/n1yQAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACeHdd188Ph8GWv18vP5/OMEEJ4npfWdb1UKBSKQRAkHMe51HW95DjOpeq8X8KBrgAA4NkajUbvHp/H43HWdd2bbrd7J4QQk8kkJ6V8c3YW37oT32QAAACfGAwGr6bTaU7TtGM+n/9gWVbYarWubNt+kFImF4tFdrPZnHuedx4EQTIMw6RhGMV+v3/T6XSk6vyfQxEDAABP9u5v/+71f/zhD6kov/lnP/tZmP+Hv//qZeLb7TY1m82y+/3++ng8ikqlUrQsK3xcd133drfbpW3bfmi321IIIVKplHU4HK6jzBo1ihgAAIi99XqdbjQa95lM5qMQQtTr9XvVmaJAEQMAAE/2rckVvg+7JgEAQOzVarVguVxeBEGQkFK+WK1WF6ozRYGJGAAAiL1qtRo2m807wzBKmqYdy+Xye9WZopA4nU6qMwAAgBjzff+taZq3qnPEke/7OdM0r370fX5NAgAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAA4NlxXTc/HA5f9nq9/Hw+zwghhOd5aV3XS4VCoRgEQcJxnEtd10uO41yqzvslHOgKAACerdFo9O7xeTweZ13Xvel2u3dCCDGZTHJSyjdnZ/GtO/FNBgAA8InBYPBqOp3mNE075vP5D5Zlha1W68q27QcpZXKxWGQ3m82553nnwf9n735CFHn3e48/jh1u8Cjd15LfDKYn6UXl4NHCstbXlQEDUosjnnXAhRTxbqQkCLnETbgXQjZuLtm7EIQDulEqSBAR91MIfeQki0lCpg+Hpp9uUlPkjjDeVcMs5k/PUPBUw/u1Knioh8/yw/eh6gmCZBiGScMwiv1+/6bT6UjV+T+HIgYAAJ7sn8a/eX33H0Eqyj2zf5QO/+wvfvHVy8S3221qNptl9/v99fF4FJVKpWhZVvi47rru7W63S9u2/dBut6UQQqRSKetwOFxHmTVqFDEAABB76/U63Wg07jOZzEchhKjX6/eqM0WBIgYAAJ7sW5MrfB++mgQAALFXq9WC5XJ5EQRBQkr5YrVaXajOFAUmYgAAIPaq1WrYbDbvDMMoaZp2LJfL71VnikLidDqpzgAAAGLM9/23pmneqs4RR77v50zTvPrR9zmaBAAAUIQiBgAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAwLPjum5+OBy+7PV6+fl8nhFCCM/z0rqulwqFQjEIgoTjOJe6rpccx7lUnfdL+KErAAB4tkaj0bvH5/F4nHVd96bb7d4JIcRkMslJKd+cncW37sQ3GQAAwCcGg8Gr6XSa0zTtmM/nP1iWFbZarSvbth+klMnFYpHdbDbnnuedB0GQDMMwaRhGsd/v33Q6Hak6/+dQxAAAwJP94z+MXt/++7+motwz9/pPwj//y95XLxPfbrep2WyW3e/318fjUVQqlaJlWeHjuuu6t7vdLm3b9kO73ZZCCJFKpazD4XAdZdaoUcQAAEDsrdfrdKPRuM9kMh+FEKJer9+rzhQFihgAAHiyb02u8H34ahIAAMRerVYLlsvlRRAECSnli9VqdaE6UxSYiAEAgNirVqths9m8MwyjpGnasVwuv1edKQqJ0+mkOgMAAIgx3/ffmqZ5qzpHHPm+nzNN8+pH3+doEgAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAM+O67r54XD4stfr5efzeUYIITzPS+u6XioUCsUgCBKO41zqul5yHOdSdd4v4YeuAADg2RqNRu8en8fjcdZ13Ztut3snhBCTySQnpXxzdhbfuhPfZAAAAJ8YDAavptNpTtO0Yz6f/2BZVthqta5s236QUiYXi0V2s9mce553HgRBMgzDpGEYxX6/f9PpdKTq/J9DEQMAAE929+vfvj7+7n0qyj3/4NXPwuyvfv7Vy8S3221qNptl9/v99fF4FJVKpWhZVvi47rru7W63S9u2/dBut6UQQqRSKetwOFxHmTVqFDEAABB76/U63Wg07jOZzEchhKjX6/eqM0WBIgYAAJ7sW5MrfB++mgQAALFXq9WC5XJ5EQRBQkr5YrVaXajOFAUmYgAAIPaq1WrYbDbvDMMoaZp2LJfL71VnikLidDqpzgAAAGLM9/23pmneqs4RR77v50zTvPrR9zmaBAAAUIQiBgAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAwLPjum5+OBy+7PV6+fl8nhFCCM/z0rqulwqFQjEIgoTjOJe6rpccx7lUnfdL+KErAAB4tkaj0bvH5/F4nHVd96bb7d4JIcRkMslJKd+cncW37sQ3GQAAwCcGg8Gr6XSa0zTtmM/nP1iWFbZarSvbth+klMnFYpHdbDbnnuedB0GQDMMwaRhGsd/v33Q6Hak6/+dQxAAAwJPN5/PXv//971NR7vnTTz+Fv/zlL796mfh2u03NZrPsfr+/Ph6PolKpFC3LCh/XXde93e12adu2H9rtthRCiFQqZR0Oh+sos0aNIgYAAGJvvV6nG43GfSaT+SiEEPV6/V51pihQxAAAwJN9a3KF78NXkwAAIPZqtVqwXC4vgiBISClfrFarC9WZosBEDAAAxF61Wg2bzeadYRglTdOO5XL5vepMUUicTifVGQAAQIz5vv/WNM1b1TniyPf9nGmaVz/6PkeTAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAeHZc180Ph8OXvV4vP5/PM0II4XleWtf1UqFQKAZBkHAc51LX9ZLjOJeq834JP3QFAADP1mg0evf4PB6Ps67r3nS73TshhJhMJjkp5Zuzs/jWnfgmAwAA+MRgMHg1nU5zmqYd8/n8B8uywlardWXb9oOUMrlYLLKbzebc87zzIAiSYRgmDcMo9vv9m06nI1Xn/xyKGAAAeLLr3wxevw9+m4pyz5+lfx4Wf/F3X71MfLvdpmazWXa/318fj0dRqVSKlmWFj+uu697udru0bdsP7XZbCiFEKpWyDofDdZRZo0YRAwAAsbder9ONRuM+k8l8FEKIer1+rzpTFChiAADgyb41ucL34atJAAAQe7VaLVgulxdBECSklC9Wq9WF6kxRYCIGAABir1qths1m884wjJKmacdyufxedaYoJE6nk+oMAAAgxnzff2ua5q3qHHHk+37ONM2rH32fo0kAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAAA8O67r5ofD4cter5efz+cZIYTwPC+t63qpUCgUgyBIOI5zqet6yXGcS9V5v4QfugIAgGdrNBq9e3wej8dZ13Vvut3unRBCTCaTnJTyzdlZfOtOfJMBAAB8YjAYvJpOpzlN0475fP6DZVlhq9W6sm37QUqZXCwW2c1mc+553nkQBMkwDJOGYRT7/f5Np9ORqvN/DkUMAAA8We83//b68P6/UlHuWfjZH4ajX/zxVy8T3263qdlslt3v99fH41FUKpWiZVnh47rrure73S5t2/ZDu92WQgiRSqWsw+FwHWXWqFHEAABA7K3X63Sj0bjPZDIfhRCiXq/fq84UBYoYAAB4sm9NrvB9+GoSAADEXq1WC5bL5UUQBAkp5YvVanWhOlMUmIgBAIDYq1arYbPZvDMMo6Rp2rFcLr9XnSkKidPppDoDAACIMd/335qmeas6Rxz5vp8zTfPqR9/naBIAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAADPjuu6+eFw+LLX6+Xn83lGCCE8z0vrul4qFArFIAgSjuNc6rpechznUnXeL+GHrgAA4NkajUbvHp/H43HWdd2bbrd7J4QQk8kkJ6V8c3YW37oT32QAAACfGAwGr6bTaU7TtGM+n/9gWVbYarWubNt+kFImF4tFdrPZnHuedx4EQTIMw6RhGMV+v3/T6XSk6vyfQxEDAABP9le/9l//9nf/mYpyz5+/yoR//yvzq5eJb7fb1Gw2y+73++vj8SgqlUrRsqzwcd113dvdbpe2bfuh3W5LIYRIpVLW4XC4jjJr1ChiAAAg9tbrdbrRaNxnMpmPQghRr9fvVWeKAkUMAAA82bcmV/g+fDUJAABir1arBcvl8iIIgoSU8sVqtbpQnSkKTMQAAEDsVavVsNls3hmGUdI07Vgul9+rzhSFxOl0Up0BAADEmO/7b03TvFWdI45838+Zpnn1o+9zNAkAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAIBnx3Xd/HA4fNnr9fLz+TwjhBCe56V1XS8VCoViEAQJx3EudV0vOY5zqTrvl/BDVwAA8GyNRqN3j8/j8Tjruu5Nt9u9E0KIyWSSk1K+OTuLb92JbzIAAIBPDAaDV9PpNKdp2jGfz3+wLCtstVpXtm0/SCmTi8Uiu9lszj3POw+CIBmGYdIwjGK/37/pdDpSdf7PoYgBAICnm//P1+L316lI9/ypGIpf/t+vXia+3W5Ts9ksu9/vr4/Ho6hUKkXLssLHddd1b3e7Xdq27Yd2uy2FECKVSlmHw+E60qwRo4gBAIDYW6/X6UajcZ/JZD4KIUS9Xr9XnSkKFDEAAPB035hc4fvw1SQAAIi9Wq0WLJfLiyAIElLKF6vV6kJ1pigwEQMAALFXrVbDZrN5ZxhGSdO0Y7lcfq86UxQSp9NJdQYAABBjvu+/NU3zVnWOOPJ9P2ea5tWPvs/RJAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAJ4d13Xzw+HwZa/Xy8/n84wQQniel9Z1vVQoFIpBECQcx7nUdb3kOM6l6rxfwg9dAQDAszUajd49Po/H46zrujfdbvdOCCEmk0lOSvnm7Cy+dSe+yQAAAD4xGAxeTafTnKZpx3w+/8GyrLDVal3Ztv0gpUwuFovsZrM59zzvPAiCZBiGScMwiv1+/6bT6UjV+T+HIgYAAJ7sb3Z/8/pf5L+kotxT/+96+Lf/42+/epn4drtNzWaz7H6/vz4ej6JSqRQtywof113Xvd3tdmnbth/a7bYUQohUKmUdDofrKLNGjSIGAABib71epxuNxn0mk/kohBD1ev1edaYoUMQAAMCTfWtyhe/DV5MAACD2arVasFwuL4IgSEgpX6xWqwvVmaLARAwAAMRetVoNm83mnWEYJU3TjuVy+b3qTFFInE4n1RkAAECM+b7/1jTNW9U54sj3/Zxpmlc/+j5HkwAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAHh2XNfND4fDl71eLz+fzzNCCOF5XlrX9VKhUCgGQZBwHOdS1/WS4ziXqvN+CT90BQAAz9ZoNHr3+Dwej7Ou6950u907IYSYTCY5KeWbs7P41p34JgMAAPjEYDB4NZ1Oc5qmHfP5/AfLssJWq3Vl2/aDlDK5WCyym83m3PO88yAIkmEYJg3DKPb7/ZtOpyNV5/8cihgAAHiyd3/9v17/v3/+51SUe/63P/3TMP9//vdXLxPfbrep2WyW3e/318fjUVQqlaJlWeHjuuu6t7vdLm3b9kO73ZZCCJFKpazD4XAdZdaoUcQAAEDsrdfrdKPRuM9kMh+FEKJer9+rzhQFihgAAHiyb02u8H34ahIAAMRerVYLlsvlRRAECSnli9VqdaE6UxSYiAEAgNirVqths9m8MwyjpGnasVwuv1edKQqJ0+mkOgMAAIgx3/ffmqZ5qzpHHPm+nzNN8+pH3+doEgAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAM+O67r54XD4stfr5efzeUYIITzPS+u6XioUCsUgCBKO41zqul5yHOdSdd4v4YeuAADg2RqNRu8en8fjcdZ13Ztut3snhBCTySQnpXxzdhbfuhPfZAAAAJ8YDAavptNpTtO0Yz6f/2BZVthqta5s236QUiYXi0V2s9mce553HgRBMgzDpGEYxX6/f9PpdKTq/J9DEQMAAE/2T+PfvL77jyAV5Z7ZP0qHf/YXv/jqZeLb7TY1m82y+/3++ng8ikqlUrQsK3xcd133drfbpW3bfmi321IIIVKplHU4HK6jzBo1ihgAAIi99XqdbjQa95lM5qMQQtTr9XvVmaJAEQMAAE/2rckVvg9fTQIAgNir1WrBcrm8CIIgIaV8sVqtLlRnigITMQAAEHvVajVsNpt3hmGUNE07lsvl96ozRSFxOp1UZwAAADHm+/5b0zRvVeeII9/3c6ZpXv3o+xxNAgAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAA4NlxXTc/HA5f9nq9/Hw+zwghhOd5aV3XS4VCoRgEQcJxnEtd10uO41yqzvsl/NAVAAA8W6PR6N3j83g8zrque9Ptdu+EEGIymeSklG/OzuJbd+KbDAAA4BODweDVdDrNaZp2zOfzHyzLClut1pVt2w9SyuRischuNptzz/POgyBIhmGYNAyj2O/3bzqdjlSd/3MoYgAA4Mn+8R9Gr2///V9TUe6Ze/0n4Z//Ze+rl4lvt9vUbDbL7vf76+PxKCqVStGyrPBx3XXd291ul7Zt+6HdbkshhEilUtbhcLiOMmvUKGIAACD21ut1utFo3GcymY9CCFGv1+9VZ4oCRQwAADzZtyZX+D58NQkAAGKvVqsFy+XyIgiChJTyxWq1ulCdKQpMxAAAQOxVq9Ww2WzeGYZR0jTtWC6X36vOFIXE6XRSnQEAAMSY7/tvTdO8VZ0jjnzfz5mmefWj73M0CQAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAgGfHdd38cDh82ev18vP5PCOEEJ7npXVdLxUKhWIQBAnHcS51XS85jnOpOu+X8ENXAADwbI1Go3ePz+PxOOu67k23270TQojJZJKTUr45O4tv3YlvMgAAgE8MBoNX0+k0p2naMZ/Pf7AsK2y1Wle2bT9IKZOLxSK72WzOPc87D4IgGYZh0jCMYr/fv+l0OlJ1/s+hiAEAgCe7+/VvXx9/9z4V5Z5/8OpnYfZXP//qZeLb7TY1m82y+/3++ng8ikqlUrQsK3xcd133drfbpW3bfmi321IIIVKplHU4HK6jzBo1ihgAAIi99XqdbjQa95lM5qMQQtTr9XvVmaJAEQMAAE/2rckVvg9fTQIAgNir1WrBcrm8CIIgIaV8sVqtLlRnigITMQAAEHvVajVsNpt3hmGUNE07lsvl96ozRSFxOp1UZwAAADHm+/5b0zRvVeeII9/3c6ZpXv3o+xxNAgAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAA4NlxXTc/HA5f9nq9/Hw+zwghhOd5aV3XS4VCoRgEQcJxnEtd10uO41yqzvsl/NAVAAA8W6PR6N3j83g8zrque9Ptdu+EEGIymeSklG/OzuJbd+KbDAAA4BODweDVdDrNaZp2zOfzHyzLClut1pVt2w9SyuRischuNptzz/POgyBIhmGYNAyj2O/3bzqdjlSd/3MoYgAA4Mnm8/nr3//+96ko9/zpp5/CX/7yl1+9THy73aZms1l2v99fH49HUalUipZlhY/rruve7na7tG3bD+12WwohRCqVsg6Hw3WUWaNGEQMAALG3Xq/TjUbjPpPJfBRCiHq9fq86UxQoYgAA4Mm+NbnC9+GrSQAAEHu1Wi1YLpcXQRAkpJQvVqvVB28Y8QAAIABJREFUhepMUWAiBgAAYq9arYbNZvPOMIySpmnHcrn8XnWmKCROp5PqDAAAIMZ8339rmuat6hxx5Pt+zjTNqx99n6NJAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAPDuu6+aHw+HLXq+Xn8/nGSGE8Dwvret6qVAoFIMgSDiOc6nreslxnEvVeb+EH7oCAIBnazQavXt8Ho/HWdd1b7rd7p0QQkwmk5yU8s3ZWXzrTnyTAQAAfGIwGLyaTqc5TdOO+Xz+g2VZYavVurJt+0FKmVwsFtnNZnPued55EATJMAyThmEU+/3+TafTkarzfw5FDAAAPNn1bwav3we/TUW558/SPw+Lv/i7r14mvt1uU7PZLLvf76+Px6OoVCpFy7LCx3XXdW93u13atu2HdrsthRAilUpZh8PhOsqsUaOIAQCA2Fuv1+lGo3GfyWQ+CiFEvV6/V50pChQxAADwZN+aXOH78NUkAACIvVqtFiyXy4sgCBJSyher1epCdaYoMBEDAACxV61Ww2azeWcYRknTtGO5XH6vOlMUEqfTSXUGAAAQY77vvzVN81Z1jjjyfT9nmubVj77P0SQAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACeHdd188Ph8GWv18vP5/OMEEJ4npfWdb1UKBSKQRAkHMe51HW95DjOpeq8X8IPXQEAwLM1Go3ePT6Px+Os67o33W73TgghJpNJTkr55uwsvnUnvskAAAA+MRgMXk2n05ymacd8Pv/Bsqyw1Wpd2bb9IKVMLhaL7GazOfc87zwIgmQYhknDMIr9fv+m0+lI1fk/hyIGAACerPebf3t9eP9fqSj3LPzsD8PRL/74q5eJb7fb1Gw2y+73++vj8SgqlUrRsqzwcd113dvdbpe2bfuh3W5LIYRIpVLW4XC4jjJr1ChiAAAg9tbrdbrRaNxnMpmPQghRr9fvVWeKAkUMAAA82bcmV/g+fDUJAABir1arBcvl8iIIgoSU8sVqtbpQnSkKTMQAAEDsVavVsNls3hmGUdI07Vgul9+rzhSFxOl0Up0BAADEmO/7b03TvFWdI45838+Zpnn1o+9zNAkAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAIBnx3Xd/HA4fNnr9fLz+TwjhBCe56V1XS8VCoViEAQJx3EudV0vOY5zqTrvl/BDVwAA8GyNRqN3j8/j8Tjruu5Nt9u9E0KIyWSSk1K+OTuLb92JbzIAAIBPDAaDV9PpNKdp2jGfz3+wLCtstVpXtm0/SCmTi8Uiu9lszj3POw+CIBmGYdIwjGK/37/pdDpSdf7PoYgBAIAn+6tf+69/+7v/TEW5589fZcK//5X51cvEt9ttajabZff7/fXxeBSVSqVoWVb4uO667u1ut0vbtv3QbrelEEKkUinrcDhcR5k1ahQxAAAQe+v1Ot1oNO4zmcxHIYSo1+v3qjNFgSIGAACe7FuTK3wfvpoEAACxV6vVguVyeREEQUJK+WK1Wl2ozhQFJmIAACD2qtVq2Gw27wzDKGmadiyXy+9VZ4pC4nQ6qc4AAABizPf9t6Zp3qrOEUe+7+dM07z60fc5mgQAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAMCz47pufjgcvuz1evn5fJ4RQgjP89K6rpcKhUIxCIKE4ziXuq6XHMe5VJ33S/ihKwAAeLZGo9G7x+fxeJx1Xfem2+3eCSHEZDLJSSnfnJ3Ft+7ENxkAAMAnBoPBq+l0mtM07ZjP5z9YlhW2Wq0r27YfpJTJxWKR3Ww2557nnQdBkAzDMGkYRrHf7990Oh2pOv/nUMQAAMDTzf/na/H761Ske/5UDMUv/+9XLxPfbrep2WyW3e/318fjUVQqlaJlWeHjuuu6t7vdLm3b9kO73ZZCCJFKpazD4XAdadaIUcQAAEDsrdfrdKPRuM9kMh+FEKJer9+rzhQFihgAAHi6b0yu8H34ahIAAMRerVYLlsvlRRAECSnli9VqdaE6UxSYiAEAgNirVqths9m8MwyjpGnasVwuv1edKQqJ0+mkOgMAAIgx3/ffmqZ5qzpHHPm+nzNN8+pH3+doEgAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAM+O67r54XD4stfr5efzeUYIITzPS+u6XioUCsUgCBKO41zqul5yHOdSdd4v4YeuAADg2RqNRu8en8fjcdZ13Ztut3snhBCTySQnpXxzdhbfuhPfZAAAAJ8YDAavptNpTtO0Yz6f/2BZVthqta5s236QUiYXi0V2s9mce553HgRBMgzDpGEYxX6/f9PpdKTq/J9DEQMAAE/2N7u/ef0v8l9SUe6p/3c9/Nv/8bdfvUx8u92mZrNZdr/fXx+PR1GpVIqWZYWP667r3u52u7Rt2w/tdlsKIUQqlbIOh8N1lFmjRhEDAACxt16v041G4z6TyXwUQoh6vX6vOlMUKGIAAODJvjW5wvfhq0kAABB7tVotWC6XF0EQJKSUL1ar1YXqTFFgIgYAAGKvWq2GzWbzzjCMkqZpx3K5/F51pigkTqeT6gwAACDGfN9/a5rmreocceT7fs40zasffZ+jSQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAADw7ruvmh8Phy16vl5/P5xkhhPA8L63reqlQKBSDIEg4jnOp63rJcZxL1Xm/hB+6AgCAZ2s0Gr17fB6Px1nXdW+63e6dEEJMJpOclPLN2Vl86058kwEAAHxiMBi8mk6nOU3Tjvl8/oNlWWGr1bqybftBSplcLBbZzWZz7nneeRAEyTAMk4ZhFPv9/k2n05Gq838ORQwAADzZu7/+X6//3z//cyrKPf/bn/5pmP8///url4lvt9vUbDbL7vf76+PxKCqVStGyrPBx3XXd291ul7Zt+6HdbkshhEilUtbhcLiOMmvUKGIAACD21ut1utFo3GcymY9CCFGv1+9VZ4oCRQwAADzZtyZX+D58NQkAAGKvVqsFy+XyIgiChJTyxWq1ulCdKQpMxAAAQOxVq9Ww2WzeGYZR0jTtWC6X36vOFIXE6XRSnQEAAMSY7/tvTdO8VZ0jjnzfz5mmefWj73M0CQAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAgGfHdd38cDh82ev18vP5PCOEEJ7npXVdLxUKhWIQBAnHcS51XS85jnOpOu+X8ENXAADwbI1Go3ePz+PxOOu67k23270TQojJZJKTUr45O4tv3YlvMgAAgE8MBoNX0+k0p2naMZ/Pf7AsK2y1Wle2bT9IKZOLxSK72WzOPc87D4IgGYZh0jCMYr/fv+l0OlJ1/s+hiAEAgCf7p/FvXt/9R5CKcs/sH6XDP/uLX3z1MvHtdpuazWbZ/X5/fTweRaVSKVqWFT6uu657u9vt0rZtP7TbbSmEEKlUyjocDtdRZo0aRQwAAMTeer1ONxqN+0wm81EIIer1+r3qTFGgiAEAgCf71uQK34evJgEAQOzVarVguVxeBEGQkFK+WK1WF6ozRYGJGAAAiL1qtRo2m807wzBKmqYdy+Xye9WZopA4nU6qMwAAgBjzff+taZq3qnPEke/7OdM0r370fY4mAQAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAgCIUMQAA8Oy4rpsfDocve71efj6fZ4QQwvO8tK7rpUKhUAyCIOE4zqWu6yXHcS5V5/0SfugKAACerdFo9O7xeTweZ13Xvel2u3dCCDGZTHJSyjdnZ/GtO/FNBgAA8InBYPBqOp3mNE075vP5D5Zlha1W68q27QcpZXKxWGQ3m82553nnQRAkwzBMGoZR7Pf7N51OR6rO/zkUMQAA8GT/+A+j17f//q+pKPfMvf6T8M//svfVy8S3221qNptl9/v99fF4FJVKpWhZVvi47rru7W63S9u2/dBut6UQQqRSKetwOFxHmTVqFDEAABB76/U63Wg07jOZzEchhKjX6/eqM0WBIgYAAJ7sW5MrfB++mgQAALFXq9WC5XJ5EQRBQkr5YrVaXajOFAUmYgAAIPaq1WrYbDbvDMMoaZp2LJfL71VnikLidDqpzgAAAGLM9/23pmneqs4RR77v50zTvPrR9zmaBAAAUIQiBgAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAwLPjum5+OBy+7PV6+fl8nhFCCM/z0rqulwqFQjEIgoTjOJe6rpccx7lUnfdL+KErAAB4tkaj0bvH5/F4nHVd96bb7d4JIcRkMslJKd+cncW37sQ3GQAAwCcGg8Gr6XSa0zTtmM/nP1iWFbZarSvbth+klMnFYpHdbDbnnuedB0GQDMMwaRhGsd/v33Q6Hak6/+dQxAAAwJPd/fq3r4+/e5+Kcs8/ePWzMPurn3/1MvHtdpuazWbZ/X5/fTweRaVSKVqWFT6uu657u9vt0rZtP7TbbSmEEKlUyjocDtdRZo0aRQwAAMTeer1ONxqN+0wm81EIIer1+r3qTFGgiAEAgCf71uQK34evJgEAQOzVarVguVxeBEGQkFK+WK1WF6ozRYGJGAAAiL1qtRo2m807wzBKmqYdy+Xye9WZopA4nU6qMwAAgBjzff+taZq3qnPEke/7OdM0r370fY4mAQAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAgCIUMQAA8Oy4rpsfDocve71efj6fZ4QQwvO8tK7rpUKhUAyCIOE4zqWu6yXHcS5V5/0SfugKAACerdFo9O7xeTweZ13Xvel2u3dCCDGZTHJSyjdnZ/GtO/FNBgAA8InBYPBqOp3mNE075vP5D5Zlha1W68q27QcpZXKxWGQ3m82553nnQRAkwzBMGoZR7Pf7N51OR6rO/zkUMQAA8GTz+fz173//+1SUe/7000/hL3/5y69eJr7dblOz2Sy73++vj8ejqFQqRcuywsd113Vvd7td2rbth3a7LYUQIpVKWYfD4TrKrFGjiAEAgNhbr9fpRqNxn8lkPgohRL1ev1edKQoUMQAA8GTfmlzh+/DVJAAAiL1arRYsl8uLIAgSUsoXq9XqQnWmKDARAwAAsVetVsNms3lnGEZJ07RjuVx+rzpTFBKn00l1BgAAEGO+7781TfNWdY448n0/Z5rm1Y++z9EkAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAnh3XdfPD4fBlr9fLz+fzjBBCeJ6X1nW9VCgUikEQJBzHudR1veQ4zqXqvF/CD10BAMCzNRqN3j0+j8fjrOu6N91u904IISaTSU5K+ebsLL51J77JAAAAPjEYDF5Np9OcpmnHfD7/wbKssNVqXdm2/SClTC4Wi+xmszn3PO88CIJkGIZJwzCK/X7/ptPpSNX5P4ciBgAAnuz6N4PX74PfpqLc82fpn4fFX/zdVy8T3263qdlslt3v99fH41FUKpWiZVnh47rrure73S5t2/ZDu92WQgiRSqWsw+FwHWXWqFHEAABA7K3X63Sj0bjPZDIfhRCiXq/fq84UBYoYAAB4sm9NrvB9+GoSAADEXq1WC5bL5UUQBAkp5YvVanWhOlMUmIgBAIDYq1arYbPZvDMMo6Rp2rFcLr9XnSkKidPppDoDAACIMd/335qmeas6Rxz5vp8zTfPqR9/naBIAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAADPjuu6+eFw+LLX6+Xn83lGCCE8z0vrul4qFArFIAgSjuNc6rpechznUnXeL+GHrgAA4NkajUbvHp/H43HWdd2bbrd7J4QQk8kkJ6V8c3YW37oT32QAAACfGAwGr6bTaU7TtGM+n/9gWVbYarWubNt+kFImF4tFdrPZnHuedx4EQTIMw6RhGMV+v3/T6XSk6vyfQxEDAABP1vvNv70+vP+vVJR7Fn72h+HoF3/81cvEt9ttajabZff7/fXxeBSVSqVoWVb4uO667u1ut0vbtv3QbrelEEKkUinrcDhcR5k1ahQxAAAQe+v1Ot1oNO4zmcxHIYSo1+v3qjNFgSIGAACe7FuTK3wfvpoEAACxV6vVguVyeREEQUJK+WK1Wl2ozhQFJmIAACD2qtVq2Gw27wzDKGmadiyXy+9VZ4pC4nQ6qc4AAABizPf9t6Zp3qrOEUe+7+dM07z60fc5mgQAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAMCz47pufjgcvuz1evn5fJ4RQgjP89K6rpcKhUIxCIKE4ziXuq6XHMe5VJ33S/ihKwAAeLZGo9G7x+fxeJx1Xfem2+3eCSHEZDLJSSnfnJ3Ft+7ENxkAAMAnBoPBq+l0mtM07ZjP5z9YlhW2Wq0r27YfpJTJxWKR3Ww2557nnQdBkAzDMGkYRrHf7990Oh2pOv/nUMQAAMCT/dWv/de//d1/pqLc8+evMuHf/8r86mXi2+02NZvNsvv9/vp4PIpKpVK0LCt8XHdd93a326Vt235ot9tSCCFSqZR1OByuo8waNYoYAACIvfV6nW40GveZTOajEELU6/V71ZmiQBEDAABP9q3JFb4PX00CAIDYq9VqwXK5vAiCICGlfLFarS5UZ4oCEzEAABB71Wo1bDabd4ZhlDRNO5bL5feqM0UhcTqdVGcAAAAx5vv+W9M0b1XniCPf93OmaV796PscTQIAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAODZcV03PxwOX/Z6vfx8Ps8IIYTneWld10uFQqEYBEHCcZxLXddLjuNcqs77JfzQFQAAPFuj0ejd4/N4PM66rnvT7XbvhBBiMpnkpJRvzs7iW3fimwwAAOATg8Hg1XQ6zWmadszn8x8sywpbrdaVbdsPUsrkYrHIbjabc8/zzoMgSIZhmDQMo9jv9286nY5Unf9zKGIAAODp5v/ztfj9dSrSPX8qhuKX//erl4lvt9vUbDbL7vf76+PxKCqVStGyrPBx3XXd291ul7Zt+6HdbkshhEilUtbhcLiONGvEKGIAACD21ut1utFo3GcymY9CCFGv1+9VZ4oCRQwAADzdNyZX+D58NQkAAGKvVqsFy+XyIgiChJTyxWq1ulCdKQpMxAAAQOxVq9Ww2WzeGYZR0jTtWC6X36vOFIXE6XRSnQEAAMSY7/tvTdO8VZ0jjnzfz5mmefWj73M0CQAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAgGfHdd38cDh82ev18vP5PCOEEJ7npXVdLxUKhWIQBAnHcS51XS85jnOpOu+X8ENXAADwbI1Go3ePz+PxOOu67k23270TQojJZJKTUr45O4tv3YlvMgAAgE8MBoNX0+k0p2naMZ/Pf7AsK2y1Wle2bT9IKZOLxSK72WzOPc87D4IgGYZh0jCMYr/fv+l0OlJ1/s+hiAEAgCf7m93fvP4X+S+pKPfU/7se/u3/+NuvXia+3W5Ts9ksu9/vr4/Ho6hUKkXLssLHddd1b3e7Xdq27Yd2uy2FECKVSlmHw+E6yqxRo4gBAIDYW6/X6UajcZ/JZD4KIUS9Xr9XnSkKFDEAAPBk35pc4fvw1SQAAIi9Wq0WLJfLiyAIElLKF6vV6kJ1pigwEQMAALFXrVbDZrN5ZxhGSdO0Y7lcfq86UxQSp9NJdQYAABBjvu+/NU3zVnWOOPJ9P2ea5tWPvs/RJAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAJ4d13Xzw+HwZa/Xy8/n84wQQniel9Z1vVQoFIpBECQcx7nUdb3kOM6l6rxfwg9dAQDAszUajd49Po/H46zrujfdbvdOCCEmk0lOSvnm7Cy+dSe+yQAAAD4xGAxeTafTnKZpx3w+/8GyrLDVal3Ztv0gpUwuFovsZrM59zzvPAiCZBiGScMwiv1+/6bT6UjV+T+HIgYAAJ7s3V//r9f/75//ORXlnv/tT/80zP+f//3Vy8S3221qNptl9/v99fF4FJVKpWhZVvi47rru7W63S9u2/dBut6UQQqRSKetwOFxHmTVqFDEAABB76/U63Wg07jOZzEchhKjX6/eqM0WBIgYAAJ7sW5MrfB++mgQAALFXq9WC5XJ5EQRBQkr5YrVaXajOFAUmYgAAIPaq1WrYbDbvDMMoaZp2LJfL71VnikLidDqpzgAAAGLM9/23pmneqs4RR77v50zTvPrR9zmaBAAAUIQiBgAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAwLPjum5+OBy+7PV6+fl8nhFCCM/z0rqulwqFQjEIgoTjOJe6rpccx7lUnfdL+KErAAB4tkaj0bvH5/F4nHVd96bb7d4JIcRkMslJKd+cncW37sQ3GQAAwCcGg8Gr6XSa0zTtmM/nP1iWFbZarSvbth+klMnFYpHdbDbnnuedB0GQDMMwaRhGsd/v33Q6Hak6/+dQxAAAwJP90/g3r+/+I0hFuWf2j9Lhn/3FL756mfh2u03NZrPsfr+/Ph6PolKpFC3LCh/XXde93e12adu2H9rtthRCiFQqZR0Oh+sos0aNIgYAAGJvvV6nG43GfSaT+SiEEPV6/V51pihQxAAAwJN9a3KF78NXkwAAIPZqtVqwXC4vgiBISClfrFarC9WZosBEDAAAxF61Wg2bzeadYRglTdOO5XL5vepMUUicTifVGQAAQIz5vv/WNM1b1TniyPf9nGmaVz/6PkeTAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAeHZc180Ph8OXvV4vP5/PM0II4XleWtf1UqFQKAZBkHAc51LX9ZLjOJeq834JP3QFAADP1mg0evf4PB6Ps67r3nS73TshhJhMJjkp5Zuzs/jWnfgmAwAA+MRgMHg1nU5zmqYd8/n8B8uywlardWXb9oOUMrlYLLKbzebc87zzIAiSYRgmDcMo9vv9m06nI1Xn/xyKGAAAeLJ//IfR69t//9dUlHvmXv9J+Od/2fvqZeLb7TY1m82y+/3++ng8ikqlUrQsK3xcd133drfbpW3bfmi321IIIVKplHU4HK6jzBo1ihgAAIi99XqdbjQa95lM5qMQQtTr9XvVmaJAEQMAAE/2rckVvg9fTQIAgNir1WrBcrm8CIIgIaV8sVqtLlRnigITMQAAEHvVajVsNpt3hmGUNE07lsvl96ozRSFxOp1UZwAAADHm+/5b0zRvVeeII9/3c6ZpXv3o+xxNAgAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAA4NlxXTc/HA5f9nq9/Hw+zwghhOd5aV3XS4VCoRgEQcJxnEtd10uO41yqzvsl/NAVAAA8W6PR6N3j83g8zrque9Ptdu+EEGIymeSklG/OzuJbd+KbDAAA4BODweDVdDrNaZp2zOfzHyzLClut1pVt2w9SyuRischuNptzz/POgyBIhmGYNAyj2O/3bzqdjlSd/3MoYgAA4Mnufv3b18ffvU9FuecfvPpZmP3Vz796mfh2u03NZrPsfr+/Ph6PolKpFC3LCh/XXde93e12adu2H9rtthRCiFQqZR0Oh+sos0aNIgYAAGJvvV6nG43GfSaT+SiEEPV6/V51pihQxAAAwJN9a3KF78NXkwAAIPZqtVqwXC4vgiBISClfrFarC9WZosBEDAAAxF61Wg2bzeadYRglTdOO5XL5vepMUUicTifVGQAAQIz5vv/WNM1b1TniyPf9nGmaVz/6PkeTAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAeHZc180Ph8OXvV4vP5/PM0II4XleWtf1UqFQKAZBkHAc51LX9ZLjOJeq834JP3QFAADP1mg0evf4PB6Ps67r3nS73TshhJhMJjkp5Zuzs/jWnfgmAwAA+MRgMHg1nU5zmqYd8/n8B8uywlardWXb9oOUMrlYLLKbzebc87zzIAiSYRgmDcMo9vv9m06nI1Xn/xyKGAAAeLL5fP7697//fSrKPX/66afwl7/85VcvE99ut6nZbJbd7/fXx+NRVCqVomVZ4eO667q3u90ubdv2Q7vdlkIIkUqlrMPhcB1l1qhRxAAAQOyt1+t0o9G4z2QyH4UQol6v36vOFAWKGAAAeLJvTa7wffhqEgAAxF6tVguWy+VFEAQJKeWL1Wp1oTpTFJiIAQCA2KtWq2Gz2bwzDKOkadqxXC6/V50pConT6aQ6AwAAiDHf99+apnmrOkcc+b6fM03z6kff52gSAABAEYoYAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAz47ruvnhcPiy1+vl5/N5RgghPM9L67peKhQKxSAIEo7jXOq6XnIc51J13i/hh64AAODZGo1G7x6fx+Nx1nXdm263eyeEEJPJJCelfHN2Ft+6E99kAAAAnxgMBq+m02lO07RjPp//YFlW2Gq1rmzbfpBSJheLRXaz2Zx7nnceBEEyDMOkYRjFfr9/0+l0pOr8n0MRAwAAT3b9m8Hr98FvU1Hu+bP0z8PiL/7uq5eJb7fb1Gw2y+73++vj8SgqlUrRsqzwcd113dvdbpe2bfuh3W5LIYRIpVLW4XC4jjJr1ChiAAAg9tbrdbrRaNxnMpmPQghRr9fvVWeKAkUMAAA82bcmV/g+fDUJAABir1arBcvl8iIIgoSU8sVqtbpQnSkKTMQAAEDsVavVsNls3hmGUdI07Vgul9+rzhSFxOl0Up0BAADEmO/7b03TvFWdI45838+Zpnn1o+9zNAkAAKAIRQwAAECDd4OWAAAgAElEQVQRihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAIBnx3Xd/HA4fNnr9fLz+TwjhBCe56V1XS8VCoViEAQJx3EudV0vOY5zqTrvl/BDVwAA8GyNRqN3j8/j8Tjruu5Nt9u9E0KIyWSSk1K+OTuLb92JbzIAAIBPDAaDV9PpNKdp2jGfz3+wLCtstVpXtm0/SCmTi8Uiu9lszj3POw+CIBmGYdIwjGK/37/pdDpSdf7PoYgBAIAn6/3m314f3v9XKso9Cz/7w3D0iz/+6mXi2+02NZvNsvv9/vp4PIpKpVK0LCt8XHdd93a326Vt235ot9tSCCFSqZR1OByuo8waNYoYAACIvfV6nW40GveZTOajEELU6/V71ZmiQBEDAABP9q3JFb4PX00CAIDYq9VqwXK5vAiCICGlfLFarS5UZ4oCEzEAABB71Wo1bDabd4ZhlDRNO5bL5feqM0UhcTqdVGcAAAAx5vv+W9M0b1XniCPf93OmaV796PscTQIAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAODZcV03PxwOX/Z6vfx8Ps8IIYTneWld10uFQqEYBEHCcZxLXddLjuNcqs77JfzQFQAAPFuj0ejd4/N4PM66rnvT7XbvhBBiMpnkpJRvzs7iW3fimwwAAOATg8Hg1XQ6zWmadszn8x8sywpbrdaVbdsPUsrkYrHIbjabc8/zzoMgSIZhmDQMo9jv9286nY5Unf9zKGIAAODJ/urX/uvf/u4/U1Hu+fNXmfDvf/X/2bufEEXC/dzjr2OHe/Eo3bHkzGB6bnpR5+DRwrLWcWXAgNTiiFkHXEgRs5GSi5Bw3BwSuNyNm5C9C0EI6EapIEFE3E8h9JFzzmJyQ6bDoem3m1tT3Iww3lXDLOZPz1DwVsP3syp4eYtn+fB7qXrNr14mvt1uU7PZLLvf76+Px6OoVCpFy7LCx3XXdW93u13atu2HdrsthRAilUpZh8PhOsqsUaOIAQCA2Fuv1+lGo3GfyWQ+CiFEvV6/V50pChQxAADwZN+aXOH78NUkAACIvVqtFiyXy4sgCBJSyher1epCdaYoMBEDAACxV61Ww2azeWcYRknTtGO5XH6vOlMUEqfTSXUGAAAQY77vvzVN81Z1jjjyfT9nmubVj+7naBIAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAADPjuu6+eFw+LLX6+Xn83lGCCE8z0vrul4qFArFIAgSjuNc6rpechznUnXeL+GHrgAA4NkajUbvHp/H43HWdd2bbrd7J4QQk8kkJ6V8c3YW37oT32QAAACfGAwGr6bTaU7TtGM+n/9gWVbYarWubNt+kFImF4tFdrPZnHuedx4EQTIMw6RhGMV+v3/T6XSk6vyfQxEDAABPN/+b1+IP16lI3/nTYih++Y9fvUx8u92mZrNZdr/fXx+PR1GpVIqWZYWP667r3u52u7Rt2w/tdlsKIUQqlbIOh8N1pFkjRhEDAACxt16v041G4z6TyXwUQoh6vX6vOlMUKGIAAODpvjG5wvfhq0kAABB7tVotWC6XF0EQJKSUL1ar1YXqTFFgIgYAAGKvWq2GzWbzzjCMkqZpx3K5/F51pigkTqeT6gwAACDGfN9/a5rmreocceT7fs40zasf3c/RJAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAJ4d13Xzw+HwZa/Xy8/n84wQQniel9Z1vVQoFIpBECQcx7nUdb3kOM6l6rxfwg9dAQDAszUajd49Po/H46zrujfdbvdOCCEmk0lOSvnm7Cy+dSe+yQAAAD4xGAxeTafTnKZpx3w+/8GyrLDVal3Ztv0gpUwuFovsZrM59zzvPAiCZBiGScMwiv1+/6bT6UjV+T+HIgYAAJ7sV7tfvf69/H0qynfqf6yHv/6zX3/1MvHtdpuazWbZ/X5/fTweRaVSKVqWFT6uu657u9vt0rZtP7TbbSmEEKlUyjocDtdRZo0aRQwAAMTeer1ONxqN+0wm81EIIer1+r3qTFGgiAEAgCf71uQK34evJgEAQOzVarVguVxeBEGQkFK+WK1WF6ozRYGJGAAAiL1qtRo2m807wzBKmqYdy+Xye9WZopA4nU6qMwAAgBjzff+taZq3qnPEke/7OdM0r350P0eTAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAeHZc180Ph8OXvV4vP5/PM0II4XleWtf1UqFQKAZBkHAc51LX9ZLjOJeq834JP3QFAADP1mg0evf4PB6Ps67r3nS73TshhJhMJjkp5Zuzs/jWnfgmAwAA+MRgMHg1nU5zmqYd8/n8B8uywlardWXb9oOUMrlYLLKbzebc87zzIAiSYRgmDcMo9vv9m06nI1Xn/xyKGAAAeLJ3f/t3r//rd79LRfnO//azn4X5f/j7r14mvt1uU7PZLLvf76+Px6OoVCpFy7LCx3XXdW93u13atu2HdrsthRAilUpZh8PhOsqsUaOIAQCA2Fuv1+lGo3GfyWQ+CiFEvV6/V50pChQxAADwZN+aXOH78NUkAACIvVqtFiyXy4sgCBJSyher1epCdaYoMBEDAACxV61Ww2azeWcYRknTtGO5XH6vOlMUEqfTSXUGAAAQY77vvzVN81Z1jjjyfT9nmubVj+7naBIAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAADPjuu6+eFw+LLX6+Xn83lGCCE8z0vrul4qFArFIAgSjuNc6rpechznUnXeL+GHrgAA4NkajUbvHp/H43HWdd2bbrd7J4QQk8kkJ6V8c3YW37oT32QAAACfGAwGr6bTaU7TtGM+n/9gWVbYarWubNt+kFImF4tFdrPZnHuedx4EQTIMw6RhGMV+v3/T6XSk6vyfQxEDAABP9q/j37y++48gFeU7s3+SDv/8r37x1cvEt9ttajabZff7/fXxeBSVSqVoWVb4uO667u1ut0vbtv3QbrelEEKkUinrcDhcR5k1ahQxAAAQe+v1Ot1oNO4zmcxHIYSo1+v3qjNFgSIGAACe7FuTK3wfvpoEAACxV6vVguVyeREEQUJK+WK1Wl2ozhQFJmIAACD2qtVq2Gw27wzDKGmadiyXy+9VZ4pC4nQ6qc4AAABizPf9t6Zp3qrOEUe+7+dM07z60f0cTQIAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAODZcV03PxwOX/Z6vfx8Ps8IIYTneWld10uFQqEYBEHCcZxLXddLjuNcqs77JfzQFQAAPFuj0ejd4/N4PM66rnvT7XbvhBBiMpnkpJRvzs7iW3fimwwAAOATg8Hg1XQ6zWmadszn8x8sywpbrdaVbdsPUsrkYrHIbjabc8/zzoMgSIZhmDQMo9jv9286nY5Unf9zKGIAAODJ/uWfRq9v//3fUlG+M/f6T8O/+OveVy8T3263qdlslt3v99fH41FUKpWiZVnh47rrure73S5t2/ZDu92WQgiRSqWsw+FwHWXWqFHEAABA7K3X63Sj0bjPZDIfhRCiXq/fq84UBYoYAAB4sm9NrvB9+GoSAADEXq1WC5bL5UUQBAkp5YvVanWhOlMUmIgBAIDYq1arYbPZvDMMo6Rp2rFcLr9XnSkKidPppDoDAACIMd/335qmeas6Rxz5vp8zTfPqR/dzNAkAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAIBnx3Xd/HA4fNnr9fLz+TwjhBCe56V1XS8VCoViEAQJx3EudV0vOY5zqTrvl/BDVwAA8GyNRqN3j8/j8Tjruu5Nt9u9E0KIyWSSk1K+OTuLb92JbzIAAIBPDAaDV9PpNKdp2jGfz3+wLCtstVpXtm0/SCmTi8Uiu9lszj3POw+CIBmGYdIwjGK/37/pdDpSdf7PoYgBAIAnu/vn374+/uf7VJTv/KNXPwmzf/nzr14mvt1uU7PZLLvf76+Px6OoVCpFy7LCx3XXdW93u13atu2HdrsthRAilUpZh8PhOsqsUaOIAQCA2Fuv1+lGo3GfyWQ+CiFEvV6/V50pChQxAADwZN+aXOH78NUkAACIvVqtFiyXy4sgCBJSyher1epCdaYoMBEDAACxV61Ww2azeWcYRknTtGO5XH6vOlMUEqfTSXUGAAAQY77vvzVN81Z1jjjyfT9nmubVj+7naBIAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAADPjuu6+eFw+LLX6+Xn83lGCCE8z0vrul4qFArFIAgSjuNc6rpechznUnXeL+GHrgAA4NkajUbvHp/H43HWdd2bbrd7J4QQk8kkJ6V8c3YW37oT32QAAACfGAwGr6bTaU7TtGM+n/9gWVbYarWubNt+kFImF4tFdrPZnHuedx4EQTIMw6RhGMV+v3/T6XSk6vyfQxEDAABPNp/PX//hD39IRfnOn/70p+Evf/nLr14mvt1uU7PZLLvf76+Px6OoVCpFy7LCx3XXdW93u13atu2HdrsthRAilUpZh8PhOsqsUaOIAQCA2Fuv1+lGo3GfyWQ+CiFEvV6/V50pChQxAADwZN+aXOH78NUkAACIvVqtFiyXy4sgCBJSyher1epCdaYoMBEDAACxV61Ww2azeWcYRknTtGO5XH6vOlMUEqfTSXUGAAAQY77vvzVN81Z1jjjyfT9nmubVj+7naBIAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAADPjuu6+eFw+LLX6+Xn83lGCCE8z0vrul4qFArFIAgSjuNc6rpechznUnXeL+GHrgAA4NkajUbvHp/H43HWdd2bbrd7J4QQk8kkJ6V8c3YW37oT32QAAACfGAwGr6bTaU7TtGM+n/9gWVbYarWubNt+kFImF4tFdrPZnHuedx4EQTIMw6RhGMV+v3/T6XSk6vyfQxEDAABPdv2bwev3wW9TUb7zJ+mfh8Vf/K+vXia+3W5Ts9ksu9/vr4/Ho6hUKkXLssLHddd1b3e7Xdq27Yd2uy2FECKVSlmHw+E6yqxRo4gBAIDYW6/X6UajcZ/JZD4KIUS9Xr9XnSkKFDEAAPBk35pc4fvw1SQAAIi9Wq0WLJfLiyAIElLKF6vV6kJ1pigwEQMAALFXrVbDZrN5ZxhGSdO0Y7lcfq86UxQSp9NJdQYAABBjvu+/NU3zVnWOOPJ9P2ea5tWP7udoEgAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAM+O67r54XD4stfr5efzeUYIITzPS+u6XioUCsUgCBKO41zqul5yHOdSdd4v4YeuAADg2RqNRu8en8fjcdZ13Ztut3snhBCTySQnpXxzdhbfuhPfZAAAAJ8YDAavptNpTtO0Yz6f/2BZVthqta5s236QUiYXi0V2s9mce553HgRBMgzDpGEYxX6/f9PpdKTq/J9DEQMAAE/W+83/eX14//9SUb6z8JP/Ho5+8T++epn4drtNzWaz7H6/vz4ej6JSqRQtywof113Xvd3tdmnbth/a7bYUQohUKmUdDofrKLNGjSIGAABib71epxuNxn0mk/kohBD1ev1edaYoUMQAAMCTfWtyhe/DV5MAACD2arVasFwuL4IgSEgpX6xWqwvVmaLARAwAAMRetVoNm83mnWEYJU3TjuVy+b3qTFFInE4n1RkAAECM+b7/1jTNW9U54sj3/Zxpmlc/up+jSQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAADw7ruvmh8Phy16vl5/P5xkhhPA8L63reqlQKBSDIEg4jnOp63rJcZxL1Xm/hB+6AgCAZ2s0Gr17fB6Px1nXdW+63e6dEEJMJpOclPLN2Vl86058kwEAAHxiMBi8mk6nOU3Tjvl8/oNlWWGr1bqybftBSplcLBbZzWZz7nneeRAEyTAMk4ZhFPv9/k2n05Gq838ORQwAADzZ//xn//Vv//P/pqJ8589fZcL//ZfmVy8T3263qdlslt3v99fH41FUKpWiZVnh47rrure73S5t2/ZDu92WQgiRSqWsw+FwHWXWqFHEAABA7K3X63Sj0bjPZDIfhRCiXq/fq84UBYoYAAB4sm9NrvB9+GoSAADEXq1WC5bL5UUQBAkp5YvVanWhOlMUmIgBAIDYq1arYbPZvDMMo6Rp2rFcLr9XnSkKidPppDoDAACIMd/335qmeas6Rxz5vp8zTfPqR/dzNAkAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAIBnx3Xd/HA4fNnr9fLz+TwjhBCe56V1XS8VCoViEAQJx3EudV0vOY5zqTrvl/BDVwAA8GyNRqN3j8/j8Tjruu5Nt9u9E0KIyWSSk1K+OTuLb92JbzIAAIBPDAaDV9PpNKdp2jGfz3+wLCtstVpXtm0/SCmTi8Uiu9lszj3POw+CIBmGYdIwjGK/37/pdDpSdf7PoYgBAICnm//Na/GH61Sk7/xpMRS//MevXia+3W5Ts9ksu9/vr4/Ho6hUKkXLssLHddd1b3e7Xdq27Yd2uy2FECKVSlmHw+E60qwRo4gBAIDYW6/X6UajcZ/JZD4KIUS9Xr9XnSkKFDEAAPB035hc4fvw1SQAAIi9Wq0WLJfLiyAIElLKF6vV6kJ1pigwEQMAALFXrVbDZrN5ZxhGSdO0Y7lcfq86UxQSp9NJdQYAABBjvu+/NU3zVnWOOPJ9P2ea5tWP7udoEgAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAM+O67r54XD4stfr5efzeUYIITzPS+u6XioUCsUgCBKO41zqul5yHOdSdd4v4YeuAADg2RqNRu8en8fjcdZ13Ztut3snhBCTySQnpXxzdhbfuhPfZAAAAJ8YDAavptNpTtO0Yz6f/2BZVthqta5s236QUiYXi0V2s9mce553HgRBMgzDpGEYxX6/f9PpdKTq/J9DEQMAAE/2q92vXv9e/j4V5Tv1P9bDX//Zr796mfh2u03NZrPsfr+/Ph6PolKpFC3LCh/XXde93e12adu2H9rtthRCiFQqZR0Oh+sos0aNIgYAAGJvvV6nG43GfSaT+SiEEPV6/V51pihQxAAAwJN9a3KF78NXkwAAIPZqtVqwXC4vgiBISClfrFarC9WZosBEDAAAxF61Wg2bzeadYRglTdOO5XL5vepMUUicTifVGQAAQIz5vv/WNM1b1TniyPf9nGmaVz+6n6NJAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAPDuu6+aHw+HLXq+Xn8/nGSGE8Dwvret6qVAoFIMgSDiOc6nreslxnEvVeb+EH7oCAIBnazQavXt8Ho/HWdd1b7rd7p0QQkwmk5yU8s3ZWXzrTnyTAQAAfGIwGLyaTqc5TdOO+Xz+g2VZYavVurJt+0FKmVwsFtnNZnPued55EATJMAyThmEU+/3+TafTkarzfw5FDAAAPNm7v/271//1u9+lonznf/vZz8L8P/z9Vy8T3263qdlslt3v99fH41FUKpWiZVnh47rrure73S5t2/ZDu92WQgiRSqWsw+FwHWXWqFHEAABA7K3X63Sj0bjPZDIfhRCiXq/fq84UBYoYAAB4sm9NrvB9+GoSAADEXq1WC5bL5UUQBAkp5YvVanWhOlMUmIgBAIDYq1arYbPZvDMMo6Rp2rFcLr9XnSkKidPppDoDAACIMd/335qmeas6Rxz5vp8zTfPqR/dzNAkAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAIBnx3Xd/HA4fNnr9fLz+TwjhBCe56V1XS8VCoViEAQJx3EudV0vOY5zqTrvl/BDVwAA8GyNRqN3j8/j8Tjruu5Nt9u9E0KIyWSSk1K+OTuLb92JbzIAAIBPDAaDV9PpNKdp2jGfz3+wLCtstVpXtm0/SCmTi8Uiu9lszj3POw+CIBmGYdIwjGK/37/pdDpSdf7PoYgBAIAn+9fxb17f/UeQivKd2T9Jh3/+V7/46mXi2+02NZvNsvv9/vp4PIpKpVK0LCt8XHdd93a326Vt235ot9tSCCFSqZR1OByuo8waNYoYAACIvfV6nW40GveZTOajEELU6/V71ZmiQBEDAABP9q3JFb4PX00CAIDYq9VqwXK5vAiCICGlfLFarS5UZ4oCEzEAABB71Wo1bDabd4ZhlDRNO5bL5feqM0UhcTqdVGcAAAAx5vv+W9M0b1XniCPf93OmaV796H6OJgEAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAPDsuK6bHw6HL3u9Xn4+n2eEEMLzvLSu66VCoVAMgiDhOM6lruslx3EuVef9En7oCgAAnq3RaPTu8Xk8Hmdd173pdrt3QggxmUxyUso3Z2fxrTvxTQYAAPCJwWDwajqd5jRNO+bz+Q+WZYWtVuvKtu0HKWVysVhkN5vNued550EQJMMwTBqGUez3+zedTkeqzv85FDEAAPBk//JPo9e3//5vqSjfmXv9p+Ff/HXvq5eJb7fb1Gw2y+73++vj8SgqlUrRsqzwcd113dvdbpe2bfuh3W5LIYRIpVLW4XC4jjJr1ChiAAAg9tbrdbrRaNxnMpmPQghRr9fvVWeKAkUMAAA82bcmV/g+fDUJAABir1arBcvl8iIIgoSU8sVqtbpQnSkKTMQAAEDsVavVsNls3hmGUdI07Vgul9+rzhSFxOl0Up0BAADEmO/7b03TvFWdI45838+Zpnn1o/s5mgQAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAMCz47pufjgcvuz1evn5fJ4RQgjP89K6rpcKhUIxCIKE4ziXuq6XHMe5VJ33S/ihKwAAeLZGo9G7x+fxeJx1Xfem2+3eCSHEZDLJSSnfnJ3Ft+7ENxkAAMAnBoPBq+l0mtM07ZjP5z9YlhW2Wq0r27YfpJTJxWKR3Ww2557nnQdBkAzDMGkYRrHf7990Oh2pOv/nUMQAAMCT3f3zb18f//N9Ksp3/tGrn4TZv/z5Vy8T3263qdlslt3v99fH41FUKpWiZVnh47rrure73S5t2/ZDu92WQgiRSqWsw+FwHWXWqFHEAABA7K3X63Sj0bjPZDIfhRCiXq/fq84UBYoYAAB4sm9NrvB9+GoSAADEXq1WC5bL5UUQBAkp5YvVanWhOlMUmIgBAIDYq1arYbPZvDMMo6Rp2rFcLr9XnSkKidPppDoDAACIMd/335qmeas6Rxz5vp8zTfPqR/dzNAkAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAIBnx3Xd/HA4fNnr9fLz+TwjhBCe56V1XS8VCoViEAQJx3EudV0vOY5zqTrvl/BDVwAA8GyNRqN3j8/j8Tjruu5Nt9u9E0KIyWSSk1K+OTuLb92JbzIAAIBPDAaDV9PpNKdp2jGfz3+wLCtstVpXtm0/SCmTi8Uiu9lszj3POw+CIBmGYdIwjGK/37/pdDpSdf7PoYgBAIAnm8/nr//whz+konznT3/60/CXv/zlVy8T3263qdlslt3v99fH41FUKpWiZVnh47rrure73S5t2/ZDu92WQgiRSqWsw+FwHWXWqFHEAABA7K3X63Sj0bjPZDIfhRCiXq/fq84UBYoYAAB4sm9NrvB9+GoSAADEXq1WC5bL5UUQBAkp5YvVanWhOlMUmIgBAIDYq1arYbPZvDMMo6Rp2rFcLr9XnSkKidPppDoDAACIMd/335qmeas6Rxz5vp8zTfPqR/dzNAkAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAIBnx3Xd/HA4fNnr9fLz+TwjhBCe56V1XS8VCoViEAQJx3EudV0vOY5zqTrvl/BDVwAA8GyNRqN3j8/j8Tjruu5Nt9u9E0KIyWSSk1K+OTuLb92JbzIAAIBPDAaDV9PpNKdp2jGfz3+wLCtstVpXtm0/SCmTi8Uiu9lszj3POw+CIBmGYdIwjGK/37/pdDpSdf7PoYgBAIAnu/7N4PX74LepKN/5k/TPw+Iv/tdXLxPfbrep2WyW3e/318fjUVQqlaJlWeHjuuu6t7vdLm3b9kO73ZZCCJFKpazD4XAdZdaoUcQAAEDsrdfrdKPRuM9kMh+FEKJer9+rzhQFihgAAHiyb02u8H34ahIAAMRerVYLlsvlRRAECSnli9VqdaE6UxSYiAEAgNirVqths9m8MwyjpGnasVwuv1edKQqJ0+mkOgMAAIgx3/ffmqZ5qzpHHPm+nzNN8+pH93M0CQAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAgGfHdd38cDh82ev18vP5PCOEEJ7npXVdLxUKhWIQBAnHcS51XS85jnOpOu+X8ENXAADwbI1Go3ePz+PxOOu67k23270TQojJZJKTUr45O4tv3YlvMgAAgE8MBoNX0+k0p2naMZ/Pf7AsK2y1Wle2bT9IKZOLxSK72WzOPc87D4IgGYZh0jCMYr/fv+l0OlJ1/s+hiAEAgCfr/eb/vD68/3+pKN9Z+Ml/D0e/+B9fvUx8u92mZrNZdr/fXx+PR1GpVIqWZYWP667r3u52u7Rt2w/tdlsKIUQqlbIOh8N1lFmjRhEDAACxt16v041G4z6TyXwUQoh6vX6vOlMUKGIAAODJvjW5wvfhq0kAABB7tVotWC6XF0EQJKSUL1ar1YXqTFFgIgYAAGKvWq2GzWbzzjCMkqZpx3K5/F51pigkTg3F77cAACAASURBVKeT6gwAACDGfN9/a5rmreocceT7fs40zasf3c/RJAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAJ4d13Xzw+HwZa/Xy8/n84wQQniel9Z1vVQoFIpBECQcx7nUdb3kOM6l6rxfwg9dAQDAszUajd49Po/H46zrujfdbvdOCCEmk0lOSvnm7Cy+dSe+yQAAAD4xGAxeTafTnKZpx3w+/8GyrLDVal3Ztv0gpUwuFovsZrM59zzvPAiCZBiGScMwiv1+/6bT6UjV+T+HIgYAAJ7sf/6z//q3//l/U1G+8+evMuH//kvzq5eJb7fb1Gw2y+73++vj8SgqlUrRsqzwcd113dvdbpe2bfuh3W5LIYRIpVLW4XC4jjJr1ChiAAAg9tbrdbrRaNxnMpmPQghRr9fvVWeKAkUMAAA82bcmV/g+fDUJAABir1arBcvl8iIIgoSU8sVqtbpQnSkKTMQAAEDsVavVsNls3hmGUdI07Vgul9+rzhSFxOl0Up0BAADEmO/7b03TvFWdI45838+Zpnn1o/s5mgQAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAMCz47pufjgcvuz1evn5fJ4RQgjP89K6rpcKhUIxCIKE4ziXuq6XHMe5VJ33S/ihKwAAeLZGo9G7x+fxeJx1Xfem2+3eCSHEZDLJSSnfnJ3Ft+7ENxkAAMAnBoPBq+l0mtM07ZjP5z9YlhW2Wq0r27YfpJTJxWKR3Ww2557nnQdBkAzDMGkYRrHf7990Oh2pOv/nUMQAAMDTzf/mtfjDdSrSd/60GIpf/uNXLxPfbrep2WyW3e/318fjUVQqlaJlWeHjuuu6t7vdLm3b9kO73ZZCCJFKpazD4XAdadaIUcQAAEDsrdfrdKPRuM9kMh+FEKJer9+rzhQFihgAAHi6b0yu8H34ahIAAMRerVYLlsvlRRAECSnli9VqdaE6UxSYiAEAgNirVqths9m8MwyjpGnasVwuv1edKQqJ0+mkOgMAAIgx3/ffmqZ5qzpHHPm+nzNN8+pH93M0CQAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAgGfHdd38cDh82ev18vP5PCOEEJ7npXVdLxUKhWIQBAnHcS51XS85jnOpOu+X8ENXAADwbI1Go3ePz+PxOOu67k23270TQojJZJKTUr45O4tv3YlvMgAAgE8MBoNX0+k0p2naMZ/Pf7AsK2y1Wle2bT9IKZOLxSK72WzOPc87D4IgGYZh0jCMYr/fv+l0OlJ1/s+hiAEAgCf71e5Xr38vf5+K8p36H+vhr//s11+9THy73aZms1l2v99fH49HUalUipZlhY/rruve7na7tG3bD+12WwohRCqVsg6Hw3WUWaNGEQMAALG3Xq/TjUbjPpPJfBRCiHq9fq86UxQoYgAA4Mm+NbnC9+GrSQAAEHu1Wi1YLpcXQRAkpJQvVqvVhepMUWAiBgAAYq9arYbNZvPOMIySpmnHcrn8XnWmKCROp5PqDAAAIMZ8339rmuat6hxx5Pt+zjTNqx/dz9EkAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAnh3XdfPD4fBlr9fLz+fzjBBCeJ6X1nW9VCgUikEQJBzHudR1veQ4zqXqvF/CD10BAMCzNRqN3j0+j8fjrOu6N91u904IISaTSU5K+ebsLL51J77JAAAAPjEYDF5Np9OcpmnHfD7/wbKssNVqXdm2/SClTC4Wi+xmszn3PO88CIJkGIZJwzCK/X7/ptPpSNX5P4ciBgAAnuzd3/7d6//63e9SUb7zv/3sZ2H+H/7+q5eJb7fb1Gw2y+73++vj8SgqlUrRsqzwcd113dvdbpe2bfuh3W5LIYRIpVLW4XC4jjJr1ChiAAAg9tbrdbrRaNxnMpmPQghRr9fvVWeKAkUMAAA82bcmV/g+fDUJAABir1arBcvl8iIIgoSU8sVqtbpQnSkKTMQAAEDsVavVsNls3hmGUdI07Vgul9+rzhSFxOl0Up0BAADEmO/7b03TvFWdI45838+Zpnn1o/s5mgQAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAMCz47pufjgcvuz1evn5fJ4RQgjP89K6rpcKhUIxCIKE4ziXuq6XHMe5VJ33S/ihKwAAeLZGo9G7x+fxeJx1Xfem2+3eCSHEZDLJSSnfnJ3Ft+7ENxkAAMAnBoPBq+l0mtM07ZjP5z9YlhW2Wq0r27YfpJTJxWKR3Ww2557nnQdBkAzDMGkYRrHf7990Oh2pOv/nUMQAAMCT/ev4N6/v/iNIRfnO7J+kwz//q1989TLx7Xabms1m2f1+f308HkWlUilalhU+rruue7vb7dK2bT+0220phBCpVMo6HA7XUWaNGkUMAADE3nq9TjcajftMJvNRCCHq9fq96kxRoIgBAIAn+9bkCt+HryYBAEDs1Wq1YLlcXgRBkJBSvlitVheqM0WBiRgAAIi9arUaNpvNO8MwSpqmHcvl8nvVmaKQOJ1OqjMAAIAY833/rWmat6pzxJHv+znTNK9+dD9HkwAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAHh2XNfND4fDl71eLz+fzzNCCOF5XlrX9VKhUCgGQZBwHOdS1/WS4ziXqvN+CT90BQAAz9ZoNHr3+Dwej7Ou6950u907IYSYTCY5KeWbs7P41p34JgMAAPjEYDB4NZ1Oc5qmHfP5/AfLssJWq3Vl2/aDlDK5WCyym83m3PO88yAIkmEYJg3DKPb7/ZtOpyNV5/8cihgAAHiyf/mn0evbf/+3VJTvzL3+0/Av/rr31cvEt9ttajabZff7/fXxeBSVSqVoWVb4uO667u1ut0vbtv3QbrelEEKkUinrcDhcR5k1ahQxAAAQe+v1Ot1oNO4zmcxHIYSo1+v3qjNFgSIGAACe7FuTK3wfvpoEAACxV6vVguVyeREEQUJK+WK1Wl2ozhQFJmIAACD2qtVq2Gw27wzDKGmadiyXy+9VZ4pC4nQ6qc4AAABizPf9t6Zp3qrOEUe+7+dM07z60f0cTQIAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAODZcV03PxwOX/Z6vfx8Ps8IIYTneWld10uFQqEYBEHCcZxLXddLjuNcqs77JfzQFQAAPFuj0ejd4/N4PM66rnvT7XbvhBBiMpnkpJRvzs7iW3fimwwAAOATg8Hg1XQ6zWmadszn8x8sywpbrdaVbdsPUsrkYrHIbjabc8/zzoMgSIZhmDQMo9jv9286nY5Unf9zKGIAAODJ7v75t6+P//k+FeU7/+jVT8LsX/78q5eJb7fb1Gw2y+73++vj8SgqlUrRsqzwcd113dvdbpe2bfuh3W5LIYRIpVLW4XC4jjJr1ChiAAAg9tbrdbrRaNxnMpmPQghRr9fvVWeKAkUMAAA82bcmV/g+fDUJAABir1arBcvl8iIIgoSU8sVqtbpQnSkKTMQAAEDsVavVsNls3hmGUdI07Vgul9+rzhSFxOl0Up0BAADEmO/7b03TvFWdI45838+Zpnn1o/s5mgQAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAMCz47pufjgcvuz1evn5fJ4RQgjP89K6rpcKhUIxCIKE4ziXuq6XHMe5VJ33S/ihKwAAeLZGo9G7x+fxeJx1Xfem2+3eCSHEZDLJSSnfnJ3Ft+7ENxkAAMAnBoPBq+l0mtM07ZjP5z9YlhW2Wq0r27YfpJTJxWKR3Ww2557nnQdBkAzDMGkYRrHf7990Oh2pOv/nUMQAAMCTzefz13/4wx9SUb7zpz/9afjLX/7yq5eJb7fb1Gw2y+73++vj8SgqlUrRsqzwcd113dvdbpe2bfuh3W5LIYRIpVLW4XC4jjJr1ChiAAAg9tbrdbrRaNxnMpmPQghRr9fvVWeKAkUMAAA82bcmV/g+fDUJAABir1arBcvl8iIIgoSU8sVqtbpQnSkKTMQAAEDsVavVsNls3hmGUdI07Vgul9+rzhSFxOl0Up0BAADEmO/7b03TvFWdI45838+Zpnn1o/s5mgQAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAMCz47pufjgcvuz1evn5fJ4RQgjP89K6rpcKhUIxCIKE4ziXuq6XHMe5VJ33S/ihKwAAeLZGo9G7x+fxeJx1Xfem2+3eCSHEZDLJSSnfnJ3Ft+7ENxkAAMAnBoPBq+l0mtM07ZjP5z9YlhW2Wq0r27YfpJTJxWKR3Ww2557nnQdBkAzDMGkYRrHf7990Oh2pOv/nUMQAAMCTXf9m8Pp98NtUlO/8SfrnYfEX/+url4lvt9vUbDbL7vf76+PxKCqVStGyrPBx3XXd291ul7Zt+6HdbkshhEilUtbhcLiOMmvUKGIAACD21ut1utFo3GcymY9CCFGv1+9VZ4oCRQwAADzZtyZX+D58NQkAAGKvVqsFy+XyIgiChJTyxWq1ulCdKQpMxAAAQOxVq9Ww2WzeGYZR0jTtWC6X36vOFIXE6XRSnQEAAMSY7/tvTdO8VZ0jjnzfz5mmefWj+zmaBAAAUIQiBgAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAwLPjum5+OBy+7PV6+fl8nhFCCM/z0rqulwqFQjEIgoTjOJe6rpccx7lUnfdL+KErAAB4tkaj0bvH5/F4nHVd96bb7d4JIcRkMslJKd+cncW37sQ3GQAAwCcGg8Gr6XSa0zTtmM/nP1iWFbZarSvbth+klMnFYpHdbDbnnuedB0GQDMMwaRhGsd/v33Q6Hak6/+dQxAAAwJP1fvN/Xh/e/79UlO8s/OS/h6Nf/I+vXia+3W5Ts9ksu9/vr4/Ho6hUKkXLssLHddd1b3e7Xdq27Yd2uy2FECKVSlmHw+E6yqxRo4gBAIDYW6/X6UajcZ/JZD4KIUS9Xr9XnSkKFDEAAPBk35pc4fvw1SQAAIi9Wq0WLJfLiyAIElLKF6vV6kJ1pigwEQMAALFXrVbDZrN5ZxhGSdO0Y7lcfq86UxQSp9NJdQYAABBjvu+/NU3zVnWOOPJ9P2ea5tWP7udoEgAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAM+O67r54XD4stfr5efzeUYIITzPS+u6XioUCsUgCBKO41zqul5yHOdSdd4v4YeuAADg2RqNRu8en8fjcdZ13Ztut3snhBCTySQnpXxzdhbfuhPfZAAAAJ8YDAavptNpTtO0Yz6f/2BZVthqta5s236QUiYXi0V2s9mce553HgRBMgzDpGEYxX6/f9PpdKTq/J9DEQMAAE/2P//Zf/3b//y/qSjf+fNXmfB//6X51cvEt9ttajabZff7/fXxeBSVSqVoWVb4uO667u1ut0vbtv3QbrelEEKkUinrcDhcR5k1ahQxAAAQe+v1Ot1oNO4zmcxHIYSo1+v3qjNFgSIGAACe7FuTK3wfvpoEAACxV6vVguVyeREEQUJK+WK1Wl2ozhQFJmIAACD2qtVq2Gw27wzDKGmadiyXy+9VZ4pC4nQ6qc4AAABizPf9t6Zp3qrOEUe+7+dM07z60f0cTQIAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAODZcV03PxwOX/Z6vfx8Ps8IIYTneWld10uFQqEYBEHCcZxLXddLjuNcqs77JfzQFQAAPFuj0ejd4/N4PM66rnvT7XbvhBBiMpnkpJRvzs7iW3fimwwAAOATg8Hg1XQ6zWmadszn8x8sywpbrdaVbdsPUsrkYrHIbjabc8/zzoMgSIZhmDQMo9jv9286nY5Unf9zKGIAAODp5n/zWvzhOhXpO39aDMUv//Grl4lvt9vUbDbL7vf76+PxKCqVStGyrPBx3XXd291ul7Zt+6HdbkshhEilUtbhcLiONGvEKGIAACD21ut1utFo3GcymY9CCFGv1+9VZ4oCRQwAADzdNyZX+D58NQkAAGKvVqsFy+XyIgiChJTyxWq1ulCdKQpMxAAAQOxVq9Ww2WzeGYZR0jTtWC6X36vOFIXE6XRSnQEAAMSY7/tvTdO8VZ0jjnzfz5mmefWj+zmaBAAAUIQiBgAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAwLPjum5+OBy+7PV6+fl8nhFCCM/z0rqulwqFQjEIgoTjOJe6rpccx7lUnfdL+KErAAB4tkaj0bvH5/F4nHVd96bb7d4JIcRkMslJKd+cncW37sQ3GQAAwCcGg8Gr6XSa0zTtmM/nP1iWFbZarSvbth+klMnFYpHdbDbnnuedB0GQDMMwaRhGsd/v33Q6Hak6/+dQxAAAwJP9aver17+Xv09F+U79j/Xw13/2669eJr7dblOz2Sy73++vj8ejqFQqRcuywsd113Vvd7td2rbth3a7LYUQIpVKWYfD4TrKrFGjiAEAgNhbr9fpRqNxn8lkPgohRL1ev1edKQoUMQAA8GTfmlzh+/DVJAAAiL1arRYsl8uLIAgSUsoXq9XqQnWmKDARAwAAsVetVsNms3lnGEZJ07RjuVx+rzpTFBKn00l1BgAAEGO+7781TfNWdY448n0/Z5rm1Y/u52gSAABAEYoYAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAz47ruvnhcPiy1+vl5/N5RgghPM9L67peKhQKxSAIEo7jXOq6XnIc51J13i/hh64AAODZGo1G7x6fx+Nx1nXdm263eyeEEJPJJCelfHN2Ft+6E99kAAAAnxgMBq+m02lO07RjPp//YFlW2Gq1rmzbfpBSJheLRXaz2Zx7nnceBEEyDMOkYRjFfr9/0+l0pOr8n0MRAwAAT/bub//u9X/97nepKN/53372szD/D3//1cvEt9ttajabZff7/fXxeBSVSqVoWVb4uO667u1ut0vbtv3QbrelEEKkUinrcDhcR5k1ahQxAAAQe+v1Ot1oNO4zmcxHIYSo1+v3qjNFgSIGAACe7FuTK3wfvpoEAACxV6vVguVyeREEQUJK+WK1Wl2ozhQFJmIAACD2qtVq2Gw27wzDKGmadiyXy+9VZ4pC4nQ6qc4AAABizPf9t6Zp3qrOEUe+7+dM07z60f0cTQIAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAODZcV03PxwOX/Z6vfx8Ps8IIYTneWld10uFQqEYBEHCcZxLXddLjuNcqs77JfzQFQAAPFuj0ejd4/N4PM66rnvT7XbvhBBiMpnkpJRvzs7iW3fimwwAAOATg8Hg1XQ6zWmadszn8x8sywpbrdaVbdsPUsrkYrHIbjabc8/zzoMgSIZhmDQMo9jv9286nY5Unf9zKGIAAODJ/nX8m9d3/xGkonxn9k/S4Z//1S++epn4drtNzWaz7H6/vz4ej6JSqRQtywof113Xvd3tdmnbth/a7bYUQohUKmUdDofrKLNGjSIGAABib71epxuNxn0mk/kohBD1ev1edaYoUMQAAMCTfWtyhe/DV5MAACD2arVasFwuL4IgSEgpX6xWqwvVmaLARAwAAMRetVoNm83mnWEYJU3TjuVy+b3qTFFInE4n1RkAAECM+b7/1jTNW9U54sj3/Zxpmlc/up+jSQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAADw7ruvmh8Phy16vl5/P5xkhhPA8L63reqlQKBSDIEg4jnOp63rJcZxL1Xm/hB+6AgCAZ2s0Gr17fB6Px1nXdW+63e6dEEJMJpOclPLN2Vl86058kwEAAHxiMBi8mk6nOU3Tjvl8/oNlWWGr1bqybftBSplcLBbZzWZz7nneeRAEyTAMk4ZhFPv9/k2n05Gq838ORQwAADzZv/zT6PXtv/9bKsp35l7/afgXf9376mXi2+02NZvNsvv9/vp4PIpKpVK0LCt8XHdd93a326Vt235ot9tSCCFSqZR1OByuo8waNYoYAACIvfV6nW40GveZTOajEELU6/V71ZmiQBEDAABP9q3JFb4PX00CAIDYq9VqwXK5vAiCICGlfLFarS5UZ4oCEzEAABB71Wo1bDabd4ZhlDRNO5bL5feqM0UhcTqdVGcAAAAx5vv+W9M0b1XniCPf93OmaV796H6OJgEAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAPDsuK6bHw6HL3u9Xn4+n2eEEMLzvLSu66VCoVAMgiDhOM6lruslx3EuVef9En7oCgAAnq3RaPTu8Xk8Hmdd173pdrt3QggxmUxyUso3Z2fxrTvxTQYAAPCJwWDwajqd5jRNO+bz+Q+WZYWtVuvKtu0HKWVysVhkN5vNued550EQJMMwTBqGUez3+zedTkeqzv85FDEAAPBkd//829fH/3yfivKdf/TqJ2H2L3/+1cvEt9ttajabZff7/fXxeBSVSqVoWVb4uO667u1ut0vbtv3QbrelEEL8//buJ0SV9c/v+OOxQwav0h1L7jmYPpNe1Fy8WljWOq4MGJBaXPG3DggjRcxGSgYhYdwMCQyzcROyycqFIPxAN4pBgoi4P4U/+sqdWZxMyOnLvU0/3UydYnK8tLNJk7M4f/qcX8FTDe/XqqB4Hj7U6sO3qKcSiYS13+8vw8waNooYAACIvNVqlazVarepVOpeCCGq1eqt6kxhoIgBAIBH+9zkCl+GryYBAEDkVSoVfz6fn/m+H5NSPlsul2eqM4WBiRgAAIi8crkc1Ov1G8MwCpqmHYrF4lvVmcIQOx6PqjMAAIAI8zzvtWma16pzRJHneRnTNC++dj2vJgEAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAPDkuK6b7ff7zzudTnY6naaEEGKxWCR1XS/kcrm87/sxx3HOdV0vOI5zrjrvx3CgKwAAeLIGg8Gbh+vhcJh2Xfeq3W7fCCHEaDTKSClfnZxEt+5ENxkAAMB7er3ei/F4nNE07ZDNZt9ZlhU0Go0L27bvpJTx2WyWXq/Xp4vF4tT3/XgQBHHDMPLdbveq1WpJ1fk/hCIGAAAebTqdvvzll18SYe757bffBj/88MMnfya+2WwSk8kkvdvtLg+HgyiVSnnLsoKH+67rXm+326Rt23fNZlMKIUQikbD2+/1lmFnDRhEDAACRt1qtkrVa7TaVSt0LIUS1Wr1VnSkMFDEAAPBon5tc4cvw1SQAAIi8SqXiz+fzM9/3Y1LKZ8vl8kx1pjAwEQMAAJFXLpeDer1+YxhGQdO0Q7FYfKs6Uxhix+NRdQYAABBhnue9Nk3zWnWOKPI8L2Oa5sXXrufVJAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAJ4c13Wz/X7/eafTyU6n05QQQiwWi6Su64VcLpf3fT/mOM65rusFx3HOVef9GA50BQAAT9ZgMHjzcD0cDtOu61612+0bIYQYjUYZKeWrk5Po1p3oJgMAAHhPr9d7MR6PM5qmHbLZ7DvLsoJGo3Fh2/adlDI+m83S6/X6dLFYnPq+Hw+CIG4YRr7b7V61Wi2pOv+HUMQAAMCjXf7Ye/nW/ykR5p7fJL8L8t//9Sd/Jr7ZbBKTySS92+0uD4eDKJVKecuygof7ruteb7fbpG3bd81mUwohRCKRsPb7/WWYWcNGEQMAAJG3Wq2StVrtNpVK3QshRLVavVWdKQwUMQAA8Gifm1zhy/DVJAAAiLxKpeLP5/Mz3/djUspny+XyTHWmMDARAwAAkVcul4N6vX5jGEZB07RDsVh8qzpTGGLH41F1BgAAEGGe5702TfNadY4o8jwvY5rmxdeu59UkAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAnhzXdbP9fv95p9PJTqfTlBBCLBaLpK7rhVwul/d9P+Y4zrmu6wXHcc5V5/0YDnQFAABP1mAwePNwPRwO067rXrXb7RshhBiNRhkp5auTk+jWnegmAwAAeE+v13sxHo8zmqYdstnsO8uygkajcWHb9p2UMj6bzdLr9fp0sVic+r4fD4IgbhhGvtvtXrVaLak6/4dQxAAAwKN1fvz7l/u3/5gIc8/cN38SDL7/00/+THyz2SQmk0l6t9tdHg4HUSqV8pZlBQ/3Xde93m63Sdu275rNphRCiEQiYe33+8sws4aNIgYAACJvtVola7XabSqVuhdCiGq1eqs6UxgoYgAA4NE+N7nCl+GrSQAAEHmVSsWfz+dnvu/HpJTPlsvlmepMYWAiBgAAIq9cLgf1ev3GMIyCpmmHYrH4VnWmMMSOx6PqDAAAIMI8z3ttmua16hxR5HlexjTNi69dz6tJAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAPDmu62b7/f7zTqeTnU6nKSGEWCwWSV3XC7lcLu/7fsxxnHNd1wuO45yrzvsxHOgKAACerMFg8Obhejgcpl3XvWq32zdCCDEajTJSylcnJ9GtO9FNBgAA8J5er/diPB5nNE07ZLPZd5ZlBY1G48K27TspZXw2m6XX6/XpYrE49X0/HgRB3DCMfLfbvWq1WlJ1/g+hiAEAgEf7i997L3/6+R8SYe753YtU8De/Mz/5M/HNZpOYTCbp3W53eTgcRKlUyluWFTzcd133ervdJm3bvms2m1IIIRKJhLXf7y/DzBo2ihgAAIi81WqVrNVqt6lU6l4IIarV6q3qTGGgiAEAgEf73OQKX4avJgEAQORVKhV/P/becQAAEANJREFUPp+f+b4fk1I+Wy6XZ6ozhYGJGAAAiLxyuRzU6/UbwzAKmqYdisXiW9WZwhA7Ho+qMwAAgAjzPO+1aZrXqnNEked5GdM0L752Pa8mAQAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAgCIUMQAA8OS4rpvt9/vPO51OdjqdpoQQYrFYJHVdL+Ryubzv+zHHcc51XS84jnOuOu/HcKArAAB4sgaDwZuH6+FwmHZd96rdbt8IIcRoNMpIKV+dnES37kQ3GQAAwHt6vd6L8Xic0TTtkM1m31mWFTQajQvbtu+klPHZbJZer9eni8Xi1Pf9eBAEccMw8t1u96rVaknV+T+EIgYAAB5v+h9eil8uE6Hu+W0+ED/810/+THyz2SQmk0l6t9tdHg4HUSqV8pZlBQ/3Xde93m63Sdu275rNphRCiEQiYe33+8tQs4aMIgYAACJvtVola7XabSqVuhdCiGq1eqs6UxgoYgAA4PE+M7nCl+GrSQAAEHmVSsWfz+dnvu/HpJTPlsvlmepMYWAiBgAAIq9cLgf1ev3GMIyCpmmHYrH4VnWmMMSOx6PqDAAAIMI8z3ttmua16hxR5HlexjTNi69dz6tJAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAPDmu62b7/f7zTqeTnU6nKSGEWCwWSV3XC7lcLu/7fsxxnHNd1wuO45yrzvsxHOgKAACerMFg8Obhejgcpl3XvWq32zdCCDEajTJSylcnJ9GtO9FNBgAA8J5er/diPB5nNE07ZLPZd5ZlBY1G48K27TspZXw2m6XX6/XpYrE49X0/HgRB3DCMfLfbvWq1WlJ1/g+hiAEAgEf7y+1fvvw7+XeJMPfU/4Ue/NW//qtP/kx8s9kkJpNJerfbXR4OB1EqlfKWZQUP913Xvd5ut0nbtu+azaYUQohEImHt9/vLMLOGjSIGAAAib7VaJWu12m0qlboXQohqtXqrOlMYKGIAAODRPje5wpfhq0kAABB5lUrFn8/nZ77vx6SUz5bL5ZnqTGFgIgYAACKvXC4H9Xr9xjCMgqZph2Kx+FZ1pjDEjsej6gwAACDCPM97bZrmteocUeR5XsY0zYuvXc+rSQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAADw5rutm+/3+806nk51OpykhhFgsFkld1wu5XC7v+37McZxzXdcLjuOcq877MRzoCgAAnqzBYPDm4Xo4HKZd171qt9s3QggxGo0yUspXJyfRrTvRTQYAAPCeXq/3YjweZzRNO2Sz2XeWZQWNRuPCtu07KWV8Npul1+v16WKxOPV9Px4EQdwwjHy3271qtVpSdf4PoYgBAIBHe/Mf/9PL//u3f5sIc89//md/FmT/y3/+5M/EN5tNYjKZpHe73eXhcBClUilvWVbwcN913evtdpu0bfuu2WxKIYRIJBLWfr+/DDNr2ChiAAAg8larVbJWq92mUql7IYSoVqu3qjOFgSIGAAAe7XOTK3wZvpoEAACRV6lU/Pl8fub7fkxK+Wy5XJ6pzhQGJmIAACDyyuVyUK/XbwzDKGiadigWi29VZwpD7Hg8qs4AAAAizPO816ZpXqvOEUWe52VM07z42vW8mgQAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAMCT47putt/vP+90OtnpdJoSQojFYpHUdb2Qy+Xyvu/HHMc513W94DjOueq8H8OBrgAA4MkaDAZvHq6Hw2Hadd2rdrt9I4QQo9EoI6V8dXIS3boT3WQAAADv6fV6L8bjcUbTtEM2m31nWVbQaDQubNu+k1LGZ7NZer1eny4Wi1Pf9+NBEMQNw8h3u92rVqslVef/EIoYAAB4tP85/PHlzf/xE2Humf6XyeDf/LvvP/kz8c1mk5hMJundbnd5OBxEqVTKW5YVPNx3Xfd6u90mbdu+azabUgghEomEtd/vL8PMGjaKGAAAiLzVapWs1Wq3qVTqXgghqtXqrepMYaCIAQCAR/vc5Apfhq8mAQBA5FUqFX8+n5/5vh+TUj5bLpdnqjOFgYkYAACIvHK5HNTr9RvDMAqaph2KxeJb1ZnCEDsej6ozAACACPM877Vpmteqc0SR53kZ0zQvvnY9ryYBAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAABAEYoYAACAIhQxAADw5Lium+33+887nU52Op2mhBBisVgkdV0v5HK5vO/7McdxznVdLziOc64678dwoCsAAHiyBoPBm4fr4XCYdl33qt1u3wghxGg0ykgpX52cRLfuRDcZAADAe3q93ovxeJzRNO2QzWbfWZYVNBqNC9u276SU8dlsll6v16eLxeLU9/14EARxwzDy3W73qtVqSdX5P4QiBgAAHu1//LfBy+v//b8SYe6Zefmvgn/77zuf/Jn4ZrNJTCaT9G63uzwcDqJUKuUtywoe7ruue73dbpO2bd81m00phBCJRMLa7/eXYWYNG0UMAABE3mq1StZqtdtUKnUvhBDVavVWdaYwUMQAAMCjfW5yhS/DV5MAACDyKpWKP5/Pz3zfj0kpny2XyzPVmcLARAwAAEReuVwO6vX6jWEYBU3TDsVi8a3qTGGIHY9H1RkAAECEeZ732jTNa9U5osjzvIxpmhdfu55XkwAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAHhyXNfN9vv9551OJzudTlNCCLFYLJK6rhdyuVze9/2Y4zjnuq4XHMc5V533YzjQFQAAPFmDweDNw/VwOEy7rnvVbrdvhBBiNBplpJSvTk6iW3eimwwAAOA9vV7vxXg8zmiadshms+8sywoajcaFbdt3Usr4bDZLr9fr08Vicer7fjwIgrhhGPlut3vVarWk6vwfQhEDAACPdvP7n14efn6bCHPPf/bimyD9u+8++TPxzWaTmEwm6d1ud3k4HESpVMpblhU83Hdd93q73SZt275rNptSCCESiYS13+8vw8waNooYAACIvNVqlazVarepVOpeCCGq1eqt6kxhoIgBAIBH+9zkCl+GryYBAEDkVSoVfz6fn/m+H5NSPlsul2eqM4WBiRgAAIi8crkc1Ov1G8MwCpqmHYrF4lvVmcIQOx6PqjMAAIAI8zzvtWma16pzRJHneRnTNC++dj2vJgEAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAPDkuK6b7ff7zzudTnY6naaEEGKxWCR1XS/kcrm87/sxx3HOdV0vOI5zrjrvx3CgKwAAeLIGg8Gbh+vhcJh2Xfeq3W7fCCHEaDTKSClfnZxEt+5ENxkAAMB7er3ei/F4nNE07ZDNZt9ZlhU0Go0L27bvpJTx2WyWXq/Xp4vF4tT3/XgQBHHDMPLdbveq1WpJ1fk/hCIGAAAebTqdvvzll18SYe757bffBj/88MMnfya+2WwSk8kkvdvtLg+HgyiVSnnLsoKH+67rXm+326Rt23fNZlMKIUQikbD2+/1lmFnDRhEDAACRt1qtkrVa7TaVSt0LIUS1Wr1VnSkMFDEAAPBon5tc4cvw1SQAAIi8SqXiz+fzM9/3Y1LKZ8vl8kx1pjAwEQMAAJFXLpeDer1+YxhGQdO0Q7FYfKs6Uxhix+NRdQYAABBhnue9Nk3zWnWOKPI8L2Oa5sXXrufVJAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAJ4c13Wz/X7/eafTyU6n05QQQiwWi6Su64VcLpf3fT/mOM65rusFx3HOVef9GA50BQAAT9ZgMHjzcD0cDtOu61612+0bIYQYjUYZKeWrk5Po1p3oJgMAAHhPr9d7MR6PM5qmHbLZ7DvLsoJGo3Fh2/adlDI+m83S6/X6dLFYnPq+Hw+CIG4YRr7b7V61Wi2pOv+HUMQAAMCjXf7Ye/nW/ykR5p7fJL8L8t//9Sd/Jr7ZbBKTySS92+0uD4eDKJVKecuygof7ruteb7fbpG3bd81mUwohRCKRsPb7/WWYWcNGEQMAAJG3Wq2StVrtNpVK3QshRLVavVWdKQwUMQAA8Gifm1zhy/DVJAAAiLxKpeLP5/Mz3/djUspny+XyTHWmMDARAwAAkVcul4N6vX5jGEZB07RDsVh8qzpTGGLH41F1BgAAEGGe5702TfNadY4o8jwvY5rmxdeu59UkAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAnhzXdbP9fv95p9PJTqfTlBBCLBaLpK7rhVwul/d9P+Y4zrmu6wXHcc5V5/0YDnQFAABP1mAwePNwPRwO067rXrXb7RshhBiNRhkp5auTk+jWnegmAwAAeE+v13sxHo8zmqYdstnsO8uygkajcWHb9p2UMj6bzdLr9fp0sVic+r4fD4IgbhhGvtvtXrVaLak6/4dQxAAAwKN1fvz7l/u3/5gIc8/cN38SDL7/00/+THyz2SQmk0l6t9tdHg4HUSqV8pZlBQ/3Xde93m63Sdu275rNphRCiEQiYe33+8sws4aNIgYAACJvtVola7XabSqVuhdCiGq1eqs6UxgoYgAA4NE+N7nCl+GrSQAAEHmVSsWfz+dnvu/HpJTPlsvlmepMYWAiBgAAIq9cLgf1ev3GMIyCpmmHYrH4VnWmMMSOx6PqDAAAIMI8z3ttmua16hxR5HlexjTNi69dz6tJAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAPDmu62b7/f7zTqeTnU6nKSGEWCwWSV3XC7lcLu/7fsxxnHNd1wuO45yrzvsxHOgKAACerMFg8Obhejgcpl3XvWq32zdCCDEajTJSylcnJ9GtO9FNBgAA8J5er/diPB5nNE07ZLPZd5ZlBY1G48K27TspZXw2m6XX6/XpYrE49X0/HgRB3DCMfLfbvWq1WlJ1/g+hiAEAgEf7i997L3/6+R8SYe753YtU8De/Mz/5M/HNZpOYTCbp3W53eTgcRKlUyluWFTzcd133ervdJm3bvms2m1IIIRKJhLXf7y/DzBo2ihgAAIi81WqVrNVqt6lU6l4IIarV6q3qTGGgiAEAgEf73OQKX4avJgEAQORVKhV/Pp+f+b4fk1I+Wy6XZ6ozhYGJGAAAiLxyuRzU6/UbwzAKmqYdisXiW9WZwhA7Ho+qMwAAgAjzPO+1aZrXqnNEked5GdM0L752Pa8mAQAAFKGIAQAAKEIRAwAAUIQiBgAAPuf+/v4+pjpE1Py/Z3L/x+xBEQMAAJ/zh19//fWUMvb/3d/fx3799ddTIcQf/ph9OL4CAAB80m+//fbnP//883//+eefDcEQ58G9EOIPv/3225//MZtwfAUAAIAitFoAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABQ5J8ABLUSWGMCQnEAAAAASUVORK5CYII=\n", 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\n", 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\n", 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29rJhwwb6+voYGRmhtbWVQqGwv3358uXs2LGD9vZ2lixZAkB9fT19fX0VrbXSqh7EIuJ44I3A+8sW/++IaKF4afLeQ9okSZIOsn37dhYvXszMmTMBWLRoUeaKKqPqQSyl9BvglEOWvbPax5UkSZU3Xs+VnhtH1pckSTVv4cKFbNq0ieHhYQYHB9m8eXPukipisu4RkyRJet5aW1vp6OigubmZhoYGFixYkLukioiUan9kiLa2ttTT05O7DEmSjkr9/f00NjbmLqMmjXZuIqI3pdQ2ke29NClJkpSJQUySJCkTg5gkSVImBjFJkqRMDGKSJEmZGMQkSZIyMYhJkqQpZ9WqVaxdu5aVK1eybds2oPgapKamJlpaWhgeHqazs5OmpiY6OzszV3t4DugqSZKmrNWrV++fXr9+PV1dXSxduhSA7u5uBgYGqKury1XeuAxikiRpSlizZg3r1q2joaGBuXPnUigUWLZsGe3t7ezZs4eNGzeydetWtmzZwuDgIENDQxQKBbq6uujo6Mhd/qgMYpIkacK2b/w5j9w/VNF9vmRuPa972yvGXKe3t5cNGzbQ19fHyMgIra2tFAqF/e3Lly9nx44dtLe3s2TJEgDq6+vp6+uraK2VZhCTJEk1b/v27SxevJiZM2cCsGjRoswVVYZBTJIkTdh4PVd6bnxqUpIk1byFCxeyadMmhoeHGRwcZPPmzblLqgh7xCRJUs1rbW2lo6OD5uZmGhoaWLBgQe6SKiJSSrlrGFdbW1vq6enJXYYkSUel/v5+Ghsbc5dRk0Y7NxHRm1Jqm8j2XpqUJEnKxCAmSZKUiUFMkiQpE4OYJElSJgYxSZKkTAxikiRJmRjEJEnSlLNq1SrWrl3LypUr2bZtG1B8DVJTUxMtLS0MDw/T2dlJU1MTnZ2dmas9PAd0lSRJU9bq1av3T69fv56uri6WLl0KQHd3NwMDA9TV1eUqb1wGMUmSNCWsWbOGdevW0dDQwNy5cykUCixbtoz29nb27NnDxo0b2bp1K1u2bGFwcJChoSEKhQJdXV10dHTkLn9UBjFJkjRhN325m4fvu6ei+2x46cu4aNmKMdfp7e1lw4YN9PX1MTIyQmtrK4VCYX/78uXL2bFjB+3t7SxZsgSA+vp6+vr6KlprpRnEJElSzdu+fTuLFy9m5syZACxatChzRZVhEJMkSRM2Xs+VnhufmpQkSTVv4cKFbNq0ieHhYQYHB9m8eXPukirCHjFJklTzWltb6ejooLm5mYaGBhYsWJC7pIqIlFLuGsbV1taWenp6cpchSdJRqb+/n8bGxtxl1KTRzk1E9KaU2iayvZcmJUmSMjGISZIkZWIQkyRJysQgJkmSlIlBTJIkKRODmCRJUiYGMUmSNOWsWrWKtWvXsnLlSrZt2wYUX4PU1NRES0sLw8PDdHZ20tTURGdnZ+ZqD88BXSVJ0pS1evXq/dPr16+nq6uLpUuXAtDd3c3AwAB1dXW5yhuXQUySJE0Ja9asYd26dTQ0NDB37lwKhQLLli2jvb2dPXv2sHHjRrZu3cqWLVsYHBxkaGiIQqFAV1cXHR0ducsflUFMkiRN2J7Nd/PbB35T0X0ee8bxnPSm3xlznd7eXjZs2EBfXx8jIyO0trZSKBT2ty9fvpwdO3bQ3t7OkiVLAKivr6evr6+itVaaQUySJNW87du3s3jxYmbOnAnAokWLMldUGQYxSZI0YeP1XOm5qfpTkxFxb0T8NCL6IqKntGxWRHwrIn5R+j652nVIkqSpa+HChWzatInh4WEGBwfZvHlz7pIqYrKGr7gopdRS9ibyjwH/mlI6G/jX0rwkSdKoWltb6ejooLm5mUsvvZQFCxbkLqkiIqVU3QNE3Au0pZQeKVt2J/D6lNKDEXE68J2U0jmH20dbW1vq6empap2SJGl0/f39NDY25i6jJo12biKit6zzaUyT0SOWgH+JiN6IWFFadmpK6cHS9K+BUyehDkmSpJoyGTfrvzaltCsiGoBvRcQd5Y0ppRQRz+qWK4W2FQBnnXXWJJQpSZI0uareI5ZS2lX6fhi4Hng18FDpkiSl74dH2a47pdSWUmqbPXt2tcuUJEmadFUNYhFxfEScsG8a+M/AbcCNwLtLq70buKGadUiSJNWial+aPBW4PiL2HeurKaVvRsSPgI0R8d+A+4C3VbkOSZKkmlPVIJZSugdoHmX5o8DF1Ty2JElSrZusccQkSZIqZtWqVaxdu5aVK1eybds2oPgapKamJlpaWhgeHqazs5OmpiY6OzszV3t4vuJIkiRNWatXr94/vX79erq6uli6dCkA3d3dDAwMUFdXl6u8cRnEJEnSlLBmzRrWrVtHQ0MDc+fOpVAosGzZMtrb29mzZw8bN25k69atbNmyhcHBQYaGhigUCnR1ddHR0ZG7/FEZxCRJ0oRt2bKFX//61xXd52mnncall1465jq9vb1s2LCBvr4+RkZGaG1tpVAo7G9fvnw5O3bsoL29nSVLlgBQX19PX19fRWutNIOYJEmqedu3b2fx4sXMnDkTgEWLFmWuqDIMYpIkacLG67nSc+NTk5IkqeYtXLiQTZs2MTw8zODgIJs3b85dUkXYIyZJkmpea2srHR0dNDc309DQwIIFC3KXVBGR0rPet11z2traUk9PT+4yJEk6KvX399PY2Ji7jJo02rmJiN6UUttEtvfSpCRJUiYGMUmSpEwMYpIkSZkYxCRJkjIxiEmSJGViEJMkScrEICZJkqacVatWsXbtWlauXMm2bduA4muQmpqaaGlpYXh4mM7OTpqamujs7Mxc7eE5oKskSZqyVq9evX96/fr1dHV1sXTpUgC6u7sZGBigrq4uV3njMohJkqQpYc2aNaxbt46Ghgbmzp1LoVBg2bJltLe3s2fPHjZu3MjWrVvZsmULg4ODDA0NUSgU6OrqoqOjI3f5ozKISZKkCfv5z/+KwaH+iu7zhPpGXvGKvxhznd7eXjZs2EBfXx8jIyO0trZSKBT2ty9fvpwdO3bQ3t7OkiVLAKivr6evr6+itVaaQUySJNW87du3s3jxYmbOnAnAokWLMldUGQYxSZI0YeP1XOm58alJSZJU8xYuXMimTZsYHh5mcHCQzZs35y6pIuwRkyRJNa+1tZWOjg6am5tpaGhgwYIFuUuqiEgp5a5hXG1tbamnpyd3GZIkHZX6+/tpbGzMXUZNGu3cRERvSqltItt7aVKSJCkTg5gkSVImBjFJkqRMDGKSJEmZGMQkSZIyMYhJkiRlYhCTJElTzqpVq1i7di0rV65k27ZtQPE1SE1NTbS0tDA8PExnZydNTU10dnZmrvbwHNBVkiRNWatXr94/vX79erq6uli6dCkA3d3dDAwMUFdXl6u8cRnEJEnSlLBmzRrWrVtHQ0MDc+fOpVAosGzZMtrb29mzZw8bN25k69atbNmyhcHBQYaGhigUCnR1ddHR0ZG7/FEZxCRJ0oT9xS92ctvQcEX3eV79i/irs+eMuU5vby8bNmygr6+PkZERWltbKRQK+9uXL1/Ojh07aG9vZ8mSJQDU19fT19dX0VorzSAmSZJq3vbt21m8eDEzZ84EYNGiRZkrqgyDmCRJmrDxeq703PjUpCRJqnkLFy5k06ZNDA8PMzg4yObNm3OXVBH2iEmSpJrX2tpKR0cHzc3NNDQ0sGDBgtwlVUSklHLXMK62trbU09OTuwxJko5K/f39NDY25i6jJo12biKiN6XUNpHtvTQpSZKUiUFMkiQpE4OYJElSJgYxSZI0rqlwT/lkq8Q5MYhJkqQxzZgxg0cffdQwVialxKOPPsqMGTOOaD8OXyFJksY0Z84cdu7cye7du3OXUlNmzJjBnDlHNsBt1YJYRMwFvgKcCiSgO6X02YhYBbwP2Pd38+MppW9Uqw5JknRkpk+fzvz583OX8YJUzR6xEeB/pJRujYgTgN6I+Fap7dMppbVVPLYkSVLNq1oQSyk9CDxYmh6MiH7gzGodT5IkaaqZlJv1I2Ie8CrgB6VFH4yIn0TENRFx8mTUIEmSVGuqHsQioh64DviTlNLjwOeB3wFaKPaYffIw262IiJ6I6PHmQEmS9EJU1SAWEdMphrD1KaV/BEgpPZRSeialtBf4IvDq0bZNKXWnlNpSSm2zZ8+uZpmSJElZVC2IRUQAVwP9KaVPlS0/vWy1xcBt1apBkiSpllXzqckLgXcCP42IvtKyjwNvj4gWikNa3Au8v4o1SJIk1axqPjW5A4hRmhwzTJIkCV9xJEmSlI1BTJIkKRODmCRJUiYGMUmSpEwMYpIkSZkYxCRJkjIxiEmSJGViEJMkScrEICZJkpSJQUySJCkTg5gkSVImBjFJkqRMDGKSJEmZGMQkSZIyMYhJkiRlYhCTJEnKxCAmSZKUiUFMkiQpE4OYJElSJgYxSZKkTAxikiRJmRjEJEmSMjGISZIkZWIQkyRJysQgJkmSlIlBTJIkKRODmCRJUiYGMUmSpEwMYpIkSZkYxCRJkjIxiEmSJGViEJMkScrEICZJkpTJtNwF1IrLNn+JB457ce4yJElSFZ3x1GNseNPy3GXsZxCTVCVpAutE1auQpFpmECv5gy2P8/QTuauQpopU/KRDp0ti31/i4OmDtk0H5tMh20X5+oY1SZUzfebj8KbcVRxgEJM0hvKwNEZwIuCYiYan8oC2b9Eh4Wzv3rK2KPsuP4YkTX0GsZJ3fe7DuUuQ8hn5LTz6C3jodnjottL37TD44IF1jm+AU5tKn/OK37PPgWnHVb6eJwbggX+HXbfCA7fCzh74zcPFtrpji8c/s1D6tMIpZ8MxPnskaeqJlCZyH0debW1tqaenJ3cZ0tSXEgw9fHDYeuh22H0H7H26uE7dscWAtS9s7fuub8hb9+O7YFdv6XNrMaj9dqjYftyJcEbLgXB2RiuceMaB3jRJmkQR0ZtSapvIuvaISS9UTz9ZDFj7A1cpfD3xyIF1TjyzGLLOfsOBwHXKy6Fuer66RxMBL55T/LzyzcVle5+BR35RDGYP3Fr8/t7fHQiU9acd6DE7sxXOeBW86OR8v0GSRmEQk6a6fb1Fh15WfOQXkJ4prjPtRdDQCOdcWgxcp50HDa+EmbPy1n4kjqmDhnOLn1e9o7js6SeL52Bfr9muXrjznw9sc8rLD/SYnVmA086H6TPy1C9JGMSkqeW3v4GH+w+5tHgbPPnYgXVOemkxbDUuOnBpcdb8YnB5oZs+A+a0FT/7DO8pXsZ84NZiOLvnu/CTfyi2HTOtdL9Z64HLmi95xdFxriTVBO8Rk2rR3r2w575nX1YcuIf9jyweW19283wTnHp+sddrxolZS58SHn/gQI/Zrt5iUHvq8WLbsfVwesvB4ezFc7zfTNKEeY+YNJU8+Rg89LODe7ke/tmBG9EJOOV3ipcTmy87ELxefJZPCj5fJ55R/DS2F+f37oVH7zpwr9muXvjBVfDMb4vtxzeUBbPW4qXNqXxZV1LNMIhJk2XvM8UerUMvK+751YF1ZpxUvFTW8o5i8Dq1CWafC8cen6/uo8Exx8DsVxQ/zZcVl408Vfx7VH6/2c+3sr9H8uT5ZUNoFOD0C2D6i7L9BElTk0FMqoYnBsoCV+n74X4YebLYHnXFe5HmvBoK7znwxKJDLtSOaccdeOJynycfhwf7DvSa/eoWuO3aYlvUwamvPHgIjdnnQp3/mZV0eNnuEYuIS4DPAnXAl1JKf3O4dSflHrFtq4pPXEnPV3oGBn5ZGgj1gQPLj5/97DG5qjUQqibf4K8Pud/s1gMPT0yfWXa/WenS5kkvNWxLL3A1f49YRNQBVwJvBHYCP4qIG1NKP8tRDwA/vbb4p13p+QrgpLPgZf/XwSPQ5xwIVdV3wmlw7h8UP1AcTmTgnoMHn/3hF+GZp4rtM085uNfszFY4/iX56peUVa4+81cDd6WU7gGIiA3Am4FsQeymk/9vHr7vnlyH1wvFQ6UPu0qff8lajnILoACptTj0yFND8Ogg3PU4PP3PwD8VV5s2A449AY6rLw6pIalqGs5o4KIPfyJ3Gfvl+jf+TOD+svmdwO+WrxARK4AVAGedddbkVSZJlRZRDFnH1QOnFZftfab4ZOxTQ/DUYPHzxO6sZUpHhWfuy13BQWr2j17g+VPQAAANR0lEQVQppW6gG4r3iFX7eBctW1HtQ0jS2J4YgKeHc1chvbDVWK9zrmp2AXPL5ueUlknS0cuxyaSjTq7RIH8EnB0R8yPiWOAy4MZMtUiSJGWRpUcspTQSER8EtlIcvuKalNLtOWqRJEnKJduF0pTSN4Bv5Dq+JElSbr6oTpIkKRODmCRJUiYGMUmSpEwMYpIkSZkYxCRJkjIxiEmSJGViEJMkScrEICZJkpSJQUySJCkTg5gkSVImBjFJkqRMDGKSJEmZGMQkSZIyMYhJkiRlYhCTJEnKxCAmSZKUiUFMkiQpE4OYJElSJgYxSZKkTAxikiRJmRjEJEmSMjGISZIkZWIQkyRJysQgJkmSlIlBTJIkKRODmCRJUiYGMUmSpEwMYpIkSZkYxCRJkjIxiEmSJGViEJMkScrEICZJkpSJQUySJCkTg5gkSVImBjFJkqRMDGKSJEmZGMQkSZIyMYhJkiRlYhCTJEnKxCAmSZKUiUFMkiQpE4OYJElSJgYxSZKkTAxikiRJmRjEJEmSMqlKEIuI/zci7oiIn0TE9RFxUmn5vIgYjoi+0ueqahxfkiRpKqhWj9i3gPNSShcAPwe6ytruTim1lD6XV+n4kiRJNa8qQSyl9C8ppZHS7PeBOdU4jiRJ0lQ2GfeIvRfYUjY/PyL+PSK+GxGvm4TjS5Ik1aRpz3fDiNgGnDZK05+llG4orfNnwAiwvtT2IHBWSunRiCgAmyKiKaX0+Cj7XwGsADjrrLOeb5mSJEk163kHsZTSG8Zqj4hlQDtwcUoplbZ5CniqNN0bEXcDrwB6Rtl/N9AN0NbWlp5vnZIkSbWqWk9NXgL8KbAopfRE2fLZEVFXmn4ZcDZwTzVqkCRJqnXPu0dsHH8HHAd8KyIAvl96QnIhsDoingb2ApenlAaqVIMkSVJNq0oQSym9/DDLrwOuq8YxJUmSphpH1pckScrEICZJkpSJQUySJCkTg5gkSVImBjFJkqRMDGKSJEmZGMQkSZIyMYhJkiRlYhCTJEnKxCAmSZKUiUFMkiQpE4OYJElSJgYxSZKkTAxikiRJmRjEJEmSMjGISZIkZWIQkyRJysQgJkmSlIlBTJIkKRODmCRJUiYGMUmSpEwMYpIkSZkYxCRJkjIxiEmSJGViEJMkScrEICZJkpSJQUySJCkTg5gkSVImBjFJkqRMDGKSJEmZGMQkSZIyMYhJkiRlYhCTJEnKxCAmSZKUiUFMkiQpE4OYJElSJgYxSZKkTAxikiRJmRjEJEmSMjGISZIkZWIQkyRJysQgJkmSlIlBTJIkKRODmCRJUiYGMUmSpEyqFsQiYlVE7IqIvtLnD8rauiLiroi4MyJ+v1o1SJIk1bJpVd7/p1NKa8sXRMQrgcuAJuAMYFtEvCKl9EyVa5EkSaopOS5NvhnYkFJ6KqX0S+Au4NUZ6pAkScqq2kHsgxHxk4i4JiJOLi07E7i/bJ2dpWWSJElHlSMKYhGxLSJuG+XzZuDzwO8ALcCDwCef475XRERPRPTs3r37SMqUJEmqSUd0j1hK6Q0TWS8ivgj8U2l2FzC3rHlOadmh++4GugHa2trSkdQpSZJUi6r51OTpZbOLgdtK0zcCl0XEcRExHzgb+GG16pAkSapV1Xxq8n9HRAuQgHuB9wOklG6PiI3Az4AR4I99YlKSJB2NqhbEUkrvHKNtDbCmWseWJEmaChxZX5IkKRODmCRJUiYGMUmSpEwMYpIkSZkYxCRJkjIxiEmSJGViEJMkScrEICZJkpSJQUySJCkTg5gkSVImBjFJkqRMDGKSJEmZGMQkSZIyMYhJkiRlYhCTJEnKxCAmSZKUiUFMkiQpE4OYJElSJgYxSZKkTAxikiRJmRjEJEmSMjGISZIkZWIQkyRJysQgJkmSlIlBTJIkKRODmCRJUiYGMUmSpEwMYpIkSZkYxCRJkjIxiEmSJGViEJMkScrEICZJkpSJQUySJCkTg5gkSVImBjFJkqRMDGKSJEmZGMQkSZIyMYhJkiRlYhCTJEnKxCAmSZKUiUFMkiQpE4OYJElSJgYxSZKkTAxikiRJmRjEJEmSMjGISZIkZTKtGjuNiH8AzinNngTsSSm1RMQ8oB+4s9T2/ZTS5dWoQZIkqdZVJYillDr2TUfEJ4HHyprvTim1VOO4kiRJU0lVgtg+ERHA24D/VM3jSJIkTUXVvkfsdcBDKaVflC2bHxH/HhHfjYjXHW7DiFgRET0R0bN79+4qlylJkjT5nnePWERsA04bpenPUko3lKbfDnytrO1B4KyU0qMRUQA2RURTSunxQ3eSUuoGugHa2trS861TkiSpVj3vIJZSesNY7RExDXgrUCjb5ingqdJ0b0TcDbwC6Hm+dUiSJE1V1bw0+QbgjpTSzn0LImJ2RNSVpl8GnA3cU8UaJEmSalY1b9a/jIMvSwIsBFZHxNPAXuDylNJAFWuQJEmqWVULYimlZaMsuw64rlrHlCRJmkocWV+SJCkTg5gkSVImBjFJkqRMDGKSJEmZGMQkSZIyMYhJkiRlYhCTJEnKxCAmSZKUiUFMkiQpE4OYJElSJgYxSZKkTAxikiRJmRjEJEmSMjGISZIkZWIQkyRJysQgJkmSlIlBTJIkKRODmCRJUiYGMUmSpEwMYpIkSZkYxCRJkjIxiEmSJGViEJMkScrEICZJkpSJQUySJCkTg5gkSVImBjFJkqRMDGKSJEmZGMQkSZIyMYhJkiRlYhCTJEnKxCAmSZKUiUFMkiQpE4OYJElSJgYxSZKkTAxikiRJmRjEJEmSMjGISZIkZWIQkyRJysQgJkmSlIlBTJIkKRODmCRJUiYGMUmSpEwMYpIkSZkYxCRJkjI5oiAWEX8YEbdHxN6IaDukrSsi7oqIOyPi98uWX1JadldEfOxIji9JkjSVHWmP2G3AW4GbyxdGxCuBy4Am4BLgcxFRFxF1wJXApcArgbeX1pUkSTrqTDuSjVNK/QARcWjTm4ENKaWngF9GxF3Aq0ttd6WU7iltt6G07s+OpA5JkqSpqFr3iJ0J3F82v7O07HDLJUmSjjrj9ohFxDbgtFGa/iyldEPlS9p/3BXAitLsUETcWa1jlXkJ8MgkHGeq8vyMz3M0Ns/P+DxHY/P8jM9zNLbJOD8vneiK4waxlNIbnkcBu4C5ZfNzSssYY/mhx+0Gup/HsZ+3iOhJKbWNv+bRyfMzPs/R2Dw/4/Mcjc3zMz7P0dhq7fxU69LkjcBlEXFcRMwHzgZ+CPwIODsi5kfEsRRv6L+xSjVIkiTVtCO6WT8iFgN/C8wG/jki+lJKv59Suj0iNlK8CX8E+OOU0jOlbT4IbAXqgGtSSrcf0S+QJEmaoo70qcnrgesP07YGWDPK8m8A3ziS41bRpF4KnYI8P+PzHI3N8zM+z9HYPD/j8xyNrabOT6SUctcgSZJ0VPIVR5IkSZkYxPC1S+OJiGsi4uGIuC13LbUoIuZGxE0R8bPSK78+lLumWhMRMyLihxHx49I5+p+5a6pFpTeQ/HtE/FPuWmpRRNwbET+NiL6I6MldT62JiJMi4tqIuCMi+iPi93LXVEsi4pzSPzv7Po9HxJ9kr+tovzRZeu3Sz4E3Uhxg9kfA21NKjvZfEhELgSHgKyml83LXU2si4nTg9JTSrRFxAtALvMV/hg6I4us3jk8pDUXEdGAH8KGU0vczl1ZTIuLDQBtwYkqpPXc9tSYi7gXaUkqOkTWKiFgHbE8pfak0MsHMlNKe3HXVotL/+3cBv5tSui9nLfaIFV+9dFdK6Z6U0m+Bfa9dUklK6WZgIHcdtSql9GBK6dbS9CDQj2+MOEgqGirNTi99ju4/BR4iIuYA/wX4Uu5aNPVExIuBhcDVACml3xrCxnQxcHfuEAYGMfC1S6qgiJgHvAr4Qd5Kak/pslsf8DDwrZSS5+hgnwH+FNibu5AaloB/iYje0ttXdMB8YDfw96XL21+KiONzF1XDLgO+lrsIMIhJFRMR9cB1wJ+klB7PXU+tSSk9k1JqofhGjVdHhJe5SyKiHXg4pdSbu5Ya99qUUitwKfDHpdsmVDQNaAU+n1J6FfAbwHueR1G6bLsI+HruWsAgBmO/jkmakNJ9T9cB61NK/5i7nlpWulxyE3BJ7lpqyIXAotI9UBuA/xQR/ydvSbUnpbSr9P0wxTEsX523opqyE9hZ1tN8LcVgpme7FLg1pfRQ7kLAIAa+dklHqHQj+tVAf0rpU7nrqUURMTsiTipNv4jiwzF35K2qdqSUulJKc1JK8yj+N+jbKaWlmcuqKRFxfOlhGEqX3P4z4JPcJSmlXwP3R8Q5pUUXU3y7jZ7t7dTIZUk4wpH1XwhSSiO+dmlsEfE14PXASyJiJ/CXKaWr81ZVUy4E3gn8tHQPFMDHS2+RUNHpwLrSk0rHABtTSg7RoOfiVOD64p97mAZ8NaX0zbwl1Zz/DqwvdSrcA7wncz01pxTi3wi8P3ct+xz1w1dIkiTl4qVJSZKkTAxikiRJmRjEJEmSMjGISZIkZWIQkyRJysQgJkmSlIlBTJIkKRODmCRJUib/P12nVuD92QF7AAAAAElFTkSuQmCC\n", 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\n", 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JyiFJku6DW173On60el1D93l6xyWc/5rXHHOdSqXC9PQ01WqVzc1NcrkcSZLsLB8aGmJ5eZlCoUB/fz8AbW1tVKvVhmZttAMvYiGEM4FnAa84YvbvhRC62Lo0ef29lkmSJN3D0tISfX19tLa2AtDb25tyosY48CIWY7wNeMi95r3koI8rSZIab6+RKx0f36wvSZKaXk9PD7Ozs9TrdWq1GnNzc2lHaogTdY+YJEnSTyyXy1EsFuns7CSTydDd3Z12pIYIMTb/myHy+XxcWVlJO4YkSSel1dVVOjo60o7RlHY7NyGESowxv5/tvTQpSZKUEouYJElSSixikiRJKbGISZIkpcQiJkmSlBKLmCRJUkosYpIk6dAZGRlhfHyc4eFhFhcXga3PIGWzWbq6uqjX65RKJbLZLKVSKeW0R+cLXSVJ0qE1Ojq6Mz01NUW5XGZgYACAiYkJ1tbWaGlpSSvenixikiTpUBgbG2NycpJMJkN7eztJkjA4OEihUGB9fZ2ZmRkWFhaYn5+nVquxsbFBkiSUy2WKxWLa8XdlEZMkSfu2NPMlvvONjYbu86HtbTztBY875jqVSoXp6Wmq1Sqbm5vkcjmSJNlZPjQ0xPLyMoVCgf7+fgDa2tqoVqsNzdpoFjFJktT0lpaW6Ovro7W1FYDe3t6UEzWGRUySJO3bXiNXOj4+NSlJkppeT08Ps7Oz1Ot1arUac3NzaUdqCEfEJElS08vlchSLRTo7O8lkMnR3d6cdqSFCjDHtDHvK5/NxZWUl7RiSJJ2UVldX6ejoSDtGU9rt3IQQKjHG/H6299KkJElSSixikiRJKbGISZIkpcQiJkmSlBKLmCRJUkosYpIkSSmxiEmSpENnZGSE8fFxhoeHWVxcBLY+g5TNZunq6qJer1Mqlchms5RKpZTTHp0vdJUkSYfW6OjozvTU1BTlcpmBgQEAJiYmWFtbo6WlJa14e7KISZKkQ2FsbIzJyUkymQzt7e0kScLg4CCFQoH19XVmZmZYWFhgfn6eWq3GxsYGSZJQLpcpFotpx9+VRUySJO3bR/5sgm/f8LWG7jPz6MfwjMErj7lOpVJhenqaarXK5uYmuVyOJEl2lg8NDbG8vEyhUKC/vx+AtrY2qtVqQ7M2mkVMkiQ1vaWlJfr6+mhtbQWgt7c35USNYRGTJEn7ttfIlY6PT01KkqSm19PTw+zsLPV6nVqtxtzcXNqRGsIRMUmS1PRyuRzFYpHOzk4ymQzd3d1pR2qIEGNMO8Oe8vl8XFlZSTuGJEknpdXVVTo6OtKO0ZR2OzchhEqMMb+f7b00KUmSlBKLmCRJUkosYpIkSSmxiEmSJKXEIiZJkpQSi5gkSVJKLGKSJOnQGRkZYXx8nOHhYRYXF4GtzyBls1m6urqo1+uUSiWy2SylUinltEfnC10lSdKhNTo6ujM9NTVFuVxmYGAAgImJCdbW1mhpaUkr3p4sYpIk6VAYGxtjcnKSTCZDe3s7SZIwODhIoVBgfX2dmZkZFhYWmJ+fp1arsbGxQZIklMtlisVi2vF3ZRGTJEn7tj73Ve745m0N3ecDHnEm51zx2GOuU6lUmJ6eplqtsrm5SS6XI0mSneVDQ0MsLy9TKBTo7+8HoK2tjWq12tCsjWYRkyRJTW9paYm+vj5aW1sB6O3tTTlRY1jEJEnSvu01cqXjc+BPTYYQrg8h/HMIoRpCWNmed24I4YMhhC9v//3gg84hSZIOr56eHmZnZ6nX69RqNebm5tKO1BAn6vUVz4gxdh3xJfJXAx+KMV4MfGj7tyRJ0q5yuRzFYpHOzk4uv/xyuru7047UECHGeLAHCOF6IB9j/M4R874IPD3GeHMI4eHAR2OMjz/aPvL5fFxZWTnQnJIkaXerq6t0dHSkHaMp7XZuQgiVIwafjulEjIhF4O9DCJUQwpXb8x4WY7x5e/oW4GEnIIckSVJTORE36/9sjPGmEEIG+GAI4bojF8YYYwjhXw3LbZe2KwEuuOCCExBTkiTpxDrwEbEY403bf38buBp4MvCt7UuSbP/97V22m4gx5mOM+fPOO++gY0qSJJ1wB1rEQghnhhDOunsa+HngWuD9wC9vr/bLwPsOMockSVIzOuhLkw8Drg4h3H2sd8cY/y6E8BlgJoTwn4AbgBcccA5JkqSmc6BFLMb4NaBzl/nfBS47yGNLkiQ1uxP1HjFJkqSGGRkZYXx8nOHhYRYXF4GtzyBls1m6urqo1+uUSiWy2SylUinltEfnJ44kSdKhNTo6ujM9NTVFuVxmYGAAgImJCdbW1mhpaUkr3p4sYpIk6VAYGxtjcnKSTCZDe3s7SZIwODhIoVBgfX2dmZkZFhYWmJ+fp1arsbGxQZIklMtlisVi2vF3ZRGTJEn7Nj8/zy233NLQfZ5//vlcfvnlx1ynUqkwPT1NtVplc3OTXC5HkiQ7y4eGhlheXqZQKNDf3w9AW1sb1Wq1oVkbzSImSZKa3tLSEn19fbS2tgLQ29ubcqLGsIhJkqR922vkSsfHpyYlSVLT6+npYXZ2lnq9Tq1WY25uLu1IDeGImCRJanq5XI5isUhnZyeZTIbu7u60IzVEiPFffW+76eTz+biyspJ2DEmSTkqrq6t0dHSkHaMp7XZuQgiVGGN+P9t7aVKSJCklFjFJkqSUWMQkSZJSYhGTJElKiUVMkiQpJRYxSZKklFjEJEnSoTMyMsL4+DjDw8MsLi4CW59BymazdHV1Ua/XKZVKZLNZSqVSymmPzhe6SpKkQ2t0dHRnempqinK5zMDAAAATExOsra3R0tKSVrw9WcQkSdKhMDY2xuTkJJlMhvb2dpIkYXBwkEKhwPr6OjMzMywsLDA/P0+tVmNjY4MkSSiXyxSLxbTj78oiJkmS9u1LX3ottY3Vhu7zrLYOHve43z7mOpVKhenpaarVKpubm+RyOZIk2Vk+NDTE8vIyhUKB/v5+ANra2qhWqw3N2mgWMUmS1PSWlpbo6+ujtbUVgN7e3pQTNYZFTJIk7dteI1c6Pj41KUmSml5PTw+zs7PU63VqtRpzc3NpR2oIR8QkSVLTy+VyFItFOjs7yWQydHd3px2pIUKMMe0Me8rn83FlZSXtGJIknZRWV1fp6OhIO0ZT2u3chBAqMcb8frb30qQkSVJKLGKSJEkpsYhJkiSlxCImSZKUEouYJElSSixikiRJKbGISZKkQ2dkZITx8XGGh4dZXFwEtj6DlM1m6erqol6vUyqVyGazlEqllNMenS90lSRJh9bo6OjO9NTUFOVymYGBAQAmJiZYW1ujpaUlrXh7sohJkqRDYWxsjMnJSTKZDO3t7SRJwuDgIIVCgfX1dWZmZlhYWGB+fp5arcbGxgZJklAulykWi2nH35VFTJIk7dtvf/lGrt2oN3SfT2x7IK+9+FHHXKdSqTA9PU21WmVzc5NcLkeSJDvLh4aGWF5eplAo0N/fD0BbWxvVarWhWRvNIiZJkpre0tISfX19tLa2AtDb25tyosawiEmSpH3ba+RKx8enJiVJUtPr6elhdnaWer1OrVZjbm4u7UgN4YiYJElqerlcjmKxSGdnJ5lMhu7u7rQjNUSIMaadYU/5fD6urKykHUOSpJPS6uoqHR0dacdoSrudmxBCJcaY38/2XpqUJElKiUVMkiQpJRYxSZKklFjEJEmSUmIRkyRJSolFTJIkKSUHVsRCCO0hhI+EEL4QQvh8COHXt+ePhBBuCiFUt/8856AySJKk+6eRkRHGx8cZHh5mcXER2PoMUjabpauri3q9TqlUIpvNUiqVUk57dAf5QtdN4L/HGD8bQjgLqIQQPri97I0xxvEDPLYkSToJjI6O7kxPTU1RLpcZGBgAYGJigrW1NVpaWtKKt6cDK2IxxpuBm7enayGEVeCRB3U8SZJ0/zY2Nsbk5CSZTIb29naSJGFwcJBCocD6+jozMzMsLCwwPz9PrVZjY2ODJEkol8sUi8W04+/qhHziKIRwIfBvgE8DTwV+LYTwUmCFrVGz752IHJIk6b753bnP84Vv/qCh+3zCI87md67IHnOdSqXC9PQ01WqVzc1NcrkcSZLsLB8aGmJ5eZlCoUB/fz8AbW1tVKvVhmZttAO/WT+E0Aa8F/ivMcYfAH8EPBboYmvE7PePst2VIYSVEMLKrbfeetAxJUlSE1taWqKvr4/W1lbOPvtsent7047UEAc6IhZCOI2tEjYVY/xrgBjjt45Y/sfAB3bbNsY4AUzA1rcmDzKnJEnan71GrnR8DvKpyQC8E1iNMf7BEfMffsRqfcC1B5VBkiTdP/T09DA7O0u9XqdWqzE3N5d2pIY4yBGxpwIvAf45hHD3BdrXAC8KIXQBEbgeeMUBZpAkSfcDuVyOYrFIZ2cnmUyG7u7utCM1RIix+a/65fP5uLKyknYMSZJOSqurq3R0dKQdoyntdm5CCJUYY34/2/tmfUmSpJRYxCRJklJiEZMkSUqJRUySJCklFjFJkqSUWMQkSZJSYhGTJEmHzsjICOPj4wwPD7O4uAhsfQYpm83S1dVFvV6nVCqRzWYplUoppz26E/LRb0mSpIMwOjq6Mz01NUW5XGZgYACAiYkJ1tbWaGlpSSvenixikiTpUBgbG2NycpJMJkN7eztJkjA4OEihUGB9fZ2ZmRkWFhaYn5+nVquxsbFBkiSUy2WKxWLa8XdlEZMkSfs3/2q45Z8bu8/zL4XLX3/MVSqVCtPT01SrVTY3N8nlciRJsrN8aGiI5eVlCoUC/f39ALS1tVGtVo+2y6ZgEZMkSU1vaWmJvr4+WltbAejt7U05UWNYxCRJ0v7tMXKl4+NTk5Ikqen19PQwOztLvV6nVqsxNzeXdqSGcERMkiQ1vVwuR7FYpLOzk0wmQ3d3d9qRGiLEGNPOsKd8Ph9XVlbSjiFJ0klpdXWVjo6OtGM0pd3OTQihEmPM72d7L01KkiSlxCImSZKUEouYJElSSixikiRJKbGISZIkpcQiJkmSlBKLmCRJOnRGRkYYHx9neHiYxcVFYOszSNlslq6uLur1OqVSiWw2S6lUSjnt0flCV0mSdGiNjo7uTE9NTVEulxkYGABgYmKCtbU1Wlpa0oq3J4uYJEk6FMbGxpicnCSTydDe3k6SJAwODlIoFFhfX2dmZoaFhQXm5+ep1WpsbGyQJAnlcplisZh2/F1ZxCRJ0r694R/ewHVr1zV0n5ecewlXPfmqY65TqVSYnp6mWq2yublJLpcjSZKd5UNDQywvL1MoFOjv7wegra2NarXa0KyNZhGTJElNb2lpib6+PlpbWwHo7e1NOVFjWMQkSdK+7TVypePjU5OSJKnp9fT0MDs7S71ep1arMTc3l3akhnBETJIkNb1cLkexWKSzs5NMJkN3d3fakRoixBjTzrCnfD4fV1ZW0o4hSdJJaXV1lY6OjrRjNKXdzk0IoRJjzO9ney9NSpIkpcQiJkmSlBKLmCRJUkosYpIkSSmxiEmSJKXEIiZJkpQSi5gkSTp0RkZGGB8fZ3h4mMXFRWDrM0jZbJauri7q9TqlUolsNkupVEo57dH5QldJknRojY6O7kxPTU1RLpcZGBgAYGJigrW1NVpaWtKKtyeLmCRJOhTGxsaYnJwkk8nQ3t5OkiQMDg5SKBRYX19nZmaGhYUF5ufnqdVqbGxskCQJ5XKZYrGYdvxdWcQkSdK+3fK61/Gj1esaus/TOy7h/Ne85pjrVCoVpqenqVarbG5uksvlSJJkZ/nQ0BDLy8sUCgX6+/sBaGtro1qtNjRro1nEJElS01taWqKvr4/W1lYAent7U07UGBaxFP3mf/1l4hkPSDsG3HY7r3vL/0k7hSTpENhr5ErHxyKWkn//h2+ncsYL0o4BQGyDH7zh9fyvq16ddhRJknbV09PD4OAg5XKZzc1N5ubmeMUrXpF2rPvMIpaC4d/4Nf7x7Ms55Ux47NnfSTsOX771PD7W8ri0Y0iSdFS5XI5isUhnZyeZTIbu7u60IzVEiDGmnWFP+Xw+rqyspB2jYQb/4A/56Lcv5qcfdgN/+RRgoQwvfR/8wx/DdR+Asx8JLQdwyfKODbjtVvi3/xku+x1oOQ2Ap73p3fzLzQ/iRa2f4PXDY40/riTpUFtdXaWjoyPtGE1pt3MTQqjEGPORCCtEAAAgAElEQVT72d4RsRR8+s6L4HT4qRs+DeEf4YKfgcc8HS76Oaj8b7jhkwdz4BAg+3x4/LPvMbvne//Mu8LPcs2DsgdzXEmStKvUilgI4dnAm4AW4E9ijK9PK8uJ9N/HXku9luOnHv4drj//8/wHToMf3Qp/lOy9cSN8qwIf/s17zGrJnMKD6t1889YH8Vuv/s/8j9e/5cRkkSTpJJdKEQshtABvBZ4F3Ah8JoTw/hjjF9LIcyJ9rPXxxNvgKd+5loXH3M7D7gxpR+JfHhB5Wu1L/M3mpVQf3pV2HEmSThppjYg9GfhKjPFrACGEaeC5QGpF7LI3/TmbB/zpzUjg1lvP4bzzbmMlfpyhpSu48PYz4PS2Az3uXr7SchO/d8ebeMAlE3z+e+fzc296V6p5JEnNZeQpjyd867tpx2iIFiIXP+yhacfYkVYReyTwjSN+3wj89JErhBCuBK4EuOCCCw480FdvfShx8+AfXAinwjPqX+CiLxXYPOMpfOW0uw78mHuH+je8/LQ7+OIZX+eajcdy/c0PTjuRJKmJbP64hTvubN7vNR6PcLBjLsetaW/WjzFOABOw9dTkQR/v+v/xnIM+BAAf//SHed0n57noAaNcsFHlinf9txNy3GN514v/hAc8oIdPto7yxd/+DGc88IFpR5IkNZHV1VU6HnVO2jHul9LqhTcB7Uf8ftT2vPu9d33mf/Jzn38a8ZTTyL1oX0+2HriOx97FHac/lMd/5VJG/+y/pB1HkqQ9jYyMMD4+zvDwMIuLi8DWZ5Cy2SxdXV3U63VKpRLZbJZSqZRy2qNLa0TsM8DFIYSL2CpgLwT+Q0pZAHjbu9/Aj+/aPNBjxLsiN5xyE113vYJzbvsij/zFVx3o8far86qX8I+/+gE66k/ngz/1R7zlXb5LTJL0/zz58Zfxre/enHaMe9j4YY0Y7uJXf33r/9Jvffdm/uSdf8yv/Nqr6H/Bv+cHP1znHe94B9d95Qu0tLTs5D8lnMJ55z4szej3kEoRizFuhhB+DVhg6/UVfxpj/HwaWe72f27/czZaDn6A8Iov5tk87Wye+ITbDvxY+3XqmQ/ksWfcyBfo5NRvn8/EA6fTjiRJaiJP4Cl8J66lHYN3/ME7eP9fvp9zH3ou5z/yfJ7Q+QRe8auv4ud+/uf4wfd/wOz73seHPvJh5j/0d9y2cRu33XYb/+7fPZOhXx/i8r7LATjtLjiPk7yIAcQY/xb427SOf2//tt7JXfz4YA9yV+Sn1p/BKfFmLvmvLzzYYx2nfOnfc93rP8+zvvJMHvaQz6QdR5LURM6Ip3H2XWcAcO3frvH9W+5o6P4fdP4DeOJzzj3mOp/7p2v5+6sX+PsPfYDNH2/ynGc+l9yTOjmNFh4YT6P/xQNc++nPcdmznsEvXrFVui656El88MN/s7WD7WfjQmp3Ze2uaW/WP9Ee99kXsXnqwd+kXn/gKXQ98PO0tDTX0ydnPfYC2uvv4Ya2HD/1Od8lJkn6f07rPIvT6+cB0LJZ55S7GvsMXcvmA3f2fzSfXVrlF3/+eZwTLoBT4dmXXcGpd55Jy+YZnHbH2ZxeP+8e01vCv9rvKXfd2dDs95VFbNtjw5f58R0H//qK004LPHnk5Qd+nJ/EU0u/yOlv/gB3NcEbNSRJzePU+LOcftcPAfjpyw7oHVzb+z96hjtoiXfu5GiJd3JqvJNT2OS0u+7g9Lt+eI9pgEDcmb7bKaek/yL1I1nEtj3zT9J/jUTaHtz5eJ71zsenHUOS1GRWV1d50EXnp5rhF/oKDA4O8rtvGGNzc5O/v+bDvOIVr+ABbQ+k9WHn8KCLzr/HNAAhpJ57LxYxSZLU9HK5HMVikc7OTjKZDN3d3WlHaogQ48Ffjruv8vl8XFlZSTuGJEknpdXVVTo6OtKO0ZR2OzchhEqMcV8vC22uRwckSZJOIhYxSZKklFjEJEmSUmIRkyRJSolFTJIkKSUWMUmSpJRYxCRJ0qEzMjLC+Pg4w8PDLC4uArC0tEQ2m6Wrq4t6vU6pVCKbzVIqlVJOe3S+0FWSJB1ao6OjO9NTU1OUy2UGBgYAmJiYYG1trem+73wki5gkSToUxsbGmJycJJPJ0N7eTpIkDA4OUigUWF9fZ2ZmhoWFBebn56nVamxsbJAkCeVymWKxmHb8XVnEJEnSvn3kzyb49g1fa+g+M49+DM8YvPKY61QqFaanp6lWq2xubpLL5UiSZGf50NAQy8vLFAoF+vv7AWhra6NarTY0a6NZxCRJUtNbWlqir6+P1tZWAHp7e1NO1BgWMUmStG97jVzp+PjUpCRJano9PT3Mzs5Sr9ep1WrMzc2lHakhHBGTJElNL5fLUSwW6ezsJJPJ0N3dnXakhggxxrQz7Cmfz8eVlZW0Y0iSdFJaXV2lo6Mj7RhNabdzE0KoxBjz+9neS5OSJEkpsYhJkiSlxCImSZKUEouYJElSSixikiRJKbGISZIkpcQiJkmSDp2RkRHGx8cZHh5mcXER2PoMUjabpauri3q9TqlUIpvNUiqVUk57dL7QVZIkHVqjo6M701NTU5TLZQYGBgCYmJhgbW2NlpaWtOLtySImSZIOhbGxMSYnJ8lkMrS3t5MkCYODgxQKBdbX15mZmWFhYYH5+XlqtRobGxskSUK5XKZYLKYdf1cWMUmStG/rc1/ljm/e1tB9PuARZ3LOFY895jqVSoXp6Wmq1Sqbm5vkcjmSJNlZPjQ0xPLyMoVCgf7+fgDa2tqoVqsNzdpoFjFJktT0lpaW6Ovro7W1FYDe3t6UEzWGRUySJO3bXiNXOj4+NSlJkppeT08Ps7Oz1Ot1arUac3NzaUdqCEfEJElS08vlchSLRTo7O8lkMnR3d6cdqSFCjDHtDHvK5/NxZWUl7RiSJJ2UVldX6ejoSDtGU9rt3IQQKjHG/H6299KkJElSSixikiRJKbGISZIkpcQiJkmSlBKLmCRJUkosYpIkSSmxiEmSpENnZGSE8fFxhoeHWVxcBLY+g5TNZunq6qJer1Mqlchms5RKpZTTHp0vdJUkSYfW6OjozvTU1BTlcpmBgQEAJiYmWFtbo6WlJa14e7KISZKkQ2FsbIzJyUkymQzt7e0kScLg4CCFQoH19XVmZmZYWFhgfn6eWq3GxsYGSZJQLpcpFotpx9+VRUySJO3b/Pw8t9xyS0P3ef7553P55Zcfc51KpcL09DTVapXNzU1yuRxJkuwsHxoaYnl5mUKhQH9/PwBtbW1Uq9WGZm20A7lHLITwP0MI14UQPhdCuDqEcM72/AtDCPUQQnX7z9sP4viSJOn+ZWlpib6+PlpbWzn77LPp7e1NO1JDHNSI2AeBcoxxM4TwBqAMXLW97Ksxxq4DOq4kSTpAe41c6fgcyIhYjPHvY4yb2z8/BTzqII4jSZJODj09PczOzlKv16nVaszNzaUdqSFOxD1iLwP+8ojfF4UQ/hH4AfBbMcalE5BBkiQdYrlcjmKxSGdnJ5lMhu7u7rQjNUSIMf5kG4awCJy/y6LfjDG+b3ud3wTywPNjjDGEcDrQFmP8bgghAWaBbIzxB7vs/0rgSoALLrggueGGG36inJIk6b5ZXV2lo6Mj7RhNabdzE0KoxBjz+9n+Jx4RizE+81jLQwiDQAG4LG63vRjjj4AfbU9XQghfBR4HrOyy/wlgAiCfz/9kbVGSJKmJHdRTk88G/j+gN8b4wyPmnxdCaNmefgxwMfC1g8ggSZLU7A7qHrH/BZwOfDCEAPCpGOMrgR5gNIRwJ3AX8MoY49oBZZAkSWpqB1LEYow/dZT57wXeexDHlCRJOmz86LckSVJKLGKSJEkpsYhJkqRDZ2RkhPHxcYaHh1lcXAS2PoOUzWbp6uqiXq9TKpXIZrOUSqWU0x6dH/2WJEmH1ujo6M701NQU5XKZgYEBACYmJlhbW6OlpSWteHuyiEmSpENhbGyMyclJMpkM7e3tJEnC4OAghUKB9fV1ZmZmWFhYYH5+nlqtxsbGBkmSUC6XKRaLacfflUVMkiTt25e+9FpqG6sN3edZbR087nG/fcx1KpUK09PTVKtVNjc3yeVyJEmys3xoaIjl5WUKhQL9/f0AtLW1Ua1WG5q10SxikiSp6S0tLdHX10draysAvb29KSdqDIuYJEnat71GrnR8fGpSkiQ1vZ6eHmZnZ6nX69RqNebm5tKO1BCOiEmSpKaXy+UoFot0dnaSyWTo7u5OO1JDhBhj2hn2lM/n48rKStoxJEk6Ka2urtLR0ZF2jKa027kJIVRijPn9bO+lSUmSpJRYxCRJklJiEZMkSUqJRUySJCklFjFJkqSUWMQkSZJSYhGTJEmHzsjICOPj4wwPD7O4uAhsfQYpm83S1dVFvV6nVCqRzWYplUoppz06X+gqSZIOrdHR0Z3pqakpyuUyAwMDAExMTLC2tkZLS0ta8fZkEZMkSYfC2NgYk5OTZDIZ2tvbSZKEwcFBCoUC6+vrzMzMsLCwwPz8PLVajY2NDZIkoVwuUywW046/K4uYJEnat9/+8o1cu1Fv6D6f2PZAXnvxo465TqVSYXp6mmq1yubmJrlcjiRJdpYPDQ2xvLxMoVCgv78fgLa2NqrVakOzNppFTJIkNb2lpSX6+vpobW0FoLe3N+VEjWERkyRJ+7bXyJWOj09NSpKkptfT08Ps7Cz1ep1arcbc3FzakRrCETFJktT0crkcxWKRzs5OMpkM3d3daUdqiBBjTDvDnvL5fFxZWUk7hiRJJ6XV1VU6OjrSjtGUdjs3IYRKjDG/n+29NClJkpQSi5gkSVJKLGKSJEkpsYhJkiSlxCImSZKUEouYJElSSixikiTp0BkZGWF8fJzh4WEWFxeBrc8gZbNZurq6qNfrlEolstkspVIp5bRH5wtdJUnSoTU6OrozPTU1RblcZmBgAICJiQnW1tZoaWlJK96eLGKSJOlQGBsbY3JykkwmQ3t7O0mSMDg4SKFQYH19nZmZGRYWFpifn6dWq7GxsUGSJJTLZYrFYtrxd2URkyRJ+/a7c5/nC9/8QUP3+YRHnM3vXJE95jqVSoXp6Wmq1Sqbm5vkcjmSJNlZPjQ0xPLyMoVCgf7+fgDa2tqoVqsNzdpoFjFJktT0lpaW6Ovro7W1FYDe3t6UEzWGRUySJO3bXiNXOj4+NSlJkppeT08Ps7Oz1Ot1arUac3NzaUdqCEfEJElS08vlchSLRTo7O8lkMnR3d6cdqSFCjDHtDHvK5/NxZWUl7RiSJJ2UVldX6ejoSDtGU9rt3IQQKjHG/H6299KkJElSSixikiRJKbGISZIkpcQiJkmSlJIDK2IhhJEQwk0hhOr2n+ccsawcQvhKCOGLIYRfOKgMkiRJzeygX1/xxhjj+JEzQghPAF4IZIFHAIshhMfFGH98wFkkSZKaShqXJp8LTMcYfxRj/DrwFeDJKeSQJEmH1MjICOPj4wwPD7O4uAhsfQYpm83S1dVFvV6nVCqRzWYplUoppz26gx4R+7UQwkuBFeC/xxi/BzwS+NQR69y4PU+SJOm4jI6O7kxPTU1RLpcZGBgAYGJigrW1NVpaWtKKt6f7VMRCCIvA+bss+k3gj4DXAnH7798HXnYc+74SuBLgggsuuC8xJUnS/cDY2BiTk5NkMhna29tJkoTBwUEKhQLr6+vMzMywsLDA/Pw8tVqNjY0NkiShXC5TLBbTjr+r+1TEYozP3M96IYQ/Bj6w/fMmoP2IxY/annfvfU8AE7D1Zv37klOSJDXI/Kvhln9u7D7PvxQuf/0xV6lUKkxPT1OtVtnc3CSXy5Ekyc7yoaEhlpeXKRQK9Pf3A9DW1ka1Wm1s1gY7yKcmH37Ezz7g2u3p9wMvDCGcHkK4CLgY+IeDyiFJkg6/paUl+vr6aG1t5eyzz6a3tzftSA1xkPeI/V4IoYutS5PXA68AiDF+PoQwA3wB2AR+1ScmJUk6JPYYudLxObARsRjjS2KMl8YYnxRj7I0x3nzEsrEY42NjjI+PMc4fVAZJknT/0NPTw+zsLPV6nVqtxtzcXNqRGuKgn5qUJEm6z3K5HMVikc7OTjKZDN3d3WlHaogQY/PfB5/P5+PKykraMSRJOimtrq7S0dGRdoymtNu5CSFUYoz5/WzvtyYlSZJSYhGTJElKiUVMkiQpJRYxSZKklFjEJEmSUmIRkyRJSolFTJIkHTojIyOMj48zPDzM4uIisPUZpGw2S1dXF/V6nVKpRDabpVQqpZz26HyhqyRJOrRGR0d3pqempiiXywwMDAAwMTHB2toaLS0tacXbk0VMkiQdCmNjY0xOTpLJZGhvbydJEgYHBykUCqyvrzMzM8PCwgLz8/PUajU2NjZIkoRyuUyxWEw7/q4sYpIkad/e8A9v4Lq16xq6z0vOvYSrnnzVMdepVCpMT09TrVbZ3Nwkl8uRJMnO8qGhIZaXlykUCvT39wPQ1tZGtVptaNZGs4hJkqSmt7S0RF9fH62trQD09vamnKgxLGKSJGnf9hq50vHxqUlJktT0enp6mJ2dpV6vU6vVmJubSztSQzgiJkmSml4ul6NYLNLZ2Ukmk6G7uzvtSA0RYoxpZ9hTPp+PKysraceQJOmktLq6SkdHR9oxmtJu5yaEUIkx5vezvZcmJUmSUmIRkyRJSolFTJIkKSUWMUmSpJRYxCRJklJiEZMkSUqJRUySJB06IyMjjI+PMzw8zOLiIrD1GaRsNktXVxf1ep1SqUQ2m6VUKqWc9uh8oaskSTq0RkdHd6anpqYol8sMDAwAMDExwdraGi0tLWnF25NFTJIkHQpjY2NMTk6SyWRob28nSRIGBwcpFAqsr68zMzPDwsIC8/Pz1Go1NjY2SJKEcrlMsVhMO/6uLGKSJGnfbnnd6/jR6nUN3efpHZdw/mtec8x1KpUK09PTVKtVNjc3yeVyJEmys3xoaIjl5WUKhQL9/f0AtLW1Ua1WG5q10SxikiSp6S0tLdHX10draysAvb29KSdqDIuYJEnat71GrnR8fGpSkiQ1vZ6eHmZnZ6nX69RqNebm5tKO1BCOiEmSpKaXy+UoFot0dnaSyWTo7u5OO1JDhBhj2hn2lM/n48rKStoxJEk6Ka2urtLR0ZF2jKa027kJIVRijPn9bO+lSUmSpJRYxCRJklJiEZMkSUqJRUySJCklFjFJkqSUWMQkSZJSYhGTJEmHzsjICOPj4wwPD7O4uAhsfQYpm83S1dVFvV6nVCqRzWYplUoppz06X+gqSZIOrdHR0Z3pqakpyuUyAwMDAExMTLC2tkZLS0ta8fZkEZMkSYfC2NgYk5OTZDIZ2tvbSZKEwcFBCoUC6+vrzMzMsLCwwPz8PLVajY2NDZIkoVwuUywW046/K4uYJEnat6WZL/Gdb2w0dJ8PbW/jaS943DHXqVQqTE9PU61W2dzcJJfLkSTJzvKhoSGWl5cpFAr09/cD0NbWRrVabWjWRrOISZKkpre0tERfXx+tra0A9Pb2ppyoMSxikiRp3/YaudLx8alJSZLU9Hp6epidnaVer1Or1Zibm0s7UkM4IiZJkppeLpejWCzS2dlJJpOhu7s77UgNEWKMjd9pCH8JPH775znAeoyxK4RwIbAKfHF72adijK/ca3/5fD6urKw0PKckSdrb6uoqHR0dacdoSrudmxBCJcaY38/2BzIiFmPceUY0hPD7wPePWPzVGGPXQRxXkiTpMDnQS5MhhAC8APh3B3kcSZKkw+igb9Z/GvCtGOOXj5h3UQjhH0MIHwshPO1oG4YQrgwhrIQQVm699dYDjilJknTi/cQjYiGEReD8XRb9ZozxfdvTLwL+4ohlNwMXxBi/G0JIgNkQQjbG+IN77yTGOAFMwNY9Yj9pTkmSpGb1ExexGOMzj7U8hHAq8Hxg57W3McYfAT/anq6EEL4KPA7wTnxJknTSOchLk88Erosx3nj3jBDCeSGElu3pxwAXA187wAySJElN6yCL2Au552VJgB7gcyGEKvAe4JUxxrUDzCBJku6HRkZGGB8fZ3h4mMXFRWDrM0jZbJauri7q9TqlUolsNkupVEo57dEd2FOTMcbBXea9F3jvQR1TkiSdXEZHR3emp6amKJfLDAwMADAxMcHa2hotLS1pxduTb9aXJEmHwtjYGJOTk2QyGdrb20mShMHBQQqFAuvr68zMzLCwsMD8/Dy1Wo2NjQ2SJKFcLlMsFvc+QAosYpIkad8+8mcTfPuGxt7enXn0Y3jG4JXHXKdSqTA9PU21WmVzc5NcLkeS7DwPyNDQEMvLyxQKBfr7+wFoa2ujWq02NGujWcQkSVLTW1paoq+vj9bWVgB6e3tTTtQYFjFJkrRve41c6fgc9Jv1JUmS7rOenh5mZ2ep1+vUajXm5ubSjtQQjohJkqSml8vlKBaLdHZ2kslk6O7uTjtSQ4QYm//rQfl8Pq6s+PJ9SZLSsLq6SkdHR9oxmtJu5yaEUIkx5vezvZcmJUmSUmIRkyRJSolFTJIkKSUWMUmSpJRYxCTp/7J3/8GNnPed5789oCUHAo4MgMxMYE7CeFsKBHTYaI19t1XhuXLMLROz2lrhqFslFycxs+ZiQ29dUM3SYZWUcS67tLXe6PbguvKlTpdNYlSJa/rkIzY+UvDCLoYHsSp3sbLEUaZgSY7H+THUamk2afX0WANp+v6YoYtRyBnOuKWn6Xm/qlxudrObH7ebM595Gt0PAChCEQMAAFCEIgYAAE6cT3ziE/LEE09IrVaTr3zlKyJybRqkQqEgxWJRLl++LI8++qgUCgV59NFHFac9Gi90BQAAJ9YnP/nJ7y8/9dRT8thjj8mHP/xhERF58sknZWdnR2KxmKp4N0URAwAAJ8Ljjz8un/vc5+T06dNy7tw5OX/+vHzkIx8R27Zld3dXvvCFL8iXv/xleeaZZ+S1114Tz/Pk/Pnz8thjj8kjjzyiOv6hKGIAAODYdr/0Tbly8VKox7wre48Mfejv3fB7nnvuOfn85z8v6+vr8sYbb8gDDzwg58+f//72j370o/Lss8+Kbdvy8MMPi4hIIpGQ9fX1ULOGjSIGAAAir9PpSKlUkng8LiIiDz74oOJE4aCIAQCAY7vZyBVuDU9NAgCAyPvABz4gzWZTLl++LK+99pp86UtfUh0pFIyIAQCAyHvggQfkkUceEdM05fTp0/L+979fdaRQaEEQqM5wU+973/uCr33ta6pjAABwR3rhhRfk/vvvVx0jkg47N5qmPRcEwfuOsz+3JgEAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAHDifOITn5AnnnhCarWafOUrXxGRa9MgFQoFKRaLcvnyZXn00UelUCjIo48+qjjt0XihKwAAOLE++clPfn/5qaeekscee0w+/OEPi4jIk08+KTs7OxKLxVTFuymKGAAAOBEef/xx+dznPienT5+Wc+fOyfnz5+UjH/mI2LYtu7u78oUvfEG+/OUvyzPPPCOvvfaaeJ4n58+fl8cee0weeeQR1fEPRREDAADH9swzz8grr7wS6jHPnj0rH/zgB2/4Pc8995x8/vOfl/X1dXnjjTfkgQcekPPnz39/+0c/+lF59tlnxbZtefjhh0VEJJFIyPr6eqhZw0YRAwAAkdfpdKRUKkk8HhcRkQcffFBxonBQxAAAwLHdbOQKt4anJgEAQOR94AMfkGazKZcvX5bXXntNvvSlL6mOFApGxAAAQOQ98MAD8sgjj4hpmnL69Gl5//vfrzpSKLQgCFRnuKn3ve99wde+9jXVMQAAuCO98MILcv/996uOEUmHnRtN054LguB9x9mfW5MAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAABAEYoYAAA4cT7xiU/IE088IbVaTb7yla+IyLVpkAqFghSLRbl8+bI8+uijUigU5NFHH1Wc9mi80BUAAJxYn/zkJ7+//NRTT8ljjz0mH/7wh0VE5Mknn5SdnR2JxWKq4t3UDzQipmnaf6tp2tc1Tbuqadr73rLtMU3TXtY07Ruapv3CgfW/eH3dy5qm/fMf5OcDAIA7x+OPPy733XefjI2NyTe+8Q0REfnIRz4iTz/9tPz+7/++fOELX5CPf/zj8iu/8ivy4IMPiud5cv78eVlYWFCc/Gg/6IjY8yLy34jI/3ZwpaZpeRH5JREpiEhWRL6iadp91zd/VkT+gYj8tYj8maZpfxwEweYPmAMAALwDXnzxU/Ka90Kox0wm7pf77vv4Db/nueeek89//vOyvr4ub7zxhjzwwANy/vz572//6Ec/Ks8++6zYti0PP/ywiIgkEglZX18PNWvYfqAiFgTBCyIimqa9ddM/FJHPB0Hwuoh8S9O0l0XkP7++7eUgCP7i+n6fv/69FDEAAHCkTqcjpVJJ4vG4iIg8+OCDihOF4+36jNh7RORPD3z919fXiYj81VvW/xdvUwYAABCym41c4dbctIhpmvYVETl7yKbfCYLg34Uf6fs/95+IyD+5/qWnado33q6fdUBGRLbfgZ9zEnFujsa5uTHOz9E4N0fj3BztHT837Xb7Z95888033smf+VY//uM/fuqzn/3s3Q8++ODlN954Q55++ukfefjhh/vf+c53Tl24cOHN559//s3vfOc7d33rW9+S559//oqIyNWrV+PPP/+8/3bmeuWVVwby+fzGW1b/5HH3v2kRC4Lgv77lVCJ/IyLnDnw9fH2d3GD9W3/ukyLy5G387NumadrXjjtb+p2Gc3M0zs2NcX6Oxrk5GufmaCrOTbfbvWAYhtJibBiG/Pmf//nZhx9+OJNOp/umabqJRMK/6667fiSdTu8ZhuHeddddI5qmJQzD2P8Qm3Vg+W3x5ptvZn6Q/z/erluTfywi85qm/Wu59mH9e0Xk/xURTUTu1TTtp+RaAfslEfnv3qYMAADgh8inP/3pVz796U+/ctT2L37xixeef/75+/e/9n3/P7wzyW7fD1TENKK8zpMAACAASURBVE0ricj/IiI/JiJLmqatB0HwC0EQfF3TtC/ItQ/hvyEiHwuC4M3r+/wzEfmyiMRE5A+CIPj6D/S/AAAA4IT6QZ+aXBSRxSO2PS4ijx+yfllEln+Qn/s2ekdvhZ4wnJujcW5ujPNzNM7N0Tg3R+Pc3EAmk/lPqjPcCi0IAtUZAABAhHW73QumafLwxCG63W7GNM2R292fuSYBAAAUYa5JuTbtkoh8Rq59bu33gyD4l4ojKaNp2jkRaYjIGREJROTJIAg+o2naJ0RkRkT2h3x/+/pt5juOpmkXROQ1EXlTRN4IguB9mqalRGRBREZE5IKI/KMgCFxVGVXQNO2n5do52PdeEamJyJDcgdeOpml/ICK2iLwaBIFxfd2h14l27a3YnxGRSRHxReQjQRD8uYrc75Qjzs/visiHROSKiHxTRKaDINjVNG1ERF4Qkf3XGP1pEAT/9B0P/Q454tx8Qo74PdI07TER+cdy7c+k/z4Igi+/46HfId/85jdHvvvd7w4ODAy88TM/8zNfFxF56aWX3vv666+/W0TkzTffjMVisTcNw9j83ve+d9fXv/514+677/6eiEg8Hvfe+973/qXK/Ie540fENE2LybVplz4oInkR+eXrUzTdqd4QkbkgCPIi8vdF5GMHzsf/HARB8fp/fuj/Ir2J/+r6edh/ZPmfi8hXgyC4V0S+ev3rO0oQBN/Yvz5E5LxcKxT7nyG9E6+dPxKRX3zLuqOukw/KtafL75Vr70/8vXcoo0p/JH/3/LRFxAiCYFREXhSRxw5s++aBa+iHtoRd90fyd8+NyCG/R2+ZUvAXReR/vf732g+lTCazrev6SwfX3XvvvX9hGMamYRibg4OD7uDg4Pf/EXzXXXe9vr8tiiVMhCImcm3qpZeDIPiLIAiuiMj+tEt3pCAItvb/JR4EwWty7V+h77nxXpBr18znri9/TkQeUpglCn5erv3F+W3VQVQJguD/FpGdt6w+6jr5hyLSCK75UxEZ0jTtx9+ZpGocdn6CIPj3QRDsvzT0T+XauybvOEdcO0f5/pSCQRB8S0QOTin4Q2dwcNB717ve9YaIiOM42VqtdqZSqWSbzWYyCAL56le/mv65n/u5dC6Xy1+6dEl74oknBnRdL5TL5cheSxSxayXjrdMuUTxE5PrtAEtE/p/rq/6Zpmn/n6Zpf6Bp2o8qC6ZeICL/XtO0567PACEiciYIgq3ry6/ItVu7d7JfEpF/e+Brrp1rjrpO+HPo7/oNEXnmwNc/pWnaf9A0bVXTtP9SVSjFDvs9uuOvnXq9fvGhhx567bvf/W7imWeekbm5uYu9Xm/znnvuCb74xS8OfPGLXwx+67d+6569vb2E6qyHoYjhUJqmJUTkiyJSCYLgu3LtVsnfE5GiiGyJyP+kMJ5qY0EQPCDXbid9TNO0DxzcGFx7FPmOfRxZ07S7RORBEfk/rq/i2jnEnX6d3Iimab8j1z4m8dT1VVsi8hNBEFgi4si1F4b/Z6ryKcLvkYhUq9WzP/3TP/3Tv/qrv3rXSy+9dLeIyNTU1Mgf/uEf/uhnPvOZbLvdPvX444+/58EHH/ypycnJn7h8+bI88sgj8id/8id73/rWt977xhtvRK738GH9G0/HdEfSNO1dcq2EPRUEwf8pIhIEwX88sP1/F5H/S1E85YIg+Jvr//2qpmmLcu02wH/UNO3HgyDYun5L6VWlIdX6oIj8+f41w7Xztxx1nfDn0HWapn1Ern1Q/eevl1UJguB1EXn9+vJzmqZ9U0TuE5Gvqcr5TrvB79E7fu1UXvjLc71L34uHeczcPe/26/f/xF/d6Hs6nU58cXEx9dxzz7340ksv6Y888sg9lmX5IiJXr16VD33oQz+yvr6+96EPfcidnp52RUTi8bjV6/U2RUReeOGFwcuXL787mUy+rXNP3qrINUMF/kyuT7t0/V/yvyTXpmi6I11/euvfiMgLQRD86wPrD35epSQiz7/T2aJA07R7NE1L7i+LyIRcOxd/LCK/fv3bfl1E/p2ahJHwy3LgtiTXzt9y1HXyxyLya9o1f19E9g7cwrxjXH+C/X8QkQeDIPAPrP+x/Q+ga5r2Xrn2UMNfqEmpxg1+j/5YRH5J07S7r08fuD+l4A+dlZWVxOTk5G4ikQgSiYRMTEzs7m+7cuXKj9x9993f0zTt6oF13x9sunz58l2vv/763e9+97tff6dz38wdPyIWBMEbTLv0t/ysiPyqiGxomrZ+fd1vy7WnSYty7VbKBREpq4mn3BkRWbzWV2VAROaDIGhpmvZnIvIFTdP+sYh8W0T+kcKMylwvp/9A/vb18a/uxGtH07R/KyI/JyIZTdP+WkT+RxH5l3L4dbIs115d8bJce9p0+h0P/A474vw8JiJ3i0j7+u/Y/msqPiAin9Q0rS8iV0XknwZBcNwPs584R5ybnzvs9+hGUwq+XW42cvV2+t73vjfU6/Uyb7755sDly5d/7MqVK3sicvX1119PpFKpHRG5Z/97v/vd7yZE5NTzzz+fF5Hg3Llz337Xu971tp6b23HHFzGRyE+79I4KguBZuTY5+1txfkQkCIK/EBHzkPXfkWtPCt7RgiC4JCLpt6z7VUVxlAqC4JeP2PR3rpPrt+A+9vYmipYjzs+/OeJ7vyjXPi5xR7iVc3P9+w+dUvCHzfj4uPcbv/EbmSeeeGKj3+9rzz77bP7Xf/3XL4nIjwwODv6ns2fPunKgiGUymV0RuWoYxqay0MdAEQMAAJE3Njbml0qlHcMwCul0uj86OnpJdaYwMNckAAC4IeaaPBpzTQIAAJxQFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAABOHMdxsrVa7UylUsk2m82kiEir1Uroul7I5XJ5z/O0crk8rOt6oVwuD6vOexRe6AoAAE6ser1+cX+50WikHMfZmp2d3RERmZ+fz7iuuz4wEN26E91kAAAAB1Sr1bMLCwuZdDrdz2azVyzL8qempkZs295zXTe2tLSUWl1dHWy1WoOe58V8348ZhpGfm5vbmpmZcVXnPwxFDAAAHNujT3fPvfjKa/Ewj3nf2aT/uw+bN5xMvNPpxBcXF1MbGxub/X5fisVi3rIsf3+74zjba2trCdu296anp10RkXg8bvV6PeaaBAAA+EGsrKwkJicnd5PJ5FURkYmJiV3VmcJAEQMAAMd2s5Er3BqemgQAAJE3Pj7uLS8vD3mep7mue6rdbg+pzhQGRsQAAEDkjY2N+aVSaccwjEI6ne6Pjo5eUp0pDFoQBKozAACACOt2uxdM09xWnSOKut1uxjTNkdvdn1uTAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAOHEcx8nWarUzlUol22w2kyIirVYroet6IZfL5T3P08rl8rCu64VyuTysOu9ReKErAAA4ser1+sX95UajkXIcZ2t2dnZHRGR+fj7juu76wEB06050kwEAABxQrVbPLiwsZNLpdD+bzV6xLMufmpoasW17z3Xd2NLSUmp1dXWw1WoNep4X830/ZhhGfm5ubmtmZsZVnf8wFDEAAHB8zY+dk1c346Ee83Tel4c+e8PJxDudTnxxcTG1sbGx2e/3pVgs5i3L8ve3O46zvba2lrBte296etoVEYnH41av19sMNWvIKGIAACDyVlZWEpOTk7vJZPKqiMjExMSu6kxhoIgBAIDju8nIFW4NT00CAIDIGx8f95aXl4c8z9Nc1z3VbreHVGcKAyNiAAAg8sbGxvxSqbRjGEYhnU73R0dHL6nOFAYtCALVGQAAQIR1u90Lpmluq84RRd1uN2Oa5sjt7s+tSQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAAJw4juNka7XamUqlkm02m0kRkVarldB1vZDL5fKe52nlcnlY1/VCuVweVp33KLzQFQAAnFj1ev3i/nKj0Ug5jrM1Ozu7IyIyPz+fcV13fWAgunUnuskAAAAOqFarZxcWFjLpdLqfzWavWJblT01Njdi2vee6bmxpaSm1uro62Gq1Bj3Pi/m+HzMMIz83N7c1MzPjqs5/GIoYAAA4to+vffzcy+7L8TCPqf+o7n/qZz91w8nEO51OfHFxMbWxsbHZ7/elWCzmLcvy97c7jrO9traWsG17b3p62hURicfjVq/X2wwza9goYgAAIPJWVlYSk5OTu8lk8qqIyMTExK7qTGGgiAEAgGO72cgVbg1PTQIAgMgbHx/3lpeXhzzP01zXPdVut4dUZwoDI2IAACDyxsbG/FKptGMYRiGdTvdHR0cvqc4UBi0IAtUZAABAhHW73QumaW6rzhFF3W43Y5rmyO3uz61JAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAnDiO42RrtdqZSqWSbTabSRGRVquV0HW9kMvl8p7naeVyeVjX9UK5XB5WnfcovNAVAACcWPV6/eL+cqPRSDmOszU7O7sjIjI/P59xXXd9YCC6dSe6yQAAAA6oVqtnFxYWMul0up/NZq9YluVPTU2N2La957pubGlpKbW6ujrYarUGPc+L+b4fMwwjPzc3tzUzM+Oqzn8YihgAADi2i7/9O+def+mleJjHvPvee/3sv3j8hpOJdzqd+OLiYmpjY2Oz3+9LsVjMW5bl7293HGd7bW0tYdv23vT0tCsiEo/HrV6vtxlm1rBRxAAAQOStrKwkJicnd5PJ5FURkYmJiV3VmcJAEQMAAMd2s5Er3BqemgQAAJE3Pj7uLS8vD3mep7mue6rdbg+pzhQGRsQAAEDkjY2N+aVSaccwjEI6ne6Pjo5eUp0pDFoQBKozAACACOt2uxdM09xWnSOKut1uxjTNkdvdn1uTAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAOHEcx8nWarUzlUol22w2kyIirVYroet6IZfL5T3P08rl8rCu64VyuTysOu9ReKErAAA4ser1+sX95UajkXIcZ2t2dnZHRGR+fj7juu76wEB06050kwEAABxQrVbPLiwsZNLpdD+bzV6xLMufmpoasW17z3Xd2NLSUmp1dXWw1WoNep4X830/ZhhGfm5ubmtmZsZVnf8wFDEAAHBsX228cG7nb7x4mMdMvSfh//yv3X/DycQ7nU58cXExtbGxsdnv96VYLOYty/L3tzuOs722tpawbXtvenraFRGJx+NWr9fbDDNr2ChiAAAg8lZWVhKTk5O7yWTyqojIxMTErupMYaCIAQCAY7vZyBVuDU9NAgCAyBsfH/eWl5eHPM/TXNc91W63h1RnCgMjYgAAIPLGxsb8Uqm0YxhGIZ1O90dHRy+pzhQGLQgC1RkAAECEdbvdC6ZpbqvOEUXdbjdjmubI7e7PrUkAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAACcOI7jZGu12plKpZJtNptJEZFWq5XQdb2Qy+Xynudp5XJ5WNf1QrlcHlad9yi80BUAAJxY9Xr94v5yo9FIOY6zNTs7uyMiMj8/n3Fdd31gILp1J7rJAAAADqhWq2cXFhYy6XS6n81mr1iW5U9NTY3Ytr3num5saWkptbq6OthqtQY9z4v5vh8zDCM/Nze3NTMz46rOfxiKGAAAOLYv/1793PZffTse5jEz537S/4XfrNxwMvFOpxNfXFxMbWxsbPb7fSkWi3nLsvz97Y7jbK+trSVs296bnp52RUTi8bjV6/U2w8waNooYAACIvJWVlcTk5ORuMpm8KiIyMTGxqzpTGChiAADg2G42coVbw1OTAAAg8sbHx73l5eUhz/M013VPtdvtIdWZwsCIGAAAiLyxsTG/VCrtGIZRSKfT/dHR0UuqM4VBC4JAdQYAABBh3W73gmma26pzRFG3282Ypjlyu/tzaxIAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAAAnjuM42VqtdqZSqWSbzWZSRKTVaiV0XS/kcrm853lauVwe1nW9UC6Xh1XnPQovdAUAACdWvV6/uL/caDRSjuNszc7O7oiIzM/PZ1zXXR8YiG7diW4yAACAA6rV6tmFhYVMOp3uZ7PZK5Zl+VNTUyO2be+5rhtbWlpKra6uDrZarUHP82K+78cMw8jPzc1tzczMuKrzH4YiBgAAjm3n6RfP9V+5FA/zmO86e4+fevi+G04m3ul04ouLi6mNjY3Nfr8vxWIxb1mWv7/dcZzttbW1hG3be9PT066ISDwet3q93maYWcNGEQMAAJG3srKSmJyc3E0mk1dFRCYmJnZVZwoDRQwAABzbzUaucGt4ahIAAETe+Pi4t7y8POR5nua67ql2uz2kOlMYGBEDAACRNzY25pdKpR3DMArpdLo/Ojp6SXWmMGhBEKjOAAAAIqzb7V4wTXNbdY4o6na7GdM0R253f25NAgAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAA4MRxHCdbq9XOVCqVbLPZTIqItFqthK7rhVwul/c8TyuXy8O6rhfK5fKw6rxH4YWuAADgxKrX6xf3lxuNRspxnK3Z2dkdEZH5+fmM67rrAwPRrTvRTQYAAHBAtVo9u7CwkEmn0/1sNnvFsix/ampqxLbtPdd1Y0tLS6nV1dXBVqs16HlezPf9mGEY+bm5ua2ZmRlXdf7DUMQAAMCxNZvNc6+++mo8zGOePn3af+ihh244mXin04kvLi6mNjY2Nvv9vhSLxbxlWf7+dsdxttfW1hK2be9NT0+7IiLxeNzq9XqbYWYNG0UMAABE3srKSmJycnI3mUxeFRGZmJjYVZ0pDBQxAABwbDcbucKt4alJAAAQeePj497y8vKQ53ma67qn2u32kOpMYWBEDAAARN7Y2JhfKpV2DMMopNPp/ujo6CXVmcKgBUGgOgMAAIiwbrd7wTTNbdU5oqjb7WZM0xy53f25NQkAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAIATx3GcbK1WO1OpVLLNZjMpItJqtRK6rhdyuVze8zytXC4P67peKJfLw6rzHoUXugIAgBOrXq9f3F9uNBopx3G2Zmdnd0RE5ufnM67rrg8MRLfuRDcZAADAAdVq9ezCwkImnU73s9nsFcuy/KmpqRHbtvdc140tLS2lVldXB1ut1qDneTHf92OGYeTn5ua2ZmZmXNX5D0MRAwAAx7b5QvXcJe/FeJjHvCdxn5+//9M3nEy80+nEFxcXUxsbG5v9fl+KxWLesix/f7vjONtra2sJ27b3pqenXRGReDxu9Xq9zTCzho0iBgAAIm9lZSUxOTm5m0wmr4qITExM7KrOFAaKGAAAOLabjVzh1vDUJAAAiLzx8XFveXl5yPM8zXXdU+12e0h1pjAwIgYAACJvbGzML5VKO4ZhFNLpdH90dPSS6kxh0IIgUJ0BAABEWLfbvWCa5rbqHFHU7XYzpmmO3O7+3JoEAABQhCIGAACgCEUMAABAEYoYAACAIhQxAAAARShiAAAAilDEAADAieM4TrZWq52pVCrZZrOZFBFptVoJXdcLuVwu73meVi6Xh3VdL5TL5WHVeY/CC10BAMCJVa/XL+4vNxqNlOM4W7OzszsiIvPz8xnXddcHBqJbd6KbDAAA4IBqtXp2YWEhk06n+9ls9oplWf7U1NSIbdt7ruvGlpaWUqurq4OtVmvQ87yY7/sxwzDyc3NzWzMzM67q/IehiAEAgGOrvPCX53qXvhcP85i5e97t1+//iRtOJt7pdOKLi4upjY2NzX6/L8ViMW9Zlr+/3XGc7bW1tYRt23vT09OuiEg8Hrd6vd5mmFnDRhEDAACRt7KykpicnNxNJpNXRUQmJiZ2VWcKA0UMAAAc281GrnBreGoSAABE3vj4uLe8vDzkeZ7muu6pdrs9pDpTGBgRAwAAkTc2NuaXSqUdwzAK6XS6Pzo6ekl1pjBoQRCozgAAACKs2+1eME1zW3WOKOp2uxnTNEdud39uTQIAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAODEcRwnW6vVzlQqlWyz2UyKiLRarYSu64VcLpf3PE8rl8vDuq4XyuXysOq8R+GFrgAA4MSq1+sX95cbjUbKcZyt2dnZHRGR+fn5jOu66wMD0a070U0GAABwQLVaPbuwsJBJp9P9bDZ7xbIsf2pqasS27T3XdWNLS0up1dXVwVarNeh5Xsz3/ZhhGPm5ubmtmZkZV3X+w1DEAADAsT36dPfci6+8Fg/zmPedTfq/+7B5w8nEO51OfHFxMbWxsbHZ7/elWCzmLcvy97c7jrO9traWsG17b3p62hURicfjVq/X2wwza9goYgAAIPJWVlYSk5OTu8lk8qqIyMTExK7qTGGgiAEAgGO72cgVbg1PTQIAgMgbHx/3lpeXhzzP01zXPdVut4dUZwoDI2IAACDyxsbG/FKptGMYRiGdTvdHR0cvqc4UBi0IAtUZAABAhHW73QumaW6rzhFF3W43Y5rmyO3uz61JAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAnDiO42RrtdqZSqWSbTabSRGRVquV0HW9kMvl8p7naeVyeVjX9UK5XB5WnfcovNAVAACcWPV6/eL+cqPRSDmOszU7O7sjIjI/P59xXXd9YCC6dSe6yQAAAA6oVqtnFxYWMul0up/NZq9YluVPTU2N2La957pubGlpKbW6ujrYarUGPc+L+b4fMwwjPzc3tzUzM+Oqzn8YihgAADi+5sfOyaub8VCPeTrvy0OfveFk4p1OJ764uJja2NjY7Pf7UiwW85Zl+fvbHcfZXltbS9i2vTc9Pe2KiMTjcavX622GmjVkFDEAABB5KysricnJyd1kMnlVRGRiYmJXdaYwUMQAAMDx3WTkCreGpyYBAEDkjY+Pe8vLy0Oe52mu655qt9tDqjOFgRExAAAQeWNjY36pVNoxDKOQTqf7o6Ojl1RnCoMWBIHqDAAAIMK63e4F0zS3VeeIom63mzFNc+R29+fWJAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAE4cx3GytVrtTKVSyTabzaSISKvVSui6XsjlcnnP87RyuTys63qhXC4Pq857FF7oCgAATqx6vX5xf7nRaKQcx9manZ3dERGZn5/PuK67PjAQ3boT3WQAAAAHVKvVswsLC5l0Ot3PZrNXLMvyp6amRmzb3nNdN7a0tJRaXV0dbLVag57nxXzfjxmGkZ+bm9uamZlxVec/DEUMAAAc28fXPn7uZffleJjH1H9U9z/1s5+64WTinU4nvri4mNrY2Njs9/tSLBbzlmX5+9sdx9leW1tL2La9Nz097YqIxONxq9frbYaZNWwUMQAAEHkrKyuJycnJ3WQyeVVEZGJiYld1pjBQxAAAwLHdbOQKt4anJgEAQOSNj497y8vLQ57naa7rnmq320OqM4WBETEAABB5Y2NjfqlU2jEMo5BOp/ujo6OXVGcKgxYEgeoMAAAgwrrd7gXTNLdV54iibrebMU1z5Hb359YkAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAThzHcbK1Wu1MpVLJNpvNpIhIq9VK6LpeyOVyec/ztHK5PKzreqFcLg+rznsUXugKAABOrHq9fnF/udFopBzH2Zqdnd0REZmfn8+4rrs+MBDduhPdZAAAAAdUq9WzCwsLmXQ63c9ms1csy/KnpqZGbNvec103trS0lFpdXR1stVqDnufFfN+PGYaRn5ub25qZmXFV5z8MRQwAABzbxd/+nXOvv/RSPMxj3n3vvX72Xzx+w8nEO51OfHFxMbWxsbHZ7/elWCzmLcvy97c7jrO9traWsG17b3p62hURicfjVq/X2wwza9goYgAAIPJWVlYSk5OTu8lk8qqIyMTExK7qTGGgiAEAgGO72cgVbg1PTQIAgMgbHx/3lpeXhzzP01zXPdVut4dUZwoDI2IAACDyxsbG/FKptGMYRiGdTvdHR0cvqc4UBi0IAtUZAABAhHW73QumaW6rzhFF3W43Y5rmyO3uz61JAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAnDiO42RrtdqZSqWSbTabSRGRVquV0HW9kMvl8p7naeVyeVjX9UK5XB5WnfcovNAVAACcWPV6/eL+cqPRSDmOszU7O7sjIjI/P59xXXd9YCC6dSe6yQAAAA6oVqtnFxYWMul0up/NZq9YluVPTU2N2La957pubGlpKbW6ujrYarUGPc+L+b4fMwwjPzc3tzUzM+Oqzn8YihgAADi2rzZeOLfzN148zGOm3pPwf/7X7r/hZOKdTie+uLiY2tjY2Oz3+1IsFvOWZfn72x3H2V5bW0vYtr03PT3tiojE43Gr1+tthpk1bBQxAAAQeSsrK4nJycndZDJ5VURkYmJiV3WmMFDEAADAsd1s5Aq3hqcmAQBA5I2Pj3vLy8tDnudpruuearfbQ6ozhYERMQAAEHljY2N+qVTaMQyjkE6n+6Ojo5dUZwqDFgSB6gwAACDCut3uBdM0t1XniKJut5sxTXPkdvfn1iQAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAABOHMdxsrVa7UylUsk2m82kiEir1Uroul7I5XJ5z/O0crk8rOt6oVwuD6vOexRe6AoAAE6ser1+cX+50WikHMfZmp2d3RERmZ+fz7iuuz4wEN26E91kAAAAB1Sr1bMLCwuZdDrdz2azVyzL8qempkZs295zXTe2tLSUWl1dHWy1WoOe58V8348ZhpGfm5vbmpmZi/frgAAAIABJREFUcVXnPwxFDAAAHNuXf69+bvuvvh0P85iZcz/p/8JvVm44mXin04kvLi6mNjY2Nvv9vhSLxbxlWf7+dsdxttfW1hK2be9NT0+7IiLxeNzq9XqbYWYNG0UMAABE3srKSmJycnI3mUxeFRGZmJjYVZ0pDBQxAABwbDcbucKt4alJAAAQeePj497y8vKQ53ma67qn2u32kOpMYWBEDAAARN7Y2JhfKpV2DMMopNPp/ujo6CXVmcKgBUGgOgMAAIiwbrd7wTTNbdU5oqjb7WZM0xy53f25NQkAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAIATx3GcbK1WO1OpVLLNZjMpItJqtRK6rhdyuVze8zytXC4P67peKJfLw6rzHoUXugIAgBOrXq9f3F9uNBopx3G2Zmdnd0RE5ufnM67rrg8MRLfuRDcZAADAAdVq9ezCwkImnU73s9nsFcuy/KmpqRHbtvdc140tLS2lVldXB1ut1qDneTHf92OGYeTn5ua2ZmZmXNX5D0MRAwAAx7bz9Ivn+q9ciod5zHedvcdPPXzfDScT73Q68cXFxdTGxsZmv9+XYrGYtyzL39/uOM722tpawrbtvenpaVdEJB6PW71ebzPMrGGjiAEAgMhbWVlJTE5O7iaTyasiIhMTE7uqM4WBIgYAAI7tZiNXuDU8NQkAACJvfHzcW15eHvI8T3Nd91S73R5SnSkMjIgBAIDIGxsb80ul0o5hGIV0Ot0fHR29pDpTGLQgCFRnAAAAEdbtdi+YprmtOkcUdbvdjGmaI7e7P7cmAQAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAgCIUMQAAcOI4jpOt1WpnKpVKttlsJkVEWq1WQtf1Qi6Xy3uep5XL5WFd1wvlcnlYdd6j8EJXAABwYtXr9Yv7y41GI+U4ztbs7OyOiMj8/HzGdd31gYHo1p3oJgMAADigWq2eXVhYyKTT6X42m71iWZY/NTU1Ytv2nuu6saWlpdTq6upgq9Ua9Dwv5vt+zDCM/Nzc3NbMzIyrOv9hKGIAAODYms3muVdffTUe5jFPnz7tP/TQQzecTLzT6cQXFxdTGxsbm/1+X4rFYt6yLH9/u+M422trawnbtvemp6ddEZF4PG71er3NMLOGjSIGAAAib2VlJTE5ObmbTCaviohMTEzsqs4UBooYAAA4tpuNXOHW8NQkAACIvPHxcW95eXnI8zzNdd1T7XZ7SHWmMDAiBgAAIm9sbMwvlUo7hmEU0ul0f3R09JLqTGHQgiBQnQEAAERYt9u9YJrmtuocUdTtdjOmaY7c7v7cmgQAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAMCJ4zhOtlarnalUKtlms5kUEWm1Wgld1wu5XC7veZ5WLpeHdV0vlMvlYdV5j8ILXQEAwIlVr9cv7i83Go2U4zhbs7OzOyIi8/PzGdd11wcGolt3opsMAADggGq1enZhYSGTTqf72Wz2imVZ/tTU1Iht23uu68aWlpZSq6urg61Wa9DzvJjv+zHDMPJzc3NbMzMzrur8h6GIAQCAY9t8oXrukvdiPMxj3pO4z8/f/+kbTibe6XTii4uLqY2Njc1+vy/FYjFvWZa/v91xnO21tbWEbdt709PTrohIPB63er3eZphZw0YRAwAAkbeyspKYnJzcTSaTV0VEJiYmdlVnCgNFDAAAHNvNRq5wa3hqEgAARN74+Li3vLw85Hme5rruqXa7PaQ6UxgYEQMAAJE3Njbml0qlHcMwCul0uj86OnpJdaYwaEEQqM4AAAAirNvtXjBNc1t1jijqdrsZ0zRHbnd/bk0CAAAoQhEDAABQhCIGAACgCEUMAABAEYoYAACAIhQxAAAARShiAADgxHEcJ1ur1c5UKpVss9lMioi0Wq2EruuFXC6X9zxPK5fLw7quF8rl8rDqvEfhha4AAODEqtfrF/eXG41GynGcrdnZ2R0Rkfn5+YzruusDA9GtO9FNBgAAcEC1Wj27sLCQSafT/Ww2e8WyLH9qamrEtu0913VjS0tLqdXV1cFWqzXoeV7M9/2YYRj5ubm5rZmZGVd1/sNQxAAAwLFVXvjLc71L34uHeczcPe/26/f/xA0nE+90OvHFxcXUxsbGZr/fl2KxmLcsy9/f7jjO9traWsK27b3p6WlXRCQej1u9Xm8zzKxho4gBAIDIW1lZSUxOTu4mk8mrIiITExO7qjOFgSIGAACO7WYjV7g1PDUJAAAib3x83FteXh7yPE9zXfdUu90eUp0pDIyIAQCAyBsbG/NLpdKOYRiFdDrdHx0dvaQ6Uxi0IAhUZwAAABHW7XYvmKa5rTpHFHW73YxpmiO3uz+3JgEAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAHDiOI6TrdVqZyqVSrbZbCZFRFqtVkLX9UIul8t7nqeVy+VhXdcL5XJ5WHXeo/BCVwAAcGLV6/WL+8uNRiPlOM7W7OzsjojI/Px8xnXd9YGB6Nad6CYDAAA4oFqtnl1YWMik0+l+Npu9YlmWPzU1NWLb9p7rurGlpaXU6urqYKvVGvQ8L+b7fswwjPzc3NzWzMyMqzr/YShiAADg2B59unvuxVdei4d5zPvOJv3ffdi84WTinU4nvri4mNrY2Njs9/tSLBbzlmX5+9sdx9leW1tL2La9Nz097YqIxONxq9frbYaZNWwUMQAAEHkrKyuJycnJ3WQyeVVEZGJiYld1pjBQxAAAwLHdbOQKt4anJgEAQOSNj497y8vLQ57naa7rnmq320OqM4WBETEAABB5Y2NjfqlU2jEMo5BOp/ujo6OXVGcKgxYEgeoMAAAgwrrd7gXTNLdV54iibrebMU1z5Hb359YkAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAThzHcbK1Wu1MpVLJNpvNpIhIq9VK6LpeyOVyec/ztHK5PKzreqFcLg+rznsUXugKAABOrHq9fnF/udFopBzH2Zqdnd0REZmfn8+4rrs+MBDduhPdZAAAAAdUq9WzCwsLmXQ63c9ms1csy/KnpqZGbNvec103trS0lFpdXR1stVqDnufFfN+PGYaRn5ub25qZmXFV5z8MRQwAABxf82Pn5NXNeKjHPJ335aHP3nAy8U6nE19cXExtbGxs9vt9KRaLecuy/P3tjuNsr62tJWzb3puennZFROLxuNXr9TZDzRoyihgAAIi8lZWVxOTk5G4ymbwqIjIxMbGrOlMYKGIAAOD4bjJyhVvDU5MAACDyxsfHveXl5SHP8zTXdU+12+0h1ZnCwIgYAACIvLGxMb9UKu0YhlFIp9P90dHRS6ozhUELgkB1BgAAEGHdbveCaZrbqnNEUbfbzZimOXK7+3NrEgAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAACeO4zjZWq12plKpZJvNZlJEpNVqJXRdL+RyubzneVq5XB7Wdb1QLpeHVec9Ci90BQAAJ1a9Xr+4v9xoNFKO42zNzs7uiIjMz89nXNddHxiIbt2JbjIAAIADqtXq2YWFhUw6ne5ns9krlmX5U1NTI7Zt77muG1taWkqtrq4OtlqtQc/zYr7vxwzDyM/NzW3NzMy4qvMfhiIGAACO7eNrHz/3svtyPMxj6j+q+5/62U/dcDLxTqcTX1xcTG1sbGz2+30pFot5y7L8/e2O42yvra0lbNvem56edkVE4vG41ev1NsPMGjaKGAAAiLyVlZXE5OTkbjKZvCoiMjExsas6UxgoYgAA4NhuNnKFW8NTkwAAIPLGx8e95eXlIc/zNNd1T7Xb7SHVmcLAiBgAAIi8sbExv1Qq7RiGUUin0/3R0dFLqjOFQQuCQHUGAAAQYd1u94Jpmtuqc0RRt9vNmKY5crv7c2sSAABAEYoYAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAJ47jONlarXamUqlkm81mUkSk1WoldF0v5HK5vOd5WrlcHtZ1vVAul4dV5z0KL3QFAAAnVr1ev7i/3Gg0Uo7jbM3Ozu6IiMzPz2dc110fGIhu3YluMgAAgAOq1erZhYWFTDqd7mez2SuWZflTU1Mjtm3vua4bW1paSq2urg62Wq1Bz/Nivu/HDMPIz83Nbc3MzLiq8x+GIgYAAI7t4m//zrnXX3opHuYx7773Xj/7Lx6/4WTinU4nvri4mNrY2Njs9/tSLBbzlmX5+9sdx9leW1tL2La9Nz097YqIxONxq9frbYaZNWwUMQAAEHkrKyuJycnJ3WQyeVVEZGJiYld1pjBQxAAAwLHdbOQKt4anJgEAQOSNj497y8vLQ57naa7rnmq320OqM4WBETEAABB5Y2NjfqlU2jEMo5BOp/ujo6OXVGcKgxYEgeoMAAAgwrrd7gXTNLdV54iibrebMU1z5Hb359YkAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAThzHcbK1Wu1MpVLJNpvNpIhIq9VK6LpeyOVyec/ztHK5PKzreqFcLg+rznsUXugKAABOrHq9fnF/udFopBzH2Zqdnd0REZmfn8+4rrs+MBDduhPdZAAAAAdUq9WzCwsLmXQ63c9ms1csy/KnpqZGbNvec103trS0lFpdXR1stVqDnufFfN+PGYaRn5ub25qZmXFV5z8MRQwAABzbVxsvnNv5Gy8e5jFT70n4P/9r999wMvFOpxNfXFxMbWxsbPb7fSkWi3nLsvz97Y7jbK+trSVs296bnp52RUTi8bjV6/U2w8waNooYAACIvJWVlcTk5ORuMpm8KiIyMTGxqzpTGChiAADg2G42coVbw1OTAAAg8sbHx73l5eUhz/M013VPtdvtIdWZwsCIGAAAiLyxsTG/VCrtGIZRSKfT/dHR0UuqM4VBC4JAdQYAABBh3W73gmma26pzRFG3282Ypjlyu/tzaxIAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAAAnjuM42VqtdqZSqWSbzWZSRKTVaiV0XS/kcrm853lauVwe1nW9UC6Xh1XnPQovdAUAACdWvV6/uL/caDRSjuNszc7O7oiIzM/PZ1zXXR8YiG7diW4yAACAA6rV6tmFhYVMOp3uZ7PZK5Zl+VNTUyO2be+5rhtbWlpKra6uDrZarUHP82K+78cMw8jPzc1tzczMuKrzH4YiBgAAju3Lv1c/t/1X346HeczMuZ/0f+E3KzecTLzT6cQXFxdTGxsbm/1+X4rFYt6yLH9/u+M422trawnbtvemp6ddEZF4PG71er3NMLOGjSIGAAAib2VlJTE5ObmbTCaviohMTEzsqs4UBooYAAA4tpuNXOHW8NQkAACIvPHxcW95eXnI8zzNdd1T7XZ7SHWmMDAiBgAAIm9sbMwvlUo7hmEU0ul0f3R09JLqTGHQgiBQnQEAAERYt9u9YJrmtuocUdTtdjOmaY7c7v7cmgQAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAMCJ4zhOtlarnalUKtlms5kUEWm1Wgld1wu5XC7veZ5WLpeHdV0vlMvlYdV5j8ILXQEAwIlVr9cv7i83Go2U4zhbs7OzOyIi8/PzGdd11wcGolt3opsMAADggGq1enZhYSGTTqf72Wz2imVZ/tTU1Iht23uu68aWlpZSq6urg61Wa9DzvJjv+zHDMPJzc3NbMzMzrur8h6GIAQCAY9t5+sVz/VcuxcM85rvO3uOnHr7vhpOJdzqd+OLiYmpjY2Oz3+9LsVjMW5bl7293HGd7bW0tYdv23vT0tCsiEo/HrV6vtxlm1rBRxAAAQOStrKwkJicnd5PJ5FURkYmJiV3VmcJAEQMAAMd2s5Er3BqemgQAAJE3Pj7uLS8vD3mep7mue6rdbg+pzhQGRsQAAEDkjY2N+aVSaccwjEI6ne6Pjo5eUp0pDFoQBKozAACACOt2uxdM09xWnSOKut1uxjTNkdvdn1uTAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAOHEcx8nWarUzlUol22w2kyIirVYroet6IZfL5T3P08rl8rCu64VyuTysOu9ReKErAAA4ser1+sX95UajkXIcZ2t2dnZHRGR+fj7juu76wEB06050kwEAABxQrVbPLiwsZNLpdD+bzV6xLMufmpoasW17z3Xd2NLSUmp1dXWw1WoNep4X830/ZhhGfm5ubmtmZsZVnf8wFDEAAHBszWbz3KuvvhoP85inT5/2H3rooRtOJt7pdOKLi4upjY2NzX6/L8ViMW9Zlr+/3XGc7bW1tYRt23vT09OuiEg8Hrd6vd5mmFnDRhEDAACRt7KykpicnNxNJpNXRUQmJiZ2VWcKA0UMAAAc281GrnBreGoSAABE3vj4uLe8vDzkeZ7muu6pdrs9pDpTGBgRAwAAkTc2NuaXSqUdwzAK6XS6Pzo6ekl1pjBoQRCozgAAACKs2+1eME1zW3WOKOp2uxnTNEdud39uTQIAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAODEcRwnW6vVzlQqlWyz2UyKiLRarYSu64VcLpf3PE8rl8vDuq4XyuXysOq8R+GFrgAA4MSq1+sX95cbjUbKcZyt2dnZHRGR+fn5jOu66wMD0a070U0GAABwQLVaPbuwsJBJp9P9bDZ7xbIsf2pqasS27T3XdWNLS0up1dXVwVarNeh5Xsz//9m7nxBFwv3c469jX27wKN2x5MxgenJ7Uefg0cKy1nFlwAtSiyNmHXAhxTUbKQlCwnFzSCBk4ybcvQtBCOhGqYsEEXE/hdBHzjmLublkOhyafrtJTZGMMN5VwyzmT89Q8FbD97MqKN6XZ/nwe6l6wzBpGEax3+/fdDodqTr/51DEAADAk13/ZvD6ffDbVJR7/iT987D4i3/46mXi2+02NZvNsvv9/vp4PIpKpVK0LCt8fO+67u1ut0vbtv3QbrelEEKkUinrcDhcR5k1ahQxAAAQe+v1Ot1oNO4zmcxHIYSo1+v3qjNFgSIGAACe7FuTK3wfvpoEAACxV6vVguVyeREEQUJK+WK1Wl2ozhQFJmIAACD2qtVq2Gw27wzDKGmadiyXy+9VZ4pC4nQ6qc4AAABizPf9t6Zp3qrOEUe+7+dM07z60fUcTQIAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAODZcV03PxwOX/Z6vfx8Ps8IIYTneWld10uFQqEYBEHCcZxLXddLjuNcqs77JfzQFQAAPFuj0ejd4/N4PM66rnvT7XbvhBBiMpnkpJRvzs7iW3fimwwAAOATg8Hg1XQ6zWmadszn8x8sywpbrdaVbdsPUsrkYrHIbjabc8/zzoMgSIZhmDQMo9jv9286nY5Unf9zKGIAAODJer/519eH9/+ZinLPwk/+KBz94k+/epn4drtNzWaz7H6/vz4ej6JSqRQtywof37uue7vb7dK2bT+0220phBCpVMo6HA7XUWaNGkUMAADE3nq9TjcajftMJvNRCCHq9fq96kxRoIgBAIAn+9bkCt+HryYBAEDs1Wq1YLlcXgRBkJBSvlitVheqM0WBiRgAAIi9arUaNpvNO8MwSpqmHcvl8nvVmaKQOJ1OqjMAAIAY833/rWmat6pzxJHv+znTNK9+dD1HkwAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAHh2XNfND4fDl71eLz+fzzNCCOF5XlrX9VKhUCgGQZBwHOdS1/WS4ziXqvN+CT90BQAAz9ZoNHr3+Dwej7Ou6950u907IYSYTCY5KeWbs7P41p34JgMAAPjEYDB4NZ1Oc5qmHfP5/AfLssJWq3Vl2/aDlDK5WCyym83m3PO88yAIkmEYJg3DKPb7/ZtOpyNV5/8cihgAAHiyv/5n//Vv//0/UlHu+fNXmfAf/8L86mXi2+02NZvNsvv9/vp4PIpKpVK0LCt8fO+67u1ut0vbtv3QbrelEEKkUinrcDhcR5k1ahQxAAAQe+v1Ot1oNO4zmcxHIYSo1+v3qjNFgSIGAACe7FuTK3wfvpoEAACxV6vVguVyeREEQUJK+WK1Wl2ozhQFJmIAACD2qtVq2Gw27wzDKGmadiyXy+9VZ4pC4nQ6qc4AAABizPf9t6Zp3qrOEUe+7+dM07z60fUcTQIAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAODZcV03PxwOX/Z6vfx8Ps8IIYTneWld10uFQqEYBEHCcZxLXddLjuNcqs77JfzQFQAAPFuj0ejd4/N4PM66rnvT7XbvhBBiMpnkpJRvzs7iW3fimwwAAOATg8Hg1XQ6zWmadszn8x8sywpbrdaVbdsPUsrkYrHIbjabc8/zzoMgSIZhmDQMo9jv9286nY5Unf9zKGIAAODp5n/1WvzhOhXpnj8thuKX//TVy8S3221qNptl9/v99fF4FJVKpWhZVvj43nXd291ul7Zt+6HdbkshhEilUtbhcLiONGvEKGIAACD21ut1utFo3GcymY9CCFGv1+9VZ4oCRQwAADzdNyZX+D58NQkAAGKvVqsFy+XyIgiChJTyxWq1ulCdKQpMxAAAQOxVq9Ww2WzeGYZR0jTtWC6X36vOFIXE6XRSnQEAAMSY7/tvTdO8VZ0jjnzfz5mmefWj6zmaBAAAUIQiBgAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAwLPjum5+OBy+7PV6+fl8nhFCCM/z0rqulwqFQjEIgoTjOJe6rpccx7lUnfdL+KErAAB4tkaj0bvH5/F4nHVd96bb7d4JIcRkMslJKd+cncW37sQ3GQAAwCcGg8Gr6XSa0zTtmM/nP1iWFbZarSvbth+klMnFYpHdbDbnnuedB0GQDMMwaRhGsd/v33Q6Hak6/+dQxAAAwJP9aver17+Xv09Fuaf+x3r46z/79VcvE99ut6nZbJbd7/fXx+NRVCqVomVZ4eN713Vvd7td2rbth3a7LYUQIpVKWYfD4TrKrFGjiAEAgNhbr9fpRqNxn8lkPgohRL1ev1edKQoUMQAA8GTfmlzh+/DVJAAAiL1arRYsl8uLIAgSUsoXq9XqQnWmKDARAwAAsVetVsNms3lnGEZJ07RjuVx+rzpTFBKn00l1BgAAEGO+7781TfNWdY448n0/Z5rm1Y+u52gSAABAEYoYAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAz47ruvnhcPiy1+vl5/N5RgghPM9L67peKhQKxSAIEo7jXOq6XnIc51J13i/hh64AAODZGo1G7x6fx+Nx1nXdm263eyeEEJPJJCelfHN2Ft+6E99kAAAAnxgMBq+m02lO07RjPp//YFlW2Gq1rmzbfpBSJheLRXaz2Zx7nnceBEEyDMOkYRjFfr9/0+l0pOr8n0MRAwAAT/bub/729X/97nepKPf87z/7WZj/+7/76mXi2+02NZvNsvv9/vp4PIpKpVK0LCt8fO+67u1ut0vbtv3QbrelEEKkUinrcDhcR5k1ahQxAAAQe+v1Ot1oNO4zmcxHIYSo1+v3qjNFgSIGAACe7FuTK3wfvpoEAACxV6vVguVyeREEQUJK+WK1Wl2ozhQFJmIAACD2qtVq2Gw27wzDKGmadiyXy+9VZ4pC4nQ6qc4AAABizPf9t6Zp3qrOEUe+7+dM07z60fUcTQIAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAODZcV03PxwOX/Z6vfx8Ps8IIYTneWld10uFQqEYBEHCcZxLXddLjuNcqs77JfzQFQAAPFuj0ejd4/N4PM66rnvT7XbvhBBiMpnkpJRvzs7iW3fimwwAAOATg8Hg1XQ6zWmadszn8x8sywpbrdaVbdsPUsrkYrHIbjabc8/zzoMgSIZhmDQMo9jv9286nY5Unf9zKGIAAODJ/mX8m9d3/xakotwz+yfp8M//8hdfvUx8u92mZrNZdr/fXx+PR1GpVIqWZYWP713Xvd3tdmnbth/a7bYUQohUKmUdDofrKLNGjSIGAABib71epxuNxn0mk/kohBD1ev1edaYoUMQAAMCTfWtyhe/DV5MAACD2arVasFwuL4IgSEgpX6xWqwvVmaLARAwAAMRetVoNm83mnWEYJU3TjuVy+b3qTFFInE4n1RkAAECM+b7/1jTNW9U54sj3/Zxpmlc/up6jSQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAADw7ruvmh8Phy16vl5/P5xkhhPA8L63reqlQKBSDIEg4jnOp63rJcZxL1Xm/hB+6AgCAZ2s0Gr17fB6Px1nXdW+63e6dEEJMJpOclPLN2Vl86058kwEAAHxiMBi8mk6nOU3Tjvl8/oNlWWGr1bqybftBSplcLBbZzWZz7nneeRAEyTAMk4ZhFPv9/k2n05Gq838ORQwAADzZ//nfo9e3/+//pqLcM/f6f4T/83/1vnqZ+Ha7Tc1ms+x+v78+Ho+iUqkULcsKH9+7rnu72+3Stm0/tNttKYQQqVTKOhwO11FmjRpFDAAAxN56vU43Go37TCbzUQgh6vX6vepMUaCIAQCAJ/vW5Arfh68mAQBA7NVqtWC5XF4EQZCQUr5YrVYXqjNFgYkYAACIvWq1GjabzTvDMEqaph3L5fJ71ZmikDidTqozAACAGPN9/61pmreqc8SR7/s50zSvfnQ9R5MAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAABAEYoYAABNYL4HAAAgAElEQVR4dlzXzQ+Hw5e9Xi8/n88zQgjheV5a1/VSoVAoBkGQcBznUtf1kuM4l6rzfgk/dAUAAM/WaDR69/g8Ho+zruvedLvdOyGEmEwmOSnlm7Oz+Nad+CYDAAD4xGAweDWdTnOaph3z+fwHy7LCVqt1Zdv2g5QyuVgsspvN5tzzvPMgCJJhGCYNwyj2+/2bTqcjVef/HIoYAAB4srt//u3r47+/T0W553979ZMw+xc//+pl4tvtNjWbzbL7/f76eDyKSqVStCwrfHzvuu7tbrdL27b90G63pRBCpFIp63A4XEeZNWoUMQAAEHvr9TrdaDTuM5nMRyGEqNfr96ozRYEiBgAAnuxbkyt8H76aBAAAsVer1YLlcnkRBEFCSvlitVpdqM4UBSZiAAAg9qrVathsNu8MwyhpmnYsl8vvVWeKQuJ0OqnOAAAAYsz3/bemad6qzhFHvu/nTNO8+tH1HE0CAAAoQhEDAABQhCIGAACgCEUMAABAEYoYAACAIhQxAAAARShiAADg2XFdNz8cDl/2er38fD7PCCGE53lpXddLhUKhGARBwnGcS13XS47jXKrO+yX80BUAADxbo9Ho3ePzeDzOuq570+1274QQYjKZ5KSUb87O4lt34psMAADgE4PB4NV0Os1pmnbM5/MfLMsKW63WlW3bD1LK5GKxyG42m3PP886DIEiGYZg0DKPY7/dvOp2OVJ3/cyhiAADgyebz+es//OEPqSj3/OlPfxr+8pe//Opl4tvtNjWbzbL7/f76eDyKSqVStCwrfHzvuu7tbrdL27b90G63pRBCpFIp63A4XEeZNWoUMQAAEHvr9TrdaDTuM5nMRyGEqNfr96ozRYEiBgAAnuxbkyt8H76aBAAAsVer1YLlcnkRBEFCSvlitVpdqM4UBSZiAAAg9qrVathsNu8MwyhpmnYsl8vvVWeKQuJ0OqnOAAAAYsz3/bemad6qzhFHvu/nTNO8+tH1HE0CAAAoQhEDAABQhCIGAACgCEUMAABAEYoYAACAIhQxAAAARShiAADg2XFdNz8cDl/2er38fD7PCCGE53lpXddLhUKhGARBwnGcS13XS47jXKrO+yX80BUAADxbo9Ho3ePzeDzOuq570+1274QQYjKZ5KSUb87O4lt34psMAADgE4PB4NV0Os1pmnbM5/MfLMsKW63WlW3bD1LK5GKxyG42m3PP886DIEiGYZg0DKPY7/dvOp2OVJ3/cyhiAADgya5/M3j9PvhtKso9f5L+eVj8xT989TLx7Xabms1m2f1+f308HkWlUilalhU+vndd93a326Vt235ot9tSCCFSqZR1OByuo8waNYoYAACIvfV6nW40GveZTOajEELU6/V71ZmiQBEDAABP9q3JFb4PX00CAIDYq9VqwXK5vAiCICGlfLFarS5UZ4oCEzEAABB71Wo1bDabd4ZhlDRNO5bL5feqM0UhcTqdVGcAAAAx5vv+W9M0b1XniCPf93OmaV796HqOJgEAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAPDsuK6bHw6HL3u9Xn4+n2eEEMLzvLSu66VCoVAMgiDhOM6lruslx3EuVef9En7oCgAAnq3RaPTu8Xk8Hmdd173pdrt3QggxmUxyUso3Z2fxrTvxTQYAAPCJwWDwajqd5jRNO+bz+Q+WZYWtVuvKtu0HKWVysVhkN5vNued550EQJMMwTBqGUez3+zedTkeqzv85FDEAAPBkvd/86+vD+/9MRbln4Sd/FI5+8adfvUx8u92mZrNZdr/fXx+PR1GpVIqWZYWP713Xvd3tdmnbth/a7bYUQohUKmUdDofrKLNGjSIGAABib71epxuNxn0mk/kohBD1ev1edaYoUMQAAMCTfWtyhe/DV5MAACD2arVasFwuL4IgSEgpX6xWqwvVmaLARAwAAMRetVoNm83mnWEYJU3TjuVy+b3qTFFInE4n1RkAAECM+b7/1jTNW9U54sj3/Zxpmlc/up6jSQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAADw7ruvmh8Phy16vl5/P5xkhhPA8L63reqlQKBSDIEg4jnOp63rJcZxL1Xm/hB+6AgCAZ2s0Gr17fB6Px1nXdW+63e6dEEJMJpOclPLN2Vl86058kwEAAHxiMBi8mk6nOU3Tjvl8/oNlWWGr1bqybftBSplcLBbZzWZz7nneeRAEyTAMk4ZhFPv9/k2n05Gq838ORQwAADzZX/+z//q3//4fqSj3/PmrTPiPf2F+9TLx7Xabms1m2f1+f308HkWlUilalhU+vndd93a326Vt235ot9tSCCFSqZR1OByuo8waNYoYAACIvfV6nW40GveZTOajEELU6/V71ZmiQBEDAABP9q3JFb4PX00CAIDYq9VqwXK5vAiCICGlfLFarS5UZ4oCEzEAABB71Wo1bDabd4ZhlDRNO5bL5feqM0UhcTqdVGcAAAAx5vv+W9M0b1XniCPf93OmaV796HqOJgEAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAPDsuK6bHw6HL3u9Xn4+n2eEEMLzvLSu66VCoVAMgiDhOM6lruslx3EuVef9En7oCgAAnq3RaPTu8Xk8Hmdd173pdrt3QggxmUxyUso3Z2fxrTvxTQYAAPCJwWDwajqd5jRNO+bz+Q+WZYWtVuvKtu0HKWVysVhkN5vNued550EQJMMwTBqGUez3+zedTkeqzv85FDEAAPB08796Lf5wnYp0z58WQ/HLf/rqZeLb7TY1m82y+/3++ng8ikqlUrQsK3x877ru7W63S9u2/dBut6UQQqRSKetwOFxHmjViFDEAABB76/U63Wg07jOZzEchhKjX6/eqM0WBIgYAAJ7uG5MrfB++mgQAALFXq9WC5XJ5EQRBQkr5YrVaXajOFAUmYgAAIPaq1WrYbDbvDMMoaZp2LJfL71VnikLidDqpzgAAAGLM9/23pmneqs4RR77v50zTvPrR9RxNAgAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAgCIUMQAAAEUoYgAA4NlxXTc/HA5f9nq9/Hw+zwghhOd5aV3XS4VCoRgEQcJxnEtd10uO41yqzvsl/NAVAAA8W6PR6N3j83g8zrque9Ptdu+EEGIymeSklG/OzuJbd+KbDAAA4BODweDVdDrNaZp2zOfzHyzLClut1pVt2w9SyuRischuNptzz/POgyBIhmGYNAyj2O/3bzqdjlSd/3MoYgAA4Ml+tfvV69/L36ei3FP/Yz389Z/9+quXiW+329RsNsvu9/vr4/EoKpVK0bKs8PG967q3u90ubdv2Q7vdlkIIkUqlrMPhcB1l1qhRxAAAQOyt1+t0o9G4z2QyH4UQol6v36vOFAWKGAAAeLJvTa7wffhqEgAAxF6tVguWy+VFEAQJKeWL1Wp1oTpTFJiIAQCA2KtWq2Gz2bwzDKOkadqxXC6/V50pConT6aQ6AwAAiDHf99+apnmrOkcc+b6fM03z6kfXczQJAACgCEUMAABAEYoYAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQCAZ8d13fxwOHzZ6/Xy8/k8I4QQnueldV0vFQqFYhAECcdxLnVdLzmOc6k675fwQ1cAAPBsjUajd4/P4/E467ruTbfbvRNCiMlkkpNSvjk7i2/diW8yAACATwwGg1fT6TSnadoxn89/sCwrbLVaV7ZtP0gpk4vFIrvZbM49zzsPgiAZhmHSMIxiv9+/6XQ6UnX+z6GIAQCAJ3v3N3/7+r9+97tUlHv+95/9LMz//d999TLx7Xabms1m2f1+f308HkWlUilalhU+vndd93a326Vt235ot9tSCCFSqZR1OByuo8waNYoYAACIvfV6nW40GveZTOajEELU6/V71ZmiQBEDAABP9q3JFb4PX00CAIDYq9VqwXK5vAiCICGlfLFarS5UZ4oCEzEAABB71Wo1bDabd4ZhlDRNO5bL5feqM0UhcTqdVGcAAAAx5vv+W9M0b1XniCPf93OmaV796HqOJgEAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAPDsuK6bHw6HL3u9Xn4+n2eEEMLzvLSu66VCoVAMgiDhOM6lruslx3EuVef9En7oCgAAnq3RaPTu8Xk8Hmdd173pdrt3QggxmUxyUso3Z2fxrTvxTQYAAPCJwWDwajqd5jRNO+bz+Q+WZYWtVuvKtu0HKWVysVhkN5vNued550EQJMMwTBqGUez3+zedTkeqzv85FDEAAPBk/zL+zeu7fwtSUe6Z/ZN0+Od/+YuvXia+3W5Ts9ksu9/vr4/Ho6hUKkXLssLH967r3u52u7Rt2w/tdlsKIUQqlbIOh8N1lFmjRhEDAACxt16v041G4z6TyXwUQoh6vX6vOlMUKGIAAODJvjW5wvfhq0kAABB7tVotWC6XF0EQJKSUL1ar1YXqTFFgIgYAAGKvWq2GzWbzzjCMkqZpx3K5/F51pigkTqeT6gwAACDGfN9/a5rmreocceT7fs40zasfXc/RJAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAJ4d13Xzw+HwZa/Xy8/n84wQQniel9Z1vVQoFIpBECQcx7nUdb3kOM6l6rxfwg9dAQDAszUajd49Po/H46zrujfdbvdOCCEmk0lOSvnm7Cy+dSe+yQAAAD4xGAxeTafTnKZpx3w+/8GyrLDVal3Ztv0gpUwuFovsZrM59zzvPAiCZBiGScMwiv1+/6bT6UjV+T+HIgYAAJ7s//zv0evb//d/U1HumXv9P8L/+b96X71MfLvdpmazWXa/318fj0dRqVSKlmWFj+9d173d7XZp27Yf2u22FEKIVCplHQ6H6yizRo0iBgAAYm+9XqcbjcZ9JpP5KIQQ9Xr9XnWmKFDEAADAk31rcoXvw1eTAAAg9mq1WrBcLi+CIEhIKV+sVqsL1ZmiwEQMAADEXrVaDZvN5p1hGCVN047lcvm96kxRSJxOJ9UZAABAjPm+/9Y0zVvVOeLI9/2caZpXP7qeo0kAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAAA8O67r5ofD4cter5efz+cZIYTwPC+t63qpUCgUgyBIOI5zqet6yXGcS9V5v4QfugIAgGdrNBq9e3wej8dZ13Vvut3unRBCTCaTnJTyzdlZfOtOfJMBAAB8YjAYvJpOpzlN0475fP6DZVlhq9W6sm37QUqZXCwW2c1mc+553nkQBMkwDJOGYRT7/f5Np9ORqvN/DkUMAAA82d0///b18d/fp6Lc87+9+kmY/Yuff/Uy8e12m5rNZtn9fn99PB5FpVIpWpYVPr53Xfd2t9ulbdt+aLfbUgghUqmUdTgcrqPMGjWKGAAAiL31ep1uNBr3mUzmoxBC1Ov1e9WZokARAwAAT/atyRW+D19NAgCA2KvVasFyubwIgiAhpXyxWq0uVGeKAhMxAAAQe9VqNWw2m3eGYZQ0TTuWy+X3qjNFIXE6nVRnAAAAMeb7/lvTNG9V54gj3/dzpmle/eh6jiYBAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAABAEYoYAACAIhQxAADw7Liumx8Ohy97vV5+Pp9nhBDC87y0ruulQqFQDIIg4TjOpa7rJcdxLlXn/RJ+6AoAAJ6t0Wj07vF5PB5nXde96Xa7d0IIMZlMclLKN2dn8a078U0GAADwicFg8Go6neY0TTvm8/kPlmWFrVbryrbtByllcrFYZDebzbnneedBECTDMEwahlHs9/s3nU5Hqs7/ORQxAADwZPP5/PUf/vCHVJR7/vSnPw1/+ctffvUy8e12m5rNZtn9fn99PB5FpVIpWpYVPr53Xfd2t9ulbdt+aLfbUgghUqmUdTgcrqPMGjWKGAAAiL31ep1uNBr3mUzmoxBC1Ov1e9WZokARAwAAT/atyRW+D19NAgCA2KvVasFyubwIgiAhpXyxWq0uVGeKAhMxAAAQe9VqNWw2m3eGYZQ0TTuWy+X3qjNFIXE6nVRnAAAAMeb7/lvTNG9V54gj3/dzpmle/eh6jiYBAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAABAEYoYAACAIhQxAADw7Liumx8Ohy97vV5+Pp9nhBDC87y0ruulQqFQDIIg4TjOpa7rJcdxLlXn/RJ+6AoAAJ6t0Wj07vF5PB5nXde96Xa7d0IIMZlMclLKN2dn8a078U0GAADwicFg8Go6neY0TTvm8/kPlmWFrVbryrbtByllcrFYZDebzbnneedBECTDMEwahlHs9/s3nU5Hqs7/ORQxAADwZNe/Gbx+H/w2FeWeP0n/PCz+4h++epn4drtNzWaz7H6/vz4ej6JSqRQtywof37uue7vb7dK2bT+0220phBCpVMo6HA7XUWaNGkUMAADE3nq9TjcajftMJvNRCCHq9fq96kxRoIgBAIAn+9bkCt+HryYBAEDs1Wq1YLlcXgRBkJBSvlitVheqM0WBiRgAAIi9arUaNpvNO8MwSpqmHcvl8nvVmaKQOJ1OqjMAAIAY833/rWmat6pzxJHv+znTNK9+dD1HkwAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAHh2XNfND4fDl71eLz+fzzNCCOF5XlrX9VKhUCgGQZBwHOdS1/WS4ziXqvN+CT90BQAAz9ZoNHr3+Dwej7Ou6950u907IYSYTCY5KeWbs7P41p34JgMAAPjEYDB4NZ1Oc5qmHfP5/AfLssJWq3Vl2/aDlDK5WCyym83m3PO88yAIkmEYJg3DKPb7/ZtOpyNV5/8cihgAAHiy3m/+9fXh/X+motyz8JM/Cke/+NOvXia+3W5Ts9ksu9/vr4/Ho6hUKkXLssLH967r3u52u7Rt2w/tdlsKIUQqlbIOh8N1lFmjRhEDAACxt16v041G4z6TyXwUQoh6vX6vOlMUKGIAAODJvjW5wvfhq0kAABB7tVotWC6XF0EQJKSUL1ar1YXqTFFgIgYAAGKvWq2GzWbzzjCMkqZpx3K5/F51pigkTqeT6gwAACDGfN9/a5rmreocceT7fs40zasfXc/RJAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAJ4d13Xzw+HwZa/Xy8/n84wQQniel9Z1vVQoFIpBECQcx7nUdb3kOM6l6rxfwg9dAQDAszUajd49Po/H46zrujfdbvdOCCEmk0lOSvnm7Cy+dSe+yQAAAD4xGAxeTafTnKZpx3w+/8GyrLDVal3Ztv0gpUwuFovsZrM59zzvPAiCZBiGScMwiv1+/6bT6UjV+T+HIgYAAJ7sr//Zf/3bf/+PVJR7/vxVJvzHvzC/epn4drtNzWaz7H6/vz4ej6JSqRQtywof37uue7vb7dK2bT+0220phBCpVMo6HA7XUWaNGkUMAADE3nq9TjcajftMJvNRCCHq9fq96kxRoIgBAIAn+9bkCt+HryYBAEDs1Wq1YLlcXgRBkJBSvlitVheqM0WBiRgAAIi9arUaNpvNO8MwSpqmHcvl8nvVmaKQOJ1OqjMAAIAY833/rWmat6pzxJHv+znTNK9+dD1HkwAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAHh2XNfND4fDl71eLz+fzzNCCOF5XlrX9VKhUCgGQZBwHOdS1/WS4ziXqvN+CT90BQAAz9ZoNHr3+Dwej7Ou6950u907IYSYTCY5KeWbs7P41p34JgMAAPjEYDB4NZ1Oc5qmHfP5/AfLssJWq3Vl2/aDlDK5WCyym83m3PO88yAIkmEYJg3DKPb7/ZtOpyNV5/8cihgAAHi6+V+9Fn+4TkW650+LofjlP331MvHtdpuazWbZ/X5/fTweRaVSKVqWFT6+d133drfbpW3bfmi321IIIVKplHU4HK4jzRoxihgAAIi99XqdbjQa95lM5qMQQtTr9XvVmaJAEQMAAE/3jckVvg9fTQIAgNir1WrBcrm8CIIgIaV8sVqtLlRnigITMQAAEHvVajVsNpt3hmGUNE07lsvl96ozRSFxOp1UZwAAADHm+/5b0zRvVeeII9/3c6ZpXv3oeo4mAQAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAgCIUMQAA8Oy4rpsfDocve71efj6fZ4QQwvO8tK7rpUKhUAyCIOE4zqWu6yXHcS5V5/0SfugKAACerdFo9O7xeTweZ13Xvel2u3dCCDGZTHJSyjdnZ/GtO/FNBgAA8InBYPBqOp3mNE075vP5D5Zlha1W68q27QcpZXKxWGQ3m82553nnQRAkwzBMGoZR7Pf7N51OR6rO/zkUMQAA8GS/2v3q9e/l71NR7qn/sR7++s9+/dXLxLfbbWo2m2X3+/318XgUlUqlaFlW+Pjedd3b3W6Xtm37od1uSyGESKVS1uFwuI4ya9QoYgAAIPbW63W60WjcZzKZj0IIUa/X71VnigJFDAAAPNm3Jlf4Pnw1CQAAYq9WqwXL5fIiCIKElPLFarW6UJ0pCkzEAABA7FWr1bDZbN4ZhlHSNO1YLpffq84UhcTpdFKdAQAAxJjv+29N07xVnSOOfN/PmaZ59aPrOZoEAABQhCIGAACgCEUMAABAEYoYAACAIhQxAAAARShiAAAAilDEAADAs+O6bn44HL7s9Xr5+XyeEUIIz/PSuq6XCoVCMQiChOM4l7qulxzHuVSd90v4oSsAAHi2RqPRu8fn8XicdV33ptvt3gkhxGQyyUkp35ydxbfuxDcZAADAJwaDwavpdJrTNO2Yz+c/WJYVtlqtK9u2H6SUycVikd1sNuee550HQZAMwzBpGEax3+/fdDodqTr/51DEAADAk737m799/V+/+10qyj3/+89+Fub//u++epn4drtNzWaz7H6/vz4ej6JSqRQtywof37uue7vb7dK2bT+0220phBCpVMo6HA7XUWaNGkUMAADE3nq9TjcajftMJvNRCCHq9fq96kxRoIgBAIAn+9bkCt+HryYBAEDs1Wq1YLlcXgRBkJBSvlitVheqM0WBiRgAAIi9arUaNpvNO8MwSpqmHcvl8nvVmaKQOJ1OqjMAAIAY833/rWmat6pzxJHv+znTNK9+dD1HkwAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAHh2XNfND4fDl71eLz+fzzNCCOF5XlrX9VKhUCgGQZBwHOdS1/WS4ziXqvN+CT90BQAAz9ZoNHr3+Dwej7Ou6950u907IYSYTCY5KeWbs7P41p34JgMAAPjEYDB4NZ1Oc5qmHfP5/AfLssJWq3Vl2/aDlDK5WCyym83m3PO88yAIkmEYJg3DKPb7/ZtOpyNV5/8cihgAAHiyfxn/5vXdvwWpKPfM/kk6/PO//MVXLxPfbrep2WyW3e/318fjUVQqlaJlWeHje9d1b3e7Xdq27Yd2uy2FECKVSlmHw+E6yqxRo4gBAIDYW6/X6UajcZ/JZD4KIUS9Xr9XnSkKFDEAAPBk35pc4fvw1SQAAIi9Wq0WLJfLiyAIElLKF6vV6kJ1pigwEQMAALFXrVbDZrN5ZxhGSdO0Y7lcfq86UxQSp9NJdQYAABBjvu+/NU3zVnWOOPJ9P2ea5tWPrudoEgAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAM+O67r54XD4stfr5efzeUYIITzPS+u6XioUCsUgCBKO41zqul5yHOdSdd4v4YeuAADg2RqNRu8en8fjcdZ13Ztut3snhBCTySQnpXxzdhbfuhPfZAAAAJ8YDAavptNpTtO0Yz6f/2BZVthqta5s236QUiYXi0V2s9mce553HgRBMgzDpGEYxX6/f9PpdKTq/J9DEQMAAE/2f/736PXt//u/qSj3zL3+H+H//F+9r14mvt1uU7PZLLvf76+Px6OoVCpFy7LCx/eu697udru0bdsP7XZbCiFEKpWyDofDdZRZo0YRAwAAsbder9ONRuM+k8l8FEKIer1+rzpTFChiAADgyb41ucL34atJAAAQe7VaLVgulxdBECSklC9Wq9WF6kxRYCIGAABir1qths1m884wjJKmacdyufxedaYoJE6nk+oMAAAgxnzff2ua5q3qHHHk+37ONM2rH13P0SQAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACeHdd188Ph8GWv18vP5/OMEEJ4npfWdb1UKBSKQRAkHMe51HW95DjOpeq8X8IPXQEAwLM1Go3ePT6Px+Os67o33W73TgghJpNJTkr55uwsvnUnvskAAAA+MRgMXk2n05ymacd8Pv/Bsqyw1Wpd2bb9IKVMLhaL7GazOfc87zwIgmQYhknDMIr9fv+m0+lI1fk/hyIGAACe7O6ff/v6+O/vU1Hu+d9e/STM/sXPv3qZ+Ha7Tc1ms+x+v78+Ho+iUqkULcsKH9+7rnu72+3Stm0/tNttKYQQqVTKOhwO11FmjRpFDAAAxN56vU43Go37TCbzUQgh6vX6vepMUaCIAQCAJ/vW5Arfh68mAQBA7NVqtWC5XF4EQZCQUr5YrVYXqjNFgYkYAACIvWq1GjabzTvDMEqaph3L5fJ71ZmikDidTqozAACAGPN9/61pmreqc8SR7/s50zSvfnQ9R5MAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAABAEYoYAAB4dlzXzQ+Hw5e9Xi8/n88zQgjheV5a1/VSoVAoBkGQcBznUtf1kuM4l6rzfgk/dAUAAM/WaDR69/g8Ho+zruvedLvdOyGEmH2wpYkAACAASURBVEwmOSnlm7Oz+Nad+CYDAAD4xGAweDWdTnOaph3z+fwHy7LCVqt1Zdv2g5QyuVgsspvN5tzzvPMgCJJhGCYNwyj2+/2bTqcjVef/HIoYAAB4svl8/voPf/hDKso9f/rTn4a//OUvv3qZ+Ha7Tc1ms+x+v78+Ho+iUqkULcsKH9+7rnu72+3Stm0/tNttKYQQqVTKOhwO11FmjRpFDAAAxN56vU43Go37TCbzUQgh6vX6vepMUaCIAQCAJ/vW5Arfh68mAQBA7NVqtWC5XF4EQZCQUr5YrVYXqjNFgYkYAACIvWq1GjabzTvDMEqaph3L5fJ71ZmikDidTqozAACAGPN9/61pmreqc8SR7/s50zSvfnQ9R5MAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAABAEYoYAAB4dlzXzQ+Hw5e9Xi8/n88zQgjheV5a1/VSoVAoBkGQcBznUtf1kuM4l6rzfgk/dAUAAM/WaDR69/g8Ho+zruvedLvdOyGEmEwmOSnlm7Oz+Nad+CYDAAD4xGAweDWdTnOaph3z+fwHy7LCVqt1Zdv2g5QyuVgsspvN5tzzvPMgCJJhGCYNwyj2+/2bTqcjVef/HIoYAAB4suvfDF6/D36binLPn6R/HhZ/8Q9fvUx8u92mZrNZdr/fXx+PR1GpVIqWZYWP713Xvd3tdmnbth/a7bYUQohUKmUdDofrKLNGjSIGAABib71epxuNxn0mk/kohBD1ev1edaYoUMQAAMCTfWtyhe/DV5MAACD2arVasFwuL4IgSEgpX6xWqwvVmaLARAwAAMRetVoNm83mnWEYJU3TjuVy+b3qTFFInE4n1RkAAECM+b7/1jTNW9U54sj3/Zxpmlc/up6jSQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAADw7ruvmh8Phy16vl5/P5xkhhPA8L63reqlQKBSDIEg4jnOp63rJcZxL1Xm/hB+6AgCAZ2s0Gr17fB6Px1nXdW+63e6dEEJMJpOclPLN2Vl86058kwEAAHxiMBi8mk6nOU3Tjvl8/oNlWWGr1bqybftBSplcLBbZzWZz7nneeRAEyTAMk4ZhFPv9/k2n05Gq838ORQwAADxZ7zf/+vrw/j9TUe5Z+MkfhaNf/OlXLxPfbrep2WyW3e/318fjUVQqlaJlWeHje9d1b3e7Xdq27Yd2uy2FECKVSlmHw+E6yqxRo4gBAIDYW6/X6UajcZ/JZD4KIUS9Xr9XnSkKFDEAAPBk35pc4fvw1SQAAIi9Wq0WLJfLiyAIElLKF6vV6kJ1pigwEQMAALFXrVbDZrN5ZxhGSdO0Y7lcfq86UxQSp9NJdQYAABBjvu+/NU3zVnWOOPJ9P2ea5tWPrudoEgAAQBGKGAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAM+O67r54XD4stfr5efzeUYIITzPS+u6XioUCsUgCBKO41zqul5yHOdSdd4v4YeuAADg2RqNRu8en8fjcdZ13Ztut3snhBCTySQnpXxzdhbfuhPfZAAAAJ8YDAavptNpTtO0Yz6f/2BZVthqta5s236QUiYXi0V2s9mce553HgRBMgzDpGEYxX6/f9PpdKTq/J9DEQMAAE/21//sv/7tv/9HKso9f/4qE/7jX5hfvUx8u92mZrNZdr/fXx+PR1GpVIqWZYWP713Xvd3tdmnbth/a7bYUQohUKmUdDofrKLNGjSIGAABib71epxuNxn0mk/kohBD1ev1edaYoUMQAAMCTfWtyhe/DV5MAACD2arVasFwuL4IgSEgpX6xWqwvVmaLARAwAAMRetVoNm83mnWEYJU3TjuVy+b3qTFFInE4n1RkAAECM+b7/1jTNW9U54sj3/Zxpmlc/up6jSQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAADw7ruvmh8Phy16vl5/P5xkhhPA8L63reqlQKBSDIEg4jnOp63rJcZxL1Xm/hB+6AgCAZ2s0Gr17fB6Px1nXdW+63e6dEEJMJpOclPLN2Vl86058kwEAAHxiMBi8mk6nOU3Tjvl8/oNlWWGr1bqybftBSplcLBbZzWZz7nneeRAEyTAMk4ZhFPv9/k2n05Gq838ORQwAADzd/K9eiz9cpyLd86fFUPzyn756mfh2u03NZrPsfr+/Ph6PolKpFC3LCh/fu657u9vt0rZtP7TbbSmEEKlUyjocDteRZo0YRQwAAMTeer1ONxqN+0wm81EIIer1+r3qTFGgiAEAgKf7xuQK34evJgEAQOzVarVguVxeBEGQkFK+WK1WF6ozRYGJGAAAiL1qtRo2m807wzBKmqYdy+Xye9WZopA4nU6qMwAAgBjzff+taZq3qnPEke/7OdM0r350PUeTAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAeHZc180Ph8OXvV4vP5/PM0II4XleWtf1UqFQKAZBkHAc51LX9ZLjOJeq834JP3QFAADP1mg0evf4PB6Ps67r3nS73TshhJhMJjkp5Zuzs/jWnfgmAwAA+MRgMHg1nU5zmqYd8/n8B8uywlardWXb9oOUMrlYLLKbzebc87zzIAiSYRgmDcMo9vv9m06nI1Xn/xyKGAAAeLJf7X71+vfy96ko99T/WA9//We//upl4tvtNjWbzbL7/f76eDyKSqVStCwrfHzvuu7tbrdL27b90G63pRBCpFIp63A4XEeZNWoUMQAAEHvr9TrdaDTuM5nMRyGEqNfr96ozRYEiBgAAnuxbkyt8H76aBAAAsVer1YLlcnkRBEFCSvlitVpdqM4UBSZiAAAg9qrVathsNu8MwyhpmnYsl8vvVWeKQuJ0OqnOAAAAYsz3/bemad6qzhFHvu/nTNO8+tH1HE0CAAAoQhEDAABQhCIGAACgCEUMAABAEYoYAACAIhQxAAAARShiAADg2XFdNz8cDl/2er38fD7PCCGE53lpXddLhUKhGARBwnGcS13XS47jXKrO+yX80BUAADxbo9Ho3ePzeDzOuq570+1274QQYjKZ5KSUb87O4lt34psMAADgE4PB4NV0Os1pmnbM5/MfLMsKW63WlW3bD1LK5GKxyG42m3PP886DIEiGYZg0DKPY7/dvOp2OVJ3/cyhiAADgyd79zd++/q/f/S4V5Z7//Wc/C/N//3dfvUx8u92mZrNZdr/fXx+PR1GpVIqWZYWP713Xvd3tdmnbth/a7bYUQohUKmUdDofrKLNGjSIGAABib71epxuNxn0mk/kohBD1ev1edaYoUMQAAMCTfWtyhe/DV5MAACD2arVasFwuL4IgSEgpX6xWqwvVmaLARAwAAMRetVoNm83mnWEYJU3TjuVy+b3qTFFInE4n1RkAAECM+b7/1jTNW9U54sj3/Zxpmlc/up6jSQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAADw7ruvmh8Phy16vl5/P5xkhhPA8L63reqlQKBSDIEg4jnOp63rJcZxL1Xm/hB+6AgCAZ2s0Gr17fB6Px1nXdW+63e6dEEJMJpOclPLN2Vl86058kwEAAHxiMBi8mk6nOU3Tjvl8/oNlWWGr1bqybftBSplcLBbZzWZz7nneeRAEyTAMk4ZhFPv9/k2n05Gq838ORQwAADzZv4x/8/ru34JUlHtm/yQd/vlf/uKrl4lvt9vU/2fvfkIU6fY0jx/LbKbxKpljyFuFk9Wdi+iLVwPDWI8rB2yQWLziXQ+4kKCdjYQ0wgztppmBpjduht67EIQLulGikUFE3Fcg5JXbvajuZiqblyRPJh0V9JRQziqhFvUnqwg4kfD9rAIOcXiWD79DnJjNZtn9fn99PB5FpVIpWpYVPq67rnu72+3Stm0/tNttKYQQqVTKOhwO11FmjRpFDAAAxN56vU43Go37TCbzUQgh6vX6vepMUaCIAQCAJ/vW5Arfh68mAQBA7NVqtWC5XF4EQZCQUr5YrVYXqjNFgYkYAACIvWq1GjabzTvDMEqaph3L5fJ71ZmikDidTqozAACAGPN9/61pmreqc8SR7/s50zSvfvR9jiYBAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAABAEYoYAACAIhQxAADw7Liumx8Ohy97vV5+Pp9nhBDC87y0ruulQqFQDIIg4TjOpa7rJcdxLlXn/RIudAUAAM/WaDR69/g8Ho+zruvedLvdOyGEmEwmOSnlm7Oz+Nad+CYDAAD4xGAweDWdTnOaph3z+fwHy7LCVqt1Zdv2g5QyuVgsspvN5tzzvPMgCJJhGCYNwyj2+/2bTqcjVef/HIoYAAB4sr//u9Hr23/5p1SUe+Ze/2n453/R++rPxLfbbWo2m2X3+/318XgUlUqlaFlW+Ljuuu7tbrdL27b90G63pRBCpFIp63A4XEeZNWoUMQAAEHvr9TrdaDTuM5nMRyGEqNfr96ozRYEiBgAAnuxbkyt8H76aBAAAsVer1YLlcnkRBEFCSvlitVpdqM4UBSZiAAAg9qrVathsNu8MwyhpmnYsl8vvVWeKQuJ0OqnOAAAAYsz3/bemad6qzhFHvu/nTNO8+tH3OZoEAABQhCIGAACgCEUMAABAEYoYAACAIhQxAAAARShiAAAAilDEAADAs+O6bn44HL7s9Xr5+XyeEUIIz/PSuq6XCoVCMQiChOM4l7qulxzHuVSd90u40BUAADxbo9Ho3ePzeDzOuq570+1274QQYjKZ5KSUb87O4lt34psMAADgE4PB4NV0Os1pmnbM5/MfLMsKW63WlW3bD1LK5GKxyG42m3PP886DIEiGYZg0DKPY7/dvOp2OVJ3/cyhiAADgye5+94fXx399n4pyzz969asw+9tff/Vn4tvtNjWbzbL7/f76eDyKSqVStCwrfFx3Xfd2t9ulbdt+aLfbUgghUqmUdTgcrqPMGjWKGAAAiL31ep1uNBr3mUzmoxBC1Ov1e9WZokARAwAAT/atyRW+D19NAgCA2KvVasFyubwIgiAhpXyxWq0uVGeKAhMxAAAQe9VqNWw2m3eGYZQ0TTuWy+X3qjNFIXE6nVRnAAAAMeb7/lvTNG9V54gj3/dzpmle/ej7HE0CAAAoQhEDAABQhCIGAACgCEUMAABAEYoYAACAIhQxAAAARShiAADg2XFdNz8cDl/2er38fD7PCCGE53lpXddLhUKhGARBwnGcS13XS47jXKrO+yVc6AoAAJ6t0Wj07vF5PB5nXde96Xa7d0IIMZlMclLKN2dn8a078U0GAADwicFg8Go6neY0TTvm8/kPlmWFrVbryrbtByllcrFYZDebzbnneedBECTDMEwahlHs9/s3nU5Hqs7/ORQxAADwZPP5/PUvv/ySinLPn376Kfz555+/+jPx7Xabms1m2f1+f308HkWlUilalhU+rruue7vb7dK2bT+0220phBCpVMo6HA7XUWaNGkUMAADE3nq9TjcajftMJvNRCCHq9fq96kxRoIgBAIAn+9bkCt+HryYBAEDs1Wq1YLlcXgRBkJBSvlitVheqM0WBiRgAAIi9arUaNpvNO8MwSpqmHcvl8nvVmaKQOJ1OqjMAAIAY833/rWmat6pzxJHv+znTNK9+9H2OJgEAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAIAiFDEAAPDsuK6bHw6HL3u9Xn4+n2eEEMLzvLSu66VCoVAMgiDhOM6lruslx3EuVef9Ei50BQAAz9ZoNHr3+Dwej7Ou6950u907IYSYTCY5KeWbs7P41p34JgMAAPjEYDB4NZ1Oc5qmHfP5/AfLssJWq3Vl2/aDlDK5WCyym83m3PO88yAIkmEYJg3DKPb7/ZtOpyNV5/8cihgAAHiy698PXr8P/pCKcs9fpX8dFn/zN1/9mfh2u03NZrPsfr+/Ph6PolKpFC3LCh/XXde93e12adu2H9rtthRCiFQqZR0Oh+sos0aNIgYAAGJvvV6nG43GfSaT+SiEEPV6/V51pihQxAAAwJN9a3KF78NXkwAAIPZqtVqwXC4vgiBISClfrFarC9WZosBEDAAAxF61Wg2bzeadYRglTdOO5XL5vepMUUicTifVGQAAQIz5vv/WNM1b1TniyPf9nGmaVz/6PkeTAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAeHZc180Ph8OXvV4vP5/PM0II4XleWtf1UqFQKAZBkHAc51LX9ZLjOJeq834JF7oCAIBnazQavXt8Ho/HWdd1b7rd7p0QQkwmk5yU8s3ZWXzrTnyTAQAAfGIwGLyaTqc5TdOO+Xz+g2VZYavVurJt+0FKmVwsFtnNZnPued55EATJMAyThmEU+/3+TafTkarzfw5FDAAAPFnv9//8+vD+31NR7ln41R+Ho9/8yVd/Jr7dblOz2Sy73++vj8ejqFQqRcuywsd113Vvd7td2rbth3a7LYUQIpVKWYfD4TrKrFGjiAEAgNhbr9fpRqNxn8lkPgohRL1ev1edKQoUMQAA8GTfmlzh+/DVJAAAiL1arRYsl8uLIAgSUsoXq9XqQnWmKDARAwAAsVetVsNms3lnGEZJ07RjuVx+rzpTFBKn00l1BgAAEGO+7781TfNWdY448n0/Z5rm1Y++z9EkAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAnh3XdfPD4fBlr9fLz+fzjBBCeJ6X1nW9VCgUikEQJBzHudR1veQ4zqXqvF/Cha4AAODZGo1G7x6fx+Nx1nXdm263eyeEEJPJJCelfHN2Ft+6E99kAAAAnxgMBq+m02lO07RjPp//YFlW2Gq1rmzbfpBSJheLRXaz2Zx7nnceBEEyDMOkYRjFfr9/0+l0pOr8n0MRAwAAT/aXv/Nf/+Ff/y0V5Z6/fpUJ//a35ld/Jr7dblOz2Sy73++vj8ejqFQqRcuywsd113Vvd7td2rbth3a7LYUQIpVKWYfD4TrKrFGjiAEAgNhbr9fpRqNxn8lkPgohRL1ev1edKQoUMQAA8GTfmlzh+/DVJAAAiL1arRYsl8uLIAgSUsoXq9XqQnWmKDARAwAAsVetVsNms3lnGEZJ07RjuVx+rzpTFBKn00l1BgAAEGO+7781TfNWdY448n0/Z5rm1Y++z9EkAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAnh3XdfPD4fBlr9fLz+fzjBBCeJ6X1nW9VCgUikEQJBzHudR1veQ4zqXqvF/Cha4AAODZGo1G7x6fx+Nx1nXdm263eyeEEJPJJCelfHN2Ft+6E99kAAAAnxgMBq+m02lO07RjPp//YFlW2Gq1rmzbfpBSJheLRXaz2Zx7nnceBEEyDMOkYRjFfr9/0+l0pOr8n0MRAwAATzf/b6/FL9epSPf8qRiKn//3V38mvt1uU7PZLLvf76+Px6OoVCpFy7LCx3XXdW93u13atu2HdrsthRAilUpZh8PhOtKsEaOIAQCA2Fuv1+lGo3GfyWQ+CiFEvV6/V50pChQxAADwdN+YXOH78NUkAACIvVqtFiyXy4sgCBJSyher1epCdaYoMBEDAACxV61Ww2azeWcYRknTtGO5XH6vOlMUEqfTSXUGAAAQY77vvzVN81Z1jjjyfT9nmubVj77P0SQAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACeHdd188Ph8GWv18vP5/OMEEJ4npfWdb1UKBSKQRAkHMe51HW95DjOpeq8X8KFrgAA4NkajUbvHp/H43HWdd2bbrd7J4QQk8kkJ6V8c3YW37oT32QAAACfGAwGr6bTaU7TtGM+n/9gWVbYarWubNt+kFImF4tFdrPZnHuedx4EQTIMw6RhGMV+v3/T6XSk6vyfQxEDAABP9le7v3r9j/IfU1Huqf9HPfzr//zXX/2Z+Ha7Tc1ms+x+v78+Ho+iUqkULcsKH9dd173d7XZp27Yf2u22FEKIVCplHQ6H6yizRo0iBgAAYm+9XqcbjcZ9JpP5KIQQ9Xr9XnWmKFDEAADAk31rcoXvw1eTAAAg9mq1WrBcLi+CIEhIKV+sVqsL1ZmiwEQMAADEXrVaDZvN5p1hGCVN047lcvm96kxRSJxOJ9UZAABAjPm+/9Y0zVvVOeLI9/2caZpXP/o+R5MAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAABAEYoYAAB4dlzXzQ+Hw5e9Xi8/n88zQgjheV5a1/VSoVAoBkGQcBznUtf1kuM4l6rzfgkXugIAgGdrNBq9e3wej8dZ13Vvut3unRBCTCaTnJTyzdlZfOtOfJMBAAB8YjAYvJpOpzlN0475fP6DZVlhq9W6sm37QUqZXCwW2c1mc+553nkQBMkwDJOGYRT7/f5Np9ORqvN/DkUMAAA82bv//j9e/79/+IdUlHv+hz/7szD/v/7nV38mvt1uU7PZLLvf76+Px6OoVCpFy7LCx3XXdW93u13atu2HdrsthRAilUpZh8PhOsqsUaOIAQCA2Fuv1+lGo3GfyWQ+CiFEvV6/V50pChQxAADwZN+aXOH78NUkAACIvVqtFiyXy4sgCBJSyher1epCdaYoMBEDAACxV61Ww2azeWcYRknTtGO5XH6vOlMUEqfTSXUGAAAQY77vvzVN81Z1jjjyfT9nmubVj77P0SQAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACeHdd188Ph8GWv18vP5/OMEEJ4npfWdb1UKBSKQRAkHMe51HW95DjOpeq8X8KFrgAA4NkajUbvHp/H43HWdd2bbrd7J4QQk8kkJ6V8c3YW37oT32QAAACfGAwGr6bTaU7TtGM+n/9gWVbYarWubNt+kFImF4tFdrPZnHuedx4EQTIMw6RhGMV+v3/T6XSk6vyfQxEDAABP9n/Gv39993+DVJR7Zv9TOvwv//U3X/2Z+Ha7Tc1ms+x+v78+Ho+iUqkULcsKH9dd173d7XZp27Yf2u22FEKIVCplHQ6H6yizRo0iBgAAYm+9XqcbjcZ9JpP5KIQQ9Xr9XnWmKFDEAADAk31rcoXvw1eTAAAg9mq1WrBcLi+CIEhIKV+sVqsL1ZmiwEQMAADEXrVaDZvN5p1hGCVN047lcvm96kxRSJxOJ9UZAABAjPm+/9Y0zVvVOeLI9/2caZpXP/o+R5MAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAABAEYoYAAB4dlzXzQ+Hw5e9Xi8/n88zQgjheV5a1/VSoVAoBkGQcBznUtf1kuM4l6rzfgkXugIAgGdrNBq9e3wej8dZ13Vvut3unRBCTCaTnJTyzdlZfOtOfJMBAAB8YjAYvJpOpzlN0475fP6DZVlhq9W6sm37QUqZXCwW2c1mc+553nkQBMkwDJOGYRT7/f5Np9ORqvN/DkUMAAA82d//3ej17b/8UyrKPXOv/zT887/offVn4tvtNjWbzbL7/f76eDyKSqVStCwrfFx3Xfd2t9ulbdt+aLfbUgghUqmUdTgcrqPMGjWKGAAAiL31ep1uNBr3mUzmoxBC1Ov1e9WZokARAwAAT/atyRW+D19NAgCA2KvVasFyubwIgiAhpXyxWq0uVGeKAhMxAAAQe9VqNWw2m3eGYZQ0TTuWy+X3qjNFIXE6nVRnAAAAMeb7/lvTNG9V54gj3/dzpmle/ej7HE0CAAAoQhEDAABQhCIGAACgCEUMAABAEYoYAACAIhQxAAAARShiAADg2XFdNz8cDl/2er38fD7PCCGE53lpXddLhUKhGARBwnGcS13XS47jXKrO+yVc6AoAAJ6t0Wj07vF5PB5nXde96Xa7d0IIMZlMclLKN2dn8a078U0GAADwicFg8Go6neY0TTvm8/kPlmWFrVbryrbtByllcrFYZDebzbnneedBECTDMEwahlHs9/s3nU5Hqs7/ORQxAADwZHe/+8Pr47++T0W55x+9+lWY/e2vv/oz8e12m5rNZtn9fn99PB5FpVIpWpYVPq67rnu72+3Stm0/tNttKYQQqVTKOhwO11FmjRpFDAAAxN56vU43Go37TCbzUQgh6vX6vepMUaCIAQCAJ/vW5Arfh68mAQBA7NVqtWC5XF4EQZCQUr5YrVYXqjNFgYkYAACIvWq1GjabzTvDMEqaph3L5fJ71ZmikDidTqozAACAGPN9/61pmreqc8SR7/s50zSvfvR9jiYBAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAABAEYoYAACAIhQxAADw7Liumx8Ohy97vV5+Pp9nhBDC87y0ruulQqFQDIIg4TjOpa7rJcdxLlXn/RIudAUAAM/WaDR69/g8Ho+zruvedLvdOyGEmEwmOSnlm7Oz+Nad+CYDAAD4xGAweDWdTnOaph3z+fwHy7LCVqt1Zdv2g5QyuVgsspvN5tzzvPMgCJJhGCYNwyj2+/2bTqcjVef/HIoYAAB4svl8/vqXX35JRbnnTz/9FP78889f/Zn4drtNzWaz7H6/vz4ej6JSqRQtywof113Xvd3tdmnbth/a7bYUQohUKmUdDofrKLNGjSIGAABib71epxuNxn0mk/kohBD1ev1edaYoUMQAAMCTfWtyhe/DV5MAACD2arVasFwuL4IgSEgpX6xWqwvVmaLARAwAAMRetVoNm83mnWEYJU3TjuVy+b3qTFFInE4n1RkAAECM+b7/1jTNW9U54sj3/Zxpmlc/+j5HkwAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAAEARihgAAHh2XNfND4fDl71eLz+fzzNCCOF5XlrX9VKhUCgGQZBwRdE/jAAAIABJREFUHOdS1/WS4ziXqvN+CRe6AgCAZ2s0Gr17fB6Px1nXdW+63e6dEEJMJpOclPLN2Vl86058kwEAAHxiMBi8mk6nOU3Tjvl8/oNlWWGr1bqybftBSplcLBbZzWZz7nneeRAEyTAMk4ZhFPv9/k2n05Gq838ORQwAADzZ9e8Hr98Hf0hFueev0r8Oi7/5m6/+THy73aZms1l2v99fH49HUalUipZlhY/rruve7na7tG3bD+12WwohRCqVsg6Hw3WUWaNGEQMAALG3Xq/TjUbjPpPJfBRCiHq9fq86UxQoYgAA4Mm+NbnC9+GrSQAAEHu1Wi1YLpcXQRAkpJQvVqvVhepMUWAiBgAAYq9arYbNZvPOMIySpmnHcrn8XnWmKCROp5PqDAAAIMZ8339rmuat6hxx5Pt+zjTNqx99n6NJAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAPDuu6+aHw+HLXq+Xn8/nGSGE8Dwvret6qVAoFIMgSDiOc6nreslxnEvVeb+EC10BAMCzNRqN3j0+j8fjrOu6N91u904IISaTSU5K+ebsLL51J77JAAAAPjEYDF5Np9OcpmnHfD7/wbKssNVqXdm2/SClTC4Wi+xmszn3PO88CIJkGIZJwzCK/X7/ptPpSNX5P4ciBgAAnqz3+39+fXj/76ko9yz86o/D0W/+5Ks/E99ut6nZbJbd7/fXx+NRVCqVomVZ4eO667q3u90ubdv2Q7vdlkIIkUqlrMPhcB1l1qhRxAAAQOyt1+t0o9G4z2QyH4UQol6v36vOFAWKGAAAeLJvTa7wffhqEgAAxF6tVguWy+VFEAQJKeWL1Wp1oTpTFJiIAQCA2KtWq2Gz2bwzDKOkadqxXC6/V50pConT6aQ6AwAAiDHf99+apnmrOkcc+b6fM03z6kff52gSAABAEYoYAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAz47ruvnhcPiy1+vl5/N5RgghPM9L67peKhQKxSAIEo7jXOq6XnIc51J13i/hQlcAAPBsjUajd4/P4/E467ruTbfbvRNCiMlkkpNSvjk7i2/diW8yAACATwwGg1fT6TSnadoxn89/sCwrbLVaV7ZtP0gpk4vFIrvZbM49zzsPgiAZhmHSMIxiv9+/6XQ6UnX+z6GIAQCAJ/vL3/mv//Cv/5aKcs9fv8qEf/tb86s/E99ut6nZbJbd7/fXx+NRVCqVomVZ4eO667q3u90ubdv2Q7vdlkIIkUqlrMPhcB1l1qhRxAAAQOyt1+t0o9G4z2QyH4UQol6v36vOFAWKGAAAeLJvTa7wffhqEgAAxF6tVguWy+VFEAQJKeWL1Wp1oTpTFJiIAQCA2KtWq2Gz2bwzDKOkadqxXC6/V50pConT6aQ6AwAAiDHf99+apnmrOkcc+b6fM03z6kff52gSAABAEYoYAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAz47ruvnhcPiy1+vl5/N5RgghPM9L67peKhQKxSAIEo7jXOq6XnIc51J13i/hQlcAAPBsjUajd4/P4/E467ruTbfbvRNCiMlkkpNSvjk7i2/diW8yAACATwwGg1fT6TSnadoxn89/sCwrbLVaV7ZtP0gpk4vFIrvZbM49zzsPgiAZhmHSMIxiv9+/6XQ6UnX+z6GIAQCAp5v/t9fil+tUpHv+VAzFz//7qz8T3263qdlslt3v99fH41FUKpWiZVnh47rrure73S5t2/ZDu92WQgiRSqWsw+FwHWnWiFHEAABA7K3X63Sj0bjPZDIfhRCiXq/fq84UBYoYAAB4um9MrvB9+GoSAADEXq1WC5bL5UUQBAkp5YvVanWhOlMUmIgBAIDYq1arYbPZvDMMo6Rp2rFcLr9XnSkKidPppDoDAACIMd/335qmeas6Rxz5vp8zTfPqR9/naBIAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAADPjuu6+eFw+LLX6+Xn83lGCCE8z0vrul4qFArFIAgSjuNc6rpechznUnXeL+FCVwAA8GyNRqN3j8/j8Tjruu5Nt9u9E0KIyWSSk1K+OTuLb92JbzIAAIBPDAaDV9PpNKdp2jGfz3+wLCtstVpXtm0/SCmTi8Uiu9lszj3POw+CIBmGYdIwjGK/37/pdDpSdf7PoYgBAIAn+6vdX73+R/mPqSj31P+jHv71f/7rr/5MfLvdpmazWXa/318fj0dRqVSKlmWFj+uu697udru0bdsP7XZbCiFEKpWyDofDdZRZo0YRAwAAsbder9ONRuM+k8l8FEKIer1+rzpTFChiAADgyb41ucL34atJAAAQe7VaLVgulxdBECSklC9Wq9WF6kxRYCIGAABir1qths1m884wjJKmacdyufxedaYoJE6nk+oMAAAgxnzff2ua5q3qHHHk+37ONM2rH32fo0kAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAAA8O67r5ofD4cter5efz+cZIYTwPC+t63qpUCgUgyBIOI5zqet6yXGcS9V5v4QLXQEAwLM1Go3ePT6Px+Os67o33W73TgghJpNJTkr55uwsvnUnvskAAAA+MRgMXk2n05ymacd8Pv/Bsqyw1Wpd2bb9IKVMLhaL7GazOfc87zwIgmQYhknDMIr9fv+m0+lI1fk/hyIGAACe7N1//x+v/98//EMqyj3/w5/9WZj/X//zqz8T3263qdlslt3v99fH41FUKpWiZVnh47rrure73S5t2/ZDu92WQgiRSqWsw+FwHWXWqFHEAABA7K3X63Sj0bjPZDIfhRCiXq/fq84UBYoYAAB4sm9NrvB9+GoSAADEXq1WC5bL5UUQBAkp5YvVanWhOlMUmIgBAIDYq1arYbPZvDMMo6Rp2rFcLr9XnSkKidPppDoDAACIMd/335qmeas6Rxz5vp8zTfPqR9/naBIAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAADPjuu6+eFw+LLX6+Xn83lGCCE8z0vrul4qFArFIAgSjuNc6rpechznUnXeL+FCVwAA8GyNRqN3j8/j8Tjruu5Nt9u9E0KIyWSSk1K+OTuLb92JbzIAAIBPDAaDV9PpNKdp2jGfz3+wLCtstVpXtm0/SCmTi8Uiu9lszj3POw+CIBmGYdIwjGK/37/pdDpSdf7PoYgBAIAn+z/j37+++79BKso9s/8pHf6X//qbr/5MfLvdpmazWXa/318fj0dRqVSKlmWFj+uu697udru0bdsP7XZbCiFEKpWyDofDdZRZo0YRAwAAsbder9ONRuM+k8l8FEKIer1+rzpTFChiAADgyb41ucL34atJAAAQe7VaLVgulxdBECSklC9Wq9WF6kxRYCIGAABir1qths1m884wjJKmacdyufxedaYoJE6nk+oMAAAgxnzff2ua5q3qHHHk+37ONM2rH32fo0kAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAAA8O67r5ofD4cter5efz+cZIYTwPC+t63qpUCgUgyBIOI5zqet6yXGcS9V5v4QLXQEAwLM1Go3ePT6Px+Os67o33W73TgghJpNJTkr55uwsvnUnvskAAAA+MRgMXk2n05ymacd8Pv/Bsqyw1Wpd2bb9IKVMLhaL7GazOfc87zwIgmQYhknDMIr9fv+m0+lI1fk/hyIGAACe7O//bvT69l/+KRXlnrnXfxr++V/0vvoz8e12m5rNZtn9fn99PB5FpVIpWpYVPq67rnu72+3Stm0/tNttKYQQqVTKOhwO11FmjRpFDAAAxN56vU43Go37TCbzUQgh6vX6vepMUaCIAQCAJ/vW5Arfh68mAQBA7NVqtWC5XF4EQZCQUr5YrVYXqjNFgYkYAACIvWq1GjabzTvDMEqaph3L5fJ71ZmikDidTqozAACAGPN9/61pmreqc8SR7/s50zSvfvR9jiYBAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAABAEYoYAACAIhQxAADw7Liumx8Ohy97vV5+Pp9nhBDC87y0ruulQqFQDIIg4TjOpa7rJcdxLlXn/RIudAUAAM/WaDR69/g8Ho+zruvedLvdOyGEmEwmOSnlm7Oz+Nad+CYDAAD4xGAweDWdTnOaph3z+fwHy7LCVqt1Zdv2g5QyuVgsspvN5tzzvPMgCJJhGCYNwyj2+/2bTqcjVef/HIoYAAB4srvf/eH18V/fp6Lc849e/SrM/vbXX/2Z+Ha7Tc1ms+x+v78+Ho+iUqkULcsKH9dd173d7XZp27Yf2u22FEKIVCplHQ6H6yizRo0iBgAAYm+9XqcbjcZ9JpP5KIQQ9Xr9XnWmKFDEAADAk31rcoXvw1eTAAAg9mq1WrBcLi+CIEhIKV+sVqsL1ZmiwEQMAADEXrVaDZvN5p1hGCVN047lcvm96kxRSJxOJ9UZAABAjPm+/9Y0zVvVOeLI9/2caZpXP/o+R5MAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAABAEYoYAAB4dlzXzQ+Hw5e9Xi8/n88zQgjheV5a1/VSoVAoBkGQcBznUtf1kuM4l6rzfgkXugIAgGdrNBq9e3wej8dZ13Vvut3unRBCTCaTnJTyzdlZfOtOfJMBAAB8YjAYvJpOpzlN0475fP6DZVlhq9W6sm37QUqZXCwW2c1mc+553nkQBMkwDJOGYRT7/f5Np9ORqvN/DkUMAAA82Xw+f/3LL7+kotzzp59+Cn/++eev/kx8u92mZrNZdr/fXx+PR1GpVIqWZYWP667r3u52u7Rt2w/tdlsKIUQqlbIOh8N1lFmjRhEDAACxt16v041G4z6TyXwUQoh6vX6vOlMUKGIAAODJvjW5wvfhq0kAABB7tVotWC6XF0EQJKSUL1ar1YXqTFFgIgYAAGKvWq2GzWbzzjCMkqZpx3K5/F51pigkTqeT6gwAACDGfN9/a5rmreocceT7fs40zasffZ+jSQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAADw7ruvmh8Phy16vl5/P5xkhhPA8L63reqlQKBSDIEg4jnOp63rJcZxL1Xm/hAtdAQDAszUajd49Po/H46zrujfdbvdOCCEmk0lOSvnm7Cy+dSe+yQAAAD4xGAxeTafTnKZpx3w+/8GyrLDVal3Ztv0gpUwuFovsZrM59zzvPAiCZBiGScMwiv1+/6bT6UjV+T+HIgYAAJ7s+veD1++DP6Si3PNX6V+Hxd/8zVd/Jr7dblOz2Sy73++vj8ejqFQqRcuywsd113Vvd7td2rbth3a7LYUQIpVKWYfD4TrKrFGjiAEAgNhbr9fpRqNxn8lkPgohRL1ev1edKQoUMQAA8GTfmlzh+/DVJAAAiL1arRYsl8uLIAgSUsoXq9XqQnWmKDARAwAAsVetVsNms3lnGEZJ07RjuVx+rzpTFBKn00l1BgAAEGO+7781TfNWdY448n0/Z5rm1Y++z9EkAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAnh3XdfPD4fBlr9fLz+fzjBBCeJ6X1nW9VCgUikEQJBzHudR1veQ4zqXqvF/Cha4AAODZGo1G7x6fx+Nx1nXdm263eyeEEJPJJCelfHN2Ft+6E99kAAAAnxgMBq+m02lO07RjPp//YFlW2Gq1rmzbfpBSJheLRXaz2Zx7nnceBEEyDMOkYRjFfr9/0+l0pOr8n0MRAwAAT9b7/T+/Prz/91SUexZ+9cfh6Dd/8tWfiW+329RsNsvu9/vr4/EoKpVK0bKs8HHddd3b3W6Xtm37od1uSyGESKVS1uFwuI4ya9QoYgAAIPbW63W60WjcZzKZj0IIUa/X71VnigJFDAAAPNm3Jlf4Pnw1CQAAYq9WqwXL5fIiCIKElPLFarW6UJ0pCkzEAABA7FWr1bDZbN4ZhlHSNO1YLpffq84UhcTpdFKdAQAAxJjv+29N07xVnSOOfN/PmaZ59aPvczQJAACgCEUMAABAEYoYAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQCAZ8d13fxwOHzZ6/Xy8/k8I4QQnueldV0vFQqFYhAECcdxLnVdLzmOc6k675dwoSsAAHi2RqPRu8fn8XicdV33ptvt3gkhxGQyyUkp35ydxbfuxDcZAADAJwaDwavpdJrTNO2Yz+c/WJYVtlqtK9u2H6SUycVikd1sNuee550HQZAMwzBpGEax3+/fdDodqTr/51DEAADAk/3l7/zXf/jXf0tFueevX2XCv/2t+dWfiW+329RsNsvu9/vr4/EoKpVK0bKs8HHddd3b3W6Xtm37od1uSyGESKVS1uFwuI4ya9QoYgAAIPbW63W60WjcZzKZj0IIUa/X71VnigJFDAAAPNm3Jlf4Pnw1CQAAYq9WqwXL5fIiCIKElPLFarW6UJ0pCkzEAABA7FWr1bDZbN4ZhlHSNO1YLpffq84UhcTpdFKdAQAAxJjv+29N07xVnSOOfN/PmaZ59aPvczQJAACgCEUMAABAEYoYAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQCAZ8d13fxwOHzZ6/Xy8/k8I4QQnueldV0vFQqFYhAECcdxLnVdLzmOc6k675dwoSsAAHi2RqPRu8fn8XicdV33ptvt3gkhxGQyyUkp35ydxbfuxDcZAADAJwaDwavpdJrTNO2Yz+c/WJYVtlqtK9u2H6SUycVikd1sNuee550HQZAMwzBpGEax3+/fdDodqTr/51DEAADA083/22vxy3Uq0j1/Kobi5//91Z+Jb7fb1Gw2y+73++vj8SgqlUrRsqzwcd113dvdbpe2bfuh3W5LIYRIpVLW4XC4jjRrxChiAAAg9tbrdbrRaNxnMpmPQghRr9fvVWeKAkUMAAA83TcmV/g+fDUJAABir1arBcvl8iIIgoSU8sVqtbpQnSkKTMQAAEDsVavVsNls3hmGUdI07Vgul9+rzhSFxOl0Up0BAADEmO/7b03TvFWdI45838+Zpnn1o+9zNAkAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAIBnx3Xd/HA4fNnr9fLz+TwjhBCe56V1XS8VCoViEAQJx3EudV0vOY5zqTrvl3ChKwAAeLZGo9G7x+fxeJx1Xfem2+3eCSHEZDLJSSnfnJ3Ft+7ENxkAAMAnBoPBq+l0mtM07ZjP5z9YlhW2Wq0r27YfpJTJxWKR3Ww2557nnQdBkAzDMGkYRrHf7990Oh2pOv/nUMQAAMCT/dXur17/o/zHVJR76v9RD//6P//1V38mvt1uU7PZLLvf76+Px6OoVCpFy7LCx3XXdW93u13atu2HdrsthRAilUpZh8PhOsqsUaOIAQCA2Fuv1+lGo3GfyWQ+CiFEvV6/V50pChQxAADwZN+aXOH78NUkAACIvVqtFiyXy4sgCBJSyher1epCdaYoMBEDAACxV61Ww2azeWcYRknTtGO5XH6vOlMUEqfTSXUGAAAQY77vvzVN81Z1jjjyfT9nmubVj77P0SQAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACeHdd188Ph8GWv18vP5/OMEEJ4npfWdb1UKBSKQRAkHMe51HW95DjOpeq8X8KFrgAA4NkajUbvHp/H43HWdd2bbrd7J4QQk8kkJ6V8c3YW37oT32QAAACfGAwGr6bTaU7TtGM+n/9gWVbYarWubNt+kFImF4tFdrPZnHuedx4EQTIMw6RhGMV+v3/T6XSk6vyfQxEDAABP9u6//4/X/+8f/iEV5Z7/4c/+LMz/r//51Z+Jb7fb1Gw2y+73++vj8SgqlUrRsqzwcd113dvdbpe2bfuh3W5LIYRIpVLW4XC4jjJr1ChiAAAg9tbrdbrRaNxnMpmPQghRr9fvVWeKAkUMAAA82bcmV/g+fDUJAABir1arBcvl8iIIgoSU8sVqtbpQnSkKTMQAAEDsVavVsNls3hmGUdI07Vgul9+rzhSFxOl0Up0BAADEmO/7b03TvFWdI45838+Zpnn1o+9zNAkAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAIBnx3Xd/HA4fNnr9fLz+TwjhBCe56V1XS8VCoViEAQJx3EudV0vOY5zqTrvl3ChKwAAeLZGo9G7x+fxeJx1Xfem2+3eCSHEZDLJSSnfnJ3Ft+7ENxkAAMAnBoPBq+l0mtM07ZjP5z9YlhW2Wq0r27YfpJTJxWKR3Ww2557nnQdBkAzDMGkYRrHf7990Oh2pOv/nUMQAAMCT/Z/x71/f/d8gFeWe2f+UDv/Lf/3NV38mvt1uU7PZLLvf76+Px6OoVCpFy7LCx3XXdW93u13atu2HdrsthRAilUpZh8PhOsqsUaOIAQCA2Fuv1+lGo3GfyWQ+CiFEvV6/V50pChQxAADwZN+aXOH78NUkAACIvVqtFiyXy4sgCBJSyher1epCdaYoMBEDAACxV61Ww2azeWcYRknTtGO5XH6vOlMUEqfTSXUGAAAQY77vvzVN81Z1jjjyfT9nmubVj77P0SQAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACeHdd188Ph8GWv18vP5/OMEEJ4npfWdb1UKBSKQRAkHMe51HW95DjOpeq8X8KFrgAA4NkajUbvHp/H43HWdd2bbrd7J4QQk8kkJ6V8c3YW37oT32QAAACfGAwGr6bTaU7TtGM+n/9gWVbYarWubNt+kFImF4tFdrPZnHuedx4EQTIMw6RhGMV+v3/T6XSk6vyfQxEDAABP9vd/N3p9+y//lIpyz9zrPw3//C96X/2Z+Ha7Tc1ms+x+v78+Ho+iUqkULcsKH9dd173d7XZp27Yf2u22FEKIVCplHQ6H6yizRo0iBgAAYm+9XqcbjcZ9JpP5KIQQ9Xr9XnWmKFDEAADAk31rcoXvw1eTAAAg9mq1WrBcLi+CIEhIKV+sVqsL1ZmiwEQMAADEXrVaDZvN5p1hGCVN047lcvm96kxRSJxOJ9UZAABAjPm+/9Y0zVvVOeLI9/2caZpXP/o+R5MAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAABAEYoYAAB4dlzXzQ+Hw5e9Xi8/n88zQgjheV5a1/VSoVAoBkGQcBznUtf1kuM4l6rzfgkXugIAgGdrNBq9e3wej8dZ13Vvut3unRBCTCaTnJTyzdlZfOtOfJMBAAB8YjAYvJpOpzlN0475fP6DZVlhq9W6sm37QUqZXCwW2c1mc+553nkQBMkwDJOGYRT7/f5Np9ORqvN/DkUMAAA82d3v/vD6+K/vU1Hu+UevfhVmf/vrr/5MfLvdpmazWXa/318fj0dRqVSKlmWFj+uu697udru0bdsP7XZbCiFEKpWyDofDdZRZo0YRAwAAsbder9ONRuM+k8l8FEKIer1+rzpTFChiAADgyb41ucL34atJAAAQe7VaLVgulxdBECSklC9Wq9WF6kxRYCIGAABir1qths1m884wjJKmacdyufxedaYoJE6nk+oMAAAgxnzff2ua5q3qHHHk+37ONM2rH32fo0kAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAAA8O67r5ofD4cter5efz+cZIYTwPC+t63qpUCgUgyBIOI5zqet6yXGcS9V5v4QLXQEAwLM1Go3ePT6Px+Os67o33W73TgghJpNJTkr55uwsvnUnvskAAAA+MRgMXk2n05ymacd8Pv/Bsqyw1Wpd2bb9IKVMLhaL7GazOfc87zwIgmQYhknDMIr9fv+m0+lI1fk/hyIGAACebD6fv/7ll19SUe75008/hT///PNXfya+3W5Ts9ksu9/vr4/Ho6hUKkXLssLHddd1b3e7Xdq27Yd2uy2FECKVSlmHw+E6yqxRo4gBAIDYW6/X6UajcZ/JZD4KIUS9Xr9XnSkKFDEAAPBk35pc4fvw1SQAAIi9Wq0WLJfLiyAIElLKF6vV6kJ1pigwEQMAALFXrVbDZrN5ZxhGSdO0Y7lcfq86UxQSp9NJdQYAABBjvu+/NU3zVnWOOPJ9P2ea5tWPvs/RJAAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAJ4d13Xzw+HwZa/Xy8/n84wQQniel9Z1vVQoFIpBECQcx7nUdb3kOM6l6rxfwoWuAADg2RqNRu8en8fjcdZ13Ztut3snhBCTySQnpXxzdhbfuhPfZAAAAJ8YDAavptNpTtO0Yz6f/2BZVthqta5s236QUiYXi0V2s9mce553HgRBMgzDpGEYxX6/f9PpdKTq/J9DEQMAAE92/fvB6/fBH1JR7vmr9K/D4m/+5qs/E99ut6nZbJbd7/fXx+NRVCqVomVZ4eO667q3u90ubdv2Q7vdlkIIkUqlrMPhcB1l1qhRxAAAQOyt1+t0o9G4z2QyH4UQol6v36vOFAWKGAAAeLJvTa7wffhqEgAAxF6tVguWy+VFEAQJKeWL1Wp1oTpTFJiIAQCA2KtWq2Gz2bwzDKOkadqxXC6/V50pConT6aQ6AwAAiDHf99+apnmrOkcc+b6fM03z6kff52gSAABAEYoYAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAz47ruvnhcPiy1+vl5/N5RgghPM9L67peKhQKxSAIEo7jXOq6XnIc51J13i/hQlcAAPBsjUajd4/P4/E467ruTbfbvRNCiMlkkpNSvjk7i2/diW8yAACATwwGg1fT6TSnadoxn89/sCyBQGfDAAAgAElEQVQrbLVaV7ZtP0gpk4vFIrvZbM49zzsPgiAZhmHSMIxiv9+/6XQ6UnX+z6GIAQCAJ+v9/p9fH97/eyrKPQu/+uNw9Js/+erPxLfbbWo2m2X3+/318XgUlUqlaFlW+Ljuuu7tbrdL27b90G63pRBCpFIp63A4XEeZNWoUMQAAEHvr9TrdaDTuM5nMRyGEqNfr96ozRYEiBgAAnuxbkyt8H76aBAAAsVer1YLlcnkRBEFCSvlitVpdqM4UBSZiAAAg9qrVathsNu8MwyhpmnYsl8vvVWeKQuJ0OqnOAAAAYsz3/bemad6qzhFHvu/nTNO8+tH3OZoEAABQhCIGAACgCEUMAABAEYoYAACAIhQxAAAARShiAAAAilDEAADAs+O6bn44HL7s9Xr5+XyeEUIIz/PSuq6XCoVCMQiChOM4l7qulxzHuVSd90u40BUAADxbo9Ho3ePzeDzOuq570+1274QQYjKZ5KSUb87O4lt34psMAADgE4PB4NV0Os1pmnbM5/MfLMsKW63WlW3bD1LK5GKxyG42m3PP886DIEiGYZg0DKPY7/dvOp2OVJ3/cyhiAADgyf7yd/7rP/zrv6Wi3PPXrzLh3/7W/OrPxLfbbWo2m2X3+/318XgUlUqlaFlW+Ljuuu7tbrdL27b90G63pRBCpFIp6/+zdz8hioSJ3ccfxw7vi6t0x5KdwfS86UPt4mphWed4MmBA6rBizgEPUsRcpORFSFgvSwIv78VLyN2DIAT0olSQICLepxB6ZXcPkzdkOixNP928NcWbEcb31DCH+dMzFDzV8P2chKLkd/zyFFqHw+E6yq1RI8QAAEDsrdfrdKPRuM9kMh+FEKJer9+r3hQFQgwAADzZt06u8H341SQAAIi9Wq0WLJfLiyAIElLKF6vV6kL1pihwIgYAAGKvWq2GzWbzzjCMkqZpx3K5/F71pigkTqeT6g0AACDGfN9/a5rmreodceT7fs40zasfvZ9HkwAAAIoQYgAAAIoQYgAAAIoQYgAAAIoQYgAAAIoQYgAAAIoQYgAA4NlxXTc/HA5f9nq9/Hw+zwghhOd5aV3XS4VCoRgEQcJxnEtd10uO41yq3vsl/KErAAB4tkaj0bvHz+PxOOu67k23270TQojJZJKTUr45O4tv7sR3GQAAwCcGg8Gr6XSa0zTtmM/nP1iWFbZarSvbth+klMnFYpHdbDbnnuedB0GQDMMwaRhGsd/v33Q6Hal6/+cQYgAA4Onmf/Na/OE6Fel3/rQYil/+41dfJr7dblOz2Sy73++vj8ejqFQqRcuywsfrruve7na7tG3bD+12WwohRCqVsg6Hw3WkWyNGiAEAgNhbr9fpRqNxn8lkPgohRL1ev1e9KQqEGAAAeLpvnFzh+/CrSQAAEHu1Wi1YLpcXQRAkpJQvVqvVhepNUeBEDAAAxF61Wg2bzeadYRglTdOO5XL5vepNUUicTifVGwAAQIz5vv/WNM1b1TviyPf9nGmaVz96P48mAQAAFCHEAAAAFCHEAAAAFCHEAAAAFCHEAAAAFCHEAAAAFCHEAADAs+O6bn44HL7s9Xr5+XyeEUIIz/PSuq6XCoVCMQiChOM4l7qulxzHuVS990v4Q1cAAPBsjUajd4+fx+Nx1nXdm263eyeEEJPJJCelfHN2Ft/cie8yAACATwwGg1fT6TSnadoxn89/sCwrbLVaV7ZtP0gpk4vFIrvZbM49zzsPgiAZhmHSMIxiv9+/6XQ6UvX+zyHEAADAk/1q96vXv5e/T0X5nfof6+Gv/+zXX32Z+Ha7Tc1ms+x+v78+Ho+iUqkULcsKH6+7rnu72+3Stm0/tNttKYQQqVTKOhwO11FujRohBgAAYm+9XqcbjcZ9JpP5KIQQ9Xr9XvWmKBBiAADgyb51coXvw68mAQBA7NVqtWC5XF4EQZCQUr5YrVYXqjdFgRMxAAAQe9VqNWw2m3eGYZQ0TTuWy+X3qjdFIXE6nVRvAAAAMeb7/lvTNG9V74gj3/dzpmle/ej9PJoEAABQhBADAABQhBADAABQhBADAABQhBADAABQhBADAABQhBADAADPjuu6+eFw+LLX6+Xn83lGCCE8z0vrul4qFArFIAgSjuNc6rpechznUvXeL+EPXQEAwLM1Go3ePX4ej8dZ13Vvut3unRBCTCaTnJTyzdlZfHMnvssAAAA+MRgMXk2n05ymacd8Pv/Bsqyw1Wpd2bb9IKVMLhaL7GazOfc87zwIgmQYhknDMIr9fv+m0+lI1fs/hxADAABP9u5v/+71f/3ud6kov/O//exnYf4f/v6rLxPfbrep2WyW3e/318fjUVQqlaJlWeHjddd1b3e7Xdq27Yd2uy2FECKVSlmHw+E6yq1RI8QAAEDsrdfrdKPRuM9kMh+FEKJer9+r3hQFQgwAADzZt06u8H341SQAAIi9Wq0WLJfLiyAIElLKF6vV6kL1pihwIgYAAGKvWq2GzWbzzjCMkqZpx3K5/F71pigkTqeT6g0AACDGfN9/a5rmreodceT7fs40zasfvZ9HkwAAAIoQYgAAAIoQYgAAAIoQYgAAAIoQYgAAAIoQYgAAAIoQYgAA4NlxXTc/HA5f9nq9/Hw+zwghhOd5aV3XS4VCoRgEQcJxnEtd10uO41yq3vsl/KErAAB4tkaj0bvHz+PxOOu67k23270TQojJZJKTUr45O4tv7sR3GQAAwCcGg8Gr6XSa0zTtmM/nP1iWFbZarSvbth+klMnFYpHdbDbnnuedB0GQDMMwaRhGsd/v33Q6Hal6/+cQYgAA4Mn+dfyb13f/EaSi/M7sn6TDP/+rX3z1ZeLb7TY1m82y+/3++ng8ikqlUrQsK3y87rru7W63S9u2/dBut6UQQqRSKetwOFxHuTVqhBgAAIi99XqdbjQa95lM5qMQQtTr9XvVm6JAiAEAgCf71skVvg+/mgQAALFXq9WC5XJ5EQRBQkr5YrVaXajeFAVOxAAAQOxVq9Ww2WzeGYZR0jTtWC6X36veFIXE6XRSvQEAAMSY7/tvTdO8Vb0jjnzfz5mmefWj9/NoEgAAQBFCDAAAQBFCDAAAQBFCDAAAQBFCDAAAQBFCDAAAQBFCDAAAPDuu6+aHw+HLXq+Xn8/nGSGE8Dwvret6qVAoFIMgSDiOc6nreslxnEvVe7+EP3QFAADP1mg0evf4eTweZ13Xvel2u3dCCDGZTHJSyjdnZ/HNnfguAwAA+MRgMHg1nU5zmqYd8/n8B8uywlardWXb9oOUMrlYLLKbzebc87zzIAiSYRgmDcMo9vv9m06nI1Xv/xxCDAAAPNm//NPo9e2//1sqyu/Mvf7T8C/+uvfVl4lvt9vUbDbL7vf76+PxKCqVStGyrPDxuuu6t7vdLm3b9kO73ZZCCJFKpazD4XAd5daoEWIAACD21ut1utFo3GcymY9CCFGv1+9Vb4oCIQYAAJ7sWydX+D78ahIAAMRerVYLlsvlRRAECSnli9VqdaF6UxQ4EQMAALFXrVbDZrN5ZxhGSdO0Y7lcfq96UxQSp9NJ9QYAABBjvu+/NU3zVvWOOPJ9P2ea5tWP3s+jSQAAAEUIMQAAAEUIMQAAAEUIMQAAAEUIMQAAAEUIMQAAAEUIMQAA8Oy4rpsfDocve71efj6fZ4QQwvO8tK7rpUKhUAyCIOE4zqWu6yXHcS5V7/0S/tAVAAA8W6PR6N3j5/F4nHVd96bb7d4JIcRkMslJKd+cncU3d+K7DAAA4BODweDVdDrNaZp2zOfzHyzLClut1pVt2w9SyuRischuNptzz/POgyBIhmGYNAyj2O/3bzqdjlS9/3MIMQAA8GR3//zb18f/fJ+K8jv/6NVPwuxf/vyrLxPfbrep2WyW3e/318fjUVQqlaJlWeHjddd1b3e7Xdq27Yd2uy2FECKVSlmHw+E6yq1RI8QAAEDsrdfrdKPRuM9kMh+FEKJer9+r3hQFQgwAADzZt06u8H341SQAAIi9Wq0WLJfLiyAIElLKF6vV6kL1pihwIgYAAGKvWq2GzWbzzjCMkqZpx3K5/F71pigkTqeT6g0AACDGfN9/a5rmreodceT7fs40zasfvZ9HkwAAAIoQYgAAAIoQYgAAAIoQYgAAAIoQYgAAAIoQYgAAAIoQYgAA4NlxXTc/HA5f9nq9/Hw+zwghhOd5aV3XS4VCoRgEQcJxnEtd10uO41yq3vsl/KErAAB4tkaj0bvHz+PxOOu67k23270TQojJZJKTUr45O4tv7sR3GQAAwCcGg8Gr6XSa0zTtmM/nP1iWFbZarSvbth+klMnFYpHdbDbnnuedB0GQDMMwaRhGsd/v33Q6Hal6/+cQYgAA4Mnm8/nrP/zhD6kov/OnP/1p+Mtf/vKrLxPfbrep2WyW3e/318fjUVQqlaJlWeHjddd1b3e7Xdq27Yd2uy2FECKVSlmHw+E6yq1RI8QAAEDsrdfrdKPRuM9kMh+FEKJer9+r3hQFQgwAADzZt06u8H341SQAAIi9Wq0WLJfLiyAIElLKF6vV6kL1pihwIgYAAGKvWq2GzWbzzjCMkqZpx3K5/F71pigkTqeT6g0AACDGfN9/a5rmreodceT7fs40zasfvZ9HkwAAAIoQYgAAAIoQYgAAAIoQYgAAAIoQYgAAAIoQYgAAAIoQYgAA4NlxXTc/HA5f9nq9/Hw+zwghhOd5aV3XS4VCoRgEQcJxnEtd10uO41yq3vsl/KErAAB4tkaj0bvHz+PxOOu67k23270TQojJZJKTUr45O4tv7sR3GQAAwCcGg8Gr6XSa0zTtmM/nP1iWFbZarSvbth+klMnFYpHdbDbnnuedB0GQDMMwaRhGsd/v33Q6Hal6/+cQYgAA4MmufzN4/T74bSrK7/xJ+udh8Rf/66svE99ut6nZbJbd7/fXx+NRVCqVomVZ4eN113Vvd7td2rbth3a7LYUQIpVKWYfD4TrKrVEjxAAAQOyt1+t0o9G4z2QyH4UQol6v36veFAVCDAAAPNm3Tq7wffjVJAAAiL1arRYsl8uLIAgSUsoXq9XqQvWmKHAiBgAAYq9arYbNZvPOMIySpmnHcrn8XvWmKCROp5PqDQAAIMZ8339rmuat6h1x5Pt+zjTNqx+9n0eTAAAAihBiAAAAihBiAAAAihBiAAAAihBiAAAAihBiAAAAihBiAADg2XFdNz8cDl/2er38fD7PCCGE53lpXddLhUKhGARBwnGcS13XS47jXKre+yX8oSsAAHi2RqPRu8fP4/E467ruTbfbvRNCiMlkkpNSvjk7i2/uxHcZAADAJwaDwavpdJrTNO2Yz+c/WJYVtlqtK9u2H6SUycVikd1sNuee550HQZAMwzBpGEax3+/fdDodqXr/5xBiAADgyXq/+T+vD+//XyrK7yz85L+Ho1/8j6++THy73aZms1l2v99fH49HUalUipZlhY/XXde93e12adu2H9rtthRCiFQqZR0Oh+sot0aNEAMAALG3Xq/TjUbjPpPJfBRCiHq9fq96UxQIMQAA8GTfOrnC9+FXkwAAIPZqtVqwXC4vgiBISClfrFarC9WbosCJGAAAiL1qtRo2m807wzBKmqYdy+Xye9WbopA4nU6qNwAAgBjzff+taZq3qnfEke/7OdM0r370fh5NAgAAKEKIAQAAKEKIAQAAKEKIAQAAKEKIAQAAKEKIAQAAKEKIAQCAZ8d13fxwOHzZ6/Xy8/k8I4QQnueldV0vFQqFYhAECcdxLnVdLzmOc6l675fwh64AAODZGo1G7x4/j8fjrOu6N91u904IISaTSU5K+ebsLL65E99lAAAAnxgMBq+m02lO07RjPp//YFlW2Gq1rmzbfpBSJheLRXaz2Zx7nnceBEEyDMOkYRjFfr9/0+l0pOr9n0OIAQCAJ/uf/+y//u1//t9UlN/581eZ8H//pfnVl4lvt9vUbDbL7vf76+PxKCqVStGyrPDxuuu6t7vdLm3b9kO73ZZCCJFKpazD4XAd5daoEWIAACD21ut1utFo3GcymY9CCFGv1+9Vb4oCIQYAAJ7sWydX+D78ahIAAMRerVYLlsvlRRAECSnli9VqdaF6UxQ4EQMAALFXrVbDZrN5ZxhGSdO0Y7lcfq96UxQSp9NJ9QYAABBjvu+/NU3zVvWOOPJ9P2ea5tWP3s+jSQAAAEUIMQAAAEUIMQAAAEUIMQAAAEUIMQAAAEUIMQAAAEUIMQAA8Oy4rpsfDocve71efj6fZ4QQwvO8tK7rpUKhUAyCIOE4zqWu6yXHcS5V7/0S/tAVAAA8W6PR6N3j5/F4nHVd96bb7d4JIcRkMslJKd+cncU3d+K7DAAA4BODweDVdDrNaZp2zOfzHyzLClut1pVt2w9SyuRischuNptzz/POgyBIhmGYNAyj2O/3bzqdjlS9/3MIMQAA8HTzv3kt/nCdivQ7f1oMxS//8asvE99ut6nZbJbd7/fXx+NRVCqVomVZ4eN113Vvd7td2rbth3a7LYUQIpVKWYfD4TrSrREjxAAAQOyt1+t0o9G4z2QyH4UQol6v36veFAVCDAAAPN03Tq7wffjVJAAAiL1arRYsl8uLIAgSUsoXq9XqQvWmKHAiBgAAYq9arYbNZvPOMIySpmnHcrn8XvWmKCROp5PqDQAAIMZ8339rmuat6h1x5Pt+zjTNqx+9n0eTAAAAihBiAAAAihBiAAAAihBiAAAAihBiAAAAihBiAAAAihBiAADg2XFdNz8cDl/2er38fD7PCCGE53lpXddLhUKhGARBwnGcS13XS47jXKre+yX8oSsAAHi2RqPRu8fP4/E467ruTbfbvRNCiMlkkpNSvjk7i2/uxHcZAADAJwaDwavpdJrTNO2Yz+c/WJYVtlqtK9u2H6SUycVikd1sNuee550HQZAMwzBpGEax3+/fdDodqXr/5xBiAADgyX61+9Xr38vfp6L8Tv2P9fDXf/brr75MfLvdpmazWXa/318fj0dRqVSKlmWFj9dd173d7XZp27Yf2u22FEKIVCplHQ6H6yi3Ro0QAwAAsbder9ONRuM+k8l8FEKIer1+r3pTFAgxAADwZN86ucL34VeTAAAg9mq1WrBcLi+CIEhIKV+sVqsL1ZuiwIkYAACIvWq1GjabzTvDMEqaph3L5fJ71ZuikDidTqo3AACAGPN9/61pmreqd8SR7/s50zSvfvR+Hk0CAAAoQogBAAAoQogBAAAoQogBAAAoQogBAAAoQogBAAAoQogBAIBnx3Xd/HA4fNnr9fLz+TwjhBCe56V1XS8VCoViEAQJx3EudV0vOY5zqXrvl/CHrgAA4NkajUbvHj+Px+Os67o33W73TgghJpNJTkr55uwsvrkT32UAAACfGAwGr6bTaU7TtGM+n/9gWVbYarWubNt+kFImF4tFdrPZnHuedx4EQTIMw6RhGMV+v3/T6XSk6v2fQ4gBAIAne/e3f/f6v373u1SU3/nffvazMP8Pf//Vl4lvt9vUbDbL7vf76+PxKCqVStGyrPDxuuu6t7vdLm3b9kO73ZZCCJFKpazD4XAd5daoEWIAACD21ut1utFo3GcymY9CCFGv1+9Vb4oCIQYAAJ7sWydX+D78ahIAAMRerVYLlsvlRRAECSnli9VqdaF6UxQ4EQMAALFXrVbDZrN5ZxhGSdO0Y7lcfq96UxQSp9NJ9QYAABBjvu+/NU3zVvWOOPJ9P2ea5tWP3s+jSQAAAEUIMQAAAEUIMQAAAEUIMQAAAEUIMQAAAEUIMQAAAEUIMQAA8Oy4rpsfDocve71efj6fZ4QQwvO8tK7rpUKhUAyCIOE4zqWu6yXHcS5V7/0S/tAVAAA8W6PR6N3j5/F4nHVd96bb7d4JIcRkMslJKd+cncU3d+K7DAAA4BODweDVdDrNaZp2zOfzHyzLClut1pVt2w9SyuRischuNptzz/POgyBIhmGYNAyj2O/3bzqdjlS9/3MIMQAA8GT/Ov7N67v/CFJRfmf2T9Lhn//VL776MvHtdpuazWbZ/X5/fTweRaVSKVqWFT5ed133drfbpW3bfmi321IIIVKplHU4HK6j3Bo1QgwAAMTeer1ONxqN+0wm81EIIer1+r3qTVEgxAAAwJN96+QK34dfTQIAgNir1WrBcrm8CIIgIaV8sVqtLlRvigInYgAAIPaq1WrYbDbvDMMoaZp2LJfL71VvikLidDqp3gAAAGLM9/23pmneqt4RR77v50zTvPrR+3k0CQAAoAghBgAAoAghBgAAoAghBgAAoAghBgAAoAghBgAAoAghBgAAnh3XdfPD4fBlr9fLz+fzjBBCeJ6X1nW9VCgUikEQJBzHudR1veQ4zqXqvV/CH7oCAIBnazQavXv8PB6Ps67r3nS73TshhJhMJjkp5Zuzs/jmTnyXAQAAfGIwGLyaTqc5TdOO+Xz+g2VZYavVurJt+0FKmVwsFtnNZnPued55EATJMAyThmEU+/3+TafTkar3fw4hBgAAnuxf/mn0+vbf/y0V5XfmXv9p+Bd/3fvqy8S3221qNptl9/v99fF4FJVKpWhZVvh43XXd291ul7Zt+6HdbkshhEilUtbhcLiOcmvUCDEAABB76/U63Wg07jOZzEchhKjX6/eqN0WBEAMAAE/2rZMrfB9+NQkAAGKvVqsFy+XyIgiChJTyxWq1ulC9KQqciAEAgNirVqths9m8MwyjpGnasVwuv1e9KQqJ0+mkegMAAIgx3/ffmqZ5q3pHHPm+nzNN8+pH7+fRJAAAgCKEGAAAgCKEGAAAgCKEGAAAgCKEGAAAgCKEGAAAgCKEGAAAeHZc180Ph8OXvV4vP5/PM0II4XleWtf1UqFQKAZBkHAc51LX9ZLjOJeq934Jf+gKAACerdFo9O7x83g8zrque9Ptdu+EEGIymeSklG/OzuKbO/FdBgAA8InBYPBqOp3mNE075vP5D5Zlha1W68q27QcpZXKxWGQ3m82553nnQRAkwzBMGoZR7Pf7N51OR6re/zmEGAAAeLK7f/7t6+N/vk9F+Z1/9OonYfYvf/7Vl4lvt9vUbDbL7vf76+PxKCqVStGyrPDxuuu6t7vdLm3b9kO73ZZCCJFKpazD4XAd5daoEWIAACD21ut1utFo3GcymY9CCFGv1+9Vb4oCIQYAAJ7sWydX+D78ahIAAMRerVYLlsvlRRAECSnli9VqdaF6UxQ4EQMAALFXrVbDZrN5ZxhGSdO0Y7lcfq96UxQSp9NJ9QYAABBjvu+/NU3zVvWOOPJ9P2ea5tWP3s+jSQAAAEUIMQAAAEUIMQAAAEUIMQAAAEUIMQAAAEUIMQAAAEUIMQAA8Oy4rpsfDocve71efj6fZ4QQwvO8tK7rpUKhUAyCIOE4zqWu6yXHcS5V7/0S/tAVAAA8W6PR6N3j5/F4nHVd96bb7d4JIcRkMslJKd+cncU3d+K7DAAA4BODweDVdDrNaZp2zOfzHyzLClut1pVt2w9SyuRischuNptzz/POgyBIhmGYNAyj2O/3bzqdjlS9/3MIMQAA8GTz+fz1H/7wh1SU3/nTn/40/OUvf/nVl4lvt9vUbDbL7vf76+PxKCqVStGyrPDxuuu6t7vdLm3b9kO73ZZCCJFKpazD4XAd5daoEWIAACD21ut1utFo3GcymY9CCFGv1+9Vb4oCIQYAAJ7sWydX+D78ahIAAMRerVYLlsvlRRAECSnli9VqdaF6UxQ4EQMAALFXrVbDZrN5ZxhGSdO0Y7lcfq96UxQSp9NJ9QYAABBjvu+/NU3zVvWOOPJ9P2ea5tWP3s+jSQAAAEUIMQAAAEUIMQAAAEUIMQAAAEUIMQAAAEUIMQAAAEUIMQAA8Oy4rpsfDocve71efj6fZ4QQwvO8tK7rpUKhUAyCIOE4zqWu6yXHcS5V7/0S/tAVAAA8W6PR6N3j5/F4nHVd96bb7d4JIcRkMslJKd+cncU3d+K7DAAA4BODweDVdDrNaZp2zOfzHyzLClut1pVt2w9SyuRischuNptzz/POgyBIhmGYNAyj2O/3bzqdjlS9/3MIMQAA8GTXvxm8fh/8NhXld/4k/fOw+Iv/9dWXiW+329RsNsvu9/vr4/EoKpVK0bKs8PG667q3u90ubdv2Q7vdlkIIkUqlrMPhcB3l1qgRYgAAIPbW63W60WjcZzKZj0IIUa/X71VvigIhBgAAnuxbJ1f4PvxqEgAAxF6tVguWy+VFEAQJKeWL1Wp1oXpTFDgRAwAAsVetVsNms3lnGEZJ07RjuVx+r3pTFBKn00n1BgAAEGO+7781TfNW9Y448n0/Z5rm1Y/ez6NJAAAARQgxAAAARQgxAAAARQgxAAAARQgxAAAARQgxAAAARQgxAADw7Liumx8Ohy97vV5+Pp9nhBDC87y0ruulQqFQDIIg4TjOpa7rJcdxLlXv/RL+0BUAADxbo9Ho3ePn8XicdV33ptvt3gkhxGQyyUkp35ydxTd34rsMAADgE4PB4NV0Os1pmnbM5/MfLMsKW63WlW3bD1LK5GKxyG42m3PP886DIEiGYZg0DKPY7/dvOp2OVL3/cwgxAADwZL3f/J/Xh/f/LxXldxZ+8t/D0S/+x1dfJr7dblOz2Sy73++vj8ejqFQqRcuywsfrruve7na7tG3bD+12WwohRCqVsg6Hw3WUW6NGiAEAgNhbr9fpRqNxn8lkPgohRL1ev1e9KQqEGAAAeLJvnVzh+/CrSQAAEHu1Wi1YLpcXQRAkpJQvVqvVhepNUeBEDAAAxF61Wg2bzeadYRglTdOO5XL5vepNUUicTifVGwAAQIz5vv/WNM1b1TviyPf9nGmaVz96P48mAQAAFCHEAAAAFCHEAAAAFCHEAAAAFCHEAAAAFCHEAAAAFCHEAADAs+O6bn44HL7s9Xr5+XyeEUIIz/PSuq6XCoVCMQiChOM4l7qulxzHuVS990v4Q1cAAPBsjUajd4+fx+Nx1nXdm263e3dIlowAACAASURBVCeEEJPJJCelfHN2Ft/cie8yAACATwwGg1fT6TSnadoxn89/sCwrbLVaV7ZtP0gpk4vFIrvZbM49zzsPgiAZhmHSMIxiv9+/6XQ6UvX+zyHEAADAk/3Pf/Zf//Y//28qyu/8+atM+L//0vzqy8S3221qNptl9/v99fF4FJVKpWhZVvh43XXd291ul7Zt+6HdbkshhEilUtbhcLiOcmvUCDEAABB76/U63Wg07jOZzEchhKjX6/eqN0WBEAMAAE/2rZMrfB9+NQkAAGKvVqsFy+XyIgiChJTyxWq1ulC9KQqciAEAgNirVqths9m8MwyjpGnasVwuv1e9KQqJ0+mkegMAAIgx3/ffmqZ5q3pHHPm+nzNN8+pH7+fRJAAAgCKEGAAAgCKEGAAAgCKEGAAAgCKEGAAAgCKEGAAAgCKEGAAAeHZc180Ph8OXvV4vP5/PM0II4XleWtf1UqFQKAZBkHAc51LX9ZLjOJeq934Jf+gKAACerdFo9O7x83g8zrque9Ptdu+EEGIymeSklG/OzuKbO/FdBgAA8InBYPBqOp3mNE075vP5D5Zlha1W68q27QcpZXKxWGQ3m82553nnQRAkwzBMGoZR7Pf7N51OR6re/zmEGAAAeLr537wWf7hORfqdPy2G4pf/+NWXiW+329RsNsvu9/vr4/EoKpVK0bKs8PG667q3u90ubdv2Q7vdlkIIkUqlrMPhcB3p1ogRYgAAIPbW63W60WjcZzKZj0IIUa/X71VvigIhBgAAnu4bJ1f4PvxqEgAAxF6tVguWy+VFEAQJKeWL1Wp1oXpTFDgRAwAAsVetVsNms3lnGEZJ07RjuVx+r3pTFBKn00n1BgAAEGO+7781TfNW9Y448n0/Z5rm1Y/ez6NJAAAARQgxAAAARQgxAAAARQgxAAAARQgxAAAARQgxAAAARQgxAADw7Liumx8Ohy97vV5+Pp9nhBDC87y0ruulQqFQDIIg4TjOpa7rJcdxLlXv/RL+0BUAADxbo9Ho3ePn8XicdV33ptvt3gkhxGQyyUkp35ydxTd34rsMAADgE4PB4NV0Os1pmnbM5/MfLMsKW63WlW3bD1LK5GKxyG42m3PP886DIEiGYZg0DKPY7/dvOp2OVL3/cwgxAADwZL/a/er17+XvU1F+p/7HevjrP/v1V18mvt1uU7PZLLvf76+Px6OoVCpFy7LCx+uu697udru0bdsP7XZbCiFEKpWyDofDdZRbo0aIAQCA2Fuv1+lGo3GfyWQ+CiFEvV6/V70pCoQYAAB4sm+dXOH78KtJAAAQe7VaLVgulxdBECSklC9Wq9WF6k1R4EQMAADEXrVaDZvN5p1hGCVN047lcvm96k1RSJxOJ9UbAABAjPm+/9Y0zVvVO+LI9/2caZpXP3o/jyYBAAAUIcQAAAAUIcQAAAAUIcQAAAAUIcQAAAAUIcQAAAAUIcQAAMCz47pufjgcvuz1evn5fJ4RQgjP89K6rpcKhUIxCIKE4ziXuq6XHMe5VL33S/hDVwAA8GyNRqN3j5/H43HWdd2bbrd7J4QQk8kkJ6V8c3YW39yJ7zIAAIBPDAaDV9PpNKdp2jGfz3+wLCtstVpXtm0/SCmTi8Uiu9lszj3POw+CIBmGYdIwjGK/37/pdDpS9f7PIcQAAMCTvfvbv3v9X7/7XSrK7/xvP/tZmP+Hv//qy8S3221qNptl9/v99fF4FJVKpWhZVvh43XXd291ul7Zt+6HdbkshhEilUtbhcLiOcmvUCDEAABB76/U63Wg07jOZzEchhKjX6/eqN0WBEAMAAE/2rZMrfB9+NQkAAGKvVqsFy+XyIgiChJTyxWq1ulC9KQqciAEAgNirVqths9m8MwyjpGnasVwuv1e9KQqJ0+mkegMAAIgx3/ffmqZ5q3pHHPm+nzNN8+pH7+fRJAAAgCKEGAAAgCKEGAAAgCKEGAAAgCKEGAAAgCKEGAAAgCKEGAAAeHZc180Ph8OXvV4vP5/PM0II4XleWtf1UqFQKAZBkHAc51LX9ZLjOJeq934Jf+gKAACerdFo9O7x83g8zrque9Ptdu+EEGIymeSklG/OzuKbO/FdBgAA8InBYPBqOp3mNE075vP5D5Zlha1W68q27QcpZXKxWGQ3m82553nnQRAkwzBMGoZR7Pf7N51OR6re/zmEGAAAeLJ/Hf/m9d1/BKkovzP7J+nwz//qF199mfh2u03NZrPsfr+/Ph6PolKpFC3LCh+vu657u9vt0rZtP7TbbSmEEKlUyjocDtdRbo0aIQYAAGJvvV6nG43GfSaT+SiEEPV6/V71pigQYgAA4Mm+dXKF78OvJgEAQOzVarVguVxeBEGQkFK+WK1WF6o3RYETMQAAEHvVajVsNpt3hmGUNE07lsvl96o3RSFxOp1UbwAAADHm+/5b0zRvVe+II9/3c6ZpXv3o/TyaBAAAUIQQAwAAUIQQAwAAUIQQAwAAUIQQAwAAUIQQAwAAUIQQAwAAz47ruvnhcPiy1+vl5/N5RgghPM9L67peKhQKxSAIEo7jXOq6XnIc51L13i/hD10BAMCzNRqN3j1+Ho/HWdd1b7rd7p0QQkwmk5yU8s3ZWXxzJ77LAAAAPjEYDF5Np9OcpmnHfD7/wbKssNVqXdm2/SClTC4Wi+xmszn3PO88CIJkGIZJwzCK/X7/ptPpSNX7P4cQAwAAT/Yv/zR6ffvv/5aK8jtzr/80/Iu/7n31ZeLb7TY1m82y+/3++ng8ikqlUrQsK3y87rru7W63S9u2/dBut6UQQqRSKetwOFxHuTVqhBgAAIi99XqdbjQa95lM5qMQQtTr9XvVm6JAiAEAgCf71skVvg+/mgQAALFXq9WC5XJ5EQRBQkr5YrVaXajeFAVOxAAAQOxVq9Ww2WzeGYZR0jTtWC6X36veFIXE6XRSvQEAAMSY7/tvTdO8Vb0jjnzfz5mmefWj9/NoEgAAQBFCDAAAQBFCDAAAQBFCDAAAQBFCDAAAQBFCDAAAQBFCDAAAPDuu6+aHw+HLXq+Xn8/nGSGE8Dwvret6qVAoFIMgSDiOc6nreslxnEvVe7+EP3QFAADP1mg0evf4eTweZ13Xvel2u3dCCDGZTHJSyjdnZ/HNnfguAwAA+MRgMHg1nU5zmqYd8/n8B8uywlardWXb9oOUMrlYLLKbzebc87zzIAiSYRgmDcMo9vv9m06nI1Xv/xxCDAAAPNndP//29fE/36ei/M4/evWTMPuXP//qy8S3221qNptl9/v99fF4FJVKpWhZVvh43XXd291ul7Zt+6HdbkshhEilUtbhcLiOcmvUCDEAABB76/U63Wg07jOZzEchhKjX6/eqN0WBEAMAAE/2rZMrfB9+NQkAAGKvVqsFy+XyIgiChJTyxWq1ulC9KQqciAEAgNirVqths9m8MwyjpGnasVwuv1e9KQqJ0+mkegMAAIgx3/ffmqZ5q3pHHPm+nzNN8+pH7+fRJAAAgCKEGAAAgCKEGAAAgCKEGAAAgCKEGAAAgCKEGAAAgCKEGAAAeHZc180Ph8OXvV4vP5/PM0II4XleWtf1UqFQKAZBkHAc51LX9ZLjOJeq934Jf+gKAACerdFo9O7x83g8zrque9Ptdu+EEGIymeSklG/OzuKbO/FdBgAA8InBYPBqOp3mNE075vP5D5Zlha1W68q27QcpZXKxWGQ3m82553nnQRAkwzBMGoZR7Pf7N51OR6re/zmEGAAAeLL5fP76D3/4QyrK7/zpT38a/vKXv/zqy8S3221qNptl9/v99fF4FJVKpWhZVvh43XXd291ul7Zt+6HdbkshhEilUtbhcLiOcmvUCDEAABB76/U63Wg07jOZzEchhKjX6/eqN0WBEAMAAE/2rZMrfB9+NQkAAGKvVqsFy+XyIgiChJTyxWq1ulC9KQqciAEAgNirVqths9m8MwyjpGnasVwuv1e9KQqJ0+mkegMAAIgx3/ffmqZ5q3pHHPm+nzNN8+pH7+fRJAAAgCKEGAAAgCKEGAAAgCKEGAAAgCKEGAAAgCKEGAAAgCKEGAAAeHZc180Ph8OXvV4vP5/PM0II4XleWtf1UqFQKAZBkHAc51LX9ZLjOJeq934Jf+gKAACerdFo9O7x83g8zrque9Ptdu+EEGIymeSklG/OzuKbO/FdBgAA8InBYPBqOp3mNE075vP5D5Zlha1W68q27QcpZXKxWGQ3m82553nnQRAkwzBMGoZR7Pf7N51OR6re/zmEGAAAeLLr3wxevw9+m4ryO3+S/nlY/MX/+urLxLfbbWo2m2X3+/318XgUlUqlaFlW+Hjddd3b3W6Xtm37od1uSyGESKVS1uFwuI5ya9QIMQAAEHvr9TrdaDTuM5nMRyGEqNfr96o3RYEQAwAAT/atkyt8H341CQAAYq9WqwXL5fIiCIKElPLFarW6UL0pCpyIAQCA2KtWq2Gz2bwzDKOkadqxXC6/V70pConT6aR6AwAAiDHf99+apnmrekcc+b6fM03z6kfv59EkAACAIoQYAACAIoQYAACAIoQYAACAIoQYAACAIoQYAACAIoQYAAB4dlzXzQ+Hw5e9Xi8/n88zQgjheV5a1/VSoVAoBkGQcBznUtf1kuM4l6r3fgl/6AoAAJ6t0Wj07vHzeDzOuq570+1274QQYjKZ5KSUb87O4ps78V0GAADwicFg8Go6neY0TTvm8/kPlmWFrVbryrbtByllcrFYZDebzbnneedBECTDMEwahlHs9/s3nU5Hqt7/OYQYAAB4st5v/s/rw/v/l4ryOws/+e/h6Bf/46svE99ut6nZbJbd7/fXx+NRVCqVomVZ4eN113Vvd7td2rbth3a7LYUQIpVKWYfD4TrKrVEjxAAAQOyt1+t0o9G4z2QyH4UQol6v36veFAVCDAAAPNm3Tq7wffjVJAAAiL1arRYsl8uLIAgSUsoXq9XqQvWmKHAiBgAAYq9arYbNZvPOMIySpmnHcrn8XvWmKCROp5PqDQAAIMZ8339rmuat6h1x5Pt+zjTNqx+9n0eTAAAAihBiAAAAihBiAAAAihBiAAAAihBiAAAAihBiAAAAihBiAADg2XFdNz8cDl/2er38fD7PCCGE53lpXddLhUKhGARBwnGcS13XS47jXKre+yX8oSsAAHi2RqPRu8fP4/E467ruTbfbvRNCiMlkkpNSvjk7i2/uxHcZAADAJwaDwavpdJrTNO2Yz+c/WJYVtlqtK9u2H6SUycVikd1sNuee550HQZAMwzBpGEax3+/fdDodqXr/5xBiAADgyf7nP/uvf/uf/zcV5Xf+/FUm/N9/aX71ZeLb7TY1m82y+/3++ng8ikqlUrQsK3y87rru7W63S9u2/dBut6UQQqRSKetwOFxHuTVqhBgAAIi99XqdbjQa95lM5qMQQtTr9XvVm6JAiAEAgCf71skVvg+/mgQAALFXq9WC5XJ5EQRBQkr5YrVaXajeFAVOxAAAQOxVq9Ww2WzeGYZR0jTtWC6X36veFIXE6XRSvQEAAMSY7/tvTdO8Vb0jjnzfz5mmefWj9/NoEgAAQBFCDAAAQBFCDAAAQBFCDAAAQBFCDAAAQBFCDAAAQBFCDAAAPDuu6+aHw+HLXq+Xn8/nGSGE8Dwvret6qVAoFIMgSDiOc6nreslxnEvVe7+EP3QFAADP1mg0evf4eTweZ13Xvel2u3dCCDGZTHJSyjdnZ/HNnfguAwAA+MRgMHg1nU5zmqYd8/n8B8uywlardWXb9oOUMrlYLLKbzebc87zzIAiSYRgmDcMo9vv9m06nI1Xv/xxCDAAAPN38b16LP1ynIv3OnxZD8ct//OrLxLfbbWo2m2X3+/318XgUlUqlaFlW+Hjddd3b3W6Xtm37od1uSyGESKVS1uFwuI50a8QIMQAAEHvr9TrdaDTuM5nMRyGEqNfr96o3RYEQAwAAT/eNkyt8H341CQAAYq9WqwXL5fIiCIKElPLFarW6UL0pCpyIAQCA2KtWq2Gz2bwzDKOkadqxXC6/V70pConT6aR6AwAAiDHf99+apnmrekcc+b6fM03z6kfv59EkAACAIoQYAACAIoQYAACAIoQYAACAIoQYAACAIoQYAACAIoQYAAB4dlzXzQ+Hw5e9Xi8/n88zQgjheV5a1/VSoVAoBkGQcBznUtf1kuM4l6r3fgl/6AoAAJ6t0Wj07vHzeDzOuq570+1274QQYjKZ5KSUb87O4ps78V0GAADwicFg8Go6neY0TTvm8/kPlmWFrVbryrbtByllcrFYZDebzbnneedBECTDMEwahlHs9/s3nU5Hqt7/OYQYAAB4sl/tfvX69/L3qSi/U/9jPfz1n/36qy8T3263qdlslt3v99fH41FUKpWiZVnh43XXdW93u13atu2HdrsthRAilUpZh8PhOsqtUSPEAABA7K3X63Sj0bjPZDIfhRCiXq/fq94UBUIMAAA82bdOrvB9+NUkAACIvVqtFiyXy4sgCBJSyher1epC9aYocCIGAABir1qths1m884wjJKmacdyufxe9aYoJE6nk+oNAAAgxnzff2ua5q3qHXHk+37ONM2rH72fR5MAAACKEGIAAACKEGIAAACKEGIAAACKEGIAAACKEGIAAACKEGIAAODZcV03PxwOX/Z6vfx8Ps8IIYTneWld10uFQqEYBEHCcZxLXddLjuNcqt77JfyhKwAAeLZGo9G7x8/j8Tjruu5Nt9u9E0KIyWSSk1K+OTuLb+7EdxkAAMAnBoPBq+l0mtM07ZjP5z9YlhW2Wq0r27YfpJTJxWKR3Ww2557nnQdBkAzDMGkYRrHf7990Oh2pev/nEGIAAODJ3v3t373+r9/9LhXld/63n/0szP/D33/1ZeLb7TY1m82y+/3++ng8ikqlUrQsK3y87rru7W63S9u2/dBut6UQQqRSKetwOFxHuTVqhBgAAIi99XqdbjQa95lM5qMQQtTr9XvVm6JAiAEAgCf71skVvg+/mgQAALFXq9WC5XJ5EQRBQkr5YrVaXajeFAVOxAAAQOxVq9Ww2WzeGYZR0jTtWC6X36veFIXE6XRSvQEAAMSY7/tvTdO8Vb0jjnzfz5mmefWj9/NoEgAAQBFCDAAAQBFCDAAAQBFCDAAAQBFCDAAAQBFCDAAAQBFCDAAAPDuu6+aHw+HLXq+Xn8/nGSGE8Dwvret6qVAoFIMgSDiOc6nreslxnEvVe7+EP3QFAADP1mg0evf4eTweZ13Xvel2u3dCCDGZTHJSyjdnZ/HNnfguAwAA+MRgMHg1nU5zmqYd8/n8B8uywlardWXb9oOUMrlYLLKbzebc87zzIAiSYRgmDcMo9vv9m06nI1Xv/xxCDAAAPNm/jn/z+u4/glSU35n9k3T453/1i6++THy73aZms1l2v99fH49HUalUipZlhY/XXde93e12adu2H9rtthRCiFQqZR0Oh+sot0aNEAMAALG3Xq/TjUbjPpPJfBRCiHq9fq96UxQIMQAA8GTfOrnC9+FXkwAAIPZqtVqwXC4vgiBISClfrFarC9WbosCJGAAAiL1qtRo2m807wzBKmqYdy+Xye9WbopA4nU6qNwAAgBjzff+taZq3qnfEke/7OdM0r370fh5NAgAAKEKIAQAAKEKIAQAAKEKIAQAAKEKIAQAAKEKIAQAAKEKIAQCAZ8d13fxwOHzZ6/Xy8/k8I4QQnueldV0vFQqFYhAECcdxLnVdLzmOc6l675fwh64AAODZGo1G7x4/j8fjrOu6N91u904IISaTSU5K+ebsLL65E99lAAAAnxgMBq+m02lO07RjPp//YFlW2Gq1rmzbfpBSJheLRXaz2Zx7nnceBEEyDMOkYRjFfr9/0+l0pOr9n0OIAQCAJ/uXfxq9vv33f0tF+Z25138a/sVf9776MvHtdpuazWbZ/X5/fTweRaVSKVqWFT5ed133drfbpW3bfmi321IIIVKplHU4HK6j3Bo1QgwAAMTeer1ONxqN+0wm81EIIer1+r3qTVEgxAAAwJN96+QK34dfTQIAgNir1WrBcrm8CIIgIaV8sVqtLlRvigInYgAAIPaq1WrYbDbvDMMoaZp2LJfL71VvikLidDqp3gAAAGLM9/23pmneqt4RR77v50zTvPrR+3k0CQAAoAghBgAAoAghBgAAoAghBgAAoAghBgAAoAghBgAAoAghBgAAnh3XdfPD4fBlr9fLz+fzjBBCeJ6X1nW9VCgUikEQJBzHudR1veQ4zqXqvV/CH7oCAIBnazQavXv8PB6Ps67r3nS73TshhJhMJjkp5Zuzs/jmTnyXAQAAfGIwGLyaTqc5TdOO+Xz+g2VZYavVurJt+0FKmVwsFtnNZnPued55EATJMAyThmEU+/3+TafTkar3fw4hBgAAnuzun3/7+vif71NRfucfvfpJmP3Ln3/1ZeLb7TY1m82y+/3++ng8ikqlUrQsK3y87rru7W63S9u2/dBut6UQQqRSKetwOFxHuTVqhBgAAIi99XqdbjQa95lM5qMQQtTr9XvVm6JAiAEAgCf71snV/2/vfkJUWRv8jj8eO2TwKt2x5J6D6TPpRc3Fq4VlrePKgAGpxRXfdUAYKWI2UjIICeNmSGCYjZuQTVYuBOEF3SgGCSLi/hS+9JU7sziZkNOXe5t+upk6xeR4aWeTJmdx/vQ5b8FTDd/PqqB4Hn7U6sfz8DyFL8OpSQAAEHmVSsWfz+dnvu/HpJTPlsvlmepMYWBFDAAARF65XA7q9fqNYRgFTdMOxWLxrepMYYgdj0fVGQAAQIR5nvfaNM1r1TmiyPO8jGmaF187nq1JAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAPDmu62b7/f7zTqeTnU6nKSGEWCwWSV3XC7lcLu/7fsxxnHNd1wuO45yrzvsxXOgKAACerMFg8ObheTgcpl3XvWq32zdCCDEajTJSylcnJ9GtO9FNBgAA8J5er/diPB5nNE07ZLPZd5ZlBY1G48K27TspZXw2m6XX6/XpYrE49X0/HgRB3DCMfLfbvWq1WlJ1/g+hiAEAgEebTqcvf/nll0SYc3777bfBDz/88MmfiW82m8RkMknvdrvLw+EgSqVS3rKs4OG967rX2+02adv2XbPZlEIIkUgkrP1+fxlm1rBRxAAAQOStVqtkrVa7TaVS90IIUa1Wb1VnCgNFDAAAPNrnVq7wZTg1CQAAIq9Sqfjz+fzM9/2YlPLZcrk8U50pDKyIAQCAyCuXy0G9Xr8xDKOgadqhWCy+VZ0pDLHj8ag6AwAAiDDP816bpnmtOkcUeZ6XMU3z4mvHszUJAACgCEUMAABAEYoYAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQCAJ8d13Wy/33/e6XSy0+k0JYQQi8Uiqet6IZfL5X3fjzmOc67resFxnHPVeT+GC10BAMCTNRgM3jw8D4fDtOu6V+12+0YIIUajUUZK+erkJLp1J7rJAAAA3tPr9V6Mx+OMpmmHbDb7zrKsoNFoXNi2fSeljM9ms/R6vT5dLBanvu/HgyCIG4aR73a7V61WS6rO/yEUMQAA8GiXP/ZevvV/SoQ55zfJ74L893/9yZ+JbzabxGQySe92u8vD4SBKpVLesqzg4b3rutfb7TZp2/Zds9mUQgiRSCSs/X5/GWbWsFHEAABA5K1Wq2StVrtNpVL3QghRrVZvVWcKA0UMAAA82udWrvBlODUJAAAir1Kp+PP5/Mz3/ZiU8tlyuTxTnSkMrIgBAIDIK5fLQb1evzEMo6Bp2qFYLL5VnSkMsePxqDoDAACIMM/zXpumea06RxR5npcxTfPia8ezNQkAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAIAnx3XdbL/ff97pdLLT6TQlhBCLxSKp63ohl8vlfd+POY5zrut6wXGcc9V5P4YLXQEAwJM1GAzePDwPh8O067pX7Xb7RgghRqNRRkr56uQkunUnuskAAADe0+v1XozH44ymaYdsNvvOsqyg0Whc2LZ9J6WMz2az9Hq9Pl0sFqe+78eDIIgbhpHvdrtXrVZLqs7/IRQxAADwaJ0f//7l/u0/JsKcM/fNnwSD7//0kz8T32w2iclkkt7tdpeHw0GUSqW8ZVnBw3vXda+3223Stu27ZrMphRAikUhY+/3+MsysYaOIAQCAyFutVslarXabSqXuhRCiWq3eqs4UBooYAAB4tM+tXOHLcGoSAABEXqVS8efz+Znv+zEp5bPlcnmmOlMYWBEDAACRVy6Xg3q9fmMYRkHTtEOxWHyrOlMYYsfjUXUGAAAQYZ7nvTZN81p1jijyPC9jmubF145naxIAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABPjuu62X6//7zT6WSn02lKCCEWi0VS1/VCLpfL+74fcxznXNf1guM456rzfgwXugIAgCdrMBi8eXgeDodp13Wv2u32jRBCjEajjJTy1clJdOtOdJMBAAC8p9frvRiPxxlN0w7ZbPadZVlBo9G4sG37TkoZn81m6fV6fbpYLE59348HQRA3DCPf7XavWq2WVJ3/QyhiAADg0f7i997Ln37+h0SYc373IhX8ze/MT/5MfLPZJCaTSXq3210eDgdRKpXylmUFD+9d173ebrdJ27bvms2mFEKIRCJh7ff7yzCzho0iBgAAIm+1WiVrtdptKpW6F0KIarV6qzpTGChiAADg0T63coUvw6lJAAAQeZVKxZ/P52e+78eklM+Wy+WZ6kxhYEUMAABEXrlcDur1+o1hGAVN0w7FYvGt6kxhiB2PR9UZAABAhHme99o0zWvVOaLI87yMaZoXXzuerUkAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABQhCIGAACgCEUMAAA8Oa7rZvv9/vNOp5OdTqcpIYRYLBZJ8xepwAAAD3pJREFUXdcLuVwu7/t+zHGcc13XC47jnKvO+zFc6AoAAJ6swWDw5uF5OBymXde9arfbN0IIMRqNMlLKVycn0a070U0GAADwnl6v92I8Hmc0TTtks9l3lmUFjUbjwrbtOyllfDabpdfr9elisTj1fT8eBEHcMIx8t9u9arVaUnX+D6GIAQCAx5v+h5fil8tEqHN+mw/ED//1kz8T32w2iclkkt7tdpeHw0GUSqW8ZVnBw3vXda+3223Stu27ZrMphRAikUhY+/3+MtSsIaOIAQCAyFutVslarXabSqXuhRCiWq3eqs4UBooYAAB4vM+sXOHLcGoSAABEXqVS8efz+Znv+zEp5bPlcnmmOlMYWBEDAACRVy6Xg3q9fmMYRkHTtEOxWHyrOlMYYsfjUXUGAAAQYZ7nvTZN81p1jijyPC9jmubF145naxIAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABPjuu62X6//7zT6WSn02lKCCEWi0VS1/VCLpfL+74fcxznXNf1guM456rzfgwXugIAgCdrMBi8eXgeDodp13Wv2u32jRBCjEajjJTy1clJdOtOdJMBAAC8p9frvRiPxxlN0w7ZbPadZVlBo9G4sG37TkoZn81m6fV6fbpYLE59348HQRA3DCPf7XavWq2WVJ3/QyhiAADg0f5y+5cv/07+XSLMOfV/oQd/9a//6pM/E99sNonJZJLe7XaXh8NBlEqlvGVZwcN713Wvt9tt0rbtu2azKYUQIpFIWPv9/jLMrGGjiAEAgMhbrVbJWq12m0ql7oUQolqt3qrOFAaKGAAAeLTPrVzhy3BqEgAARF6lUvHn8/mZ7/sxKeWz5XJ5pjpTGFgRAwAAkVcul4N6vX5jGEZB07RDsVh8qzpTGGLH41F1BgAAEGGe5702TfNadY4o8jwvY5rmxdeOZ2sSAABAEYoYAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAT47rutl+v/+80+lkp9NpSgghFotFUtf1Qi6Xy/u+H3Mc51zX9YLjOOeq834MF7oCAIAnazAYvHl4Hg6Hadd1r9rt9o0QQoxGo4yU8tXJSXTrTnSTAQAAvKfX670Yj8cZTdMO2Wz2nWVZQaPRuLBt+05KGZ/NZun1en26WCxOfd+PB0EQNwwj3+12r1qtllSd/0MoYgAA4NHe/Mf/9PL//u3fJsKc85//2Z8F2f/ynz/5M/HNZpOYTCbp3W53eTgcRKlUyluWFTy8d133ervdJm3bvms2m1IIIRKJhLXf7y/DzBo2ihgAAIi81WqVrNVqt6lU6l4IIarV6q3qTGGgiAEAgEf73MoVvgynJgEAQORVKhV/Pp+f+b4fk1I+Wy6XZ6ozhYEVMQAAEHnlcjmo1+s3hmEUNE07FIvFt6ozhSF2PB5VZwAAABHmed5r0zSvVeeIIs/zMqZpXnzteLYmAQAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAQBGKGAAAgCIUMQAA8OS4rpvt9/vPO51OdjqdpoQQYrFYJHVdL+Ryubzv+zHHcc51XS84jnOuOu/HcKErAAB4sgaDwZuH5+FwmHZd96rdbt8IIcRoNMpIKV+dnES37kQ3GQAAwHt6vd6L8Xic0TTtkM1m31mWFTQajQvbtu+klPHZbJZer9eni8Xi1Pf9eBAEccMw8t1u96rVaknV+T+EIgYAAB7tfw5/fHnzf/xEmHOm/2Uy+Df/7vtP/kx8s9kkJpNJerfbXR4OB1EqlfKWZQUP713Xvd5ut0nbtu+azaYUQohEImHt9/vLMLOGjSIGAAAib7VaJWu12m0qlboXQohqtXqrOlMYKGIAAODRPrdyhS/DqUkAABB5lUrFn8/nZ77vx6SUz5bL5ZnqTGFgRQwAAEReuVwO6vX6jWEYBU3TDsVi8a3qTGGIHY9H1RkAAECEeZ732jTNa9U5osjzvIxpmhdfO56tSQAAAEUoYgAAAIpQxAAAABShiAEAAChCEQMAAFCEIgYAAKAIRQwAADw5rutm+/3+806nk51OpykhhFgsFkld1wu5XC7v+37McZxzXdcLjuOcq877MVzoCgAAnqzBYPDm4Xk4HKZd171qt9s3QggxGo0yUspXJyfRrTvRTQYAAPCeXq/3YjweZzRNO2Sz2XeWZQWNRuPCtu07KWV8Npul1+v16WKxOPV9Px4EQdwwjHy3271qtVpSdf4PoYgBAIBH+x//bfDy+n//r0SYc2Ze/qvg3/77zid/Jr7ZbBKTySS92+0uD4eDKJVKecuygof3ruteb7fbpG3bd81mUwohRCKRsPb7/WWYWcNGEQMAAJG3Wq2StVrtNpVK3QshRLVavVWdKQwUMQAA8GifW7nCl+HUJAAAiLxKpeLP5/Mz3/djUspny+XyTHWmMLAiBgAAIq9cLgf1ev3GMIyCpmmHYrH4VnWmMMSOx6PqDAAAIMI8z3ttmua16hxR5HlexjTNi68dz9YkAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAnhzXdbP9fv95p9PJTqfTlBBCLBaLpK7rhVwul/d9P+Y4zrmu6wXHcc5V5/0YLnQFAABP1mAwePPwPBwO067rXrXb7RshhBiNRhkp5auTk+jWnegmAwAAeE+v13sxHo8zmqYdstnsO8uygkajcWHb9p2UMj6bzdLr9fp0sVic+r4fD4IgbhhGvtvtXrVaLak6/4dQxAAAwKPd/P6nl4ef3ybCnPOfvfgmSP/uu0/+THyz2SQmk0l6t9tdHg4HUSqV8pZlBQ/vXde93m63Sdu275rNphRCiEQiYe33+8sws4aNIgYAACJvtVola7XabSqVuhdCiGq1eqs6UxgoYgAA4NE+t3KFL8OpSQAAEHmVSsWfz+dnvu/HpJTPlsvlmepMYWBFDAAARF65XA7q9fqNYRgFTdMOxWLxrepMYYgdj0fVGQAAQIR5nvfaNM1r1TmiyPO8jGmaF187nq1JAAAARShiAAAAilDEAAAAFKGIAQAAKEIRAwAAUIQiBgAAoAhFDAAAPDmu62b7/f7zTqeTnU6nKSGEWCwWSV3XC7lcLu/7fsxxnHNd1wuO45yrzvsxXOgKAACerMFg8ObheTgcpl3XvWq32zdCCDEajTJSylcnJ9GtO9FNBgAA8J5er/diPB5nNE07ZLPZd5ZlBY1G48K27TspZXw2m6XX6/XpYrE49X0/HgRB3DCMfLfbvWq1WlJ1/g+hiAEAgEebTqcvf/nll0SYc3777bfBDz/88MmfiW82m8RkMknvdrvLw+EgSqVS3rKs4OG967rX2+02adv2XbPZlEIIkUgkrP1+fxlm1rBRxAAAQOStVqtkrVa7TaVS90IIUa1Wb1VnCgNFDAAAPNrnVq7wZTg1CQAAIq9Sqfjz+fzM9/2YlPLZcrk8U50pDKyIAQCAyCuXy0G9Xr8xDKOgadqhWCy+VZ0pDLHj8ag6AwAAiDDP816bpnmtOkcUeZ6XMU3z4mvHszUJAACgCEUMAABAEYoYAACAIhQxAAAARShiAAAAilDEAAAAFKGIAQCAJ8d13Wy/33/e6XSy0+k0JYQQi8Uiqet6IZfL5X3fjzmOc67resFxnHPVeT+GC10BAMCTNRgM3jw8D4fDtOu6V+12+0YIIUajUUZK+erkJLp1J7rJAAAA3tPr9V6Mx+OMpmmHbDb7zrKsoNFoXNi2fSeljM9ms/R6vT5dLBanvu/HgyCIG4aR73a7V61WS6rO/yEUMQAA8GiXP/ZevvV/SoQ55zfJ74L893/9yZ+JbzabxGQySe92u8vD4SBKpVLesqzg4b3rutfb7TZp2/Zds9mUQgiRSCSs/X5/GWbWsFHEAABA5K1Wq2StVrtNpVL3QghRrVZvVWcKA0UMAAA82udWrvBlODUJAAAir1Kp+PP5/Mz3/ZiU8tlyuTxTnSkMrIgBAIDIK5fLQb1evzEMo6Bp2qFYLL5VnSkMsePxqDoDAACIMM/zXpumea06RxR5npcxTfPia8ezNQkAAKAIRQwAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAIAnx3XdbL/ff97pdLLT6TQlhBCLxSKp63ohl8vlfd+POY5zrut6wXGcc9V5P4YLXQEAwJM1GAzePDwPh8O067pX7Xb7RgghRqNRRkr56uQkunUnuskAAADe0+v1XozH44ymaYdsNvvOsqyg0Whc2LZ9J6WMz2az9Hq9Pl0sFqe+78eDIIgbhpHvdrtXrVZLqs7/IRQxAADwaJ0f//7l/u0/JsKcM/fNnwSD7//0kz8T32w2iclkkt7tdpeHw0GUSqW8ZVnBw3vXda+3223Stu27ZrMphRAikUhY+/3+MsysYaOIAQCAyFutVslarXabSqXuhRCiWq3eqs4UBooYAAB4tM+tXOHLcGoSAABEXqVS8efz+Znv+zEp5bPlcnmmOlMYWBEDAACRVy6Xg3q9fmMYRkHTtEOxWHyrOlMYYsfjUXUGAAAQYZ7nvTZN81p1jijyPC9jmubF145naxIAAEARihgAAIAiFDEAAABFKGIAAACKUMQAAAAUoYgBAAAoQhEDAABPjuu62X6//7zT6WSn02lKCCEWi0VS1/VCLpfL+74fcxznXNf1guM456rzfgwXugIAgCdrMBi8eXgeDodp13Wv2u32jRBCjEajjJTy1clJdOtOdJMBAAC8p9frvRiPxxlN0w7ZbPadZVlBo9G4sG37TkoZn81m6fV6fbpYLE59348HQRA3DCPf7XavWq2WVJ3/QyhiAADg0f7i997Ln37+h0SYc373IhX8ze/MT/5MfLPZJCaTSXq3210eDgdRKpXylmUFD+9d173ebrdJ27bvms2mFEKIRCJh7ff7yzCzho0iBgAAIm+1WiVrtdptKpW6F0KIarV6qzpTGChiAADg0T63coUvw6lJAAAQeZVKxZ/P52e+78eklM+Wy+WZ6kxhYEUMAABEXrlcDur1+o1hGAVN0w7FYvGt6kxhiB2PR9UZAABAhHme99o0zWvVOaLI87yMaZoXXzuerUkAAABFKGIAAACKUMQAAAAUoYgBAIDPub+/v4+pDhE1/++b3P8xc1DEAADA5/zh119/PaWM/X/39/exX3/99VQI8Yc/Zh6urwAAAJ/022+//fnPP//833/++WdDsIjz4F4I8Yfffvvtz/+YSbi+AgAAQBFaLQAAgCIUMQAAAEUoYgAAAIpQxAAAABShiAEAACjyT49xJh1UoF/DAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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GhhgcHCSfz1MoFPA8j1QqRSKRcB1/TkeriL2HWacljTGnWGufnv7YCNx7lHKIiIjIEdj2+c+zb3xLWfe5MnQ+m6699rDrZDIZent7yWazlEolotEonufNLG9tbWV0dJR4PE5TUxMAgUCAbDZb1qzlVvEiZoxZA7wN+PCs2X9rjKll6tTkoy9ZJiIiIvIiIyMjNDY24vf7AWhoaHCcqDwqXsSstbuBjS+Z975Kf6+IiIiU33wjV7I4erK+iIiIVL36+nr6+/spFovk83kGBgZcRyqLo3WNmIiIiMgrFo1GSSQSRCIRgsEgdXV1riOVhbG2+p8MEYvF7NjYmOsYIiIix6Xx8XFCoZDrGFVprmNjjMlYa2ML2V6nJkVEREQcURETERERcURFTERERMQRFTERERERR1TERERERBxRERMRERFxREVMRERElpz29na6urpoa2tjeHgYmHoNUjgcpra2lmKxSDKZJBwOk0wmHac9ND3QVURERJasjo6Omemenh5SqRTNzc0ApNNpcrkcPp/PVbx5qYiJiIjIktDZ2Ul3dzfBYJCamho8z6OlpYV4PM7ExAR9fX0MDQ0xODhIPp+nUCjgeR6pVIpEIuE6/pxUxERERGTBRvoe5LknCmXd50k1AS5917mHXSeTydDb20s2m6VUKhGNRvE8b2Z5a2sro6OjxONxmpqaAAgEAmSz2bJmLTcVMREREal6IyMjNDY24vf7AWhoaHCcqDxUxERERGTB5hu5ksXRXZMiIiJS9err6+nv76dYLJLP5xkYGHAdqSw0IiYiIiJVLxqNkkgkiEQiBINB6urqXEcqC2OtdZ1hXrFYzI6NjbmOISIiclwaHx8nFAq5jlGV5jo2xpiMtTa2kO11alJERETEERUxEREREUdUxEREREQcURETERERcURFTERERMQRFTERERERR1TEREREZMlpb2+nq6uLtrY2hoeHganXIIXDYWpraykWiySTScLhMMlk0nHaQ9MDXUVERGTJ6ujomJnu6ekhlUrR3NwMQDqdJpfL4fP5XMWbl4qYiIiILAmdnZ10d3cTDAapqanB8zxaWlqIx+NMTEzQ19fH0NAQg4OD5PN5CoUCnueRSqVIJBKu489JRUxEREQW7I4b0zz72G/Lus/ga17LZS1XHXadTCZDb28v2WyWUqlENBrF87yZ5a2trYyOjhKPx2lqagIgEAiQzWbLmrXcVMRERESk6o2MjNDY2Ijf7wegoaHBcaLyUBETERGRBZtv5EoWR3dNioiISNWrr6+nv7+fYrFIPp9nYGDAdaSy0IiYiIiIVL1oNEoikSASiRAMBqmrq3MdqSyMtdZ1hnnFYjE7NjbmOoaIiMhxaXx8nFAo5DpGVZrr2BhjMtba2EK216lJEREREUdUxEREREQcURETERERcURFTERERMQRFTERERERR1TERERERBxRERMREZElp729na6uLtra2hgeHgamXoMUDoepra2lWCySTCYJh8Mkk0nHaQ9ND3QVERGRJaujo2Nmuqenh1QqRXNzMwDpdJpcLofP53MVb14qYiIiIrIkdHZ20t3dTTAYpKamBs/zaGlpIR6PMzExQV9fH0NDQwwODpLP5ykUCnieRyqVIpFIuI4/JxUxERERWbCJgYfZ/9Tusu7zhFPXcOI7XnfYdTKZDL29vWSzWUqlEtFoFM/zZpa3trYyOjpKPB6nqakJgEAgQDabLWvWclMRExERkao3MjJCY2Mjfr8fgIaGBseJykNFTERERBZsvpErWZyK3zVpjHnUGHOPMSZrjBmbnrfBGPNDY8xvpv+ur3QOERERWbrq6+vp7++nWCySz+cZGBhwHaksjtbjKy6z1tbOehP5x4H/staeA/zX9GcRERGROUWjURKJBJFIhCuvvJK6ujrXkcrCWGsr+wXGPArErLXPzZr3APAWa+3TxphTgB9Za8871D5isZgdGxuraE4RERGZ2/j4OKFQyHWMqjTXsTHGZGYNPh3W0RgRs8B/GmMyxpirpue92lr79PT0NuDVRyGHiIiISFU5Ghfrv9lau9UYEwR+aIzZMnuhtdYaY142LDdd2q4COOOMM45CTBEREZGjq+IjYtbardN/nwVuAy4Cnpk+Jcn032fn2C5trY1Za2Mnn3xypWOKiIiIHHUVLWLGmDXGmLUvTAO/B9wLfB/4wPRqHwBur2QOERERkWpU6VOTrwZuM8a88F03W2v/wxjzC6DPGPNnwGPAuyqcQ0RERKTqVLSIWWt/C0TmmL8DuLyS3y0iIiJS7Y7Wc8REREREyqa9vZ2uri7a2toYHh4Gpl6DFA6Hqa2tpVgskkwmCYfDJJNJx2kPTa84EhERkSWro6NjZrqnp4dUKkVzczMA6XSaXC6Hz+dzFW9eKmIiIiKyJHR2dtLd3U0wGKSmpgbP82hpaSEejzMxMUFfXx9DQ0MMDg6Sz+cpFAp4nkcqlSKRSLiOPycVMREREVmwwcFBtm3bVtZ9btq0iSuvvPKw62QyGXp7e8lms5RKJaLRKJ7nzSxvbW1ldHSUeDxOU1MTAIFAgGw2W9as5aYiJiIiIlVvZGSExsZG/H4/AA0NDY4TlYeKmIiIiCzYfCNXsji6a1JERESqXn19Pf39/RSLRfL5PAMDA64jlYVGxERERKTqRaNREokEkUiEYDBIXV2d60hlYax92fu2q04sFrNjY2OuY4iIiByXxsfHCYVCrmNUpbmOjTEmY62NLWR7nZoUERERcURFTERERMQRFTERERERR1TERERERBxRERMRERFxREVMRERExBEVMREREVly2tvb6erqoq2tjeHhYWDqNUjhcJja2lqKxSLJZJJwOEwymXSc9tD0QFcRERFZsjo6Omame3p6SKVSNDc3A5BOp8nlcvh8Plfx5qUiJiIiIktCZ2cn3d3dBINBampq8DyPlpYW4vE4ExMT9PX1MTQ0xODgIPl8nkKhgOd5pFIpEomE6/hzUhETERGRBXvwwc+SL4yXdZ9rAyHOPfdTh10nk8nQ29tLNpulVCoRjUbxPG9meWtrK6Ojo8TjcZqamgAIBAJks9myZi03FTERERGpeiMjIzQ2NuL3+wFoaGhwnKg8VMRERERkweYbuZLF0V2TIiIiUvXq6+vp7++nWCySz+cZGBhwHaksNCImIiIiVS8ajZJIJIhEIgSDQerq6lxHKgtjrXWdYV6xWMyOjY25jiEiInJcGh8fJxQKuY5RleY6NsaYjLU2tpDtdWpSRERExBEVMRERERFHVMREREREHFERExEREXFERUxERETEERUxEREREUdUxERERGTJaW9vp6uri7a2NoaHh4Gp1yCFw2Fqa2spFoskk0nC4TDJZNJx2kPTA11FRERkyero6JiZ7unpIZVK0dzcDEA6nSaXy+Hz+VzFm5eKmIiIiCwJnZ2ddHd3EwwGqampwfM8WlpaiMfjTExM0NfXx9DQEIODg+TzeQqFAp7nkUqlSCQSruPPSUVMREREFuxTv3mSewvFsu7zgsBqPnvO6YddJ5PJ0NvbSzabpVQqEY1G8TxvZnlrayujo6PE43GampoACAQCZLPZsmYtNxUxERERqXojIyM0Njbi9/sBaGhocJyoPFTEREREZMHmG7mSxdFdkyIiIlL16uvr6e/vp1gsks/nGRgYcB2pLDQiJiIiIlUvGo2SSCSIRCIEg0Hq6upcRyoLY611nWFesVjMjo2NuY4hIiJyXBofHycUCrmOUZXmOjbGmIy1NraQ7XVqUkRERMQRFTERERERR1TERERERBxRERMRERFxREVMRERExBEVMRERERFHKlbEjDE1xpg7jDH3G2PuM8b85fT8dmPMVmNMdvrf2yuVQURERI5N7e3tdHV10dbWxvDwMDD1GqRwOExtbS3FYpFkMkk4HCaZTDpOe2iVfKBrCfgba+0vjTFrgYwx5ofTy75sre2q4HeLiIjIcaCjo2Nmuqenh1QqRXNzMwDpdJpcLofP53MVb14VK2LW2qeBp6en88aYceC0Sn2fiIiIHNs6Ozvp7u4mGAxSU1OD53m0tLQQj8eZmJigr6+PoaEhBgcHyefzFAoFPM8jlUqRSCRcx5/TUXnFkTHmTOANwM+AS4CPGmPeD4wxNWq282jkEBERkSPzmYH7uP+pXWXd5+tPXcen3xE+7DqZTIbe3l6y2SylUoloNIrneTPLW1tbGR0dJR6P09TUBEAgECCbzZY1a7lV/GJ9Y0wA+C7wV9baXcBXgdcBtUyNmP3dIba7yhgzZowZ2759e6VjioiISBUbGRmhsbERv9/PunXraGhocB2pLCo6ImaMWcFUCeux1n4PwFr7zKzlXwf+ba5trbVpIA1T75qsZE4RERFZmPlGrmRxKnnXpAG+AYxba/9+1vxTZq3WCNxbqQwiIiJybKivr6e/v59isUg+n2dgYMB1pLKo5IjYJcD7gHuMMS+coL0WeI8xphawwKPAhyuYQURERI4B0WiURCJBJBIhGAxSV1fnOlJZGGur/6xfLBazY2NjrmOIiIgcl8bHxwmFQq5jVKW5jo0xJmOtjS1kez1ZX0RERMQRFTERERERR1TERERERBxRERMRERFxREVMRERExBEVMRERERFHVMRERERkyWlvb6erq4u2tjaGh4eBqdcghcNhamtrKRaLJJNJwuEwyWTScdpDOyov/RYRERGphI6Ojpnpnp4eUqkUzc3NAKTTaXK5HD6fz1W8eamIiYiIyJLQ2dlJd3c3wWCQmpoaPM+jpaWFeDzOxMQEfX19DA0NMTg4SD6fp1Ao4HkeqVSKRCLhOv6cVMRERERk4QY/DtvuKe8+N22GK79w2FUymQy9vb1ks1lKpRLRaBTP82aWt7a2Mjo6Sjwep6mpCYBAIEA2mz3ULquCipiIiIhUvZGRERobG/H7/QA0NDQ4TlQeKmIiIiKycPOMXMni6K5JERERqXr19fX09/dTLBbJ5/MMDAy4jlQWGhETERGRqheNRkkkEkQiEYLBIHV1da4jlYWx1rrOMK9YLGbHxsZcxxARETkujY+PEwqFXMeoSnMdG2NMxlobW8j2OjUpIiIi4oiKmIiIiIgjKmIiIiIijqiIiYiIiDiiIiYiIiLiiIqYiIiIiCMqYiIiIrLktLe309XVRVtbG8PDw8DUa5DC4TC1tbUUi0WSySThcJhkMuk47aHpga4iIiKyZHV0dMxM9/T0kEqlaG5uBiCdTpPL5fD5fK7izUtFTERERJaEzs5Ouru7CQaD1NTU4HkeLS0txONxJiYm6OvrY2hoiMHBQfL5PIVCAc/zSKVSJBIJ1/HnpCImIiIiC/bFn3+RLbktZd3n+RvO55qLrjnsOplMht7eXrLZLKVSiWg0iud5M8tbW1sZHR0lHo/T1NQEQCAQIJvNljVruamIiYiISNUbGRmhsbERv98PQENDg+NE5aEiJiIiIgs238iVLI7umhQREZGqV19fT39/P8VikXw+z8DAgOtIZaERMZEK2rN7N0Mj/+U6hojIglnfypcN07xmw8ns3PW8m0DAqhNOIBqNkkgkiEQiBINB6urqnOUpJ2OtdZ1hXrFYzI6NjbmOIbIo9z5wL+98coLdywKuo4iIHJGbTjS8+nXnOM2wfn+BM04KOs0wl/HxcUKh0IvmGWMy1trYQrbXiJhIBfz20YdpfGInu31rOav4BK/at8d1JBGRBTEAvHiQZuW6s1l9cK+LOAAUfSvZeUKAZc89y+lVWMaOhIqYSJk9vW0rb3/oCfK+E/F2jvPvf/Qe15FERF65yUnGx+/n3DUr4eABOLh/1r/pz5Oll29nfGAPvnz+suXgOwF8K6b/nvDiz8uWgzEv2mR77jmeWr6aHdNl7NRjqIypiImU0a7nJ3jrPVuYWL6Rzbt+oxImIkvfsmWwzAcnrDn0OnbyJSXtwFQ5W7b8JYVrBZjF3yd48oaTmNyxnW0r/Gw/YQ3Ldmxn08aTj+BHVQ8VMZEy2bN7N5f+7OfsWBHkvN2P8MN3/rHrSCIiR4dZBstXTv2rkFdvPJnJHdt5doWfZ1b4MTue49UbT6rY9x0tenyFSBns2b2bS0fu5JkVQV5bfIIfxxtdRxIROeacsvFkTtq/GzBsW7Ga7bkdriPXjEJaAAAgAElEQVQdMRUxkSO0Z/duLv/xHWxdeQo1+55i+Hd+13UkEZFj1mknBdm4vwAYnlq+ih0TOdeRjoiKmMgRuvKO/+SR1aezaf+z/PjS38G/5jDXUYiIyBE7/aQgN37u03Rf949c+3//lv7b+4Gp1yCFw2Fqa2spFoskk0nC4TDJZNJx4kPTNWIiR+Ctt9/CA+vO4aTSDu68+CKVMBGRo+RV/jUcOLifD3yiDcMku/K76OnpIZVK0dzcDEA6nSaXy+Hz+RynPTQVMZFX6O3f+zb3rg9x4sEJfrj5fNa96kTXkUREjmmdnZ10d3cTDAapqanB8zw+++EPcvGV76Dw/E76+voYGhpicHCQfD5PoVDA8zxSqRSJRMJ1/DmpiIm8Ao23fItfnrSZtQfz/ODsGk7ZdJrrSCIiR8W2z3+efeNbyrrPlaHz2XTttYddJ5PJ0NvbSzabpVQqEY1G8TyPwMpVrD64j7d94IP8z9138a4/eDvv/ZP3AhAIBMhms2XNWm4qYiKLlPjOjdx1coQ1k7u5rWY9rz3zda4jiYgc80ZGRmhsbMTv9wPQ0NAws+zVa9cRKO3BYthWmmTv3iKrVq12FXVRVMREFuF9N/8LP97kscrupXfDci447wLXkUREjqr5Rq5ced2Gk1huD2JZxkP7S5yzbJ/rSAuiuyZFFujP/jXNDzd5rGQfN64qUveGN7qOJCJy3Kivr6e/v59isUg+n2dgYOBl66w7YSUnTB7gID5+s3dpFDGNiIkswF9038C/17yRFRzgeruDt1zyB64jiYgcV6LRKIlEgkgkQjAYpK6ubs71Tg0EWDW5j73LVjIJHCyV8C2v3rpjrLXzr+VYLBazY2NjrmPIceqvb/wqN59xMcsp0bXncd4d/1+uI4mIHFXj4+OEQiHXMRZly8QE+5adwAp7gPMCgYo9wmKuY2OMyVhrYwvZXqcmRQ4jNV3CfBzkcxMPqYSJiCwR56xdywl2PwfMCh4s5Dl48KDrSHNyNlZnjLkC+EfAB/yLtfYLrrKIzKWj+wb+3xlvZBmTXPvsvbS8u2XR+7jvvvu45557yh9OROQoOvvss8nl3L1KyOfzsXbtWpYtW/j4kc/n49zAWh4oFNhvTuA3+V2cf+L6CqZ8ZZwUMWOMD7geeBvwJPALY8z3rbX3u8gj8lJf6knz1ZqLMMD/fuqX/EXzhxa9j1//+td873vfY926daxevTRuoxYRmcvk5CSlUsnZ9+/du5cDBw6wYcOGRZex89asYcvuPexbtpIHJnKcd+KGCiZdPFcjYhcBD1lrfwtgjOkF3gk4K2K/82+3UTLV+woEOboeOcUD4M+f+DnJD1y96O3vv/9+brvtNtav3s7rVz6AmSx3QhGRo2cFYVZRcPb9JR/s3W/ZuXMnGzZswBiz4G19y5dzrn81DxSL7F22igd35jh3ffWUMVdF7DTgiVmfnwRe9CwAY8xVwFUAZ5xxRsUDPeA/s+LfIUuHwfLBx3/Gp1o+suhtH3zwQW699VY2rN5GuPbH2OX7wC78PxoiIlVnxZ/Bqt3Ovn454N8/yZ59sHPnTtavX7+oMrZixQrOsZM8uHc/e30r2bdvLytXrqpc4EWo2vs5rbVpIA1Td01W+vu2/e4bKv0VsuREF73Fb3/7W77zne+wbuUzhCN3smzvOp5+6K28568/V4F8IiJHx/j4OOvWublrslQqsSf3GMtW7WG1heJemJgwnHjiiYsqYytPWMk5k5Ps3b+flSvXVDDx4ri6a3IrUDPr8+nT80SWrMcff5xvf/vbrFnxLBdG7mTZAT9bx+tVwkREjsDy5cvxb3gN7PPjW1lk9Yq9FItFUqkUX/rSl2hra2N4eBiYeg1SOBymtraWYrFIMpkkHA6TTCYBWLVqNSeue5XLn/MyrkbEfgGcY4w5i6kC9m7gTxxlETliW7du5V//9V9ZuWw7tZE7WTa5gsd/9Wbe/4m/dR1NRGTJW758OatfVUPx+cfxrdzDKgsHDhxg3759fOYzn5kZGevp6SGVStHc3AxAOp0ml8tV7Bli5eCkiFlrS8aYjwJDTD2+4pvW2vtcZBE5Utu2beNb3/oWK8wOopE7McDjY2/i/W1fdh1NROSYseKEE/i/X+nhpptu5OTgek7ZdBprNr+B5uZmGhsbmZiYoK+vj6GhIQYHB8nn8xQKBTzPI5VKkUgkXP+EOTm7Rsxa+wPgB66+X6Qctm/fzre+9S3M5A68C3+MWXaArXe/mfe3X+86mohIRYz0PchzT5T3DsqTagJc+q5zD7tOJpPhlltv5Rc//wV78k9w6e++k8iFF3DgwAGKxSKtra2Mjo4Sj8dpamoCIBAIkM1my5q13Kr2Yn2RapfL5bjppps4uP85Ltp8J6wo8vRdb6a5/auuo4mIHHNGRkZobGxk/caTWLlqFW///cvxrdjPMjNJsVikUHD3eI0joSIm8go8//zzdHd3s3/vc7zxghFYtYtnfvpm/qT9a66jiYhU1HwjV0eDf00AfH7M5HJ8yw+wfNkBdu3a5fShs6+U3jUpskj5fJ7u7m727H6Oi14/Av4d7LjrEt7T/nXX0UREjln19fX09/dTLBbJ5/MMDf0nFj9MLmP5CXtZsWw/+/fvZ9++fa6jLopGxEQWYffu3dx0003s2vUsbwqNwtpn2XnXpfzxp//FdTQRkWNaNBolkUgQiUQIBoPU1dWxyr8Gy0rM5HJW+gssM5Ps3r2bYrG4ZF4tZ6yt+LNSj1gsFrNjY2OuY8hxrlgs0t3dzXPPbeWN5/2EZeufIP+zS/nDT3zTdTQRkYoaHx8nFHLzQNeF2Pns0yxf8Tx2WYm9xbWUJlewYcMGVq2q/NPz5zo2xpiMtTa2kO11alJkAfbt20dPTw/PPvsUbzz7bpZteJzdv7hEJUxEpAqsD57Cgf3rMNbHqtV5fOYAuVyOvXv3uo42LxUxkXns37+fm2++maeeeoI3nv0zlgV/y96xN9OQutF1NBERmbbh1aeyf+9ajPWx2l/AZ0rkcrmqv2ZMRUzkMEqlEt/5znd47LFHqDvrFyzf9BD7/+cS/uCaG11HExGRl9i46XT2FwMYa1jtz7Nsuozt37/fdbRDUhETOYSDBw9yyy238PDDD1F31v9wwmkPcPDXb+LKv7nJdTQRETmEjafUsH9PAAC/v4ChxI4dO6q2jKmIicxhcnKS733vezzwwBaiZ/yKVTX3Y++/iN/7q391HU1EROax8dQzOLA7AFj8q/PA1MjYgQMHXEd7GT2+Ytrzzz/vOoJUkTvuuIP77ruX2tPvYc2Z98ADMWJ/otcWiYgsFRtPfQ07nnqMFWsKrFldYHdxLTt27OCkk05i+fLqqT/Vk8Sxr3zlK1V/QZ8cXZtPuZ+1r/0V5uE3EE18lRM3bHAdSUREprW3txMIBNi1axf19fW89a1vZWRkhKuvvpoVK1Zw11138YUv/zMD37+d3/v9S/hcx/+ZKWPBYBBjjOufAKiIzXj729/OwYMHXceQKvHET75E4Jxf4nvsQsLxf1AJExGpUh0dHTPTPT09pFIpmpubAUin0+RyOZ5/5glYVmDNqgL7D7yqakoYqIjN2PnknzG5vOg6hlSJQHgvvifDvO53/paTTznddRwREQE6Ozvp7u4mGAxSU1OD53m0tLQQj8eZmJigr6+PoaEhBgcHyefzFAoFPM8jlUrxtkvfyPJAnpXsYs/utVPvq6wCKmLTDj52NsY36TqGVIlS8QROv+yvqHntOa6jiIhUlTtuTPPsY78t6z6Dr3ktl7Vcddh1MpkMvb29ZLNZSqUS0WgUz/Nmlre2tjI6Oko8HqepqQmAQCBANpudWWfn04+ybNkka9dXRwkDFbEZl/95r+sIIiIicggjIyM0Njbi9/sBaGhoWPQ+1p9yZplTHTkVMREREVmw+UauZHH0HDERERGpevX19fT391MsFsnn8wwMDLiOVBYaERMREZGqF41GSSQSRCIRgsEgdXV1riOVhbHWus4wr1gsZsfGxlzHEBEROS6Nj48TCoVcx6hKcx0bY0zGWhtbyPY6NSkiIiLiiIqYiIiIiCMqYiIiIiKOqIiJiIiIOKIiJiIiIuKIipiIiIiIIypiIiIisuS0t7fT1dVFW1sbw8PDwNRrkMLhMLW1tRSLRZLJJOFwmGQy6TjtoemBriIiIrJkdXR0zEz39PSQSqVobm4GIJ1Ok8vl8Pl8ruLNS0VMREREloTOzk66u7sJBoPU1NTgeR4tLS3E43EmJibo6+tjaGiIwcFB8vk8hUIBz/NIpVIkEgnX8eekIiYiIiILNjHwMPuf2l3WfZ5w6hpOfMfrDrtOJpOht7eXbDZLqVQiGo3ied7M8tbWVkZHR4nH4zQ1NQEQCATIZrNlzVpuKmIiIiJS9UZGRmhsbMTv9wPQ0NDgOFF5qIiJiIjIgs03ciWLo7smRUREpOrV19fT399PsVgkn88zMDDgOlJZaERMREREql40GiWRSBCJRAgGg9TV1bmOVBbGWus6w7xisZgdGxtzHUNEROS4ND4+TigUch2jKs11bIwxGWttbCHb69SkiIiIiCMqYiIiIiKOqIiJiIiIOKIiJiIiIuKIipiIiIiIIypiIiIiIo6oiImIiMiS097eTldXF21tbQwPDwNTr0EKh8PU1tZSLBZJJpOEw2GSyaTjtIemB7qKiIjIktXR0TEz3dPTQyqVorm5GYB0Ok0ul8Pn87mKNy8VMREREVkSOjs76e7uJhgMUlNTg+d5tLS0EI/HmZiYoK+vj6GhIQYHB8nn8xQKBTzPI5VKkUgkXMefk4qYiIiILNjg4CDbtm0r6z43bdrElVdeedh1MpkMvb29ZLNZSqUS0WgUz/Nmlre2tjI6Oko8HqepqQmAQCBANpsta9Zyq8g1YsaYLxljthhjfm2Muc0Yc+L0/DONMUVjTHb63w2V+H4RERE5toyMjNDY2Ijf72fdunU0NDS4jlQWlRoR+yGQstaWjDFfBFLANdPLHrbW1lboe0VERKSC5hu5ksWpyIiYtfY/rbWl6Y93A6dX4ntERETk+FBfX09/fz/FYpF8Ps/AwIDrSGVxNK4R+yDwnVmfzzLG/A+wC/iktXbkKGQQERGRJSwajZJIJIhEIgSDQerq6lxHKgtjrX1lGxozDGyaY9EnrLW3T6/zCSAG/JG11hpjVgIBa+0OY4wH9ANha+2uOfZ/FXAVwBlnnOE99thjryiniIiIHJnx8XFCoZDrGFVprmNjjMlYa2ML2f4Vj4hZa996uOXGmBYgDlxup9uetXYfsG96OmOMeRg4FxibY/9pIA0Qi8VeWVsUERERqWKVumvyCuD/AA3W2j2z5p9sjPFNT78WOAf4bSUyiIiIiFS7Sl0j9s/ASuCHxhiAu621VwP1QIcx5gAwCVxtrc1VKIOIiIhIVatIEbPWnn2I+d8FvluJ7xQRERFZavTSbxERERFHVMREREREHFERExERkSWnvb2drq4u2traGB4eBqZegxQOh6mtraVYLJJMJgmHwySTScdpD00v/RYREZElq6OjY2a6p6eHVCpFc3MzAOl0mlwuh8/ncxVvXipiIiIisiR0dnbS3d1NMBikpqYGz/NoaWkhHo8zMTFBX18fQ0NDDA4Oks/nKRQKeJ5HKpUikUi4jj8nFTERERFZsAcf/Cz5wnhZ97k2EOLccz912HUymQy9vb1ks1lKpRLRaBTP82aWt7a2Mjo6Sjwep6mpCYBAIEA2my1r1nJTERMREZGqNzIyQmNjI36/H4CGhgbHicpDRUxEREQWbL6RK1kc3TUpIiIiVa++vp7+/n6KxSL5fJ6BgQHXkcpCI2IiIiJS9aLRKIlEgkgkQjAYpK6uznWksjDWWtcZ5hWLxezY2JjrGCIiIsel8fFxQqGQ6xhVaa5jY4zJWGtjC9lepyZFREREHFERExEREXFERUxERETEERUxEREREUdUxEREREQcURETERERcURFTERERJac9vZ2urq6aGtrY3h4GJh6DVI4HKa2tpZisUgymSQcDpNMJh2nPTQ90FVERESWrI6Ojpnpnp4eUqkUzc3NAKTTaXK5HD6fz1W8eamIiYiIyJLQ2dlJd3c3wWCQmpoaPM+jpaWFeDzOxMQEfX19DA0NMTg4SD6fp1Ao4HkeqVSKRCLhOv6cVMRERERkwT71mye5t1As6z4vCKzms+ecfth1MpkMvb29ZLNZSqUS0WgUz/Nmlre2tjI6Oko8HqepqQmAQCBANpsta9ZyUxETERGRqjcyMkJjYyN+vx+AhoYGx4nKQ0VMREREFmy+kStZHN01KSIiIlWvvr6e/v5+isUi+XyegYEB15HKQiNiIiIiUvWi0SiJRIJIJEIwGKSurs51pLIw1lrXGeYVi8Xs2NiY6xgiIiLHpfHxcUKhkOsYVWmuY2OMyVhrYwvZXqcmRURERBxRERMRERFxREVMRERExBEVMRERERFHVMREREREHFERExEREXFERUxERESWnPb2drq6umhra2N4eBiYeg1SOBymtraWYrFIMpkkHA6TTCYdpz00PdBVRERElqyOjo6Z6Z6eHlKpFM3NzQCk02lyuRw+n89VvHmpiImIiMiS0NnZSXd3N8FgkJqaGjzPo6WlhXg8zsTEBH19fQwNDTE4OEg+n6dQKOB5HqlUikQi4Tr+nFTEREREZME+M3Af9z+1q6z7fP2p6/j0O8KHXSeTydDb20s2m6VUKhGNRvE8b2Z5a2sro6OjxONxmpqaAAgEAmSz2bJmLTcVMREREal6IyMjNDY24vf7AWhoaHCcqDxUxERERGTB5hu5ksXRXZMiIiJS9err6+nv76dYLJLP5xkYGHAdqSw0IiYiIiJVLxqNkkgkiEQiBINB6urqXEcqC2OtdZ1hXrFYzI6NjbmOISIiclwaHx8nFAq5jlGV5jo2xpiMtTa2kO11alJERETEERUxEREREUdUxEREREQcURETERERcaRiRcwY026M2WqMyU7/e/usZSljzEPGmAeMMb9fqQwiIiIi1azSj6/4srW2a/YMY8zrgXcDYeBUYNgYc6619mCFs4iIiIhUFRenJt8J9Fpr91lrHwEeAi5ykENERESWqPb2drq6umhra2N4eBiYeg1SOBymtraWYrFIMpkkHA6TTCYdpz20So+IfdQY835gDPgba+1O4DTg7lnrPDk9T0RERGRROjo6ZqZ7enpIpVI0NzcDkE6nyeVy+Hw+V/HmdURFzBgzDGyaY9EngK8CnwXs9N+/Az64iH1fBVwFcMYZZxxJTBERETkGdHZ20t3dTTAYpKamBs/zaGlpIR6PMzExQV9fH0NDQwwODpLP5ykUCnieRyqVIpFIuI4/pyMqYtbaty5kPWPM14F/m/64FaiZtfj06Xkv3XcaSMPUk/WPJKeIiIiUyeDHYds95d3nps1w5RcOu0omk6G3t5dsNkupVCIajeJ53szy1tZWRkdHicfjNDU1ARAIBMhms+XNWmaVvGvylFkfG4F7p6e/D7zbGLPSGHMWcA7w80rlEBERkaVvZGSExsZG/H4/69ato6GhwXWksqjkNWJ/a4ypZerU5KPAhwGstfcZY/qA+4ES8Be6Y1JERGSJmGfkShanYiNi1tr3WWs3W2svtNY2WGufnrWs01r7OmvtedbawUplEBERkWNDfX09/f39FItF8vk8AwMDriOVRaXvmhQRERE5YtFolEQiQSQSIRgMUldX5zpSWRhrq/86+FgsZsfGxlzHEBEROS6Nj48TCoVcx6hKcx0bY0zGWhtbyPZ616SIiIiIIypiIiIiIo6oiImIiIg4oiImIiIi4oiKmIiIiIgjKmIiIiIijqiIiYiIyJLT3t5OV1cXbW1tDA8PA1OvQQqHw9TW1lIsFkkmk4TDYZLJpOO0h6YHuoqIiMiS1dHRMTPd09NDKpWiubkZgHQ6TS6Xw+fzuYo3LxUxERERWRI6Ozvp7u4mGAxSU1OD53m0tLQQj8eZmJigr6+PoaEhBgcHyefzFAoFPM8jlUqRSCRcx5+TipiIiIgs2Bd//kW25LaUdZ/nbzifay665rDrZDIZent7yWazlEolotEonufNLG9tbWV0dJR4PE5TUxMAgUCAbDZb1qzlpiImIiIiVW9kZITGxkb8fj8ADQ0NjhOVh4qYiIiILNh8I1eyOLprUkRERKpefX09/f39FItF8vk8AwMDriOVhUbEREREpOpFo1ESiQSRSIRgMEhdXZ3rSGVhrLWuM8wrFovZsbEx1zFERESOS+Pj44RCIdcxqtJcx8YYk7HWxhayvU5NioiIiDiiIiYiIiLiiIqYiIiIiCMqYiIiIiKOqIiJiIiIOKIiJiIiIuKIipiIiIgsOe3t7XR1ddHW1sbw8DAw9RqkcDhMbW0txWKRZDJJOBwmmUw6TntoeqCriIiILFkdHR0z0z09PaRSKZqbmwFIp9Pkcjl8Pp+rePNSERMREZElobOzk+7uboLBIDU1NXieR0tLC/F4nImJCfr6+hgaGmJwcJB8Pk+hUMDzPFKpFIlEwnX8OamIiYiIyIJt+/zn2Te+paz7XBk6n03XXnvYdTKZDL29vWSzWUqlEtFoFM/zZpa3trYyOjpKPB6nqakJgEAgQDabLWvWclMRExERkao3MjJCY2Mjfr8fgIaGBseJykNFTERERBZsvpErWRzdNSkiIiJVr76+nv7+forFIvl8noGBAdeRykIjYiIiIlL1otEoiUSCSCRCMBikrq7OdaSyMNZa1xnmFYvF7NjYmOsYIiIix6Xx8XFCoZDrGFVprmNjjMlYa2ML2V6nJkVEREQcURETERH5/9u7/9i47/qO4893zompcRQUp5fUjUugaahzLnbunG3SQgQdKxTdzAxeDcNbTec2CPijSnWCA9WzDJaoZFjRVE14BeFKLo6VLqZecbIYVZ2NxIZdLmta90doQ9MSt0kuDnfNucnZn/3hiwnBjp34zl87fj3+6fc+n/ve95WPvo3e+Xx/fEQ8okJMRERExCMqxEREREQ8okJMRERExCMqxEREREQ8okJMRERElpympiZaW1tpbGykr68PmFwGKRAIUFFRQSqVIhKJEAgEiEQiHqedmV7oKiIiIktWc3Pz1HZHRwfRaJS6ujoA2traiMfj+Hw+r+LNSoWYiIiILAktLS20t7fj9/spKSkhFApRX19POBxmdHSUrq4uDhw4QG9vL4lEgmQySSgUIhqNUltb63X8aakQExERkTnr73qZk8eSWf3NdSWFfOSuLZf9ztDQEJ2dncRiMdLpNMFgkFAoNNXf0NDAwMAA4XCYmpoaAAoLC4nFYlnNmm0qxERERGTR6+/vp7q6moKCAgCqqqo8TpQdKsRERERkzmabuZIro6cmRUREZNHbuXMn3d3dpFIpEokEPT09XkfKCs2IiYiIyKIXDAapra2lvLwcv9/P9u3bvY6UFeacy/6Pmu0BPpT5+D5g1DlXYWabgGHgpUzfL51zX5rt9yorK93g4GDWc4qIiMjshoeHKS0t9TrGojTd2JjZkHOuci7752RGzDk39YyomX0XOHNR92+ccxW5OK6IiIjIUpLTS5NmZsBdwO25PI6IiIjIUpTrm/U/ArzlnHvlorYPmNmvzewZM/vITDua2X1mNmhmgydOnMhxTBEREZGFd9UzYmbWB2yYpuubzrmfZrY/D/zkor7jwE3OuVNmFgK6zSzgnPv9pT/inGsD2mDyHrGrzSkiIiKyWF11Ieac+/jl+s0sD/gMMPXaW+fcu8C7me0hM/sNsAXQnfgiIiKy7OTy0uTHgRedc29caDCz683Ml9n+IHAL8GoOM4iIiIgsWrksxD7HH1+WBNgJ/J+ZxYC9wJecc/EcZhAREZFrUFNTE62trTQ2NtLX1wdMLoMUCASoqKgglUoRiUQIBAJEIhGP084sZ09NOufqp2l7AngiV8cUERGR5aW5uXlqu6Ojg2g0Sl1dHQBtbW3E43F8Pp9X8WalN+uLiIjIktDS0kJ7ezt+v5+SkhJCoRD19fWEw2FGR0fp6uriwIED9Pb2kkgkSCaThEIhotEotbW1sx/AAyrEREREZM6e/nEbb/82u7d3+9//QT5Wf99lvzM0NERnZyexWIx0Ok0wGCQUmnoekIaGBgYGBgiHw9TU1ABQWFhILBbLatZsUyEmIiIii15/fz/V1dUUFBQAUFVV5XGi7FAhJiIiInM228yVXJlcv1lfREREZN527txJd3c3qVSKRCJBT0+P15GyQjNiIiIisugFg0Fqa2spLy/H7/ezfft2ryNlhTm3+FcPqqysdIODevm+iIiIF4aHhyktLfU6xqI03diY2ZBzrnIu++vSpIiIiIhHVIiJiIiIeESFmIiIiIhHVIiJiIiIeESFmIiIiIhHVIiJiIiIeESFmIiIiCw5TU1NtLa20tjYSF9fHzC5DFIgEKCiooJUKkUkEiEQCBCJRDxOOzO90FVERESWrObm5qntjo4OotEodXV1ALS1tRGPx/H5fF7Fm5UKMREREVkSWlpaaG9vx+/3U1JSQigUor6+nnA4zOjoKF1dXRw4cIDe3l4SiQTJZJJQKEQ0GqW2ttbr+NNSISYiIiJzNtrzG8797p2s/uaq4vfyvr+5+bLfGRoaorOzk1gsRjqdJhgMEgqFpvobGhoYGBggHA5TU1MDQGFhIbFYLKtZs02FmIiIiCx6/f39VFdXU1BQAEBVVZXHibJDhZiIiIjM2WwzV3Jl9NSkiIiILHo7d+6ku7ubVCpFIpGgp6fH60hZoRkxERERWfSCwSC1tbWUl5fj9/vZvn2715GywpxzXmeYVWVlpRscHPQ6hoiIyLI0PDxMaWmp1zEWpenGxsyGnHOVc9lflyZFREREPKJCTERERMQjKsREREREPKJCTERERMQjKsREREREPKJCTERERMQjKsRERERkyWlqaqU/wrEAAAqvSURBVKK1tZXGxkb6+vqAyWWQAoEAFRUVpFIpIpEIgUCASCTicdqZ6YWuIiIismQ1NzdPbXd0dBCNRqmrqwOgra2NeDyOz+fzKt6sVIiJiIjIktDS0kJ7ezt+v5+SkhJCoRD19fWEw2FGR0fp6uriwIED9Pb2kkgkSCaThEIhotEotbW1XseflgoxERERmbPe3l5GRkay+psbNmzgzjvvvOx3hoaG6OzsJBaLkU6nCQaDhEKhqf6GhgYGBgYIh8PU1NQAUFhYSCwWy2rWbFMhJiIiIotef38/1dXVFBQUAFBVVeVxouxQISYiIiJzNtvMlVwZPTUpIiIii97OnTvp7u4mlUqRSCTo6enxOlJWaEZMREREFr1gMEhtbS3l5eX4/X62b9/udaSsMOec1xlmVVlZ6QYHB72OISIisiwNDw9TWlrqdYxFabqxMbMh51zlXPbXpUkRERERj6gQExEREfGICjERERERj6gQExEREfGICjERERERj6gQExEREfGICjERERFZcpqammhtbaWxsZG+vj5gchmkQCBARUUFqVSKSCRCIBAgEol4nHZmeqGriIiILFnNzc1T2x0dHUSjUerq6gBoa2sjHo/j8/m8ijerec2ImdnfmdnzZjZhZpWX9EXN7IiZvWRmn7io/ZOZtiNm9vX5HF9ERESWj5aWFrZs2cKOHTt46aWXAKivr2fv3r08+uijdHV18eCDD/KFL3yBqqoqkskkoVCIPXv2eJx8ZvOdETsMfAb4wcWNZrYV+BwQAIqBPjPbkul+BPhr4A3gV2b2pHPuhXnmEBERkQXw8svfIpEczupvri4sZcuWBy/7naGhITo7O4nFYqTTaYLBIKFQaKq/oaGBgYEBwuEwNTU1ABQWFhKLxbKaNdvmVYg554YBzOzSrk8Dnc65d4HXzOwI8GeZviPOuVcz+3VmvqtCTERERGbU399PdXU1BQUFAFRVVXmcKDtydY/YjcAvL/r8RqYN4Ngl7X+eowwiIiKSZbPNXMmVmbUQM7M+YMM0Xd90zv00+5GmjnsfcF/mY9LMXsrVsS6yDji5AMeRP6Zx94bG3Rsad29o3Ofh4MGDt42Pj6evdL/x8fE8n893xftN54YbbljxyCOP5FdVVaXS6TR79+69rqam5vypU6dWHD16dPzw4cPjp06dWnVhG2BiYqLg8OHDZ7Nx/JmMjIzkbd269blLmt8/1/1nLcSccx+/4lTwJlBy0eeNmTYu037pcduAtqs49lUzs8G5rpYu2aNx94bG3Rsad29o3Ofn0KFDR8vKyq64kD18+HBpWVlZVm4oKysr49lnn91QU1Ozrqio6Hx5efnpwsLCs6tWrbquqKjoTFlZ2elVq1ZturCd2W1bto4/k/Hx8XXzObdydWnySeBxM/sekzfr3wL8L2DALWb2ASYLsM8Bf5+jDCIiInINeeihh0YeeuihkZn6n3jiiaMXfz579uyvcx5qnuZViJlZNfCvwPXAU2YWc859wjn3vJl1MXkTfhr4inNuPLPPV4EDgA/4kXPu+Xn9CURERESWqPk+NbkP2DdDXwvQMk37z4Cfzee4ObSgl0JlisbdGxp3b2jcvaFx98C6detOeJ1hsdMSRxfJ3JcmC0zj7g2Nuzc07t7QuHtjw4YNekBiFirERERERDyitSaZXHYJ+D6T96096pz7jseRlgUzOwokgHEgrSeacsfMfgSEgbedc2WZtrXAHmATcBS4yzl3eqbfkCszw5g3AfcCFy7XfCNzu4ZkiZmVAI8B6wEHtDnnvq/zPbfGxsZWvvbaax9Ip9MrAYqKik4UFxe/fezYseJTp06ty8vLSwMUFxe/uXbt2jPepl1clv2MmJn5mFx26U5gK/D5zBJNsjA+5pyrUBGWcz8GPnlJ29eBnzvnbgF+nvks2fNj/nTMAf4lc85XqAjLiTTwgHNuK/AXwFcyf6frfM8hM2Pjxo1v3Hbbbc+XlpYOnzx50v/OO++8B+D6669/q6ys7IWysrIXVIT9qWVfiDG59NIR59yrzrlzwIVll0SuGc65/wbilzR/GmjPbLcDf7ugoa5xM4y55Jhz7rhz7tnMdgIYZnJlF53vOZSfn39+9erVZwHy8vIm8vPzU+fOnVuVy2Pu3r27uLGxcf39999f3N3dvRpg//79hZs3bw7ceuutW5PJpO3atWvj5s2bA7t27dqYyyzzoUuTk/+Datklbzjgv8zMAT/QzbQLbr1z7nhme4TJSzmSe181s38EBpmcudHlsRwxs03ANuB/0Pm+YMbGxlaNjY0VrF69OplMJgtPnjzpj8fjRQUFBWdvuummYytXrhzP5vEefvjh313Yfuyxx9bu3r37+Je//OU4wOOPP77u9OnTsby8xVvuLN5kshzscM69aWZ+4KCZvZiZRZAF5pxzmYJYcuvfgG8x+Y+QbwHfBe7xNNE1yswKgSeA+51zvzezqT6d77mTTqdXHDly5OYbb7zxWF5e3sT69evf3rhx4+8Ajh07duPrr79ecvPNNx+92t//2te+tmHPnj3rioqKzhcXF5/btm3b2c9+9rObwuHwmdOnT/ueeuqptc8888ya/fv3r0kmk76zZ8/6ysrKtj7wwAPH77333kX5jx4VYpdfjklyyDn3Zua/b5vZPiYvE6sQWzhvmdkNzrnjZnYD8LbXga51zrm3Lmyb2b8D/+lhnGuWma1ksgjrcM79R6ZZ53uW3D/8esmL74wV/GmPY3xs7Drz5aVXjMXXczT+x7OObsLGx8au851+Of/SPW9973vOPlx607FL2y/W399fsG/fvrXPPffcC+fPn6eiomLrtm3bptaR3L1798lf/OIXheFw+MwXv/jF0wAFBQXbXnzxxReu8o+6IHSPGPyKzLJLZraKyWWXnvQ40zXPzN5rZqsvbAN3AIe9TbXsPAncndm+G/iph1mWhUwBcEE1Ouezzianvn4IDDvnvndRl873nHJMvPvue2zFiokVK1ee/0PzxNRU5ER6PM9WrJi42iM8/fTThZ/61KdGV69ePbF27dqJO+64Y3SeoReFZT8j5pxLa9klT6wH9mUuF+QBjzvn9nsb6dplZj8BPgqsM7M3gH8GvgN0mdk/Ab8F7vIu4bVnhjH/qJlVMHlp8iiwy7OA166/BP4BeM7MYpm2b6DzPWumm7k6c+ZM4SuvvPKh/Pz8lNk5H0y+qiIej69NpVLXAaxavWps06bNv83Pzz9/6f7L2bIvxGDRL7t0TXLOvQqUe51juXDOfX6Grr9a0CDLyAxj/sMFD7LMOOcGAJuhW+d7jqxZsyZZWVk5dGl7Nl9XcfvttyfvueeeTd/+9rePnz9/3g4ePPi+u+++e8kvoaRCTERERBa9HTt2nK2uro6XlZUFioqKzn/4wx9+x+tM2WDO6cERERERmdmhQ4eOlpeXa93IaRw6dGhdeXn5pqvdXzfri4iIiHhEhZiIiIiIR1SIiYiIiHhEhZiIiIjMZmJiYmKmp1GXrcyYXPW70UCFmIiIiMzu8IkTJ9aoGPuDiYkJO3HixBrm+WJmvb5CRERELiudTjeMjIw8OjIyUoYmcS6YAA6n0+mG+fyIXl8hIiIi4hFVtSIiIiIeUSEmIiIi4hEVYiIiIiIeUSEmIiIi4hEVYiIiIiIe+X8ie3p4QJO37gAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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2xvT0NPPz88zNzVGtVllfXydJEkqlEoVCIe34OzpWRey3OOS0ZAjhwTHGm7c+9gKfP0Y5JEnST+GW17yGO1eureuY92k7n7Ne/vIj7lMul5mamqJSqbCxsUEulyNJku3tg4ODLC0tkc/n6evrA6ClpYVKpVLXrPW270UshHAq8BTghYes/rMQQgebpyavv8c2SZKkn7C4uEhvby/Nzc0A9PT0pJyoPva9iMUYfwDc/x7rnrPf3ytJkupvt5krHR2frC9Jkhped3c3MzMz1Go1qtUqs7OzaUeqi2N1jZgkSdK9lsvlKBQKtLe3k8lk6OrqSjtSXYQYG//JEJ2dnXF5eTntGJIknZBWVlZoa2tLO0ZD2um3CSGUY4ydezneU5OSJEkpsYhJkiSlxCImSZKUEouYJElSSixikiRJKbGISZIkpcQiJkmSDpzh4WHGxsYYGhpiYWEB2HwNUjabpaOjg1qtRrFYJJvNUiwWU057eD7QVZIkHVgjIyPby5OTk5RKJfr7+wEYHx9ndXWVpqamtOLtyiImSZIOhNHRUSYmJshkMrS2tpIkCQMDA+TzedbW1pienmZ+fp65uTmq1Srr6+skSUKpVKJQKKQdf0cWMUmStGeL01/mO99Yr+uYD2ht4UnPetQR9ymXy0xNTVGpVNjY2CCXy5Ekyfb2wcFBlpaWyOfz9PX1AdDS0kKlUqlr1nqziEmSpIa3uLhIb28vzc3NAPT09KScqD4sYpIkac92m7nS0fGuSUmS1PC6u7uZmZmhVqtRrVaZnZ1NO1JdOCMmSZIaXi6Xo1Ao0N7eTiaToaurK+1IdRFijGln2FVnZ2dcXl5OO4YkSSeklZUV2tra0o7RkHb6bUII5Rhj516O99SkJElSSixikiRJKbGISZIkpcQiJkmSlBKLmCRJUkosYpIkSSmxiEmSpANneHiYsbExhoaGWFhYADZfg5TNZuno6KBWq1EsFslmsxSLxZTTHp4PdJUkSQfWyMjI9vLk5CSlUon+/n4AxsfHWV1dpampKa14u7KISZKkA2F0dJSJiQkymQytra0kScLAwAD5fJ61tTWmp6eZn59nbm6OarXK+vo6SZJQKpUoFAppx9+RRUySJO3Z1e8c59s3fK2uY2Ye9nAuHLj0iPuUy2WmpqaoVCpsbGyQy+VIkmR7++DgIEtLS+Tzefr6+gBoaWmhUqnUNWu9WcQkSVLDW1xcpLe3l+bmZgB6enpSTlQfFjFJkrRnu81c6eh416QkSWp43d3dzMzMUKvVqFarzM7Oph2pLpwRkyRJDS+Xy1EoFGhvbyeTydDV1ZV2pLoIMca0M+yqs7MzLi8vpx1DkqQT0srKCm1tbWnHaEg7/TYhhHKMsXMvx3tqUpIkKSUWMUmSpJRYxCRJklJiEZMkSUqJRUySJCklFjFJkqSUWMQkSdKBMzw8zNjYGENDQywsLACbr0HKZrN0dHRQq9UoFotks1mKxWLKaQ/PB7pKkqQDa2RkZHt5cnKSUqlEf38/AOPj46yurtLU1JRWvF1ZxCRJ0oEwOjrKxMQEmUyG1tZWkiRhYGCAfD7P2toa09PTzM/PMzc3R7VaZX19nSRJKJVKFAqFtOPvyCImSZL2bG32On74zR/UdcyfecipnPG0Rxxxn3K5zNTUFJVKhY2NDXK5HEmSbG8fHBxkaWmJfD5PX18fAC0tLVQqlbpmrTeLmCRJaniLi4v09vbS3NwMQE9PT8qJ6sMiJkmS9my3mSsdnX2/azKEcH0I4XMhhEoIYXlr3ZkhhA+HEL6y9ef99juHJEk6uLq7u5mZmaFWq1GtVpmdnU07Ul0cq8dXXBhj7DjkTeQvA/4pxvhI4J+2PkuSJO0ol8tRKBRob2/nkksuoaurK+1IdRFijPv7BSFcD3TGGL9zyLovAb8SY7w5hPBg4KMxxkcfbozOzs64vLy8rzklSdLOVlZWaGtrSztGQ9rptwkhlA+ZfDqiYzEjFoF/DCGUQwiXbq17UIzx5q3lW4AHHYMckiRJDeVYXKz/SzHGm0IIGeDDIYRrD90YY4whhP80LbdV2i4FOOecc45BTEmSpGNr32fEYow3bf35beBK4HHAt7ZOSbL157d3OG48xtgZY+x84AMfuN8xJUmSjrl9LWIhhFNDCKfdvQz8GvB54APAb2/t9tvAVfuZQ5IkqRHt96nJBwFXhhDu/q53xxg/FEL4NDAdQvgd4AbgWfucQ5IkqeHsaxGLMX4NaN9h/XeBi/bzuyVJkhrdsXqOmCRJUt0MDw8zNjbG0NAQCwsLwOZrkLLZLB0dHdRqNYrFItlslmKxmHLaw/MVR5Ik6cAaGRnZXp6cnKRUKtHf3w/A+Pg4q6urNDU1pRVvVxYxSZJ0IIyOjjIxMUEmk6G1tZUkSRgYGCCfz7O2tsb09DTz8/PMzc1RrVZZX18nSRJKpRKFQiHt+DuyiEmSpD2bm5vjlltuqeuYZ511FpdccskR9ymXy0xNTVGpVNjY2CCXy5Ekyfb2wcFBlpaWyOfz9PX1AdDS0kKlUqlr1nqziEmSpIa3uLhIb28vzc3NAPT09KScqD4sYpIkac92m7nS0fGuSUmS1PC6u7uZmZmhVqtRrVaZnZ1NO1JdOCMmSZIaXi6Xo1Ao0N7eTiaToaurK+1IdRFi/E/v2244nZ2dcXl5Oe0YkiSdkFZWVmhra0s7RkPa6bcJIZRjjJ17Od5Tk5IkSSmxiEmSJKXEIiZJkpQSi5gkSVJKLGKSJEkpsYhJkiSlxCImSZIOnOHhYcbGxhgaGmJhYQHYfA1SNpulo6ODWq1GsVgkm81SLBZTTnt4PtBVkiQdWCMjI9vLk5OTlEol+vv7ARgfH2d1dZWmpqa04u3KIiZJkg6E0dFRJiYmyGQytLa2kiQJAwMD5PN51tbWmJ6eZn5+nrm5OarVKuvr6yRJQqlUolAopB1/RxYxSZK0Z1/+8quprq/UdczTWtp41KP++Ij7lMtlpqamqFQqbGxskMvlSJJke/vg4CBLS0vk83n6+voAaGlpoVKp1DVrvVnEJElSw1tcXKS3t5fm5mYAenp6Uk5UHxYxSZK0Z7vNXOnoeNekJElqeN3d3czMzFCr1ahWq8zOzqYdqS6cEZMkSQ0vl8tRKBRob28nk8nQ1dWVdqS6CDHGtDPsqrOzMy4vL6cdQ5KkE9LKygptbW1px2hIO/02IYRyjLFzL8d7alKSJCklFjFJkqSUWMQkSZJSYhGTJElKiUVMkiQpJRYxSZKklFjEJEnSgTM8PMzY2BhDQ0MsLCwAm69BymazdHR0UKvVKBaLZLNZisViymkPzwe6SpKkA2tkZGR7eXJyklKpRH9/PwDj4+Osrq7S1NSUVrxdWcQkSdKBMDo6ysTEBJlMhtbWVpIkYWBggHw+z9raGtPT08zPzzM3N0e1WmV9fZ0kSSiVShQKhbTj78giJkmS9uyPv3Ijn1+v1XXMx7T8LK9+5NlH3KdcLjM1NUWlUmFjY4NcLkeSJNvbBwcHWVpaIp/P09fXB0BLSwuVSqWuWevNIiZJkhre4uIivb29NDc3A9DT05NyovqwiEmSpD3bbeZKR8e7JiVJUsPr7u5mZmaGWq1GtVpldnY27Uh14YyYJElqeLlcjkKhQHt7O5lMhq6urrQj1UWIMaadYVednZ1xeXk57RiSJJ2QVlZWaGtrSztGQ9rptwkhlGOMnXs53lOTkiRJKbGISZIkpcQiJkmSlBKLmCRJUkosYpIkSSmxiEmSJKVk34pYCKE1hHB1COGLIYQvhBBesrV+OIRwUwihsvXfU/crgyRJOj4NDw8zNjbG0NAQCwsLwOZrkLLZLB0dHdRqNYrFItlslmKxmHLaw9vPB7puAP8rxviZEMJpQDmE8OGtbW+MMY7t43dLkqQTwMjIyPby5OQkpVKJ/v5+AMbHx1ldXaWpqSmteLvatyIWY7wZuHlruRpCWAEeul/fJ0mSjm+jo6NMTEyQyWRobW0lSRIGBgbI5/Osra0xPT3N/Pw8c3NzVKtV1tfXSZKEUqlEoVBIO/6OjskrjkII5wK/APwL8ETgxSGE5wLLbM6afe9Y5JAkST+dV81+gS9+87a6jvnzDzmdVz4te8R9yuUyU1NTVCoVNjY2yOVyJEmyvX1wcJClpSXy+Tx9fX0AtLS0UKlU6pq13vb9Yv0QQgvwfuAPYoy3AW8FHgF0sDlj9obDHHdpCGE5hLB866237ndMSZLUwBYXF+nt7aW5uZnTTz+dnp6etCPVxb7OiIUQTmGzhE3GGP8eIMb4rUO2/w3wDzsdG2McB8Zh812T+5lTkiTtzW4zVzo6+3nXZADeDqzEGP/8kPUPPmS3XuDz+5VBkiQdH7q7u5mZmaFWq1GtVpmdnU07Ul3s54zYE4HnAJ8LIdx9gvblwG+FEDqACFwPvHAfM0iSpONALpejUCjQ3t5OJpOhq6sr7Uh1EWJs/LN+nZ2dcXl5Oe0YkiSdkFZWVmhra0s7RkPa6bcJIZRjjJ17Od4n60uSJKXEIiZJkpQSi5gkSVJKLGKSJEkpsYhJkiSlxCImSZKUEouYJEk6cIaHhxkbG2NoaIiFhQVg8zVI2WyWjo4OarUaxWKRbDZLsVhMOe3hHZOXfkuSJO2HkZGR7eXJyUlKpRL9/f0AjI+Ps7q6SlNTU1rxdmURkyRJB8Lo6CgTExNkMhlaW1tJkoSBgQHy+Txra2tMT08zPz/P3Nwc1WqV9fV1kiShVCpRKBTSjr8ji5gkSdq7uZfBLZ+r75hnXQCXvPaIu5TLZaampqhUKmxsbJDL5UiSZHv74OAgS0tL5PN5+vr6AGhpaaFSqRxuyIZgEZMkSQ1vcXGR3t5empubAejp6Uk5UX1YxCRJ0t7tMnOlo+Ndk5IkqeF1d3czMzNDrVajWq0yOzubdqS6cEZMkiQ1vFwuR6FQoL29nUwmQ1dXV9qR6iLEGNPOsKvOzs64vLycdgxJkk5IKysrtLW1pR2jIe3024QQyjHGzr0c76lJSZKklFjEJEmSUmIRkyRJSolFTJIkKSUWMUmSpJRYxCRJklJiEZMkSQfO8PAwY2NjDA0NsbCwAGy+BimbzdLR0UGtVqNYLJLNZikWiymnPTwf6CpJkg6skZGR7eXJyUlKpRL9/f0AjI+Ps7q6SlNTU1rxdmURkyRJB8Lo6CgTExNkMhlaW1tJkoSBgQHy+Txra2tMT08zPz/P3Nwc1WqV9fV1kiShVCpRKBTSjr8ji5gkSdqz133qdVy7em1dxzz/zPO5/HGXH3GfcrnM1NQUlUqFjY0NcrkcSZJsbx8cHGRpaYl8Pk9fXx8ALS0tVCqVumatN4uYJElqeIuLi/T29tLc3AxAT09PyonqwyImSZL2bLeZKx0d75qUJEkNr7u7m5mZGWq1GtVqldnZ2bQj1YUzYpIkqeHlcjkKhQLt7e1kMhm6urrSjlQXIcaYdoZddXZ2xuXl5bRjSJJ0QlpZWaGtrS3tGA1pp98mhFCOMXbu5XhPTUqSJKXEIiZJkpQSi5gkSVJKLGKSJEkpsYhJkiSlxMdXSCeYaz51Ne/81GvYCHekHUXSAfG8rhGu/3ZIO0ZdBE7iYZlHpR1jm0VMOoG84orL+HTzEt88LdB8111px5F0QPww3EXtpI20Y/yEv/qzt9B8ajM/qK6TPCHhv/7yE1i+psyriq/m5JNP5j1z/x9/+bo38/GFRbqf/CSKw/8LgFNiYxVKi5h0Avj69V/hNf/wQj59+re5/48jl3y/iz/7/f9z7wa7fRU++FL4/Pvh7C7o/Wu4/yPqG1hSQ1lZWaHtAY31HLEHNmdoObWFl77ypdvr3vR//4rhVwzT398PwPv/9u9ZXV2lqakprZi7sohJx7nXvvVlfKLp//L10+AXfnAq//38l3Hxr/beu8Gu+wjM/C784Fb41VfAE/9faPKfEUnHxujoKBMTE2QyGVpbW0mShIGBAfL5PGtra0xPTzM/P8/c3BzVapX19XWSJKFUKlEoFNKOvyP/BZWOU2trq7z8b5/Dp1pu4L4xctFalr94yfS9G+yHt8PCMHzqr+EBj4bfeg885BfqmlfSwXDLa17DnSvX1nXM+7Sdz1kvf/kR9ymXy0xNTVGpVNjY2CCXy5FB5GtVAAAgAElEQVQkyfb2wcFBlpaWyOfz9PX1AdDS0kKlUqlr1nqziEnHofG/fSMLP5hg5fQf8/O1U/hvZzyf5z//f9y7wW76DPz9pfDdr8Avvgie/Eo45WfrG1iSdrG4uEhvby/Nzc0A9PT0pJyoPixi0nHm96/oY7llhR/dJ9B923n82fPew6ktLUc/0I83YOnP4WOvg1Mz8JwZeMSF9Q8s6UDZbeZKR8fniEnHiZm5aZ7zli6u/rkvkdlo4lk/fAZv/h+z966Effc6eMd/g6tH4eefAb/7CUuYpFR1d3czMzNDrVajWq0yOzubdqS6cEZMOg689E2/TbllmbXmwBOqD2Ko9/9w9kPPOfqBYoTld8A/vgKaToFffztc0Ff/wJJ0lHK5HIVCgfb2djKZDF1dXWlHqosQY0w7w646Ozvj8vJy2jGkhrNc+SRv+eeX8umW7/PQH0a6ar/Mq3//zfdusOotcNWL4asfhodfCM94C5z+kPoGlnQgrays0NbWWI+vaBQ7/TYhhHKMsXMvxzsjJh1QQ3/5Yj59349yY0uga/3neOHj/pRf7HrSvRvsi1fB7B/Aj26HS/4Mul4AJ3nlgiTtt9SKWAjhYuBNQBPwv2OMr00ri3SQ3HjTv/PqK5/Pp067hZ/7ceTitRyvf8m77t1gd3wf5i6Hf3sPPLgDnvk38MDGefWHJB3vUiliIYQm4M3AU4AbgU+HED4QY/xiGnmkg+IN40MsxRm+elqk/faf5dcf+gf0/s6z791g1y/BlZfBbTdB9x/CL//h5nVhkqRjJq0ZsccBX40xfg0ghDAFPB1IrYi98K+ezF38OK2vl3Z1V/gxn2v+Lk0RLvz+o7niMTng6/CRPzmqcT5z/W28/T4ZfnCf+0DrCzdXrgJXvq3umSUdH37nvMfx9dVvpR2jLk6KkYfd/6y0Y2xLq4g9FPjGIZ9vBH7x0B1CCJcClwKcc869uPvrKH2u+WZ+cFJjvQhUuqfz7/gZfu3Ws/mdu66GxauP+vjX/ewLmM51c1PT2YTo/3hI2ptnn3Qyt518Lx6F04BO4UdpR/gJDXuxfoxxHBiHzbsm9/v7PvH8L+z3V0ip+fcbvsboNVfxfzO/xKn8gMFrP8CfvGgo7ViSDoiVlRXaTj817RjHpbSK2E1A6yGfz95aJ6nO/vJNr2a6rY2vPOhCfuGOz9J/02082xIm6YAbHh6mpaWF2267je7ubp785CezuLjIZZddximnnMI111zD0NAQH/zgB3nqU5/K61//+rQj7yitIvZp4JEhhPPYLGC/Cfz3lLJIx6W1732Pkav+mr+/4CIC8OzrP8QfP+OFnHG/+6UdTZLqZmRkZHt5cnKSUqlEf38/AOPj46yurtLU1JRWvF2lUsRijBshhBcD82w+vuIdMUbPDUp18rf/+womH3o/Kg+7mEf96CsUVr7E773kFWnHkqSfyujoKBMTE2QyGVpbW0mShIGBAfL5PGtra0xPTzM/P8/c3BzVapX19XWSJKFUKlEoFNKOv6PUrhGLMX4Q+GBa3y8dr/7ora/mvY/u5naaecYtH+FPLvwtHvBrv5F2LEnHicXpL/Odb6zXdcwHtLbwpGcd+RmG5XKZqakpKpUKGxsb5HI5kiTZ3j44OMjS0hL5fJ6+vs1Xs7W0tFCpVOqatd4a9mJ9SUdn4QPv522ssnT+0zj7x9/g+Z/9J172P0d2P1CSDoDFxUV6e3tpbm4GoKenJ+VE9WERk44DI28a4n2PeSK3hvP4te/9M3/wkF8gZwmTtA92m7nS0fFlctIB9oXyv/Db7/8r3nrB07mLk7js81fxrmf+HrnH/9e0o0lSXXV3dzMzM0OtVqNarTI7O5t2pLpwRkw6oF7/56/kvY9N+Pczf4n/ur7M837YzNN+/1Vpx5KkfZHL5SgUCrS3t5PJZOjq6ko7Ul2EGPf9Wak/tc7Ozri8vJx2DKkhfOeWmxn6yLuZPetJ3Jc76PvKx/nTF3pHpKT9s7KyQltbW9oxGtJOv00IoRxj7NzL8c6ISQfIW6/4U/7u0Q/n2gdfxAV3foH/fsO3eZ4lTJIOLIvYlt999xvYOMlL5tS4fnTyyXzsMd38mCYK3/hHXvW0F3DGxT6cVZIOMovYlg+d9XhuD75HS43tERvX0fe5z/H//s/htKNIkurAIrZlktsh3p52DOmI2jp/lTOe8utpx5Ak1YlFbMsTfvUpaUeQJEknGC+KkiRJSolFTJIkHTjDw8OMjY0xNDTEwsICsPkapGw2S0dHB7VajWKxSDabpVgsppz28Dw1KUmSDqyRkf94ndvk5CSlUon+/n4AxsfHWV1dpampKa14u7KISZKkA2F0dJSJiQkymQytra0kScLAwAD5fJ61tTWmp6eZn59nbm6OarXK+vo6SZJQKpUoFAppx9+RRUySJO3Z1e8c59s3fK2uY2Ye9nAuHLj0iPuUy2WmpqaoVCpsbGyQy+VIkmR7++DgIEtLS+Tzefr6+gBoaWmhUqnUNWu9WcQkSVLDW1xcpLe3l+bmZgB6enpSTlQfFjFJkrRnu81c6eh416QkSWp43d3dzMzMUKvVqFarzM7Oph2pLpwRkyRJDS+Xy1EoFGhvbyeTydDV1ZV2pLoIMca0M+yqs7MzLi8vpx1DkqQT0srKCm1tbWnHaEg7/TYhhHKMsXMvx3tqUpIkKSUWMUmSpJRYxCRJklJiEZMkSUqJRUySJCklFjFJkqSUWMQkSdKBMzw8zNjYGENDQywsLACbr0HKZrN0dHRQq9UoFotks1mKxWLKaQ/PB7pKkqQDa2RkZHt5cnKSUqlEf38/AOPj46yurtLU1JRWvF1ZxCRJ0oEwOjrKxMQEmUyG1tZWkiRhYGCAfD7P2toa09PTzM/PMzc3R7VaZX19nSRJKJVKFAqFtOPvyCImSZL2bG32On74zR/UdcyfecipnPG0Rxxxn3K5zNTUFJVKhY2NDXK5HEmSbG8fHBxkaWmJfD5PX18fAC0tLVQqlbpmrTeLmCRJaniLi4v09vbS3NwMQE9PT8qJ6sMiJkmS9my3mSsdHe+alCRJDa+7u5uZmRlqtRrVapXZ2dm0I9WFM2KSJKnh5XI5CoUC7e3tZDIZurq60o5UFyHGmHaGXXV2dsbl5eW0Y0iSdEJaWVmhra0t7RgNaaffJoRQjjF27uV4T01KkiSlxCImSZKUEouYJElSSixikiRJKbGISZIkpcQiJkmSlBKLmCRJOnCGh4cZGxtjaGiIhYUFYPM1SNlslo6ODmq1GsVikWw2S7FYTDnt4flAV0mSdGCNjIxsL09OTlIqlejv7wdgfHyc1dVVmpqa0oq3K4uYJEk6EEZHR5mYmCCTydDa2kqSJAwMDJDP51lbW2N6epr5+Xnm5uaoVqusr6+TJAmlUolCoZB2/B1ZxCRJ0p7Nzc1xyy231HXMs846i0suueSI+5TLZaampqhUKmxsbJDL5UiSZHv74OAgS0tL5PN5+vr6AGhpaaFSqdQ1a73tyzViIYTXhxCuDSF8NoRwZQjhjK3154YQaiGEytZ/b9uP75ckSceXxcVFent7aW5u5vTTT6enpyftSHWxXzNiHwZKMcaNEMLrgBJw+da262KMHfv0vZIkaR/tNnOlo7MvM2Ixxn+MMW5sffwkcPZ+fI8kSToxdHd3MzMzQ61Wo1qtMjs7m3akujgW14g9H/i7Qz6fF0L4V+A24BUxxsVjkEGSJB1guVyOQqFAe3s7mUyGrq6utCPVRYgx3rsDQ1gAztph0x/FGK/a2uePgE7gmTHGGEK4D9ASY/xuCCEBZoBsjPG2Hca/FLgU4JxzzkluuOGGe5VTkiT9dFZWVmhra0s7RkPa6bcJIZRjjJ17Of5ez4jFGJ98pO0hhAEgD1wUt9pejPFO4M6t5XII4TrgUcDyDuOPA+MAnZ2d964tSpIkNbD9umvyYuAPgZ4Y4+2HrH9gCKFpa/nhwCOBr+1HBkmSpEa3X9eI/RVwH+DDIQSAT8YYLwO6gZEQwo+Au4DLYoyr+5RBkiSpoe1LEYsx/pfDrH8/8P79+E5JkqSDxpd+S5IkpcQiJkmSlBKLmCRJOnCGh4cZGxtjaGiIhYUFYPM1SNlslo6ODmq1GsVikWw2S7FYTDnt4fnSb0mSdGCNjIxsL09OTlIqlejv7wdgfHyc1dVVmpqa0oq3K4uYJEk6EEZHR5mYmCCTydDa2kqSJAwMDJDP51lbW2N6epr5+Xnm5uaoVqusr6+TJAmlUolCoZB2/B1ZxCRJ0p59+cuvprq+UtcxT2tp41GP+uMj7lMul5mamqJSqbCxsUEulyNJku3tg4ODLC0tkc/n6evrA6ClpYVKpVLXrPVmEZMkSQ1vcXGR3t5empubAejp6Uk5UX1YxCRJ0p7tNnOlo+Ndk5IkqeF1d3czMzNDrVajWq0yOzubdqS6cEZMkiQ1vFwuR6FQoL29nUwmQ1dXV9qR6iLEGNPOsKvOzs64vLycdgxJkk5IKysrtLW1pR2jIe3024QQyjHGzr0c76lJSZKklFjEJEmSUmIRkyRJSolFTJIkKSUWMUmSpJRYxCRJklJiEZMkSQfO8PAwY2NjDA0NsbCwAGy+BimbzdLR0UGtVqNYLJLNZikWiymnPTwf6CpJkg6skZGR7eXJyUlKpRL9/f0AjI+Ps7q6SlNTU1rxdmURkyRJB8Lo6CgTExNkMhlaW1tJkoSBgQHy+Txra2tMT08zPz/P3Nwc1WqV9fV1kiShVCpRKBTSjr8ji5gkSdqzP/7KjXx+vVbXMR/T8rO8+pFnH3GfcrnM1NQUlUqFjY0NcrkcSZJsbx8cHGRpaYl8Pk9fXx8ALS0tVCqVumatN4uYJElqeIuLi/T29tLc3AxAT09PyonqwyImSZL2bLeZKx0d75qUJEkNr7u7m5mZGWq1GtVqldnZ2bQj1YUzYpIkqeHlcjkKhQLt7e1kMhm6urrSjlQXIcaYdoZddXZ2xuXl5bRjSJJ0QlpZWaGtrS3tGA1pp98mhFCOMXbu5XhPTUqSJKXEIiZJkpQSi5gkSVJKLGKSJEkpsYhJkiSlxCImSZKUEouYJEk6cIaHhxkbG2NoaIiFhQVg8zVI2WyWjo4OarUaxWKRbDZLsVhMOe3h+UBXSZJ0YI2MjGwvT05OUiqV6O/vB2B8fJzV1VWamprSircri5gkSToQRkdHmZiYIJPJ0NraSpIkDAwMkM/nWVtbY3p6mvn5eebm5qhWq6yvr5MkCaVSiUKhkHb8HVnEJEnSnr1q9gt88Zu31XXMn3/I6bzyadkj7lMul5mamqJSqbCxsUEulyNJku3tg4ODLC0tkc/n6evrA6ClpYVKpVLXrPVmEZMkSQ1vcXGR3t5empubAejp6Uk5UX1YxCRJ0p7tNnOlo+Ndk5IkqeF1d3czMzNDrVajWq0yOzubdqS6cEZMkiQ1vFwuR6FQoL29nUwmQ1dXV9qR6iLEGNPOsKvOzs64vLycdgxJkk5IKysrtLW1pR2jIe3024QQyjHGzr0c76lJSZKklFjEJEmSUmIRkyRJSolFTJIkKSX7VsRCCMMhhJtCCJWt/556yLZSCOGrIYQvhRD+235lkCRJamT7/fiKN8YYxw5dEUL4eeA3gSzwEGAhhPCoGOOP9zmLJElSQ0nj1OTTgakY450xxq8DXwUel0IOSZJ0QA0PDzM2NsbQ0BALCwvA5muQstksHR0d1Go1isUi2WyWYrGYctrD2+8ZsReHEJ4LLAP/K8b4PeChwCcP2efGrXWSJElHZWRkZHt5cnKSUqlEf38/AOPj46yurtLU1JRWvF39VEUshLAAnLXDpj8C3gq8Gohbf74BeP5RjH0pcCnAOeec89PElCRJx4HR0VEmJibIZDK0traSJAkDAwPk83nW1taYnp5mfn6eubk5qtUq6+vrJElCqVSiUCikHX9HP1URizE+eS/7hRD+BviHrY83Aa2HbD57a909xx4HxmHzyfo/TU5JklQncy+DWz5X3zHPugAuee0RdymXy0xNTVGpVNjY2CCXy5Ekyfb2wcFBlpaWyOfz9PX1AdDS0kKlUqlv1jrbz7smH3zIx17g81vLHwB+M4RwnxDCecAjgU/tVw5JknTwLS4u0tvbS3NzM6effjo9PT1pR6qL/bxG7M9CCB1snpq8HnghQIzxCyGEaeCLwAbwe94xKUnSAbHLzJWOzr7NiMUYnxNjvCDG+NgYY0+M8eZDto3GGB8RY3x0jHFuvzJIkqTjQ3d3NzMzM9RqNarVKrOzs2lHqov9vmtSkiTpp5bL5SgUCrS3t5PJZOjq6ko7Ul2EGBv/OvjOzs64vLycdgxJkk5IKysrtLW1pR2jIe3024QQyjHGzr0c77smJUmSUmIRkyRJSolFTJIkKSUWMUmSpJRYxCRJklJiEZMkSUqJRUySJB04w8PDjI2NMTQ0xMLCArD5GqRsNktHRwe1Wo1isUg2m6VYLKac9vB8oKskSTqwRkZGtpcnJycplUr09/cDMD4+zurqKk1NTWnF25VFTJIkHQijo6NMTEyQyWRobW0lSRIGBgbI5/Osra0xPT3N/Pw8c3NzVKtV1tfXSZKEUqlEoVBIO/6OLGKSJGnPXvep13Ht6rV1HfP8M8/n8sddfsR9yuUyU1NTVCoVNjY2yOVyJEmyvX1wcJClpSXy+Tx9fX0AtLS0UKlU6pq13ixikiSp4S0uLtLb20tzczMAPT09KSeqD4uYJEnas91mrnR0vGtSkiQ1vO7ubmZmZqjValSrVWZnZ9OOVBfOiEmSpIaXy+UoFAq0t7eTyWTo6upKO1JdhBhj2hl21dnZGZeXl9OOIUnSCWllZYW2tra0YzSknX6bEEI5xti5l+M9NSlJkpQSi5gkSVJKLGKSJEkpsYhJkiSlxCImSZKUEouYJElSSixikiTpwBkeHmZsbIyhoSEWFhaAzdcgZbNZOjo6qNVqFItFstksxWIx5bSH5wNdJUnSgTUyMrK9PDk5SalUor+/H4Dx8XFWV1dpampKK96uLGKSJOlAGB0dZWJigkwmQ2trK0mSMDAwQD6fZ21tjenpaebn55mbm6NarbK+vk6SJJRKJQqFQtrxd2QRkyRJe3bLa17DnSvX1nXM+7Sdz1kvf/kR9ymXy0xNTVGpVNjY2CCXy5Ekyfb2wcFBlpaWyOfz9PX1AdDS0kKlUqlr1nqziEmSpIa3uLhIb28vzc3NAPT09KScqD4sYpIkac92m7nS0fGuSUmS1PC6u7uZmZmhVqtRrVaZnZ1NO1JdOCMmSZIaXi6Xo1Ao0N7eTiaToaurK+1IdRFijGln2FVnZ2dcXl5OO4YkSSeklZUV2tra0o7RkHb6bUII5Rhj516O99SkJElSSixikiRJKbGISZIkpcQiJkmSlBKLmCRJUkosYpIkSSmxiEmSpANneHiYsbExhoaGWFhYADZfg5TNZuno6KBWq1EsFslmsxSLxZTTHp4PdJUkSQfWyMjI9vLk5CSlUon+/n4AxsfHWV1dpampKa14u7KISZKkA2F0dJSJiQkymQytra0kScLAwAD5fJ61tTWmp6eZn59nbm6OarXK+vo6SZJQKpUoFAppx9+RRUySJO3Z4vSX+c431us65gNaW3jSsx51xH3K5TJTU1NUKhU2NjbI5XIkSbK9fXBwkKWlJfL5PH19fQC0tLRQqVTqmrXeLGKSJKnhLS4u0tvbS3NzMwA9PT0pJ6oPi5gkSdqz3WaudHS8a1KSJDW87u5uZmZmqNVqVKtVZmdn045UF86ISZKkhpfL5SgUCrS3t5PJZOjq6ko7Ul2EGGP9Bw3h74BHb308A1iLMXaEEM4FVoAvbW37ZIzxst3G6+zsjMvLy3XPKUmSdreyskJbW1vaMRrSTr9NCKEcY+zcy/H7MiMWY9y+RzSE8Abg+4dsvi7G2LEf3ytJknSQ7OupyRBCAJ4F/Op+fo8kSdJBtN8X6z8J+FaM8SuHrDsvhPCvIYSPhRCedLgDQwiXhhCWQwjLt9566z7HlCRJOvbu9YxYCGEBOGuHTX8UY7xqa/m3gPccsu1m4JwY43dDCAkwE0LIxhhvu+cgMcZxYBw2rxG7tzklSZIa1b0uYjHGJx9pewjhZOCZwPZjb2OMdwJ3bi2XQwjXAY8CvBJfkiSdcPbz1OSTgWtjjDfevSKE8MAQQtPW8sOBRwJf28cMkiRJDWs/i9hv8pOnJQG6gc+GECrA+4DLYoyr+5hBkiQdh4aHhxkbG2NoaIiFhQVg8zVI2WyWjo4OarUaxWKRbDZLsVhMOe3h7dtdkzHGgR3WvR94/359pyRJOrGMjIxsL09OTlIqlejv7wdgfHyc1dVVmpqa0oq3K5+sL0mSDoTR0VEmJibIZDK0traSJAkDAwPk83nW1taYnp5mfn6eubk5qtUq6+vrJElCqVSiUCjs/gUpsIhJkqQ9u/qd43z7hvpe3p152MO5cODSI+5TLpeZmpqiUqmwsbFBLpcjSbbvB/z/27vjmDjP+w7g358PQ3w55ArIGWPIXNlxA3fJwR1sk2ahLtvSJrpdy3IrbcNmkpFaSvuHRXRK6RSGSJBqiXWppmgqdas6Eh5GTk3DUuyaKsogUrdCdsjE2ImX0JDExDYH3p05Yo579scdlBLOgO89ngO+n3/83vPce++PR6/Rl+d9731QV1eH/v5+uN1ueL1eAIDFYoHf7ze0VqMxiBEREVHa6+vrQ1VVFcxmMwDA4/ForsgYDGJERES0aivNXNHapPrJ+kRERERJq6ysRFdXF8LhMILBILq7u3WXZAjOiBEREVHaczqdqK6uhsPhgNVqRUVFhe6SDCFKpf/qQeXl5WpggA/fJyIi0mFkZATFxcW6y0hLy42NiAwqpcpXsz8vTRIRERFpwiBGREREpAmDGBEREZEmDGJEREREmjCIEREREWnCIEZERESkCYMYERERbThNTU1obW1FY2Mjent7AcSWQbLZbCgtLUU4HIbP54PNZoPP59NcbWJ8oCsRERFtWM3NzQvb7e3taGhoQE1NDQCgra0NgUAAJpNJV3krYhAjIiKiDaGlpQXHjx+H1WpFUVERXC4Xamtr4Xa7MTU1hc7OTpw9exY9PT0IBoMIhUJwuVxoaGhAdXW17vKXxSBGREREqzbV/b+49fFNQz8zs+BufO6v9932PYODg+jo6IDf70ckEoHT6YTL5Vror6urQ39/P9xuN7xeLwDAYrHA7/cbWqvRGMSIiIgo7fX19aGqqgpmsxkA4PF4NFdkDAYxIiIiWrWVZq5obfitSSIiIkp7lZWV6OrqQjgcRjAYRHd3t+6SDMEZMSIiIkp7TqcT1dXVcDgcsFqtqKio0F2SIUQppbuGFZWXl6uBgQHdZRAREW1JIyMjKC4u1l1GWlpubERkUClVvpr9eWmSiIiISBMGMSIiIiJNGMSIiIiINGEQIyIiItKEQYyIiIhIEwYxIiIiIk0YxIiIiGjDaWpqQmtrKxobG9Hb2wsgtgySzWZDaWkpwuEwfD4fbDYbfD6f5moT4wNdiYiIaMNqbm5e2G5vb0dDQwNqamoAAG1tbQgEAjCZTLrKWxGDGBEREW0ILS0tOH78OKxWK4qKiuByuVBbWwu3242pqSl0dnbi7Nmz6OnpQTAYRCgUgsvlQkNDA6qrq3WXvywGMSIiIlq1np4ejI+PG/qZ+fn5eOSRR277nsHBQXR0dMDv9yMSicDpdMLlci3019XVob+/H263G16vFwBgsVjg9/sNrdVoDGJERESU9vr6+lBVVQWz2QwA8Hg8misyBoMYERERrdpKM1e0NvzWJBEREaW9yspKdHV1IRwOIxgMoru7W3dJhuCMGBEREaU9p9OJ6upqOBwOWK1WVFRU6C7JEKKU0l3DisrLy9XAwIDuMoiIiLakkZERFBcX6y4jLS03NiIyqJQqX83+vDRJREREpAmDGBEREZEmDGJEREREmjCIEREREWnCIEZERESkCYMYERERkSYMYkRERLThNDU1obW1FY2Njejt7QUQWwbJZrOhtLQU4XAYPp8PNpsNPp9Pc7WJ8YGuREREtGE1NzcvbLe3t6OhoQE1NTUAgLa2NgQCAZhMJl3lrSipGTER+VsReVtEoiJSvqSvQUQui8glEfnSovYvx9sui8h3kzk+ERERbR0tLS04cOAADh48iEuXLgEAamtrcerUKRw7dgydnZ147rnn8Pjjj8Pj8SAUCsHlcuHkyZOaK08s2RmxYQB/A+BHixtFpATA1wHYABQA6BWRA/HulwD8FYAPAfxWRF5VSl1Isg4iIiJaB++88zyCoRFDPzPbUowDB5677XsGBwfR0dEBv9+PSCQCp9MJl8u10F9XV4f+/n643W54vV4AgMVigd/vN7RWoyUVxJRSIwAgIku7vgKgQyn1KYD3ReQygD+O911WSr0X368j/l4GMSIiIkqor68PVVVVMJvNAACPx6O5ImOk6h6xPQB+s+j1h/E2ABhb0v4nKaqBiIiIDLbSzBWtzYpBTER6AeQv0/WPSqlfGF/SwnG/BeBb8ZchEbmUqmMtkgfg+joch/4Qx10PjrseHHc9OO5JOHfu3ANzc3ORte43NzeXYTKZ1rzfcnbv3r3tpZdeyvJ4POFIJIJTp07t8Hq9sxMTE9tGR0fnhoeH5yYmJjLntwEgGo2ah4eHp404fiLj4+MZJSUl55c0/9Fq918xiCml/nLNVQEfASha9Low3obbtC89bhuAtjs49h0TkYHVrpZOxuG468Fx14PjrgfHPTlDQ0Ojdrt9zUF2eHi42G63G3JDmd1ux1tvvZXv9XrzcnNzZx0Ox6TFYpnOzMzckZube8Nut09mZmbund+O71Zm1PETmZuby0vm3ErVpclXAZwQkR8gdrP+fQD+G4AAuE9EPo9YAPs6gG+mqAYiIiLaRI4ePTp+9OjR8UT9r7zyyuji19PT0yE25r8AAAZZSURBVP+T8qKSlFQQE5EqAP8K4B4Ar4mIXyn1JaXU2yLSidhN+BEA31ZKzcX3+Q6AswBMAH6qlHo7qZ+AiIiIaINK9luTpwGcTtDXAqBlmfZfAvhlMsdNoXW9FEoLOO56cNz14LjrwXHXIC8v75ruGtIdlzhaJH5fGq0zjrseHHc9OO56cNz1yM/P5xckVsAgRkRERKQJ15pEbNklAD9E7L61Y0qp72suaUsQkVEAQQBzACL8RlPqiMhPAbgBXFVK2eNtOQBOAtgLYBTA15RSk4k+g9YmwZg3AXgKwPzlmu/Fb9cgg4hIEYCXAewCoAC0KaV+yPM9tWZmZra///77n49EItsBIDc391pBQcHVsbGxgomJibyMjIwIABQUFHyUk5NzQ2+16WXLz4iJiAmxZZceAVAC4BvxJZpoffy5UqqUISzlfgbgy0vavgvg10qp+wD8Ov6ajPMzfHbMAeBf4ud8KUNYSkQAPKOUKgHwpwC+Hf+dzvM9hUQEhYWFHz7wwANvFxcXj1y/ft168+bNuwDgnnvu+cRut1+w2+0XGMI+a8sHMcSWXrqslHpPKXULwPyyS0SbhlLqPwEEljR/BcDx+PZxAF9d16I2uQRjTimmlLqilHorvh0EMILYyi4831MoKytrNjs7exoAMjIyollZWeFbt25lpvKY9fX1BY2NjbuOHDlS0NXVlQ0AZ86csezfv992//33l4RCITl8+HDh/v37bYcPHy5MZS3J4KXJ2H9QLrukhwLwKxFRAH7Em2nX3S6l1JX49jhil3Io9b4jIn8PYACxmRteHksREdkLoAzAf4Hn+7qZmZnJnJmZMWdnZ4dCoZDl+vXr1kAgkGs2m6fvvffese3bt88ZebwXX3zx4/ntl19+Oae+vv7K008/HQCAEydO5E1OTvozMtI37qRvZbQVHFRKfSQiVgDnRORifBaB1plSSsUDMaXWvwF4HrE/Qp4H8M8AntRa0SYlIhYArwA4opT6PxFZ6OP5njqRSGTb5cuX9+3Zs2csIyMjumvXrquFhYUfA8DY2NieDz74oGjfvn2jd/r5zz77bP7JkyfzcnNzZwsKCm6VlZVNP/bYY3vdbveNyclJ02uvvZbzxhtv7Dxz5szOUChkmp6eNtnt9pJnnnnmylNPPZWWf/QwiN1+OSZKIaXUR/F/r4rIacQuEzOIrZ9PRGS3UuqKiOwGcFV3QZudUuqT+W0R+TGA/9BYzqYlItsRC2HtSqmfx5t5vhvkyMgHRRdvzpg/26MwNzOzQ0wZkW0zgV0YDfzhrKOKytzMzA7T5DtZS/e8/+67pl8svndsaftifX195tOnT+ecP3/+wuzsLEpLS0vKysoW1pGsr6+//uabb1rcbveNJ554YhIAzGZz2cWLFy/c4Y+6LniPGPBbxJddEpFMxJZdelVzTZueiNwtItnz2wAeBjCst6ot51UAh+LbhwD8QmMtW0I8AMyrAs95w0ls6usnAEaUUj9Y1MXzPaUUop9+epds2xbdtn377O+bowtTkdHIXIZs2xa90yO8/vrrlkcffXQqOzs7mpOTE3344Yenkiw6LWz5GTGlVITLLmmxC8Dp+OWCDAAnlFJn9Ja0eYnIvwP4IoA8EfkQwD8B+D6AThH5BwC/A/A1fRVuPgnG/IsiUorYpclRAIe1Fbh5/RmAvwNwXkT88bbvgee7YZabubpx44bl3Xff/UJWVlZY5JYJiD2qIhAI5ITD4R0AkJmdObN37/7fZWVlzS7dfyvb8kEMSPtllzYlpdR7ABy669gqlFLfSND1F+tayBaSYMx/su6FbDFKqX4AkqCb53uK7Ny5M1ReXj64tN3Ix1U89NBDoSeffHLvCy+8cGV2dlbOnTv3uUOHDm34JZQYxIiIiCjtHTx4cLqqqipgt9ttubm5sw8++OBN3TUZQZTiF0eIiIgosaGhoVGHw8F1I5cxNDSU53A49t7p/rxZn4iIiEgTBjEiIiIiTRjEiIiIiDRhECMiIqKVRKPRaKJvo25Z8TG542ejAQxiREREtLLha9eu7WQY+71oNCrXrl3biSQfzMzHVxAREdFtRSKRuvHx8WPj4+N2cBJnXhTAcCQSqUvmQ/j4CiIiIiJNmGqJiIiINGEQIyIiItKEQYyIiIhIEwYxIiIiIk0YxIiIiIg0+X9JW8Q94nL1ZAAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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LZVZWVshmsxSLRfL5fNLxN3W4itjvst9hyRDC42KMt62/7ASuO0w5JEnSz+H2P/9z7l+8vqpjHtN0Hqe9+tUH3WZhYYGxsTFKpRKrq6tkMhmy2ezG+t7eXubm5sjlcnR1dQHQ0NBAqVSqatZq2/EiFkI4DvgN4Pf3e/svQwgtrB2avPER6yRJkn7C7OwsnZ2d1NfXA9DR0ZFwourY8SIWY/whcPIj3nvxTn+uJEmqvq1mrnRovLO+JEmqee3t7UxMTFCpVCiXy0xOTiYdqSoO1zlikiRJP7NMJkM+n6e5uZlUKkVbW1vSkaoixFj7d4ZobW2N8/PzSceQJOmItLi4SFNTU9IxatJm300IYSHG2Lqd/T00KUmSlBCLmCRJUkIsYpIkSQmxiEmSJCXEIiZJkpQQi5gkSVJCLGKSJGnX6e/vZ2hoiL6+PmZmZoC1xyCl02laWlqoVCoUCgXS6TSFQiHhtAfmDV0lSdKuNTAwsLE8OjpKsViku7sbgOHhYZaWlqirq0sq3pYsYpIkaVcYHBxkZGSEVCpFY2Mj2WyWnp4ecrkcy8vLjI+PMz09zdTUFOVymZWVFbLZLMVikXw+n3T8TVnEJEnSts2Of507b16p6pinNDZw4QuefNBtFhYWGBsbo1Qqsbq6SiaTIZvNbqzv7e1lbm6OXC5HV1cXAA0NDZRKpapmrTaLmCRJqnmzs7N0dnZSX18PQEdHR8KJqsMiJkmStm2rmSsdGq+alCRJNa+9vZ2JiQkqlQrlcpnJycmkI1WFM2KSJKnmZTIZ8vk8zc3NpFIp2trako5UFSHGmHSGLbW2tsb5+fmkY0iSdERaXFykqakp6Rg1abPvJoSwEGNs3c7+HpqUJElKiEVMkiQpIRYxSZKkhFjEJEmSEmIRkyRJSohFTJIkKSEWMUmStOv09/czNDREX18fMzMzwNpjkNLpNC0tLVQqFQqFAul0mkKhkHDaA/OGrpIkadcaGBjYWB4dHaVYLNLd3Q3A8PAwS0tL1NXVJRVvSxYxSZK0KwwODjIyMkIqlaKxsZFsNktPTw+5XI7l5WXGx8eZnp5mamqKcrnMysoK2WyWYrFIPp9POv6mLGKSJGnbPvmeYb5/07erOmbq8U/gop7LD7rNwsICY2NjlEolVldXyWQyZLPZjfW9vb3Mzc2Ry+Xo6uoCoKGhgVKpVNWs1WYRkyRJNW92dpbOzk7q6+sB6OjoSDhRdVjEJEnStm01c6VD41WTkiSp5rW3tzMxMUGlUqFcLjM5OZl0pKpwRkySJNW8TCZDPp+nubmZVCpFW1tb0pGqIsQYk86wpdbW1jg/P590DEmSjkiLi4s0NTUlHaMmbfbdhBAWYoyt29nfQ5OSJEkJsYhJkiQlxCImSZKUEIuYJElSQixikiRJCbGISZIkJcQiJkmSdp3+/n6Ghobo6+tjZmYGWHsMUjqdpqWlhUqlQqFQIJ1OUygUEk57YN7QVZIk7VoDAwMby6OjoxSLRbq7uwEYHh5maWmJurq6pOJtySImSZJ2hcHBQUZGRkilUjQ2NpLNZunp6SGXy7G8vMz4+DjT09NMTU1RLpdZWVkhm81SLBbJ5/NJx9+URUySJG3b8uS3+NF3f1jVMX/h9OM48TlPPOg2CwsLjI2NUSqVWF1dJZPJkM1mN9b39vYyNzdHLpejq6sLgIaGBkqlUlWzVptFTJIk1bzZ2Vk6Ozupr68HoKOjI+FE1WERkyRJ27bVzJUOzY5fNRlCuDGE8JUQQimEML/+3r4QwidCCN9Y/33STueQJEm7V3t7OxMTE1QqFcrlMpOTk0lHqorDdfuKi2KMLfs9ifwq4F9jjE8C/nX9tSRJ0qYymQz5fJ7m5mYuvfRS2trako5UFSHGuLMfEMKNQGuM8c793rsBeGaM8bYQwuOAT8UYzz3QGK2trXF+fn5Hc0qSpM0tLi7S1NSUdIyatNl3E0JY2G/y6aAOx4xYBP4lhLAQQrh8/b3HxhhvW1++HXjsYcghSZJUUw7HyfrPiDHeGkJIAZ8IIVy//8oYYwwh/NS03HppuxzgrLPOOgwxJUmSDq8dnxGLMd66/vv7wDXA04DvrR+SZP339zfZbzjG2BpjbD311FN3OqYkSdJht6NFLIRwXAjh+IeXgd8ErgM+Arx0fbOXAh/eyRySJEm1aKcPTT4WuCaE8PBnvS/G+PEQwheA8RDC7wE3AS/Y4RySJEk1Z0eLWIzx20DzJu/fBVy8k58tSZJU6w7XfcQkSZKqpr+/n6GhIfr6+piZmQHWHoOUTqdpaWmhUqlQKBRIp9MUCoWE0x6YjziSJEm71sDAwMby6OgoxWKR7u5uAIaHh1laWqKuri6peFuyiEmSpF1hcHCQkZERUqkUjY2NZLNZenp6yOVyLC8vMz4+zvT0NFNTU5TLZVZWVshmsxSLRfL5fNLxN2URkyRJ2zY1NcXtt99e1TFPO+00Lr300oNus7CwwNjYGKVSidXVVTKZDNlsdmN9b28vc3Nz5HI5urq6AGhoaKBUKlU1a7VZxCRJUs2bnZ2ls7OT+vp6ADo6OhJOVB0WMUmStG1bzVzp0HjVpCRJqnnt7e1MTExQqVQol8tMTk4mHakqnBGTJEk1L5PJkM/naW5uJpVK0dbWlnSkqggx/tTztmtOa2trnJ+fTzqGJElHpMXFRZqampKOUZM2+25CCAsxxtbt7O+hSUmSpIRYxCRJkhJiEZMkSUqIRUySJCkhFjFJkqSEWMQkSZISYhGTJEm7Tn9/P0NDQ/T19TEzMwOsPQYpnU7T0tJCpVKhUCiQTqcpFAoJpz0wb+gqSZJ2rYGBgY3l0dFRisUi3d3dAAwPD7O0tERdXV1S8bZkEZMkSbvC4OAgIyMjpFIpGhsbyWaz9PT0kMvlWF5eZnx8nOnpaaampiiXy6ysrJDNZikWi+Tz+aTjb8oiJkmStu3rX3895ZXFqo55fEMTT37yaw+6zcLCAmNjY5RKJVZXV8lkMmSz2Y31vb29zM3Nkcvl6OrqAqChoYFSqVTVrNVmEZMkSTVvdnaWzs5O6uvrAejo6Eg4UXVYxCRJ0rZtNXOlQ+NVk5Ikqea1t7czMTFBpVKhXC4zOTmZdKSqcEZMkiTVvEwmQz6fp7m5mVQqRVtbW9KRqiLEGJPOsKXW1tY4Pz+fdAxJko5Ii4uLNDU1JR2jJm323YQQFmKMrdvZ30OTkiRJCbGISZIkJcQiJkmSlBCLmCRJUkIsYpIkSQmxiEmSJCXEIiZJknad/v5+hoaG6OvrY2ZmBlh7DFI6naalpYVKpUKhUCCdTlMoFBJOe2De0FWSJO1aAwMDG8ujo6MUi0W6u7sBGB4eZmlpibq6uqTibckiJkmSdoXBwUFGRkZIpVI0NjaSzWbp6ekhl8uxvLzM+Pg409PTTE1NUS6XWVlZIZvNUiwWyefzScfflEVMkiRt22u/cQvXrVSqOuZTGh7D65905kG3WVhYYGxsjFKpxOrqKplMhmw2u7G+t7eXubk5crkcXV1dADQ0NFAqlaqatdosYpIkqebNzs7S2dlJfX09AB0dHQknqg6LmCRJ2ratZq50aLxqUpIk1bz29nYmJiaoVCqUy2UmJyeTjlQVzohJkqSal8lkyOfzNDc3k0qlaGtrSzpSVYQYY9IZttTa2hrn5+eTjiFJ0hFpcXGRpqampGPUpM2+mxDCQoyxdTv7e2hSkiQpIRYxSZKkhFjEJEmSEmIRkyRJSohFTJIkKSEWMUmSpITsWBELITSGED4ZQvhaCOGrIYQ/Wn+/P4RwawihtP7z7J3KIEmSHp36+/sZGhqir6+PmZkZYO0xSOl0mpaWFiqVCoVCgXQ6TaFQSDjtge3kDV1XgT+JMX4xhHA8sBBC+MT6urfEGId28LMlSdIRYGBgYGN5dHSUYrFId3c3AMPDwywtLVFXV5dUvC3tWBGLMd4G3La+XA4hLAJn7NTnSZKkR7fBwUFGRkZIpVI0NjaSzWbp6ekhl8uxvLzM+Pg409PTTE1NUS6XWVlZIZvNUiwWyefzScff1GF5xFEI4Wzgl4HPAxcArwwhvASYZ23W7AeHI4ckSfr5vG7yq3ztu/dUdcxfOn0vf/ac9EG3WVhYYGxsjFKpxOrqKplMhmw2u7G+t7eXubk5crkcXV1dADQ0NFAqlaqatdp2/GT9EEID8EHgj2OM9wB/BzwRaGFtxuzNB9jv8hDCfAhh/o477tjpmJIkqYbNzs7S2dlJfX09e/fupaOjI+lIVbGjM2IhhD2slbDRGOOHAGKM39tv/TuBj262b4xxGBiGtWdN7mROSZK0PVvNXOnQ7ORVkwF4F7AYY/yr/d5/3H6bdQLX7VQGSZL06NDe3s7ExASVSoVyuczk5GTSkapiJ2fELgBeDHwlhPDwAdpXA78bQmgBInAj8Ps7mEGSJD0KZDIZ8vk8zc3NpFIp2trako5UFSHG2j/q19raGufn55OOIUnSEWlxcZGmpqakY9Skzb6bEMJCjLF1O/t7Z31JkqSEWMQkSZISYhGTJElKiEVMkiQpIRYxSZKkhFjEJEmSEmIRkyRJu05/fz9DQ0P09fUxMzMDrD0GKZ1O09LSQqVSoVAokE6nKRQKCac9sMPy0G9JkqSdMDAwsLE8OjpKsViku7sbgOHhYZaWlqirq0sq3pYsYpIkaVcYHBxkZGSEVCpFY2Mj2WyWnp4ecrkcy8vLjI+PMz09zdTUFOVymZWVFbLZLMVikXw+n3T8TVnEJEnS9k1dBbd/pbpjnnY+XPqGg26ysLDA2NgYpVKJ1dVVMpkM2Wx2Y31vby9zc3Pkcjm6uroAaGhooFQqHWjImmARkyRJNW92dpbOzk7q6+sB6OjoSDhRdVjEJEnS9m0xc6VD41WTkiSp5rW3tzMxMUGlUqFcLjM5OZl0pKpwRkySJNW8TCZDPp+nubmZVCpFW1tb0pGqIsQYk86wpdbW1jg/P590DEmSjkiLi4s0NTUlHaMmbfbdhBAWYoyt29nfQ5OSJEkJsYhJkiQlxCImSZKUEIuYJElSQixikiRJCbGISZIkJcQiJkmSdp3+/n6Ghobo6+tjZmYGWHsMUjqdpqWlhUqlQqFQIJ1OUygUEk57YN7QVZIk7VoDAwMby6OjoxSLRbq7uwEYHh5maWmJurq6pOJtySImSZJ2hcHBQUZGRkilUjQ2NpLNZunp6SGXy7G8vMz4+DjT09NMTU1RLpdZWVkhm81SLBbJ5/NJx9+URUySJG3bG//9jVy/dH1Vxzxv33lc+bQrD7rNwsICY2NjlEolVldXyWQyZLPZjfW9vb3Mzc2Ry+Xo6uoCoKGhgVKpVNWs1WYRkyRJNW92dpbOzk7q6+sB6OjoSDhRdVjEJEnStm01c6VD41WTkiSp5rW3tzMxMUGlUqFcLjM5OZl0pKpwRkySJNW8TCZDPp+nubmZVCpFW1tb0pGqIsQYk86wpdbW1jg/P590DEmSjkiLi4s0NTUlHaMmbfbdhBAWYoyt29nfQ5OSJEkJsYhJkiQlxCImSZKUEIuYJElSQixikiRJCfH2FZJ0AN/51jeYHPgLwgNJJ5GS9bRXXMb3vn1j0jGq5rFPODvpCBssYpK0ib95bYHVb9zOg/EHwJ6k40jJig8S42rSKX7C0Nuu5rjj6imvrPD0tjbaL7iAz33hC1z52j727Dmayfe/nze99a3866c+zcXP/HX6rroKgFBjBwMtYpK0n3vuvpv/9Uf/jdXKEnAUdcen+OM/uKg6g8cHofw9uOcWuPvhn1vhgR/+5HZH7YETzoC9Z8IJZ64tn3AmnNAIe89Ye33sCdXJpEMTI9x7137//G7Z75/nrWu/y7cBj7hH57Enrv3z2/+fZ/3JQEjirzhki3X1nHbaSUnH+AkNDcfS0PAYXveawsZ7H//ENK+96r/T/cIXADA6Ps7Szd+krq7uxzseVffIoRJlEZOkde95619w9+dvYPWhOzk6nMzxZ8Nlx34QPvLB6n7Qcam1/xCfei784rPWlveesf4f6jPW1h9VW//XrnUhwHGnrP2c3rL5Ng8+sFbGNsrZzeuFbX35P6+F+5YPb+6f12+Nw93JV4bBt/0DI+//KKlT9tF4+mPJPrWJnpe9jNyzLmT5njKo2GW0AAAgAElEQVTjH7qG6U98gql/nqS88kNWVn5I9lcvpPjKl5F/7m+tDXL0sfCYE5P9Q/aT/LcqSTXgrS+/jAfvuRtYZc+xp9J9+j+z74Rj4ZlvhPOeTVVmLkKA406Fo4/5+cdS7arbAyeetfZzIPeXobKLytity5A6F4Db3/CX3H/9DVUd/pjzzuW0q/7HQbdZWPgiY//8aUqlL7G6ukrmab9C9teeCcfeBSc00nvZ85j78o3k/suz6fqd5wHQcOI+Sl/6yk8OVGOTkBYxSUe0yfH38p1rPsWDD32fo8KJ7Hlc4JUnTkC2By7607WZD6najjl+7We3uG0Fjv6FteWjjlr7n4pqOuqoH49/ALPXfo7O5z2P+r1rs1kdHc+Fo45e27fu6LX9919+2BbjJs0iJumI9Vev7IU7y8R4L3v2pPitMz7NuU1NcMln4LTzk44n1aTTXv3qpCM8qngSgqQjzpfnr+XqF11GvON2AkdRd8rJ/GH2C5z70r+Cl05awqQa1N7ezsTEBJVKhXK5zOTkZNKRqsIZMUlHlLe+6pXEW37AQ/Fu9tSlaHn8DbRf8tvwa38Hex6TdDxJB5DJZMjn8zQ3N5NKpWhra0s6UlWEGOPWWyWstbU1zs/PJx1D0i52y8038aE/fR0P3H8nIRzDUSc08Me/eTQ8q3/tSkVJB7S4uEhTU1PSMWrSZt9NCGEhxti6nf2dEZP0qPf2gSIPfO1WHoxL7DnqVB57zr3kr7gCzvqVpKNJOsIlVsRCCJcAbwPqgH+IMb4hqSySHp3uuftu/td/+2NWf7h2m4CjjzuVnpf+Gnsv/L1t3adr8StfY/Cbn+bOYxt2OqpU06567HnU3X1X0jGqoi4+xBNPPDXpGBsSKWIhhDrg7cBvALcAXwghfCTG+LUk8kh69Bn92zdz1+x1rD50B3VhH48551h+/8+G4Ni929r/rSPv4B/OfAJ3nfA0Tn/oNsIj75QuHUEeCkexGmrrjvSPFknNiD0N+GaM8dsAIYQx4LlAYkXs6hdf9lNPpJC0O4UIDzxYJsb72XNMiuf9ycs4s/nCbe37/du/x6s++wE+0firnBLv5BVf+hBn/ccndjixVNuOf/kbSN11W9IxqiIeFeCEk5OOsSGpInYGcPN+r28BfuJkjRDC5cDlAGeddZC7E1fJAz+6G/jRjn+OpMPjqHACdWfu4w/f/Pfb3ue9Y/+Hvz7lBG4+6QKesfIFukbeR/N1t/MQ1NzduKXD6cGXRo774YNJx6iKB2rs7Pgai/NjMcZhYBjWrprc6c/7k3+q8rPkJO0alZV7+ZOPvpOPpH6Neu7l926Y5Hl/9z6OeghmL2nkZX/5Ufb8Qm3fnVvaSYuLizzmUXLVZK3dpCapInYr0Ljf6zPX35Okw+pjH/sobzz6Xm547K/TfP91PP9DH+SCz1zPTY8LPPTfXsnlHX+QdERJm+jv76ehoYF77rmH9vZ2nvWsZzE7O8sVV1zBnj17uPbaa+nr6+NjH/sYz372s3nTm96UdORNJVXEvgA8KYRwDmsF7IXA/59QFklHoPvuu48/e/87GD/jaURO5ndv/hc6r343J90buPYZJ5F/28c57rjtndgvKTkDAwMby6OjoxSLRbq7uwEYHh5maWmJurravdAgkSIWY1wNIbwSmGbt9hXvjjF+NYksko48n7/2c7xu+Qa+eOYz+cUHvsnzP/HP/Obk57j95MBX/2sXl73s9UlHlLSJwcFBRkZGSKVSNDY2ks1m6enpIZfLsby8zPj4ONPT00xNTVEul1lZWSGbzVIsFsnn80nH31Ri54jFGD8GfCypz5d0ZBp8z1/zv896CuVj0nR8/1M8/+p3cuZdDzH/y4/h4rd8iItOOzvpiFJNmx3/OnfevFLVMU9pbODCFzz5oNssLCwwNjZGqVRidXWVTCZDNpvdWN/b28vc3By5XI6uri4AGhoaKJVKVc1abTV7sr4kVdO3rv8GV339k8w+/kLOePAWXvb5j/Bf/s/HuacBrv29C7is8A9JR5R0ELOzs3R2dlJfXw9AR0dHwomqwyIm6VHvb977Tt55RiPfb2jl4uVref7wezj3O/dw3Xl7+KW/eAeXNf1a0hGlXWOrmSsdmq2f8SFJu9Qdd9zBZR/8G/78zAyrHM0VX/kQhddezZm33cNnOs/l+RNfJm0Jk3aF9vZ2JiYmqFQqlMtlJicnk45UFc6ISXpUGnv/P/GWE4/hpn3P4Ffu/SLP/z//SPY/buGbZx3FSa/t5/cvfH7SESUdgkwmQz6fp7m5mVQqRVtbW9KRqiLEWPvP9WltbY3z8/M7+hldk+/kR0fZS6VHgxjgS8f+Env4EV3fnqXrb/43e1bhC79+Gj1vmfbmrNIhWlxcpOlRckPXatvsuwkhLMQYW7ezv81j3U2POY37wjFJx5BUJen7b+C5H/1nfv1fv8zNj4XKH76cl//Of086liT9BIvYut//wJsIq4+O52hJR7oQH+LcLy9zwgp8/lf28ty3fZSTTjw16ViS9FMsYut++do7qL8/6RSSquV7J0HpD59NzyvenHQUSTogi9i6x049Oq6+kLTmnH2nc+yx9UnHkKSDsoitO/P0X0w6giRJOsJ4HzFJkqSEWMQkSdKu09/fz9DQEH19fczMzABrj0FKp9O0tLRQqVQoFAqk02kKhULCaQ/MQ5OSJGnXGhgY2FgeHR2lWCzS3d0NwPDwMEtLS9TV1SUVb0sWMUmStCsMDg4yMjJCKpWisbGRbDZLT08PuVyO5eVlxsfHmZ6eZmpqinK5zMrKCtlslmKxSD6fTzr+pixikiRp2z75nmG+f9O3qzpm6vFP4KKeyw+6zcLCAmNjY5RKJVZXV8lkMmSz2Y31vb29zM3Nkcvl6OrqAqChoYFSqVTVrNVmEZMkSTVvdnaWzs5O6uvXbkvT0dGRcKLqsIhJkqRt22rmSofGqyYlSVLNa29vZ2JigkqlQrlcZnLy0XEjdmfEJElSzctkMuTzeZqbm0mlUrS1tSUdqSpCjDHpDFtqbW2N8/PzSceQJOmItLi4SFNTU9IxatJm300IYSHG2Lqd/T00KUmSlBCLmCRJUkIsYpIkSQmxiEmSJCXEIiZJkpQQi5gkSVJCLGKSJGnX6e/vZ2hoiL6+PmZmZoC1xyCl02laWlqoVCoUCgXS6TSFQiHhtAfmDV0lSdKuNTAwsLE8OjpKsViku7sbgOHhYZaWlqirq0sq3pYsYpIkaVcYHBxkZGSEVCpFY2Mj2WyWnp4ecrkcy8vLjI+PMz09zdTUFOVymZWVFbLZLMVikXw+n3T8TVnEJEnSti1PfosfffeHVR3zF04/jhOf88SDbrOwsMDY2BilUonV1VUymQzZbHZjfW9vL3Nzc+RyObq6ugBoaGigVCpVNWu1WcQkSVLNm52dpbOzk/r6egA6OjoSTlQdFjFJkrRtW81c6dB41aQkSap57e3tTExMUKlUKJfLTE5OJh2pKpwRkyRJNS+TyZDP52lubiaVStHW1pZ0pKoIMcakM2yptbU1zs/PJx1DkqQj0uLiIk1NTUnHqEmbfTchhIUYY+t29vfQpCRJUkIsYpIkSQmxiEmSJCXEIiZJkpQQi5gkSVJCLGKSJEkJsYhJkqRdp7+/n6GhIfr6+piZmQHWHoOUTqdpaWmhUqlQKBRIp9MUCoWE0x6YN3SVJEm71sDAwMby6OgoxWKR7u5uAIaHh1laWqKuri6peFuyiEmSpF1hcHCQkZERUqkUjY2NZLNZenp6yOVyLC8vMz4+zvT0NFNTU5TLZVZWVshmsxSLRfL5fNLxN2URkyRJ2zY1NcXtt99e1TFPO+00Lr300oNus7CwwNjYGKVSidXVVTKZDNlsdmN9b28vc3Nz5HI5urq6AGhoaKBUKlU1a7XtyDliIYQ3hRCuDyF8OYRwTQjhxPX3zw4hVEIIpfWfd+zE50uSpEeX2dlZOjs7qa+vZ+/evXR0dCQdqSp2akbsE0AxxrgaQngjUASuXF/3rRhjyw59riRJ2kFbzVzp0OzIjFiM8V9ijKvrLz8HnLkTnyNJko4M7e3tTExMUKlUKJfLTE5OJh2pKg7HOWKXAf+03+tzQgj/AdwDvCbGOHsYMkiSpF0sk8mQz+dpbm4mlUrR1taWdKSqCDHGn23HEGaA0zZZ9acxxg+vb/OnQCvwvBhjDCEcAzTEGO8KIWSBCSAdY7xnk/EvBy4HOOuss7I33XTTz5RTkiT9fBYXF2lqako6Rk3a7LsJISzEGFu3s//PPCMWY3zWwdaHEHqAHHBxXG97Mcb7gfvXlxdCCN8CngzMbzL+MDAM0Nra+rO1RUmSpBq2U1dNXgL8D6Ajxnjvfu+fGkKoW19+AvAk4Ns7kUGSJKnW7dQ5Yn8DHAN8IoQA8LkY4xVAOzAQQngAeAi4Isa4tEMZJEmSatqOFLEY4y8e4P0PAh/cic+UJEnabXzotyRJUkIsYpIkSQmxiEmSpF2nv7+foaEh+vr6mJmZAdYeg5ROp2lpaaFSqVAoFEin0xQKhYTTHpgP/ZYkSbvWwMDAxvLo6CjFYpHu7m4AhoeHWVpaoq6uLql4W7KISZKkXWFwcJCRkRFSqRSNjY1ks1l6enrI5XIsLy8zPj7O9PQ0U1NTlMtlVlZWyGazFItF8vl80vE3ZRGTJEnb9vWvv57yymJVxzy+oYknP/m1B91mYWGBsbExSqUSq6urZDIZstnsxvre3l7m5ubI5XJ0dXUB0NDQQKlUqmrWarOISZKkmjc7O0tnZyf19fUAdHR0JJyoOixikiRp27aaudKh8apJSZJU89rb25mYmKBSqVAul5mcnEw6UlU4IyZJkmpeJpMhn8/T3NxMKpWira0t6UhVEWKMSWfYUmtra5yfn086hiRJR6TFxUWampqSjlGTNvtuQggLMcbW7ezvoUlJkqSEWMQkSZISYhGTJElKiEVMkiQpIRYxSZKkhFjEJEmSEmIRkyRJu05/fz9DQ0P09fUxMzMDrD0GKZ1O09LSQqVSoVAokE6nKRQKCac9MG/oKkmSdq2BgYGN5dHRUYrFIt3d3QAMDw+ztLREXV1dUvG2ZBGTJEm7wuDgICMjI6RSKRobG8lms/T09JDL5VheXmZ8fJzp6WmmpqYol8usrKyQzWYpFovk8/mk42/KIiZJkrbttd+4hetWKlUd8ykNj+H1TzrzoNssLCwwNjZGqVRidXWVTCZDNpvdWN/b28vc3By5XI6uri4AGhoaKJVKVc1abRYxSZJU82ZnZ+ns7KS+vh6Ajo6OhBNVh0VMkiRt21YzVzo0XjUpSZJqXnt7OxMTE1QqFcrlMpOTk0lHqgpnxCRJUs3LZDLk83mam5tJpVK0tbUlHakqQowx6Qxbam1tjfPz80nHkCTpiLS4uEhTU1PSMWrSZt9NCGEhxti6nf09NClJkpQQi5gkSVJCLGKSJEkJsYhJkiQlxCImSZKUEIuYJElSQixikiRp1+nv72doaIi+vj5mZmaAtccgpdNpWlpaqFQqFAoF0uk0hUIh4bQH5g1dJUnSrjUwMLCxPDo6SrFYpLu7G4Dh4WGWlpaoq6tLKt6WLGKSJGlXGBwcZGRkhFQqRWNjI9lslp6eHnK5HMvLy4yPjzM9Pc3U1BTlcpmVlRWy2SzFYpF8Pp90/E1ZxCRJ0ra9bvKrfO2791R1zF86fS9/9pz0QbdZWFhgbGyMUqnE6uoqmUyGbDa7sb63t5e5uTlyuRxdXV0ANDQ0UCqVqpq12ixikiSp5s3OztLZ2Ul9fT0AHR0dCSeqDouYJEnatq1mrnRovGpSkiTVvPb2diYmJqhUKpTLZSYnJ5OOVBXOiEmSpJqXyWTI5/M0NzeTSqVoa2tLOlJVhBhj0hm21NraGufn55OOIUnSEWlxcZGmpqakY9Skzb6bEMJCjLF1O/t7aFKSJCkhFjFJkqSEWMQkSZISYhGTJElKyI4VsRBCfwjh1hBCaf3n2futK4YQvhlCuCGE8Fs7lUGSJKmW7fTtK94SYxza/40Qwi8BLwTSwOnATAjhyTHGB3c4iyRJUk1J4tDkc4GxGOP9McbvAN8EnpZADkmStEv19/czNDREX18fMzMzwNpjkNLpNC0tLVQqFQqFAul0mkKhkHDaA9vpGbFXhhBeAswDfxJj/AFwBvC5/ba5Zf09SZKkQzIwMLCxPDo6SrFYpLu7G4Dh4WGWlpaoq6tLKt6Wfq4iFkKYAU7bZNWfAn8HvB6I67/fDFx2CGNfDlwOcNZZZ/08MSVJ0qPA4OAgIyMjpFIpGhsbyWaz9PT0kMvlWF5eZnx8nOnpaaampiiXy6ysrJDNZikWi+Tz+aTjb+rnKmIxxmdtZ7sQwjuBj66/vBVo3G/1mevvPXLsYWAY1u6s//PklCRJVTJ1Fdz+leqOedr5cOkbDrrJwsICY2NjlEolVldXyWQyZLPZjfW9vb3Mzc2Ry+Xo6uoCoKGhgVKpVN2sVbaTV00+br+XncB168sfAV4YQjgmhHAO8CTg33cqhyRJ2v1mZ2fp7Oykvr6evXv30tHRkXSkqtjJc8T+MoTQwtqhyRuB3weIMX41hDAOfA1YBf6rV0xKkrRLbDFzpUOzYzNiMcYXxxjPjzE+NcbYEWO8bb91gzHGJ8YYz40xTu1UBkmS9OjQ3t7OxMQElUqFcrnM5ORk0pGqYqevmpQkSfq5ZTIZ8vk8zc3NpFIp2trako5UFSHG2j8PvrW1Nc7PzycdQ5KkI9Li4iJNTU1Jx6hJm303IYSFGGPrdvb3WZOSJEkJsYhJkiQlxCImSZKUEIuYJElSQixikiRJCbGISZIkJcQiJkmSdp3+/n6Ghobo6+tjZmYGWHsMUjqdpqWlhUqlQqFQIJ1OUygUEk57YN7QVZIk7VoDAwMby6OjoxSLRbq7uwEYHh5maWmJurq6pOJtySImSZJ2hcHBQUZGRkilUjQ2NpLNZunp6SGXy7G8vMz4+DjT09NMTU1RLpdZWVkhm81SLBbJ5/NJx9+URUySJG3bG//9jVy/dH1Vxzxv33lc+bQrD7rNwsICY2NjlEolVldXyWQyZLPZjfW9vb3Mzc2Ry+Xo6uoCoKGhgVKpVNWs1WYRkyRJNW92dpbOzk7q6+sB6OjoSDhRdVjEJEnStm01c6VD41WTkiSp5rW3tzMxMUGlUqFcLjM5OZl0pKpwRkySJNW8TCZDPp+nubmZVCpFW1tb0pGqIsQYk86wpdbW1jg/P590DEmSjkiLi4s0NTUlHaMmbfbdhBAWYoyt29nfQ5OSJEkJsYhJkiQlxCImSZKUEIuYJElSQixikiRJCbGISZIkJcQiJkmSdp3+/n6Ghobo6+tjZmYGWHsMUjqdpqWlhUqlQqFQIJ1OUygUEk57YN7QVZIk7VoDAwMby6OjoxSLRbq7uwEYHh5maWmJurq6pOJtySImSZJ2hcHBQUZGRkilUjQ2NpLNZunp6SGXy7G8vMz4+DjT09NMTU1RLpdZWVkhm81SLBbJ5/NJx9+URUySJG3b7X/+59y/eH1Vxzym6TxOe/WrD7rNwsICY2NjlEolVldXyWQyZLPZjfW9vb3Mzc2Ry+Xo6uoCoKGhgVKpVNWs1WYRkyRJNW92dpbOzk7q6+sB6OjoSDhRdVjEJEnStm01c6VD41WTkiSp5rW3tzMxMUGlUqFcLjM5OZl0pKpwRkySJNW8TCZDPp+nubmZVCpFW1tb0pGqIsQYk86wpdbW1jg/P590DEmSjkiLi4s0NTUlHaMmbfbdhBAWYoyt29nfQ5OSJEkJsYhJkiQlxCImSZKUEIuYJElSQixikiRJCbGISZIkJcQiJkmSdp3+/n6Ghobo6+tjZmYGWHsMUjqdpqWlhUqlQqFQIJ1OUygUEk57YN7QVZIk7VoDAwMby6OjoxSLRbq7uwEYHh5maWmJurq6pOJtySImSZJ2hcHBQUZGRkilUjQ2NpLNZunp6SGXy7G8vMz4+DjT09NMTU1RLpdZWVkhm81SLBbJ5/NJx9+URUySJG3b7PjXufPmlaqOeUpjAxe+4MkH3WZhYYGxsTFKpRKrq6tkMhmy2ezG+t7eXubm5sjlcnR1dQHQ0NBAqVSqatZqs4hJkqSaNzs7S2dnJ/X19QB0dHQknKg6LGKSJGnbtpq50qHxqklJklTz2tvbmZiYoFKpUC6XmZycTDpSVTgjJkmSal4mkyGfz9Pc3EwqlaKtrS3pSFURYozVHzSEfwLOXX95IrAcY2wJIZwNLAI3rK/7XIzxiq3Ga21tjfPz81XPKUmStra4uEhTU1PSMWrSZt9NCGEhxti6nf13ZEYsxrhxjWgI4c3A3fut/laMsWUnPleSJGk32dFDkyGEALwA+P928nMkSZJ2o50+Wf9C4Hsxxm/s9945IYT/CCF8OoRw4YF2DCFcHkKYDyHM33HHHTscU5Ik6fD7mWfEQggzwGmbrPrTGOOH15d/F/jH/dbdBpwVY7wrhJAFJkII6RjjPY8cJMY4DAzD2jliP2tOSZKkWvUzF7EY47MOtj6EcDTwPGDjtrcxxvuB+9eXF0II3wKeDHgmviRJOuLs5KHJZwHXxxhvefiNEMKpIYS69eUnAE8Cvr2DGSRJkmrWThaxF/KThyUB2oEvhxBKwAeAK2KMSzuYQZIkPQr19/czNDREX18fMzMz/6+9+4+Nu77vOP785BwbjrNS2eYSTEzTETKcO7Djc7ZJi6KWbbSgm1sPr26HNwwzRKL9AwWd6HXCiwyWGinrqCY01aMVRjJzrKRxyaiTxRViNtK2xuwiDOZHBoYAMflxduaLz8Tn++yPu7hpsGMnvvPXP16Pf/je53Pf+77z0Zfolc/3xwdILYPk8/koLy8nHo8TCoXw+XyEQiGHq51Z1p6atNbWT9O2H9ifrWOKiIjIytLU1DS13dbWRjgcpq6uDoCWlhai0Sgul8up8malN+uLiIjIktDc3Exrayter5eSkhICgQD19fUEg0FGRkbo6Ojg8OHDdHV1MTo6SiwWIxAIEA6Hqa2tnf0ADlAQExERkTl75fkWTn2Y2du7vV/+Pb5W/8gVv9PX10d7ezuRSIREIkFFRQWBwNTzgDQ0NNDb20swGKSmpgYAj8dDJBLJaK2ZpiAmIiIii15PTw/V1dW43W4AqqqqHK4oMxTEREREZM5mm7mSq5PtN+uLiIiIzNv27dvp7OwkHo8zOjrKwYMHnS4pIzQjJiIiIoteRUUFtbW1lJWV4fV62bp1q9MlZYSxdvGvHlRZWWmPHtXL90VERJwwMDBAaWmp02UsStONjTGmz1pbOZf9dWlSRERExCEKYiIiIiIOURATERERcYiCmIiIiIhDFMREREREHKIgJiIiIuIQBTERERFZcnbt2sWePXtobGyku7sbSC2D5PP5KC8vJx6PEwqF8Pl8hEIhh6udmV7oKiIiIktWU1PT1HZbWxvhcJi6ujoAWlpaiEajuFwup8qblYKYiIiILAnNzc20trbi9XopKSkhEAhQX19PMBhkZGSEjo4ODh8+TFdXF6Ojo8RiMQKBAOFwmNraWqfLn5aCmIiIiMzZyMH/5cKn5zP6m7nFN/ClP7/1it/p6+ujvb2dSCRCIpGgoqKCQCAw1d/Q0EBvby/BYJCamhoAPB4PkUgko7VmmoKYiIiILHo9PT1UV1fjdrsBqKqqcriizFAQExERkTmbbeZKro6emhQREZFFb/v27XR2dhKPxxkdHeXgwYNOl5QRmhETERGRRa+iooLa2lrKysrwer1s3brV6ZIywlhrna5hVpWVlfbo0aNOlyEiIrIiDQwMUFpa6nQZi9J0Y2OM6bPWVs5lf12aFBEREXGIgpiIiIiIQxTERERERByiICYiIiLiEAUxEREREYcoiImIiIg4REFMRERElpxdu3axZ88eGhsb6e7uBlLLIPl8PsrLy4nH44RCIXw+H6FQyOFqZ6YXuoqIiMiS1dTUNLXd1tZGOBymrq4OgJaWFqLRKC6Xy6nyZqUgJiIiIktCc3Mzra2teL1eSkpKCAQC1NfXEwwGGRkZoaOjg8OHD9PV1cXo6CixWIxAIEA4HKa2ttbp8qelICYiIiJz1tXVxdDQUEZ/c926ddxzzz1X/E5fXx/t7e1EIhESiQQVFRUEAoGp/oaGBnp7ewkGg9TU1ADg8XiIRCIZrTXTFMRERERk0evp6aG6uhq32w1AVVWVwxVlhoKYiIiIzNlsM1dydfTUpIiIiCx627dvp7Ozk3g8zujoKAcPHnS6pIzQjJiIiIgsehUVFdTW1lJWVobX62Xr1q1Ol5QRxlrrdA2zqqystEePHnW6DBERkRVpYGCA0tJSp8tYlKYbG2NMn7W2ci7769KkiIiIiEMUxEREREQcoiAmIiIi4hAFMRERERGHKIiJiIiIOERBTERERMQhCmIiIiKy5OzatYs9e/bQ2NhId3c3kFoGyefzUV5eTjweJxQK4fP5CIVCDlc7M73QVURERJaspqamqe22tjbC4TB1dXUAtLS0EI1GcblcTpU3q3nNiBlj/tIY86YxJmmMqbysL2yMOW6MeccY8/VL2r+RbjtujPnBfI4vIiIiK0dzczObNm1i27ZtvPPOOwDU19ezb98+nnvuOTo6OnjyySe5//77qaqqIhaLEQgE2Lt3r8OVz2y+M2L9wF8AP7200RizGfgO4AOKgW5jzKZ097PAnwEfA78xxrxkrX1rnnWIiIjIAnj33acYjQ1k9DfzPaVs2vTkFb/T19dHe3s7kUiERCJBRUUFgUBgqr+hoYHe3l6CwSA1NTUAeDweIpFIRmvNtHkFMWvtAIAx5vKubwLt1trPgQ+MMceBP0j3HbfWvp/erz39XQUxERERmVFPTw/V1dW43W4AqqqqHK4oM7J1j9jNwH9e8vnjdBvAicva/zBLNYiIiEiGzTZzJVdn1iBmjOkG1k3T9XfW2l9mvl5YSGIAAAeXSURBVKSp4z4CPJL+GDPGvJOtY12iCDizAMeR36Vxd4bG3Rkad2do3OfhyJEjd0xOTiaudr/Jyckcl8t11ftN56abblr17LPP5lVVVcUTiQT79u27vqamZuLs2bOrBgcHJ/v7+yfPnj2be3EbIJlMuvv7+8cycfyZDA0N5WzevPmNy5q/PNf9Zw1i1to/veqq4BOg5JLP69NtXKH98uO2AC3XcOxrZow5OtfV0iVzNO7O0Lg7Q+PuDI37/Bw7dmzQ7/dfdZDt7+8v9fv9GbmhzO/38/rrr6+rqakpKiwsnCgrKxv2eDxjubm51xcWFp7z+/3Dubm5Gy5up3fbkqnjz2RycrJoPudWti5NvgS8aIz5Mamb9W8D/hswwG3GmK+QCmDfAf4qSzWIiIjIMrJ79+6h3bt3D83Uv3///sFLP4+Njf1P1ouap3kFMWNMNfBPwI3Ay8aYiLX269baN40xHaRuwk8A37PWTqb3+T5wGHABP7fWvjmvP4GIiIjIEjXfpyYPAAdm6GsGmqdp/xXwq/kcN4sW9FKoTNG4O0Pj7gyNuzM07g4oKio67XQNi52WOLpE+r40WWAad2do3J2hcXeGxt0Z69at0wMSs1AQExEREXGI1poktewS8BNS9609Z639kcMlrQjGmEFgFJgEEnqiKXuMMT8HgsApa60/3VYA7AU2AIPAt621wzP9hlydGcZ8F/AwcPFyzQ/Tt2tIhhhjSoAXgLWABVqstT/R+Z5d4+Pjqz/44IOvJBKJ1QCFhYWni4uLT504caL47NmzRTk5OQmA4uLiTwoKCs45W+3isuJnxIwxLlLLLt0DbAa+m16iSRbG16y15QphWfc88I3L2n4A/Npaexvw6/RnyZzn+eKYA/xj+pwvVwjLigTwuLV2M/BHwPfSf6frfM8iYwzr16//+I477niztLR04MyZM97z589fB3DjjTd+5vf73/L7/W8phH3Rig9ipJZeOm6tfd9aewG4uOySyLJhrf0PIHpZ8zeB1vR2K/CtBS1qmZthzCXLrLUnrbWvp7dHgQFSK7vofM+ivLy8ifz8/DGAnJycZF5eXvzChQu52Tzmzp07ixsbG9c+9thjxZ2dnfkAhw4d8mzcuNF3++23b47FYmbHjh3rN27c6NuxY8f6bNYyH7o0mfofVMsuOcMC/26MscBPdTPtgltrrT2Z3h4idSlHsu/7xpi/AY6SmrnR5bEsMcZsALYA/4XO9wUzPj6eOz4+7s7Pz4/FYjHPmTNnvNFotNDtdo/dcsstJ1avXj2ZyeM988wzn17cfuGFFwp27tx58tFHH40CvPjii0XDw8ORnJzFG3cWb2WyEmyz1n5ijPECR4wxb6dnEWSBWWttOhBLdv0z8BSpf4Q8BfwD8JCjFS1TxhgPsB94zFr7f8aYqT6d79mTSCRWHT9+/Nabb775RE5OTnLt2rWn1q9f/ynAiRMnbv7oo49Kbr311sFr/f0nnnhi3d69e4sKCwsniouLL2zZsmXsvvvu2xAMBs8NDw+7Xn755YJXX311zaFDh9bEYjHX2NiYy+/3b3788cdPPvzww4vyHz0KYldejkmyyFr7Sfq/p4wxB0hdJlYQWzifGWNustaeNMbcBJxyuqDlzlr72cVtY8y/AP/mYDnLljFmNakQ1mat/UW6Wed7hjw28FHJ2+fH3V/ssUyOj19vXDmJVePRtQxGf3fW0SbN5Pj49a7hd/Mu3/P2G64be6b0lhOXt1+qp6fHfeDAgYI33njjrYmJCcrLyzdv2bJlah3JnTt3nnnttdc8wWDw3IMPPjgM4Ha7t7z99ttvXeMfdUHoHjH4Delll4wxuaSWXXrJ4ZqWPWPMDcaY/IvbwN1Av7NVrTgvAQ+ktx8AfulgLStCOgBcVI3O+YwzqamvnwED1tofX9Kl8z2rLMnPP7/OrFqVXLV69cRvm5NTU5HJxGSOWbUqea1HeOWVVzz33nvvSH5+frKgoCB59913j8yz6EVhxc+IWWsTWnbJEWuBA+nLBTnAi9baQ86WtHwZY/4V+CpQZIz5GPh74EdAhzHmb4EPgW87V+HyM8OYf9UYU07q0uQgsMOxApevPwb+GnjDGBNJt/0Qne8ZM93M1blz5zzvvffe7+fl5cWNueCC1KsqotFoQTwevx4gNz93fMOGjR/m5eVNXL7/Srbigxgs+mWXliVr7ftAmdN1rBTW2u/O0PUnC1rICjLDmP9swQtZYay1vYCZoVvne5asWbMmVllZ2Xd5eyZfV3HXXXfFHnrooQ1PP/30yYmJCXPkyJEvPfDAA0t+CSUFMREREVn0tm3bNlZdXR31+/2+wsLCiTvvvPO80zVlgrFWD46IiIjIzI4dOzZYVlamdSOncezYsaKysrIN17q/btYXERERcYiCmIiIiIhDFMREREREHKIgJiIiIrNJJpPJmZ5GXbHSY3LN70YDBTERERGZXf/p06fXKIz9VjKZNKdPn17DPF/MrNdXiIiIyBUlEomGoaGh54aGhvxoEueiJNCfSCQa5vMjen2FiIiIiEOUakVEREQcoiAmIiIi4hAFMRERERGHKIiJiIiIOERBTERERMQh/w9BpxDMIxwtyAAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 3.920498283945505 \n", + "\n", + "\n" + ] + }, + { + "ename": "InvalidArgumentError", + "evalue": "Reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero\n\t [[{{node model_41/flatten/Reshape}}]]\n\t [[{{node vad_out_13/concat}}]]", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0mvad_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msamples6\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mno_100\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;32melif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mno_140\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mno_160\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mvad_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msamples7\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m-\u001b[0m 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file_path)\u001b[0m\n\u001b[1;32m 7\u001b[0m X_concat_tensor = np.concatenate([mag_L_instances_sub , phase_L_instances_sub , \n\u001b[1;32m 8\u001b[0m mag_R_instances_sub , phase_R_instances_sub], axis = -1)\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mvad_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_concat_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0mvad_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvad_pred\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mpred\u001b[0m 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\u001b[0mreset_metrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.conda/envs/tf.kr/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mmodel_iteration\u001b[0;34m(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps, mode, validation_in_fit, **kwargs)\u001b[0m\n\u001b[1;32m 327\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 328\u001b[0m \u001b[0;31m# Get outputs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 329\u001b[0;31m \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m 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run_metadata=self.run_metadata)\n\u001b[0m\u001b[1;32m 3077\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_fetch_callbacks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3078\u001b[0m return nest.pack_sequence_as(self._outputs_structure,\n", + "\u001b[0;32m~/.conda/envs/tf.kr/lib/python3.7/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1437\u001b[0m ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m 1438\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m 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to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mInvalidArgumentError\u001b[0m: Reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero\n\t [[{{node model_41/flatten/Reshape}}]]\n\t [[{{node vad_out_13/concat}}]]" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "rmse_list = []\n", + "k = 0\n", + "\n", + "for i in val_list:\n", + " if(i < 50):\n", + " vad_pred, pred = predict(model, samples1[i])\n", + " elif((i >= 50) & (i < no_20)):\n", + " vad_pred, pred = predict(model, samples2[i - 50])\n", + " elif((i >= no_20) & (i < no_40)):\n", + " vad_pred, pred = predict(model, samples3[i - no_20])\n", + " elif((i >= no_40) & (i < no_80)):\n", + " vad_pred, pred = predict(model, samples4[i - no_40])\n", + " elif((i >= no_80) & (i < no_100)):\n", + " vad_pred, pred = predict(model, samples5[i - no_80])\n", + " elif((i >= no_100) & (i < no_140)):\n", + " vad_pred, pred = predict(model, samples6[i - no_100])\n", + " elif((i >= no_140) & (i < no_160)):\n", + " vad_pred, pred = predict(model, samples7[i - no_140])\n", + " error = label_instances[i].reshape(-1, 1) - pred\n", + " plt.figure(figsize=(10, 8))\n", + " plt.title('%d degree' %(edge_list[k]))\n", + " plt.plot(range(0, len(label_instances[i])), label_instances[i], label='actual')\n", + " plt.plot(range(0, len(pred)), pred, label='predict')\n", + " #plt.plot(range(0, len(error)), error, label='diff')\n", + " plt.ylim(-100, 100)\n", + " plt.legend()\n", + " plt.show()\n", + " \n", + " for j in range(0, len(error)):\n", + " if((vad_label_instances[i][j] == 0) & (vad_pred[j] != 0) | ((vad_label_instances[i][j] != 0) & (vad_pred[j] == 0))):\n", + " error[j] = 180\n", + " \n", + " rmse = np.sqrt((error ** 2).mean())\n", + " rmse_list.append(rmse)\n", + " print('RMSE:', rmse, '\\n\\n')\n", + " k = k + 1\n", + " \n", + "print('total_rmse:', np.array(rmse_list).mean())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Indices with : 0~11(Class-1), 12~24(Class-2), 25~37(Class-3), 38~49(Class-4) and Class-0 is assigned to the noise\n", + "\n", + "# 모델1. (2D CNN + Bidirectional GRU) based Siamese Network\n", + "\n", + "`X_train` has the shape (batch_size, height, width, channels). The first and the Second channels pertains to the left, and the third and fourth is about the right channel." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.layers import TimeDistributed, Bidirectional\n", + "from tensorflow.keras.layers import Conv2D, Conv1D, MaxPooling2D, MaxPooling1D, Input, Flatten, Dropout\n", + "from tensorflow.keras import layers, models\n", + "from tensorflow.keras import regularizers\n", + "from tensorflow.keras.utils import multi_gpu_model\n", + "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\n", + "from sklearn.metrics import confusion_matrix, classification_report\n", + "\n", + "print('Training Set Data Shape (X_train, y_train, y_train_vad) : ', X_train.shape, y_train.shape, y_train_vad.shape)\n", + "print('Validation Set Data Shape (X_val, y_val, y_val_vad) : ', X_val.shape, y_val.shape, y_val_vad.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# reset_keras_session(100)\n", + "\n", + "if 'siamese_classifier' in locals():\n", + " del siamese_classifier\n", + " \n", + "input_spectrogram = Input(shape=(X_train.shape[1], X_train.shape[2], int(X_train.shape[3]/2) ) )\n", + "\n", + "conv_1 = Conv2D(64, (3, 3), activation='relu', padding='same')(input_spectrogram)\n", + "conv_1_pool = MaxPooling2D((3, 2))(conv_1)\n", + "\n", + "conv_2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv_1_pool)\n", + "conv_2_pool = MaxPooling2D((3, 2))(conv_2)\n", + "\n", + "conv_3 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv_2_pool)\n", + "conv_3_pool = MaxPooling2D((3, 2))(conv_3)\n", + "\n", + "conv_4 = Conv2D(64, (3, 3), activation='elu', padding='same')(conv_3_pool)\n", + "conv_4_pool = MaxPooling2D((3, 2))(conv_4)\n", + "\n", + "shape_conv_4_pool = conv_4_pool.get_shape().as_list() # (None, height, width, channel)\n", + "conv_5 = Conv2D(128, (shape_conv_4_pool[1], 1), padding='valid', activation='relu')(conv_4_pool)\n", + "# 앞의 conv_5 필터 수를 256 으로 늘리면 학습이 잘 진행되지 않음 \n", + "\n", + "shape_conv_5 = conv_5.get_shape().as_list()\n", + "reshaped = layers.Reshape((shape_conv_5[2], shape_conv_5[3]))(conv_5) # reshape to (timesteps, features) explicitly \n", + "bgru = Bidirectional(layers.GRU(units=128))(reshaped) # GRU units 의 수를 늘리면? \n", + "\n", + "fc1 = layers.Dense(128, activation='elu')(bgru)\n", + "fc1_drop = Dropout(0.5)(fc1)\n", + "\n", + "dense_out = layers.Dense(32, kernel_regularizer=regularizers.l2(0.01), activation='linear')(fc1_drop)\n", + "\n", + "feature_extract_model = models.Model(inputs=input_spectrogram, outputs=dense_out, name='feat_ext_model')\n", + "\n", + "print(feature_extract_model.summary())\n", + "# show_model_graph(feature_extract_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input_2 (InputLayer) (None, 512, 100, 2) 0 \n", + "__________________________________________________________________________________________________\n", + "input_3 (InputLayer) (None, 512, 100, 2) 0 \n", + "__________________________________________________________________________________________________\n", + "feat_ext_model (Model) (None, 32) 395680 input_2[0][0] \n", + " input_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "Merge (Concatenate) (None, 64) 0 feat_ext_model[1][0] \n", + " feat_ext_model[2][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_1 (Dropout) (None, 64) 0 Merge[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_2 (Dense) (None, 32) 2080 dropout_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_3 (Dense) (None, 11) 363 dense_2[0][0] \n", + "==================================================================================================\n", + "Total params: 398,123\n", + "Trainable params: 398,123\n", + "Non-trainable params: 0\n", + "__________________________________________________________________________________________________\n", + "None\n" + ] + } + ], + "source": [ + "X_L_train = X_train[:,:,:,:2]\n", + "X_R_train = X_train[:,:,:, 2:]\n", + "\n", + "X_L_val = X_val[:,:,:,:2]\n", + "X_R_val = X_val[:,:,:,2:]\n", + "\n", + "left_inputs_inst = Input(shape=(X_L_train.shape[1], X_L_train.shape[2], X_L_train.shape[3]))\n", + "right_inputs_inst = Input(shape=(X_R_train.shape[1], X_R_train.shape[2], X_R_train.shape[3]))\n", + "\n", + "left_ext_features = feature_extract_model(left_inputs_inst)\n", + "right_ext_features = feature_extract_model(right_inputs_inst)\n", + "\n", + "merged_responses = keras.layers.concatenate([left_ext_features, right_ext_features], axis=-1,\n", + " name='Merge')\n", + "merge_drop = Dropout(0.5)(merged_responses)\n", + "\n", + "fc = layers.Dense(32, activation='selu')(merge_drop)\n", + "\n", + "# out = layers.Dense(11, activation='softmax')(fc)\n", + "class_out = layers.Dense(11, activation='softmax', name='class_out')(fc)\n", + "vad_out = layers.Dense(1, activation='sigmoid', name='vad_out')(fc)\n", + "\n", + "#siamese_classifier = models.Model(inputs=[left_inputs_inst, right_inputs_inst], outputs=out)\n", + "#siamese_classifier = models.Model(inputs=[left_inputs_inst, right_inputs_inst], outputs=out)\n", + "siamese_classifier = models.Model(inputs=[left_inputs_inst, right_inputs_inst], outputs=[vad_out, class_out])\n", + "\n", + "\n", + "print(siamese_classifier.summary())\n", + "#show_model_graph(siamese_classifier)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 23053 samples, validate on 7809 samples\n", + "Epoch 1/100\n", + "23053/23053 [==============================] - 142s 6ms/sample - loss: 1.4687 - acc: 0.7767 - val_loss: 1.4274 - val_acc: 0.7336\n", + "Epoch 2/100\n", + "23053/23053 [==============================] - 150s 6ms/sample - loss: 1.0969 - acc: 0.7907 - val_loss: 1.1938 - val_acc: 0.7336\n", + "Epoch 3/100\n", + "23053/23053 [==============================] - 147s 6ms/sample - loss: 0.9874 - acc: 0.7891 - val_loss: 1.1554 - val_acc: 0.7336\n", + "Epoch 4/100\n", + "23053/23053 [==============================] - 148s 6ms/sample - loss: 0.9194 - acc: 0.7897 - val_loss: 1.1013 - val_acc: 0.7336\n", + "Epoch 5/100\n", + "23053/23053 [==============================] - 151s 7ms/sample - loss: 0.8638 - acc: 0.7916 - val_loss: 1.0424 - val_acc: 0.7336\n", + "Epoch 6/100\n", + "23053/23053 [==============================] - 149s 6ms/sample - loss: 0.8378 - acc: 0.7917 - val_loss: 1.0248 - val_acc: 0.7336\n", + "Epoch 7/100\n", + "23053/23053 [==============================] - 152s 7ms/sample - loss: 0.9185 - acc: 0.7851 - val_loss: 1.1923 - val_acc: 0.7336\n", + "Epoch 8/100\n", + "23053/23053 [==============================] - 156s 7ms/sample - loss: 0.9468 - acc: 0.7925 - val_loss: 1.1663 - val_acc: 0.7336\n", + "Epoch 9/100\n", + "23053/23053 [==============================] - 150s 6ms/sample - loss: 0.9227 - acc: 0.7919 - val_loss: 1.1368 - val_acc: 0.7336\n", + "Epoch 10/100\n", + "23053/23053 [==============================] - 150s 7ms/sample - loss: 0.9051 - acc: 0.7894 - val_loss: 1.0820 - val_acc: 0.7336\n", + "Epoch 11/100\n", + "23053/23053 [==============================] - 150s 7ms/sample - loss: 0.8941 - acc: 0.7875 - val_loss: 1.0680 - val_acc: 0.7336\n", + "Epoch 12/100\n", + "23053/23053 [==============================] - 140s 6ms/sample - loss: 0.8318 - acc: 0.7877 - val_loss: 1.0478 - val_acc: 0.7336\n", + "Epoch 13/100\n", + "23053/23053 [==============================] - 138s 6ms/sample - loss: 0.8326 - acc: 0.7890 - val_loss: 1.0202 - val_acc: 0.7336\n", + "Epoch 14/100\n", + "23053/23053 [==============================] - 155s 7ms/sample - loss: 0.8034 - acc: 0.7882 - val_loss: 1.0075 - val_acc: 0.7336\n", + "Epoch 15/100\n", + "23053/23053 [==============================] - 150s 7ms/sample - loss: 0.7735 - acc: 0.7895 - val_loss: 1.0023 - val_acc: 0.7266\n", + "Epoch 16/100\n", + "23053/23053 [==============================] - 151s 7ms/sample - loss: 0.7564 - acc: 0.7877 - val_loss: 0.9712 - val_acc: 0.7265\n", + "Epoch 17/100\n", + "23053/23053 [==============================] - 150s 7ms/sample - loss: 0.7431 - acc: 0.7878 - val_loss: 0.9784 - val_acc: 0.7333\n", + "Epoch 18/100\n", + "23053/23053 [==============================] - 139s 6ms/sample - loss: 0.7372 - acc: 0.7887 - val_loss: 0.9613 - val_acc: 0.7279\n", + "Epoch 19/100\n", + "23053/23053 [==============================] - 140s 6ms/sample - loss: 0.7438 - acc: 0.7880 - val_loss: 0.9635 - val_acc: 0.7270\n", + "Epoch 20/100\n", + "23053/23053 [==============================] - 140s 6ms/sample - loss: 0.7331 - acc: 0.7873 - val_loss: 0.9479 - val_acc: 0.7336\n", + "Epoch 21/100\n", + "23053/23053 [==============================] - 152s 7ms/sample - loss: 0.7213 - acc: 0.7904 - val_loss: 0.9553 - val_acc: 0.7274\n", + "Epoch 22/100\n", + "23053/23053 [==============================] - 155s 7ms/sample - loss: 0.7247 - acc: 0.7900 - val_loss: 0.9435 - val_acc: 0.7315\n", + "Epoch 23/100\n", + "23053/23053 [==============================] - 155s 7ms/sample - loss: 0.7180 - acc: 0.7887 - val_loss: 0.9440 - val_acc: 0.7336\n", + "Epoch 24/100\n", + "23053/23053 [==============================] - 156s 7ms/sample - loss: 0.7150 - acc: 0.7899 - val_loss: 0.9269 - val_acc: 0.7265\n", + "Epoch 25/100\n", + "23053/23053 [==============================] - 150s 7ms/sample - loss: 0.7026 - acc: 0.7899 - val_loss: 0.9153 - val_acc: 0.7265\n", + "Epoch 26/100\n", + "23053/23053 [==============================] - 150s 7ms/sample - loss: 0.7119 - acc: 0.7874 - val_loss: 0.9179 - val_acc: 0.7267\n", + "Epoch 27/100\n", + "23053/23053 [==============================] - 147s 6ms/sample - loss: 0.7022 - acc: 0.7893 - val_loss: 0.9250 - val_acc: 0.7336\n", + "Epoch 28/100\n", + "23053/23053 [==============================] - 150s 6ms/sample - loss: 0.6926 - acc: 0.7895 - val_loss: 0.9143 - val_acc: 0.7336\n", + "Epoch 29/100\n", + "23053/23053 [==============================] - 149s 6ms/sample - loss: 0.7047 - acc: 0.7904 - val_loss: 0.9400 - val_acc: 0.7350\n", + "Epoch 30/100\n", + "23053/23053 [==============================] - 150s 7ms/sample - loss: 0.6940 - acc: 0.7919 - val_loss: 0.9275 - val_acc: 0.7336\n", + "Epoch 31/100\n", + "23053/23053 [==============================] - 149s 6ms/sample - loss: 0.6900 - acc: 0.7894 - val_loss: 0.9387 - val_acc: 0.7336\n", + "Epoch 32/100\n", + "23053/23053 [==============================] - 151s 7ms/sample - loss: 0.6955 - acc: 0.7919 - val_loss: 0.9316 - val_acc: 0.7336\n", + "Epoch 33/100\n", + "23053/23053 [==============================] - 151s 7ms/sample - loss: 0.6893 - acc: 0.7914 - val_loss: 0.9197 - val_acc: 0.7343\n", + "Epoch 34/100\n", + "23053/23053 [==============================] - 152s 7ms/sample - loss: 0.6904 - acc: 0.7909 - val_loss: 0.9170 - val_acc: 0.7284\n", + "Epoch 35/100\n", + "23053/23053 [==============================] - 151s 7ms/sample - loss: 0.6888 - acc: 0.7929 - val_loss: 0.9391 - val_acc: 0.7292\n", + "Epoch 36/100\n", + "23053/23053 [==============================] - 149s 6ms/sample - loss: 0.6913 - acc: 0.7914 - val_loss: 0.9172 - val_acc: 0.7325\n", + "Epoch 37/100\n", + "23053/23053 [==============================] - 149s 6ms/sample - loss: 0.6870 - acc: 0.7920 - val_loss: 0.9135 - val_acc: 0.7335\n", + "Epoch 38/100\n", + "23053/23053 [==============================] - 149s 6ms/sample - loss: 0.6800 - acc: 0.7944 - val_loss: 0.9107 - val_acc: 0.7335\n", + "Epoch 39/100\n", + "23053/23053 [==============================] - 150s 6ms/sample - loss: 0.6746 - acc: 0.7942 - val_loss: 0.9282 - val_acc: 0.7365\n", + "Epoch 40/100\n", + "23053/23053 [==============================] - 149s 6ms/sample - loss: 0.6850 - acc: 0.7937 - val_loss: 0.9176 - val_acc: 0.7321\n", + "Epoch 41/100\n", + "23053/23053 [==============================] - 149s 6ms/sample - loss: 0.6779 - acc: 0.7947 - val_loss: 0.9257 - val_acc: 0.7345\n", + "Epoch 42/100\n", + "23053/23053 [==============================] - 148s 6ms/sample - loss: 0.6712 - acc: 0.7942 - val_loss: 0.9077 - val_acc: 0.7333\n", + "Epoch 43/100\n", + "23053/23053 [==============================] - 153s 7ms/sample - loss: 0.6683 - acc: 0.7960 - val_loss: 0.9263 - val_acc: 0.7336\n", + "Epoch 44/100\n", + "23053/23053 [==============================] - 155s 7ms/sample - loss: 0.6782 - acc: 0.7940 - val_loss: 0.9240 - val_acc: 0.7381\n", + "Epoch 45/100\n", + "23053/23053 [==============================] - 155s 7ms/sample - loss: 0.6811 - acc: 0.7942 - val_loss: 0.9186 - val_acc: 0.7301\n", + "Epoch 46/100\n", + "23053/23053 [==============================] - 154s 7ms/sample - loss: 0.6666 - acc: 0.7977 - val_loss: 0.9162 - val_acc: 0.7326\n", + "Epoch 47/100\n", + "23053/23053 [==============================] - 155s 7ms/sample - loss: 0.6549 - acc: 0.7994 - val_loss: 0.9009 - val_acc: 0.7385\n", + "Epoch 48/100\n", + "23053/23053 [==============================] - 154s 7ms/sample - loss: 0.6557 - acc: 0.7967 - val_loss: 0.8917 - val_acc: 0.7353\n", + "Epoch 49/100\n", + "23053/23053 [==============================] - 156s 7ms/sample - loss: 0.6592 - acc: 0.8007 - val_loss: 0.9314 - val_acc: 0.7334\n", + "Epoch 50/100\n", + "23053/23053 [==============================] - 152s 7ms/sample - loss: 0.6557 - acc: 0.7995 - val_loss: 0.8996 - val_acc: 0.7402\n", + "Epoch 51/100\n", + "23053/23053 [==============================] - 156s 7ms/sample - loss: 0.6512 - acc: 0.7996 - val_loss: 0.9055 - val_acc: 0.7395\n", + "Epoch 52/100\n", + "23053/23053 [==============================] - 156s 7ms/sample - loss: 0.6530 - acc: 0.7978 - val_loss: 0.9128 - val_acc: 0.7335\n", + "Epoch 53/100\n", + "23053/23053 [==============================] - 149s 6ms/sample - loss: 0.6521 - acc: 0.8012 - val_loss: 0.8971 - val_acc: 0.7330\n", + "Epoch 54/100\n", + "23053/23053 [==============================] - 146s 6ms/sample - loss: 0.6484 - acc: 0.8012 - val_loss: 0.8961 - val_acc: 0.7362\n", + "Epoch 55/100\n", + "23053/23053 [==============================] - 145s 6ms/sample - loss: 0.6417 - acc: 0.8010 - val_loss: 0.9060 - val_acc: 0.7348\n", + "Epoch 56/100\n", + "23053/23053 [==============================] - 145s 6ms/sample - loss: 0.6552 - acc: 0.8001 - val_loss: 0.8930 - val_acc: 0.7339\n", + "Epoch 57/100\n", + "23053/23053 [==============================] - 145s 6ms/sample - loss: 0.6419 - acc: 0.8016 - val_loss: 0.9021 - val_acc: 0.7366\n", + "Epoch 58/100\n", + "23053/23053 [==============================] - 144s 6ms/sample - loss: 0.6342 - acc: 0.8049 - val_loss: 0.8904 - val_acc: 0.7370\n", + "Epoch 59/100\n", + "23053/23053 [==============================] - 156s 7ms/sample - loss: 0.6408 - acc: 0.8060 - val_loss: 0.8953 - val_acc: 0.7380\n", + "Epoch 60/100\n", + "23053/23053 [==============================] - 157s 7ms/sample - loss: 0.6462 - acc: 0.8020 - val_loss: 0.8801 - val_acc: 0.7402\n", + "Epoch 61/100\n", + "23053/23053 [==============================] - 154s 7ms/sample - loss: 0.6407 - acc: 0.8005 - val_loss: 0.8830 - val_acc: 0.7400\n", + "Epoch 62/100\n", + "23053/23053 [==============================] - 150s 7ms/sample - loss: 0.6316 - acc: 0.8010 - val_loss: 0.8912 - val_acc: 0.7345\n", + "Epoch 63/100\n", + "23053/23053 [==============================] - 149s 6ms/sample - loss: 0.6385 - acc: 0.8020 - val_loss: 0.8702 - val_acc: 0.7385\n", + "Epoch 64/100\n", + "23053/23053 [==============================] - 148s 6ms/sample - loss: 0.6223 - acc: 0.8071 - val_loss: 0.8826 - val_acc: 0.7350\n", + "Epoch 65/100\n", + "23053/23053 [==============================] - 148s 6ms/sample - loss: 0.6344 - acc: 0.8057 - val_loss: 0.9028 - val_acc: 0.7303\n", + "Epoch 66/100\n", + "23053/23053 [==============================] - 148s 6ms/sample - loss: 0.6305 - acc: 0.8055 - val_loss: 0.8879 - val_acc: 0.7353\n", + "Epoch 67/100\n", + "23053/23053 [==============================] - 149s 6ms/sample - loss: 0.6236 - acc: 0.8041 - val_loss: 0.8832 - val_acc: 0.7368\n", + "Epoch 68/100\n", + "23053/23053 [==============================] - 150s 7ms/sample - loss: 0.6172 - acc: 0.8088 - val_loss: 0.8566 - val_acc: 0.7366\n", + "Epoch 69/100\n", + "23053/23053 [==============================] - 150s 6ms/sample - loss: 0.6187 - acc: 0.8068 - val_loss: 0.8691 - val_acc: 0.7363\n", + "Epoch 70/100\n", + "23053/23053 [==============================] - 151s 7ms/sample - loss: 0.6352 - acc: 0.8036 - val_loss: 0.8720 - val_acc: 0.7321\n", + "Epoch 71/100\n", + "23053/23053 [==============================] - 139s 6ms/sample - loss: 0.6248 - acc: 0.8083 - val_loss: 0.8644 - val_acc: 0.7374\n", + "Epoch 72/100\n", + "23053/23053 [==============================] - 139s 6ms/sample - loss: 0.6658 - acc: 0.7999 - val_loss: 0.8622 - val_acc: 0.7416\n", + "Epoch 73/100\n", + "23053/23053 [==============================] - 138s 6ms/sample - loss: 0.6332 - acc: 0.8041 - val_loss: 0.8730 - val_acc: 0.7381\n", + "Epoch 74/100\n", + "23053/23053 [==============================] - 138s 6ms/sample - loss: 0.6305 - acc: 0.8056 - val_loss: 0.8895 - val_acc: 0.7298\n", + "Epoch 75/100\n", + "23053/23053 [==============================] - 138s 6ms/sample - loss: 0.6183 - acc: 0.8063 - val_loss: 0.8957 - val_acc: 0.7329\n", + "Epoch 76/100\n", + "23053/23053 [==============================] - 137s 6ms/sample - loss: 0.6142 - acc: 0.8090 - val_loss: 0.8672 - val_acc: 0.7380\n", + "Epoch 77/100\n", + "23053/23053 [==============================] - 137s 6ms/sample - loss: 0.6589 - acc: 0.8007 - val_loss: 0.9314 - val_acc: 0.7374\n", + "Epoch 78/100\n", + "23053/23053 [==============================] - 138s 6ms/sample - loss: 0.6443 - acc: 0.8006 - val_loss: 0.9184 - val_acc: 0.7345\n", + "Epoch 79/100\n", + "23053/23053 [==============================] - 137s 6ms/sample - loss: 0.6156 - acc: 0.8074 - val_loss: 0.8703 - val_acc: 0.7376\n", + "Epoch 80/100\n", + "23053/23053 [==============================] - 137s 6ms/sample - loss: 0.6206 - acc: 0.8081 - val_loss: 0.8820 - val_acc: 0.7394\n", + "Epoch 81/100\n", + "23053/23053 [==============================] - 137s 6ms/sample - loss: 0.6136 - acc: 0.8087 - val_loss: 0.9273 - val_acc: 0.7306\n", + "Epoch 82/100\n", + "23053/23053 [==============================] - 137s 6ms/sample - loss: 0.6281 - acc: 0.8061 - val_loss: 0.8770 - val_acc: 0.7358\n", + "Epoch 83/100\n", + "23053/23053 [==============================] - 154s 7ms/sample - loss: 0.6245 - acc: 0.8048 - val_loss: 0.9014 - val_acc: 0.7269\n", + "Epoch 84/100\n", + "23053/23053 [==============================] - 153s 7ms/sample - loss: 0.6207 - acc: 0.8089 - val_loss: 0.8948 - val_acc: 0.7411\n", + "Epoch 85/100\n", + "23053/23053 [==============================] - 155s 7ms/sample - loss: 0.6145 - acc: 0.8067 - val_loss: 0.8909 - val_acc: 0.7313\n", + "Epoch 86/100\n", + "23053/23053 [==============================] - 156s 7ms/sample - loss: 0.6140 - acc: 0.8092 - val_loss: 0.8852 - val_acc: 0.7394\n", + "Epoch 87/100\n", + "23053/23053 [==============================] - 155s 7ms/sample - loss: 0.6192 - acc: 0.8057 - val_loss: 0.9141 - val_acc: 0.7293\n", + "Epoch 88/100\n", + "23053/23053 [==============================] - 156s 7ms/sample - loss: 0.6146 - acc: 0.8109 - val_loss: 0.8782 - val_acc: 0.7370\n", + "Epoch 89/100\n", + "23053/23053 [==============================] - 156s 7ms/sample - loss: 0.6000 - acc: 0.8122 - val_loss: 0.8662 - val_acc: 0.7388\n", + "Epoch 90/100\n", + "23053/23053 [==============================] - 154s 7ms/sample - loss: 0.6198 - acc: 0.8067 - val_loss: 0.8938 - val_acc: 0.7356\n", + "Epoch 91/100\n", + "23053/23053 [==============================] - 149s 6ms/sample - loss: 0.6326 - acc: 0.8051 - val_loss: 0.8817 - val_acc: 0.7329\n", + "Epoch 92/100\n", + "23053/23053 [==============================] - 139s 6ms/sample - loss: 0.6210 - acc: 0.8069 - val_loss: 0.8677 - val_acc: 0.7338\n", + "Epoch 93/100\n", + "23053/23053 [==============================] - 139s 6ms/sample - loss: 0.6046 - acc: 0.8122 - val_loss: 0.8590 - val_acc: 0.7394\n", + "Epoch 94/100\n", + "23053/23053 [==============================] - 139s 6ms/sample - loss: 0.6102 - acc: 0.8100 - val_loss: 0.8921 - val_acc: 0.7353\n", + "Epoch 95/100\n", + "23053/23053 [==============================] - 139s 6ms/sample - loss: 0.6045 - acc: 0.8087 - val_loss: 0.8705 - val_acc: 0.7374\n", + "Epoch 96/100\n", + "23053/23053 [==============================] - 139s 6ms/sample - loss: 0.6281 - acc: 0.8096 - val_loss: 0.9291 - val_acc: 0.7347\n", + "Epoch 97/100\n", + "23053/23053 [==============================] - 139s 6ms/sample - loss: 0.6321 - acc: 0.8075 - val_loss: 0.8848 - val_acc: 0.7353\n", + "Epoch 98/100\n", + "23053/23053 [==============================] - 139s 6ms/sample - loss: 0.5966 - acc: 0.8139 - val_loss: 0.8586 - val_acc: 0.7402\n", + "Epoch 99/100\n", + "23053/23053 [==============================] - 138s 6ms/sample - loss: 0.6160 - acc: 0.8064 - val_loss: 0.8691 - val_acc: 0.7412\n", + "Epoch 100/100\n", + "23053/23053 [==============================] - 137s 6ms/sample - loss: 0.6086 - acc: 0.8124 - val_loss: 0.8901 - val_acc: 0.7384\n" + ] + } + ], + "source": [ + "from tensorflow.keras.utils import multi_gpu_model\n", + "siamese_classifier = multi_gpu_model(siamese_classifier, gpus=8)\n", + "\n", + "siamese_classifier.compile(optimizer ='adam',loss={'vad_out':'binary_crossentropy','class_out':'categorical_crossentropy'}, \n", + " metrics ={'vad_out':'acc','class_out':'acc'})\n", + "\n", + "callbacks_list = [keras.callbacks.EarlyStopping(monitor='val_loss', patience=20),\n", + " keras.callbacks.ModelCheckpoint(filepath='Best_SSL_STFT_siamese_2DConv_RNN_2output.h5', \n", + " monitor='val_loss', save_best_only=True, mode='auto'),\n", + " keras.callbacks.ReduceLROnPlateau(monitor='val_loss',\n", + " factor=0.1, patience=50)]\n", + "history = siamese_classifier.fit([X_L_train, X_R_train], [vad_train, y_train],\n", + " epochs=100, batch_size=64,\n", + " callbacks=callbacks_list,\n", + " validation_data=([X_L_val, X_R_val], [vad_val, vad_train]),\n", + " shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sklearn.metrics \n", + "best_model = keras.models.load_model(os.path.join('.', 'Best_SSL_STFT_siamese__2DConv_RNN_2output.h5'))\n", + "vad_pred, pred = best_model.predict(X_val)\n", + "\n", + "cm_vad = sklearn.metrics.confusion_matrix(np.argmax(vad_val, axis=1), \n", + " np.argmax(vad_pred, axis=1))\n", + "\n", + "cm_class = sklearn.metrics.confusion_matrix(np.argmax(y_val, axis=1), \n", + " np.argmax(pred, axis=1))\n", + "\n", + "acc_vad = sklearn.metrics.accuracy_score(np.argmax(vad_val, axis=1), \n", + " np.argmax(vad_pred, axis=1))\n", + "\n", + "acc_class = sklearn.metrics.accuracy_score(np.argmax(y_val, axis=1),\n", + " np.argmax(pred, axis=1))\n", + "\n", + "print(\"Accuracy : \", acc_vad)\n", + "cm_vad\n", + "print(\"Accuracy : \", acc_class)\n", + "cm_class" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy : 0.7365859905237546\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[5626, 0, 5, 0, 0, 0, 0, 0, 78, 0, 20],\n", + " [ 43, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " [ 177, 0, 10, 0, 0, 0, 0, 0, 8, 0, 0],\n", + " [ 757, 0, 11, 0, 0, 0, 0, 0, 2, 0, 0],\n", + " [ 54, 0, 2, 0, 0, 0, 0, 0, 0, 0, 9],\n", + " [ 299, 0, 0, 0, 0, 0, 0, 0, 20, 0, 0],\n", + " [ 85, 0, 0, 0, 0, 0, 0, 0, 78, 0, 0],\n", + " [ 38, 0, 0, 0, 0, 0, 0, 0, 0, 0, 57],\n", + " [ 64, 0, 0, 0, 0, 0, 0, 0, 97, 0, 0],\n", + " [ 131, 0, 0, 0, 0, 0, 0, 0, 118, 0, 0],\n", + " [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16]])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# You can restore the best saved model and compare :\n", + "import sklearn.metrics \n", + "best_model = keras.models.load_model(os.path.join('.', 'Best_SSL_STFT_Siamese_2DConv_RNN.h5'))\n", + "y_val_pred = best_model.predict([X_L_val, X_R_val])\n", + "cm = sklearn.metrics.confusion_matrix(np.argmax(y_val, axis=1), \n", + " np.argmax(y_val_pred, axis=1))\n", + "acc = sklearn.metrics.accuracy_score(np.argmax(y_val, axis=1),\n", + " np.argmax(y_val_pred, axis=1))\n", + "print(\"Accuracy : \", acc)\n", + "cm" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([172, 117, 167, 224, 152, 293, 235, 84, 95, 215])" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def predict_utterances(model, utt_inst_inputs, utt_inst_labels, test_indices) :\n", + " \"\"\"\n", + " utt_inst_inputs : ndarray. 50 elements. Each element of the shape: (frames, 513, 100, 4)\n", + " utt_inst_labels : ndarray. 50 elements. Each element of the shape: (frames,)\n", + " \"\"\"\n", + " labels_pred = []\n", + " \n", + " X = utt_inst_inputs[[test_indices]]\n", + " y = utt_inst_labels[[test_indices]]\n", + " \n", + " for i, x_utter in enumerate(X) : # for the instances in each utterance sample \n", + " x_L = x_utter[:, :, :, :2]\n", + " x_R = x_utter[:, :, :, 2:]\n", + " \n", + " labels_pred.append( np.argmax(model.predict([x_L, x_R]), axis=1) )\n", + " \n", + " return np.array(labels_pred)\n", + "\n", + "# Get predictions\n", + "test_idx = idx[-10:]\n", + "predictions=predict_utterances(best_model, total_instances_tensors, total_label_tensors, test_idx)\n", + "\n", + "test_idx" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(0, 0),\n", + " (0, 0),\n", + " (0, 0),\n", + " (0, 0),\n", + " (0, 0),\n", + " (8, 0),\n", + " (8, 0),\n", + " (8, 0),\n", + " (8, 8),\n", + " (8, 8),\n", + " (8, 8),\n", + " (8, 8),\n", + " (8, 8),\n", + " (8, 8),\n", + " (8, 8),\n", + " (8, 8),\n", + " (8, 8),\n", + " (8, 8),\n", + " (8, 8),\n", + " (8, 8),\n", + " (8, 8),\n", + " (8, 8),\n", + " (8, 8),\n", + " (8, 0),\n", + " (0, 0),\n", + " (0, 0),\n", + " (0, 0),\n", + " (0, 0)]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "index = 7\n", + "list(zip(total_label_tensors[test_idx][index], predictions[index]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Best Model의 Validation Set에 대한 Confusion Matrix 분석\n", + "- 특히 Class_0 (Noise)에 대한 판단에 오류가 많다. " + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 5729\n", + "1 47\n", + "2 195\n", + "3 770\n", + "4 65\n", + "5 319\n", + "6 163\n", + "7 95\n", + "8 161\n", + "9 249\n", + "10 16\n", + "dtype: int64" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 실제 ground truth 의 class 분포 \n", + "pd.Series(np.argmax(y_val, axis=1)).value_counts().sort_index() " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 7274\n", + "1 3\n", + "2 29\n", + "8 401\n", + "10 102\n", + "dtype: int64" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 예측의 class 분포\n", + "pd.Series(np.argmax(y_val_pred, axis=1)).value_counts().sort_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- class-0 (Noise)가 실제로 45개 그 중 35개만 맞춤. 특히 ground-true가 class-2 (60도) 인데, 이를 class-0 (noise)라 틀리게 분류한 것이 11개 \n", + "- class-0 noise를 class-3 또는 class-4라 잘 못 분류한 것이 10개" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 모델2. (2D CNN + 1D CNN) 기반 Siamese \n", + "- 앞서 만든 데이터셋 활용 : `X_L_train, X_R_train, y_train, X_L_val, X_R_val, y_val` " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "((23053, 512, 100, 2),\n", + " (23053, 512, 100, 2),\n", + " (23053, 11),\n", + " (7809, 512, 100, 2),\n", + " (7809, 512, 100, 2),\n", + " (7809, 11))" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#reset_keras_session()\n", + "\n", + "# 데이터 구조 확인\n", + "X_L_train.shape, X_R_train.shape, y_train.shape, X_L_val.shape, X_R_val.shape, y_val.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "input_4 (InputLayer) (None, 512, 100, 2) 0 \n", + "_________________________________________________________________\n", + "conv2d_5 (Conv2D) (None, 510, 98, 32) 608 \n", + "_________________________________________________________________\n", + "max_pooling2d_4 (MaxPooling2 (None, 170, 49, 32) 0 \n", + "_________________________________________________________________\n", + "conv2d_6 (Conv2D) (None, 168, 47, 64) 18496 \n", + "_________________________________________________________________\n", + "max_pooling2d_5 (MaxPooling2 (None, 56, 23, 64) 0 \n", + "_________________________________________________________________\n", + "conv2d_7 (Conv2D) (None, 56, 23, 128) 73856 \n", + "_________________________________________________________________\n", + "max_pooling2d_6 (MaxPooling2 (None, 18, 11, 128) 0 \n", + "_________________________________________________________________\n", + "conv2d_8 (Conv2D) (None, 18, 11, 256) 295168 \n", + "_________________________________________________________________\n", + "max_pooling2d_7 (MaxPooling2 (None, 6, 5, 256) 0 \n", + "_________________________________________________________________\n", + "conv2d_9 (Conv2D) (None, 1, 5, 512) 786944 \n", + "_________________________________________________________________\n", + "reshape_1 (Reshape) (None, 5, 512) 0 \n", + "_________________________________________________________________\n", + "conv1d (Conv1D) (None, 3, 1024) 1573888 \n", + "_________________________________________________________________\n", + "flatten (Flatten) (None, 3072) 0 \n", + "_________________________________________________________________\n", + "dense_4 (Dense) (None, 128) 393344 \n", + "_________________________________________________________________\n", + "dropout_2 (Dropout) (None, 128) 0 \n", + "_________________________________________________________________\n", + "dense_5 (Dense) (None, 32) 4128 \n", + "=================================================================\n", + "Total params: 3,146,432\n", + "Trainable params: 3,146,432\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "if 'siamese_classifier' in locals():\n", + " del siamese_classifier\n", + " \n", + "input_spectrogram = Input(shape=(X_train.shape[1], X_train.shape[2], int(X_train.shape[3]/2) ) )\n", + "\n", + "conv_1 = Conv2D(32, (3, 3), activation='relu', padding='valid')(input_spectrogram)\n", + "conv_1_pool = MaxPooling2D((3, 2))(conv_1)\n", + "\n", + "conv_2 = Conv2D(64, (3, 3), activation='relu', padding='valid')(conv_1_pool)\n", + "conv_2_pool = MaxPooling2D((3, 2))(conv_2)\n", + "\n", + "conv_3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv_2_pool)\n", + "conv_3_pool = MaxPooling2D((3, 2))(conv_3)\n", + "\n", + "conv_4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv_3_pool)\n", + "conv_4_pool = MaxPooling2D((3, 2))(conv_4)\n", + "\n", + "shape_conv_4_pool = conv_4_pool.get_shape().as_list() # (None, height, width, channel)\n", + "conv_5 = Conv2D(512, (shape_conv_4_pool[1], 1), padding='valid', activation='relu')(conv_4_pool)\n", + "\n", + "shape_conv_5 = conv_5.get_shape().as_list()\n", + "reshaped = layers.Reshape((shape_conv_5[2], shape_conv_5[3]))(conv_5) # reshape to (timesteps, features) explicitly \n", + "\n", + "conv_6 = Conv1D(1024, kernel_size=3, activation='relu')(reshaped)\n", + "flatten = layers.Flatten()(conv_6)\n", + "\n", + "fc1 = layers.Dense(128, activation='relu')(flatten)\n", + "fc1_drop = Dropout(0.5)(fc1)\n", + "\n", + "dense_out = layers.Dense(32, kernel_regularizer=regularizers.l2(0.01), activation='linear')(fc1_drop)\n", + "\n", + "feature_extract_model = models.Model(inputs=input_spectrogram, outputs=dense_out, name='feat_ext_model')\n", + "feature_extract_model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input_5 (InputLayer) (None, 512, 100, 2) 0 \n", + "__________________________________________________________________________________________________\n", + "input_6 (InputLayer) (None, 512, 100, 2) 0 \n", + "__________________________________________________________________________________________________\n", + "feat_ext_model (Model) (None, 32) 3146432 input_5[0][0] \n", + " input_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "Merge (Concatenate) (None, 64) 0 feat_ext_model[1][0] \n", + " feat_ext_model[2][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_3 (Dropout) (None, 64) 0 Merge[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_6 (Dense) (None, 32) 2080 dropout_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_7 (Dense) (None, 11) 363 dense_6[0][0] \n", + "==================================================================================================\n", + "Total params: 3,148,875\n", + "Trainable params: 3,148,875\n", + "Non-trainable params: 0\n", + "__________________________________________________________________________________________________\n", + "None\n" + ] + } + ], + "source": [ + "left_inputs_inst = Input(shape=(X_L_train.shape[1], X_L_train.shape[2], X_L_train.shape[3]))\n", + "right_inputs_inst = Input(shape=(X_R_train.shape[1], X_R_train.shape[2], X_R_train.shape[3]))\n", + "\n", + "left_ext_features = feature_extract_model(left_inputs_inst)\n", + "right_ext_features = feature_extract_model(right_inputs_inst)\n", + "\n", + "merged_responses = keras.layers.concatenate([left_ext_features, right_ext_features], axis=-1,\n", + " name='Merge')\n", + "merge_drop = Dropout(0.5)(merged_responses)\n", + "\n", + "fc = layers.Dense(32, activation='selu')(merge_drop)\n", + "\n", + "#out = layers.Dense(11, activation='softmax')(fc)\n", + "class_out = layers.Dense(11, activation='softmax', name='class_out')(fc)\n", + "vad_out = layers.Dense(1, activation='sigmoid', name='vad_out')(fc)\n", + "\n", + "siamese_classifier = models.Model(inputs=[left_inputs_inst, right_inputs_inst], outputs=[vad_out, class_out])\n", + "\n", + "print(siamese_classifier.summary())\n", + "#show_model_graph(siamese_classifier) " + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 23053 samples, validate on 7809 samples\n", + "Epoch 1/100\n", + "23053/23053 [==============================] - 103s 4ms/sample - loss: 1.2953 - acc: 0.7797 - val_loss: 1.2470 - val_acc: 0.7336\n", + "Epoch 2/100\n", + "23053/23053 [==============================] - 105s 5ms/sample - loss: 1.0139 - acc: 0.7925 - val_loss: 1.1916 - val_acc: 0.7336\n", + "Epoch 3/100\n", + "23053/23053 [==============================] - 101s 4ms/sample - loss: 0.9711 - acc: 0.7925 - val_loss: 1.1691 - val_acc: 0.7336\n", + "Epoch 4/100\n", + "23053/23053 [==============================] - 101s 4ms/sample - loss: 0.9509 - acc: 0.7925 - val_loss: 1.1590 - val_acc: 0.7336\n", + "Epoch 5/100\n", + "23053/23053 [==============================] - 100s 4ms/sample - loss: 0.9446 - acc: 0.7925 - val_loss: 1.1547 - val_acc: 0.7336\n", + "Epoch 6/100\n", + "23053/23053 [==============================] - 100s 4ms/sample - loss: 0.9407 - acc: 0.7925 - val_loss: 1.1522 - val_acc: 0.7336\n", + "Epoch 7/100\n", + "23053/23053 [==============================] - 100s 4ms/sample - loss: 0.9360 - acc: 0.7925 - val_loss: 1.1504 - val_acc: 0.7336\n", + "Epoch 8/100\n", + "23053/23053 [==============================] - 100s 4ms/sample - loss: 0.9360 - acc: 0.7925 - val_loss: 1.1494 - val_acc: 0.7336\n", + "Epoch 9/100\n", + "23053/23053 [==============================] - 100s 4ms/sample - loss: 0.9359 - acc: 0.7925 - val_loss: 1.1492 - val_acc: 0.7336\n", + "Epoch 10/100\n", + "23053/23053 [==============================] - 101s 4ms/sample - loss: 0.9339 - acc: 0.7925 - val_loss: 1.1494 - val_acc: 0.7336\n", + "Epoch 11/100\n", + "23053/23053 [==============================] - 100s 4ms/sample - loss: 0.9341 - acc: 0.7925 - val_loss: 1.1499 - val_acc: 0.7336\n", + "Epoch 12/100\n", + "23053/23053 [==============================] - 100s 4ms/sample - loss: 0.9340 - acc: 0.7925 - val_loss: 1.1499 - val_acc: 0.7336\n", + "Epoch 13/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 0.9327 - acc: 0.7925 - val_loss: 1.1508 - val_acc: 0.7336\n", + "Epoch 14/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 0.9336 - acc: 0.7925 - val_loss: 1.1505 - val_acc: 0.7336\n", + "Epoch 15/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 0.9322 - acc: 0.7925 - val_loss: 1.1508 - val_acc: 0.7336\n", + "Epoch 16/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 0.9314 - acc: 0.7925 - val_loss: 1.1511 - val_acc: 0.7336\n", + "Epoch 17/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 0.9312 - acc: 0.7925 - val_loss: 1.1518 - val_acc: 0.7336\n", + "Epoch 18/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 0.9312 - acc: 0.7925 - val_loss: 1.1522 - val_acc: 0.7336\n", + "Epoch 19/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 0.9307 - acc: 0.7925 - val_loss: 1.1532 - val_acc: 0.7336\n", + "Epoch 20/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 0.9304 - acc: 0.7925 - val_loss: 1.1532 - val_acc: 0.7336\n", + "Epoch 21/100\n", + "23053/23053 [==============================] - 100s 4ms/sample - loss: 0.9302 - acc: 0.7925 - val_loss: 1.1538 - val_acc: 0.7336\n", + "Epoch 22/100\n", + "23053/23053 [==============================] - 100s 4ms/sample - loss: 0.9299 - acc: 0.7925 - val_loss: 1.1542 - val_acc: 0.7336\n", + "Epoch 23/100\n", + "23053/23053 [==============================] - 100s 4ms/sample - loss: 0.9295 - acc: 0.7925 - val_loss: 1.1549 - val_acc: 0.7336\n", + "Epoch 24/100\n", + "23053/23053 [==============================] - 100s 4ms/sample - loss: 0.9295 - acc: 0.7925 - val_loss: 1.1555 - val_acc: 0.7336\n", + "Epoch 25/100\n", + "23053/23053 [==============================] - 100s 4ms/sample - loss: 0.9291 - acc: 0.7925 - val_loss: 1.1553 - val_acc: 0.7336\n", + "Epoch 26/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 0.9292 - acc: 0.7925 - val_loss: 1.1560 - val_acc: 0.7336\n", + "Epoch 27/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 0.9295 - acc: 0.7925 - val_loss: 1.1564 - val_acc: 0.7336\n", + "Epoch 28/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 0.9293 - acc: 0.7925 - val_loss: 1.1564 - val_acc: 0.7336\n", + "Epoch 29/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 0.9295 - acc: 0.7925 - val_loss: 1.1571 - val_acc: 0.7336\n", + "Epoch 30/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 0.9294 - acc: 0.7925 - val_loss: 1.1576 - val_acc: 0.7336\n", + "Epoch 31/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 0.9291 - acc: 0.7925 - val_loss: 1.1578 - val_acc: 0.7336\n", + "Epoch 32/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 0.9288 - acc: 0.7925 - val_loss: 1.1581 - val_acc: 0.7336\n", + "Epoch 33/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 0.9290 - acc: 0.7925 - val_loss: 1.1582 - val_acc: 0.7336\n", + "Epoch 34/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 0.9290 - acc: 0.7925 - val_loss: 1.1584 - val_acc: 0.7336\n", + "Epoch 35/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 0.9289 - acc: 0.7925 - val_loss: 1.1584 - val_acc: 0.7336\n", + "Epoch 36/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 0.9282 - acc: 0.7925 - val_loss: 1.1589 - val_acc: 0.7336\n", + "Epoch 37/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 0.9290 - acc: 0.7925 - val_loss: 1.1582 - val_acc: 0.7336\n", + "Epoch 38/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 0.9283 - acc: 0.7925 - val_loss: 1.1584 - val_acc: 0.7336\n", + "Epoch 39/100\n", + "23053/23053 [==============================] - 100s 4ms/sample - loss: 0.9285 - acc: 0.7925 - val_loss: 1.1583 - val_acc: 0.7336\n", + "Epoch 40/100\n", + "23053/23053 [==============================] - 100s 4ms/sample - loss: 0.9277 - acc: 0.7925 - val_loss: 1.1586 - val_acc: 0.7336\n", + "Epoch 41/100\n", + "23053/23053 [==============================] - 100s 4ms/sample - loss: 0.9288 - acc: 0.7925 - val_loss: 1.1587 - val_acc: 0.7336\n", + "Epoch 42/100\n", + "23053/23053 [==============================] - 101s 4ms/sample - loss: 0.9287 - acc: 0.7925 - val_loss: 1.1586 - val_acc: 0.7336\n", + "Epoch 43/100\n", + "23053/23053 [==============================] - 100s 4ms/sample - loss: 0.9280 - acc: 0.7925 - val_loss: 1.1585 - val_acc: 0.7336\n", + "Epoch 44/100\n", + "23053/23053 [==============================] - 100s 4ms/sample - loss: 0.9279 - acc: 0.7925 - val_loss: 1.1587 - val_acc: 0.7336\n", + "Epoch 45/100\n", + "23053/23053 [==============================] - 100s 4ms/sample - loss: 0.9284 - acc: 0.7925 - val_loss: 1.1594 - val_acc: 0.7336\n", + "Epoch 46/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 0.9281 - acc: 0.7925 - val_loss: 1.1584 - val_acc: 0.7336\n", + "Epoch 47/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 0.9282 - acc: 0.7925 - val_loss: 1.1589 - val_acc: 0.7336\n", + "Epoch 48/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 0.9286 - acc: 0.7925 - val_loss: 1.1588 - val_acc: 0.7336\n", + "Epoch 49/100\n", + "23053/23053 [==============================] - 99s 4ms/sample - loss: 0.9277 - acc: 0.7925 - val_loss: 1.1592 - val_acc: 0.7336\n", + "Epoch 50/100\n", + "23053/23053 [==============================] - 100s 4ms/sample - loss: 0.9290 - acc: 0.7925 - val_loss: 1.1591 - val_acc: 0.7336\n", + "Epoch 51/100\n", + "23053/23053 [==============================] - 100s 4ms/sample - loss: 0.9277 - acc: 0.7925 - val_loss: 1.1579 - val_acc: 0.7336\n", + "Epoch 52/100\n", + "23053/23053 [==============================] - 100s 4ms/sample - loss: 0.9284 - acc: 0.7925 - val_loss: 1.1584 - val_acc: 0.7336\n" + ] + } + ], + "source": [ + "#siamese_classifier.compile(optimizer ='adam',loss='categorical_crossentropy', metrics =['acc'])\n", + "\n", + "#callbacks_list = [keras.callbacks.EarlyStopping(monitor='acc', patience=50),\n", + "# keras.callbacks.ModelCheckpoint(filepath='Best_SSL_STFT_Siamese_2DConv_1DConv.h5', \n", + "# monitor='val_loss', save_best_only=True, mode='auto'),\n", + "# keras.callbacks.ReduceLROnPlateau(monitor='val_loss',\n", + "# factor=0.1, patience=50)]\n", + "#history = siamese_classifier.fit([X_L_train, X_R_train], y_train,\n", + "# epochs=100, batch_size=64, \n", + "# callbacks=callbacks_list,\n", + "# validation_data=([X_L_val, X_R_val], y_val),\n", + "# shuffle=False)\n", + "\n", + "from tensorflow.keras.utils import multi_gpu_model\n", + "siamese_classifier = multi_gpu_model(siamese_classifier, gpus=8)\n", + "\n", + "siamese_classifier.compile(optimizer ='adam',loss={'vad_out':'binary_crossentropy','class_out':'categorical_crossentropy'}, \n", + " metrics ={'vad_out':'acc','class_out':'acc'})\n", + "\n", + "callbacks_list = [keras.callbacks.EarlyStopping(monitor='val_loss', patience=20),\n", + " keras.callbacks.ModelCheckpoint(filepath='Best_SSL_STFT_siamese_2DConv_1DCNN_2output.h5', \n", + " monitor='val_loss', save_best_only=True, mode='auto'),\n", + " keras.callbacks.ReduceLROnPlateau(monitor='val_loss',\n", + " factor=0.1, patience=50)]\n", + "history = siamese_classifier.fit([X_L_train, X_R_train], [vad_train, y_train],\n", + " epochs=100, batch_size=64,\n", + " callbacks=callbacks_list,\n", + " validation_data=([X_L_val, X_R_val], [vad_val, vad_train]),\n", + " shuffle=False)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sklearn.metrics \n", + "best_model = keras.models.load_model(os.path.join('.', 'Best_SSL_STFT_siamese__2DConv_1DCNN_2output.h5'))\n", + "vad_pred, pred = best_model.predict(X_val)\n", + "\n", + "cm_vad = sklearn.metrics.confusion_matrix(np.argmax(vad_val, axis=1), \n", + " np.argmax(vad_pred, axis=1))\n", + "\n", + "cm_class = sklearn.metrics.confusion_matrix(np.argmax(y_val, axis=1), \n", + " np.argmax(pred, axis=1))\n", + "\n", + "acc_vad = sklearn.metrics.accuracy_score(np.argmax(vad_val, axis=1), \n", + " np.argmax(vad_pred, axis=1))\n", + "\n", + "acc_class = sklearn.metrics.accuracy_score(np.argmax(y_val, axis=1),\n", + " np.argmax(pred, axis=1))\n", + "\n", + "print(\"Accuracy : \", acc_vad)\n", + "cm_vad\n", + "print(\"Accuracy : \", acc_class)\n", + "cm_class" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy : 0.7336406710206173\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[5729, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " [ 47, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " [ 195, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " [ 770, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " [ 65, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " [ 319, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " [ 163, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " [ 95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " [ 161, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " [ 249, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " [ 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "best_model = keras.models.load_model(os.path.join('.', 'Best_SSL_STFT_Siamese_2DConv_1DConv.h5'))\n", + "y_val_pred = best_model.predict([X_L_val, X_R_val])\n", + "cm = sklearn.metrics.confusion_matrix(np.argmax(y_val, axis=1), \n", + " np.argmax(y_val_pred, axis=1))\n", + "acc = sklearn.metrics.accuracy_score(np.argmax(y_val, axis=1),\n", + " np.argmax(y_val_pred, axis=1))\n", + "print(\"Accuracy : \", acc)\n", + "cm" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([172, 117, 167, 224, 152, 293, 235, 84, 95, 215])" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predictions=predict_utterances(best_model, total_instances_tensors, total_label_tensors, test_idx)\n", + "test_idx" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(0, 0),\n", + " (0, 0),\n", + " (0, 0),\n", + " (0, 0),\n", + " (0, 0),\n", + " (8, 0),\n", + " (8, 0),\n", + " (8, 0),\n", + " (8, 0),\n", + " (8, 0),\n", + " (8, 0),\n", + " (8, 0),\n", + " (8, 0),\n", + " (8, 0),\n", + " (8, 0),\n", + " (8, 0),\n", + " (8, 0),\n", + " (8, 0),\n", + " (8, 0),\n", + " (8, 0),\n", + " (8, 0),\n", + " (8, 0),\n", + " (8, 0),\n", + " (8, 0),\n", + " (0, 0),\n", + " (0, 0),\n", + " (0, 0),\n", + " (0, 0)]" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "index = 7\n", + "list(zip(total_label_tensors[test_idx][index], predictions[index]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:tf.kr]", + "language": "python", + "name": "conda-env-tf.kr-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}