{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import os \n",
"os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\n",
"import pickle\n",
"\n",
"import tensorflow as tf\n",
"import matplotlib.pyplot as plt\n",
"from datetime import datetime\n",
"\n",
"from architectures.bicycle_GAN import *\n",
"from architectures.utils.toolbox import *"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"task='TEST'\n",
"\n",
"PATH='test12'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LEARNING_RATE:0.0004\n",
"BETA1:0.5\n",
"BATCH_SIZE:16\n",
"EPOCHS:3\n",
"SAVE_SAMPLE_PERIOD:400\n",
"SEED:1\n",
"d_sizes:{'conv_layers': [(8.0, 4, 2, False, 1, <function lrelu at 0x7fcab0a15ea0>, <tensorflow.python.ops.init_ops.TruncatedNormal object at 0x7fcaaacad9b0>), (16, 4, 2, 'bn', 0.8, <function lrelu at 0x7fcab0a15ea0>, <tensorflow.python.ops.init_ops.TruncatedNormal object at 0x7fcaaacadb70>), (32, 4, 2, 'bn', 1, <function lrelu at 0x7fcab0a15ea0>, <tensorflow.python.ops.init_ops.TruncatedNormal object at 0x7fcaaacadba8>), (64, 4, 2, 'bn', 0.8, <function lrelu at 0x7fcab0a15ea0>, <tensorflow.python.ops.init_ops.TruncatedNormal object at 0x7fcaaacadbe0>), (128, 4, 1, 'bn', 1, <function lrelu at 0x7fcab0a15ea0>, <tensorflow.python.ops.init_ops.TruncatedNormal object at 0x7fcaaacadc18>)], 'dense_layers': [(512, 'bn', 0.8, <function lrelu at 0x7fcab0a15ea0>, <tensorflow.python.ops.init_ops.TruncatedNormal object at 0x7fcaaacadc50>), (64, 'bn', 0.8, <function lrelu at 0x7fcab0a15ea0>, <tensorflow.python.ops.init_ops.TruncatedNormal object at 0x7fcaaacadc88>), (16, False, 0.8, <function lrelu at 0x7fcab0a15ea0>, <tensorflow.python.ops.init_ops.TruncatedNormal object at 0x7fcaaacadcc0>)], 'readout_layer_w_init': <tensorflow.python.ops.init_ops.TruncatedNormal object at 0x7fcaaacadcf8>}\n",
"g_sizes_dec:{'deconv_layers': [(64, 4, 2, 'bn', 1, <function lrelu at 0x7fcab0a15ea0>, <tensorflow.python.ops.init_ops.TruncatedNormal object at 0x7fcaaacadd30>), (32, 4, 2, 'bn', 0.8, <function lrelu at 0x7fcab0a15ea0>, <tensorflow.python.ops.init_ops.TruncatedNormal object at 0x7fcaaacadd68>), (16, 4, 2, 'bn', 1, <function lrelu at 0x7fcab0a15ea0>, <tensorflow.python.ops.init_ops.TruncatedNormal object at 0x7fcaaacadda0>), (8.0, 4, 2, 'bn', 1, <function lrelu at 0x7fcab0a15ea0>, <tensorflow.python.ops.init_ops.TruncatedNormal object at 0x7fcaaacaddd8>), (1, 4, 2, False, 0.8, <function identity at 0x7fcabdb328c8>, <tensorflow.python.ops.init_ops.TruncatedNormal object at 0x7fcaaacade10>)]}\n",
"g_sizes_enc:{'latent_dims': 128, 'conv_layers': [(8.0, 4, 2, False, 1, <function lrelu at 0x7fcab0a15ea0>, <tensorflow.python.ops.init_ops.TruncatedNormal object at 0x7fcaaacade48>), (16, 4, 2, 'bn', 0.8, <function lrelu at 0x7fcab0a15ea0>, <tensorflow.python.ops.init_ops.TruncatedNormal object at 0x7fcaaacade80>), (32, 4, 2, 'bn', 1, <function lrelu at 0x7fcab0a15ea0>, <tensorflow.python.ops.init_ops.TruncatedNormal object at 0x7fcaaacadeb8>), (64, 4, 2, 'bn', 1, <function lrelu at 0x7fcab0a15ea0>, <tensorflow.python.ops.init_ops.TruncatedNormal object at 0x7fcaaacadef0>), (128, 4, 2, 'bn', 0.8, <function lrelu at 0x7fcab0a15ea0>, <tensorflow.python.ops.init_ops.TruncatedNormal object at 0x7fcaaacadf28>)]}\n",
"e_sizes:{'latent_dims': 128, 'conv_layers': [(8.0, 4, 2, False, 1, <function lrelu at 0x7fcab0a15ea0>, <tensorflow.python.ops.init_ops.RandomNormal object at 0x7fcaaacadf98>), (16, 4, 2, 'bn', 1, <function lrelu at 0x7fcab0a15ea0>, <tensorflow.python.ops.init_ops.RandomNormal object at 0x7fcaaacadfd0>), (32, 4, 2, 'bn', 0.8, <function lrelu at 0x7fcab0a15ea0>, <tensorflow.python.ops.init_ops.RandomNormal object at 0x7fcaaacb1048>), (64, 4, 2, 'bn', 1, <function lrelu at 0x7fcab0a15ea0>, <tensorflow.python.ops.init_ops.RandomNormal object at 0x7fcaaacb1080>), (128, 4, 2, 'bn', 0.8, <function lrelu at 0x7fcab0a15ea0>, <tensorflow.python.ops.init_ops.RandomNormal object at 0x7fcaaacb10b8>)], 'dense_layers': [(256, 'bn', 0.8, <function lrelu at 0x7fcab0a15ea0>, <tensorflow.python.ops.init_ops.RandomNormal object at 0x7fcaaacb10f0>), (128, 'bn', 0.8, <function lrelu at 0x7fcab0a15ea0>, <tensorflow.python.ops.init_ops.RandomNormal object at 0x7fcaaacb1128>), (16, False, 0.8, <function lrelu at 0x7fcab0a15ea0>, <tensorflow.python.ops.init_ops.RandomNormal object at 0x7fcaaacb1160>)], 'readout_layer_w_init': <tensorflow.python.ops.init_ops.RandomNormal object at 0x7fcaaacb1198>}\n",
"cost_type:FEATURE\n",
"validating_size:1000\n",
"test_size:1000\n",
"n_batches:2\n",
"reco_path:/disk/lhcb_data/davide/HCAL_project_full_event/reco/\n",
"true_path:/disk/lhcb_data/davide/HCAL_project_full_event/true/\n",
"discr_steps:1\n",
"gen_steps:1\n",
"vae_steps:1\n",
"latent_weight:1\n",
"cycl_weight:1\n",
"kl_weight:1\n"
]
}
],
"source": [
"if task == 'TEST' and os.path.exists(PATH+'/hyper_parameters.pkl'):\n",
" with open(PATH+'/hyper_parameters.pkl', 'rb') as f: \n",
" hyper_dict = pickle.load(f)\n",
" for key, item in hyper_dict.items():\n",
" print(key+':'+str(item))\n",
" \n",
" reco_path = hyper_dict['reco_path']\n",
" true_path = hyper_dict['true_path']\n",
" #true_path_p = hyper_dict['true_path_p']\n",
" #true_path_K = hyper_dict['true_path_K']\n",
" n_batches = hyper_dict['n_batches']\n",
" test_size = hyper_dict['test_size']\n",
" LEARNING_RATE = hyper_dict['LEARNING_RATE']\n",
" BETA1 = hyper_dict['BETA1']\n",
" BATCH_SIZE = hyper_dict['BATCH_SIZE']\n",
" EPOCHS = hyper_dict['EPOCHS']\n",
" SAVE_SAMPLE_PERIOD = hyper_dict['SAVE_SAMPLE_PERIOD']\n",
" SEED = hyper_dict['SEED']\n",
" d_sizes = hyper_dict['d_sizes']\n",
" g_sizes_enc = hyper_dict['g_sizes_enc']\n",
" g_sizes_dec = hyper_dict['g_sizes_dec']\n",
" e_sizes = hyper_dict['e_sizes']\n",
" cost_type = hyper_dict['cost_type']\n",
" validating_size=hyper_dict['validating_size']\n",
" cycl_weight=hyper_dict['cycl_weight']\n",
" latent_weight=hyper_dict['latent_weight']\n",
" kl_weight=hyper_dict['kl_weight']\n",
" discr_steps=hyper_dict['discr_steps']\n",
" gen_steps=hyper_dict['gen_steps']\n",
" vae_steps=hyper_dict['vae_steps']\n",
" \n",
"\n",
"if task == 'TEST' and not os.path.exists(PATH+'/hyper_parameters.pkl'):\n",
" \n",
" print('Missing hyperparameter dictionary in save folder')\n",
" \n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"test_size=3000\n",
"n_batches=2\n",
"start_batch=2\n",
"\n",
"train_true, test_true, train_TIS_true, test_TIS_true, train_TOS_true, test_TOS_true, train_reco, test_reco = load_data(true_path, reco_path, n_batches, start_batch, test_size=test_size)\n",
" \n",
"train_true, train_reco = delete_undetected_events_double(train_true, train_reco)\n",
"test_true, test_reco = delete_undetected_events_double(test_true, test_reco)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3000, 52, 64, 1)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_true.shape"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 792x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#if preprocess != False:\n",
"draw_one_sample(train_true, train_reco,\n",
" min_true=min_true, max_true=max_true, \n",
" min_reco=min_reco, max_reco=max_reco,\n",
" save=False, PATH=PATH\n",
" )\n",
"#else:\n",
"# draw_one_sample(train_true,train_reco)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((37000, 52, 64, 1), (3000, 52, 64, 1))"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_true.shape, test_true.shape"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"h=test_reco[0].shape[0]\n",
"w=test_reco[0].shape[1]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"def HCAL():\n",
"\n",
" \n",
" tf.reset_default_graph()\n",
" \n",
" _, n_H_A, n_W_A ,n_C = train_true.shape\n",
" _, n_H_B, n_W_B ,n_C = train_reco.shape\n",
" \n",
" gan = bicycle_GAN(n_H_A, n_W_A, n_H_B, n_W_B, n_C,\n",
" min_true=min_true, max_true=max_true, \n",
" min_reco=min_reco, max_reco=max_reco,\n",
" d_sizes=d_sizes, g_sizes_enc=g_sizes_enc, g_sizes_dec=g_sizes_dec, e_sizes=e_sizes,\n",
" lr=LEARNING_RATE, beta1=BETA1,\n",
" cost_type=cost_type, cycl_weight=cycl_weight, latent_weight=latent_weight, kl_weight=kl_weight,\n",
" discr_steps=discr_steps, gen_steps=gen_steps, vae_steps=vae_steps,\n",
" batch_size=BATCH_SIZE, epochs=EPOCHS,\n",
" save_sample=SAVE_SAMPLE_PERIOD, path=PATH, seed= SEED)\n",
" \n",
" vars_D = [v for v in tf.trainable_variables() if 'discriminator' in v.name]\n",
" vars_G = [v for v in tf.trainable_variables() if 'generator' in v.name]\n",
" vars_E = [v for v in tf.trainable_variables() if 'encoder' in v.name]\n",
" \n",
" if task == 'TEST':\n",
" \n",
" vars_to_train=tf.trainable_variables()\n",
" vars_all = tf.global_variables()\n",
" vars_to_init = list(set(vars_all)-set(vars_to_train))\n",
" init_op = tf.variables_initializer(vars_to_init)\n",
" \n",
" saver=tf.train.Saver()\n",
" \n",
" # Add ops to save and restore all the variables.\n",
" \n",
" gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.15)\n",
" \n",
" with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:\n",
" \n",
" sess.run(init_op)\n",
" \n",
" if task=='TEST':\n",
" \n",
" print('\\n Evaluate model on test set...')\n",
" \n",
" #if os.path.exists(PATH+'/pretrained/'+PATH+'.ckpt.index'):\n",
" # saver.restore(sess,PATH+'/'+PATH+'pretrained.ckpt')\n",
" \n",
" if os.path.exists(PATH+'/'+PATH+'bicycle.ckpt.index'):\n",
" saver.restore(sess, PATH+'/'+PATH+'bicycle.ckpt')\n",
" \n",
" print('Model restored.')\n",
" \n",
" gan.set_session(sess)\n",
" \n",
" #test_reco_NN=gan.get_samples_A_to_B(test_true.reshape(test_true.shape[0],n_H_A,n_W_A,n_C))\n",
" test_reco_NN=np.zeros_like(test_true)\n",
" for i in range(len(test_true)):\n",
" test_reco_NN[i]=gan.get_sample_A_to_B(test_true[i].reshape(1,n_H_A,n_W_A,n_C))\n",
"\n",
" done = True\n",
" while not done:\n",
" \n",
"\n",
" if preprocess:\n",
" draw_nn_sample(test_true, test_reco, 1, preprocess,\n",
" min_true=min_true, max_true=max_true, \n",
" min_reco=min_reco, max_reco=max_reco,\n",
" f=gan.get_sample_A_to_B, save=False, is_training=False, PATH=PATH)\n",
" else:\n",
" draw_nn_sample(test_true, test_reco, 1, preprocess,\n",
" f=gan.get_sample_A_to_B, save=False, is_training=False)\n",
" \n",
" ans = input(\"Generate another?\")\n",
" if ans and ans[0] in ('n' or 'N'):\n",
" done = True\n",
" \n",
" done = True\n",
" while not done:\n",
" \n",
" if preprocess:\n",
" draw_nn_sample(test_true, test_reco, 20, preprocess,\n",
" min_true=min_true, max_true=max_true, \n",
" min_reco=min_reco, max_reco=max_reco,\n",
" f=gan.get_sample_A_to_B, save=False, is_training=False)\n",
" else:\n",
" draw_nn_sample(test_true, test_reco, 20, preprocess,\n",
" f=gan.get_sample_A_to_B, save=False, is_training=False)\n",
" \n",
" ans = input(\"Generate another?\")\n",
" if ans and ans[0] in ('n' or 'N'):\n",
" done = True\n",
" \n",
" return test_reco_NN"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Convolutional Network architecture detected for discriminator B\n",
"Convolutional Network architecture detected for encoder B\n",
"Encoder_B\n",
"Convolution\n",
"Input for convolution shape (?, 52, 64, 1)\n",
"Encoder output shape (?, 128)\n",
"Generator_A_to_B\n",
"Input for generator encoded shape (?, 52, 64, 1)\n",
"Output of generator encoder, \n",
" and input for generator decoder shape (?, 1, 1, 1024)\n",
"Generator output shape (?, 52, 64, 1)\n",
"Generator_A_to_B\n",
"Input for generator encoded shape (?, 52, 64, 1)\n",
"Output of generator encoder, \n",
" and input for generator decoder shape (?, 1, 1, 1024)\n",
"Generator output shape (?, 52, 64, 1)\n",
"Encoder_B\n",
"Convolution\n",
"Input for convolution shape (?, 52, 64, 1)\n",
"Encoder output shape (?, 128)\n",
"Discriminator_B\n",
"Input for convolution shape (?, 52, 64, 1)\n",
"minibatch features shape (?, 10)\n",
"Feature output shape (?, 64)\n",
"Logits shape (?, 1)\n",
"Discriminator_B\n",
"Input for convolution shape (?, 52, 64, 1)\n",
"minibatch features shape (?, 10)\n",
"Feature output shape (?, 64)\n",
"Logits shape (?, 1)\n",
"Discriminator_B\n",
"Input for convolution shape (?, 52, 64, 1)\n",
"minibatch features shape (?, 10)\n",
"Feature output shape (?, 64)\n",
"Logits shape (?, 1)\n",
"Generator_A_to_B\n",
"Input for generator encoded shape (?, 52, 64, 1)\n",
"Output of generator encoder, \n",
" and input for generator decoder shape (?, 1, 1, 1024)\n",
"Generator output shape (?, 52, 64, 1)\n",
"\n",
" Evaluate model on test set...\n",
"INFO:tensorflow:Restoring parameters from test12/test12bicycle.ckpt\n",
"Model restored.\n"
]
}
],
"source": [
"if __name__=='__main__':\n",
"\n",
" if task == 'TEST': \n",
" if not os.path.exists(PATH+'/checkpoint'):\n",
" print('No checkpoint to test')\n",
" else:\n",
" test_reco_NN =HCAL()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"scale=test_reco_NN.std()/test_reco.std()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.6232238"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scale"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"test_reco_MC_hist= test_reco.reshape(test_reco.shape[0],test_reco.shape[1]*test_reco.shape[2])\n",
"test_reco_MC_hist = np.sum(test_reco_MC_hist,axis=1)\n",
"max_MC_hist = np.max(test_reco_MC_hist)\n",
"\n",
"test_reco_NN_test=test_reco_NN/0.9\n",
"test_reco_NN_hist= test_reco_NN_test.reshape(test_reco_NN_test.shape[0],test_reco_NN.shape[1]*test_reco_NN.shape[2])\n",
"test_reco_NN_hist = np.sum(test_reco_NN_hist,axis=1)\n",
"max_NN_hist = np.max(test_reco_NN_hist)\n",
"\n",
"test_true_hist= test_true.reshape(test_true.shape[0],test_true.shape[1]*test_true.shape[2])\n",
"test_true_hist = np.sum(test_true_hist,axis=1)\n",
"max_true_hist = np.max(test_true_hist)\n",
"\n",
"#test_reco_NN_hist_rescaled=(test_reco_NN_hist/max_NN_hist)*max_MC_hist\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"diffNN = test_reco_NN_hist-test_true_hist\n",
"diffMC = test_reco_MC_hist-test_true_hist"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1296x432 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.subplot(1,3,1)\n",
"plt.tick_params(labelsize=12);\n",
"h_reco = plt.hist(test_true_hist/1000,bins=30,range=(0,190), edgecolor='black');\n",
"plt.xlabel('E_T (GeV)', fontsize=15)\n",
"plt.ylabel('dN/dE_T', fontsize=15)\n",
"plt.title('Total true E_T/event', fontsize=15)\n",
"plt.subplot(1,3,2)\n",
"plt.tick_params(labelsize=12);\n",
"h_reco = plt.hist(test_reco_MC_hist/1000,bins=30,range=(0,140), edgecolor='black');\n",
"plt.xlabel('E_T (GeV)', fontsize=15)\n",
"\n",
"plt.title('Total Reco E_T/event from Geant4', fontsize=15)\n",
"plt.subplot(1,3,3)\n",
"plt.tick_params(labelsize=12);\n",
"h_nn = plt.hist(test_reco_NN_hist/1000, bins=30,range=(0,140), edgecolor='black');\n",
"plt.xlabel('E_T (GeV)', fontsize=15)\n",
"\n",
"plt.title('Total Reco E_T/event from BicycleGAN', fontsize=15)\n",
"fig = plt.gcf()\n",
"fig.set_size_inches(18,6)\n",
"plt.savefig(PATH+'/distribution.eps', format='eps', dpi=100)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAt8AAAEWCAYAAAC+BfslAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAHohJREFUeJzt3Xu85XVd7/HXW25eqAe3Qee2Z5AzhtBJtB2hlg9Ki0vkSIgNmRJpY4+DqT20ArHAY1SnTM1MOhQE3kBOYE5JJpBEdhTYIOFwyzlc9gwzwYiimIYNfs4f67dlsWfPvq/f2nvN6/l4rMdav+/vsj7r+/jN3u/9ne/6/VJVSJIkSeq9p/S7AEmSJGl3YfiWJEmSWmL4liRJklpi+JYkSZJaYviWJEmSWmL4liRJklpi+JYkSZJaYviWJCDJfUkeTPKMrrbXJ7mua7mSfCnJU7rafjfJxT2o55eSfG4hHS/JuU0fvGlc+1ua9nO72r4/yfuSjCb5ZpJNzfJBc6lhgppWN++950I8niSNZ/iWpCfsCbx5im2WAetaqGWh+jfgtHFtr23aAUiyN3AtcARwHPD9wIuAh4Gj2ilTkhYmw7ckPeGPgLcl2W+Sbf4QeOd0R0aT/Eoz6vvVJBuSLGvadxphTXJdM9r+XODPgRc2o8aPNOsvTvLnSa5O8miSf0qyarbHm6WbgKcnOaJ5jyOApzXtY14LDAEnVdUdVfXdqnqoqt5VVVftop9elOSmJF9vnl/Ute6+JC/rWj43yUeaxeub50eaz/bCZpT/X5L8aXO8u5K8dLbHm00nSdKuGL4l6QkjwHXA2ybZ5krgG8AvTXWwJD8J/D7wKmApcD9w2VT7VdWdwK8Cn6+qfauq+4+BVwPvAg4CbgU+OsfjzcaH6QRs6IyCf2jc+pcBn66qb07nYEkOAD4FvB84EHgP8KkkB05j95c0z/s1n+3zzfKPAvfQ6adzgCub95nt8SRpXhi+JenJfgf4tSRLdrG+gN8GfifJPlMc69XARVV1S1U9BpxFZ/R59Rzq+1RVXd8c7+zmeCvncLzZ+AhwapK96EzB+ci49QcC22ZwvJ8BvlxVH66qHVV1KXAX8LNzqPEh4H1V9V9V9XHg7uZ9JKmvDN+S1KWqNgJ/B5w5yTZXAaPA+ikOt4zOaPfYft+kM+95+RxK3DzueF9t3mdOkvx4M83im0lun2zbqhoFNgG/Ryc0bx63ycN0Rvqn60n91LifufXTA1VV4443536SpLkyfEvSzs4BfoXJw9876Iw8P32SbbYCq8YWmiupHAg8APxH09y9/7O6XncHx27fG+VOsi9wQPM+sz1eZ2XVPzfTLPatqiMm27bxIeCt7DzlBOAa4NjuK8dM4Un91Bii00/Q+Wwz/VzLk2Tc8bbO4XiSNC8M35I0TlVtAj4OvGmSba4DvsTOV/7o9jHg9CRHNlNUfg+4oaruq6rtdMLlLybZI8kvA4d27fsgsKK5cki3E5L8WNP+ruZ4m+dwvNn6OPDTwOUTrPswnRH6K5IcluQpSQ5M8vYkJ0yw/VXAc5L8QpI9k/w8cDid/4GAztz2dUn2SjIMvLJr3+3Ad4FnjzvmwcCbmn1OAZ7bvM9sjydJ88LwLUkT+5/AVCO376Az8jyhqrqWzvzwK+jMgT6UJ1+m8FeA36AzTeMI4P92rftH4Hbg35N8pav9Y3RG5r8K/DCdeeVzOd6sVNW3q+qaqvr2BOseo/Oly7uAq+l8QfVGOl9+vGGC7R8GTqQzkv4w8JvAiVU1Vudv0+m7rwHvpNMHY/t+CzgP+JckjyQ5ull1A7AG+Eqz/pXN+8z2eJI0L/LkKXGSpIUqnZv5bKmqd/S7loUsyS8Br6+qH+t3LZI0niPfkiRJUksM35IkSVJLnHYiSZIktcSRb0mSJKkle/a7gF466KCDavXq1f0uQ5IkSQPu5ptv/kpV7eruyN8z0OF79erVjIyM9LsMSZIkDbgk4+/UOyGnnUiSJEktMXxLkiRJLTF8S5IkSS0xfEuSJEktMXxLkiRJLTF8S5IkSS0xfEuSJEktMXxLkiRJLTF878aGVq0iyYweQ6tW9btsSZKkRWug73CpyW0eHeWKu7bOaJ+TD1vWo2okSZIGnyPfkiRJUksM35IkSVJLDN+SJElSSwzfkiRJUksM35IkSVJLDN+SJElSSwzfkiRJUksM35IkSVJLDN+SJElSS/oWvpOsTPLZJHcmuT3Jm5v2c5M8kOTW5nFC1z5nJdmU5O4kx/ardkmSJGk2+nl7+R3AW6vqliTfB9yc5Opm3Xur6t3dGyc5HFgHHAEsA65J8pyqerzVqiVJkqRZ6tvId1Vtq6pbmtePAncCyyfZZS1wWVU9VlX3ApuAo3pfqSRJkjQ/FsSc7ySrgecDNzRNb0xyW5KLkuzftC0HNnfttoUJwnqS9UlGkoxs3769h1VLkiRJM9P38J1kX+AK4C1V9Q3gfOBQ4EhgG/DHY5tOsHvt1FB1QVUNV9XwkiVLelS1JEmSNHN9Dd9J9qITvD9aVVcCVNWDVfV4VX0X+AuemFqyBVjZtfsKYGub9UqSJElz0c+rnQS4ELizqt7T1b60a7OTgI3N6w3AuiT7JDkEWAPc2Fa9kiRJ0lz182onLwZeA3wpya1N29uBU5McSWdKyX3AGwCq6vYklwN30LlSyhle6USSJEmLSd/Cd1V9jonncV81yT7nAef1rChJkiSph/r+hUtJkiRpd2H4liRJklpi+JYkSZJaYviWJEmSWmL4liRJklpi+JYkSZJaYviWJEmSWmL4liRJklpi+JYkSZJaYviWJEmSWmL4liRJklpi+JYkSZJaYviWJEmSWmL4liRJklpi+JYkSZJaYviWJEmSWtK38J1kZZLPJrkzye1J3ty0H5Dk6iRfbp73b9qT5P1JNiW5LckL+lW7JEmSNBv9HPneAby1qp4LHA2ckeRw4Ezg2qpaA1zbLAMcD6xpHuuB89svWZIkSZq9voXvqtpWVbc0rx8F7gSWA2uBS5rNLgFe0bxeC3yoOr4A7JdkactlS5IkSbO2IOZ8J1kNPB+4AXhmVW2DTkAHDm42Ww5s7tptS9M2/ljrk4wkGdm+fXsvy5YkSZJmpO/hO8m+wBXAW6rqG5NtOkFb7dRQdUFVDVfV8JIlS+arTEmSJGnO+hq+k+xFJ3h/tKqubJofHJtO0jw/1LRvAVZ27b4C2NpWrZIkSdJc9fNqJwEuBO6sqvd0rdoAnNa8Pg34ZFf7a5urnhwNfH1seookSZK0GOzZx/d+MfAa4EtJbm3a3g78AXB5ktcBo8ApzbqrgBOATcC3gNPbLVeSJEmam76F76r6HBPP4wZ46QTbF3BGT4uSJEmSeqjvX7jU4BhatYokM34MrVrV79IlSZJa0c9pJxowm0dHueKumX8H9uTDlvWgGkmSpIXHkW9JkiSpJYZvSZIkqSWGb0mSJKklhm9JkiSpJYZvSZIkqSWGb0mSJKklhm9JkiSpJYZvSZIkqSWGb0mSJKklhm9JkiSpJYZvSZIkqSWGb0mSJKklhm9JkiSpJYZvSZIkqSWGb0mSJKklfQ3fSS5K8lCSjV1t5yZ5IMmtzeOErnVnJdmU5O4kx/anakmSJGl2+j3yfTFw3ATt762qI5vHVQBJDgfWAUc0+3wwyR6tVSpJkiTNUV/Dd1VdD3x1mpuvBS6rqseq6l5gE3BUz4qTJEmS5lm/R7535Y1JbmumpezftC0HNndts6Vpe5Ik65OMJBnZvn17G7VKkiRJ07IQw/f5wKHAkcA24I+b9kywbe3UUHVBVQ1X1fCSJUt6V6UkSZI0QwsufFfVg1X1eFV9F/gLnphasgVY2bXpCmBr2/VJkiRJs7XgwneSpV2LJwFjV0LZAKxLsk+SQ4A1wI1t1ydJkiTN1p79fPMklwLHAAcl2QKcAxyT5Eg6U0ruA94AUFW3J7kcuAPYAZxRVY/3o25JkiRpNvoavqvq1AmaL5xk+/OA83pXkSRJktQ7C27aiSRJkjSoDN+SJElSSwzfkiRJUksM34vE0KpVJJnxY2jVqn6XLkmSpEZfv3Cp6ds8OsoVd838suYnH7asB9VIkiRpNhz5liRJklpi+JYkSZJaYviWJEmSWmL4liRJklpi+JYkSZJaYviWJEmSWmL4liRJklpi+JYkSZJaYviWJEmSWmL4liRJklpi+JYkSZJaMmn4TnJY1+t9xq07uldFSZIkSYNoqpHvj3W9/vy4dR+c65snuSjJQ0k2drUdkOTqJF9unvdv2pPk/Uk2JbktyQvm+v6SJElSm6YK39nF64mWZ+Ni4LhxbWcC11bVGuDaZhngeGBN81gPnD8P7y9JkiS1ZqrwXbt4PdHyjFXV9cBXxzWvBS5pXl8CvKKr/UPV8QVgvyRL51qDJEmS1JY9p1i/Isn76Yxyj72mWV7eo5qeWVXbAKpqW5KDm/blwOau7bY0bdu6d06yns7IOENDQz0qUZIkSZq5qcL3b3S9Hhm3bvxyr000zWWn0fequgC4AGB4eHjOo/OSJEnSfJk0fFfVJZOt75EHkyxtRr2XAg817VuAlV3brQC2tl6dJEmSNEuThu8kf8skc7ur6uXzXhFsAE4D/qB5/mRX+xuTXAb8KPD1sekpkiRJ0mIw1bSTdzfPPwc8C/hIs3wqcN9c3zzJpcAxwEFJtgDn0Andlyd5HTAKnNJsfhVwArAJ+BZw+lzfX5IkSWrTVNNO/gkgybuq6iVdq/42yfVzffOqOnUXq146wbYFnDHX95QkSZL6Zbq3l1+S5NljC0kOAZb0piRJkiRpME017WTMrwPXJbmnWV5Nczk/SZIkSdMz1Rcul1bVtqr6dJI1wGHNqruq6rHelydJkiQNjqlGvi9Ksj9wHfBp4HNVtaPnVUmSJEkDaKovXB6f5Kl0rkhyEvDuJKN0gvinq2q09yVKkiRJg2HKOd9V9Z80YRu+92XL44EPJHlWVR3V2xIlSZKkwTDdL1x+T1XdC3wQ+GCSvee/JEmSJGkwTfWFy3t58h0u07VcVXVorwqTJEmSBs1UI9/D45afArwKeBvwxZ5UJEmSJA2oSW+yU1UPV9XDwNeAE4HPAi8EfqaqTm6hPu3mhlatIsmMHkOrVvW7bEmSpAlNNe1kL+CX6dxk53PA2qr6f20UJgFsHh3liru2zmifkw9b1qNqJEmS5maqaSf3AjuA9wGjwPOSPG9sZVVd2cPaJEmSpIEyVfi+unn+oeYxZuyLl4ZvSZIkaZqmCt8b6YTsdD3Dk6+AIkmSJGkapgrf+zbPPwD8CPBJOgH8Z4Hre1iXJEmSNHCmur38OwGSfAZ4QVU92iyfC/yfnlcnSZIkDZBJLzXYZQj4Ttfyd4DV816NJEmSNMCme3v5DwM3JvkEnfneJwGX9KwqIMl9wKPA48COqhpOcgDwcTrB/z7gVVX1tV7WIUmSJM2XaY18V9V5wOl0brbzCHB6Vf1+Lwtr/ERVHVlVY3faPBO4tqrWANc2y5IkSdKiMN2Rb6rqFuCWHtYyHWuBY5rXlwDXAb/Vr2IkSZKkmZjunO9+KOAzSW5Osr5pe2ZVbQNong8ev1OS9UlGkoxs3769xXIlSZKkyU175LsPXlxVW5McDFyd5K7p7FRVFwAXAAwPD3s9ckmSJC0YC3bku6q2Ns8PAZ8AjgIeTLIUoHl+qH8VSpIkSTOzIMN3kmck+b6x18BP07nb5gbgtGaz0+jc9EeSJElaFBbqtJNnAp9IAp0aP1ZVn05yE3B5ktcBo8ApfaxRkiRJmpEFGb6r6h7geRO0Pwy8tP2KJEmSpLlbkNNOJEmSpEFk+JYkSZJaYviWJEmSWmL4liRJklpi+NZuZ2jVKpLM6DG0alW/y5YkSQNgQV7tROqlzaOjXHHX1hntc/Jhy3pUjSRJ2p048i1JkiS1xPAtSZIktcTwLUmSJLXE8C1JkiS1xPAtSZIktcTwLUmSJLXE8N0jXktakiRJ43md7x7xWtKSJEkaz5FvSZIkqSWGb0mSJKklhm9JkiSpJYsufCc5LsndSTYlObPf9UiSJEnTtajCd5I9gD8DjgcOB05Ncnh/q5Ik9ZtXmJof9qPUe4vtaidHAZuq6h6AJJcBa4E7+lqVJKmvvMLU/LAf58fQqlVsHh2d0T4rh4YYvf/+HlWkBaWqFs0DeCXwl13LrwE+MG6b9cAIMDI0NFT9snJoqIAZPVZOUu9sjteLYy72GhfK57bG3afGtj+3NQ7W+WONOzvnnHNmfLxzzjln0ffjfH/u2RyvF8dsu8ZeAkZqGnk2nW0XhySnAMdW1eub5dcAR1XVr020/fDwcI2MjLRZonZDSWY1UjTZv735PqY19q/GXhzTGtuxED73VJ95MYywWqN2F0lurqrhqbZbbNNOtgAru5ZXADP/CS5J0gBYDOHPGqUnW1RfuARuAtYkOSTJ3sA6YEOfa5IkSZKmZVGNfFfVjiRvBP4B2AO4qKpu73NZkiRJ0rQsqvANUFVXAVf1uw5JkiRpphZd+JYkqQ0rh4ZmfBm9lUNDPapG0qAwfEuSNIH5/hKeYV4SGL4lSWqFV9SQBIvvaieSJEnSomX4liRJklpi+JYkSZJa4pxvSdKk/KKgJM0fw7ckaVJ+UVCS5o/TTiRJkqSWOPItSWrVbKaxjO0nSYud4VuS1CqnsUjanTntRJIkSWqJ4VuSJElqidNOJA0sL5EnSVpoDN+SBpZziyVJC43hW5IGiFcSkaSFbcHN+U5ybpIHktzaPE7oWndWkk1J7k5ybD/rlKSFaPT++6mqGT/8XwJJasdCHfl+b1W9u7shyeHAOuAIYBlwTZLnVNXj/ShQGuO8YkmSNF0LNXxPZC1wWVU9BtybZBNwFPD5/pal3Z0jhpoL/3iTpN3Lgpt20nhjktuSXJRk/6ZtObC5a5stTduTJFmfZCTJyPbt29uoVZJmbTbTRPyDT5IWr76E7yTXJNk4wWMtcD5wKHAksA3447HdJjhU7dRQdUFVDVfV8JIlS3r2GSRJkqSZ6su0k6p62XS2S/IXwN81i1uAlV2rVwBb57k0SZIkqWcW3LSTJEu7Fk8CNjavNwDrkuyT5BBgDXBj2/VJkiRJs7UQv3D5h0mOpDOl5D7gDQBVdXuSy4E7gB3AGV7pRJIkSYvJggvfVfWaSdadB5zXYjmSJEnSvFlw004kSZKkQWX4liRJklpi+JYkSZJaYviWJEmSWmL4liRJklpi+JYkSZJaYviWJEmSWmL4liRJklqy4G6yI2n+rRwa4uTDls14Hz3ZbPpxbD9JksDwLe0WRu+/v98lDAT7UZI0V047kSRJklpi+JYkSZJaYviWJEmSWmL4liRJklpi+JYkSZJaYviWJEmSWmL4liRJklrSl/Cd5JQktyf5bpLhcevOSrIpyd1Jju1qP65p25TkzParliRJkuamXzfZ2Qj8HPC/uxuTHA6sA44AlgHXJHlOs/rPgJ8CtgA3JdlQVXe0V7KkXvIunJKk3UFfwndV3QmQZPyqtcBlVfUYcG+STcBRzbpNVXVPs99lzbaGb2lAePdISdLuYKHN+V4ObO5a3tK07ap9J0nWJxlJMrJ9+/aeFSpJkiTNVM9GvpNcAzxrglVnV9Und7XbBG3FxH8k1EQHqKoLgAsAhoeHJ9xGkiRJ6oeehe+qetksdtsCrOxaXgFsbV7vql1SHzhHW5KkmevXFy53ZQPwsSTvofOFyzXAjXRGxNckOQR4gM6XMn+hb1VKco62JEmz0JfwneQk4E+BJcCnktxaVcdW1e1JLqfzRcodwBlV9XizzxuBfwD2AC6qqtv7UbvUBkeVJUkaTKka3GnRw8PDNTIy0u8yJEmSNOCS3FxVw1Ntt9CudiJJkiQNLMO3JEmS1BLDtyRJktQSw7ckSZLUEsO3JEmS1BLDtyRJktQSw7ckSZLUEsO3JEmS1JKBvslOku1Av+6BfRDwlT699+7A/u0t+7e37N/esn97y/7tLfu3t3rZv6uqaslUGw10+O6nJCPTucuRZsf+7S37t7fs396yf3vL/u0t+7e3FkL/Ou1EkiRJaonhW5IkSWqJ4bt3Luh3AQPO/u0t+7e37N/esn97y/7tLfu3t/rev875liRJklriyLckSZLUEsO3JEmS1BLD9zxLclySu5NsSnJmv+tZ7JKsTPLZJHcmuT3Jm5v2A5JcneTLzfP+/a51MUuyR5IvJvm7ZvmQJDc0/fvxJHv3u8bFKsl+Sf46yV3NefxCz9/5k+TXm58NG5NcmuSpnr9zk+SiJA8l2djVNuE5m473N7/zbkvygv5Vvjjson//qPkZcVuSTyTZr2vdWU3/3p3k2P5UvXhM1L9d696WpJIc1Cz35fw1fM+jJHsAfwYcDxwOnJrk8P5WtejtAN5aVc8FjgbOaPr0TODaqloDXNssa/beDNzZtfy/gPc2/fs14HV9qWow/Anw6ao6DHgenX72/J0HSZYDbwKGq+oHgT2AdXj+ztXFwHHj2nZ1zh4PrGke64HzW6pxMbuYnfv3auAHq+qHgH8DzgJoft+tA45o9vlgkzW0axezc/+SZCXwU8BoV3Nfzl/D9/w6CthUVfdU1XeAy4C1fa5pUauqbVV1S/P6UTrBZTmdfr2k2ewS4BX9qXDxS7IC+BngL5vlAD8J/HWzif07S0m+H3gJcCFAVX2nqh7B83c+7Qk8LcmewNOBbXj+zklVXQ98dVzzrs7ZtcCHquMLwH5JlrZT6eI0Uf9W1Weqakez+AVgRfN6LXBZVT1WVfcCm+hkDe3CLs5fgPcCvwl0X2mkL+ev4Xt+LQc2dy1vado0D5KsBp4P3AA8s6q2QSegAwf3r7JF7310fiB9t1k+EHik6xeB5/HsPRvYDvxVM63nL5M8A8/feVFVDwDvpjOStQ34OnAznr+9sKtz1t978++Xgb9vXtu/8yDJy4EHqupfx63qS/8avudXJmjzWo7zIMm+wBXAW6rqG/2uZ1AkORF4qKpu7m6eYFPP49nZE3gBcH5VPR/4D5xiMm+aecdrgUOAZcAz6Pw38niev73jz4t5lORsOtMtPzrWNMFm9u8MJHk6cDbwOxOtnqCt5/1r+J5fW4CVXcsrgK19qmVgJNmLTvD+aFVd2TQ/OPZfQ83zQ/2qb5F7MfDyJPfRmSb1k3RGwvdr/hsfPI/nYguwpapuaJb/mk4Y9/ydHy8D7q2q7VX1X8CVwIvw/O2FXZ2z/t6bJ0lOA04EXl1P3ITF/p27Q+n8gf6vze+6FcAtSZ5Fn/rX8D2/bgLWNN+035vOlyQ29LmmRa2Zf3whcGdVvadr1QbgtOb1acAn265tEFTVWVW1oqpW0zlf/7GqXg18Fnhls5n9O0tV9e/A5iQ/0DS9FLgDz9/5MgocneTpzc+Ksf71/J1/uzpnNwCvba4acTTw9bHpKZq+JMcBvwW8vKq+1bVqA7AuyT5JDqHzxcAb+1HjYlVVX6qqg6tqdfO7bgvwgubnc1/OX+9wOc+SnEBn5HAP4KKqOq/PJS1qSX4M+GfgSzwxJ/ntdOZ9Xw4M0fkFfEpVTfQFC01TkmOAt1XViUmeTWck/ADgi8AvVtVj/axvsUpyJJ0vs+4N3AOcTmfgw/N3HiR5J/DzdP6r/ovA6+nM2fT8naUklwLHAAcBDwLnAH/DBOds80fPB+hcXeJbwOlVNdKPuheLXfTvWcA+wMPNZl+oql9ttj+bzjzwHXSmXv79+GPqCRP1b1Vd2LX+PjpXSPpKv85fw7ckSZLUEqedSJIkSS0xfEuSJEktMXxLkiRJLTF8S5IkSS0xfEuSJEktMXxL0oBI8niSW7seE95NM8n7krykeb1nkt9L8uWu/c6e4n0uTvKGcW2vSHJVkr2TXN91kxtJUhfDtyQNjm9X1ZFdjz8Yv0GSA4Cjq+r6pul36dya/b9X1ZHAjwN7TfE+l9K5KVO3dcClVfUd4Fo6196WJI3jdb4laUAk+WZV7TvFNuuBZVV1bpKnA5uB1VX16C62/0XgTXRuEnQD8D+aVWN3idvWHGcUOKSqHk3yPOD3q+qE+flkkjQ4HPmWpMHxtHHTTiYafX4xcHPz+r8Bo5ME7+fSGcF+cTMq/jjw6qp6HLgSeFWz6cuBz3YdZyPwI/PzkSRpsDgnT5IGx7ebkDyZpcD2iVYkOR14M3Ag8CLgpcAPAzd17sLM04CHms0vBf4I+BM6U04+NHacqno8yXeSfN+ugr0k7a4M35K0e/k28NTm9SZgaCwkV9VfAX+VZCOwBxDgkqo6a4Lj/AuwtJli8iJ2ngO+D/CfPfkEkrSIOe1EknYvd9KZbkJVfQu4EPhAkqcCJNmDzvxu6Hxx8pVJDm7WHZBkVbNvAZcDlwBXVdX3gnaSA4HtVfVf7XwkSVo8DN+SNDjGz/ne6WonwKeAY7qWzwa2ARuTfBH4ZzqBemtV3QG8A/hMktuAq+lMWxlzKfA84LJx7/ETwFXz8YEkadB4tRNJ2s0k+RxwYlU90qPjXwmcVVV39+L4krSYOfItSbuftwJDvThwkr2BvzF4S9LEHPmWJEmSWuLItyRJktQSw7ckSZLUEsO3JEmS1BLDtyRJktQSw7ckSZLUkv8PSi2LZc2qFcYAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 864x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"idx = np.arange(0,140, step=140/30)\n",
"diff=plt.bar(idx, \n",
" height=(h_nn[0]-h_reco[0]), edgecolor='black', \n",
" linewidth=1, color='lightblue',width = 3, align = 'edge') \n",
"plt.xlabel('E (GeV)')\n",
"plt.ylabel('dN/dE')\n",
"plt.title(\"NN output - MC output\")\n",
"fig = plt.gcf()\n",
"fig.set_size_inches(12,4)\n",
"plt.savefig(PATH+'/difference.eps', format='eps',dpi=100)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x360 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.subplot(1,2,1)\n",
"plt.tick_params(labelsize=15);\n",
"\n",
"\n",
"h_reco = plt.hist(diffMC/1000,bins=30, range=(-110,25), edgecolor='black');\n",
"plt.xlabel('ET recoMC - ET tracking (GeV)', fontsize=15)\n",
"plt.ylabel('dN/dETdiff', fontsize=15)\n",
"plt.title('Resolution as simulated by Geant4', fontsize=15)\n",
"plt.subplot(1,2,2)\n",
"plt.tick_params(labelsize=15);\n",
"\n",
"\n",
"h_nn = plt.hist(diffNN/1000,bins=30, range=(-110,25), edgecolor='black');\n",
"plt.xlabel('ET recoNN - ET tracking (GeV)', fontsize=15)\n",
"\n",
"#plt.ylabel('dN/dETdiff', fontsize=15)\n",
"plt.title('Resolution as simulated by NN', fontsize=15)\n",
"fig = plt.gcf()\n",
"fig.set_size_inches(12,5)\n",
"plt.savefig(PATH+'/resolution.eps', format='eps', dpi=100)\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.subplot(1,2,1)\n",
"plt.hist2d(test_true_hist/1000,test_reco_NN_hist/1000, range=([[0,140],[0,140]]), bins=30);\n",
"plt.xlabel('True ET (GeV)', fontsize=15)\n",
"plt.ylabel('NN Reco ET (GeV)', fontsize=15)\n",
"fig=plt.gcf()\n",
"fig.set_size_inches(5,5)\n",
"plt.subplot(1,2,2)\n",
"plt.hist2d(test_true_hist/1000,test_reco_MC_hist/1000, range=([[0,140],[0,140]]), bins=30);\n",
"plt.xlabel('True ET (GeV)', fontsize=15)\n",
"plt.ylabel('MC Reco ET (GeV)', fontsize=15)\n",
"fig=plt.gcf()\n",
"fig.set_size_inches(10,5)\n",
"plt.savefig(PATH+'/scatterplot.eps', format='eps', dpi=100)\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"test_reco_inner = get_inner_HCAL(test_reco)\n",
"test_reco_outer = get_outer_HCAL(test_reco)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"test_reco_inner_NN = get_inner_HCAL(test_reco_NN_test)\n",
"test_reco_outer_NN = get_outer_HCAL(test_reco_NN_test)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(279970.38, 550056.0)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_reco_inner_NN.sum(axis=0).max(),test_reco_inner.sum(axis=0).max()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7fca1c62be80>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(test_reco_NN[14].reshape(52,64))"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7fca1c52b9e8>"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.subplot(1,2,1)\n",
"plt.imshow(test_reco_inner_NN[4].reshape(28,32))\n",
"plt.colorbar()\n",
"plt.subplot(1,2,2)\n",
"plt.imshow(test_reco_outer_NN[4].reshape(26,32))\n",
"plt.colorbar()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7fca1c3cf5c0>"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.subplot(1,2,1)\n",
"plt.imshow(test_reco_inner[4].reshape(28,32))\n",
"plt.colorbar()\n",
"plt.subplot(1,2,2)\n",
"plt.imshow(test_reco_outer[4].reshape(26,32))\n",
"plt.colorbar()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"indices_MC_inner, indices_MC_outer, triggered_true_inner_MC, triggered_true_outer_MC, _, _ = get_triggered_events(test_true, test_reco_inner, test_reco_outer)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(531, 188)"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(indices_MC_inner), len(indices_MC_outer)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"indices_NN_inner, indices_NN_outer, triggered_true_inner_NN, triggered_true_outer_NN, _, _ = get_triggered_events(test_true, test_reco_inner_NN, test_reco_outer_NN)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(172, 124)"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(indices_NN_inner), len(indices_NN_outer)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"total_MC=np.concatenate((triggered_true_outer_MC,triggered_true_inner_MC))\n",
"total_NN=np.concatenate((triggered_true_outer_NN,triggered_true_inner_NN))"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"a_MC=plt.hist(total_MC/1000, range=(0,140), bins=40, histtype='step', label='MC Triggered events',)# range=(0,160))\n",
"a_NN=plt.hist(total_NN/1000, range=(0,140), bins=40, histtype='step', label='NN Triggered events',)# range=(0,160))\n",
"b=plt.hist(test_true_hist/1000, range=(0,140), bins=40,histtype='step', label='ET from Tracking',)# range=(0,160))\n",
"plt.tick_params(labelsize='xx-large')\n",
"plt.xlabel('ET (GeV)', fontsize=20)\n",
"plt.ylabel('dN/dET (a.u.)', fontsize=20)\n",
"plt.legend(fontsize='xx-large')\n",
"fig = plt.gcf()\n",
"fig.set_size_inches(20,10)\n",
"plt.savefig(PATH+'/eff_1.png',dpi=100)"
]
},
{
"cell_type": "code",
"execution_count": 215,
"metadata": {},
"outputs": [],
"source": [
"#a_MC_np, a_MC_edges = np.histogram(total_MC/1000,bins=40, )#range=(0,200) )#, label='MC Triggered events')\n",
"#a_NN_np, a_NN_edges = np.histogram(total_NN/1000,bins=40, )#range=(0,200) )#, label='NN Triggered events')\n",
"#a_true_np, a_true_edges = np.histogram(test_true_hist/1000, bins=40, )#range=(0,140) )\n",
"#\n",
"#bincenters = 0.5*(a_NN_edges[1:]+a_NN_edges[:-1])\n",
"#\n",
"#a_MC_std = np.sqrt(a_MC_np)\n",
"#a_NN_std = np.sqrt(a_NN_np)\n",
"#a_true_std = np.sqrt(a_true_np)\n",
"#\n",
"#plt.errorbar(bincenters, a_true_np,yerr=a_NN_std, xerr=0.5 , fmt='o')\n",
"#plt.errorbar(bincenters, a_MC_np, yerr=a_MC_std, xerr=0.5 , fmt='o', label='MC Triggered events')\n",
"#plt.errorbar(bincenters, a_NN_np, yerr=a_NN_std, xerr=0.5 , fmt='o',label='NN Triggered events')\n",
"#plt.tick_params(labelsize='xx-large')\n",
"#plt.xlabel('ET (GeV)', fontsize=20)\n",
"#plt.ylabel('dN/dET (a.u.)', fontsize=20)\n",
"#plt.legend(fontsize='xx-large')\n",
"#fig = plt.gcf()\n",
"#fig.set_size_inches(16,10)\n",
"#plt.savefig(PATH+'/eff_1.png',dpi=100)"
]
},
{
"cell_type": "code",
"execution_count": 216,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0., 1., 19., 32., 60., 104., 137., 142., 170., 205., 216.,\n",
" 200., 183., 197., 147., 166., 166., 130., 107., 113., 90., 72.,\n",
" 71., 47., 42., 37., 32., 31., 18., 7., 11., 11., 6.,\n",
" 6., 3., 4., 4., 6., 0., 4.])"
]
},
"execution_count": 216,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b[0]"
]
},
{
"cell_type": "code",
"execution_count": 217,
"metadata": {},
"outputs": [],
"source": [
"eff_MC = np.zeros_like(b[0])\n",
"eff_NN = np.zeros_like(b[0])\n",
"for i in range(len(b[0])):\n",
" if b[0][i]!=0:\n",
" eff_MC[i]=a_MC[0][i]/b[0][i]\n",
" eff_NN[i]=a_NN[0][i]/b[0][i]"
]
},
{
"cell_type": "code",
"execution_count": 304,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"idx = np.arange(0,140, step=140/40)\n",
"#plt.subplot(1,2,2)\n",
"plt.plot(idx,eff_NN, label='Efficiency simulated by NN')\n",
"plt.plot(idx,eff_MC, label='Efficiency simulated by MC')\n",
"plt.legend(fontsize='xx-large')\n",
"plt.tick_params(labelsize='xx-large')\n",
"plt.xlabel('ET (GeV)', fontsize=20)\n",
"plt.ylabel('eff (a.u.)', fontsize=20)\n",
"#plt.subplot(1,2,1)\n",
"#a_MC=plt.hist(triggered_true_inner_MC/1000,bins=40,histtype='step', range=(0, 160), label='MC Triggered events')\n",
"#a_NN=plt.hist(triggered_true_inner_NN/1000,bins=40,histtype='step', range=(0, 160), label='NN Triggered events')\n",
"#b=plt.hist(test_true_hist/1000,bins=40,histtype='step', range=(0, 160), label='ET from Tracking')\n",
"#plt.tick_params(labelsize='xx-large')\n",
"#plt.xlabel('ET (GeV)', fontsize=20)\n",
"#plt.ylabel('dN/dET (a.u.)', fontsize=20)\n",
"#plt.legend(fontsize='xx-large')\n",
"fig = plt.gcf()\n",
"fig.set_size_inches(15,10)\n",
"\n",
"plt.savefig(PATH+'/eff_2.png',dpi=100)"
]
},
{
"cell_type": "code",
"execution_count": 219,
"metadata": {},
"outputs": [],
"source": [
"efficiencies={\n",
" 'MC':total_MC,\n",
" 'NN':total_NN,\n",
" 'true':test_true\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 220,
"metadata": {},
"outputs": [],
"source": [
"with open(PATH+'/efficiencies_full.pickle','wb') as f:\n",
" pickle.dump(efficiencies, f, protocol=2)"
]
},
{
"cell_type": "code",
"execution_count": 221,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"four_cells_diff_NN= np.array([\n",
" four_cells(test_reco_NN_test[i]).sum()- test_true[i].sum() for i in range(len(test_reco))\n",
"]) \n",
"four_cells_diff_MC= np.array([\n",
" four_cells(test_reco[i]).sum() - test_true[i].sum() for i in range(len(test_reco))\n",
"])\n",
"plt.hist(four_cells_diff_NN/1000, bins=30,range=(-200,0), label = 'NN max 4 cells ET - E tracking',histtype='step')\n",
"plt.hist(four_cells_diff_MC/1000, bins=30,range=(-200,0), label = 'MC max 4 cells ET - E tracking', histtype='step')\n",
"plt.legend(loc=2);\n",
"plt.xlabel('Sum of 4 max cells ET - ET true (GeV)')\n",
"plt.ylabel('dN/dET')\n",
"fig = plt.gcf()\n",
"fig.set_size_inches(8,6)\n",
"plt.savefig(PATH+'/four_cells_diff_combined.eps', format='eps', dpi=100)"
]
},
{
"cell_type": "code",
"execution_count": 222,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"four cells diff mean -746.0126953125, std 1924.9454345703125\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"four_cells_diff= four_cells_diff_NN-four_cells_diff_MC\n",
"plt.title('4 max_cells ET from Geant4 - 4 max cells ET from NN (GeV)')\n",
"plt.hist((four_cells_diff)/1000, bins=30, label = 'MC-NN', histtype='step');\n",
"plt.xlabel('E_T (GeV)')\n",
"plt.legend();\n",
"print('four cells diff mean {0}, std {1}'.format(four_cells_diff.mean(), four_cells_diff.std()))\n",
"fig = plt.gcf()\n",
"fig.set_size_inches(8,6)\n",
"plt.savefig(PATH+'/four_cells_diff.eps', format='eps', dpi=100)"
]
},
{
"cell_type": "code",
"execution_count": 223,
"metadata": {},
"outputs": [],
"source": [
"i=2"
]
},
{
"cell_type": "code",
"execution_count": 224,
"metadata": {},
"outputs": [],
"source": [
"X_A=test_true[i]\n",
"X_B=test_reco[i]\n",
"sample_nn=test_reco_NN[i]\n",
"n_H_A=n_H_B=52\n",
"n_W_A=n_W_B=64"
]
},
{
"cell_type": "code",
"execution_count": 225,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.subplot(1,3,1)\n",
"plt.gca().set_title('RealET from sim (GeV)')\n",
"plt.xlabel('X')\n",
"plt.ylabel('Y')\n",
"ax = plt.gca()\n",
"im = ax.imshow(X_A.reshape(n_H_A,n_W_A)/1000)\n",
"# create an axes on the right side of ax. The width of cax will be 5%\n",
"# of ax and the padding between cax and ax will be fixed at 0.05 inch.\n",
"divider = make_axes_locatable(ax)\n",
"cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
"\n",
"plt.colorbar(im, cax=cax)\n",
"plt.subplots_adjust(wspace=0.4,hspace=0.2)\n",
"\n",
"plt.subplot(1,3,2)\n",
"plt.gca().set_title('RecoET Geant 4 (GeV)')\n",
"#plt.imshow(X_B.reshape(n_H_B,n_W_B))\n",
"plt.xlabel('X')\n",
"plt.ylabel('Y')\n",
"ax = plt.gca()\n",
"im = ax.imshow(X_B.reshape(n_H_B,n_W_B)/1000)\n",
"# create an axes on the right side of ax. The width of cax will be 5%\n",
"# of ax and the padding between cax and ax will be fixed at 0.05 inch.\n",
"divider = make_axes_locatable(ax)\n",
"cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
"\n",
"plt.colorbar(im, cax=cax)\n",
"plt.subplots_adjust(wspace=0.4,hspace=0.2)\n",
"\n",
"plt.subplot(1,3,3)\n",
"plt.gca().set_title('RecoET from NN (GeV)')\n",
"#plt.imshow(sample_nn.reshape(n_H_B,n_W_B)/10)\n",
"plt.xlabel('X')\n",
"plt.ylabel('Y')\n",
"ax = plt.gca()\n",
"im = ax.imshow(sample_nn.reshape(n_H_B,n_W_B)/1000)\n",
"# create an axes on the right side of ax. The width of cax will be 5%\n",
"# of ax and the padding between cax and ax will be fixed at 0.05 inch.\n",
"divider = make_axes_locatable(ax)\n",
"cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
"\n",
"plt.colorbar(im, cax=cax)\n",
"plt.subplots_adjust(wspace=0.4,hspace=0.2)\n",
"\n",
"fig = plt.gcf()\n",
"fig.set_size_inches(10,8)\n",
"plt.savefig(PATH+'/single_event_{0}.png'.format(i))"
]
},
{
"cell_type": "code",
"execution_count": 226,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7fc42f7f4898>"
]
},
"execution_count": 226,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.subplot(1,2,1)\n",
"plt.imshow(test_reco_inner_NN[0].reshape(28,32))\n",
"plt.colorbar()\n",
"plt.subplot(1,2,2)\n",
"plt.imshow(test_reco_outer_NN[0].reshape(26,32))\n",
"plt.colorbar()"
]
},
{
"cell_type": "code",
"execution_count": 227,
"metadata": {},
"outputs": [],
"source": [
"with open('/disk/lhcb_data/davide/HCAL_project_full_event/csv/MCtracker_info.pickle', 'rb') as f:\n",
" tracks=pickle.load(f)\n",
" \n",
"pos_rejected={}\n",
"i=0\n",
"while os.path.exists('/disk/lhcb_data/davide/HCAL_project_full_event/reco/rejected'+str(i)+'.pickle'):\n",
"#while i < 5:\n",
" file_path = '/disk/lhcb_data/davide/HCAL_project_full_event/reco/rejected'+str(i)+'.pickle'\n",
" with open(file_path, 'rb') as handle:\n",
" pos_rejected[i]=pickle.load(handle)\n",
" i+=1"
]
},
{
"cell_type": "code",
"execution_count": 228,
"metadata": {},
"outputs": [],
"source": [
"width_X=8404.0\n",
"width_Y=6828.0\n",
"#number of events\n",
"batch_size=3000\n",
"n_batches=len(tracks['xProjections'])\n",
"\n",
"X_pixels=64\n",
"Y_pixels=52"
]
},
{
"cell_type": "code",
"execution_count": 229,
"metadata": {},
"outputs": [],
"source": [
"for j in range(n_batches):\n",
" for i in range(batch_size):\n",
" tracks[\"xProjections\"][j][i][0]/=(width_X/2)\n",
" tracks[\"yProjections\"][j][i][0]/=(width_Y/2)\n",
" \n",
" tracks[\"xProjections\"][j][i][0]*=32\n",
" tracks[\"yProjections\"][j][i][0]*=26\n",
" \n",
" tracks[\"xProjections\"][j][i][0]+=32\n",
" tracks[\"yProjections\"][j][i][0]+=26"
]
},
{
"cell_type": "code",
"execution_count": 236,
"metadata": {},
"outputs": [],
"source": [
"inner={}\n",
"outer={}\n",
"\n",
"\n",
"for n in range(start_batch,n_batches):\n",
" \n",
" inner[n]={}\n",
" outer[n]={}\n",
" \n",
" inner[n]['TOS']=np.empty(shape=(batch_size,),dtype='bool')\n",
" inner[n]['TIS']=np.empty(shape=(batch_size,),dtype='bool')\n",
" inner[n]['ET']=np.empty(shape=(batch_size,))\n",
" \n",
" outer[n]['TOS']=np.empty(shape=(batch_size,),dtype='bool')\n",
" outer[n]['TIS']=np.empty(shape=(batch_size,),dtype='bool')\n",
" outer[n]['ET']=np.empty(shape=(batch_size,))\n",
" \n",
" for event in range(batch_size):\n",
" \n",
" if event not in pos_rejected[n]:\n",
" \n",
" value_inner, pos_inner = get_4_max_cells(test_reco_inner[event])\n",
" value_outer, pos_outer = get_4_max_cells(test_reco_outer[event])\n",
" \n",
" inner[n]['ET'][event]=value_inner.sum()\n",
" outer[n]['ET'][event]=value_outer.sum()\n",
" \n",
" if value_inner.sum()>3680:\n",
" \n",
" for ntrack in range(3):\n",
" \n",
" y=int(np.floor(tracks['yProjections'][n][event][0][ntrack]))\n",
" x=int(np.floor(tracks['xProjections'][n][event][0][ntrack]))\n",
" \n",
"\n",
" if pos[0]==y or pos[0]==y+1 or pos[1]==x or pos[1]==x+1:\n",
" inner[n]['TOS'][event]=True\n",
" \n",
" else:\n",
" inner[n]['TIS'][event]=True\n",
" \n",
" \n",
" if value_outer.sum()>3680:\n",
" \n",
" for ntrack in range(3):\n",
" \n",
" y=int(np.floor(tracks['yProjections'][n][event][0][ntrack]))\n",
" x=int(np.floor(tracks['xProjections'][n][event][0][ntrack]))\n",
" \n",
"\n",
" if pos[0]==y or pos[0]==y+1 or pos[1]==x or pos[1]==x+1:\n",
" outer[n]['TOS'][event]=True\n",
" else:\n",
" outer[n]['TIS'][event]=True\n",
" \n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 307,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"TOS_evts = plt.hist(inner[2]['ET'][inner[2]['TOS']],range=(0.001,1.25e4),bins=100)\n",
"TIS_evts = plt.hist(inner[2]['ET'][inner[2]['TIS']],range=(0.001,1.25e4),bins=100)"
]
},
{
"cell_type": "code",
"execution_count": 325,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.5181666666666667"
]
},
"execution_count": 325,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(inner[2]['TIS'].mean()+outer[2]['TIS'].mean())/2"
]
},
{
"cell_type": "code",
"execution_count": 308,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tot_evts=plt.hist(inner[2]['ET'],range=(0.001,1.25e4),bins=100);"
]
},
{
"cell_type": "code",
"execution_count": 314,
"metadata": {},
"outputs": [],
"source": [
"#eff_MC = np.zeros_like(b[0])\n",
"TIS_eff_NN = np.zeros_like(tot_evts[0])\n",
"TOS_eff_NN = np.zeros_like(tot_evts[0])\n",
"for i in range(len(tot_evts[0])):\n",
" if tot_evts[0][i]!=0:\n",
" TIS_eff_NN[i]=TIS_evts[0][i]/tot_evts[0][i]\n",
" TOS_eff_NN[i]=TOS_evts[0][i]/tot_evts[0][i]"
]
},
{
"cell_type": "code",
"execution_count": 315,
"metadata": {},
"outputs": [],
"source": [
"idx2 = np.arange(0.001,1.25e4, step=1.25e4/100)"
]
},
{
"cell_type": "code",
"execution_count": 316,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(100,)"
]
},
"execution_count": 316,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"idx2.shape"
]
},
{
"cell_type": "code",
"execution_count": 318,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fc453e19be0>]"
]
},
"execution_count": 318,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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EltWnGmE6LCp0yqPckXIiYsRNbKiKk4nPaETueuFXED8RdQNzsZcYE5i6PhsKVAEXRUnuJew4iWyZVi/AcM1F1aH92TJu9CdUcYhhQLY9LUMIQBPkbvHcuz7Gw5OPfUM1TxQyUu66Mk5TwzY4TrrnLiOPWlpG9/tV5e7m9NxFi2AdJuXOVOXuMfNnppO78Ny1dTjitYt47gWqgIuiJPcSdpwgcv/G7Qfxd9/+2Yq/TxraXR9DFQe1Cu3Plqko5E5pYkO0l9JbRqh2ILoCSLNlxods5N6f5y5IVFf6hPkFPXctLaMkfnTPXdgyes7dUT13oWrTlHHKiYil2DI+KD/hmj6zhHIXkdGkhQQUU+5FqoCLoiT3EmZ4HaC3dELa/X7t9oP4xA17V/x90tDq+RiqOqhXnEJpGYAnZhK9ZaxdIZn024HoJJFmy0wI5a6vrx/PveNjqFYDYPbcg0HTMvyF5eO+5rkLW0ZX5Wr/GalqU8gzbX+AUUcSsLzP5Lnr7yFOdtRFEDDZzz1xlTFIWqZAFXBRlORewozWLP96ApR7s+tjvtV/8mA50epx5T6Um9xNtgxNeO6Rck+mZVylWCjTlun5mBiqmo/px3PvehiuVxEwkui/QgZOyyStDuGtCx89ikLq5B4RroxCpir3dM/d0W2ZIErLcFsmTbk78AKmbDwnrzISz82LAlXARVGSewkzZHXqyqdlml0PHS/oazj1cqPV9VGvOKhX8nru/JiqRu5dzxyFzFTuwpaxeu4Bxqy2TB+ee8fHcM2BB7pMyp0mlbvyutKvFuRumbLkxDz3UNWaiozkWj1Z0aqDkaRyV3PuPAppsKYUW8YPmDx5JE9Eg5F7v1XARVGSewkzTmDTsGZY+r/QLvDHskxo93wMVSm3ZXK0/LXaMrknMQWa5x4qd8sJrt3z0ag6qJomNvXpuTeqrnGghdpTPS8SOXdtHSKlQrUNVd3HdlhkIUlVm2bLpCl36sJh8d8Dk557mi0TkbsXBFGbBm0dNJwBWzQt028iqShKci9hxgkk96UO/yOZb6+eNSNsmbrr5OoKabJlXGpKy+RT7vWK3ZZhjMn18QZjOTZUtYIc+XN2uXL34US5bvEyKWrYBickd9mvBYipYRmFDL12R0QhA4Nyp2LMHuWDMlLIM22tJuXOYhWq2Z67r9oyhqsM/efMiyJVwEVRknsJM04guQsyXS3fXSXPetXJ1RXSmJZxUtIyho1WY1rG8N49n8EPGIaqjrk1sImotIIcgaWuz5U7oWbl3ifxiJ+B92sxeO7a5CFqGcSheu6AeQ5q3rUy6iaHcIev5bM8yp177kJh28m9oHLv8wRaFCW5lzDjRCp3Qe6rZMvwhl1AveqgntF6Vz5HeO6OtqFqGcphVO6OwZYxWEKiqKoulHueIiYt1ifQ7HoYrobKXbcbCtgyjtrq2Oi5x22ZqFd7dCJnQYAK8TVyd+TEIxNSY5vESZC7WqHa9cxXGbrnbqvkdQfYUC1SBVwUJbmXMKN5nP8B1EZX/K1a0nNfHeUuNnKHKjwKmadCtevzXjSuQu6uWtATIn9axp5zV9dXq9BEi4M8BTkCSx0fjVrouSdsmWKeO4Co06K2DiaVeyVcatKWCdScfAifpNsyJEixZWgyLSOVu7Rl0j8zL2Cye6bxKkN/bl6UnnuJVccJ6gjZ8wNpZcy3Vke5txTyzB2F7AUxvx3gyj0xiSmn515L8dzFyW+oSvPbMrmU++Dk7lClYZppQ9WLN8oS81HV6k5PTDpSLCTTySexVpsCpm5EwCGE+k7PuSvK3WfWDVVxVZB2ZWFDkSrgoijJvYQZJ6g6talsXq7WhmpEnn1EIb1ANvsSqDgk1s89CJhMkmSlZYS9k2bLyA3VXOTuxx8L19MMPXc+rUi3ZTww0l9ZvFm5KxuqciB03JZRo5C+1n8GyPbc005EjDpwbZ57rg1VB14QRCSsXomI4SP6c3OiSBVwUZTkXsKME0bu0R/Iam2oCjKvV/qpUPUTyt2lcc+9p1gxWcqdUoKqY57GlPDcrRWqJv84IhLxOiItoyvPYspdeO5mq0N462JD1TFUn+qdIwEYo5oqiNKuIAHThqrquft5PffkhqraZybtyiJt3f1uWhdFLnInhFxNCLmXEHIfIeT3DY+fSgi5lhByCyHkp4SQ5yz/UkucUJxMyl0hz3qF2x6BRsY61OHYAtyWiZ6nxiKzessAfFM14aeD970BhOdusmXyee5L4YlUKHe9cyJNI0wLsjx34a2LCKRpEEegDfQAuOeeacukkTthCHyVuPn3vEI1p+duUO6e2s2yiHJ/JKVlCCEOgA8CeDaAcwG8khByrnbYHwP4DGPsYgCvAPCPy73QEicYrZkTU53aUch9lTx3fUMVsFeKCnDP3WTLKMrdz6/cAZgLlKDYMtV+bJkkuYvPulF1jKPoqJI1zwup3P30DdXIc082BfP0gR6AcX36Wu3kzu9XVXbkuafZMra0TLQOdZ5q2pWFDY805X4ZgPsYY/czxroAPgXghdoxDMBY+P04gAPLt8QSq4LVsGVW23MP2w8A2QM7Op4fy7gDgOvQGIn3Yso9GZF0tRJ0Y4ES4p577gpVg+euKneTMuadGfv03J10z13YMsJrp4a+MTILryv3DM+dUctaKX8PnYgDRsByFjGpOXcovWU8bxlsmUeKcgewBcBDyu194X0q3gHgVYSQfQC+DuDNy7K6EqsDrwt0F0+oLTNSc1fNc4+UMZXKPSsOabZliKbWle/9bOVutFwQnXzqfVWoJj138VkP1xy+oZooq+/flomnZQxFTKJRVqjYTR0ffZGW6UO5OykWEgnXoVoo6li+ruUqQ/3M/CBQyD1ZcQsUI/ciVcBFkYfcTVk43ZB8JYCPMca2AngOgH8jJNlJnxDyOkLITkLIziNHjvS/2hInBm3REXLl2/0Kwtk4Xl+1IibVcx8KyT1buRtsGRqPQva8NM89kKpXwFigpKzFXqGa3gRLQLR5aFRdBEj2PHdYYFfDFmR57nIgtNxQNXjuBuUeZHnuaSci0TNeVdlhBFGegDM2VD2fgYHCZ8RYlCVes1880myZfQC2Kbe3Imm7/AaAzwAAY+y/AdQBTOkvxBj7MGPsUsbYpdPT08VWXGLlcUKrU/kfyMax+iqmZUy2TIbnbkrLaP3c1bSMTu5G5W7y02GKQuaxZQyeu6bcdXIvptzVtIypcZiIQgrlnvTcE0O0AeOGb2ytzEdgsZDE68grAkCO5as4NNeGqvh9eVolrz+wcn9k2TI3AjiLEHI6IaQKvmH6Ze2YvQCeAQCEkHPAyb2U5o9WNMN2vydgOHYrptxXP+cusutZnSE7vSDhuetFTILoqy5NbKia0zKO2XPvRlHNWqX4hqpQ7sNVF8zkuSPFx7YgU7nLyUNaEZMhC09oNJ0o05ZJ2/wVQ7h15U5o9DvK4bkDyTYI/oCe+yOK3BljHoA3AbgGwN3gqZg7CSF/Rgh5QXjYWwG8lhByG4BPAng1Yyw9S1bikQthy9RX3pYRyn3TeB3tXmCdRJQHfsDwf763W5JYXkhbxlVsmYzOkEZbRitiEkRfd2k+5V6xp2WqLoVDCWqug64XIPbnlVGQIyCUe6NqVu4OfKCwcmdGe0jYL8KOIZTyCUcxNSxaFOjKPT0Kadv8FfaO58dji0K55/PchXKPr1X13NOuLNLWfaLIPddpmjH2dfCNUvW+tyvf3wXgiuVdWolVQ3uOf62PD/xS19x5CBvH6rhwm/lE0er6oASYGuGj3xbaHmojxf7z375/Du/+xj3YNDGEF1y4OffzWj1usVBK5IZqpnI32jLxSUyC3IeqTrLPe2BOyxxfMveWEScddWKTWGvexmHiRDpcC3Pu0D33Iso9HCIdMKCSotydSJXrrQ9kz/c+yN2BD73jpQAxTHsSDbuqeT33mHJfviImXgU83PfziqCsUC2RRHuef10Gcv+jL9yO/3P9buvjSx0fw1VXDn8exHc/vtQBABxd6PT1vHaXz08F0IfnbkjLUK7chaoWBFGvODk9d3NaxkbuEpQCIJm2TLPDT6Q1l4JR16Dck/3fs5CZcxdpGVcldwqixAsDq+eeYctY0zKGIdxhH/WKm69xmIiueqCxvveBryr3R7ktU+IkRCdU7rWx9OMyMN/u4ehiF7NNO2G3eh6Gqg7Ghvgf5CDTmGaW+PscW+qP3FsKedZDqyVrYAf33HVbRlGxUG0Zx+C5B8YKVZstI046NVlkZdhUzVGhOlx1QQgBI06ic6JboIjJnHNPrkNGIAF4JD4oRC90Avgc1DTbgyv39A3VeGyRRxDTPfds5R63ZYqRe7+b1kVRknuJJNpzgFsHBpzQ/sDRJQBIJfeljo/hmovReqjcB9hUnWnydMTRhW7GkXG0eoEkd6Hg02wZxpjVlgGi4iXxtV51EsM6fN/iuRs3VH1pwUTj+AybqhlFTM2Oj0YtrLo02TIphGmDOS1j99wBu9XRl3JPiW1Kco/FFqO0TNc6IDv6zNS0DIkp9+j4QuRe4ARaFCW5l0iiPT+wageAPSG5z6VYLc0uV81jgtwHaEEgTiJ9K3eFPIVyT7NlvIAhYDAWMQFRBNJTNlSNaZlEzt1SxNSLbCOjLQOE5J7eOKzZ4xYYAAQkbssEvg+HsL7JPWsSU6TcNc89ptyF5x4dw/J47lm2jKcSMVfuSc/dvKEqkk4+o9qJaLAN1SJVwEVRkrvAh64AfvSB1V7FIwOdeaC+fOQ+27Qr6WbXw3AtsmUGUe7Hw/c5stifcm+r5Jmj/YBpODYQ2TKertxze+72IqbIc7fZMk6mLdPseJFyp/FpRXKjsGhvGastw9/DiVWfxgkzMNkyNHllEXvflNimaC8caJufAVRbJr3wS/y+9GIqcSXSZpXiyr20ZU4gGAMO3w08fOdqr+TE4thu4Lq/4j+/ivb8smymCnJf6vrGbodAqNyrrqLci5O7OIkcWyzuuddcCkIyyD18LNlbJlTuoWKXaZlK/rRMR4856uuzDfXI6bk3QuXONOUui3OUrHkexNIyFjXsMQqi/Kx6dpwFJuVumKakvi+CWEWrCpGXj9kyYR/1vj13re+9SOB0SbUQuRepAi6KktwBoNfkl4mieOdkwR3/CVz3bmDpaPz+zvLaMoDdmhGTgRpVBw4lg3nuYkO1T+Wu2jKEENTd9J7ukXLX0zLCcw9tmUAUH1EEDLE2wrbeMozFG47J9em2TBHPvetjuCrik3HlLjszLotyj7fa1ScP2QgzvqFqV+6B74MSZlfujkG5h2P5Km5azl313PnnG2gnIvGaXVRSryxsKJX7iYaI/rVOMnIXP68oWhJom20ZP2D4wi37chUJMcaw5+gSJhtcRc21zITbDGOIhBCM1d2BPHexodrq+X0VMqm2DIDMaUxWW8ZVYoGI2zIA4CuK3JaW4a8fJ422suFrt2WyPfeljodGTSh3zZYxDMzIg8hzN8cLSdBLzAxNWB1aFSsg5qCafweeYXKTCjGvVY8tBiLn7mUodxLtkeiVvGJ/oEuqqVcWNqRW1i4z1jy5+wHDh67bnbqph84C/3qyKXfx84peMgLtOaNy/+cf3I/f/fRt+OpPszs6H1vqYqHt4aKweMmu3KNNvrGhysBpGTGurh/13u75qCsqfChjSLYg1qphEhOQtGUkuWcpd8tmqW4bmY7J5bkryp1p04pEHxY1sZIHWTl3MJ9HHxXoajiKQmobqhZlHFlIZpIU/j7Tki3mKKR2QqQuQEjMc49tPIc/W49UCyn3IlXARbHmyf2O/XN4zzfvwTV3HLIf1DnJlXtLU+6dpOd+3+FFvO+/fgYA2H1kCVkQMciLT+XNx2xxyKWOh0ZIOGP1SmHPnTGGmWYPp0/x6r8jffjurYRyz7BlehZbRnruYYxOU+5qYsbWWwYwkLuhyKqQ596JPHeQuDIOiir3jJy7aSC0bsswrbkYkDz5qJCtfC0nImKyZcJ8eUTulsIv0bpAkrtrXKtHKtYrizQUqQIuijVP7vtmWgCAQ/Nt+0GS3GfjZ/K1DpNy93t8D0JR7n7A8LbP3YahioMtE0O4/8hi5kvfH5K7UO4mcvcDho4XSMIZG3ILt/1t9fim7ZmnjAAkqjGkAAAgAElEQVTob1NVVcYA976L2DKu7G2uK/dw0zEk+yBgYAyghpw7gNiMVMZYWMSk2TKJOaoW5R6qRMb4cOzhmsVz9wt67kTx3EnSlgHzEWg0w0BjalhWsaqkR6iV3E1j+VRQi3JnsnEYEwcmPXeiXWVRba3h5+STYp67g6BU7suF/bNNAMDBuRRyF547WFLFrmUIUlfJXVhUinL/6A/24Oa9s3jnC87DBVvHcX9O5e5SgvM285PErEGRN+VkoMGV+/ElbisIcj+a05YJAoZ2T+nTAk7GaQ3MxGOJrpCubsvoyl1stPL77Z57dGIR32fbMukbql0/gBewKC2jkXtR5e4Yu0JGr2tS7rZ4YdJzT1fuNs/dtKHKx/K5qLoEXdG503S1I5uOCc/dNdoyHq1ZTz5p4FXA/SWSimLtk3uo3B9OVe4L0fcnkzXTMih32TSMk/L+2Rbe91/34pnnbsALL9qMHdPD2Hu8GWtta8Keo0s4dX0Dk40qCAHmDFl3UeIvstdj9eKeu7gy6Fe5S/JUbJmhipPafsBqy1DdlokTs1CD4quTiEImbZloBCC1HgMgVKH2DVUxPzVKy7hwofSbNzTvyoN4FDJpdZiGUwRaa4FoQ1UhPeJwlWuAJG1rzj05EITqtox4fsJzj29+J4qphC1Dq6CW9aWBQtl4XmGseXIXtkyacmeC0ICTZ1M18KOrFDUtIyyq0Jb5xu0H0fEC/PFzzwEhBDumRuAFDHuPN1Nffs/RJZy+fhiUEowPVYzKfUlpQQuEtkzBtIxIymwYq2O07uLYUj7lrg7CEKhXnNT2A0L5JWwZvYgpJHFxEhCEIb5albtiuajDsYHoaiFRN0DdWNWnrtzl/NQwLQPqghKGwPfDw8Pj+91QVT138X4x5e7D14ayceWurD88Pl7EVLGSu9hQtZ2IxMYsY3FyD6gyrIMfmPzMRHVrmGYKiAOirEMMy/YLKHcWBHBJ/83ZimLNk/v+2dBzn2tZj+k156MbJ4tyb89BTktMUe7fufswzt4wgtPW843KHdP8a5o1EwQMDxxbkpubE0MVo+feVAY2A1y5t3p+5lWBCTPh668brmB6pJZ7Q9VM7llRyNCWydF+wKUkQfrCezf1luGvryh3ZQQgAJkG6rdxmJzCJDZUhUINlahsu1swCunFyD1LuTtGz11tUaDvCajIim3KaljNlmHE1Tz3FFsmTDPp3TPF1UBAq3D7jEIWrQIuijVN7owx7J9pgRD+x29LQHSXFOV6sih39eeMkXuk3OdaPdz4wHE845wN8uEd09z2SNtUfXihjXYvwPaQ3McbVYvnrit3/sddpDPkTKjUJxpVrB+p5rZl5PBpLS2Ty5YxTGICIJVhzw9QcWi8/woi793UWwaw2TL8MUoJqo5lGlOK5x7NTxW2DP+shQqO2u725wc7as5drkNR7syDr0chdavD0BYY1IVLArAgeZIN/PTYpngd1XMXA7WrDkEvCKuATZ+ZbBfM00x690xxIgqcmvXKwgYvYyN4ubGmyX2+5WGh4+ExG0YB2H33XnMWi6wOAPD0as21ivAKhREa30QWtkx9DN/fdQRewPCMx54iHx4fqmBqpJqq3PeEj+1QlLsp5x6Re5SWAYq1IBC2zMRQBeuHa7k3VNsWWyZ9Q9Vmy8RVbM/nzcEcTd1Gnnt2EVNbs2XEceYK1RTPXTuRilSMIJzA0JkxD2JpGfF+sShkUrknMuy+WbkD8eEY0Y+Wvla5MRvoyp3bMowpG8AJ5R7FVp2Q3GNrjZF7n8rd0Ld+JbGmyX1fmJR5/Gk8a23z3YPWPA6y9egxB4szJ8no11C57wvWw1dVvFTu4/jO3Ycx2ajIrLrAjqkR3H/Urtz3HOPkfnpo4YwPVYwbqk1NTY7Wirf9nW32MFZ34ToUU6N9KHcTubtZUUibLZNsP2BW7jbPXcQck7ZMPKppGpJtiEISBwjJV85PVTx3ILI4/IK2DKUElOieu2bLJJS7NihENBfTlDugqF0FvqHRmArHsKEqerpEiSZm3oSWnjuD69DEWsVrMqdmvbKwwSuYSCqKNU3uIilz6XZOTocs5M7a81jAEGYxjNbcSULuoXLfE2wEi0UhObn71VFce+9hPP0xpyQU5o7p4UzlXq9QbBjlV0MTDfOGqu4Djw0Vb/s70+xicpj3n18/XMNMsyfTKmmIhmNHfwrccy+SltH6uXsMlZhy58+zpmVMnruwjVRyN7UGTvGPAYNydwS585Muk8q9/5ieS6nVczdNHtITKCbPPVpf8v9NIDP55rXKkX66cg83VIFwUzyX5655/+HxzOVjIU1XFjYEGRHO5cbaJvdwM/XS09YBsBcyke4CFlgDs2wU3uKxE7a+VUVI6A+yDXDas1FnyPYcUGng5v2LmG32cNU5pySeumN6GMeWupizVJ3uObqE7WFSBohsGbVxFhBtqA4paRmgmHI/vtTFRIOT+9RoTd6XBX3DEhBdHJl1Y7fjBXCUjVIBacuInHsQwKU0XsWJPDl3e1pGHNcvuavzU+XxiCwOU/OuvHAosadlmIdA61+uWx0k8BAwAuooJwGp3A22TIaFJKOQyufhhBuqVbVzZ5rnHqZl9Jy7SMvADW1cw8nHBtkLviT3wbFvpoWhioOtk0MYrbk4PLsIXP8+YPah2HG0u4AFDGEGI0DzJCH35nH4oHiITfOCkm5os4R9Zb5z92G4lOCpZ08nnrpjim+q7rZYM3uUpAzAN1QZS26USuWu5NyBbM/92GIHf/utn8XId7bZk03KpkIFn8d3t3nu6mM6TFOYAMWWESTuC+UuKleF587XbZqhCsRjjqb1VV1qqFDVPXc/rtw7uucuinXinjt1+ycelxKZBNLtIaNy1wqUWODxWaUKiDz5GJR7RhTSNXjuYspUzDrL4bknUjti9qtQ7v2Qe+m5Lx/2z7SwZXIIhBBsHK9j276vAt/9c+CuL8aOc3uLWEIDbXccTmfG8mprDK3jmMcIZjAa3g5/7nBQx3fufhiXnb5OEq6KrDjkwdk2tkwMydsTod0yq3WGFDl3Mf1I2jIZyv2ff7AHf/+dXbhlb7QRPNPsYl2o3NeP8D+8ozl898iWiUchAfs0JtNwbECJQippGbcPz71qqD7V0zIAb4+Qq3GYErmba/VASbR5Hc0ZDW0GvzjxOA7R0jI5bBlNuetVrFFqxWTLZG2omjz3aEMV4JZZpucu0jLQTpoASEjupisLG4p23iyKtU3usy1JMlvGXDz72Mf5A1oipuovousMw6tNot6b019mTYI1j+M4G8E8CxW2IPf2PDrOCHYdXoxFIFVsW9eAS4kxDtnu+Wj1fKwbieavToSKWs+6t7q8aZiwb4arDihJ99x52+H9AHgzM4HZZi+yZcL3zjNuz1bEJH4WEzq9IJGUAZQiJtlbhqHi0HjnROWrrtwdSlBxiGbLJCtojRObMmyZo4sdTI3U5HsSjTxFWb1TyHMnfXvusdYCLNnzPY/nTi1rld59TLlzpS42VPN67tAbmAUefEZAnGp4M79yz7riWG6seXLfOsnJ/Xn+d7ExOASAAE2F3AMftaCFXmUUaKzDSDCfnEy0BuEtHsMMG8Es4xaLqtznGP/MnrRjvfG5FYfi1PUNo3IXkcTJhoHcNbtlqetHXQrBB2Vktf390e6jMvW06zBvG9H1Aix2PGnLCOWep+2vyXPPZctUkn86QonL9gNBgIpDEspdfNVz7kC4WWpIy6hXCkU89yMLHUyHexGAqtz5Z80yEihpSHruyQiiCr3jIwm8RBWrnuaJPT8rLSPuV1S5Cx+gTn7P3eeTshJ95cVVRsqVhQ1y87Uk98HQ7Ho4vtTFlskhwOvgWcf+DbcEZ4Kdci6wpPjqYTokqI6gMjKFKjx0WwuWV1078JdCcodQ7qIVwTwW0AAAbF03ZHm2PQ4ppiEJogWA8SFO9HrWvdX1o6KaEFnNwz5/0z6M1V08ZsOoVO5ivJ5Iy4zVXVQdmqtKtd31QUicPCNy79eWSfZzd2kyLeNZ0jJAkrjbPR/1CgUhRDnGyZFzj3vuR0LlLhC1xRXkHqpKd9C0TNzq4MpdIzPqxguADLYM1U4+KgJDi2AVhFJ4jC6P567ZMiTw4cFJvbKwoWgVcFGsWXIXMcgtE0PATf+Kse7DeJ/3cnRr6+Kbpm1B7mOoj08BAA4/fPCEr/eEo3Ucs2wEQW0ivB0p91l/CCM11+i3C5wxPYwHjjUTg58F0U4oyn089NL1rLvay10gre3vQruHb955CC+4aDPO2zwmyV20HhBXC4SQsEo1n3Ifqjgx8hQWja2/DCf3pC3jhJlvdRITz7krzbWUr7rnDiQtl7bWjhjoI+eueO4J5a4pY2HL2KyONKQqd2ZQ7jphGmwZoW5NUcM8Vxk+opNM4PtwwrF8SXK3ee4BXIck+8oHHp8slXJlYUPRKuCiWLPkvi+MQW4bJcD334eZ6Sfgh8H5WHIn4rZM2BGS1kcxOsk95mOH1z65u+1ZzGAUW7Zs5ndIz30OR706No3XU5+/Y3oYXS+QJ1EBnWiBiNwTnnuvP+X+9dsPot0L8NJLtuLMDSM4ONfGQrsnI4/q1cL6kWq+DVWlV7qA2FC1tSCwpWUA7rurvWUqDoUQ6F4Q2TVA0nMHwiSMtqGaIPdc7QciogoChqOLcXIXqRhZoi/a7hawDFI9d9PMUL2dr2HOqlC3gcH2MA3U1uGDgoTHqT1dZM5dbqime+5E657JLSQn9crChqJVwEWxdsk9JJ0zD30dWHwYc0/8PQAE83TcaMvQoXFMTnFynz328Ile7rLjUz/Zi/ddc6/5wV4LbtDGLBvB2VtOQYdVEDRnAK8LeG0c7tawacJuyQBRjxk9Dhl57tEfXtWlGK46Sc+940W56xBpbX8/d9M+nDE9jIu2TeCsU3jK577DiwlbBgCmRmq5lLs6n1Qg14aqwXMHeGMvuXEa8PYD/Sn3pOder5qUe35yn2v10PMZplVbhsbnjGZZHWngyt2cljFNHtILg3gWXt9QDdeXYsuknYg8EnV8jMbyVVB18+bcw7SM1j1Tbv6mXFnYULQKuCjWLLnvn2mh6hCM/vSjwIbzMfbYpwMAjrExoDPHiQy8OhUAKo1xrJveCABYnD28OoteRnzhlv34yA/uNxfihCq9Ux3H1klemdtZPC5PdIc6FWzOUO4ix/7g0fimqsmWEbd15d40qNKxIdfYOOyBo0u48YEZvOzx20AIwVlh3/ZdhxeNVwu8v0xe5R7/M5BRSJ1AQ9hsGYBvkorPvOsF8bSMFoU0KXfdcjHaMq7TV85d7D3ElLs+0MIwpDovnETOXfHcbco9pobtaZkgzZZJWasPB0QM1pCVoU7fnrusBxCvEe4PpF1Z2DBIFXARrF1yn23h6tHdIA/fCVz2OkwOV1F1KQ4HYTok9N3bi5zoqsMTqIxwz7099+hvHnZgroV2L8A9Bw2bw2EvGTa0DlMjNcyyEfQWj8l2vwfbVWwaT1fu6xpVOJQkNi1nmj0MV53E8OjxoQrmtJw7H/uWVO6m9sDf38XbQjzvgk0AeByz6lLcd3gxahqmXC1MhZ47y0g+tbvx+amAotyL2DKUKmkZpqVlwvYDPpPH6tA3VPURgKZjAKR67kcWkuQulbEfT8s4RTZUHbvn7hg8d1AXFeLLvizUoNwFeZrSKLk9d6nco3x5fs+dp2VkAzNB7mEL47QrCxsGqQIugrVL7jNN/Aq+CQxNAo97OQgh2DRex/5umA4Jfff2Ik+J1Ecm+bEAvKVHd5WqHzDZR+fmvYairLCvjDu8DtOjNcxiBMFSpNznMYxNE+nKnVKC9cNVHF2IE/aM0gZAxUQjSdpNA7FODlfR6vkJS0RUm4q9AIcS7Jgaxq6HFzCz1MVQxYl551MjNXT9AAud9MtmE3nWc22o2mwZItsP8H7uhpx7mnLX+sa0DJ+RIPfYiSvFljlqUO7C0mC6ci+0oWrvLePAMBBa7gXwn9PU812QJzPZHkE2SQagUrmL/jnEcZXeMvk8d2G/xJQ7oalXFtY1DVAFXAS5yJ0QcjUh5F5CyH2EkN+3HPOLhJC7CCF3EkL+Y3mX2T96x/fi0vZ/A5f8GlDl0b6NY3U81Obfi0Im0ct9aHQCcCpo0RHQ1qOb3I8udqR6NJJ7qNyro9OYHqnxQqb2jEwOLbAGNmcod4CTRVK5dzE5nCQIU/OwZteLxr4pxwHJzdfZZld2fRQ4a8OotGXWDcdPKOvDQqajC+nWjHlDtVgRExBuqCozVCtOsrdMes6dJoqY9PXVwttd1XJLIXezco+TkxxSXSgKmaLcYZg8JNsNh6TL/ETPd6pV0KqQnrubFBEC3Jbx469BXTnspOfl6y0T9eCJK/e0KwsbBqkCLoJMcieEOAA+CODZAM4F8EpCyLnaMWcB+AMAVzDGzgPwlhVYa250PB/Pbn8dBAx4wmvk/RvH69jdDP+Dh7ZMrzmHHnMwMsI36DrVcVS7s5nTgA4vtHHf4UdmHl40TButu5zcO4vA3D75eBCSe2Niiit3NgynMy9tmQUMZSp3gKtj3deeafZi3rfAuNbTPQgYV83V+H908dwZLTY50+zFNkwB4KxTRrB/toWDc62YJSPWBiBz3J4pjVJ3s9oPmIuYgNBzl/3cA60rZHZaplaJ92o3e+6GIdnUBVgAiI1N1XNf6KDqUowqFlhizqjszFjQc5cbqnHP3TF47lEMM1LDCVtGT/OoyKHcfeKAhGP2PNlF0kUldUNV8dx9i+ceDh9JvbKwYJAq4CLIo9wvA3AfY+x+xlgXwKcAvFA75rUAPsgYmwEAxtiq7kgeOjqDVzjfxcGNVwETp8r7N47XsWshJK1QufutOSxiCOMhqfi1SUxg0doeWOBPv3QnXvl/b0h0Onwk4EBI7s86dyMeOt5C5zO/AXzk52XlrWhrPDq5AcM1F0vOKGq9OcWWaWRGIYGQ3DVlPNvsWsi9irlmT1oJbc8HY7Aq9yS5J+2eM08ZAWPArQ/NJt4zr3Jv95K2h+tQVBwiq0N1dLxAKkAdPC0TFSsVSstkRSHlrFWV3MNjRMRQ89ynR2qxLD+VnrvoLTOA556m3JlvUO6CMEOSNtoywjYyRSH5z+imrJXPaY0rd9WWyeW5O4pyF5uh4fCRtCsLGwapAi6CPOS+BYDaRnFfeJ+KswGcTQj5ISHkx4SQq00vRAh5HSFkJyFk55EjK9c3femBm7COLGL27JfG7t80VscRvwEGIpU7ay9ggQ1J4qDD6zBBFvHQjH0ANGMMN+w5jiMLHdx5YN563GpBkPvzLtyEC8hu1HZ/E1g4CMzsAcDJvclqmJ7kc1J71QlUg5Y84ZHaWKwtgA1To1Uc1TYtZ5TujComGhV0/UAS5pLWpVBAkHTSlkm+rkjMNLt+QtULm+a4YUiIAGMMM80eRuvJn5UP7Ejx3NOUu+znXiAto9gyQcAw2+om1heN41PWJwg0UJS4kpZRLRkgSsUklXuxKGS6525uCiZ7rRj6zwh1GwQpyj2N3BVbxleVO83nufsBg0OpJOLYhipx0q8sLBikCrgI8pB78n+gnKws4QI4C8CVAF4J4COEkInEkxj7MGPsUsbYpdPTyVayywW/y4mZDsd7o2wcryMAhV+fjAqZOvNYREMW2lRHpzCJBZmTN2H3kSW8rvOv+HT1z/C9nz3yYpMHZtsYrbl40o71+F+Vz8Mj4X+mfTcBAHqLxzCDEZwyxtU5q4e/qtm9AIDR8XW53mc63LQUjb78gGG+3TNvqGqFTC1txJ6A3ZZJXhGctn5Yql+d+MXrps1CPb7UxVyrh9PDFsYqahXzNCbPD+AHzO65U8VzT0xiig/ryErLHJzns2jFRKtobRZbBjCT+0KS3AVpiYIgOQ2pYBFTP5470TcpDYkaqm/4qshhIam2TKC02k23ZaKrDC/MueuVqOJElHZlYcMgVcBFkIfc9wHYptzeCuCA4ZgvMcZ6jLE9AO4FJ/tVQeDxS3FaiVsLG8NNwnZ1UqpU2uG93IU9UB+bxiRZTCX3nQ8cx8/Tm/BEeg9uvue+lfgRBsL+2RY2TwyhfugmXElvxWeHfwWoNIB9NwIAgiXeemBDSO40TAlh9kG0UMfGySTZmSAIQ2yqzrV6YCxJtEByo1QMj9CVu31DtZfw1asulUO49ROKeN1mCrnvDhufnaGRJ8AnM5mUezQ/1fynU3FIrPtjvLdMnpx7VMS0O2yvcMZ0/PdhtmV0cvdTyT3RFjfw4DEKYjjhZMGhVMm5R1YHCwJUSNKWIZrVYVLu0Z6AyZbJtpACRNOeAmUjs5K6oRpZWb7IucuNZ7H5y4ePpF5Z2DBAFXAR5PlN3gjgLELI6YSQKoBXAPiydswXATwdAAghU+A2zf3LudB+EPQ42biV+H9o4SMvORPSlnG9RXTosPQjneH1GCUt7DpwHDbcsftBnEn5+a2y/0bj8OfVxIHZFjZP1IFr34UldxLvnbsSwaYLgf07AQCkzcldVCy6I6FSn3kwt98ORJuWYlN1xlApKiCah4me7nLsm5Zzr1ccDFUczCgboVHXx+TrCmtmnUb8FYei6lB5EjFBtCzWyROw2zLZ5M6VO2Ms9NwV5R4SoB8q+7TeMowx6/rSbZm4597zAxxvdmPVqYCq3KOTQaK/S07ElXtkdQRq1aoKbcQfhYHcU20Z4bnb0zKq5y5UN6Gu0rkz3XP3wrSM3vdenIhSryxsaxqgCrgIMsmdMeYBeBOAawDcDeAzjLE7CSF/Rgh5QXjYNQCOEULuAnAtgP/NGFu1PKEvyL0aJ6npkRrqFYrjGJPKveItouMqfzwNTnQ33Xu/TJ3o6D5wg/z+8eQe/PC+R1bR04HZFp7k/gy4/1o8cM7rMNOr4uj4BcCh2wGvg0pnFkvumCw0qo/x4i02uxdzwRA2Z7QeEBDkLqJ2gpBtOXcAcjRf06LcAe6Xq165OCGYrgjODMnddEIZqjqptszuI4uouTQ2WESgXjGTu5iSVKtYbBmHohcw6btXjWmZULlbopAB48fsPrKE0bor+9Orx6hrARBtqGq2zPGlLhiDQbmHryk3Cnu8IVYBOI6alonUsIg6gprTMqpy17PwUt2mpWVSrjIC4oAKW0bJlxNCUHVoTs+dRJ67dpWRdmVhX1TxKuAiyPXbZIx9nTF2NmPsDMbYX4b3vZ0x9uXwe8YY+1+MsXMZY49jjH1qJRedud7wP5Wr2TKUEmxfP4xD3rD03Gv+EnoGcp/EIj7+owcSr314vo0tS3cgAAXbeAEud+/F9+5dvs1hOdQ58IGd/yJ98rxodj0sNFt44eEPAiMbsO6pvwkAuJOcBfhd4NDtqPfm0K1EWyLDYTdM4new0IdyF4QRKfdku18Bvae7PrBZP1a1ZcT3JgIX5G46oQxXnUxb5vSpaNariqGKY0zLCLVsVe6UFzEJsnMd3q5X7ZyYlZbh7xNg95FFnDE9Eku5AP157uLEO5Wl3JnP+7EUgM1z9y0DofV2uaYqVkGetihkjzmpFlIsLaMN1K6IFhGqLcMYTxppnrvegydS7v3bMoMkkopgTVaoClumUqslHtu+fhgPdRq8kCfwMRQsIaiORgcMcXL/hR1VfPIne7GkVTje+MAMLiG70Fn3WJCznonzsAc33PtQZpl7Hhyca+EJf/ltfOg7dwOf/w3gq28BPnIV8PEXAQ/8MNdrHJht483uF7Bh4S7gOX+NTdPrsXGsjmsXw0jovhvRCBYQ1Cflc0Ymo83tBdbIbD0gMDFUgUNJ0pax5NyBqKd7pNyTKmayUY1tqEZdH5Ove+VjTsEvP/FUPP60ycRjQ1VHvo8Ju48s4oxTzPsLmyfquP/IUuL3GtkyZiKshI3Den6cwNVESVZvGQDo9HxJ7oljlBOAhMVzN/WVAaJUjOx3Yhp1lxPJtAwnVc8yVi4izFANG7LwjmEOqoRou5sCRlTPPR5BrLg0Se7CnlGGdTg0WYkq1pp6ZWHDIzDn/qgDCzdUK9WkAt0+NYwHWkMAGLBwEBV4YCq5h8r9hWfXMN/28Pmb98Wev3PPEVxMd6N6+hOBU58MBz42L92Bnz3M/VHGWKoVkIYv3XoA7eYCzrnuN4E7vwBc9SfAM/8MePgO4GPPAb7yO1GRigULu36INzlfxNEzXgKcy8sRLjltAtfsc8BGNwG7vwsHAUgjSsRMrptCwDjRLGCI+/U5oLcgmDX0eBEYqjioOlSqcDkcO5dyt7/u+FAF73rx4zBSS54kGlXXqtzbPR8PHW8ayRMALj51EocXOnLqk4DYxLS3/OWqUCRmhPXlKp0Ts9IyAC++eni+gzNOSW72RkVM2Z67UO6n6GkZfRSdqad6Tlg9d9mN0abcVatDs2UMc1Dl83PsDwTENWyoCuWukLso/BKfg9I4zHUI9Na+Yq2pVxYWDFIFXARrktwR7my71aRyP32qgaN+SObHee4b9bHogFC5nzHcxUXbJvAvP3wgVqh0+P7bMEJacLY9Edj2BDAQPIHci+/97DB+sOsonveBH+CJ7/o29hw1D49Ow1dufhCfG3k/nurcjj/0XosfbXk1cMXvAG+5HXjym4GbPgZ8/a32MYCdRZz5w7fiINaj88x3y7tfeslWPDzfwf7GuWB7ruefzUgUE50eG8JcOJFpgTWwMactA/DL/SOKLVNxiJFoCSEYb0TNw5qd5GBqAV25m7o+5kEjxZZ58FgTATMnZQDg4lO5bXXrQ7Ox+6UtY8m5V8J+7p7WHMyk3A3CXaryuw/y+olU5W4qYsppy7iaMiaBh6Co527pLeMpEUQVUUfK0JYxZOGlurUp9wwLSVXuTEupVB0a9XPnByjkHvfcE8o9tJBSryxsGKAKuAjWJrmHnnu1ZlDu64dxDJzcg+M80ENVcheKtnUcv/5zp+PQ0WP43l28hmux42Hs6K388W2XAfVxkI3n48qh+4NWI0kAACAASURBVPA33/oZXvXPN2C22QMhBG/+5M3xHiFdH9fdezi+Cabg3kMLGDl8M87z7kTnWe/Bjeuej9/8t5v4tKHKEPDMPweueAuw86PAN94mCf5bdz2M2wQBfftPMdLch7f23oAN06fI177qsafgwq3j+PKxzSAeV6K1cBMV4B0UZ8NB2b3KqNVyMGFqNGpBMBtWkeoescDEUEWO4Wtacu4A9+znWj2pBtPsnjQ0UmyZ3SlJGQB47MYx1FyKW7TePNm2DAltGeG588/CjXnuYRLD8DkJVX5XGrn36bmP1tzESVQqY0nuy6XcI3KXVZ0W5a6qYaYpd6qtL/b8HFcZAXFAEbdlxPvGPHe+kAS5i7QM1bpnSlsm5crChtJzXwYwP7RlKklyP31qGMdZWJl5lJO72xiPDqg0AKcG7NuJ593/F7il/luY/PzL8ZVb9+HmB2dwMdmFXm0SWLeDH3/qk3F+cC/GKsAfP/ccfPf3nob3vfxC3LF/Hn/1jXsAAHuPNfGSD/0Ir/6XG/Hzf/M9fOGWfYnxdF++bT+ucO8EIxRDl/wSPvrqJ8ClBG/73G38yoEQ4OffATzpTcBPPgx8//349I178dqP78Qf/OftwMIh4KZ/xY8mn4+HRi+KNdgihOB3n3k2rm+eJu8bnoh89prrYIlyEiF15bPIgWmlBcHMkrk6VeCxm8Zw7b2HcffBeTS7HmouNfrOE40qGIOcyDTb7KHmUqPKT0OjZrdlRMxwh0W5V12K87eM45a9FuWeNolJtWUcodxpTLmbfm4gIu67DszDoQSnrmskj0m1ZZKeu+63A0qMMLRxRM+UInBo1AmTr4MBQSCjjvpAaD1G6ITDq+PrG9Rzj6Y9MS2CGLNlxHsonnsQMAQMsbSMXGuY7Em9srBhgCrgIliT5A6/iy5zQA39P6ZHa2iFSRH/yG4AQKWhFNMSAjTWA/d8FfSuL6K37cm4iN2DH3/2/XjLp2/FJXQXyLbL+HEAcOrlqARt/OTXT8FrnrIDNdfBM8/dgFc/eTv+5YcP4L3fvAfP+8D3cWC2hT9+7jkYrrl416e/h+ve/SLs28f9fMYYvnTrAVzduBdk88VAfRzb1jXwR889FzfvncUnb9wbre1ZfwGc83x4178ff/2fP8REo4K7Ds5j/voPAoGHz1ReZIwyPu3saThbLoEfeutj6zbEHm85/ITnNPojd7UFgan/i4q3P+9cjA1V8MZP3IwjC51EL3cB0VVSKPaZJXO/miw0Ko60f3TsPrKEzeP11DYLF2+bwO3752JXW9Jzt9kylLcfEEQeU+4y586MSRkgbsucFvastx2Trdy55z5lIHcZIxTK3VBIlBcJzz183Szl7ivkntxQTfPcszd/GU0qd2ry3MO1qp67z6LNcN1bF8o97crCigGqgItgTZI78bvowTIZnRCMr+cTl8gs99xrI1qnhKvfDTzv74C33oPRX/8i2Pan4k+HPott/l6cSQ/APfWy6NjTnsy/7v0RsHgE+OIbgf/7DPzBs7bjvM1j+MfrdmPrZANfedPP4TVP2YGvvfnn8IkLb8czetfhix9/P44sdHDz3hkcn5nBmd17gdOfKl/6pZdsweU71uE937hHeqcgBD/Z8UaQXgtvn/wvfPK1l2MIbdRu+Rjw2OfilsV1RnInhOANv3Ahfsa2AgDWTW2MPd6tclKvjyRTJ2lQWxCY+r/Ejh2t4QOvvBgPHFvCF27db4xBAlGsUZK7oTo1D7JsGVtSRuDiUyfR8QLccyjqH5QvLRMpd5vnblXuIZkfXezKUYY6qmmNw2Ibqi6Ohk3DdBBK0WPR5qdoiFUEPOeu2DLh+/tWzz2eEefKPX6Mq2/4qms3tAjWYfTc3Sgtw3Puyia0Ysv4Ms1EzcqdOOlXFjYMUAVcBGuW3LvETgZbpyewgAYqcw8AABqjGqGd9yLg0v/JN1oJAXn+36HKuvjCxD/wx7cp5D66EZg8nXvhH3g8cNt/APt3onbPl/HhX7sUf/Scc/D51z8Zp67nl9cUAc4++BUAwFM61+PV//ITfOLHe/Hkys940YVC7oQQ/OWLH4d2L8BffO0uPHS8ibd86hb80n8ex3drz8DzO1/DOcOLeMPEDah58wgufxMOzrWsRUhPPmM99g+fD49RrF8f7+3j1/gJrjHWH7nLQqbFDo5bOkKquHzHerz1WY8BY+aMO6D0l1kStkxB5V5zjVl1xhh2HzbHDFWYNlXz2TJKEVPYy4RPK4rSMq6lq6R60jAlZQB+oqg4JFfjMJstA/Ah0pFyL27LJDz38P0j5R7/W9RH/DkssBcxBcnfn2m4h46YLSMHaosNVRK2H1A2oRVy95Q6BHkiCtTNX7eQ5z5IFXARrFly92C/9Dl9/TCOsTG4Pe67DmcR2vozgKf+b9DZBwBCgc2XxB/ffgVw/H5g80XAG24Aph4D/OTD2DIxhNc+dUfcK97zPWB+H7DlUlxI7sPSod34z1v245em9gC0Amy7PPbSZ0yP4LeuPANfuvUArnr/dfjGHYfwm089A0/89feCsAD43nvwKvY13Bychfvq56HnM2yxRBkJITjnF9+JWy//W7gV7eQXNg8bHV9veKYdaiHTbIYtI/D6p52Bq8/biHM3jRkfX5dQ7t3EMI48aFQc9HyW2MR+eL6Dpa5vTcoIbBqv45TRWsx3z9NbhqdlCip3xe5JO/norYFj5B4W5HigWGh7KeQedU40td3NC7GfwBjTlHt8I1Mu1UCYSPR8p/AYNSv3PGkZZQi3vpGZbsu40j5zKJEnGXkiChuhpV1Z2DBIFXARnBjz5wSDBF30UpT79qlhHGOj2E4OAQBG8qjVK34buONzQHUYqGl/dM94B3DBK4DtP8d98cteC3z994B9O4Gtl8aPveXfOZG++J+A//9SfPCiB/GSn27A5eROfkVQTW6gveHKM3DrQ7PYMlHHbz/jrKjI6PH/A7jxI5gE8Ife72D6Bu7Np7UP2HL6Y7Dl9Mck7neGeUpoYl1/5C6U+4PHltDzWaotI0ApwYdedYk9VTMcbx5mahqWB+Kk2ur6Me86KykjQAjBxadOxBIzkedu7wrJWHQSSEvLmKCeNNJOPvrEJtPmYHjhY7RlAN45cbk8dwDhJmRkD8mEiS0K6Smeu95/BuEc1KLKnbqg4RBumS+XaRnKixONG6qOUl1MZGtfpu0PpF1Z2BdVvAq4CNakcqdBD36acp9qyMRMi1UxPpIk1ATcGvDqrwOv+GTysZFp4PSnRJusF74CqI7yVIuK1gxw91eBC34RmDoL2HIpzjv+bfz0/7sMo8fvjFkyKuoVBx//9cvw7pdcEK8efcrvAW4dbOI03Dp8BT53E9+gzdsbRkVj02PQZhVs2rqjr+eJvie7wiKuvPaJjdgBYLTGGzzNNLthT3PzdKcsiA3bZi+urmRDrgzPHeC++wPHmrJKNrP9QGjDiJRO32kZxZbZYWhFHB1HLV0hI/94UZB7mnJnQrknpyHlRdQ7J66GA4vnLnvJi/QIgsSmK18fBQl6iftznYhIpNxlN8YwIVRx8nruBNThz1E9d1A39crCuqQBqoCLYG2Su9+FR+xksH09V+4AsICG1ftNYHg9MLoh+7jaKHDRL/Mq00Wl3/vtnwP8DnDRr/Db578UOPRT1G77NwDMSu5WjG0CfvHjIC/9CJ5x3mZJKEXI/aKrfhGd370X60/Z3NfzJhtVOJRgV9ietojC1kEIwUSjgplmDwttD37ACm+oAtFgEIHdR5YwXHUSVZsmXLxN+O5cvXe8AJSY+8IAkMMghNcvvPW4ck9Ly/Dj1w9Xjb105HEV3ZZJ+sfZ5E6XVbn7QdyWsU0eosqIusD3QQkzKnePONFkKfX5OU5EjLqRLaMp96obRjdzeO56sZJacGW7srAvqvTcBwYNuvConQzWDVex5PJ0SJM0UlVkYTzhNbxS9uZ/je675d+BDY8DNl3Ib5/3IgAE+N57AXcI2HKp8aVScfYvANsuw7PO5emXkZqLMcNkoSwQSjE+0Z8lA0QtCO4Lyb2IN27CRKOK2Wa3cAETYB/YIZIyeX7vj9s6DocS3Br67h2PD8e2PVfYMO3wPW29ZbI89yzLKN2W4US00OXvl+65xysviyDW9VL13LXioWipEbl7luZi+vpU5LFlQBy4WlrGzeu5K2kZR4tCqsNHbFcW1iUNUAVcBGvSc3dYD36K504IAWlMAU2gTdM31Qpj+mxgx9OB//5H4OBP+X+eg7cCV78nsm/GNgOnPonHKM+4CkjpT52Fy3esx2jNxaaJ+sqcrFIwNVKTFZV5NlTzYLJRwYxK7sMDKHctDrn78CKeuCPfiaxRdfGYDaP47E37sOvwIu46OG/NuAOQwyBEBLMSU+5hWsZnxr4yQGTj2JIyAjWX4ua9s3j9v/OuoS/cMo+rgZh/PN/l/9VsJ1yfqLaMjx4pRgexfvWK1SHHymmNshwnyoj7sv9Mkqx92JR79lUGoxVOxIAk7ngRE4PZc9fTMsqViDZ8xHZlYcMgVcBFsCaVuxOkkzsAVMd5eX7HWSFyB4CnvQ0Y2wIc3cX72Gy7nPvtKs5/Cf/aryWjoepSvPGqM/Gyx28d6HWKQC2SybOhmgcTjSpmlnpyU7XISUPdUBUIAoZD821jD3cbfvmJp2K07sr+78953CbrsZVQuTdDW0bc5lWc2crddShe/viteN4F6fbYs87biMlGBbuPLOL7u47iczcfDH/AyD9uesBwNZo+pCNAnNwLK/fw9blyTxYxJWwZZZNSKHe9ipWvjxqVex5yh5KW0Xu6cM89pYgpPAk7lETdMwMvMXzEdmVhwyBx0yJYs8q966Rvkg5PbgAOAr1KvpFyhXDak4HX/yD9mMe9DHjwh8D5Lxv47X7raWcM/BpFoA6TEK19B8Vko4LbHhrMlhkObRm1BcFS10PA+lvnqy4/Da+6/LTsAxFFH4UtI5W7Q+QGqB8E0r4x4a9ffmHm+7zx6WfijU8/EwDw5k/egpmHwslhClF1A4p6ylWGTxRbxjANKS+snntgVu6uotxtnSOBeFRTBWU+ejRjv4S6cEkAFgSJsXxV2VtGKfyy5dyVyKPndbnuVj33fpT7APsaRbBmyT1I8dwBYHKKKyN/Jck9D4YmgZd/bHXXMCBE1G6s7lqLc/rFZKOK2WYvdQBIFky2zHybfz82tDL/9SuusGXEhqpQ7hSe6HOeotyLoOZSNP3w9TRyT2sCpyp3x9C8Ky/saZl05c4JM8VzV4Zcx57PPDCSccUtCNiPLBdXScvwIibRX0fbUFVy7jLP7nuJ4SO2KwsbBqkCLoI1acu4rAeWQe7TG7cAQHxQR4lCEBt2aemOfjE5XEXXD3BgtgVKgLH6YDl3AdGMrMjr5UGF6rZMf2mZIqi5FC3BMQpRdQJijWwCiE0rooa2u3lhVu6+opj1KGQUL5T90A3krq5PRT5bJvTFvV7kuYdXVXxYh91zl/32HRJ1cAz8xPAR25WFDSfallmz5O7TdKLZumUb/6Y+kXpciWyIQqbl2kwFIqW+5+gSJhpV4yi8LJhsGUnuy2Qf6RBXLtKW6bNCtQhqroOWpyp3/t6dgBgbjwmoc0aXLy0Tee5Ma9glj1eUu1DDuroHwisLiy2TtdbYKL8gPpZPeO7yNTTP3VPSMjHPXbOQbFcWNgxSBVwEa9KWqeRQ7uPj49j5hPdjx4VPP0GrWrsQ5L5cm6lAdKLg5F7sdesVCkIQax4mbJnRAnHRPJAbqpoto09iqloqXIugVqFoegSoIOYfdwJqraQFls+WEfsM1py71uI2Ikxftigweu42WybPVYZU7p5sESxWUQ1/Jz5xOAFaiph4WsZBwAiIaiGJClvLlYUNpee+DKjAA3OyVeSlz33NCVjN2sfUKP+si2x62iBea+/xJi7aVuzqihDC2/4qyn2hvcK2jIhCarbMyip3ipYvyD1SoW2foFbPUu4huQ9gy0jl7usbqqEtk1DuwuroIQinpulZeICffKiBPPOciKQv7vUSEUTxO+kxGpK7XsQUpWUAwAPlyl1rYWy7srCB7xWUtsxAcFkvF7mXWB5MS1tm+QhTXAX4Qb5+NTYMaXNUV9yWofEipkqfvWWKoOY68MSfsuq5+9meO5Wee7IzY15kVqjqnrvSlyWwNBcT6zN67ob+78mDRM94bsuoXrcgd4+ZK1RV5Q5EkUd9+Ei/tsyJVu5rktyrOZV7ieXBZKOKU9c1rF0ei0D17wfx8vWe7itty7hSuXt8BidR0jIi5+6zQnsINtRcGlU+ap57WlqGERpT7npnxryIp2WieGGk3PX2A9zqiFWxGq4amLInEHs/Q4tgHeL1fK8XjuWLqE4kmnosuU+heu7idxSAAsyPlHv4OTFQ45WFDbk2gpcRa86WYUGAGumBONl9Q0osDygluP5ty7t3oV4FDKLc9SHZ860eGlXHWtgzKKpOFIWsKFn2FU3LVCg8RKQas2VScu4BcUFZE4B5GlJeOKpyd5Nq2LRZ6oV9bUTnSFMUciDlLjdU+TrUjUzhuXtMnBBVz92RLX+lcidCuQ/mufMq4FK5F4bY9GDOylx2lzgxqDgUo2FXx+VV7r0V89uBeG+ZitJiQJ1W5C+z5151aKRMVc/dS7dl1GlFLktOQ8oLN5aWSdoyppmhIkYYWKwbIG4bqbC1CFYR99zjM1dVz12s1VTE5Ci2TGz4iCR313hlYcMgiaQiWHPk3u20AADELZX7ox2ir/sgG7WNhOfurVgBExB57K2eH6tCVdMy3rIrdwcA4cShEFUrw5aJb6gGA2+oBhq5y+pXC7nH2gIb0m18fUHi/jwWkjqnVR/Ll0XugZyhGqaAQPlraEVZXLkn12cDRYCgYCKpCNYcuXvdcNZoacs86iEmMq0r0DRMoFF14kVMK6zcKzFbRlHumi3jLOMcTaHOGXW5xRASdjtjQ5WRaKAFVbod9gtxEospdxaAycpQA7kTTpiBmARlsG4YiYZcq3BYHuUe5dP1LpLi99INFCuLRX1jzMo92kMQr227srChVO4DotdpAwDIAB0WSzwyIOyYQW2ZJd2WWaGkDBBtqHa8IEbuK12hCkBR7pxwWl66584oJ0+922G/cGI5d9VzD68K0pS78NwNNiq3PUy2TPaJiCieu67cxVzb+IZqsnFYzHNXN1QV5d4XuQ+wr1EEa47cu92Q3Culcn+0Q2ykDmTL1Fyt/YBXqN99XlQU0lZtmcQkppTGYf1CWC+c3LUN1dS0DCcnvdthv7B67kGG5878aMKRTbkbyNPNkcmPhnD3Ej1dkraM1jjMjyt3UewVaNW0tisLG/JccSwn1hy5+z1O7rT03B/1EIp9oLSMVsS00spdV+vq9yuWc68I5e7GVKgHmmnLOMyHJ/LbA3ruvtY4jAQeAsarPHUIq0NXw/H1WWwZZF9lSHXt9RI9XcTvqGPx3NXeMkCUljFvqD7KlTsh5GpCyL2EkPsIIb+fctzLCCGMEFJgpNDywOsKci9tmUc7tkwMYajiLENaxkcQMDDGsND2TkhaBkDCc/fCNaxEhSrAbYI4uTvp5B72PNe7HfaLNOXuWSgmCAuAbC0K+Pr4ySf2PN+HQ1gmuUt17XuJfHnkuedLy0jlrg0fsV1Z2OCcYM8987dJeGL/gwCeCWAfgBsJIV9mjN2lHTcK4LcB3LASC80LsaFKK/XVXEaJZcCvPuk0POOcU1KbX2WhEcYp254PxrgvvFIFTECc0E0qPmAr4blzwgg0z91Hem8ZRvmGqt7tsF/Ecu5KV8i0gdCJKKTBczcpd98PTxcZVxl6Wiaecw9tmRi5++KJSoVqdNJULaTCtkyOCOdyIs9fzWUA7mOM3c8Y6wL4FIAXGo77cwDvBdBexvX1Db8Xknup3B/1qFcc7MiYJZoFdUj2fHtlWw8AGqGrnrtMlAShcl/+tEygee5Zyh2EK/e0gRl5IEiQ95ZRNlRTBkKLjpTSczfk3BlxEso9GsuX/juMhnD3EgO1K+GGajcQG6rhZ0YooOyNSOUerjXQrjJMVxZpyLMRvJzI8z9sC4CHlNv7wvskCCEXA9jGGPvqMq6tELzQc3dK5V4CwFAl6uk+HzY9X0lbhrcc4N/HbBkSqduVqFAFkraMz7JsGRcO/ES3w34hTlx6bxkSePBJ+oi/aFqTgdzD9amIhnvk3VD1Ej1dpOceaGkZ0Y9GS8sEWEbl/gjz3E3/C5l8kBAK4G8BvDXzhQh5HSFkJyFk55EjR/Kvsg8EoS3jVMsN1RLAcGjLNHueotxXVj1VZN9wNS0Txu98tuwVqsKWEfHC+IZqCpkQB64a8Vtmzx1ptoxmdZjikqBuNORaPC+nhSSVe9BL5MvF76cXgBdDaeRuVu5+Ymyg6coiDc4AVcBFkIfc9wHYptzeCuCAcnsUwPkAriOEPADgcgBfNm2qMsY+zBi7lDF26fT0dPFVpyAId/6dMgpZAtE0pqWOv+JTmASiHu5Jz73rxVXhckCo84jcfXk7PedegYMg0e2wX5jTMn7YsMtuyxAlLmkrYtKVu1hr1lWGVO6e2FCNjhe2TE8MyRafmewkGe8tI8g90GKbpiuLNAzSVrkI8pD7jQDOIoScTgipAngFgC+LBxljc4yxKcbYdsbYdgA/BvACxtjOFVlxBoTn7lZLW6ZENI2p1T0xnjsQXfbHbBlZ3BQW9ixrzj0kd0I1zz09CokwLbOsyp3ENymt5C56tUvlbtgjM6Vlcip34eGzwEsqd7GhGiN3T+4XWJW73gfHcGWRuqZHmufOGPMAvAnANQDuBvAZxtidhJA/I4S8YKUX2C+YF9oypXIvgWhDtdn1FM99hW2ZkLj1rpD/r72zjbXlKuv4/1lr9t7nnlPoC1yw9oW2oYgNElou0ApBAhTaBktMNCnBUKUGgiFRMSoNCVG+GMAgEomAAiEEeauoTVPSKKBfQOAiWFqg9vLaWrCtBQq09+w9M8sPs9bMmjVrzazZe2affXafX3Jz98ucvdfsfc4z//Vfz3oeoNi5at8fAqKinZ5ry2SQ7baMSJBQjnSxouduZ8sQlQGTnIVMm8rqaFHuoqnc05YqkjbVgmoKgUAqZGqCe+Z47qpWrlnpAmGV5657wHpmFiFW3QW8DFHvpJS6GcDNzmNvDBz7vNWHtTwmuE/Yc2dQ2TIPzStb5lFj2zLajkmcPHcA2F+YLj/D7h+ceYJ7Z7aMUapmV3dHBkqIMltGK94yuLcod+V47r5drD5l3Nbco/6jVXB3OzeVqZAmu8fjudtrImWZAbfhd49smTzXyZibpNwPG7kJ7rMjBzwSZhOwm2Q/eHKBIxO5Ut58DMbT9Sv3rHZ/KGaJLOqT15S7wE6L514Gs/2ipvsgyt28bp41UhBt3IDp28QEkWBCRe0bQxap3KWsCoe5PVfN9xL03J0dxMr13MtUyHjlvuou4GXYuuCu9Ic4Yc+dga3c09HL/RrKbBnhUe5pvT/nUMwS3bAjz+qbmFpsGRPMy13dSwb30nPPTHCX2papbx6yyU0aYVl/xvPepia7FdzzyLRN44ubHaq2527SVds8d/v7MQXWGs1Henjuq+4CXoatC+5gW4ax2LVtmZHL/RrKbJmacq+qRRb3Bw7uE1F2Nyo6DwmozgVVrVTnugfCksFd6GCZ2QXI8mY1Rhslkprn7k+F1MHWqF7AqiLZPtbEUu6uLUNEmEiBeRb23OvKvbBfGoXQPDOLEKvuAl6GrQvuSndTn7Itw6BYPJtKUQX3kTNlzHva/wO2557V7g9FYctU/rFRqm0WFDnBfVnlDhQXq6bnHrZlyhzxsuZ7M1umHJ/ZlYoqz71zQdXMBPLUm4I4lQKLtM1zrz435cwyhBXcgfrMIsSqu4CXYeuCO7I5ckXeEqLMI5MjutXeT06mo9aVMSRlcG/JlhkwFRLQtozluZug2pUtAwC53tW9rHIH6s1ITHAXLbZMI2D6FphNsDWqF2hty1cbj75YFNkyzYbaE0mtee415S4SSJWXaZuVcm/OLEKsugt4GbYuuFO6jzkS0MDZCMzhZU9Xhnzw4fXYMqamey1bRrqe+/DZMgtl8tyr1L9W5S7rwd1XvCuWxKrJUqjhrFGN0aYsl5unWCjp/3u1+qCWP5eF2/LZSEe5u235JlLEe+7lhajefMQ3swix6l6CZdi+CJjNMSdujs1UHNGt9h48uaYFVaPcxRqzZSZSB/dKuU8ktds/wg3uwyp3ailxW2aaOM2rbcoF31pwD+fF2xh1rfLMW42x3XPPazMrsxPV57kX4+tukr3qLuBl2LrgTvkCaVz6PvMIYXea4GfzdG3KPSk3MbXluQ9vy9jBvXMDE6wCWIMod0LqLKi2KXellXtbLrxRuSagF7fDbflsygXafIFENZX7NBHRee6memaj+YhnZhGClfsAUDbHAqzcmYrdqcT//XSONFdrXVBNavXcR86WSQTmtnLv2sAElCrSbPwbWrm7O0Nr2Mo9dEzZB7XytNva8tV+1DyfZ5DIoRwbZ5YInFxkgTx3n+eeNZqP+GYWITL23FdH5HMs2JZhLHanEj94sFCna1HuormJSTq2zBjZMou88twz6g7uwqjIVJfJ7likbCMR5OS5F557qDmF2brfVlzMr9wjd6hKiUxR5bk72TJ7prdu0HO3Pjudz+42H/GNL0Sl3NcXm7YyuGcc3BmL3VmC+39aqNO1eO5JMxWyWVtm2D+9qaPcU8jWLkyAZW2kq9syUjaVe2tbOVNRMcJztxcs29ryuWSQQJYioWbBrt2pxM/maVyeu5BIkDWbj3hmFsGxRJZNGJItDO4LpBzcGYvdiYTScWe92TIe5T6i5z7XStUEzC7lbgINGVsmImCGqGfLVLZMeEG1SC9sa8VnVK5PucdciFJIULavx+Qo92mCh/azOM9dX4jc5iP9lHu4KclYcHBnth6zSxUYv9wvr2LwogAAG+tJREFUYOW52/XcpZMtM3Se+0QUDZ9rC6pxtowJgHIV5R7Kc6dAMLNtmcAFoFLuVfBsa8vnkkFYwd1R7jNbuTc7MSVucCcF5Iu6LeOZWYSILZswJFsX3CXbMoyDaZINjF/uF7BSIZPwJqYxPPd5Lop0vTxDqjq6MKGyZURmlPuqO1TtbJnMuzPUYNIL2+rPVK3yLFumpS2fS0ayPDc3qO5NEzw0dxdUteeeucq9eFxkc8dzb84sQrgt+tbBFgb3BbI1Llowm8/uZL3KfeLpxCTXkC1TFQ4znnuHcteBxgTAZDDlLkvlrgLKnUSCxCxSdij33OO5e2vROGSogrvXc99v8dztmZWoLoJ+5R6R5x65EDwk2xfc1QI5B3fG4ohly6yl/ICnh2qp3EerLSOKhUmzoKq6PXej1IdT7s6CqidLxaBEAkEKIl90KndlKeO25h4udnB388t3pwn207zeVDxQW4ZK5b5fS+30zSxCqKzef3UdbF1wT9QCufC07GIesZgm2bOk26oYgqqee7jk79DZMrOJ1Mo91Vv6Y2yZ4nORue477Gt1F4nPc5ctC6om2Mp8HsyFL5WxbXu0teVzyCHLc3OV+95MNxUn4fHcVcNzN2P1Kfc+m5jYllkBDu6Mi1lQXYclA1QLqYk3FXJc5U66L2ln/1RUC6gy1wuqKyl3YeW5V557sAqiVsMy3w8G96pVnu2591DuJMpzc+2QXd3EpaqkaXnuTrZMdRH0K3cV47nnrNxXJmFbhnEwf8jrWEwFrPIDwreJaUTPXenAk+1joeI990SrW2+ru0h8nrtUzWqM1Q9U7929oNrMlokZaw5ZnltIuS8gPJ57M1vGN1bvzCI0lpSV+8pMsICSrNyZirUr9zJbxlN+YLQ8d1lZBunJKFvG5LUnytgyK+S5y2ZtGV81RuvNy/fuVO65ZXu0teVzyEiW5+YG1VK559157mSNNasp9+bMIkTVlISD+9IkSNmWYWqYBdV1bGAC7GwZS7mvIc+9rHuS7mMes6AqneA+cG0ZXzVGA0UFd+NpW8q4rS2fQ44quLvVGPemRrlTp+duFLo7Vt/MIoTqsflqKLYuuE/VAmDlzliYJtnrUu5Vtky4/MA4nnsReFR6Eos8xnMvPpeJmiOzqx0uQTNbJitSHYOee/XeoeBuVK5RvfbtmFlGThITo9wbm5iK+1UlzWp9oMhzb+5Enai6LeOdWQToUzZhKLYuuE+Qsi3D1ChtmTV57hNPD9XRs2USWVPuKURnbRkTaKZqHiwBEIvruatsDkEqqNyN6p2qeTAXvrI9bOWuF6QjZhk5SUx1cHdLBBvlPs9tz11n0ASU+zSk3CPy3MuLEiv35cizDBPKAMnNsZmKIwfludupkLSOPHcdeBYno8oPGGU8xaJIo1yBRm0ZXYysS7lPsQjbMokvuMeV/AWKbk9T+Lf9G+Vul2yoee7SE9yxqKV2+mYWISrlzp77UsznxS8U1jj1YTafR+0k2JtKnHXaepqmP/aUGRJBONW6mAhBEDRetszORFQBOtuPS4WsKffVQkHDc0/9BbsMVFPuAVumtD3qwT1VIqqNpq3cXVumrtwXrdky5gLVVO6ei0+AsovTGpX7+i4ja2C+fxI7AChh5c5UzBKJf/3DX8FjT1nP78Xzn/w4/NsfPa/xfokQo9aWMQGaUqPc29W4CZ47tMBJrPbZuLVlSCv3UFs5E9x3aBGsP2NUrqvcM8iowJWTxA75OzeZbJm5EkBWT5d0s2XMOHaoPsvwziyCg1m/ct+q4J7OTRd39tyZOmeeuh7VDhQq/ezTdxuPS0FA4cqMm+eOYnPOTleeuzXDHcRzt5t1VG/if28r6Ic898Sj3Iu2fHGzDJ8/bpgmAhNJ2M+aBcIanru1b8ZeQ/DOLAKoHjtrh2KrbJmFCe6s3JkNJPFsahoKW7kDiKrnbqcTrhrcizx3y5bRhDz3WsAMeu76Z+3gqTKkodx5B1VT2c1x7E4T7OfNzUpubZn6haj5mn2UO+e5L0k61yvjHNyZDcQs0klBIBojz91S7jG2jKXcQ7tEY2l47oZQcLcDZuAYo3Lt4NnW3MPFriXvq8a4N5U4mTWDezNbxn8h8s0sQiid5bPKLuC+bFlwfxgAQBO2ZZjNwwSMoVU7AEylWEK5W7YMrRYKGtkymtB2+5AatpEB5R4b3GuZLZ6FzN1ZgpN5vVeqUgqZ47nbmTn2BcM7swihc+FX2QXcly0L7roXZLJzwCNhmCYmYAzttwOFz28H1Zh67rWgNXCeezWw5ZW7ufgY1QsUyj2PDFuqxXMHfMpdlufgy3MvxmqnQjZnFkF65OcPRdSnRERXENEdRHSCiF7vef51RPQ1IrqViD5FRE8YfqjdmEa1csK2DLN5mI1LYyh3oL61PYPEtGPHKQmBVBVjCjXMiKXRicm8R1C5Wwo2qNz1MdYO0GJBNVK52zMIj2I+MpV4OK3bMmb2Yee52z9rL/56ZxYh8nTlXcB96QzuRCQBvBPAlQAuAvAyIrrIOezLAI4ppZ4K4AYAbxl6oDFUyp2DO7N5jKncgbo6LXaodms3EyjH8tzDwT3CczfH2MpdhTs3uShPTrrN3jTBw5n1gEi8yr32s5Zy980sgvRYKxiKGOX+TAAnlFLfUkrNAXwEwEvtA5RSn1FKPaTv/geAs4cdZhzZXNelnnJwZzaPynMfxw11lXuX514cVxwT2iUaS6O2TDmmQHC3lXQwXVIiU1RTxqSy6LHWPXdPtswswcNZQLnbLRIT//qAb2YRgvJs5V3AfYn5LTsLwF3W/bv1YyGuA/BJ3xNE9CoiOk5Ex++77774UUaS611xbMswm8joyt0KQjENsgGUaYWrBncpBJQC8lw5qZD+BUQR8LFdMtNdyrxeHm6o7dJly+xNJR6yHZWgcvdn//hmFkHydOVdwH2JeTffb6LyHkj0mwCOAXir73ml1HuUUseUUseOHj0aP8pIsoVu9MvBndlA5IjZMkA9CMUrdxPcV1voM0XS0lzVrIsozz1UfwbFwjAtacvUAnEgz73uucty3UAEbJm61SOROzOLEJSnyFfMSOpLzLvdDeAc6/7ZAO5xDyKiFwJ4A4CrlVL7wwyvH7ne8pxMOVuG2TwSOW5wl7XgHue5m8yT2AyUEELn7We5qi2QUqi0gL2w2BKsc9P02/ycilfusIKpz5bZmznKnfzZMiHlDhRrG1ELqj1SOIci5hv9IoALieh8IpoCuAbAjfYBRHQxgHejCOz3Dj/MONRCtwtjz53ZQIyPO5YtY+dQpxCYynjlHso1j8WcU5rnvfPcQ8XFgCL/npb13GvK3ZPnPk3KbKFiHEnZB7aW556ELaTMmVmE6LP5aig6v32lVArgtQBuAfB1AB9TSt1ORG8ioqv1YW8FcAqAjxPRV4joxsDLjYrSnjsrd2YTGXMTE1APYIqSWoPuEDmG8tyLc8pzONkyfs9dRiyoAvrio6rgKVQaP1ZPkS+bvZmsB1yRIFdN5d42Vnd8QVS+9uAeZbQppW4GcLPz2But2y8ceFxLoXR1twkHd2YDGdtzt2vFhOwQl4wEoAbIlpG2cm/PUgHqyj3kywNaGSvbc19OuSe+bJlpUjU4KQZlZcvYO1RbgrszswhBfS5KA7FVO1RZuTObjGnYMXT/1PL1rYqDIcXsYpR7KNc8+r2F5blH5LnXqiN2KffcVu5ZtIVEHbbM3rSp3CvP3Soclvjz3KvxxSyobqbnfmgwwX064+DObB7Vguo4f3Z2rZg2NWxjMk+G89zrwT3UM7Sm3FuCe04SpJwF1cjMnk7PfZbUc88DnnutGbeT2unOLEL0spMGYquCO7JiM8GUlTuzgYyd555M4gKmTT7Qgqq5YLnK3bczFHCqI3YodxpCuQdqy9RyzwN57vaFwbW73JlFcCx9snwGYruCe7qPhZJrrd/AMLGMvaCaWNVQQ1kqLnm5iWnFPPeAcg/VLxeRs4ycJISt3BHvuZfNNwI1XYpsGUe56zx3u7ZM24XInVmEIJWtXL+nL1sV3CmbY7FdzaWYLWJ05W4HzMjSsiZQtu0SjaHy3OsLqqGLTN3qaAnuju0hVRbs3NQclK7PHlDMRbZMfUHVq9xbFn/7pEKyLbMClM2xWFGBMMxYjF0VcjKxGnJHK3fdTWg05d6dCtk21ozqwV0gi78Qmc5KgeBeZMu4yr3pudvVM33KXUQo916brwZiu4J7PscC6yuGzzB9GFu5TyxbxtecwkepJgdS7mnmeu4h5W5ly3TYMuTsUO3ruYdquvjy3H3ZMsVryNprluOLXlDtYScNxHYF94yDO7O5jF0VcjnlPlC2jPSnQoYaQgs71bDDlhHL2jImuAfObSeR9Q5UAeUOVBeIhi1DccGdelyUhmKrgrvIF0iJgzuzmYyv3Kvf/dhGzJVyXzXPvQglbuGw0EWGhMBCL2a25eQXtocV3HvYMqLDcxeCMHFy2DO9oOp+R6Z6ZkO5OzOL4FhYua+GyOcc3JmNpcxzH2kT09TOlgkoZhdVLqgO47m7yr2tIXRIDbvjq3vuefxYRXtwB+oZRqE8d/s13LG6M4vgUPpk+QzElgV3Vu7M5jK2cp9Nk6K5BZxslBbKFMiBass0Coe1BvfiPVttGY9yjx2rCcRtC5nTaT24l567jAzuFBncVbbyBbQvWxbc58g4uDMbytjZMrNElNkfsQuq4yv39kwYIMKWgWvLxI3VBOKspY66G9xTTyokUJVEdm0Zd2YRQrLnvhoyXyALdH5hmINmdOWeVNkfctIvuMfuaA0ha6mQnlZ0Hkrl3mbLiKSmjBMVr9xFhHKf1WyZKs/dXfQux5osqdzBwX0lpFogE3FeI8Osm7GzZQrlrmvGR25iMip4deWuyw9krnIP/z0az90NmLXxkYSs2TI5VKSAMy3+2naGzmZxyr2cZTjv7c4sQvSpiTMUWxXcE7VAzsqd2VBGV+4TUSrM6OA+inK3PPeWC1nIx3bH59oysTn5MZ77TsNz1+UHXFuG/LMMd2YRok+Wz1BwcGeYNTF2bZlZIivl3tOWWXUTky/PfaEkqC24U/f6QE5V8FR5joTqC7ZtlLZMm3K3u7a1eu7+9QF3ZhEcS5/8/IHYquAu1QI52zLMhjJ2m71ZYin32OBuAmXkpqcQvmyZ0M5QQx7luctCrQPIMp1P3le5twT3nR1/tkwjFbK8EDUXVGNsmT4zjqHYquA+4eDObDBj57kXyr0IIJPI4F6q4EGzZXQ1xo5aKiZgtqVLKku5p+mi11hNueE2r3unVO4ECFHmubvlB/JgKmQPW4YXVJdnggWU5ODObCZr8dx1gatJz01MY3juXSVuK+XeciGylbsO7rGNSGJsmSOz4r3NDKZU7jLguTsXIntm0Uah3NmWWZoEafRKOsOsm/Vky/SzZYZT7s1mHV3KPQ9YHTaKquCZpsaWiQzuiVHu4XHs7kyLjV+mgmTIcyf/hUjFKnfVY2ftQGxVcJ+qBcDKndlQxlbu00SUPrddIbINk8ERW2gshFe5d3nuEbYMRFIG99wo9wEXVPemhZVlPoeubJlGzZ4+yp1tmeWZIIVKZt0HMswBMHa2zFRWC6rTyOA+uOee5dGeu7Fl2oqcKZFAqly/9nKee5vXvTtLkEGWmSxlVUiqf0cqlApJ8cGdlfuSpIs5JClW7szGMna2TGIF98m0ny0T62OHMB51miuACBlkt+dequGWsdLynrsslXv4+EK5izJ4Z7mCoKJiZH2siX+s1syijQT5yusafdma4D7ffxgAQBzcmQ1lbOUOVAGzVjOljYGCey1bBkBOorPzUB6R566s4Jn19txNl6kW5T4tlLsZS5qrRqaM/Rq+TUxmZhEizzIIUqzcl2Uxnxc32JZhNpSxPXfACu7RtswInjsKyyVWubd57iQSJCiCp/HcY8cqtNBrC+57s0K555Zy9118q1mG87lG2DJpz7WCodii4K6Ve2QKGMOsmyrPfbw/OxPIZpHK3QQct2ZKX2rZMijSILuUu/G52ypHKpFAkEKeZdUmplhbxij3lqBqlLuxs9JMeS++Zqxe5d4R3LMyP58XVJdioW0Zwcqd2VDWpdxzRZhN+zW0WNWWMadklLttdYSI8dzNxSdNF8j1gmpb/Xcb87rttkyRLWOCe5bn3k1mJpvGrdljzyxClJuvVvyM+7I1wT1dFLYMK3dmU1mH565EghQCsyRSJQq/Iu0LESERVKYS5hHB3QTd1iJnJvMmXVSpkJG16s05tdoy0wSZEqWFVHjuPuUe2sRUzSxC5D131g7F9gT3+UkAACU7BzwShvEzdrYMUAShDBKzJO5P2yj2VZU7UFy00j62jOjexGTUbqHc015jNReNNlvmiFHuqrKVfBffkIVkzyxCsOe+ItliHwAgJ2zLMJvJOpQ7RIK0T3AvlfvqO7sTQUU9d8TZMooSpEq0Vo40ajdPF2Wee6wtU6rsluOnSbGYanb2BrNlRMBCsmYWIfKsX5bPUET9BhDRFUR0BxGdIKLXe56fEdFH9fOfJ6Lzhh5oF0a5xzYGZph1U3nuIy6oigQZBJLYRVs5jC0DOMrdykAJYWYZbdjKWJXKPe5ClER47gB0cO9Q7sIodye+yG7lnvXM8hmKzt8AIpIA3gngSgAXAXgZEV3kHHYdgB8qpZ4I4C8BvHnogXaRzQvlLqas3JnNZC3KPSJg1g43yj2yuUcbiRRVtkyMctcXolaMcs9S5DrPPTZIygjlDhTBP9W2TMhzN6UDGs1HrJlFiL75+UMRc3l/JoATSqlvKaXmAD4C4KXOMS8F8AF9+wYALyCiEX+Dm+RpEdyTCXvuzGayjmwZyKQzqNrQSMo9tbb0h1AkkXaM1Ywrszz3trZ8NpXn3m0PLUrPPQ8qd1/zkRjPPc90ssealXvMu50F4C7r/t0AnhU6RimVEtGPATwGwP1DDNLmi5/4Kxy97W8bjx/Ni1RI9tyZTaXMcx8xuJNIkPdYSjP57UMs9iWCcNOt9+D4dx7AO1JATdvHoWLGaoLn+38VR5W2ZSLzxYUs0kI7C3YJiR8+nOHyt/077vnRwzjnjN3mMST9Y9Xje+hdl+MngfdJlFlQXW+ee8w36vtNVEscAyJ6FYBXAcC5554b8dZNklMegwd2z/c+973Jo/HUJz99qddlmLF5ylmn4tXPvQDPOP+M0d5j77Lr8N17noPHRx7/xGddhc/d9Z94xpMvWfm9X/3cC/CF7zwAAPjs7itw8ZPOaz3+tMuuxR3f/qWGUrQ599gVOP6tz0Dkxcz8e5Nn4im/+IzoMX3hSa/Dzz3txa3H/OSS1+BLdz2IC085BRc+/hQ870mPaxxz9NmvwJdvPxeXOo+f84yX4Phdn4XI563v8X35VJx/8eXR4x4CUqoRg+sHEF0G4E+VUi/W968HAKXUn1vH3KKP+RwRJQB+AOCoannxY8eOqePHjw9wCgzDMI8ciOhLSqljXcfFzN++COBCIjqfiKYArgFwo3PMjQCu1bd/HcCn2wI7wzAMMy6dtoz20F8L4BYAEsD7lFK3E9GbABxXSt0I4L0APkhEJwA8gOICwDAMwxwQUasoSqmbAdzsPPZG6/ZJAL8x7NAYhmGYZdmaHaoMwzBMBQd3hmGYLYSDO8MwzBbCwZ1hGGYL4eDOMAyzhXRuYhrtjYnuA/DdJX/8sRihtMGa2YZzALbjPPgcNgM+hzieoJQ62nXQgQX3VSCi4zE7tDaZbTgHYDvOg89hM+BzGBa2ZRiGYbYQDu4MwzBbyGEN7u856AEMwDacA7Ad58HnsBnwOQzIofTcGYZhmHYOq3JnGIZhWjh0wb2rWfdBQkTnENFniOjrRHQ7Ef2efvwMIvoXIrpT/3+6fpyI6B36XG4lokus17pWH38nEV0bes8Rz0US0ZeJ6CZ9/3zd/PxO3Qx9qh8PNkcnouv143cQUXvHhOHHfxoR3UBE39Dfx2WH7Xsgoj/Qv0e3EdGHiWjnMHwPRPQ+IrqXiG6zHhvssyeipxPRV/XPvINo+JaegXN4q/59upWI/pGITrOe837GoXgV+h4HRSl1aP6hKDn8TQAXAJgC+C8AFx30uKzxnQngEn37UQD+G0VT8bcAeL1+/PUA3qxvXwXgkyg6WV0K4PP68TMAfEv/f7q+ffqaz+V1AP4ewE36/scAXKNvvwvAa/Tt3wXwLn37GgAf1bcv0t/PDMD5+nuTaxz/BwD8jr49BXDaYfoeULSu/DaAI9bn/1uH4XsA8FwAlwC4zXpssM8ewBcAXKZ/5pMArlzTObwIQKJvv9k6B+9njJZ4FfoeBz2HdfyiDviBXwbgFuv+9QCuP+hxtYz3nwFcDuAOAGfqx84EcIe+/W4AL7OOv0M//zIA77Yerx23hnGfDeBTAJ4P4Cb9R3S/9Ytdfg8o6vxfpm8n+jhyvxv7uDWM/9EoAiM5jx+a7wFVX+Iz9Od6E4AXH5bvAcB5TmAc5LPXz33Derx23Jjn4Dz3awA+pG97P2ME4lXb39OQ/w6bLeNr1n3WAY2lFT0tvhjA5wE8Xin1fQDQ/5smjaHzOejzfDuAPwaQ6/uPAfAjpXSH4vp4as3RAZjm6Ad5DhcAuA/A+7W19HdEtIdD9D0opf4HwF8A+B6A76P4XL+Ew/U92Az12Z+lb7uPr5tXopg1AP3Poe3vaTAOW3CPasR90BDRKQD+AcDvK6UebDvU85hqeXx0iOglAO5VSn3JfrhlPBt3DiiU6yUA/kYpdTGAn6GwAkJs3DloT/qlKKb5Pw9gD8CVLePZuHOIpO+4D/x8iOgNAFIAHzIPeQ478HM4bMH9bgDnWPfPBnDPAY3FCxFNUAT2DymlPqEf/l8iOlM/fyaAe/XjofM5yPN8NoCrieg7AD6Cwpp5O4DTqGh+7o6nHKt+/lQUrRYP8hzuBnC3Uurz+v4NKIL9YfoeXgjg20qp+5RSCwCfAPDLOFzfg81Qn/3d+rb7+FrQC7svAfBypT0V9D+H+xH+HgfjsAX3mGbdB4ZetX8vgK8rpd5mPWU3EL8WhRdvHn+Fzhi4FMCP9ZT1FgAvIqLTtYJ7kX5sdJRS1yulzlZKnYfi8/20UurlAD6Dovm57xx8zdFvBHCNzuI4H8CFKBbC1nEOPwBwFxH9gn7oBQC+hkP0PaCwYy4lol39e2XO4dB8Dw6DfPb6uZ8Q0aX6c3mF9VqjQkRXAPgTAFcrpR6yngp9xt54pb+X0Pc4HGMvrIywyHEViiyUbwJ4w0GPxxnbc1BMr24F8BX97yoUHtunANyp/z9DH08A3qnP5asAjlmv9UoAJ/S/3z6g83keqmyZC/Qv7AkAHwcw04/v6Psn9PMXWD//Bn1ud2CEjIaOsT8NwHH9XfwTioyLQ/U9APgzAN8AcBuAD6LIxtj47wHAh1GsEyxQqNfrhvzsARzTn8k3Afw1nIXzEc/hBAoP3fxtv6vrM0YgXoW+xyH/8Q5VhmGYLeSw2TIMwzBMBBzcGYZhthAO7gzDMFsIB3eGYZgthIM7wzDMFsLBnWEYZgvh4M4wDLOFcHBnGIbZQv4fQgfxO/snDhkAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(idx2,TOS_eff_NN)\n",
"plt.plot(idx2,TIS_eff_NN)"
]
},
{
"cell_type": "code",
"execution_count": 320,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{0: array([False, False, False, ..., False, False, False]),\n",
" 1: array([False, True, False, ..., False, False, True]),\n",
" 2: array([False, False, False, ..., False, False, False]),\n",
" 3: array([ True, False, True, ..., False, False, False]),\n",
" 4: array([False, False, False, ..., False, True, False])}"
]
},
"execution_count": 320,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tracks['L0HadronDec_TIS']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Davide Lancierini