Regression_GammaEscan-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import root_numpy as rnp\n",
    "import os\n",
    "import sys\n",
    "import re\n",
    "import glob\n",
    "import h5py\n",
    "import numpy as np\n",
    "import os\n",
    "#os.environ['KERAS_BACKEND'] = 'theano'\n",
    "os.environ['KERAS_BACKEND'] = 'tensorflow'\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '5'\n",
    "from keras.models import Sequential, Model, model_from_json\n",
    "from keras.layers import Dense, Activation, Input, Dense, Dropout, merge, Reshape, Convolution3D, MaxPooling3D, Flatten\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Declaring functions for loading and saving models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def loadModel(name, weights = False):\n",
    "    json_file = open('%s.json' % name, 'r')\n",
    "    loadedmodeljson = json_file.read()\n",
    "    json_file.close()\n",
    "    \n",
    "    model = model_from_json(loadedmodeljson)\n",
    "    \n",
    "    #load weights into new model\n",
    "    if weights == True:\n",
    "        model.load_weights('%s.h5' % name)\n",
    "    #print model.summary()\n",
    "    \n",
    "    print(\"Loaded model from disk\")\n",
    "    return model\n",
    "\n",
    "\n",
    "def saveModel(model, name = \"neural network\"):\n",
    "    model_name = name\n",
    "    model.summary()\n",
    "    model.save_weights('%s.h5' % model_name, overwrite=True)\n",
    "    model_json = model.to_json()\n",
    "    with open(\"%s.json\" % model_name, \"w\") as json_file:\n",
    "        json_file.write(model_json)\n",
    "        \n",
    "def saveLosses(hist, name=\"neural network\"):    \n",
    "    loss = np.array(hist.history['loss'])\n",
    "    valoss = np.array(hist.history['val_loss'])\n",
    "    \n",
    "    f = h5py.File(\"%s_losses.h5\" % name, \"w\")\n",
    "    f.create_dataset('loss', data=loss)\n",
    "    f.create_dataset('val_loss', data=valoss)\n",
    "    f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Defining models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "input_1 (InputLayer)             (None, 25, 25, 25)    0                                            \n",
      "____________________________________________________________________________________________________\n",
      "input_2 (InputLayer)             (None, 5, 5, 60)      0                                            \n",
      "____________________________________________________________________________________________________\n",
      "reshape_1 (Reshape)              (None, 25, 25, 25, 1) 0           input_1[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "reshape_2 (Reshape)              (None, 5, 5, 60, 1)   0           input_2[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "convolution3d_1 (Convolution3D)  (None, 22, 22, 22, 3) 195         reshape_1[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "convolution3d_2 (Convolution3D)  (None, 4, 4, 55, 10)  250         reshape_2[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "maxpooling3d_1 (MaxPooling3D)    (None, 11, 11, 11, 3) 0           convolution3d_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "maxpooling3d_2 (MaxPooling3D)    (None, 2, 2, 27, 10)  0           convolution3d_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)              (None, 3993)          0           maxpooling3d_1[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "flatten_2 (Flatten)              (None, 1080)          0           maxpooling3d_2[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "merge_1 (Merge)                  (None, 5073)          0           flatten_1[0][0]                  \n",
      "                                                                   flatten_2[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "dense_1 (Dense)                  (None, 1000)          5074000     merge_1[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)              (None, 1000)          0           dense_1[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "energy (Dense)                   (None, 1)             1001        dropout_1[0][0]                  \n",
      "====================================================================================================\n",
      "Total params: 5,075,446\n",
      "Trainable params: 5,075,446\n",
      "Non-trainable params: 0\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "Regression model\n",
    "'''\n",
    "\n",
    "# ECAL input\n",
    "input1 = Input(shape = (25, 25, 25)) # This returns a tensor\n",
    "r = Reshape( (25,25,25,1))(input1)\n",
    "model1 = Convolution3D(3, 4, 4, 4, activation='relu')(r) # a layer instance is callable on a tensor, and returns a tensor\n",
    "model1 = MaxPooling3D()(model1)\n",
    "model1 = Flatten()(model1)\n",
    "\n",
    "# HCAL input\n",
    "input2 = Input(shape = (5, 5, 60))\n",
    "r = Reshape( (5,5,60,1))(input2)\n",
    "model2 = Convolution3D(10, 2, 2, 6, activation='relu')(r)\n",
    "model2 = MaxPooling3D()(model2)\n",
    "model2 = Flatten()(model2)\n",
    "\n",
    "# join the two input models\n",
    "bmodel = merge([model1,model2], mode='concat') # branched model\n",
    "\n",
    "# fully connected ending\n",
    "bmodel = (Dense(1000, activation='relu')) (bmodel)\n",
    "bmodel = (Dropout(0.5)) (bmodel)\n",
    "\n",
    "#oc = Dense(1,activation='sigmoid', name='particle_label')(bmodel) # output particle classification \n",
    "oe = Dense(1,activation='linear', name='energy')(bmodel) # output energy regression\n",
    "\n",
    "#classification, will not use yet\n",
    "#bimodel = Model(input=[input1,input2], output=[oc,oe])\n",
    "#bimodel.compile(loss=['binary_crossentropy', 'mse'], optimizer='sgd')\n",
    "#bimodel.summary()\n",
    "\n",
    "# energy regression model\n",
    "emodel = Model(input=[input1,input2], output=oe)\n",
    "emodel.compile(loss= 'mse', optimizer='adam')\n",
    "emodel.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Danny's generator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if __package__ is None:\n",
    "    import sys, os\n",
    "    sys.path.append(os.path.realpath(\"/data/shared/Software/CMS_Deep_Learning\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from CMS_Deep_Learning.io import gen_from_data, retrieve_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# defining directory to get the data from:\n",
    "data_dir = \"/bigdata/shared/LCD/NewV1/GammaEscan/GammaEscan_1_1.h5\"\n",
    "\n",
    "\n",
    "## Trying the data without the generator first\n",
    "\n",
    "# training set\n",
    "train1 = retrieve_data(data_dir, data_keys=[[\"ECAL\", \"HCAL\"], \"target\"])\n",
    "\n",
    "# validation set: (need to change this)\n",
    "#val1 = retrieve_data(data_dir, data_keys=[[\"ECAL\", \"HCAL\"], \"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# reshaping things for the training set\n",
    "(xe,xh), y = train1\n",
    "#print xe.shape\n",
    "\n",
    "#print y\n",
    "y = y[:, 1:]\n",
    "#print y.shape\n",
    "#print y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "10000/10000 [==============================] - 4s - loss: 12111.8271     \n",
      "Epoch 2/50\n",
      "10000/10000 [==============================] - 3s - loss: 303.6184     \n",
      "Epoch 3/50\n",
      "10000/10000 [==============================] - 3s - loss: 241.0949     \n",
      "Epoch 4/50\n",
      "10000/10000 [==============================] - 3s - loss: 206.8849     \n",
      "Epoch 5/50\n",
      "10000/10000 [==============================] - 3s - loss: 200.4263     \n",
      "Epoch 6/50\n",
      "10000/10000 [==============================] - 3s - loss: 199.5512     \n",
      "Epoch 7/50\n",
      "10000/10000 [==============================] - 3s - loss: 195.2283     \n",
      "Epoch 8/50\n",
      "10000/10000 [==============================] - 3s - loss: 198.4973     \n",
      "Epoch 9/50\n",
      "10000/10000 [==============================] - 3s - loss: 195.8630     \n",
      "Epoch 10/50\n",
      "10000/10000 [==============================] - 3s - loss: 200.8132     \n",
      "Epoch 11/50\n",
      "10000/10000 [==============================] - 3s - loss: 198.3170     \n",
      "Epoch 12/50\n",
      "10000/10000 [==============================] - 3s - loss: 191.7023     \n",
      "Epoch 13/50\n",
      "10000/10000 [==============================] - 3s - loss: 195.3450     \n",
      "Epoch 14/50\n",
      "10000/10000 [==============================] - 3s - loss: 197.0565     \n",
      "Epoch 15/50\n",
      "10000/10000 [==============================] - 3s - loss: 195.0711     \n",
      "Epoch 16/50\n",
      "10000/10000 [==============================] - 3s - loss: 193.7862     \n",
      "Epoch 17/50\n",
      "10000/10000 [==============================] - 3s - loss: 195.9674     \n",
      "Epoch 18/50\n",
      "10000/10000 [==============================] - 3s - loss: 198.9075     \n",
      "Epoch 19/50\n",
      "10000/10000 [==============================] - 3s - loss: 198.7262     \n",
      "Epoch 20/50\n",
      "10000/10000 [==============================] - 3s - loss: 190.3186     \n",
      "Epoch 21/50\n",
      "10000/10000 [==============================] - 3s - loss: 193.2701     \n",
      "Epoch 22/50\n",
      "10000/10000 [==============================] - 3s - loss: 188.3092     \n",
      "Epoch 23/50\n",
      "10000/10000 [==============================] - 3s - loss: 192.5533     \n",
      "Epoch 24/50\n",
      "10000/10000 [==============================] - 3s - loss: 191.5516     \n",
      "Epoch 25/50\n",
      "10000/10000 [==============================] - 3s - loss: 190.8765     \n",
      "Epoch 26/50\n",
      "10000/10000 [==============================] - 3s - loss: 187.2469     \n",
      "Epoch 27/50\n",
      "10000/10000 [==============================] - 3s - loss: 189.5273     \n",
      "Epoch 28/50\n",
      "10000/10000 [==============================] - 3s - loss: 186.5553     \n",
      "Epoch 29/50\n",
      "10000/10000 [==============================] - 3s - loss: 192.1469     \n",
      "Epoch 30/50\n",
      "10000/10000 [==============================] - 3s - loss: 198.2160     \n",
      "Epoch 31/50\n",
      "10000/10000 [==============================] - 3s - loss: 193.0357     \n",
      "Epoch 32/50\n",
      "10000/10000 [==============================] - 3s - loss: 204.9666     \n",
      "Epoch 33/50\n",
      "10000/10000 [==============================] - 3s - loss: 189.8713     \n",
      "Epoch 34/50\n",
      "10000/10000 [==============================] - 3s - loss: 195.1957     \n",
      "Epoch 35/50\n",
      "10000/10000 [==============================] - 3s - loss: 189.8351     \n",
      "Epoch 36/50\n",
      "10000/10000 [==============================] - 3s - loss: 191.0446     \n",
      "Epoch 37/50\n",
      "10000/10000 [==============================] - 3s - loss: 189.4428     \n",
      "Epoch 38/50\n",
      "10000/10000 [==============================] - 3s - loss: 186.2125     \n",
      "Epoch 39/50\n",
      "10000/10000 [==============================] - 3s - loss: 194.5492     \n",
      "Epoch 40/50\n",
      "10000/10000 [==============================] - 3s - loss: 184.8964     \n",
      "Epoch 41/50\n",
      "10000/10000 [==============================] - 3s - loss: 194.3190     \n",
      "Epoch 42/50\n",
      "10000/10000 [==============================] - 3s - loss: 189.2124     \n",
      "Epoch 43/50\n",
      "10000/10000 [==============================] - 3s - loss: 194.1575     \n",
      "Epoch 44/50\n",
      "10000/10000 [==============================] - 3s - loss: 196.2227     \n",
      "Epoch 45/50\n",
      "10000/10000 [==============================] - 3s - loss: 192.8709     \n",
      "Epoch 46/50\n",
      "10000/10000 [==============================] - 3s - loss: 191.2555     \n",
      "Epoch 47/50\n",
      "10000/10000 [==============================] - 3s - loss: 184.1833     \n",
      "Epoch 48/50\n",
      "10000/10000 [==============================] - 3s - loss: 190.8202     \n",
      "Epoch 49/50\n",
      "10000/10000 [==============================] - 3s - loss: 185.7501     \n",
      "Epoch 50/50\n",
      "10000/10000 [==============================] - 3s - loss: 189.7726     \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.\r\n",
      "W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.\r\n",
      "W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.\r\n",
      "W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.\r\n",
      "W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.\r\n",
      "W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.\r\n",
      "I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties: \r\n",
      "name: GeForce GTX 1080\r\n",
      "major: 6 minor: 1 memoryClockRate (GHz) 1.7335\r\n",
      "pciBusID 0000:0c:00.0\r\n",
      "Total memory: 7.92GiB\r\n",
      "Free memory: 6.05GiB\r\n",
      "I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0 \r\n",
      "I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0:   Y \r\n",
      "I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:0c:00.0)\r\n"
     ]
    }
   ],
   "source": [
    "hist1 = emodel.fit([xe, xh], y, nb_epoch=50,\n",
    "                    #validation_data = [vx,vy], vy,\n",
    "                    verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def show_losses( histories ):\n",
    "    plt.figure(figsize=(5,5))\n",
    "    #plt.ylim(bottom=0)\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.title('Training Error by Epoch')\n",
    "    colors = []\n",
    "    do_acc = False\n",
    "    \n",
    "    for label,loss in histories:\n",
    "        color = tuple(np.random.random(3))\n",
    "        colors.append(color)\n",
    "        l = label\n",
    "        vl= label + \" validation\"\n",
    "        \n",
    "        if 'acc' in loss.history:\n",
    "            l += ' (acc %2.4f)' % (loss.history['acc'][-1])\n",
    "            do_acc = True\n",
    "            \n",
    "        if 'val_acc' in loss.history:\n",
    "            vl += ' (val acc %2.4f)' % (loss.history['val_acc'][-1])\n",
    "            do_acc = True\n",
    "        plt.plot(loss.history['loss'], label=l, color=color)\n",
    "        \n",
    "        if 'val_loss' in loss.history:\n",
    "            plt.plot(loss.history['val_loss'], lw=2, ls='dashed', label=vl, color=color)\n",
    "\n",
    "    plt.legend()\n",
    "    plt.yscale('log')\n",
    "    plt.show()\n",
    "    \n",
    "    if not do_acc: return\n",
    "    \n",
    "    plt.figure(figsize=(5,5))\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Accuracy')\n",
    "    \n",
    "    for i,(label,loss) in enumerate(histories):\n",
    "        color = colors[i]\n",
    "        if 'acc' in loss.history:\n",
    "            plt.plot(loss.history['acc'], lw=2, label=label+\" accuracy\", color=color)\n",
    "            \n",
    "        if 'val_acc' in loss.history:\n",
    "            plt.plot(loss.history['val_acc'], lw=2, ls='dashed', label=label+\" validation accuracy\", color=color)\n",
    "    \n",
    "    plt.legend(loc='lower right')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Function to reshape the data, parameter to Danny's generator\n",
    "def reshapeData(inp):\n",
    "    (xe, xh), y = inp\n",
    "    energy = [y[:,1:]]\n",
    "    return (xe, xh), energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Using Danny's generator\n",
    "\n",
    "train_dir = \"/home/vitoriabp/CaloImage_2017/GammaEscan/train\"\n",
    "valid_dir = \"/home/vitoriabp/CaloImage_2017/GammaEscan/valid\"\n",
    "test_dir = \"/home/vitoriabp/CaloImage_2017/GammaEscan/test\"\n",
    "\n",
    "# training set:\n",
    "train2 = gen_from_data(train_dir, batch_size=500, data_keys=[[\"ECAL\", \"HCAL\"], \"target\"], prep_func=reshapeData)\n",
    "\n",
    "# validation set:\n",
    "val2 = gen_from_data(valid_dir, batch_size=500, data_keys=[[\"ECAL\", \"HCAL\"], \"target\"], prep_func=reshapeData)\n",
    "\n",
    "#testing set:\n",
    "test2 = gen_from_data(valid_dir, batch_size=500, data_keys=[[\"ECAL\", \"HCAL\"], \"target\"], prep_func=reshapeData)\n",
    "\n",
    "#check = ModelCheckpoint(filepath=\"temp.hdf5\", verbose=1)\n",
    "#early = EarlyStopping(monitor='val_loss', patience=10, verbose=1, mode='auto')\n",
    "#emodel.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 25, 25, 25)\n",
      "(500, 5, 5, 60)\n",
      "(500, 1)\n"
     ]
    }
   ],
   "source": [
    "# check training data\n",
    "#print(train2)\n",
    "#for x in train2:\n",
    "    #print('moo')\n",
    "    #pass\n",
    "(xe2, xh2), y2 = next(train2)\n",
    "#txe2 = ecal input\n",
    "#txh2 = hcal input\n",
    "#ty2 = energy target\n",
    "\n",
    "print(xe2.shape)\n",
    "print(xh2.shape)\n",
    "print(y2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "1000/1000 [==============================] - 12s - loss: 182.8920 - val_loss: 30.1014\n",
      "Epoch 2/50\n",
      "1000/1000 [==============================] - 13s - loss: 233.9115 - val_loss: 16.7284\n",
      "Epoch 3/50\n",
      "1000/1000 [==============================] - 0s - loss: 196.2614 - val_loss: 35.2665\n",
      "Epoch 4/50\n",
      "1000/1000 [==============================] - 14s - loss: 227.5551 - val_loss: 27.2878\n",
      "Epoch 5/50\n",
      "1000/1000 [==============================] - 12s - loss: 167.8668 - val_loss: 13.0923\n",
      "Epoch 6/50\n",
      "1000/1000 [==============================] - 16s - loss: 189.5012 - val_loss: 35.7396\n",
      "Epoch 7/50\n",
      "1000/1000 [==============================] - 11s - loss: 179.1544 - val_loss: 31.2451\n",
      "Epoch 8/50\n",
      "1000/1000 [==============================] - 0s - loss: 195.6350 - val_loss: 20.2603\n",
      "Epoch 9/50\n",
      "1000/1000 [==============================] - 10s - loss: 197.9208 - val_loss: 18.5359\n",
      "Epoch 10/50\n",
      "1000/1000 [==============================] - 0s - loss: 186.9233 - val_loss: 27.4769\n",
      "Epoch 11/50\n",
      "1000/1000 [==============================] - 10s - loss: 182.0564 - val_loss: 19.9727\n",
      "Epoch 12/50\n",
      "1000/1000 [==============================] - 15s - loss: 179.2143 - val_loss: 18.1257\n",
      "Epoch 13/50\n",
      "1000/1000 [==============================] - 0s - loss: 216.5001 - val_loss: 18.2810\n",
      "Epoch 14/50\n",
      "1000/1000 [==============================] - 10s - loss: 204.6296 - val_loss: 24.7237\n",
      "Epoch 15/50\n",
      "1000/1000 [==============================] - 16s - loss: 183.1688 - val_loss: 15.7462\n",
      "Epoch 16/50\n",
      "1000/1000 [==============================] - 14s - loss: 190.6143 - val_loss: 16.5706\n",
      "Epoch 17/50\n",
      "1000/1000 [==============================] - 11s - loss: 213.4270 - val_loss: 25.9195\n",
      "Epoch 18/50\n",
      "1000/1000 [==============================] - 0s - loss: 189.7890 - val_loss: 15.5607\n",
      "Epoch 19/50\n",
      "1000/1000 [==============================] - 13s - loss: 196.1408 - val_loss: 18.9180\n",
      "Epoch 20/50\n",
      "1000/1000 [==============================] - 0s - loss: 200.5760 - val_loss: 16.8274\n",
      "Epoch 21/50\n",
      "1000/1000 [==============================] - 12s - loss: 205.0261 - val_loss: 17.0407\n",
      "Epoch 22/50\n",
      "1000/1000 [==============================] - 11s - loss: 172.3310 - val_loss: 35.6185\n",
      "Epoch 23/50\n",
      "1000/1000 [==============================] - 0s - loss: 182.7713 - val_loss: 14.0795\n",
      "Epoch 24/50\n",
      "1000/1000 [==============================] - 12s - loss: 192.5765 - val_loss: 14.6678\n",
      "Epoch 25/50\n",
      "1000/1000 [==============================] - 13s - loss: 172.8643 - val_loss: 17.8881\n",
      "Epoch 26/50\n",
      "1000/1000 [==============================] - 11s - loss: 190.8247 - val_loss: 17.6172\n",
      "Epoch 27/50\n",
      "1000/1000 [==============================] - 10s - loss: 164.4130 - val_loss: 19.2821\n",
      "Epoch 28/50\n",
      "1000/1000 [==============================] - 0s - loss: 225.0165 - val_loss: 28.4806\n",
      "Epoch 29/50\n",
      "1000/1000 [==============================] - 13s - loss: 186.9067 - val_loss: 14.1676\n",
      "Epoch 30/50\n",
      "1000/1000 [==============================] - 0s - loss: 189.7929 - val_loss: 32.3403\n",
      "Epoch 31/50\n",
      "1000/1000 [==============================] - 7s - loss: 174.3801 - val_loss: 30.5708\n",
      "Epoch 32/50\n",
      "1000/1000 [==============================] - 9s - loss: 215.4912 - val_loss: 16.8898\n",
      "Epoch 33/50\n",
      "1000/1000 [==============================] - 0s - loss: 188.0268 - val_loss: 21.1827\n",
      "Epoch 34/50\n",
      "1000/1000 [==============================] - 10s - loss: 202.1201 - val_loss: 23.7285\n",
      "Epoch 35/50\n",
      "1000/1000 [==============================] - 12s - loss: 181.6510 - val_loss: 24.5771\n",
      "Epoch 36/50\n",
      "1000/1000 [==============================] - 12s - loss: 185.6347 - val_loss: 21.1953\n",
      "Epoch 37/50\n",
      "1000/1000 [==============================] - 9s - loss: 165.0308 - val_loss: 76.1071\n",
      "Epoch 38/50\n",
      "1000/1000 [==============================] - 0s - loss: 188.1158 - val_loss: 18.5295\n",
      "Epoch 39/50\n",
      "1000/1000 [==============================] - 13s - loss: 177.1975 - val_loss: 16.9803\n",
      "Epoch 40/50\n",
      "1000/1000 [==============================] - 0s - loss: 191.2516 - val_loss: 15.7197\n",
      "Epoch 41/50\n",
      "1000/1000 [==============================] - 8s - loss: 176.2804 - val_loss: 17.1013\n",
      "Epoch 42/50\n",
      "1000/1000 [==============================] - 12s - loss: 172.5232 - val_loss: 18.3275\n",
      "Epoch 43/50\n",
      "1000/1000 [==============================] - 0s - loss: 173.1006 - val_loss: 16.9765\n",
      "Epoch 44/50\n",
      "1000/1000 [==============================] - 8s - loss: 172.9162 - val_loss: 30.6495\n",
      "Epoch 45/50\n",
      "1000/1000 [==============================] - 10s - loss: 194.7239 - val_loss: 19.3505\n",
      "Epoch 46/50\n",
      "1000/1000 [==============================] - 13s - loss: 180.5443 - val_loss: 15.9029\n",
      "Epoch 47/50\n",
      "1000/1000 [==============================] - 9s - loss: 202.4096 - val_loss: 16.0485\n",
      "Epoch 48/50\n",
      "1000/1000 [==============================] - 0s - loss: 168.8641 - val_loss: 21.7705\n",
      "Epoch 49/50\n",
      "1000/1000 [==============================] - 13s - loss: 169.1408 - val_loss: 16.6005\n",
      "Epoch 50/50\n",
      "1000/1000 [==============================] - 0s - loss: 174.7108 - val_loss: 16.2511\n"
     ]
    }
   ],
   "source": [
    "hist2 = emodel.fit_generator(train2, samples_per_epoch=1000, \n",
    "                           nb_epoch=50,\n",
    "                           validation_data = val2, \n",
    "                           nb_val_samples=1000, verbose=1,\n",
    "                        #validation_split=0.2,\n",
    "                  #callbacks=[EarlyStopping(monitor='val_loss', patience=5, verbose=1, mode='min'),\n",
    "                            #ModelCheckpoint(filepath='simple.h5', verbose=0)]\n",
    "                            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SJUvg4eFhFcf777+P2NhY9O7dG7Nnz8aNGzewaNEiREZG4scffzTtp6qUQn5+Pnr16oX2\n7dtjzpw52L59O+bOnYvw8HDExcWZ2hw9ejRWrFiBkSNH4plnnkFqaioSEhJw4MABfP3119BqtaY2\njx49iuHDhyMuLg6jR49Go0aNAACLFy9G06ZN8eCDD8LNzQ0bN27EU089BRHB2LFjAQBvvfUWnn76\nafj6+mLKlCkQEdSqVQsAcP78eXTo0AFZWVl45plnUL16dSxfvhwxMTFYt26d1fV9+eWXUa1aNUya\nNAm3bt2Ch4eH3defnK80VvlCRKrcF4BWACQlJUWodKWkpEhxr/WN6zfll4O/lfrXjes3nfLZpk+f\nLkopGTt2rKksLy9P6tWrJ1qtVt544w1T+ZUrV8Tb21tGjBghIiLz588XjUYjSUlJpjq5ubnSsWNH\n8fPzk8zMTBER+fjjj0UpJXPmzDHVy8/Pl6ioKNFoNLJ8+XJTeffu3aVly5aSk5NjEWenTp2kUaNG\npte7du0SjUYjycnJxf6s8fHxotVq5dChQ6ay9PR0CQoKEo1GI2lpaSIikpmZKYGBgTJmzBiL48+f\nPy8BAQESFxdnKouNjRWNRiOzZs2yqNuqVStp27at6fWePXtEKSUffPCBRb1t27aJUsriGoaGhopG\no5EvvvjC6jNkZWVZlfXu3VvCw8Mtypo2bSrR0dFWdcePHy8ajUb27t1rKsvMzJQGDRpIgwYNTGW7\ndu0SpZSEh4fLrVu3rNqhslXU7yDj+wBaiYO5pUr3UKl8OfH7KQzr/VSpnyfp84WIaH6XU9pSSmHU\nqFGm1xqNBm3atMEnn3yCkSNHmsr9/f3RqFEjHD9+HACwefNm1K5dG0OHDjXV0Wq1GDduHIYPH47k\n5GT07dsXmzdvhru7O8aMGWNxzvj4eOzZs8dUlp6ejp07d+Lll1+2+ou7Z8+emDFjBs6cOYM6derY\n9Tm3bt2KDh06oFmzZqaygIAAPProo/jvf/9rKtu2bRsyMjIwdOhQXLp0ySLm++67Dzt37rRq27wn\nCgCRkZFYuXKl6fXatWsREBCA7t27W7R57733wsfHBzt37rS4jmFhYejRo4fVeapVq2b699WrV5GT\nk4OoqChs27YN165dg6+v722vwZYtW9CuXTt06NDBVKbT6TB69Gj85z//wS+//IImTZqY3ouNjWWv\ntIphQqVyIzS8HpI+X1gm53Gmgs9Y9Pf3h6enJ6pXr25VfvnyZQD658TedZd1Uo+IiICIIC0tzVSv\nTp068Pb2tqhnHMY0+v333yEimDp1KqZMmWLVrlIK58+ftzuhpqWloWPHjlbl4eHhNuOIjo62GYOf\nn59FmaenJ4KCgizKAgMDkZ6ebnp97NgxXLlyBcHBwTbbPH/+vEVZWFiYzc/w9ddf48UXX8Q333xj\n8XxepRQyMjKKTKhpaWlo3769VbnxsYRpaWkWCTU0NPS27VHlw4RK5YaXt6fTeo5lyTh/V1QZgFJb\n6Zmfnw8AmDhxInr16mWzTsHkV1pxKKWwcuVK09yjOTc3y185hV2ngm3WqlULq1evtnn9atasafHa\ny8vLqs7x48fRo0cPREREYN68eahXrx48PDywadMmzJ8/33T9nMlWHFS5MaESuUBISAgOHz5sVX7k\nyBEAf/duQkJC8OWXX+LGjRsWvdSjR49aHNegQQMAgLu7u8WqVGfG+/vvv1uVHzt2zOJ1w4YNISKo\nWbOm0+Jo2LAhduzYgY4dO1oM25bExo0bkZ2djY0bN+LOO+80le/YscOqbmGbXoSEhODXX3+1Kjd+\nz0JCQuyKjSoP3jZD5AJ9+/bF2bNnsWbNGlNZXl4eEhIS4Ovri6ioKFO9nJwcLFq0yFQvPz8fCQkJ\nFr/4a9asia5duyIxMRFnz561Ot/FixcdirdXr17Yt28fDh06ZCq7fPkyVq9ebVXPz88Pr7zyCnJz\nc50Sx5AhQ5Cbm4uXXnrJ6r28vLxirdI09oTNe6IZGRlYtmyZVV2dTmfztqS+ffviu+++w7fffmsq\nu379Ot555x2EhYVZDPdS1cQeKpELjB49GomJiYiNjcUPP/xgum1m3759eOutt6DT6QAA/fv3R6dO\nnTB58mSkpqaiSZMmWL9+vc17Gt9++21ERkaiWbNm+Oc//4kGDRrg3Llz2LdvH/766y/8+OOPprol\nHXp+7rnnsHLlSvTo0QPx8fHQ6XRYsmQJQkJCkJ6ebkruvr6+WLRoER5//HG0atUKQ4cORc2aNXHy\n5Els2rQJnTt3xoIFC0p07qioKMTFxeG1117DgQMH0LNnT7i7u+O3337D2rVrsWDBAgwcOPC2bRiP\n6devH+Li4nDt2jUsWbIEtWrVsvoDpHXr1li8eDFmzZqF8PBwBAcHIzo6GpMnT0ZSUhJ69+6NcePG\noXr16li2bBnS0tKwfv36En0mqqQcXSZcEb9guG0mKipK+vfvL6tXr77NomtyRElum6lopk+fLhqN\nRi5dumRRHhsbK35+flb1u3btKs2bNze9vnDhgowaNUqCg4PF09NTWrRoIStWrLA6Lj09XZ544gkJ\nCAiQwMBAiY2NlYMHD1rdNiMikpqaKrGxsXLHHXdItWrVpF69ehITEyMbNmww1bHnthkRkYMHD0qX\nLl3Ey8tL6tevL7Nnz5aEhATRaDRy/vx5i7rJycnSp08fCQwMFG9vb7nrrrtk5MiRsn///iKv0/Tp\n00Wr1VqVL1myRNq2bSs6nU78/f2lRYsW8vzzz8vZs2dNdcLCwiQmJsZm/J999pm0bNlSvL29pUGD\nBvLmm2/K0qVLLW77ERE5d+6c9O/fX/z9/UWj0VjcQpOamipDhgyR6tWri7e3t7Rv3162bNlicR7j\n9V23bl0RV5TKQmG/g1avXi39+/eXqKgop902o6SUFkmUZ0qpVgBSUlJS0KpVK1eHU6nt378frVu3\nBq915TR+/Hi8++67yMzM5Ib7VC4V9TvI+D6A1iKy35FzcQ6ViIql4J64ly5dwsqVKxEZGclkSgTO\noRJVaVlZWUUu6gkKCoKbmxs6dOiArl27IiIiAmfPnsV7772Ha9euYerUqWUULVH5xoRKVIWtWbMG\nI0aMKPR9pRR27tyJqKgoPPDAA1i7di3effddKKXQunVrLF26FJ06dSrDiInKLyZUoiqsd+/e2L59\n+23rtGjRAgAwc+ZMzJw5syzCIqqQmFCJqrBatWrZ3NGIiEqOi5KIiIicgAmViIjICZhQiYiInKBK\nz6FOmDAB/v7+GDZsGIYNG+bqcIiIqIwkJSUhKSmpWHtBF1eVTqjz5s3j7j1ERFWQsSNltlOSwzjk\nS0RE5ARMqERERE7AhEpUQaWlpUGj0WDFihUlPjY5ORkajQa7d+8uhcioPNBoNBbPkF22bBk0Gg1O\nnjxZ5LGhoaEYOXKkU+OJjY1FWFiYU9ssb5hQiaoobmhfuEWLFmH58uWuDsOplFLF/p7b+7Nx5swZ\nzJgxw+JB9OZtajSVO+VU7k9HRGSHhQsXVrqE+vjjj+PmzZuoX79+qZ3j9OnTmDFjBg4cOGD13pIl\nS3D06NFSO3d5wIRKRJVewUfPucKtW7fgyudPK6Xg4eFRque43efTarVwd3cv1fO7GhMqkZ2mT58O\njUaDY8eO4bHHHkNAQACCg4Mxbdo0AMCpU6cwYMAA+Pv7o06dOpg7d65VGxcuXMCoUaNQu3ZteHl5\noWXLljbnRDMyMhAbG4uAgAAEBgZixIgRuHLlis24fv31VwwaNAhBQUHw8vJC27ZtsXHjRqd85kOH\nDqFLly7w9vZGvXr1MGvWLCxdutTm3NyWLVsQFRUFHx8f+Pn5oV+/fvjll18s6sTGxsLX1xenT5/G\ngAED4Ovri+DgYEyaNMnql7OIYP78+WjatCm8vLxQu3ZtjBkzxuo6hIaGIiYmBtu2bUPbtm3h5eWF\nd955BwCwdOlSdO/eHbVq1YKnpyfuueceLF682OL4sLAw/Pzzz9i1axc0Gg00Gg26detmej81NRWD\nBw9GUFAQdDodOnTogM2bN1u0YZyjXrNmDaZMmYK6detCp9Ph2rVrVtc0NzcXQUFBGDVqlNV7165d\ng5eXF5577jkAQE5ODqZNm4Y2bdogICAAPj4+iIqKwq5du2x9uywUNoc6c+ZM1KtXDzqdDt27d7f6\nHgFAeno6Jk6ciObNm8PX1xf+/v7o27evxdBucnIy2rVrB6UUYmNjodFooNVqTT/PtuZQb9y4gWef\nfRb169eHp6cnGjdujDlz5lidX6PRYNy4cfjkk0/QrFkzeHp6omnTpti6dWuRn7ssVen7UIkcYZxn\neuSRR9CkSRO8/vrr2LRpE2bNmoXq1asjMTER3bt3x+zZs7Fq1SpMmjQJ7dq1Q+fOnQHoe01dunTB\n8ePHER8fj9DQUHz00UeIjY1FRkYG4uPjTeeKiYnB3r17MXbsWDRu3BgbNmzAE088YTXX9fPPP6Nz\n586oW7cunn/+eeh0Onz44YcYMGAA1q9fjwcffNDuz3v69GlER0dDq9XihRdegLe3N5YsWQIPDw+r\nON5//33Exsaid+/emD17Nm7cuIFFixYhMjISP/74o2nYUSmF/Px89OrVC+3bt8ecOXOwfft2zJ07\nF+Hh4YiLizO1OXr0aKxYsQIjR47EM888g9TUVCQkJODAgQP4+uuvodVqTW0ePXoUw4cPR1xcHEaP\nHo1GjRoBABYvXoymTZviwQcfhJubGzZu3IinnnoKIoKxY8cCAN566y08/fTT8PX1xZQpUyAipgcI\nnD9/Hh06dEBWVhaeeeYZVK9eHcuXL0dMTAzWrVtndX1ffvllVKtWDZMmTcKtW7ds9hDd3Nzw0EMP\nYcOGDUhMTISb29+/ljds2IDs7GzTxjNXr17Fe++9h2HDhmH06NG4du0a/ve//6F379747rvv0Lx5\n80K/f7bmUKdOnYpZs2ahX79+6NOnD/bv34+ePXsiJyfHot7x48fx6aefYvDgwQgLC8O5c+eQmJiI\nrl274pdffkHt2rURERGBl156CdOmTUNcXBwiIyMBAB07diz0/P3790dycjKefPJJtGjRAlu3bsWk\nSZNw+vRpq8S6Z88erF+/Hk899RR8fX2xYMECDBo0CCdPnkRgYGChn7tMiUiV+wLQCoCkpKQIla6U\nlBQpybVesfgjub/V0EK/nhw0scg2nhw0sdDjVyz+yNGPZDJ9+nRRSsnYsWNNZXl5eVKvXj3RarXy\nxhtvmMqvXLki3t7eMmLECFPZ/PnzRaPRSFJSkqksNzdXOnbsKH5+fpKZmSkiIh9//LEopWTOnDmm\nevn5+RIVFSUajUaWL19uKu/evbu0bNlScnJyLGLt1KmTNGrUyPR6165dotFoJDk5udifNz4+XrRa\nrRw6dMhUlp6eLkFBQaLRaCQtLU1ERDIzMyUwMFDGjBljcfz58+clICBA4uLiTGWxsbGi0Whk1qxZ\nFnVbtWolbdu2Nb3es2ePKKXkgw8+sKi3bds2UUpZXMPQ0FDRaDTyxRdfWH2GrKwsq7LevXtLeHi4\nRVnTpk0lOjraqu748eNFo9HI3r17TWWZmZnSoEEDadCggals165dopSS8PBwuXXrllU7BRk/x6ZN\nmyzK+/btaxFbfn6+1fc2IyNDateuLU8++aRFuVJKZsyYYXq9bNkyi+/ThQsXpFq1ahITE2Nx3Asv\nvCBKKYuf1ezsbKuY09LSxNPTU2bOnGkq++GHH0QpZfEzaRQbGythYWGm18af61dffdWi3uDBg0Wr\n1crx48ctPounp6ekpqaayg4dOiRKKXn77betzmWuqN9BxvcBtBIHcwuHfKlcybx2A+fPXCz069KF\n9CLbuHQhvdDjM6/dcGq8SimLoTqNRoM2bdpARCxuO/D390ejRo1w/PhxU9mWLVtQu3ZtDB061FSm\n1Woxbtw4ZGZmIjk5GQCwefNmuLu7Y8yYMRbnjY+PtxgWTU9Px86dOzF48GBkZGTg0qVLpq+ePXvi\n2LFjOHPmjN2fdevWrejQoQOaNWtmKgsICMCjjz5qUW/btm3IyMjA0KFDLWJQSuG+++7Dzp07rdo2\n74kCQGRkpMW1Wrt2LQICAtC9e3eLNu+99174+PhYtRkWFoYePXpYnadatWqmf1+9ehWXLl1CVFQU\njh8/bnOKAtUKAAAgAElEQVQ4tqAtW7agXbt26NChg6lMp9Nh9OjROHHihM0h7eLMW3br1g01atTA\nmjVrTGVXrlzB9u3bLX4+lFKmHqyIID09HdnZ2WjTpg32799f5HnMbd++HTk5ORYjIQAwfvx4q7rm\nc5/5+fm4fPkyvL290ahRoxKf12jLli1wc3OzOv+zzz6L/Px8bNmyxaL8/vvvR2hoqOl1s2bN4Ofn\nZ/Fz4mpVesiXe/mWPz6+3giuU6PQ94NqFj20E1QzEJnXrhfavrMVXDXp7+8PT09PVK9e3ar88uXL\nptdpaWm46667rNqLiIiAiCAtLQ0AcPLkSdSpUwfe3paxG4cxjX7//XeICKZOnYopU6ZYtauUwvnz\n51GnTp2SfUCzeI3Dd+bCw8NtxhEdHW0zBj8/P4syT09PBAUFWZQFBgYiPf3vP56OHTuGK1euIDg4\n2Gab58+ftygr7H7Hr7/+Gi+++CK++eYb3Ljx9x9XSilkZGTA19fX5nFGaWlpaN++vVV5RESE6f0m\nTZqYys0TwO1otVo8/PDDSEpKQk5ODtzd3bFu3Trk5uZiyJAhFnWXL1+OuXPn4ujRoxZDsw0aNCjW\nucw/C2D9/atRo4bVEKoY5q8XLVqE1NRU5OXlAdBftxo1Cv//tajz33HHHdDpdBbl5tfSXL169aza\nKPhzUhLcy9fJuJdv+fOPuEH4R9wgh9p496M3nBRN8Rjn7ooqA26/CtJR+fn5AICJEyeiV69eNusU\n/OVZWnEopbBy5UqbDy83nyMECr9WBdusVasWVq9ebfMa1qxZ0+K1l5eXVZ3jx4+jR48eiIiIwLx5\n81CvXj14eHhg06ZNmD9/vun6OZOtOAozdOhQJCYmYsuWLYiJicGHH36Ixo0bW4wIrFy5EiNGjMDA\ngQPx3HPPITg4GFqtFq+88kqp9tRmzZqFadOm4cknn8TMmTNRvXp1aDQaPPPMM6Vy3Wxx9v9TpbGX\nb5VOqESuFBISgsOHD1uVHzlyBMDfvZuQkBB8+eWXuHHjhkUvteA9fcYeiru7u8WqVGfG+/vvv1uV\nHzt2zOJ1w4YNISKoWbOm0+Jo2LAhduzYgY4dO1oM25bExo0bkZ2djY0bN+LOO+80le/YscOqbmEb\nG4SEhODXX3+1Kjd+z0JCQuyKDQCioqJQp04drFmzBp06dcLOnTsxdepUizrr1q1Dw4YNsXbtWoty\n48rykjDGeuzYMYue9MWLF616fevWrUO3bt1Mq6WNrly5YvHHTEk2hAgJCcGOHTtw/fp1i16qM66l\nq3AOlchF+vbti7Nnz1rMm+Xl5SEhIQG+vr6Iiooy1cvJycGiRYtM9fLz85GQkGDxC6xmzZro2rUr\nEhMTcfbsWavzXbx40aF4e/XqhX379lncKnH58mWsXr3aqp6fnx9eeeUV5ObmOiWOIUOGIDc312Ir\nPaO8vLxiDdsZezjmPaqMjAwsW7bMqq5Op7N5W1Lfvn3x3Xff4dtvvzWVXb9+He+88w7CwsIshntL\nSimFQYMGYePGjXj//feRl5dnNdxrq5f27bffYt++fSU+X48ePeDm5oaEhASL8nnz5lnV1Wq1Vj3B\njz76CH/99ZdFmTExFnZLl7m+ffsiNzcX//3vf63Or9Fo0KdPn2J9jvKEPVQiFxk9ejQSExMRGxuL\nH374wXTbzL59+/DWW2+Zfjn1798fnTp1wuTJk5GamoomTZpg/fr1NhfRvP3224iMjESzZs3wz3/+\nEw0aNMC5c+ewb98+/PXXX/jxxx9NdUs6VPbcc89h5cqV6NGjB+Lj46HT6bBkyRKEhIQgPT3dlNx9\nfX2xaNEiPP7442jVqhWGDh2KmjVr4uTJk9i0aRM6d+6MBQsWlOjcUVFRiIuLw2uvvYYDBw6gZ8+e\ncHd3x2+//Ya1a9diwYIFGDhw4G3bMB7Tr18/xMXF4dq1a1iyZAlq1apl9QdI69atsXjxYsyaNQvh\n4eEIDg5GdHQ0Jk+ejKSkJPTu3Rvjxo1D9erVsWzZMqSlpWH9+vUl+ky2PPLII0hISMCLL76IZs2a\nWc2T9+vXD+vXr8eAAQPwwAMP4Pjx40hMTMQ999yDzMzMEp2rRo0amDhxIl577TX069cPffv2xY8/\n/ojPP//cagi9X79+ePnllzFy5Eh07NgRhw8fxqpVq9CwYUOLeg0bNkRAQAAWL14MHx8f6HQ6tG/f\n3mZvs3///oiOjsYLL7yA1NRU020zGzduxIQJEyrmvr+OLhOuiF/gbTNlpqS3zVQk06dPF41GI5cu\nXbIoj42NFT8/P6v6Xbt2lebNm1uUXbhwQUaNGiXBwcHi6ekpLVq0kBUrVlgdm56eLk888YQEBARI\nYGCgxMbGysGDB61umxERSU1NldjYWLnjjjukWrVqUq9ePYmJiZENGzaY6thz24yIyMGDB6VLly7i\n5eUl9evXl9mzZ0tCQoJoNBo5f/68Rd3k5GTp06ePBAYGire3t9x1110ycuRI2b9/f5HXavr06aLV\naq3KlyxZIm3bthWdTif+/v7SokULef755+Xs2bOmOmFhYVa3ghh99tln0rJlS/H29pYGDRrIm2++\nKUuXLrW4nURE5Ny5c9K/f3/x9/cXjUZjcQtNamqqDBkyRKpXry7e3t7Svn172bJli8V5jNd33bp1\nRVxRa/Xr1xeNRmN1O4nRa6+9JmFhYeLl5SWtW7eWzZs3S2xsrMVtOyIiGo1GXnrpJdPrgrfNGL38\n8sty5513ik6nk+7du8svv/wiYWFhMnLkSFOdW7duyaRJk0z1oqKi5Ntvv5Xo6Gjp1q2bRXsbN26U\npk2bioeHh8XPp60Yr1+/Ls8++6zUrVtXqlWrJo0aNZK5c+dafWaNRiPjxo2zKi8Ypy1leduMklJc\nJFFeKaVaAUhJSUnhoqRSZpzw57WuvMaPH493330XmZmZ3HCfyp2ifgeZLUpqLSL23QNkwDlUIiq2\ngnviXrp0CStXrkRkZCSTKVV5nEMlquKysrKKXNQTFBQENzc3dOjQAV27dkVERATOnj2L9957D9eu\nXbNajUpUFTGhElVxa9aswYgRIwp9XymFnTt3IioqCg888ADWrl2Ld999F0optG7dGkuXLkWnTp3K\nMGKi8okJlaiK6927N7Zv337bOi1atACgfzLJzJkzyyIsogqHCZWoiqtVq5bNHY2IqGS4KImIiMgJ\nmFCJiIicgAmViIjICZhQiYiInIAJlYiIyAmq9CpfPmCciKhq4gPGnYwPGCdX6Nq1q2mzhOLUvXz5\nssUj04jIcaXxgHEO+RKVMaUUNJq//9c7c+YMZsyYYTNpunJ/3C1btmDGjBkuOz9RRcOESlTGvvji\nC2zdutX0+vTp05gxYwYOHDjgwqisbd682eYDvYnItio95EvkCm5ulv/blddHKJbXuIjKK/ZQiex0\n+PBhaDQafPbZZ6ay/fv3Q6PRoE2bNhZ1+/Tpgw4dOgDQz4t269YNAJCcnIx27dpBKYXY2FhoNBpo\ntVqsWLHC4vgjR44gOjoaOp0OdevWxRtvvGEVz4ULFzBq1CjUrl0bXl5eaNmypVU7ycnJ0Gg02L17\nt0V5WloaNBqNqf6IESOwcOFCAIBGozHFVRJvv/02GjZsCG9vb7Rv3x5fffWVxWc3ys7Oxosvvoi7\n7roLnp6eqF+/Pv7v//4P2dnZFvU0Gg3GjRuHTz75BM2aNYOnpyeaNm1q0dsnciX2UIns1LRpUwQE\nBGD37t3o168fAGDPnj3QaDQ4ePAgMjMz4ePjAxHBvn37MGbMGACW86IRERF46aWXMG3aNMTFxSEy\nMhIA0LFjR1Ody5cvo0+fPhg4cCCGDh2KtWvXYvLkyWjevDl69eoFQP8Iti5duuD48eOIj49HaGgo\nPvroI8TGxiIjIwPx8fGm9oozLztmzBicPn0a27dvx6pVq0rcW120aBHi4+PRpUsX/Pvf/8aJEycw\nYMAABAYGol69eqZ6IoL+/ftj7969iIuLQ+PGjXH48GHMmzcPx44dw/r16y3a3bNnD9avX4+nnnoK\nvr6+WLBgAQYNGoSTJ08iMDCwRDESORsTKpUbOTdv4fIff5X6eao3vBPuXtUcbkcphU6dOmHPnj2m\nsj179uChhx7CJ598gr1796Jnz544cOAArl69akqW5oKDg9GnTx9MmzYNHTp0wPDhw63qnDlzBu+/\n/77pvZEjRyIkJAT/+9//TAk1MTERv/76K1atWoWhQ4cC0CfFqKgoTJkyBSNHjoROpyv2Z7vvvvtw\n9913Y/v27SW+pSwnJwfTpk3Dfffdhx07dpgWYDVv3hxPPPGERUJdtWoVvvzyS+zevdvUgweAe+65\nB2PHjsU333yD9u3bm8qPHj2KI0eOIDQ0FIC+t9+iRQskJSXhqaeeKlGcRM7GhErlxuU//sKKAZNK\n/TyPf/wGajVt4JS2IiMjMXXqVNy8eRNeXl746quv8Oqrr+LEiRPYs2cPevbsaeq12vvMUB8fH4tE\n6+7ujnbt2uH48eOmsi1btqB27dqmZAoAWq0W48aNw/Dhw5GcnIy+ffva/0FL4IcffsClS5fw+uuv\nW6xmHj58OMaPH29Rd+3atYiIiMDdd9+NS5cumcqjo6MhIti5c6dFQr3//vtNyRQAmjVrBj8/P4tr\nQeQqTKhUblRveCce/9h6brA0zuMskZGRyMnJwb59+1C3bl1cuHABkZGR+Omnn0w916+++gpNmjRB\nQECAXeeoW7euVVlgYCAOHz5sep2Wloa77rrLql5ERAREBGlpaXad2x5paWlQSqFhw4YW5Vqt1iIZ\nAsCxY8dw9OhR1KxZ06odpRTOnz9vUWbeuzUKDAxEenq644ETOYgJlcoNd69qTus5lpU2bdrA09MT\nu3fvRr169RAcHIzw8HBERkZi0aJFyM7Oxp49ezBw4EC7z1HYYiB7VuEWNn+al5dX4racIT8/H82a\nNcO8efNsfp6CCdSZ14LI2ZhQiRxgHH7dvXs36tevb5onjYyMxK1bt7Bq1SqcO3cOUVFRhbbhjM0b\nQkJCLHqsRkeOHDG9D+h7cyKCK1euWNQ7ceKE0+IKCQmBiOD3339Hly5dTOV5eXk4ceIEWrRoYSpr\n2LAhDh06hOjoaLvORVSe8LYZIgdFRkbi22+/xa5du0wJNSgoCI0bN8brr78OpZTNBUlGxsVCBZNc\nSfTt2xdnz57FmjVrTGV5eXlISEiAr6+vKbGFhIRAq9Va3TazcOFCqwRqjOvq1asliqVNmzYICgrC\nu+++i/z8fFP5ypUrrYZmhwwZgj///BPvvvuuVTtZWVm4ceNGic5N5ErsoRI5KDIyErNmzcKpU6cs\nEmdUVBQSExMRFhaGO+64o9DjGzZsiICAACxevBg+Pj7Q6XRo3769qVdZHKNHj0ZiYiJiY2Pxww8/\nmG6b2bdvH9566y1TcvTz88PgwYOxYMEC07k/++wzXLhwwarN1q1bQ0QQHx+PXr16QavV4pFHHiky\nFnd3d0yfPh3jxo1DdHQ0hgwZghMnTmDZsmUIDw+3SNz/+Mc/8OGHH2Ls2LHYuXMnOnXqhLy8PBw5\ncgQfffQRtm3bxv22qcJgD5XIQR07doRWq4Wfn5/FcGZkZCSUUjaHe82TipubG1asWAGtVouxY8ea\nVuXaqltYG56enkhOTsajjz6KFStWYOLEibhy5QqWLVuGp59+2uK4hIQEDBgwAImJiZg6dSpCQ0Ox\nfPlyq/YHDhyIcePGYevWrXj88cdt3tJTmH/9619YsGABTp06hUmTJuGrr77Cxo0b4e/vD09PT4vP\n8Mknn+C1117DTz/9hEmTJuGll15CSkoKJkyYgLvvvtuirq1rUVg5UVlTVXEyXynVCkBKSkoK//ot\nZcYnOfBak4igZs2aePjhh5GYmOjqcKiKKOp3kNnTZlqLyH5HzsUeKhE53a1bt6zKli9fjsuXL3MB\nElVanEMlomJLT0+32mPXnFarRY0aNfDNN99gwoQJGDx4MIKCgpCSkoL33nsPzZs3x6BBg8owYqKy\nw4RKRMU2cOBAi/ndgkJDQ3H8+HGEhoaifv36SEhIwOXLl1G9enXExsbi1VdftXraDlFlwZ9sIiq2\nuXPn3nZXIi8vLwD623M+/vjjsgqLqFyo0gk1468LANfJEBXbvffe6+oQiMqtKr0oKfv6TVeHQERE\nlUSlS6hKKS+l1Aml1Oyi6ubetF6JSEREZI9Kl1ABvABgX3Eq5tzMKuVQiIioqqhUc6hKqXAAjQBs\nBNC0qPo5Nwtf/k/OZdyknYioLJXl755KlVABvAlgIoBiPck5h0O+pa5GjRrw9vbGY4895upQiKiK\n8vb2Ro0aNUr9POUioSqlIgFMAtAaQB0AA0Tk0wJ1/gV9sqwN4CCAeBH53uz9GAC/isjvSqlOAIrc\n3DMniwm1tNWvXx9HjhzBxYsXXR0KEVVRNWrUQP369Uv9POUioQLQATgA4H8A1hd8Uyn1CIA5AEYD\n+A7ABABblVJ3i4jxN3V7AEOVUoMB+AJwU0pliMjMwk7KRUllo379+mXyw0xE5ErlIqGKyOcAPgcA\nZfuxERMAJIrICkOdMQAeADASwGxDG/8B8B/D+08AuOd2yRTgkC8RETlPuUiot6OUcod+KPgVY5mI\niFJqO4AOjrT99mersfnPHy3Khg0bhmHDhjnSLBERlUNJSUlISkqyKMvIyHBa++U+oQKoAUAL4FyB\n8nPQr+i1IiLWD3e0YWTHfpjwwTzHoiMiogrBVofJ7PFtDquM96EWG4d8iYjIWSpCQr0IIA9ArQLl\ntQCcdaRhJlQiInKWcp9QRSQHQAqA7sYyw8Kl7gD2OtL2ipStiImJsRpTJyKiyi0pKQkxMTGYMGGC\n09pUIuK0xuwOQikdgHDo7x3dD+DfAHYCuCwip5RSQwAsAzAGf982MwhAYxG5YMf5WgFIeaHNYMz8\n/kPnfAgiIqpwzOZQW4vIfkfaKi+LktpAn0DF8DXHUL4cwEgR+VApVQPAS9AP9R4A0MueZGoul3v5\nEhGRk5SLhCoiyShi+FlEFgJY6Mzzcg6ViIicpdzPoZam3Owc5OfmuToMIiKqBMpFD9VVNl48hD9i\nYvDoPx7jZg5ERFWIcZMHZ27sUC4WJZU146KkcXWjMfO7j+BbJ8jVIRERkQs4c1FSlR7yBYDs6zdd\nHQIREVUCTKhMqERE5ARMqEyoRETkBEyo13kvKhEROa7Kr/I99MIEjL74NFf5EhFVIVzl6yTGVb7j\nQ3rg8VnP4t5He7s6JCIicgGu8nUSN08PDvkSEZFTVOmE6u7liRwuSiIiIieo4gmVPVQiInKOqp1Q\nPavxthkiInKKKr3Kd/XPO/Hpie+R3jyQq3yJiKoQrvJ1EuMq3zcejkeofzAG/W+Kq0MiIiIX4Cpf\nJ3H3qsY5VCIicooqn1BzmFCJiMgJqnxC5aIkIiJyBiZUJlQiInKCqp1QPT2YUImIyCmqdEJ18/JE\nblY28nPzXB0KERFVcFX6PtQ3Vybi+pmzqLViBR4fOcLV4RARURnhfahOYrwP9dN3VuHo6+swZs87\n8K0T5OqwiIiojPE+VCdx864GAJxHJSIih1XphOruyYRKRETOUcUTqgcAIPsGN3cgIiLHVOmE6uZl\n6KFmsodKRESOqdIJ1d3bEwCHfImIyHFVOqFq3d2gcdMyoRIRkcOqdEJVSsHd25NPnCEiIodV6Y0d\nJkyYgPS0E7j8lT/uGz3A1eEQEVEZ4cYOTmLc2CElJQUH/rMcoZ1botsU7pRERFTVcGMHJ/LQeXEO\nlYiIHFblE6q7zpMJlYiIHFblEyp7qERE5AxMqDov5HCnJCIichATqs4L2ZlMqERE5BgmVM6hEhGR\nEzChcg6ViIicgAmVPVQiInICJlSdF3KzspGfl+fqUIiIqAJjQtV5AQBX+hIRkUOYUA0JlSt9iYjI\nEVV+c3wP0aLmtUzOoxIRVSHcHN9JzDfHr+tVHcv7P4vH1r2GOi3ucnVoRERUhrg5vhN56DwBANmc\nQyUiIgcwoZrmUDnkS0RE9mNC9TEkVM6hEhGRA6p8QtV6uENpNUyoRETkkCqfUJVShu0HOYdKRET2\nq/IJFTA+wo09VCIish8TKgz7+XJjByIicgATKvjEGSIichwTKgB3PnGGiIgcxIQK4xwqh3yJiMh+\nTKjgkC8RETmOCRWGhMpFSURE5AAmVBhW+bKHSkREDmBCBYd8iYjIcUyo0PdQuSiJiIgcUeUfMO7v\n749OYc2guXkL+Xl50Gi1rg6LiIhKGR8w7iTmDxhv1aoVft28F5+Om4NxP65ANV+dq8MjIqIywgeM\nO5m78SHjXOlLRER2YkKF2UPGuTCJiIjsxIQKs4TKhUlERGQnJlToV/kC7KESEZH9mFBh1kPNZEIl\nIiL7MKHCbFESe6hERGQnJlQAbtU8oLQaJlQiIrIbEyoApRQf4UZERA5hQjXgfr5EROQIJlQDD50n\nN3YgIiK7MaEasIdKRESOYEI1cOczUYmIyAFMqAZclERERI5gQjXgkC8RETmCCdXAw5uLkoiIyH5M\nqAYePuyhEhGR/exKqEqp3kqpzmav/6WUOqCUWq2UCnReeGWHQ75EROQIe3uobwDwAwClVDMAcwBs\nBhAGYK5zQitbHjpPLkoiIiK7udl5XBiAXwz/fhjAZyLyH6VUK+gTa4XjofNCzs1byM/Lg0ardXU4\nRERUwdjbQ80G4G34dw8A2wz/vgxDz7WsKaX8lVLfK6X2K6UOKaWeLMnx7t76J86wl0pERPawt4f6\nFYC5SqmvAbQD8Iih/G4AfzojMDtcBRApIllKKS8APyul1olIenEO9vAxPhM1C9V8daUYJhERVUb2\n9lCfBpALYBCAsSLyl6G8D4DPnRFYSYmesXvpZfivKu7xpoeM3+DCJCIiKjm7eqgichJAPxvlExyO\nyAFKKX8AyQDCAUwSkcvFPdaUUK9zyJeIiErO3ttmWhlW9xpfP6iU+lgp9YpSysOO9iKVUp8qpf5S\nSuUrpWJs1PmXUipVKXVTKfWNUqptwToikiEiLaFfNPWoUqpmcWPw0OnnUHnrDBER2cPeId9E6OdL\noZRqAOADADcADAYw2472dAAOAHgKgBR8Uyn1CPS35rwI4F4ABwFsVUrVsNWYiFww1IksbgDuxh5q\nJhMqERGVnL0J9W7oEyCgT6K7RWQ4gFjob6MpERH5XESmicgnsD3vOQFAooisEJGjAMZAn8BHGiso\npYKVUj6Gf/sDiALwa3FjYA+ViIgcYe8qX4W/k3EPAJ8Z/n0KgM1eo72UUu4AWgN4xVgmIqKU2g6g\ng1nVEADvKKWM8b0lIj8X9zxu1TygtBreNkNERHaxN6H+AGCKIal1ATDWUB4G4JwzAjNTA4DWRrvn\nADQyvhCR76EfDi62CRMmwN/f3/Q69eyPuLmzPloO72V/tEREVC4lJSUhKSnJoiwjI8Np7dubUMcD\nWAVgAIBZIvK7oXwQgL3OCKwszJs3D61atTK9XhwZh6b3tHFhREREVFqGDRuGYcOGWZTt378frVu3\ndkr79t42cwhAMxtvTQKQ51BE1i4a2qxVoLwWgLPOPBEf4UZERPZy6PFtSqnWSqnHDF+tRCRLRHKc\nFRwAGNpLAdDd7LzK8NqpvWE+wo2IiOxlVw9VKRUMYA3086dXDMUBSqmdAIYablspSXs66DdjMK7w\nbaCUagHgsoicgv4JNsuUUikAvoN+1a83gGX2xG9knEM1DgO484kzRERVgnE+tTzMoSYA8AFwj4gc\nAQClVBMAywEsADDsNsfa0gbATujvQRXo7zmFob2RIvKh4Z7Tl6Af6j0AoFdJE3dBBedQ+UxUIqKq\nwdiRcvkcKoDeAHoYkykAiMgvSql/4e8nzxSbiCSjiOFnEVkIYGFJ2y4JD50Xrv51vjRPQURElZS9\nc6gaALbmSnMcaNPluCiJiIjsZW/y+xLAW0qpO4wFSqk7AcwzvFchcVESERHZy94h36cBfArghFLq\nlKGsHoDDAB5zRmBloeCiJA+dF7K5KImIqNIrN4uSROSUUqoV9NsONjYUHwFwFMA0AKOdE17psl6U\n5Ikc9lCJiCq98rQoCSIiAL4wfAEADLe6jEIFSagFuXt7IufmLeTn5UGj1bo6HCIiqkAq7AKi0mB8\nyDjvRSUiopJiQjXj4WN8JioTKhERlQwTqhljDzX7BudRiYioZEo0h6qUWl9ElQAHYilztlb5AkD2\ndfZQiYgqs/KwyreoM2cAWGFnLGWu4Cpfd29PAOC9qERElZzLV/mKyAinnLWc+nsOlQmViIhKhnOo\nZjx07KESEZF9mFDNuFXzgNJqeNsMERGVGBOqGaUUH+FGRER2sXunpMqg4CpfwPDEGSZUIqJKrTys\n8q1UCq7yBQwPGefGDkRElVpprPLlkG8BHj5enEMlIqISY0ItwF3HIV8iIio5JtQCuCiJiIjswYRa\nABclERGRPZhQC+CiJCIisgcTagEePl582gwREZVYlb5txuZ9qDovPm2GiKiS432oTmb7PlRP5HAO\nlYioUuN9qGXA3dsTOTdvIT8vz9WhEBFRBcKEWoDxIePc3IGIiEqCCbUA0zNROY9KREQlwIRagLGH\nyntRiYioJJhQC3D3Nj5knD1UIiIqPibUAthDJSIiezChFmCaQ81kQiUiouKr0veh2t7YQT/ky1W+\nRESVFzd2cDJbGzu4VfOA0mo45EtEVIlxY4cyoJTiE2eIiKjEmFBt4DNRiYiopJhQbeAj3IiIqKSY\nUG3w8PHioiQiIioRJlQb3HWcQyUiopJhQrWBi5KIiKikmFBt4KIkIiIqKSZUG7goiYiISooJ1QYP\nHy9k32APlYiIio8J1QYPnReyrmQi91aOq0MhIqIKokon1AkTJiAmJgZJSUkW5Q27tUHOzVvY8fL/\nXBQZERGVpqSkJMTExGDChAlOa1OJiNMaqyiUUq0ApKSkpFjt5Wt0eO2X+Hzy2+g5Mw4thvYs2wCJ\niEQdmx8AACAASURBVKhMmO3l21pE9jvSVpXeHP92mg3qhnM//YHtM/6HGneH4M5WjVwdEhERlWNV\nesi3KNH/iUWdFnfhk6ffQOa5y64Oh4iIyjEm1NvQergjJuFZKKXwydNvIi+bi5SIiMg2JtQi+NQM\nxINvT8K5n/7Ajpffc3U4RERUTjGhFsMdLe9Gjxn/xMGkbTj4wReuDoeIiMohLkoqpuZDeuDcT8ex\nY8YS1GwUgjvuvdvVIRERUTnCHmoJdJsyArWbh+Pjf81G5vl0V4dDRETlCBNqCWg93BHz34lQSuGj\n2JeQ8ed5V4dERETlBBOqDTs278HE0S/h6pVrVu/51AzEkOUvIjcrG+8/9H/484cjLoiQiIjKGybU\nAlKPncSUcbOx/bM9eHLQRFy+aD20GxReF4+tew017q6HNf+YjsNrvzS9d+P6TSx45X/47ZfjZRk2\nUZnLSL+K/Px8V4dBVG4woZq5eSMLk+JeRp07g/H+pgRcvngFowY+i3NnLlrV9Qr0xeBl09BsUDd8\nPvlt7HxlGdL++BOP9x+H9/77AaaMex25uXku+BREpe/Mn+fRp91j+PeoGcjh/dlEAJhQLcyethB/\nnjiD2YlT0Ozexnhvw1xk3byFkQ9NwF8nz1jV17q74f6XRqP7tFH4YelnSOg9DnlZ2ZiV8H84diQV\na5Z94oJPUT5lpF/F0v9+gD07vsWtrGxXh0MOmvfyO/DwcMfXO7/Hc2NmIScnt0zP/+N3PyH9UkaZ\nnpOoKEyoBpvW78CG1VswedbTCG8cBgCoH3Yn3tswD1o3LUY89G+kHjtp89iD165h560bqKnRoq/O\nDx3bNMWgx/th4ezluHDuUll+jHLpakYmxg6bjITXliL+H1PQtenDePbJGfj0w22l/ksx+1Y23p2/\nCjGdY/F+4lqOGjjB93sPYtvGZEycMQZzlryIPTu+xeSxZZdUN6//EiMGTMCQ++Pww76DZXJOouJg\nQgVw4vdTmPncfDzwcHc8OLSXxXt16gbjvfVz4efvg5EDn8WvP/9heu/mjZuYPPYVvDVrCfr+azBG\nfjYXSgQrH/4/tPXSIVTrjrcmzkdO1q2y/kjlxvXMG3j6sf/gz5NnkbR1IdbtWoJ/PvMoLpy7hBcn\nvInuLYZg5EP/xvJFH9kcBXDEt3v2Y0iPMUic+z5CGtTF3JfeweP94nH0p9+dep7C3Lh+E3/8loaf\nD/6KyvJUp9zcPMye+jaat26CvgO7I6rHfZjz7jQkf/ENnn/qlVJPqinfHMKL/34TfaPbISzkDowe\n/BwWz3kfeXn8Q4lcr8o/vq1JxD34R7945GTnYvXnb8Nb52XzmCuXr+Kp4ZNxKu0MFq56BdVrBGDC\nyOk4deI0Xn7rOfR4IBIAcPPKNXw5cyn+/P4XXP3rgvGECAytgxp310ONu+ojqOGdcPf2hNbdTf9V\nzR1adzdoDK89vL2gqxkAjZu2TK5H9o0sZJw6hysnzyLrSiY8fL3h5e+Dav4+8ArwgaefD9x1nlBK\nlajdmzdu4qnh/8Gxo//f3n3HRV3/ARx/fY4NgiIoAu69985Vriy1oVmW2jYrGzasfg2zbZaj1LJl\n2tA0szJLTXNvcU/cypC9N9zn98fBeSAg4+BQ3s/H4x7c+N73++bDce/v9zPP8dXSj2nZNvdkGJHh\n0Wz+dyeb1u5g55Z9ZGVmcf+jd/HEi2Nxq+Ja4t8nMjyaT6fO558V/9GxWxv+99GzNG5Wn8P7jvPO\nyzM5G3iBsU+M5IkXxuLi6lyiY2itiY6MJSQojNCgMEKDwrkcHG66HxzO5ZBw4mKu9BK/96HhvPLe\n0xgM1/c57JLvfmfam/P48e/PadXuygpMG9ds56Xx73LzrT35cO7/sLfyZ9eYlcWe3zex8LU51LGz\nxz4jCwdXJ6jvyx/7j9G4e2ve//wVatbyLnQ/MVFx/LV8HRGhkYx5YsQ1txdlLykxmW9m/4ynV1XG\nPjGy2N8zpWXN5dsqdULt06cPYcFRZMY78M+m32jSokGh70uIT+LZcW9w8ugZHBztcfeowqwFU81V\nxHmlxCfywoiX0TEJjBjWj+gzQUQGXiI5MvbaMdoZcK/lhYefNx5+NUw//Wvg4VsDZWcgKTKW5Mg4\nkqKyf0bGkhwZS1xYNEYAJwe0gx1ZBgPpSpGmjaRkZZFhNFLXxxsfdzd0QjKxF8OKFI/B3g5nDzc8\n/GtQo1k9arZsQI3m9ajRvD7OHm5X/+7JqTz74JscPXCSOd+/QwP/miRFxJKelEJ6UirpSSlkJKeS\nnpxKRlIqyfGJBB4+w6HDgRicHelz602069EWJ3dXHKu44FTFFQcXJ+xdnLB3dsTB2RF7J0cMDvbm\nf8CsrCyWLviTr6cvxMXOjnGP3UW3bm1IT0whNS6RlJgEkqLiOLzjEOePnaWKgwM1q3lgTEkjPSkl\nu+CVaX8KlFLk/HcoV2fSq7oSbYBLScmcC4/K1Rbs6uaCb20favnXwK+2D7X8a+Lr74Nv7ZqcPHKa\naW/O4/aRA3j70xetnmzKS0xUHMN7PcSA23sz5ZMXrnp9w+rtvDz+Hfrf1pv357ya6/dMiU0g6tQl\nIrNvUacukRKbQNU6PnjW98Wzni+e9WrhWd+PKj6eKIOBrIxMLu48QuDqHQSu3UVqTAJpdgY6jRpA\ni8HdCQ44wcFf/iUpPIYYOzhvUDz1+Uv0GtA9V1xGo5HdWw/w209/89/qbbigcHdxIi4zk0eeu58x\nj4/AydmxzMuvKIyZWaDAYFeyz4jWulQJKSsjk/TEFNISkkhLSCYtIdn8OD0pFUc3Z9xqeOJWoxpu\n3tVw8XRHZZ8kZqRn4ODoUKxY1/+9lY/fmkdsdBzpaRk8+uxoJr7ycLkk1cWLF7N48WLi4uLYvHkz\nSEItmZyEOnv6F3w3YzlvTZ/E3Q/cVqT3piSn8NpTHwIwdeZLVPX0KHT7wGNnGT34SSa++ggPP30v\nAGkJSWSmZZCVnklWRibGDNPPnFt6QhLxIZHZtwjTz+AIEsOi0RbDFBzcnHHzqoard1WSM7M4fSGE\nkMgY7ABnOzvcHOxxMdjhaFA4GDV2WUaUUZOKJiEri0xnR2o2qUOLHm1pf0tnvBvVxsXTnbSEZFJj\nE0mNTyI1NsH0My6R1NhEYi+FEX7sHFGnLpGVXb3n4V+Dms3r492sLsbMLOJCIjmwKYCM+GQ8XZ3J\nSsm/ytvBxQkHV2cc3VxwcHPGYDCQHJ9I7OVodHoG9kX4p1IGA/bODmCwIzUpBbtCPs9OHm64VvfA\npboHODpwLPA8wWFRNGrbhF639iQuJp6wkAjCQiMID40iKjyKrOw2V28XF3zs7XFJy0QBBhcn3BvX\nxq9DM5r0aY971SqkxCSQHBVHcnS86RYVR0p0PGmJySQZjWwPOIp/y4Y89vqj1GhSBzfvarm+OLTR\nSFJELHHBEaa/e7Dpb5+RkoqdowP2Tg6mn44OZGoICQ4jKCiM+k3q0rxtE1MNh70dBnt7DPZ22DnY\nYefkiJtXVarU8sLJ3TXX8ZKTUogMj6ZuA/9rljPAe6/MYvUfG/lz2/dU96qW7zZrlq5l1suz6Nq2\nKb06tyb6TBBRpy6RFGE6aVN2BqrX98OriemzFncpnJgLocQHR5o/2/bOjlSrV4vEy9GkxiXiUbsm\np5OSOJuaymerZuNf19d8vKyMTE7/u5s93/9F6L6TpGgjbu2bMObDiUQGhbNx2b8c23oAlZCCt4sz\nVQCdcaV6OEkbSXN0oFmPNrTp14nqDfyo3tAPZw83MlLTycy+ZaVlkJGWTmZqGllpGRgc7HF0c8bB\n1RkHF+cr950dzQmmKNKTUgg5EEjQnuMEB5wg9EAgBns76vZsQ/1e7WnQuz1Va9cETNXt+3cdpl6j\n2uYr66z0DEIOBHJ+60EubDtE2NGzuNXwNJ2k5JyoZN+vWscHO0d7kqPjib0QSsyFy8ReuEzsRdPP\nmAuhpMYmFhirwd7OlPAtKDsDLtU9SEzP4HJ0HFWqe1CvWX3qNamLk7srDq5OOGaXkatXVXxaNaRK\nreqEBIXx0etz2LJuF30HdueV9yeyftUWPp06n4eeGsVzrz9m/qymJ6cSdeoSTu6uVK1dE7tiJG1z\nOSen4lhAbZRcoZZSTkJtU7s3Q++4jfc/f6VMz4imT/mC5T/+ze+bv6WWf80Ct4uPTWDGO1+xa+t+\nuvXqQL/BPejWu6O5WtKYmUVCWBQ6y4hbDU+Ugz1r/tjAt58v4WzgBTr3bMcjE++jY7c2ODk75vs7\nZWVkYgQO7D7Cf6u3sXHNDkKDwqji4UavW7rSs19nGjSuQ90G/oWeLGRlZBJ9LoSI4+cJP3Ge8GPn\niTx1EXsnB8JiE4iIT6TH4J406tiMKjWrU6Wm6azWyd0NR1dn7F0cCz0L37ZhD9Ne/5zIoHBG3DeE\nkfcOwl4pMlPTibocxbkT57l46iJB54IJDwrHmJ5BdR8vhtw7iMZtm+Dk7oqzuxtOHq44ubvh5O56\nVRW61prfF69mxrtfkRBn+iJxdnaiUfP6NG3RgCYtGtKkZUOaNG9AteqmskhPzP4C3HvlCzAjzwmD\nk4cbrl5Vca3ugatXVRxcnUkIjSQs8CLpFtXADm7OeNb1xcnDlYTQKBJCI80nKQBO7q54+NfAwdWZ\nrPQMkuOTSYpPJC0phaz0TAyAnVIorTEU4fPr4OKEW01P3Gp6EpOcwpGT50lMTaNHz/Y0qOtLWs6J\nU3wSaXGJpMYnY7Az4OrlAc5OHDgcSIvOrWjXqz2uXlVx8XQnMSyaqDNBRJ0JJvpMECnZv59Ra4yu\nTrj4eZPh4kSinSImM5PwpGSiY+KJiYrDxdWZUQ8OY/Qjd1LVowpxQWGmL/bzocScD8XF051GA7sy\n7ZOF7Nyyn+9WfHpVs4GliMALLPnfPBL2n8LBojyMTg54NfKnTpsmVG/oR/UGfji5uxJz4TJnA46z\nf91uMqLiqGpnh7LC12HOia5bDdPJrpv3lSu6CyHhnDxyGl8nJ4yXowk/dg6dZcS5WhVqd2qBf+fm\nZKZlcH7LAUIOBKKzjHjU9SG1qhu7z1zkZGQ0Hg4O9GvbjHqursQcP09Gciounu7U7dEGvw7NSI6K\nM5XhhVBiz4eaP5+mk09HMpJTzbG61fTEs14tqtXzxbNuLdxqeuLk7prr5ljF9NPeyZGM1DRTjVhE\nLEmRMQRs2MuGFRuwy8yicT0/4iNjSY5LxNneHo8qrjjaGTCmZeT6H1GuzoQkJZPs7MDgh4cx8KFh\nVPE2naD98PkSfpr2Pf17tKe5vw/hx88RfTYEsvOUsjNQ1b+m6SShwZUTBndfb5Kj4kgIjSQ+1HQx\nkhAaSUL2hYm9syNP78p/tTBJqKVkrvJtN4y/ty4pVXtdUSQmJHFXn0do17kVn3z9Vr7bbFyznfdf\nnU1KciqD77iZgB2HOH/mEk7OjnTv3ZG+g3rQZ2B3vGtWJy01nZXL1rJg7i8EX7xM7wHdePSZ0bTv\n0qrYsWmtOXn0DBvX7GDD6m25Ol15VHOnbgN/6tT3o24Df+o29MfXvybVvapR3bsa7lWr5EraGRmZ\nTH7iPbZt2M2sBe/Qs1/n4heWhfS0dBZ+uYxvZy/GvVoV2nVuydH9JwkNNk35WKOWF207tqB1h+a0\n6dic9l1al6g6NSoihsP7jtOgSV1q1/PFrhjVbVkZmUQGXkQZDOYkY+dQ8JoTOzfsYcrj79K0ji+j\nRgwgJSyatPgk3H29TFX7/qZbVf8aZBkMbNuwhy3rdrF9414iw6NxcXWmW68O9Ly5Czfd3Bm/OrXY\nuGYHn0yZR0RoJPeOG8ZDT92Di5MTxswsMlLTSYqMJfFyFAlh0RzZdoCjOw+jUtLxruKGysoiISUV\n1+oeNGzVCFdPd5w9quBc1Q2nqlXQWUaSouJYv3w9pKXTpEFtUrKvvo2ZWdg7OeLZ0A+vRv54Na6D\nV6PaeDXyJ+DYad59ZTb29vZ4elXF06sanl5Vqe5dDc/qpsdBF0NZ8fM/ZGZmMuyeQYx7YiT1GtXO\nVV4fvzWPJd/9wawFU+kzsHsBpZrbvq37+W3mYpp3acGQB4fh5Vt4O6nWmi3rdjF9yjziLoVz28Ae\ntG7ViPjEZGLjEomKiScyKpaIiBguh0eTnJJGpy6tePKFMXhV8zA1W2Tf0pNSSU9MITk6O+lExJjK\nPzyG5Oh4cv5bkoxGou3Au2VDOt/Zl5tHDcTFov+G1ppd/+1h1dylhO8PxNdgj5tSoBRojREIz8rE\n4O9N33FDufXhodg7XH3lprU2VYmfDyX6XAhpiclUq+NDtXq1qFa3VoFXbdcSERbFR6/PYf3fW+k3\nqAevffgsPtnlfPrEOZYt+ou/fl1HSnIqvft3ZeTY27HPyOK7d74mPTSK1nV8cUpOIy37RNbDz/Te\n+BDTmP8MrbGvUY2OQ3rik928lJ6YQsyFUKLPmU4UYs6HEnvxcq6TUAAXTw88/LzN/1Puft54+HrT\n7Lae+V5kSEItpZyE+tuyP7hr5PByOeY/K/7jtac/ZN7PH9CzXxfz8zFRcXz81jz+WfEfvfp35c1p\nz+PjVwMw9T7elN1p58CeoxiNRlp3aE5YSDiR4TEMGtaXhyfeS/PWja0WZ1JiMpfOhXDhXDCXzgdz\n8WwwF88Fc+l8CFERuWeNsre3wzP7C7K6dzWSEpM5fvg0M797m979u1ktppCgMD57/xvCL0eZk2fb\nji3M5XS9ObzvOE+PeR2/2j7MW/xhrurTuJh4Nq/bxfq/t7Jj017SUtNp1Kw+N2Un0A5dW+PodHV7\nX1pqOovmL+Pbz5bgVsWF515/jKEjB2AwGNBas2PTXj774DtOHDlNv0E9mPjaIzRuVh+AVcvXMfWl\nGTRt2YhPv5li/mLMseq39bw+8SO+WvoxXXt1AExf1GkJyTi6ORdY01CU9rz42ASW/fAXP3+zgujI\nWG4ZchMPPTWKNh1b8NM3vzH9rS/434fPMurBYcUp4hLJSM/g529/56uZP5KUmAxAde9q2e3hNamV\nfXN1c+HbzxYTFRHNhBfHMfaJkYWeyJ04fJrXnv6A0KAwnn1hHENH9OdyTDzrVm1h/d9bOXPyPC6u\nzvTu343+t/cmNjqOpQtXcubkeeo1rM2oh4YxdORAsmISuLTrCB5+NajVoRlbN+3lh/nLORRwjDoN\n/Hjg0bsYfu/gAjtWWoPWmj+WrOHTd+bj4GDPK+9NZNCwPvn+nZOTUvh7xX8sW7jSfKLesl1T3pz2\nPC3aNkFrTVxQOGGHzxB6+DRKKWq2bIBPywas37qP91/9jHseHMZr708ssDOfMSuL+JBIEi9H4epV\nFXdfbxxcnIr1O0lCLSXLXr4dO3Ysl2NqrRk/ajKXQ8L5df3XODk7snblJj783xyysrJ45d2nuO3u\n/gV+AcVExbH1v91sXrcTd48qPDjhnqvO5staYkIS4ZcjiYmKIzoy1nyLiYolOiqWhLgkHnjsriJf\nSVRmgcfO8uToV/GoWoX357zKkf0n+e+frezdfpDMzCzadmpJ/9t6ccuQm6hT36/I+70cHM6s975m\n9R8badOxOWMeH8Hyn1axe+sB2ndpxXP/e4wO3Vpf9b6jB0/ywiNTycrK4tNvptCuc0vAdIJ1Z++H\nadelFZ98lX/tijWkpaazavk6Fn6xjAtng2jVvhnHDgYybsI9THrz8TI7bn7iYxOIiYrDx68GzgV8\nOackpzBv+kJ++noFTVs2ZMonL9CibZNc2xiNRn6Yv5zPP/qORk3r8eG812jYpN5V+zp36iLr/t7C\n+lVbOXHkNHZ2BvoN7smoB4fRtVeHa56UHN53nB+/+o11qzbj6ORI3Qb+uU4AzPf9auDpVZWsrCwy\nM41kZmSSmZlJVmYWmZlZZGZkorU2JS8FBoMBpRTKoDAoA/FxCcyYOp9dW/czbNRAXnxrgrkppDBa\naw7vO0745ShuvrVnkWuAfl+8mqkvzeCu+4fwxrTnit1DPiYqjtMnznHq+DliY+J56uUH891OEmop\n2SKhApw9dYFRAyZw38N3cDk4nHWrttD/tl689sEzeNesXm5xiIrhwtkgJtz7CqHB4djZGejcsz23\nDLmJm2/tWerhHAE7DzHtjbkEHjtLw6b1ePa1R+g7qEehX85RETG8+NhUjh4M5H8fPMNd9w/hsw++\n5advfmPF5u/wq+1TqpiKwmg0smntTn786lf86/ry9owXK/RQo6MHTzL1xRmcPnGeMeNH8ORL43Bx\ndSYsNJK3nvuYXVv3M27CPUx85aF8axbyCgkKw8HBnho+XsWOJTQonNV/bCDk0mXT0K3gcEKDws1X\n29bgV6cWb0x7rtTNOUX159K1TJn0CcNHDeKJF8eijUaMRo0xy4jRaDS11WcZSU1N42zgBXMCPX3i\nPJHh0QA4ODrQrFUjFq2cne9nSRJqKdkqoQLMfv8bFsz9BU+varz2wUQGDs2/ukRUDpHh0Rzce5TO\nPdpds8d4cWVmZnH6+DmatGxQ5KuCjPQMpr05l19/WMXQkQNY8+cmHn1mNBNeHGvV2G4kGRmZ/Dj/\nV76c8QPeNaszcsztfP/FUhydHHl31mS69ynf75i8EuKTTMk1OJy4mHjs7Oywd7DD3t4Oe3t77B3s\nsp8zDUHTWptuRtNPY/ZPpRTtOrfAxbXsqpTzs2r5Ot58bnqRFmKoXc+Xxs0b0KRFg+yf9anboHah\nVfKSUEvJlgk1JTmV3xevZvCd/QoceiCErf36w1989MZcavh4sWLTtwVWfYorLp4L5t3Js9iz7QD9\nb+vFmx9PKlKVqLi2M4EXCA+NxGBQGOzsTD+VAYOdAYNB4eDgQJ0GfiXqYCoJtZRsmVCFuF6cOn4O\nR0eHcm+rv55prTl76iINm9SVmqfrhDUTasH9+4UQldq1Zg4TV1NK0ajp1R2PROVQcVv7hRBCiOuI\nJFQhhBDCCiShCiGEEFYgCVUIIYSwAkmoQgghhBVIQhVCCCGsQBKqEEIIYQWSUIUQQggrkIQqhBBC\nWIEkVCGEEMIKJKEKIYQQVnDDJFSlVG2l1Aal1FGl1AGl1EhbxySEEKLyuJEmx88EntNaH1JK+QAB\nSqlVWusUWwcmhBDixnfDXKFqrS9rrQ9l3w8DIoHqto1KCCFEZXHDJFRLSqlOgEFrHWzrWIQQQlQO\nFSKhKqV6K6X+VEoFK6WMSqnh+WzztFLqnFIqRSm1UynVpYB9VQcWAo+XddxCCCFEjgqRUAE34ADw\nFKDzvqiUuhf4FJgCdAAOAmuUUt55tnMEVgAfaK13lXXQQgghRI4KkVC11qu11m9prf8AVD6bTALm\na60Xaa1PABOAZOCRPNstBNZrrX8u24iFEEKI3CpEQi2MUsoB6ASsz3lOa62BdUAPi+1uAu4B7lRK\n7VdK7VNKtSrveIUQQlRO18OwGW/ADgjL83wY0CzngdZ6G8X8fSZNmkTVqlVzPTd69GhGjx5dskiF\nEEJUWIsXL2bx4sW5nouLi7Pa/q+HhFpmZs6cSceOHW0dhhBCiHKQ3wXTvn376NSpk1X2X+GrfDGN\nJ80CfPI87wNcLv9whBBCiKtV+ISqtc4AAoD+Oc8ppVT24+22iksIIYSwVCGqfJVSbkBjrvTwbaiU\nagdEa60vATOA75VSAcBuTL1+XYHvS3PcnDZUaTcVQojKJac91ZptqMrUYda2lFJ9gQ1cPQZ1odb6\nkextngImY6rqPQA8o7XeW8LjdQQCAgICpA1VCCEqMYs21E5a632l2VeFuELVWm/iGtXPWut5wLzy\niUgIIYQongrfhiqEEEJcDyShCiGEEFZQIap8bUU6JQkhrCkzM4vTJ87RpEUD7OzsbB2OKMQN2ymp\nvEmnJCFEWZj8xHusXbmJYaMG8u6sybYORxSBNTslSZWvEEJYQVZWFmtXbgJg5dJ/bRyNsAVJqEII\nYQWXQyJyPY6PTbBRJMJWJKEKIYQVeNeoTsMmdc2Pgy/KzKiVjSRUIYSwAidnR4bcfYv5cfAlSaiV\njfTylV6+QggrqV3X13xfrlArtrLo5VupE6os3yaEsCb/urXM94MuhtowEnEtORdS1ly+rVInVCGE\nsKYmLRrw1dKP8a/ni49vDVuHI8qZJFQhhLASF1cXuvbqYOswhI1IpyQhhBDCCiShCiGEEFZQqat8\npZevEMIadm7ex4E9R6nbwJ/ufTpQ3dvT1iGJa5C5fK1E5vIVQljTx2/N4+dvVgDwzfJP6NyjnY0j\nEkUlc/kKIUQFcvFcsPl+3Qb+NoxE2JIkVCGEKKWchOri6kwNHy8bRyNspVK3oQohRGllZmYRkj0r\nUp36fgB8+/ligi6E4urmwstTn7RleKIcyRWqEEKUQsily2RmZgFQt6E/Sil+/Oo3Vvz8D//+tdnG\n0YnyJAlVCCFKIb/209r1TFMQRlyOIi013SZxifInCVUIIUrh0rkQ8/2chOpfx5RQtdaEBoXZJC5R\n/ip1G6qMQxVClJZ7VTfadGzOhbPB1GtQGwD/eharzly6TP3GdWwVniiArDZjZbLajBCitIaOHMjQ\nkQMB0xUpXLlCBQi6IKvOVERlsdqMVPkKIYSVKKWA3Mu4ybqolYckVCGEsLLaFlW+IZckoVYWlbrK\nVwghyoKPX01atm2CX51adOzWxtbhiHIiCVUIIazM3t6On1fPs3UYopxJla8QQghhBZJQhRBCCCuQ\nhCqEECWUEJ9EZVwCU+SvUrehysQOQojSuHfgBOJi4mnZrilfL5tu63BEMcgC41YiC4wLIUorPS2d\n7o2GYTQaadm2iXRCuk7JAuNCCGFjQRdDMRqNgCwqLkwkoQohRAlcPHv1KjP5SYhP4uypC+URkrCx\nSt2GWhJJiclMfuI97O3t+Hj+mzg5O9o6JCGEDVw6b7HKTMP8E+qDw5/j4N5jODg6sOvsXxgMcg1z\nI5O/bjEt/3EVp0+cZ8TYoWRmZpb58bTW0otQiAoov3VQ86pazQOAjPQMIi5HlUtcwnYkoRbTNOJe\nIAAAIABJREFUri37ycjI4LkH3yQsNKLMj/fn0rVMengKsdHxZX4sYRsxUXHs2BQgJ07XmdxVvrXz\n3ca/nsUk+TKn7w1PEmoxZGVlcXDvUaIjY/H0qkqDxnXL9HjnT1/iw//NYePaHYwa+AR7dxws0+OJ\n8vfW89O5uc1Inhz9aqValSQuJp5jhwLJzMyydSgllnOF6lHNnWrVPfLdxr+uxbqolejvW1lJG2ox\nBB49S2JCMgAdu7cxL9VUViLCo3FxcSI1JZXw0EjG3zOZx567n/GTxmBvb1emxxblo5Z/TfP9Y4cC\nc61ScqNKSU5hZP/xxMcmsPHIr9jbu9g6pBKZ9f07XDgbRHJiSoHbWK6LKgn1xidXqMVgeYXYuUc7\nq+47NSWNlx5/h/kzfjAfp0vPdvyy7ku69moPgNFo5KuZP/LEvZNJSU616vGFbbRs28R8//ihUzaM\npPzs332EiMtRtO7QnLOnLto6nBJr1qoRg4b15c7Rtxa4jeUJUtBFWWj8RicJtRgCdhwy3+/co61V\n93365HnWrdrCF58s4s9f1pqfr1nLmy8Wf8TEVx/Gzs5gjmPJd79b9fiifOW0l7Zo29T83LFKklAP\n7j0OmD7Hlu2QNyLLhcZD5Ar1hlepE+rlkKJ3KjIajezbfQSAap4eNGxa76ptLpwNIiKsZD35Ao+e\nMd9v2qpRrtfs7Ox47Nn7+WrZdHM189KFK8nKun7bnyq7h+54nidGTWblsrVU964GwPHDpypFx6RD\nAcfM99t2blngdlprEhOSyiOkMuPq5oKnl+nvK1W+N75K3YZ674jR9LqlO+PGjb3mXL6njp8jPjYB\nMLWfWo4n01rzxy9rmPbGXNp2askXiz8s9nizwGNnzfebtWyY7zadurflplu6cHDvMQYM7U1qShpu\nVVyLdRxhe3Ex8RwKOI7WmrjYBFq0bcK2//YQH5tASFBYrna3G43RaOTwPtMVqnfN6vjV9rlqm4z0\nDOZM+54Na7ZRw8eLb5d/Wt5hWtX7n0/G3aNKrqtVYXtlMZdvpU6ovh5NePrRSQwdOfCa2/rXrcXH\n898gYMch2ndpleu15KQU5k1fSEpyKru27OOH+ct58Ml7ihWLZUJtWkBCBXj9w+eoVt0dF9frsyOH\ngH27jpivRDv3bIeLqzPb/tsDmNpRb+SEejbwgrljX9tOLfLt2GfvYM/GNdu5eDaYoPOhxETF4elV\ntbxDtZqe/brYOgSrOhN4gepe1a7rvwlgXhTFYi7fUqvUVb4A//2zrUjbVXF3Y9Cwvrz2wTMMueuW\nXK+5VXHlvdmTzV8On3/0HccOBRY5Bq01gcdNCdXHtwZVPfPvgg/gW7tmmSXTC2eC2LxuV6WodrSl\n3J3b2tKijaljksFgIDQ43FZhFZnWmvGjXubrWT+Za22K6mDAcfP9dp1b5buNUop+g3sCpivaLet3\nlTxYYTUnDp/m4TsnMaLfY/z641+2DqdCqvQJdfuGvVbpMdu1VwcefvpeADIzMvnf0x8VeYxdSFAY\nifGmtqKmrQq+Oi1LYaGRjBn6DM+Oe4NZ731jkxgqi73bTQlVKUXHbm3o3LMdC/+czbbAPxg7foSN\no7u2Let2sXvrAeZ+/D0vj3+3WO89tNei/bRTiwK3u/nWnub7G1ZvL36Qwurcq1XhwJ6jAPz20z/m\nhQHEFZU+oaamprFrS6lW7DF78uUHzcMgzp+5xNEDJ4v0vsCjV6p7m7RoYJVYiuunr5aTEJcIwMIv\nlspk3mUkLibeXL3frFUjPKq541G1Cu06t8TF1dnG0V2b1ppvP19sfnzfI3cW6/3tu7Si78Du1Kjl\nRUuLHs55tenY3NxZa8dG65z0WkvwpcvMfPdrlv/0N2cCb+z/k83/7uTFx6by16//4u5RhZ79OgMQ\nGhTGjk0BNo6u4qn0CRWsdwbs4GDPyLFDzY+LOrORd01P7rh3MC3bNqF1+2ZWiaU4MtIzWPnrulzP\nfTLlS6n6LQN520+vN/t2HeZg9lVmw6b16Duoe7Hef9f9Q5i98F3WBiwudGEJOzs7+g7qAVj3pNca\nThw+xcIvlvLuyzNZ99dmW4dTptau3MT6v7fyxrMfc3jfcUaMud382vIfV9kwsoqpUidUZ2cnADat\n3WG1KdAsvyRzqvaupU3HFkyd+RI/r57HLUN6WSWO4nBwdGD5hq8Y+8SV6sbtG/eyZf3uco/lRpe3\n/fR6893nS8z3H5l4b4lXTynKLGM3D75S7btxzY4SHacsXDxnscrMDbwOamZmFpvXmdqv3aq40qVn\nO3oP6EYNn+qA6Xsz/HKkLUOscCp1Qm3f1dQpQhkMBF+wziwmder70bx1YwYM7c2gYX2tss+iKO2Y\n1Orenrw4ZQLTvnzD/Nwnb39BRnpGaUOrEIxGIwE7D5mHbNjKg0/ew/tzXuWu0bfSsVsbm8ZSXCeO\nnGbbBlNvZN/aPgy+4+YyPV7XXh3M1eD7dx8p0xqT6MgYtm/cw4I5S3j1qQ+Y9uZcEuLzHwOba5WZ\nApZtuxHs33XY3Oms1y1dcXRyxMHBnjtHDwEgK8vIH7+ssWWIFU6lHjYzfNQgJk1+mradW2BnZ525\ncZVSLFn7hVX2dS1aawJ2HmLJd3+QnpbBZ4uK10EkP4OG9eGXBW3Yt+swDRrVISEhierZA9PL2pnA\nC2zfsIexT4y06n5DgsJ49K4XCA0Op1f/rsz54X2r7r84atby5va7+3P73f1tFkNJLZjzi/n+g0/e\ng4ND2X59OLs4Mfmdp6hdz5f2XVuX2dzZkx55mw2rr+7tv2NjALMWTKV+4zq5ns+9bFv+q8zktW3D\nHg7sOUrwxcu89sEzuHu4lS7ocvCfRZlYdhK76/4hfDP7Z7TW/PbTPzwy8T6rfX9e7yp1Qm3YpB4d\nOrYudJt//9rMnm0H6NSjHT37da5Q/whGo5Epkz4xz8By4UwQ9RoV7R+8IEopXn1/IpHhUeU6fu7d\nybPMbTLd+3SyauesWn41zFc3OzbuJToyhurenlbbf2WQnpbOmcDzAFT3rsad9xU8f6013XX/kDI/\nRi2/Gvk+f/7MJcbcPpEP5v6PPgO6mZ/PSaieXtWK/H2w7q/NrFi8GoBxT4ykeZvGpYy6bGmt2Zjd\nt8TewZ6bbulqfs2vtg89b+5MwI7DdOvVgeSk1Ar1vWhLlbrKtyjW/bWFpQtX8sqE9zh/5pKtw8nF\nzs6OUQ8ONz9e8v0fVtlv05YNy30wumUCXbZopVX3bTAYuC37ijAry8iaPzZZdf9loaJ1CHN0cmTp\nuvnM+HYKL055AmcXU/+D0yfPM/Pdrxk9+ClzdfD1pn2XVnTu2Y4x40fw3meT+frXT2jcvD4AyUmp\n2Ntf+ZpMSU4xLxRet4FfkY/hZzFL0vUwSf7JI2fMY6K73tT+qoT52vvP8O/+Jbw940VJphYkoRYi\np0oVTHNy5gzAr0juvG+wuXPVn7+sJSkx2cYRlcztIwbg7GJqL1u1fD3JSQUviVUSt424UsX61/J1\nhWxpO3u2HeCFR99mSJcHKlQnnBwGg4FbhvTi9hEDzM9dOHOJhV8s5fjhU+zcXPGGUSTEJxEWWnjH\nmcF39OObXz/hpbcnMHTkQLr0bMeilZ8xYGhvnnn14Vwnl7k6JBWj/dS/zvW1LuqGNVdGPlhW9+ao\nXc9XEmk+JKEW4uLZYCLDowHo0LV1hVyDtKqnB0PuNs3clJSYzMpl/9o4opJx93DjNovf4+8V/5Vq\nf4u+XMacaQsIDTKdZTduVp/mrU3VbEcPnOT86YpV2wAQF5vAf/9sIzQ4nOPFmGnLljr3aGdu29y1\nZX+B223fuJfD+46XWyc3rTVr/tjIXX0e4a3npxf7it/VzYXp89/koezJWsz7NWq69mpPLb8a1G9U\np4B3X612Pct1USv+Feqd993K5HeeostN7c3Dl2wlPjaBOdMWmHscV2SSUAuxx2KIQ6fu1h/iYDQa\n2bZhDxFhUaWq4httMbj+lwV/FGlfX8/6idMnz5f4mGXhHosxvMsWrixxmWSkZ/D9vKV8M/tn7uj9\nkLm35u0WV6mrfltfumDLQAuLdrVjh6+PpdyqenrQInsyk8BjZ4mOjMl3u2lvzGXs0Ge5uc1Iqw1R\nK0jwpcs8df9rvPLk+0SGR7Nryz5W/76h2PtRSl3VEap5m8Z8tXQ6q/f+zCMT7yvyvvzrXl9XqL61\na3L/Y3fx9bLp1KzlbbM4tNa8/MR7fDP7Z5578E327Tpss1iKQhJqISzXP+1UBmMGL50P4ekH/sfA\nDvfxxrPTSryfpi0b0qm7aQjGudOXCr1SANMQhLkff8/Imx/nk7e/LPFxS+riueB8zzZbtG1C6w7N\nATh59AxH9p8o0f7X/72V6MhYAPoO6mGumrr1zpvN4yZXLV9f4dop/erUwqOaO2CaN/V60a1XB/P9\n3VsPXPV6bHQ8F84GAabJIMqypicqIoZH73oh1yw+fQd2L3SZuJIqTq/j6t7VzE0a10NCrSj2bj9o\nntRDa81bz0+3enOQNUlCtZCelm6eq9Ky/dTZxZmW7QqeJq2w/QXsPMRXM3/Mt6rLcoUZyzPYksiZ\nAs5gMHDsYOHVhd/PvTL8obCVbfKKi4kvdccTrTUfvT6HZ8e9wQuPvn3VFc3IsVdmYllaws5JSxde\neZ9lp60aPl50692Bmr7eDBrWh9SUtBLtvyRee/pDPnn7Szb/u7PAbZRS5nb6yPDo62bQfLfeHc33\nd229+mTukMXY37adSpfYMjIyC1xzOCM9g5cef8e8znEtvxrM/G4qsxe+a/MVfJRS5uXbgi9dlnlw\ni6hzz3a5xsYHXQhl5ntf2zCiwlXqYTOWZr33NcsW/UVKcirrDiwhKTGF8OzODO27tCzRmLt3X5nF\nyqWmNs0uPdvToVvuITq51kAt5aT4N996ExNeHMewUQML/fI4feIcm7K/1Gv51WDInUUbnP/rj6v4\n/MNvSYhL4s2Pn2f4vYNKNPZs3aotbN+4F4BjBwPNZ+05Bg/vx9yPv6dHn06Memh4frso1OkT58zV\nQg2b1L1qNqL3P3+Fqp4e5TpuLi4mntW/b0Brzd7tB+kzsODp+lq2bWI+Iz9+6JRNq9uKqn2XVjg6\nOZCelpFv7UiuBcULmRC/MEmJybz3ymy2/rebNh2aM+/nD6/aZtqbc9m/+wgANWp58cOqz6nh41Wi\n45WF1u2b4e7hRu16vqSlpskSjEWglGLw8L40alqXMbc/S2pKKssWruTmwT3N8wpXJJX6CnXSpEkM\nHz6cxYsXo5QiKTEZo9HI5n934ejkyOPPP0DHbm3o0bdkf7hO3a58mec3r2/g0TPm+01bNirRMXLY\n29sx4cWx1zwT/37eUvP9MU+MxMHRoUj7Pxt4gbiYBIxGI1NfmsE9t4xn3aotxao2TUpMZvqUK5Ne\nTH73KVzdcn+puLg68/fOH3hn1sslmtfY8ur0nnHDrqqWq+7tWe6D0Iszf29zi57kxw7Zrh01IT6J\nR+9+gVXL112zzdPZxcm8RnDIpcsE5Zl17KDFCjPtSniF6urmwoHdR0iIS2T3tgMkJuSexSgiLIr1\nq7YC4OjkwIxv365QyRRg6syX+P6PWbz32Ss3VDLVWnN4/wnefuHTXN8v1tS4eQMmvfm4+fHUF2cQ\nn72YR0ktXryY4cOHM2nSpNKGZ1apE+rMmTP5888/GT16dJ7lorbh4+vN05Mf4rsVM4q9WHiOa83r\nm3OF6urmYq4OKkshQWH8lT0JflVPd+4uxqD5ia88bF6jEuDsqYu89Pg7PHDbRHZsCihSYv3y00Xm\nq/5e/bty86035btdUZN8XslJKaxabups5OzizNB7rr1wfHGUtM21OPP35qxWBKYr1PKWnJTC9/OW\ncnv3sQTsPMzrz0zj/VdmXfN9t93dnzHjR/DZovfwrlnd/HxmZpa5LdzHtwY+BUyicC2Wa6RmZmSa\nF2TPUcPHi59Wz6VZq0a8NX0SbbLb4kXZiwiL4sFhz/H7ktV8+cmiXCdQ1nTPuKHm5gVHJ4cCq/6L\navTo0fz555/MnDnTGuEBlTyhWmrdobl50ucdmwOs0vDtX7cWvv41AdNZenpauvm1+LhE88DpJi0a\nlHiS8eJY/O0K8/37Hr7zqqvDwri6uTBrwVS+WzHDfDUCpmrbJ0e/ylczfyz0/b8vWc0P85cD4OTs\nyKvvTbT6VHL//bPVPA53yF03W3WcXEJ8EvcOnMDKZf8WO7HmXf+0MLXr+TJy7O28+t7TPPnSuBLH\nW1yJCUl889nP3NZ1DLPe+9o8h6uzsxP3PnTHNd9/53238tLbE+gzoJt50gcwVcHnLL3WrpQdg3Kd\n9K65eoUov9o+/Pj3HIaOtO6JlChczVreDB1pGpucmprGxLGvc9Ki9s1aDAYDU2e8yJjxI1i67ksa\nNa1n9WOUliTUbAaDwXwGnJ6WYW7nKw2lFJ2yr1JTU9M4euBKZ6FTx6+0nzZtVbrq3qJKSjSdJDi7\nOHPfw9f+ksxPx25tWPD7TD5b9J65Q5OdnYHBw/sV+r6Z735lvv/Ys/dTu17pOmHl57a7+/Plkmn0\nv60Xo8YNs+q+P//wWwKPneXN5z5mwn2vcul8yLXfRP7rnxZGKcUb057nvkfuNA9HKamE+CTW/73l\nmle6i75cxpCuY5jz0QJiY+LNcQwe3o8f/5lTqmnyoiNjzVP7lbanbYdubXCvWgWAret35dvRr6zn\nF76RbVyznXHDnmXBnCVczj7ZL6o3pj1nvnpMiEvkydGvmnt2W1Mt/5q89PaECltlLgnVQt5qX2uw\nrOKzrPoLDQrDzs5U/M2K0dO2pLTWODo60KRFA6Z8MglPr6ol3pdSij4DurFk7Rd8NO9/PP78mKsm\nEM8rpxdzvYa1S1yFfi0Gg4HufTry6TdTSp2MLBmNxlwrj+zaso97bhnPgrm/kJGRWeh7bbX+aXRU\nLOOGPsOLj73D0oV/FrptbHS8eXF5g8HA7SP6s3zj10z78nUaN6tfqjh69uvM6r0/syZgMUMtZlgq\nCQcHe/OcuokJyey1GNZ2Pdu36zDzZ/xAXPbJTGllpGew/u8tfPDaZ9esTVny3e9sXLOdrKws/vtn\nG4cCjjP7g285deJcsY7p6OTIrAVvm3txR0fGMuHeV4qdmK/1/1Thaa0r3Q3oCOiAgABtKT0tXd/U\ndLhu5ztA92p+p05Pz9ClFXQhRLfzHaDb+Q7Qj9/zUq7XUlPS9LGDgToyPLrUx6noFn/3u/5q5o86\n5FKYrUMpsU3/7tS3dr7f/Pds5ztA39N/vD5y4ESB7/n4rXnmbTes3lYucSbEJ+rRg580H3f2+98U\nun1UZIy+qdkd+q3np+vzZy6VS4wltXblJvPv9f6rs20djlWMHzVZt/MdoHs0HqY/nTpfh4VGlGg/\np0+e1x+9Pkf3bXm3uYwK+2zGxSbobg2H6na+A/Stne/XPZsMN8eRmpJWohjiYuL1Pf3Hm48//KaH\ndFRE0b7fThw5rQd1HK3/Wr5OG43GEh2/JAICAjSggY66lLlFrlAtODg60Ku/6Qy4ll8NwkMjSr1P\nvzqmdtS6Df1plOds38nZkRZtm+BV48Zf+eS+h+/g8ecfwLd2zRK9PyE+qdRrvuaVlprOv39t5vmH\npxRpOrg+A7qxfOM3PPD43eY278BjZxl7+7MFrrM64LbePPjkKFp3aF4u65+mpabz/MNTzD2E7ewM\n5skyClLdqxqr9/zE1JkvUa9h6VYrKms9+3XG0cnUaa2iLVZREsGXLrN3u2kyjOSkFBZ9uYzbuo3l\n1ac+KNIasDp7vPyz495kRL/HWPzd7+Zqe4B/V24u8L3//rWZ1BRT+3ZocLi5/8FNt3TBydmxRL+P\nRzV3vlj8EXWyFw6o5V/zqqFx+TEajXzw2meEhUbw+sSPSjSFakpyqtW/I4pLXesPdiNSSnUEAgIC\nAujYsWOu186duoi9gz116hd9JYlrSUxIooq7TCRdEudOXWTR/F/5Z8UGPv7ydfMYzuSkFBLik/Dx\nLfk4zQVzf2H2+98A8PTkh3Bxc6F9l1ZFGq5z5MBJ3nlpBoHHztK5Zzu+Xja9zNbrLKrMzCxeemwq\nG9eaJtav6unOdytmVsjOG6Wx5s9NNG3RgAZN6to6FKsIvhjKwi+W8fuS1aSn5W4XbtaqEVNnvmSe\nh9qS1prxoyazZ1vu2amcnB25+dabGD5qEN16dyhwmFjO1KdLF65k6/rd5uQ97cs3GDy8b6l+p5Cg\nMBbM/YWX356Ao9O1k/Mfv6xhyqRPAFOz0LL184v0Pkuz3/+GRV8uo2Ytb2r51zTd/GpQy78mvfp3\nLXBI4b59++jUqRNAJ631vmIdNA9JqHkSqqhYNv+7k2cffBOATt3bcNPNXdmxOYD9u48weHhf3v/8\n1RLvOzQonCFdHwDArYorSYnJuLq58Nmid+nc49rtnRkZmfw4/1duGdKr1OvQlpbRaOStSZ/wV/aZ\nvYurM18tmy7DR64jkeHR/PztClb89A8x0XGAaXjI6r0/U92rWr7v+fB/n/PL96Y28lp+NXhg/Aju\nuHcwHtmdt4oq6EIoq5avw8XVmTHjR5TLqIOFXyzjUMAxkpNSOLzvOIkJpivkLxZ/RI++nYq9v9ee\n/pB/ClhUY/b37xQ4yb81E6p0iRMV2k23dMG3tg+hQWEE7DxMwM4rk2Pv2BSA0Wgs8T+/b+2adOrR\nloAdh8zVXclJKRw7GFikhOrgYM/DxZggvSwd3neCv7PH4Do4OjBrwVSbJNOkxGQ2rt3B7i37adqq\nIQ88dne5x3C98q5ZnWdfe5QnJo3l3782sWTBn9RvXKfAZAowZvwIDgUcZ8wTIxg0rG+JeznXrufL\nEy+MLWnoJXJw71H++yd358+Bw/qUKJmC6f+5eevGXA4Oz1XtDaaq5/IgCVVUaHZ2dowYcxtzPlqQ\n6/na9Xzp0a8zqSlpxRpPm9ftd/fPtQjCY8/dz7gJZdMLuThSklMI2HmY44dOUb9xHQYO7VPo9u06\nt+Tj+W/wxjPT+GDuq7nm1y1PSYnJvD7xI8A0b68k1OJzcnZk6MiBDB05kLTU9EK3rVPfj8Vr5pVT\nZNblWsU11+MmLRrw8tQnS7y/Z197lGdfexQwtaeGhURwOSSCy8HhVm3CK4xU+UqVb4WXlprOJ29/\nQXRkLF1uak/Pfp2p26DoizsXJiE+iXtueZzLIRHc/9hdvDz1SZu3hQKcP32JO/s8ApjmaZ753dtF\nel9URIzNO7nd3fdRzp66CMDWk79L/wGRr8jwaDIzMnGt4oqrm4vN1puWKl9RqTg5O/L6R8+Vyb7d\nPdz45d8viQiLonHzBmVyjJKo29Df3K5rudh4VlZWoXMR2zqZAnTt1cGcUHs1u5Ovf/2ELuU4Bldc\nHyynqLxRyLCZchYbHc/gTvfzzLg3+H3xaluHIzAtkl2RkimYJlho3to0g9blkAheGv8OQ7o8wKIv\nf7VxZNeWt7rZxfXawyaEuBHIFWo527P9AGGhEYSFRpTJ9HvixtGibVNzJ6x1f20B4FBA/uNdK5JO\neRYAKI+ZwISoCOQKtRzN+/h7Xh7/rvlxs1Iu2SZubL1u6ZrrsbOL83UxV61H1Srcmr3O7p333Vri\n1YOEuN5U/P/OG0idPB1pmrSsWNWMomLp3qcjn//wHmGhkbRu14zGLRrYrONGcX0w51WefGmc1TqP\nCXE9kIRajvJWhTVqWt82gYjrRu/sqTCvNwaDocJPYyiEtUmVbznyq+1jXvKsXeeWudaNFEIIcX2T\nK9Ry9snXb7F+1RYGlXKuTCGEEBWLJNRyVreBf4WZrk4IIYT1SJWvEEIIYQU3VEJVSv2mlIpWSi21\ndSxCCCEqlxsqoQKzgPJdMkEIIYTgBkuoWuvNQKKt47iRLV682NYhXHekzEpGyq34pMxs64ZKqKLs\nyT9s8UmZlYyUW/FJmdlWhUioSqneSqk/lVLBSimjUmp4Pts8rZQ6p5RKUUrtVEp1sUWsQgghRH4q\nREIF3IADwFPAVQu0KqXuBT4FpgAdgIPAGqWUd3kGKYQQQhSkQiRUrfVqrfVbWus/gPxWd54EzNda\nL9JanwAmAMnAI/lsqwrYhxBCCFFmKvzEDkopB6AT8EHOc1prrZRaB/TIs+2/QFvATSl1EbhHa70r\nn906Axw/XvGXwqpo4uLi2LevVIvaVzpSZiUj5VZ8UmbFZ5EHSr1wr9L6qhpWm1JKGYE7tdZ/Zj/2\nBYKBHpbJUSk1Deijte6R/54KPcb9wE9WClkIIcT17wGt9c+l2UGFv0ItI2uAB4DzQKptQxFCCGFD\nzkB9THmhVK6HhBoJZAE+eZ73AS6XZIda6yigVGciQgghbhjbrbGTCtEpqTBa6wwgAOif85xSSmU/\ntkohCCGEEKVVIa5QlVJuQGOu9M5tqJRqB0RrrS8BM4DvlVIBwG5MvX5dge9tEK4QQghxlQrRKUkp\n1RfYwNVjUBdqrR/J3uYpYDKmqt4DwDNa673lGqgQQghRgAqRUIUQQojrXYVvQy0LMo1h4Yo4FeQ7\nSqkQpVSyUupfpVRjW8RaUSilXlNK7VZKxSulwpRSK5RSTfNs46SUmquUilRKJSilflVK1bRVzLam\nlJqglDqolIrLvm1XSt1q8bqU1zUopV7N/h+dYfGclFseSqkp2eVkeTtm8bpVyqzSJVSZxrBIrjUV\n5CvARGA80BVIwlSGjuUZZAXTG/gc6AYMAByAtUopF4ttZgG3AyOAPoAfsLyc46xILgGvAB0xTd7y\nH/CHUqpF9utSXoXIvhAYj+k7zJKUW/6OYGoyrJV962XxmnXKTGtdqW7ATmC2xWMFBAGTbR1bRbwB\nRmB4nudCgEkWjz2AFGCUreOtKDfAO7vselmUURpwl8U2zbK36WrreCvKDYgCHpbyumY5VQFOArdg\n6n8yI/t5Kbf8y2sKsK+A16xWZpXqCtViGsP1Oc9pU+ldNY2hyJ9SqgGmszvLMowHdiGTKJU/AAAF\nkklEQVRlaKkapqv76OzHnTD1qrcst5PARaTcUEoZlFL3Yeq9vwMpr2uZC6zUWv+X5/nOSLkVpEl2\nM9YZpdSPSqk62c9b7bNWIYbNlCNvwA4Iy/N8GKYzEnFttTAlivzKsFb5h1PxZI+TngVs1VrntNPU\nAtKzTz4sVepyU0q1xpRAnYEETFcJJ5RSHZDyylf2iUd7TMkzLx+k3PKzE3gI01W9L/A2sDn782e1\n/83KllCFKA/zgJbkbqMR+TsBtAOqAiOBRUqpPrYNqeJSStXGdLI2QJsmvRFFoLW2nFbwiFJqN3AB\nGIUVp5+tVFW+lME0hpXQZUztzlKG+VBKzQFuA/pprUMsXroMOCqlPPK8pVKXm9Y6U2t9Vmu9X2v9\nOqYONs8h5VWQTkANYJ9SKkMplQH0BZ5TSqVjuqpyknIrnNY6DgjENKGQ1T5rlSqhapnGsNS01ucw\nfcgsy9ADU+/WSl2G2cn0DuBmrfXFPC8HAJnkLrdmQF1MVZ7CxAA4IeVVkHVAG0xVvu2yb3uBHy3u\nZyDlViilVBWgEaYOllb7rFXGKl+ZxvAaijAV5CzgDaXUaUwr9ryLqaf0HzYIt0JQSs0DRgPDgSSl\nVM4VfJzWOlVrHa+U+haYoZSKwdRe+BmwTWu92zZR25ZS6gPgH0ydP9wxrQDVFxgk5ZU/rXUScMzy\nOaVUEhCltT6e/VjKLQ+l1HRgJaZqXn9gKqYkusSan7VKl1C11kuzx5y+w5VpDAdrrSNsG1mF0pkr\nU0FqTON2ARYCj2itP1ZKuQLzMfVm3QIM0Vqn2yLYCmICprLamOf5h4FF2fcnYWpy+BXTVdhq4Oly\niq8iqonpM+ULxAGHMCXTnJ6rUl5Fk3esuJTb1WpjWmHMC4gAtgLdtWnlMbBSmcnUg0IIIYQVVKo2\nVCGEEKKsSEIVQgghrEASqhBCCGEFklCFEEIIK5CEKoQQQliBJFQhhBDCCiShCiGEEFYgCVUIIYSw\nAkmoQgghhBVIQhVCFItSyqiUGm7rOISoaCShCnEdUUotyE5oWdk/c+7/bevYhKjsKt3k+ELcAP4B\nHuLKakAAabYJRQiRQ65Qhbj+pGmtI7TW4Ra3ODBXx05QSv2tlEpWSp1RSo2wfLNSqrVSan3265FK\nqfnZS/ZZbvOIUuqIUipVKRWslPosTww1lFK/KaWSlFKBSqlhZfw7C1HhSUIV4sbzDrAMaAv8BCzJ\nXjCZ7GX31gBRQCdgJDAA+DznzUqpJ4E5wJdAa0xrvJ7Oc4y3gCWYFrv+G/hJKVWt7H4lISo+Wb5N\niOuIUmoBMAZItXhaAx9orT9SShmBeVrriRbv2QEEaK0nKqUeBz4EamutU7NfH4Jp8WVfrXWEUioI\n+FZrPaWAGIzAO1rrt7MfuwKJwK1a67VW/pWFuG5IG6oQ15//MC1obtmGGm1xf2ee7XcA7bLvNwcO\n5iTTbNsw1VY1U0oB+GUfozCHc+5orZOVUvGYFgwXotKShCrE9SdJa32ujPadUsTtMvI81kgTkqjk\n5B9AiBtP93weH8++fxxop5RysXi9F5AFnNBaJwLngf5lHaQQNxq5QhXi+uOklPLJ81ym1joq+/49\nSqkAYCum9tYuwCPZr/0EvA0sVEpNxVRN+xmwSGsdmb3N28AXSqkITEN0PICeWus5ZfT7CHFDkIQq\nxPXnViAkz3MngZbZ96cA9wFzgVDgPq31CQCtdYpSajAwG9gNJAO/Ai/m7EhrvUgp5QRMAqYDkdnb\nmDfJJybp3SgqPenlK8QNJLsH7p1a6z9tHYsQlY20oQohhBBWIAlViBuLVDkJYSNS5SuEEEJYgVyh\nCiGEEFYgCVUIIYSwAkmoQgghhBVIQhVCCCGsQBKqEEIIYQWSUIUQQggrkIQqhBBCWIEkVCGEEMIK\n/g9A3mKB/Vi8gAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f924cccbc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_losses([(\"model_generator\", hist2), (\"without_gen\", hist1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "input_1 (InputLayer)             (None, 25, 25, 25)    0                                            \n",
      "____________________________________________________________________________________________________\n",
      "input_2 (InputLayer)             (None, 5, 5, 60)      0                                            \n",
      "____________________________________________________________________________________________________\n",
      "reshape_1 (Reshape)              (None, 25, 25, 25, 1) 0           input_1[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "reshape_2 (Reshape)              (None, 5, 5, 60, 1)   0           input_2[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "convolution3d_1 (Convolution3D)  (None, 22, 22, 22, 3) 195         reshape_1[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "convolution3d_2 (Convolution3D)  (None, 4, 4, 55, 10)  250         reshape_2[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "maxpooling3d_1 (MaxPooling3D)    (None, 11, 11, 11, 3) 0           convolution3d_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "maxpooling3d_2 (MaxPooling3D)    (None, 2, 2, 27, 10)  0           convolution3d_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)              (None, 3993)          0           maxpooling3d_1[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "flatten_2 (Flatten)              (None, 1080)          0           maxpooling3d_2[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "merge_1 (Merge)                  (None, 5073)          0           flatten_1[0][0]                  \n",
      "                                                                   flatten_2[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "dense_1 (Dense)                  (None, 1000)          5074000     merge_1[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)              (None, 1000)          0           dense_1[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "energy (Dense)                   (None, 1)             1001        dropout_1[0][0]                  \n",
      "====================================================================================================\n",
      "Total params: 5,075,446\n",
      "Trainable params: 5,075,446\n",
      "Non-trainable params: 0\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "emodel.summary()"
   ]
  },
  {
   "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, 25, 25, 25)    0                                            \n",
      "____________________________________________________________________________________________________\n",
      "input_2 (InputLayer)             (None, 5, 5, 60)      0                                            \n",
      "____________________________________________________________________________________________________\n",
      "reshape_1 (Reshape)              (None, 25, 25, 25, 1) 0           input_1[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "reshape_2 (Reshape)              (None, 5, 5, 60, 1)   0           input_2[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "convolution3d_1 (Convolution3D)  (None, 22, 22, 22, 3) 195         reshape_1[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "convolution3d_2 (Convolution3D)  (None, 4, 4, 55, 10)  250         reshape_2[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "maxpooling3d_1 (MaxPooling3D)    (None, 11, 11, 11, 3) 0           convolution3d_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "maxpooling3d_2 (MaxPooling3D)    (None, 2, 2, 27, 10)  0           convolution3d_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)              (None, 3993)          0           maxpooling3d_1[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "flatten_2 (Flatten)              (None, 1080)          0           maxpooling3d_2[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "merge_1 (Merge)                  (None, 5073)          0           flatten_1[0][0]                  \n",
      "                                                                   flatten_2[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "dense_1 (Dense)                  (None, 1000)          5074000     merge_1[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)              (None, 1000)          0           dense_1[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "energy (Dense)                   (None, 1)             1001        dropout_1[0][0]                  \n",
      "====================================================================================================\n",
      "Total params: 5,075,446\n",
      "Trainable params: 5,075,446\n",
      "Non-trainable params: 0\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "saveModel(emodel, \"regression_1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 25, 25, 25)\n",
      "(500, 5, 5, 60)\n",
      "(1,)\n"
     ]
    }
   ],
   "source": [
    "# define testing data\n",
    "(txe2, txh2), ty2 = next(test2)\n",
    "#txe2 -> ecal input\n",
    "#txh2 -> hcal input\n",
    "#ty2 -> energy target\n",
    "\n",
    "print(txe2.shape)\n",
    "print(txh2.shape)\n",
    "print(ty2[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 1)\n"
     ]
    }
   ],
   "source": [
    "pred = emodel.predict([txe2, txh2])\n",
    "print pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#print pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nToo complicated :P\\ndef compareEnergy(predictedEnergies, targets):\\n    diff = []\\n    for i in range(len(predictedEnergies)):\\n        d = predictedEnergies[i] - targets[0][i]\\n        #print d\\n        diff.append(d)\\n    #print diff\\n    return diff\\n'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This function returns the difference between the precicted energy and the real (target) energy of the particle.\n",
    "# \"targets\" is an array of lists, of size 1. This is why we need to do targets[0][i] instead of only targets[i].\n",
    "'''\n",
    "Too complicated :P\n",
    "def compareEnergy(predictedEnergies, targets):\n",
    "    diff = []\n",
    "    for i in range(len(predictedEnergies)):\n",
    "        d = predictedEnergies[i] - targets[0][i]\n",
    "        #print d\n",
    "        diff.append(d)\n",
    "    #print diff\n",
    "    return diff\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#print len(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#print len(ty2[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500,)\n",
      "(500,)\n"
     ]
    }
   ],
   "source": [
    "# Defining array of values that will contain the difference between the predicted energy and the target energy\n",
    "\n",
    "diff = pred-ty2\n",
    "\n",
    "# defining the relative diference to the target energy of the particle\n",
    "rDif = diff/ty2\n",
    "\n",
    "#print(diff.shape)\n",
    "#print rDif.shape\n",
    "\n",
    "diff = diff.reshape((diff.shape[0],))\n",
    "rDif = rDif.reshape((rDif.shape[0],))\n",
    "\n",
    "print(diff.shape)\n",
    "print rDif.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# This function returns the difference between the target energy and the predicted energy\n",
    "def dif(target, predicted):\n",
    "    dif = target - predicted\n",
    "    dif = dif.reshape((dif.shape[0],))\n",
    "    return dif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# This function returns the relative difference to the target energy of the particle\n",
    "def rDif(target, predicted):\n",
    "    dif = target - predicted\n",
    "    rDif = dif/target\n",
    "    rDif = rDif.reshape((rDif.shape[0],))\n",
    "    return rDif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500\n",
      "500\n"
     ]
    }
   ],
   "source": [
    "print ty2.size\n",
    "print pred.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# This function plots the predicted energy against the target energy.\n",
    "def plotPredictedXTarget(target, predicted):\n",
    "    plt.figure(figsize=(5,5))\n",
    "    plt.xlabel(\"Target energy (GeV)\")\n",
    "    plt.ylabel(\"Predicted energy (GeV)\")\n",
    "    plt.title(\"Comparing the predicted energy with the real energy of the particle\")\n",
    "    \n",
    "    plt.scatter(target, predicted, color='g', alpha = 0.5)\n",
    "    \n",
    "    plt.xlim(0, 550)\n",
    "    plt.ylim(0, 550)\n",
    "    \n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/matplotlib/axes/_axes.py:531: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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oTksTg6FkSni+x9GtR5MIJ7jtqdu000m1H22jVIcxZsxW08aYAvCu4K9Rnm3Y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\nKImIiIiUoEBJREREpAQFSiIiIiIl/H+OOMm2ntVaSQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f929eddea90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotPredictedXTarget(ty2, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.mlab as mlab\n",
    "from scipy.stats import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Plots histogram of the difference between the predicted energy and the target energy (GeV)\n",
    "\n",
    "def histEDif(difference):\n",
    "    # the histogram of the data\n",
    "    n, bins, patches = plt.hist(difference, 1000, normed=1, facecolor='green', alpha=0.75)\n",
    "    \n",
    "    # best fit of data\n",
    "    (mu, sigma) = norm.fit(difference)\n",
    "\n",
    "    # add a 'best fit' line\n",
    "    y = mlab.normpdf( bins, mu, sigma)\n",
    "    l = plt.plot(bins, y, 'r--', linewidth=1)\n",
    "\n",
    "    plt.xlabel('Difference between the predicted energy and the target energy (GeV)')\n",
    "    plt.ylabel('Probability')\n",
    "    plt.title('Histogram of the energy difference for the regression model')\n",
    "    \n",
    "    plt.xlim(-25, 25)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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K+fTEhUZPHxfZ5RayLjPnk3zHQFfOeV1ajijfk8CFZvYjwlODnyZ0qCa6tfUH4A/RcTIL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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f91c475d9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histEDif(diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Plots histogram of the normalized energy difference (%)\n",
    "\n",
    "def histRelDif(difference):\n",
    "    # the histogram of the data\n",
    "    n, bins, patches = plt.hist(difference, 300, normed=1, facecolor='green', alpha=0.75)\n",
    "    std = np.std( difference )\n",
    "    # best fit of data\n",
    "    print std\n",
    "    (mu, sigma) = norm.fit(difference, range = (-std,std))\n",
    "\n",
    "    print mu, sigma\n",
    "    # add a \"best fit\" line\n",
    "    y = mlab.normpdf( bins, mu, sigma)\n",
    "    l = plt.plot(bins, y, 'r--', linewidth=1)\n",
    "\n",
    "    plt.xlim(-0.2, 0.2)\n",
    "    plt.xlabel('Relative difference between the predicted energy and the target energy (%)')\n",
    "    plt.ylabel('Occurrences')\n",
    "    plt.title('Histogram of the relative energy difference for the regression model')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'function' object is not iterable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-35-87a76eebaf69>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhistRelDif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrDif\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-34-6b33853404cf>\u001b[0m in \u001b[0;36mhistRelDif\u001b[0;34m(difference)\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mhistRelDif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdifference\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;31m# the histogram of the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpatches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdifference\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfacecolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'green'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.75\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0mstd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mdifference\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;31m# best fit of data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mhist\u001b[0;34m(x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, hold, data, **kwargs)\u001b[0m\n\u001b[1;32m   2963\u001b[0m                       \u001b[0mhisttype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhisttype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malign\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malign\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2964\u001b[0m                       \u001b[0mrwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2965\u001b[0;31m                       stacked=stacked, data=data, **kwargs)\n\u001b[0m\u001b[1;32m   2966\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2967\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwashold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/matplotlib/__init__.pyc\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1817\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m   1818\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1819\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1820\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1821\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/matplotlib/axes/_axes.pyc\u001b[0m in \u001b[0;36mhist\u001b[0;34m(self, x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)\u001b[0m\n\u001b[1;32m   5933\u001b[0m             \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5934\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5935\u001b[0;31m             \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_normalize_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'x'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5936\u001b[0m         \u001b[0mnx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# number of datasets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5937\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/matplotlib/axes/_axes.pyc\u001b[0m in \u001b[0;36m_normalize_input\u001b[0;34m(inp, ename)\u001b[0m\n\u001b[1;32m   5864\u001b[0m             \"\"\"\n\u001b[1;32m   5865\u001b[0m             if (isinstance(x, np.ndarray) or\n\u001b[0;32m-> 5866\u001b[0;31m                     not iterable(cbook.safe_first_element(inp))):\n\u001b[0m\u001b[1;32m   5867\u001b[0m                 \u001b[0;31m# TODO: support masked arrays;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5868\u001b[0m                 \u001b[0minp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/matplotlib/cbook.pyc\u001b[0m in \u001b[0;36msafe_first_element\u001b[0;34m(obj)\u001b[0m\n\u001b[1;32m   2560\u001b[0m         raise RuntimeError(\"matplotlib does not support generators \"\n\u001b[1;32m   2561\u001b[0m                            \"as input\")\n\u001b[0;32m-> 2562\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\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   2563\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2564\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'function' object is not iterable"
     ]
    },
    {
     "data": {
      "image/png": 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AAHSJBQCgSywAAF1iAQDoEgsAQJdYAAC6xAIA0CUWAIAusQAAdIkFAKBLLAAAXWIBAOgS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\nAECXWAAAusQCANAlFgCArv8Px/C1g8U/IJgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f91b47d7590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histRelDif(rDif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# next plot: relative energy difference against target energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# This function plots the relative energy difference against the target energy.\n",
    "def plotRelXTarget(target, relative):\n",
    "    plt.figure(figsize=(5,5))\n",
    "    plt.xlabel(\"Target energy (GeV)\")\n",
    "    plt.ylabel(\"Relative energy difference (%)\")\n",
    "    plt.title(\"Comparing the relative energy difference with the target energy of the particle\")\n",
    "    \n",
    "    plt.scatter(target, relative, color='g', alpha = 0.5)\n",
    "    \n",
    "    plt.xlim(0, 500)\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plotRelXTarget(ty2, rDif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Danny's input handler to grab all the data from the test set and do the same plots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "testDir = '/home/vitoriabp/CaloImage_2017/GammaEscan/test' # test directory where you get the targets from\n",
    "mod = '/home/vitoriabp/CaloImage_2017/regression_1.json' # model file\n",
    "w = '/home/vitoriabp/CaloImage_2017/regression_1.h5' # weight file\n",
    "\n",
    "from CMS_Deep_Learning.io import simple_grab\n",
    "\n",
    "# y is the target\n",
    "y,  = simple_grab(['Y'], data = testDir, label_keys='target')\n",
    "pred, = simple_grab(['predictions'], data = testDir, label_keys='target', input_keys=['ECAL', 'HCAL'], model = mod, weights = w)\n",
    "\n",
    "\n",
    "y = y[:, 1:]\n",
    "#print(y.shape)\n",
    "#print y\n",
    "#print pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plotPredictedXTarget(y, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "histEDif(dif(y, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "histRelDif(rDif(y, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plotRelXTarget(y, rDif(y, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As there is so much data for such a big energy range, we can divide the data into bins containing a smaller energy range, in order to further understand the distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bin1 = y[0, 100]\n",
    "#bin2 = y[101, 200]\n",
    "#bin3 = y[201, 300]\n",
    "#bin4 = y[301, 400]\n",
    "#bin5 = y[401, 500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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    "name": "ipython",
    "version": 2
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   "file_extension": ".py",
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