v4_struct_phase.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "apJbCsBHl-2A"
   },
   "outputs": [],
   "source": [
    "## Choose GPUs and CPUs\n",
    "\n",
    "#If want to choose GPU to use\n",
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"1,4\"\n",
    "#This will make it so that only the 0th GPU is visible to TensorFlow.\n",
    "#In your case, you can choose any in the range [0, 3].\n",
    "#If you wanted, for example, the 0th and 2nd GPUs to both be visible, replace \"0\" with \"0,2\"\n",
    "\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras import backend as K\n",
    "from keras.datasets import mnist\n",
    "\n",
    "from keras.layers import Conv2D, MaxPool2D\n",
    "from keras.models import Sequential\n",
    "from keras.utils import to_categorical, multi_gpu_model, plot_model\n",
    "from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D, Dropout\n",
    "from keras.models import Model\n",
    "\n",
    "GPU=True\n",
    "CPU=False\n",
    "num_cores = 4\n",
    "\n",
    "if GPU:\n",
    "    num_GPU = 2\n",
    "    num_CPU = 1\n",
    "if CPU:\n",
    "    num_CPU = 1\n",
    "    num_GPU = 0\n",
    "\n",
    "config = tf.ConfigProto(intra_op_parallelism_threads=num_cores,\\\n",
    "        inter_op_parallelism_threads=num_cores, allow_soft_placement=True,\\\n",
    "        device_count = {'CPU' : num_CPU, 'GPU' : num_GPU},\\\n",
    "#        gpu_options=tf.GPUOptions(per_process_gpu_memory_fraction=0.5))\n",
    "                       )\n",
    "session = tf.Session(config=config)\n",
    "K.set_session(session)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "# Config the matplotlib backend as plotting inline in IPython\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "h,w=32,32\n",
    "nepochs=10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class ModelMGPU(Model):\n",
    "    def __init__(self, ser_model, gpus):\n",
    "        pmodel = multi_gpu_model(ser_model, gpus)\n",
    "        self.__dict__.update(pmodel.__dict__)\n",
    "        self._smodel = ser_model\n",
    "\n",
    "    def __getattribute__(self, attrname):\n",
    "        '''Override load and save methods to be used from the serial-model. The\n",
    "        serial-model holds references to the weights in the multi-gpu model.\n",
    "        '''\n",
    "        # return Model.__getattribute__(self, attrname)\n",
    "        if 'load' in attrname or 'save' in attrname:\n",
    "            return getattr(self._smodel, attrname)\n",
    "\n",
    "        return super(ModelMGPU, self).__getattribute__(attrname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "real_intens_test=np.load('data_numpy_arrays/real_intens_test.npy')\n",
    "real_phase_test=np.load('data_numpy_arrays/real_phase_test.npy')\n",
    "ft_test=np.load('data_numpy_arrays/ft_test.npy')\n",
    "\n",
    "real_intens_train=np.load('data_numpy_arrays/real_intens_train.npy')\n",
    "real_phase_train=np.load('data_numpy_arrays/real_phase_train.npy')\n",
    "ft_train=np.load('data_numpy_arrays/ft_train.npy')\n",
    "ft_train_unstrained=np.load('data_numpy_arrays/ft_unstrained.npy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Autoencoder-structure predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 32, 32, 1)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        320       \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 4, 4, 128)         147584    \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 4, 4, 128)         147584    \n",
      "_________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2 (None, 8, 8, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 8, 8, 64)          73792     \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 8, 8, 64)          36928     \n",
      "_________________________________________________________________\n",
      "up_sampling2d_2 (UpSampling2 (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_11 (Conv2D)           (None, 16, 16, 32)        18464     \n",
      "_________________________________________________________________\n",
      "conv2d_12 (Conv2D)           (None, 16, 16, 32)        9248      \n",
      "_________________________________________________________________\n",
      "up_sampling2d_3 (UpSampling2 (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_13 (Conv2D)           (None, 32, 32, 1)         289       \n",
      "=================================================================\n",
      "Total params: 720,321\n",
      "Trainable params: 720,321\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "Train on 180000 samples, validate on 20000 samples\n",
      "Epoch 1/10\n",
      "179968/180000 [============================>.] - ETA: 0s - loss: 0.1858\n",
      "Epoch 00001: saving model to v4_struct_weights/tmp_weights_struct/weights.01.hdf5\n",
      "180000/180000 [==============================] - 65s 363us/step - loss: 0.1858 - val_loss: 0.1542\n",
      "Epoch 2/10\n",
      "179968/180000 [============================>.] - ETA: 0s - loss: 0.1447\n",
      "Epoch 00002: saving model to v4_struct_weights/tmp_weights_struct/weights.02.hdf5\n",
      "180000/180000 [==============================] - 66s 368us/step - loss: 0.1447 - val_loss: 0.1479\n",
      "Epoch 3/10\n",
      "179968/180000 [============================>.] - ETA: 0s - loss: 0.1375\n",
      "Epoch 00003: saving model to v4_struct_weights/tmp_weights_struct/weights.03.hdf5\n",
      "180000/180000 [==============================] - 66s 366us/step - loss: 0.1375 - val_loss: 0.1449\n",
      "Epoch 4/10\n",
      "179968/180000 [============================>.] - ETA: 0s - loss: 0.1329\n",
      "Epoch 00004: saving model to v4_struct_weights/tmp_weights_struct/weights.04.hdf5\n",
      "180000/180000 [==============================] - 66s 366us/step - loss: 0.1329 - val_loss: 0.1426\n",
      "Epoch 5/10\n",
      "179968/180000 [============================>.] - ETA: 0s - loss: 0.1295\n",
      "Epoch 00005: saving model to v4_struct_weights/tmp_weights_struct/weights.05.hdf5\n",
      "180000/180000 [==============================] - 66s 365us/step - loss: 0.1295 - val_loss: 0.1458\n",
      "Epoch 6/10\n",
      "179968/180000 [============================>.] - ETA: 0s - loss: 0.1269\n",
      "Epoch 00006: saving model to v4_struct_weights/tmp_weights_struct/weights.06.hdf5\n",
      "180000/180000 [==============================] - 65s 364us/step - loss: 0.1269 - val_loss: 0.1415\n",
      "Epoch 7/10\n",
      "179968/180000 [============================>.] - ETA: 0s - loss: 0.1247\n",
      "Epoch 00007: saving model to v4_struct_weights/tmp_weights_struct/weights.07.hdf5\n",
      "180000/180000 [==============================] - 66s 365us/step - loss: 0.1247 - val_loss: 0.1432\n",
      "Epoch 8/10\n",
      "179968/180000 [============================>.] - ETA: 0s - loss: 0.1229\n",
      "Epoch 00008: saving model to v4_struct_weights/tmp_weights_struct/weights.08.hdf5\n",
      "180000/180000 [==============================] - 64s 355us/step - loss: 0.1229 - val_loss: 0.1421\n",
      "Epoch 9/10\n",
      "179968/180000 [============================>.] - ETA: 0s - loss: 0.1210\n",
      "Epoch 00009: saving model to v4_struct_weights/tmp_weights_struct/weights.09.hdf5\n",
      "180000/180000 [==============================] - 64s 356us/step - loss: 0.1210 - val_loss: 0.1391\n",
      "Epoch 10/10\n",
      "179968/180000 [============================>.] - ETA: 0s - loss: 0.1193\n",
      "Epoch 00010: saving model to v4_struct_weights/tmp_weights_struct/weights.10.hdf5\n",
      "180000/180000 [==============================] - 66s 364us/step - loss: 0.1193 - val_loss: 0.1396\n"
     ]
    }
   ],
   "source": [
    "\n",
    "K.clear_session()\n",
    "np.random.seed(123)\n",
    "model=Sequential()\n",
    "\n",
    "\n",
    "input_img = Input(shape=(h, w, 1))  # adapt this if using `channels_first` image data format\n",
    "\n",
    "x = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)\n",
    "x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)\n",
    "x = MaxPooling2D((2, 2), padding='same')(x)\n",
    "#x = Dropout(0.5)(x)\n",
    "x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)\n",
    "x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)\n",
    "x = MaxPooling2D((2, 2), padding='same')(x)\n",
    "#x = Dropout(0.5)(x)\n",
    "x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)\n",
    "x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)\n",
    "x = MaxPooling2D((2, 2), padding='same')(x)\n",
    "#encoded = Dropout(0.5)(x)\n",
    "encoded=x\n",
    "\n",
    "# at this point the representation is \n",
    "\n",
    "x = Conv2D(128, (3, 3), activation='relu', padding='same')(encoded)\n",
    "x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)\n",
    "x = UpSampling2D((2, 2))(x)\n",
    "#x = Dropout(0.5)(x)\n",
    "x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)\n",
    "x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)\n",
    "x = UpSampling2D((2, 2))(x)\n",
    "#x = Dropout(0.5)(x)\n",
    "x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)\n",
    "x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)\n",
    "x = UpSampling2D((2, 2))(x)\n",
    "#x = Dropout(0.5)(x)\n",
    "decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)\n",
    "\n",
    "autoencoder = Model(input_img, decoded)\n",
    "#autoencoder.compile(optimizer='adam', loss='binary_crossentropy')\n",
    "\n",
    "parallel_model = ModelMGPU(autoencoder, gpus=num_GPU)\n",
    "parallel_model.compile(optimizer='adam', loss='binary_crossentropy')\n",
    "\n",
    "print (autoencoder.summary())\n",
    "plot_model(autoencoder, to_file='paper_data/str_model.png')\n",
    "\n",
    "checkpoints=keras.callbacks.ModelCheckpoint('v4_struct_weights/tmp_weights_struct/weights.{epoch:02d}.hdf5', \n",
    "                                            monitor='val_loss', verbose=1, save_best_only=False, \n",
    "                                            save_weights_only=False, mode='auto', period=1)\n",
    "\n",
    "history=parallel_model.fit(ft_train, real_intens_train, shuffle=True, batch_size=256, verbose=1, \n",
    "                           epochs=nepochs, validation_split = 0.1, callbacks=[checkpoints])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\naxarr[1].plot(epochs,hist.history['acc'], 'C0o', label='Training')\\naxarr[1].plot(epochs,hist.history['val_acc'], 'C0-', label='Validation')\\nplt.legend(loc='center right', bbox_to_anchor=(1.5, 0.5))\\nplt.xlabel('Epochs')\\nplt.ylabel('Accuracy')\\nplt.tight_layout()\\nplt.grid()\\nplt.tick_params(which='both', width=2)\\nplt.tick_params(which='major', length=7)\\nplt.tick_params(which='minor', length=5)\\n\""
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XjfFOS0vDbrdXOFTEarXicDhISUkhICDgou/x/vvvYzabCQgI4JlnniEhIQF/f39Gjx5N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\nqOXJ+J4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e64118e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist=history\n",
    "epochs=np.asarray(history.epoch)+1\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "plt.style.use('seaborn-white')\n",
    "matplotlib.rc('font',family='Times New Roman')\n",
    "matplotlib.rcParams['font.size'] = 20\n",
    "\n",
    "f, axarr = plt.subplots(1, sharex=True, figsize=(8, 6))\n",
    "\n",
    "axarr.set(ylabel='Loss')\n",
    "axarr.plot(epochs,hist.history['loss'], 'C3o', label='Training')\n",
    "axarr.plot(epochs,hist.history['val_loss'], 'C3-', label='Validation')\n",
    "axarr.grid()\n",
    "axarr.legend(loc='center right', bbox_to_anchor=(1.5, 0.5))\n",
    "\n",
    "\"\"\"\n",
    "axarr[1].plot(epochs,hist.history['acc'], 'C0o', label='Training')\n",
    "axarr[1].plot(epochs,hist.history['val_acc'], 'C0-', label='Validation')\n",
    "plt.legend(loc='center right', bbox_to_anchor=(1.5, 0.5))\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.tight_layout()\n",
    "plt.grid()\n",
    "plt.tick_params(which='both', width=2)\n",
    "plt.tick_params(which='major', length=7)\n",
    "plt.tick_params(which='minor', length=5)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.save('paper_data/str_history', history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "minepoch=10\n",
    "from keras.models import Model, load_model\n",
    "model=load_model('v4_struct_weights/tmp_weights_struct/weights.%02d.hdf5' %minepoch)\n",
    "#score=model.evaluate(ft_test, test_dataset, verbose=1)\n",
    "#print (score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 32, 32, 1)\n"
     ]
    }
   ],
   "source": [
    "preds_intens=(model.predict(ft_test))\n",
    "print (preds_intens.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use 0.1 as threshold for intensity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "preds_intens=np.where(preds_intens<0.1,0,preds_intens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot some instances of the test data, it's FT and the CNN prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ddf245950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ddf245ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ntrain=120000\n",
    "ntest=10000\n",
    "plt.viridis()\n",
    "n = 5\n",
    "f,ax=plt.subplots(4,n,figsize=(20, 12))\n",
    "for i in range(0,n):\n",
    "    j=int(round(np.random.rand()*ntest))\n",
    "\n",
    "    # display original intens\n",
    "    ax[2,i].imshow(real_intens_test[j].reshape(h, w))\n",
    "    ax[2,i].get_xaxis().set_visible(False)\n",
    "    ax[2,i].get_yaxis().set_visible(False)\n",
    "    \n",
    "    # display original intens\n",
    "    ax[1,i].imshow(real_phase_test[j].reshape(h, w))\n",
    "    ax[1,i].get_xaxis().set_visible(False)\n",
    "    ax[1,i].get_yaxis().set_visible(False)\n",
    "\n",
    "    # display FT\n",
    "    ax[0,i].imshow(ft_test[j].reshape(h, w))\n",
    "    ax[0,i].get_xaxis().set_visible(False)\n",
    "    ax[0,i].get_yaxis().set_visible(False)\n",
    "    \n",
    "    # display reconstruction\n",
    "    ax[3,i].imshow(preds_intens[j].reshape(h, w))\n",
    "    ax[3,i].get_xaxis().set_visible(False)\n",
    "    ax[3,i].get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f0eacbda890>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J2traaOXKlZk4JMvYs2cP7dy5k5566ilyOvnvpfb29ridY4iIGhoaaNGiRRk5\n2YmQz6Egp/aCfNqPZXI6pp9yTsAwDHP16tXmww8/bEajUTMcDpvf/e53zW9+85tmNBrNxCFZwu7d\nu8277rrLPHLkiFlfX2/W19ebJ0+eNP/yl7+Yjz32mBkMBs3HH3/cfOWVV8xIJGKapmm2tLSYP/7x\nj83u7u6MHTfymRhyai/Ip/1YKacZa53p9/tp48aNdPToUdJ1nRYvXkxr164ll8uVicPJuF27dtH6\n9espGo3G/fqmTZto6dKl9P3vf5/27t1LZWVldNNNN1FNTQ0tX7484+ubIp+DIaf2gnzaj9VyivWQ\nAQAALADLLwIAAFgABmQAAAALwIAMAABgARiQAQAALAADMgAAgAVgQAYAALAADMgAAAAWgAEZAADA\nAjAgAwAAWAAGZAAAAAv4f2QjDeQij7uUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e5d44d3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i=0\n",
    "f,axarr=plt.subplots(1,4,figsize=(8, 6))\n",
    "axarr[0].imshow(real_intens_test[i].squeeze())\n",
    "axarr[1].imshow(real_phase_test[i].squeeze())\n",
    "axarr[2].imshow(ft_test[i].squeeze())\n",
    "axarr[3].imshow(preds_intens[i].squeeze())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Autoencoder-predict phase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 32, 32, 1)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        320       \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 4, 4, 128)         147584    \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 4, 4, 128)         147584    \n",
      "_________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2 (None, 8, 8, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 8, 8, 64)          73792     \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 8, 8, 64)          36928     \n",
      "_________________________________________________________________\n",
      "up_sampling2d_2 (UpSampling2 (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_11 (Conv2D)           (None, 16, 16, 32)        18464     \n",
      "_________________________________________________________________\n",
      "conv2d_12 (Conv2D)           (None, 16, 16, 32)        9248      \n",
      "_________________________________________________________________\n",
      "up_sampling2d_3 (UpSampling2 (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_13 (Conv2D)           (None, 32, 32, 1)         289       \n",
      "=================================================================\n",
      "Total params: 720,321\n",
      "Trainable params: 720,321\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "Train on 180000 samples, validate on 20000 samples\n",
      "Epoch 1/10\n",
      "179968/180000 [============================>.] - ETA: 0s - loss: 0.6677\n",
      "Epoch 00001: saving model to v4_struct_weights/tmp_weights_phase/weights.01.hdf5\n",
      "180000/180000 [==============================] - 66s 369us/step - loss: 0.6677 - val_loss: 0.6642\n",
      "Epoch 2/10\n",
      "179968/180000 [============================>.] - ETA: 0s - loss: 0.6584\n",
      "Epoch 00002: saving model to v4_struct_weights/tmp_weights_phase/weights.02.hdf5\n",
      "180000/180000 [==============================] - 65s 364us/step - loss: 0.6584 - val_loss: 0.6626\n",
      "Epoch 3/10\n",
      "179968/180000 [============================>.] - ETA: 0s - loss: 0.6542\n",
      "Epoch 00003: saving model to v4_struct_weights/tmp_weights_phase/weights.03.hdf5\n",
      "180000/180000 [==============================] - 66s 365us/step - loss: 0.6542 - val_loss: 0.6610\n",
      "Epoch 4/10\n",
      "179968/180000 [============================>.] - ETA: 0s - loss: 0.6527\n",
      "Epoch 00004: saving model to v4_struct_weights/tmp_weights_phase/weights.04.hdf5\n",
      "180000/180000 [==============================] - 65s 360us/step - loss: 0.6527 - val_loss: 0.6591\n",
      "Epoch 5/10\n",
      "179968/180000 [============================>.] - ETA: 0s - loss: 0.6518\n",
      "Epoch 00005: saving model to v4_struct_weights/tmp_weights_phase/weights.05.hdf5\n",
      "180000/180000 [==============================] - 62s 347us/step - loss: 0.6518 - val_loss: 0.6604\n",
      "Epoch 6/10\n",
      "179968/180000 [============================>.] - ETA: 0s - loss: 0.6513\n",
      "Epoch 00006: saving model to v4_struct_weights/tmp_weights_phase/weights.06.hdf5\n",
      "180000/180000 [==============================] - 65s 358us/step - loss: 0.6513 - val_loss: 0.6599\n",
      "Epoch 7/10\n",
      "179968/180000 [============================>.] - ETA: 0s - loss: 0.6507\n",
      "Epoch 00007: saving model to v4_struct_weights/tmp_weights_phase/weights.07.hdf5\n",
      "180000/180000 [==============================] - 64s 357us/step - loss: 0.6507 - val_loss: 0.6589\n",
      "Epoch 8/10\n",
      "179968/180000 [============================>.] - ETA: 0s - loss: 0.6506\n",
      "Epoch 00008: saving model to v4_struct_weights/tmp_weights_phase/weights.08.hdf5\n",
      "180000/180000 [==============================] - 64s 353us/step - loss: 0.6506 - val_loss: 0.6577\n",
      "Epoch 9/10\n",
      "179968/180000 [============================>.] - ETA: 0s - loss: 0.6499\n",
      "Epoch 00009: saving model to v4_struct_weights/tmp_weights_phase/weights.09.hdf5\n",
      "180000/180000 [==============================] - 64s 353us/step - loss: 0.6499 - val_loss: 0.6564\n",
      "Epoch 10/10\n",
      "179968/180000 [============================>.] - ETA: 0s - loss: 0.6498\n",
      "Epoch 00010: saving model to v4_struct_weights/tmp_weights_phase/weights.10.hdf5\n",
      "180000/180000 [==============================] - 64s 355us/step - loss: 0.6498 - val_loss: 0.6567\n"
     ]
    }
   ],
   "source": [
    "\n",
    "K.clear_session()\n",
    "np.random.seed(123)\n",
    "model=Sequential()\n",
    "\n",
    "\n",
    "input_img = Input(shape=(h, w, 1))  # adapt this if using `channels_first` image data format\n",
    "\n",
    "x = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)\n",
    "x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)\n",
    "x = MaxPooling2D((2, 2), padding='same')(x)\n",
    "#x = Dropout(0.5)(x)\n",
    "x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)\n",
    "x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)\n",
    "x = MaxPooling2D((2, 2), padding='same')(x)\n",
    "#x = Dropout(0.5)(x)\n",
    "x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)\n",
    "x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)\n",
    "x = MaxPooling2D((2, 2), padding='same')(x)\n",
    "#encoded = Dropout(0.5)(x)\n",
    "encoded=x\n",
    "\n",
    "# at this point the representation is \n",
    "\n",
    "x = Conv2D(128, (3, 3), activation='relu', padding='same')(encoded)\n",
    "x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)\n",
    "x = UpSampling2D((2, 2))(x)\n",
    "#x = Dropout(0.5)(x)\n",
    "x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)\n",
    "x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)\n",
    "x = UpSampling2D((2, 2))(x)\n",
    "#x = Dropout(0.5)(x)\n",
    "x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)\n",
    "x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)\n",
    "x = UpSampling2D((2, 2))(x)\n",
    "#x = Dropout(0.5)(x)\n",
    "decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)\n",
    "\n",
    "autoencoder = Model(input_img, decoded)\n",
    "#autoencoder.compile(optimizer='adam', loss='binary_crossentropy')\n",
    "\n",
    "parallel_model = ModelMGPU(autoencoder, gpus=num_GPU)\n",
    "parallel_model.compile(optimizer='adam', loss='binary_crossentropy')\n",
    "\n",
    "print (autoencoder.summary())\n",
    "plot_model(autoencoder, to_file='paper_data/ph_model.png')\n",
    "\n",
    "\n",
    "checkpoints=keras.callbacks.ModelCheckpoint('v4_struct_weights/tmp_weights_phase/weights.{epoch:02d}.hdf5', \n",
    "                                            monitor='val_loss', verbose=1, save_best_only=False, \n",
    "                                            save_weights_only=False, mode='auto', period=1)\n",
    "\n",
    "history=parallel_model.fit(ft_train, real_phase_train, shuffle=True, batch_size=256, \n",
    "                        verbose=1, epochs=nepochs, validation_split = 0.1, callbacks=[checkpoints])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.save('paper_data/ph_history', history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\naxarr[1].plot(epochs,hist.history['acc'], 'C0o', label='Training')\\naxarr[1].plot(epochs,hist.history['val_acc'], 'C0-', label='Validation')\\nplt.legend(loc='center right', bbox_to_anchor=(1.5, 0.5))\\nplt.xlabel('Epochs')\\nplt.ylabel('Accuracy')\\nplt.tight_layout()\\nplt.grid()\\nplt.tick_params(which='both', width=2)\\nplt.tick_params(which='major', length=7)\\nplt.tick_params(which='minor', length=5)\\n\""
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iIyNhY2NTp35qamooKSnBsmXLsGnTJvTo0QNubm5YsGBBtTv2mpqa2LZtGxYtWoQ333wT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\n/M4uTQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e55827e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist=history\n",
    "epochs=np.asarray(history.epoch)+1\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "plt.style.use('seaborn-white')\n",
    "matplotlib.rc('font',family='Times New Roman')\n",
    "matplotlib.rcParams['font.size'] = 20\n",
    "\n",
    "f, axarr = plt.subplots(1, sharex=True, figsize=(8, 6))\n",
    "\n",
    "axarr.set(ylabel='Loss')\n",
    "axarr.plot(epochs,hist.history['loss'], 'C0o', label='Training')\n",
    "axarr.plot(epochs,hist.history['val_loss'], 'C0-', label='Validation')\n",
    "axarr.grid()\n",
    "axarr.legend(loc='center right', bbox_to_anchor=(1.5, 0.5))\n",
    "\n",
    "\"\"\"\n",
    "axarr[1].plot(epochs,hist.history['acc'], 'C0o', label='Training')\n",
    "axarr[1].plot(epochs,hist.history['val_acc'], 'C0-', label='Validation')\n",
    "plt.legend(loc='center right', bbox_to_anchor=(1.5, 0.5))\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.tight_layout()\n",
    "plt.grid()\n",
    "plt.tick_params(which='both', width=2)\n",
    "plt.tick_params(which='major', length=7)\n",
    "plt.tick_params(which='minor', length=5)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "minepoch=10\n",
    "from keras.models import Model, load_model\n",
    "model=load_model('v4_struct_weights/tmp_weights_phase/weights.%02d.hdf5' %minepoch)\n",
    "#score=model.evaluate(ft_test, test_dataset, verbose=1)\n",
    "#print (score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 32, 32, 1)\n"
     ]
    }
   ],
   "source": [
    "preds_phase=(model.predict(ft_test))*2*np.pi-np.pi #MAP BACK TO -pi to pi phases\n",
    "print (preds_phase.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0dd954d6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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K6dDtMetp6ZVxrvT851/OGUL91iYxd+Fra1eF+vSpA8U2shpgfxke6RTLrr0qXh9fM/N0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\nxSDg8cEDxTYrxw+Gejnb5shXlkPde/yF8rhrMUS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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0dd954dad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.viridis()\n",
    "n = 7\n",
    "f,ax=plt.subplots(4,n,figsize=(20, 12))\n",
    "for i in range(0,n):\n",
    "    j=int(round(np.random.rand()*ntest))\n",
    "\n",
    "    # display original intens\n",
    "    ax[0,i].imshow(real_intens_test[j].reshape(h, w))\n",
    "    ax[0,i].get_xaxis().set_visible(False)\n",
    "    ax[0,i].get_yaxis().set_visible(False)\n",
    "    \n",
    "    # display original intens\n",
    "    ax[1,i].imshow(real_phase_test[j].reshape(h, w))\n",
    "    ax[1,i].get_xaxis().set_visible(False)\n",
    "    ax[1,i].get_yaxis().set_visible(False)\n",
    "\n",
    "    # display FT\n",
    "    ax[2,i].imshow(ft_test[j].reshape(h, w))\n",
    "    ax[2,i].get_xaxis().set_visible(False)\n",
    "    ax[2,i].get_yaxis().set_visible(False)\n",
    "    \n",
    "    # display reconstruction\n",
    "    ax[3,i].imshow(preds_phase[j].reshape(h, w))\n",
    "    ax[3,i].get_xaxis().set_visible(False)\n",
    "    ax[3,i].get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f0ddf354590>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TbHuEIpUePS9/zOYJtXv1Yx2/sdfMyU17du4or9OvmcycfX2Eaaeh66Cv4bV+jmrDnpwQ\nneYprTNCw5fDgjeknNifeq/tqHvy3fq8bbniqJlz/ehBNX5f7wEzZzCp1+e6J3lwqm3z3l+pr1Xj\nn09fbOYcK+k8uqmTNmYr6/U6Pzx6nZnT8w8/N9uwfNzXqu+lGzrNrH2Tsmt1Xu2nttjz+ttFnaPm\nfgYVEXmsoZu7T1RsfEY1uz4ndY92yZTNFMmW9Bqema7ZSaLjuLrKfjYJnLRONz8vLvhGDQAAAABi\nhgs1AAAAAIgZLtQAAAAAIGa4UAMAAACAmIlp6lx8Jd91mdk2cb1uMD37gYaZc+PWl9X45tFdZs51\nOV1gZCBpm1MmZeEOpJvTx8229+fH1fgXg7ap5X+u36TGjaE+M4f+p93HLRwiIhJEushAPbKFGeZC\nHX8lN/NWRGYbeluzYSMkVbcZy26T35SnmEiy5hSU8BQTkZbeFvmaEEPmLipIMtdzeuwmaAd2qTEF\nXuoj9jy+8oer1Dgs2KokiaZTGGPWJp5HTmPmMPTEjFM8ZLhgC+D0ZnUm+uScLYBTzDuFSzI2rmYn\nbEGoKOc8H+s8FVicRt1Bwb7FNp3iOkHO09D1hNOE2tapkKGX9PMxt8nzd1fnaWzZJV0yTn/lnon5\n4wnrIr40fYgEQ7pYQWWDjdl3v2e/Gn9q7VNmzruyE2o8lvIUWDB/U7ex18jaRtnXF3Txkt/sf97M\n2dfUnwWeOvUOM+d/F/TnnsqMfS8oXqEbGoe795o5WD6RZzmI3NAatZ9VP3jJPjX+qFM4RERkQ1qf\n//0tu585501lrp4zcxIt+5rJOMt6umHXx/ScXueT0zb28229PmY22jW9pt++JGE/Bpk1dCUKLPGN\nGgAAAADEDBdqAAAAABAzXKgBAAAAQMyQo7YAt5m1m48mItL6UEmN/8O2fzJz/nXPq2q82vM79EJC\n/+43lTi76+hiwiacBE6mQSW0vxeunNK3Gyn7frCLuHvhE//d2WIbPbadlr5VT2rXXKjjcabVY+c0\ndRwFVRvXGU+Ompv/lKrbFsOJps7DiFo2lygKiNHFKK9PSKJ3fj3pHdfPf27cPo+hk0rWKtrcMvdP\nfZlBm6tQ7NUJiamkp3mzsymTssdz8YDuFH1Rz8Kdow/lR8y2/dOjatwO7Tob9tj7H1qn1/lKza6h\nYaD3leptmjnN4/p1FKXs89Hq1a+ZzJx9DVXW621JTwpnZa0+nvwJz3PvpJW2z2jsTcbnr9cc0ue/\nvsauYdcMHlHjK518NBGRdWm9n1zi7DLBezzrfL+Tezzq6Wy/MXVYjdenZ8yc2Yv1e8H/PWxz9UcH\n9OcHeqWvMM+LN+zRMXrpukkz53eGnlVjNx9NRCQUHVfTof3MOd3SOZz1pid/vWLXXjdfPVO2r6tk\nRb/PROWKmZNI6gjMzdlE35STLxxmPU+au4kcNQAAAAAAF2oAAAAAEDNcqAEAAABAzHChBgAAAAAx\nQzGRBVQ36wTEmWttxvadl/5YjX+nuM/MGUnqBPKzLRSyGG4zYxGRE4FOyPz+9HYzp+dlnRDac+Sk\nmWPTOhE3mYSn6IOjHumk8orphClysq2bBc96iomUnSaWiYYvOdjef7q2iGIidV2IIfI0vI4CInIx\nMqd0sYnQOd31QXve3IIWzX7Pjp2lplW1CeP1jD5H/T02IIpO0+lWYGO4kNJzBlK2FXPoZHqXGjYR\n/oqx19V4LFs2cw6P2Oao5ZaO9fzArJlzpKSLTc2V7f1HOf2kZafsay9w6kKkPV2nG0P6NbT6Wfta\nmHmn3ne2YpPl3UbZAwfn9xM0Q7HlUCAi0u7RMRrl7fM/nNaxNZD0FFaShdfrs+V+zsh5PvINOC/9\n1Sn7elib14V0Il9doYZT/GmRx4gOWURj5uyALsLxO2t+aea8O6fXtUyi18wphXpVmGjbok0nGvp2\njZp9b8h6FpeUU48qXfN8Nqjq95Cw5lkgs/r+ki0bkYlFfHwwsb4Cgc03agAAAAAQM1yoAQAAAEDM\ncKEGAAAAADFDjtoCWn36B6pDo7YZ5LvzuqnlQNI2nlzKnDRXLbI//H22sV6Nf3Zwi5mz9hX9g91g\nzyudPTCsiFZkf4jdcPIY3ebWIiKlQOekzTTtnHrD+R14zca5L7/GNLxueH4s3nJyHtq2Was4j+PA\nX15n50CSbZFke/45TzpPd9v2K5W2k5LYM2F/nF91/tbXyNvzHzhNoPtytin2u4eO6XHvETNnW9bJ\nLUvZdS5wDvGG4l4z55/L29Q440lUKKbtMT594hI1nmvZhtduM+9g2s5J9OtcuyjpeRtO6P0UpuyU\ndsFpeN2056ftvGSrq+z56TuiX0P9P5h/zlrJusza9BOI2Ma3npygvNOFPOP5HLCcnw1895VyDrzl\n+fv9eE3nXmZn7YONntl9jkeHc+HmpPnyCFcPzqnx5flxM8f3+dV1PNBrxiv1tWbOREUnNUd1e0Dp\nqo2jbFnvO1Xx5KbXnYbXns8GCWdbIvTkqC0m3ywGyZZ8owYAAAAAMcOFGgAAAADEDBdqAAAAABAz\nXKgBAAAAQMxQTGQBmTmdaP76jG3+96vWqBpf4SS9i4jkErbZX6e4Da6PBzax8rGT79LHs9sWhiju\nm1Zj2zYb3Sj0nMmKk1jrKyYy3daxXvIUE2nV9RKSsTUYJFW32bipuj6mRM2TMNxwGl63PMVEsCj9\nR5qSTs8/n4FT9GPmHXZ9avXr81Zba89jmHUKcWTsnGxWn7dcyp5Ht6BHX9JWoBlO6nhYm7Lx6DZ7\nH/Y1xe59VY3/8ui/NXPW99hm1o1Ax/pcwybdZ9P6cUQZ+9rLOA3A272eOSV9fsobbNK9WxBm5lJ7\nDvsP6fNRHLcFWCav0QVPar+3/fT/h7VTIj/ZZW4DkXTVOW+eN8y5QMdoGNnXh/v+vZzFRURssamj\n7UEz55XpMTUuHI9BhQUobvGQyLMWr+rRxUQ2epqb5xJFNa6Gds14rd2nxw0bM6WarlCVLPuKiZhN\nkmro10Oyaj8bSE03vPYWGkt4qvu4FhPG7m5oeA0AAAAA4EINAAAAAGKGCzUAAAAAiBly1BbQc+SU\nHr9su38+vvVKNb42f9TMKSSW7nfoboNrt7m1iG1wvXavbfIa7rbNYdH96pH9/XbV+UH7rNPcWkRk\ntqW3zTVs896oppcQXwNLt7n1G9t0/CUa9nfw0nJy1AIbszS4XpzZLVlJFubPX+A2uPb87j4zp8+l\npy+0NHTqimT7bZJiNq3jrxXYXIVjdZ3j8FPZauaERb1mhjJh5gwk9TFXPTlBp0L94DcXT5g5G/PT\nZlsuqR/H916+wswZGdY5H4mmXefbTefxe1IpIifdrDlkH0f/fns7V2NQ77w+bF/D+RN6331H5l93\n7XZTPL22ISIpZw1Llm3O4q/q+vNCvc8msvWbLUvHzYcTEZkLdVzvrm00c6aP6dfnlgOeZGSsKHd9\nTrgdsEWkGer3a18dAjdGypHNERtv6wbXk42imVOp6HU21bDH48tfz5QX/mwQNT2fFxyJtH6s7YJd\ni8OFe3uTowYAAAAAsLhQAwAAAICY4UINAAAAAGKGCzUAAAAAiBmKiSwg2POKGg9ttcUL3EIdvxxZ\nZ+asTk2qcTHhZvMv8ng8ycBug2u3ubWIbXDtNrcWocH1+eKy/3mbGj/97/6bmVOJdBbtycAmA083\ndTGRqqfBb7Ku/9aT8uSY+4qJJN1iIk2bsBy6Da5DTzULLEqmKpI8o8l54aQ+J9VV9m929TE9pzVq\ni9KkevS2dNqeo0LGaXid9jW81qtPLbDNm+87/EE1fke/LXPxgf4DahxE9nE9eEyv4f9mtS2iNJyq\nmG2ltH49rB0rmTmZlH780yP2BRFUnMeWtStv/le64EhzwL6Gmv06y91tUC4isupZ/Vw3Bmwhl0So\nbzf7jvnXeVjLioybm0BEMtO6W2/uhH1PH3eK5Mx5CjyMOtUJ7BnqnIansNTBto7rn05vMXPyx/VH\nxczMnJnD54cV5oRW0tMnerauPwc2PeujGyMnAxuzx5rDavxaecDMCao6ZrLlxRUaSzZ1JCWqdTMn\n9DW4dkR9vWrc6vFVbXLGvh7ZMQhsvlEDAAAAgJjhQg0AAAAAYoYLNQAAAACIGS7UAAAAACBmKCby\nNhUP2ATy7Es6sfJHWy8zc67Lv6b3c5aXyLXIdmT/ZUMXL3GLm4iIrN2rk9zD3TaBHuenamQTdudC\nnfg+0+41c2abOvG4XrcFHpJ1nX2bsnm/kq7ZbNxk3UkGbti4jpyE4QP/7f1251iUKPHGf2+a26AX\nIF8hCjexOjVr3y7CjD63YdYubLNVHUen6jkzZ397VI03Ds2aOQPZmhrPOMVuREQONcbUOJewSecb\nizNqvLey2syZzPaZbXtLet5woWrmFNO6eEhlwBbgmY504R5PfSjJzOnnurrWnp/iYT1Otm0mfKqh\nb9cYtHPy03pO+oynOayLeOoDQUTCF/R7aP5ffMDM2Tet43pirV1nN6T02pdJdK6ciFt8rBTadfYn\nle1q/OLB9WbOmlec1/nzL3fg6NBR7hLhWdJPlvWa+Vpg17m+pP6MezzoN3PGG7pITsVTaCxR1XGc\nscul/7NBw1mzA1ugKnK2JTL2/sN+/b7TKtq1L8x4niSXe7NF3KTT+EYNAAAAAGKGCzUAAAAAiBku\n1AAAAAAgZshRe5vc36WLiBSv1r9N3zO7xswpjenf6671JCakEgtfN8+GNufiRyWdE5d9qWDmFA/o\nBtcx6OGHZfLB/3WH2faXNz2oxjMtm+9zqqHz2IKaXS5yDTdHzf6AO9Xw5ajpXImoZbtzRm7Da5y1\ngUN1Safmz9/k1c75nrO/328M6XMZFu15jGp6XWuIzRVY6DYitnH6ayl7X+v6T6lxrW1zJk/WdQ7Q\nUM4mRhya1TnF/XmbhVUv2H1PlnVuWdbT3Hsuo/PvZk/Z11VPr07kbLft81F6p37tJUJ7fpLO3bds\n+pNMvUs/jpRNUZLiMb2xdMn8Y/D0w8Wv0TthY/bYlM7vOdhcZea8K3vYbOsUt3nxKy2bb/SDyW1q\n3PeizSEdfJHPD7HnLBG+1261pD8b/qJ2sZkzknxJjcfbQ2bOZEPnts1VbLP3dE0fUNKT7JqueRpe\nV/VngajuSXx3PisnhwfNlPJGvSAG9hDNuhqmPAloK5CT5mIZBgAAAICYWdQ3alEUyTe/+U158MEH\n5ciRI7Jq1SrZuXOn3HLLLZJIzF+RVqtV+cIXviB79uyRKIrkhhtukNtuu01Sqc5VMQIWg5hFtyFm\n0W2IWXQbYhbdZlHfqH31q1+V559/Xv70T/9UHnjgAbn44ovlnnvukXvuuef0nGazKbfeeqvUajV5\n+OGH5Rvf+Ibs2rVL/uRP/mTJDh74dYhZdBtiFt2GmEW3IWbRbRa8UGs2mzI9PS1/8Rd/Iddcc428\n//3vl/vvv1+2b98uf//3fy9TU1MiIvK1r31Nnn/+ebnzzjslkUhIJpOR2267Tb71rW/Jz372syV/\nIMCbiFl0G2IW3YaYRbchZtGNFvzpY7lcls985jNqWyqVkg9/+MOyZ88eGR8fl7GxMfn6178ul112\nmQwPzydqX3311ZLNZuWhhx6S66+/vvNHHxM9kzph9/DrI2bOi5t0U+qL0q+bOcWEU7zBU3CkFNqv\n3d3iJcVxm/3oK4JyviJmF3ayrQsjzHqKiVSbTkGFhv27TrrmjH3FRGq26ELCKSYSNm0xkQP/9Vqz\n7Xy11DF78oq8JHrmz3Gypc9TkLDFKjJlvS1/wq49zQHnNhX7lhI4YdQctutaMKTX0PJJG4+TSX27\nnpytjFGr6zW00V741/11z5xSwxZkaoc6/mdes81i8wNOxvxh+zjmVrlPiH1dJZ2k9jBrX1fNPn0+\nIs8vstK6/opkynY/7R59w8LM/Os1aAbi6VMrIqyzrsKELXqQntLnf0/VNpO+sXBQjYuJsys01ors\nOns80K+Rb5y80cw5/PMNarzxl7bqQ7j7/Pj8cD7HrBs2vvXAXWterdjCd5fnx9V4qm3Xuama/vzg\nKzSWqev3j0zVrj1p72cDHX9R3VOFxL1Nj12vm0X9WEPfW0EiBpVCFmHBd7EzA/VMhUJBksmkbNy4\nUQ4cOCCvvfaabN+uO9xns1nZsGGD7Nq1S6IoUr//BZYKMYtuQ8yi2xCz6DbELLrRWVd9fOaZZ+SG\nG26QkZEROXbsmIiIDA3ZMp7iACVrAAAVTUlEQVR9fX1SKpXk1KlT5t+A5UTMotsQs+g2xCy6DTGL\nODurPmrj4+Py4x//WL797W+LiEipVBIRf2C/WSGnXq/LwMCA+XdgORCz6DbELLoNMYtuQ8wi7s7q\nQu2uu+6S22+/XbZs2SIiIvn8G3kBLU/T2kbjjd+XDg7ahnTni57D+q8rhZdtjtrj77xSja/NHzVz\ncgn9e91GZJ/PFxvrzDY3J27TJI2CXcSsVgp07kSpZbtBVuu6eXGqZr+ATzlpGRlPA8tU3ROPbk6a\n5zxc6DoZs2FaJHHGat8q6p/tFI57cpd63Q6qdr9pJ4EpW7KTqqv1ftJlG0dBn9MctcfGTCtIveVY\nRKTm5FWemLANfhNOHl2UtTlBMyM1s61Zc3LLPM1RWy3nmNJ2TmZS7ydh0zQk2Vr4Z1Vtt8G15yZu\nnkp9zE5KN/Sk0ub58xNVUiL7FjyU0y7kdTZ9Ys5s6z2mT9IzJzeZOa8N6rV4dcrGfm4RH9XKoc3l\n+WH1HWr8xO7tZs76Z3X8p3/47IL3dT45X2I22XSaN3vyWhOBnvOzYxebOWNZHcdJTx7XbE1/XkiU\nbXy6nw1STc9ng6qNdTd/PfDkryecFgnhgLsYijQG9ftMYPu4e5uCx9HbPsz7779fRkdH5dOf/vTp\nbZs2vbH4zM7Omvmzs7MyPDwsuZznWQKWATGLbkPMotsQs+g2xCy6wdu6UHvsscfkhRdekLvvvltt\n37p1q4yMjMjBg7p6UaPRkImJiVhWyMGFgZhFtyFm0W2IWXQbYhbdYtEXak888YQ88sgj8sUvflHS\n6fmvOScnJyWRSMiOHTvkxRdfVH+F2LVrlwRBIDt27OjsUQOLQMyi2xCz6DbELLoNMYtusqgLtccf\nf1y+9KUvyWc/+1k5evSoHDhwQPbt2yc/+MEP5K/+6q8kkUjIH/zBH8jmzZvlK1/5ioiIVKtVue++\n+2THjh1y7bUXTj8kxAMxi25DzKLbELPoNsQsus2CGaqPPvqo3HnnnRKGoXz84x83/37vvfeKyBsJ\nmF/72tfk7rvvlk984hMShqF85CMfkVtuuaXjBx03wZ5X1Hh463Vmzs+u2KLGvxyxRUFGU8fV+ERg\ns8wfn77SbCu8rBM7ew6ftMdotpy/iNmFffn7H1LjS949bua0Gnp5SDVsIYKU0+A6VbeFGRI1mwwc\nOQnCUfvCLoCz1DHbLogkzqhZsPYpXXigssYplCG2MIwvGbw+4CRs532Ns/XY089XGqv07aK2/Rti\nZVInjFcytslpIuPs3NNMuv+A3nbqHZ5m0nX7fEQtfbtUyb59hnnnNRPa5yPhhHr+5MKFQ5KeWjtu\nA9fcjKeQyxq973aPnZNz7l8VIHmLP+WyzmrBvoNmW+HaVWrsFv4SEfnJmkvV+KL0c2bOsHMeWp53\n9H1tG7P/cPw9atz/fNbM6d89qcbn82eF8zlmI09xI1dqTgdSNWmLcPyo551qvL5YMnNqDR1H6crC\n636uZCMrVbEFcKK6rkKSzNq4TvTqAjy11T1mTtP26bb35dajimn/6wUv1G666Sa56aabFrWzkZER\n+dKXvnTOBwWcC2IW3YaYRbchZtFtiFl0oy4pTgkAAAAAFw4u1AAAAAAgZs6q4TXeWvGA/U1v9qVh\nNf4/Wy83cy7PTqjx842NZs7PDm4x29bu1b/9dXPmgIXMNWxfmLCml4ds3dMsdzE5ap6GldLSTS33\nf/6axRwmzlJuWiR5Rs7Zye36fLdsqoIUphbOUUs39LaBXzXNnMn36PvyNRnNTelkgcZqT7KA25i6\n7cntcppSZ2fsnbmP1W0CKyISVu1bY6Kh95Up2X23A33cSft0SKas769l0yskzOn9+HJA3ObiNU8z\n66zzVpSu2TktJ5fjzCbmUe38zllaagOv6ObBpS0DZs5PL9Lv6e8p/MrM2ZadUePpwObtPDL7XrNt\nz/71anzxXhuQwSv7zTZ0H7OuepbQpPNizp5wk7REJnJDatwYtWthvaJz1LItz2eDqrOGVe1Kkpyr\nmW1u/nqiYHORw01r1biy1r4eQmdTlPY0AHfeUqKF04VXBN+oAQAAAEDMcKEGAAAAADHDhRoAAAAA\nxAwXagAAAAAQMxQTWQLhC3vNtuLVH1Djl2bWmDkvDehtj554t5mT220TK4v7pvX9L+oogXknXh41\n2xJZJxm4bqZI2m2K3LAJw4mGTWAPfQVGsGSyc5EqBtLO638P0zaL2i1OUR+xieduQYvkmE3qHjik\nY6K83u6n7vThTXgaVYvTGzVVt3PSTqGO/Em7m+wpN2btfhpDnqzyRSSam8bUnoR+N2E9N2PnuHfW\nLtoZbnPxTMVzX85TnZmzB+Q2pj1TWBd5i3/GAqJn96hx/2XvN3N2H9IFPx7psUVBbujXBcJ21zaY\nOQ+/ZG839KxTXOf7T/36g8V5z1fIyZWa0ZcFM0G/mZNw1l7f2pNxiomkKp73/NDzaTXjXJaMDJkp\n5S16Qayt8hSEyi7cvXoxBVjigG/UAAAAACBmuFADAAAAgJjhQg0AAAAAYoYctWXSM9lW48NHbU7Q\nt3v1b8yf3rfZzNnwcttsC3fbnDjgXCWd36GnfDlqDf0b82Td8zt0Tz7avv9y1TkdG96eVjEhQWH+\nd/x9x/Q6kq148sYGnTypXk+z5H69LUot3PjUzWsTEYmS+naNIXs8CedmmTkzRXLTelJuzuZAzG7R\n+3Ybo4qIZOc8j8PJwwi97576dkm7XEuupI9p+BfTZk5to84L8TV0rY84x+hrcOukh/rOT+g0gg3O\n6E8e8qfcjuo/YBv8VlfpDuw/yG81c6Y36q7ox+YGzZzE0bzZ1neUduV4e5INZw2bOrvLhFRDryuJ\n0LNAJTyJv6PDajj7bvtZ+dQlemE6c82avz899q7XMc1Jc7EMAwAAAEDMcKEGAAAAADHDhRoAAAAA\nxAwXagAAAAAQMxQTWSY9vyqp8cDzNkHy6ZpOIu5/1SbUF/fZDq6kC2MppEyDYZt5625L1mzhkKhp\nG15jmUWiEqero3rpbxVtUnf/YV0JY26jXY9CpzhFmDVTpOUUCkm2bBwlnEXMV4TDTQb3JYef2dRb\nRKQwZWOv0a+LLlRXe4qk9JpNEjiPLW3rQiyqoWyQ1fdX2u5p6LpBP9fu8yMisubpqhpnDrxu5kzc\npAtS+Rp5u4VcVPEX3lw6KvHU82bbyMC1avz6oA2+Xa2L1Dis2eDvn7DntvC6roDTJbUTsJKcMHKL\ncoiIJFt6UuApyFQbcYuRFcycTMFTJGlUL7Ru4RARkVbRKVQS+NY1e0zd6jx6KAAAAABwfuBCDQAA\nAABihgs1AAAAAIgZctSWSfDSq2o8urpo5mRLumtf/xHbYTjY80pnDwz4NYKC/h14um5/B56qO0ks\nnubWr/5H28AVy6s5KJI4o2H1wAF93qKU/Ztdu0dvy1TMFNNo1NtU1ElD8OWfDb+ic8nqw3ZHtWEn\n58GT6xZmdIyeuMLmRbR172DJlO3xDO63yVlu3pjbTFpEZGif3ujmf4mIVMf0Yxt8ZsIzZ73ej00P\nlMaQzuWY+L0tZo6bZ9p/2CacuLkc5XXzG6KqYIkV9p9Q47GeVWbOdE3HcdKTO1h8zXNun91zbgeH\nC4+zrPpyvSI3j80zp9mvJ51Ke94cEnabmy/ta2bt5siFWfte4B5jN+MbNQAAAACIGS7UAAAAACBm\nuFADAAAAgJjhQg0AAAAAYoZiIisk9aNfmG2jl+uiC24BEmAlnXyXTdgd2Kez2l/9w9XLdTh4G/In\nRJLl+fNXHdPVKXzFKvqO6MIY1UttVnfoFArJzXpa6jqb6qM2y3vifbowRtLWpJGEs5+wYfez9icl\nNa5sskWbWj36dvUR+/fKRr/dtvbJU2pcvsg2Jk5VdaWU6rq8meM+Z1M3rDNzXJHnnboxqI8xXfM1\npNfjVsE+Z+75OLMASdSk5/VSC/YfUuO+jD3Z6eqgGvsKPOSP28ovNLjGOfMU5Qgz0YJz3KIgbc/a\n43vfMdvchd/HN8Xdj6dxd7fgGzUAAAAAiBku1AAAAAAgZrhQAwAAAICYIUctRshJQ7c5eLNtKIz4\nSUT6p/79R3QuVaZsu1AnGzo7KTebMXMyZZ130Bi2eQhuE+zCpE0oaPXq2/lysvIn9O0GDtbMnKn3\n9uvjGbLH46Y8ZD15ddmy3TZzWZ+zHzsn8/IRNQ432mbvCTdXwpPfkSvpSYUT9vwUjs2p8dR1w2ZO\n23le0xV7zKMv6FzE8vr58xzWRTx9vbGEgpf3mW3Zlxe+HfloWBKLyO1y13gREXHz2HwWMcWXorao\nZtZdnJPm4hs1AAAAAIgZLtQAAAAAIGa4UAMAAACAmOFCDQAAAABihmIiAHCeS4S6iEVps176w7R9\nK+g/oouJtHtsBrdbBMRXrCLM6jnNPrufXMm5nSeBPHD6bU9c12PmuEVIMmW7n4FDupt27kTdzClf\nZPedqeqDKh6umDnV67aocWPAPtakUxek76jt7j29TTcAb/bbQi6lzbp4SNN3X04lkDObWc/vx+77\nTeF5lJAPYAX5Cof4ioIspiqOe7vzvJIO36gBAAAAQMxwoQYAAAAAMcOFGgAAAADEDDlqAHCey1RD\nSbXnE45Gfn5S/fupK0fMbfr2ltQ4uHLQ7tjJDfA1Ps04zaOTLZtQUB/WfzM0TaFFpGdS58xl52yC\nQ7Pf2ebZT21EH2R5XXHh/YiYvIjK2n4zJVXXj63vWGDmVNaknPtKmTlJJ22t5cnrSzn9vnPTnkQN\n92ae5yM/o2+Xrs9PCpqBeNLaAOCtLSZvbDE9sc82j+08wjdqAAAAABAzXKgBAAAAQMxwoQYAAAAA\nMbNiOWpB8MZv99vp5gIzAevNuHkzjpbD6fsqe5ozAQv5/3GzEjEbtHTMtiLdAyxo6r5d3jkNm0sV\nphLO2B5D6CY5tW2CQVTV+4l8fdQaOsEqStrkhbDmbPOlbTlvOaFnjtuP7Y0bOrfzvHUlnMcatOy5\nDuv6SXIfl4hIWNN/Q/XlaURO+zff8bjHnPCEXtDQT0CieUaOWuuNGFiJmOWzAc7Gin42qPDZAGep\n8us/H6zYhdrU1JSIiExuPLhSh4DzwNTUlFx00UXLdl8iIplHv7ss94fz00rE7KmDj6ntM+7El+1t\nZ90NL57dMXSqGEU3fmyv+TYeXMScRejUx9DF3P9KxCyfDXAuViJmU//46LLcH85fvrhNRJHvb5dL\nr16vy+7du2VsbExSKc+fYYG3EASBTE1NyRVXXCH5fH5Z7pOYxbkgZtFtiFl0G2IW3eit4nbFLtQA\nAAAAAH4UEwEAAACAmOFCDQAAAABihgs1AAAAAIiZFav6KPJG8tx9990nTz75pCSTSbnqqqvkjjvu\nkEKhsJKH1ZWiKJJvfvOb8uCDD8qRI0dk1apVsnPnTrnlllskkZiv0VytVuULX/iC7NmzR6Iokhtu\nuEFuu+02EmAXiZjtLOJ26RGznUXMLj1itrOI2aVHzHYWMTtvRYuJ/NEf/ZGcOnVKHnjgAclkMnLH\nHXfIzMyM/N3f/Z06EVjYV77yFTl48KB8/OMfl3a7LV/96lflySeflFtuuUU+97nPiYhIs9mUT33q\nU3LRRRfJPffcI+12W2699VbZtGmT/Nmf/dkKP4LuQMx2FnG79IjZziJmlx4x21nE7NIjZjuLmD1D\ntEK+973vRVu3bo127959etuRI0eirVu3Rg8//PBKHVZXajQa0T333KO2tdvt6GMf+1i0bdu2aHJy\nMoqiKHrggQeibdu2RSdPnjw976mnnoq2bt0a/fSnP13WY+5GxGxnEbdLj5jtLGJ26RGznUXMLj1i\ntrOIWW3FctQeeughGRwclMsvv/z0to0bN8r69evloYceWqnD6krlclk+85nPqG2pVEo+/OEPSxiG\nMj4+LiIiX//61+Wyyy6T4eHh0/OuvvpqyWazPOeLQMx2FnG79IjZziJmlx4x21nE7NIjZjuLmNVW\n5EKtXC7Lc889J+vWrTNfCW/evFn27t0rpVJpJQ6tKw0PD8vIyIjZXigUJJlMysaNG+XAgQPy2muv\nybp169ScbDYrGzZskF27dklES71fi5jtPOJ2aRGznUfMLi1itvOI2aVFzHYeMautyIXa8ePHJQgC\nGRoaMv/W19cnURSdvmLG2XvmmWfkhhtukJGRETl27JiIyK99zkulkpw6dWq5D7FrELPLh7jtDGJ2\n+RCznUHMLh9itjOI2eVzocbsilR9nJ2dFRH/E/xmpZZ6vb6sx3S+GR8flx//+Mfy7W9/W0Tk9F90\nFnrOBwYGlu8guwgxuzyI284hZpcHMds5xOzyIGY7h5hdHhdyzK7IN2r5fF5ERFqtlvm3ZrMpInJe\nPLkr6a677pLbb79dtmzZIiJv/Zw3Gg0RERkcHFy+A+wyxOzyIG47h5hdHsRs5xCzy4OY7Rxidnlc\nyDG7IhdqmzZtEhGRmZkZ828zMzOSSqVk9erVy31Y5437779fRkdH5dOf/vTpbW8+52/+9edMs7Oz\nMjw8LLlcbtmOsdsQs0uPuO0sYnbpEbOdRcwuPWK2s4jZpXehx+yKXKj19fXJ9u3b5dChQ+bfDh8+\nLFdddZUUi8UVOLLu99hjj8kLL7wgd999t9q+detWGRkZkYMHD6rtjUZDJiYm5Prrr1/Ow+w6xOzS\nIm47j5hdWsRs5xGzS4uY7TxidmkRsyt0oSYisnPnTpmampK9e/ee3nbo0CE5fvy47NixY6UOq6s9\n8cQT8sgjj8gXv/hFSafn0w8nJyclkUjIjh075MUXX1R/gdi1a5cEQcBzvgjE7NIgbpcOMbs0iNml\nQ8wuDWJ26RCzS4OYfUMiWqH6lWEYyq233ipDQ0Ny7733ShAEcvvtt0uj0ZD777+fTu5v0+OPPy5/\n8zd/I5///OelUCiIyBvP8eHDh+WHP/yh/Pmf/7nU63W5+eab5cYbb5Q//uM/lmq1Kr//+78v27Zt\nk7vuumuFH0H8EbOdR9wuLWK284jZpUXMdh4xu7SI2c4jZuet2IWaiEi1WpV77rlH9uzZI8lkUq6/\n/nq57bbbJJvNrtQhdaVHH31U7rzzTgnD0Pvv9957r/zWb/2WiIicPHlS7r77bpmYmJAwDOUjH/mI\n3HLLLSwki0TMdg5xuzyI2c4hZpcHMds5xOzyIGY7h5jVVvRCDQAAAABgrViOGgAAAADAjws1AAAA\nAIgZLtQAAAAAIGa4UAMAAACAmOFCDQAAAABihgs1AAAAAIgZLtQAAAAAIGa4UAMAAACAmOFCDQAA\nAABi5v8BBCeZ9C1hh9oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0dd95d5b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i=0\n",
    "f,axarr=plt.subplots(1,5,figsize=(15, 10))\n",
    "axarr[0].imshow(real_intens_test[i].squeeze())\n",
    "axarr[1].imshow(real_phase_test[i].squeeze())\n",
    "axarr[2].imshow(np.log10(ft_test[i].squeeze()))\n",
    "axarr[3].imshow(preds_intens[i].squeeze())\n",
    "axarr[4].imshow(preds_phase[i].squeeze())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Zero phase when lying outside the object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "preds_phase3=np.where(preds_intens<0.1,0,preds_phase)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0de0e83590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6VtbzxGIxefjhh2XZsvcfBpubm+W0007Lshch0we1T0yF2icmQ/0TU6H2iakE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\n7QFkV6MnjtuP/Zzz369AXLZSmdCoh5+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pHj+F+YtOPebCeyMjWF7l7puA5Y2zMuqNX/ZQ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1D2CNp9XoGo4xvl+jjin0PbK9HKfk+s5J9nAlFYrP4iXF5ejnfffRdVVVXYtGkTmpqaUFZWBsMI\n/pqf/exnaG5uxqOPPoqnnnoKSUlJuOeee/DCCy8MxEciCgmzT6rqz5lAzD/ZRaj5z8vLAwA0Njb6\nPdbY2Ain04msrKw+3+/hhx/GzJkzMXPmzJCWr/vKMt2/RhNFCrNPqrKy3xMr+ecgEIUnwBxJBNmn\n775c3pIlS+ByueBwOLB48WLs3r0bW7ZsCfia7svl3XPPPbjoooswffp0/PrXv8bo0aPx5JNPBhxl\nJRoUzD6pykL2AeafbCbE/KempqKoqChgj4ba2loUFxfD7Xb3+lbPPPMMnE4nysrKQl680aNHIz4+\nHuedd17IryEKCbNPqrKw3xMr+ecgEIXHZwS+BdDX5fIC6e1yea2trRztp6HD7JOqLGQfYP7JZizk\nf8GCBWhoaEB19TdTn2pqalBfX4/S0tKe+wLl+YUXXsDhw4exdOlScX99fX2vi7dv3z4sXLgwpOkG\nRJYw+6Qqi/s9sZB/tXoCHZOXY0t7T/YE+vsZOR9x2MdyPqL7E9P83QguW6wKdY5k9+XyCgsLA14u\n76233kJzc7NfcHNzcwO+b6DL5RENJmafQqUfOizq+ENBnhgjrMyNZ/6jm9UeQmaZke4pVCB7CqUM\nk31kzpj6aeU1DH6fCCv5nzNnDl555RWsWbMGK1euhK7rWLlyJaZPn47Zs2cDANauXYvHHnsMTzzx\nBK677joAwHPPPYfXXnsNDz30EI4ePQoA8Hq9OHToED766CPcc889+OKLL7BixQrMmDED3/3udwEA\n1dXV2LVrF+69996B+fAWdKTLfejsUXIfepxLHtAkOOIt/f2+ev7U67IHz7umHj87m8eL+mDTaFHX\nnhgh6qSPZD+qMdXyKMB9RPa38r3fe9/QWDyGYPYpVOb8J78vH08tKhR1+xh5ZoxmWn+Hulei1V6I\nsZB/pQaBKPICzYkMNEcy1MvlhTp6GehyeUSDidknVYWafYD5J/uxkn9N07B69WqsWLECc+fOhaZp\nmDZtGsrKynoGRTMyMuB2uzFs2DAA6DloAIAbbrjB729u3LgRAOByudDY2Ih7770Xq1evxsUXX4zL\nL78cDzzwQMQ+K9HZmH1SlZXsA7GRfw4CUVgcRoCVIkCjz4G4XN4zzzxjdXGJIobZJ1WFmn2A+Sf7\nsZJ/AEhOTsayZcuCPl5SUoKSkpKe+o477sAdd9zR53K43W48++yzISwxUWQw+6Qqq9kHoj//HASi\nsDh0Aw6H4Xef2WBfLo9ooDH7pKpQsw8w/2Q/VvJPZCfMPqnKjtlXahBI//BjUY/MkvMPXc1y9vuw\nT+Wvk/rBGG/kMBAM+HdHD7BODNXl8ogGDLNPqgox+wDzb3cR7ymUK3sKedyyzmyUfV/i/7Sv74WM\nNAv5V5nPKfs/uePlQHCiw9wVR/YEavXJffAmn+wR0uyTU0I/MPX82fbFJFH/9ej5onZ9mCSXr07+\nJ+adku+XXGvqC2o6JuilPYh9MPsUIeb1J/7gEC1IqGyYfaUGgSjyuhplmedI+j9vqC6XRzRQmH1S\nVajZB5h/sh8r+SeyE2afVGXH7PMS8RQWh8/oOkXu7FsUXS6SaKAw+6QqK9kHmH+yF6v5J7ILZp9U\nZcfscxCIwuMzAt8CmDNnDqZOnYo1a9bAMAx4vd6Al8ubPHkytm/f3vO65557Dtu2bcNtt92Go0eP\n4siRIzh06BB+//vf49e//vVgfEoif8w+qcpC9gHmn2zGYv6JbIPZJ1XZMPtKTwdz7nxH1CMnFIja\n3EOI/Dl8PjhM58M5fIHPjxvIy+URDTZmn1RlJfsA80/BhdtTaChYzT918RmyR9CXPtkDqMYrewB9\n0DFG1Dubx4v6YNNoUdeeGCHqpI8SRT2mWvYgch/5Qi7f+zKLZuYORipi9klVdsy+0oNAFAE+Aw5z\nZ6xeRkYH6nJ5RIOO2SdVWcw+wPyTjfQj/0S2wOyTqmyYfQ4CUXgMo+tmvo/I7ph9UhWzTypj/klV\nzD6pyobZ5yAQhUcPsFLE+MgoUUiYfVIVs08qY/5JVcw+qcqG2ecg0FnYA8g6h8+AA+Y5krG9UhCF\ngtknVTH7pDLmPzSaV/6b/LM1RdTbvywWdXWr7PHz95qxonZ9mCRqd538+3mnvKJOrj0tav3gIVHH\ndjePocHsk6rsmH0OAlF4DB9gboxl8KuVFMDsk6qYfVIZ80+qYvZJVTbMPgeBKCwO3YDDIUdCHTE+\nR5IoFMw+qYrZJ5Ux/6QqZp9UZcfscxCIwuPzwf+k2tgeGSUKCbNPqmL2SWXMP6mK2SdV2TD7HASi\n8BgGYL5kHgzAMRQLQzSImH1SFbNPKmP+Q5LU4BH16Y/SRb3u5JWiTjkmD0nGHNJF7T7yhah971f3\n+v56r49SvzD7pCobZp+DQBQenwH/kVADcA7FwhANImafVMXsk8qYf1IVs0+qsmH2OQhE4dF1+P/e\nosf0SkEUEmafVMXsk8qYf1IVs0+qsmH2OQhE4fH5/LujO2J7jiRRSJh9UhWzTypj/klVzD6pyobZ\n5yAQhcdnBFgpYrtbOlFImH1SFbNPKmP+QxL/p32iztMvFrXPKZtpJJ5oFrV+8JB8fgSXjfqJ2SdV\n2TD7HASi8BjG182yxJ1DsihEg4rZJ1Ux+6Qy5p9UxeyTqmyYfQ4CUXh0HTBMcyQdvCYDKYDZJ1Ux\n+6Qy5p9UxeyTqmyY/aCDQLre9cG8cZ2DtjAUPbr/37tzEJTP13U7mxb7J+0y/+pi9pl9VTH7zL7K\nVM7/YGRf934pap8hp4N5jDb5ONfDQaNy9gFu+1WmcvaDDgI1NDQAAE7lHh20haHo09DQgHPPPTfo\n44bug+GTK45hnjN5Fl3XsWrVKuzatQuapqG4uBgVFRVISkrqdTna2trw85//HAcPHoRhGLjyyitR\nVlYGp3Ng2rIz/8TsM/uqinT2gdjIP7NPQOxt+yOxjgxK9k9WW3v+2IFZDApOxewD3PZTbO73hJv/\noINAEydORGVlJTIzMwfsYIOil67raGhowMSJE3t/ok//+rJ54tVBn15eXo6WlhZUVVUhPj4eFRUV\nKCsrw7p16+BwOAK+prOzE4sWLcK5556LTZs2wev1YtGiRVi6dCmWL19u8ZOFhvlXF7PP7KtqoLIP\nxEb+mX21xeK2P1LrCLOvNpWzDzD/KovV/Z6I5N8g6ofPPvvMKCgoMK4eNt/4TspCcbt62HyjoKDA\n+Oyzz8Rr/vCHPxgFBQXGgQMHeu779NNPjYKCAmPTpk1B3+uZZ54xxo0bZ5w+fbrnvr/97W9GQUGB\nsXv37sh/OKJeMPukqv5k3zCYf7KHaNz2cx2hwcDsk6qidb8nEvnXQhsqIgrCwDcd03tugZ9aWVmJ\n9PR0TJgwoee+3Nxc5OTkoLKyMuhbvPjiixg/fjyGDx/ec9/FF18Ml8vV6+uIBhSzT6qykH2A+Seb\niaJtP9cRGlTMPqkqyvZ7IpF/DgJReHQ98M2ktbUV+/fvR3Z2tt8pcPn5+aiurkZzc7Pf644cOYLj\nx48jOztb3O9yuXDOOedgz549MPwu2Uc0CJh9UlWI2QeYf7KhKNn2cx2hQcfsk6qiaL8nUvnnIBCF\nxetoh8fRJm5eR7vf8+rr66HrOjIyMvweS01NhWEYqKur83vs888/B4Cgr2tubkZLS0sEPgmRNcw+\nqSrU7APMP9lPtGz7uY7QYGP2SVXRtN8TqfwHbQxN1Bu32420tDTU45OAj6elpcHtdvfUTU1NAAIH\ntrsJW3u7/8rUPVLa1+vS0tIsfgKi/mH2SVVWsw8w/2Qf0bbt5zpCg4XZJ1VF435PpPLPQSDql/T0\ndLz22mtobW0N+Ljb7UZ6enpPnZiYCADweDx+z+3s7ASAgGHt7XUdHR09y0I0WJh9UpXV7APMP9lH\ntG37uY7QYGH2SVXRuN8TqfxzEIj6LT09PeSNbF5eHgCgsbHR77HGxkY4nU5kZWUFfV33qOrZmpqa\nMHz4cCQkJFhZbKKwMfukKivZB5h/spdo2vZzHaHBxOyTqqJtvydS+WdPIBoUqampKCoqQk1Njd9j\ntbW1KC4u9judDgAKCgowYsQIHD16VNzf0dGBkydPYtq0aQO2zESRwOyTyph/UtVAZ5/rCEUrZp9U\nFiv55yAQDZoFCxagoaEB1dXVPffV1NSgvr4epaWlPfedPbKpaRpKS0vxwQcfiPv37NkDXdfF64ii\nFbNPKmP+SVUDmX2uIxTNmH1SWSzkn4NANGjmzJmDqVOnYs2aNTAMA16vFytXrsT06dMxe/ZsAMDa\ntWsxefJkbN++ved1t99+O/Lz8/Hss88CANra2rBq1SqUlpbisssuG5LPQmQFs08qY/5JVQOdfa4j\nFK2YfVJZLOTfYYRyIXmiCGlra8OKFStw8OBBaJqGadOmoaysDC6XCwCwdetWPPLII/jFL36BK664\noud1p0+fxrJly3Dy5En4fD7MmjULt956KxwOx1B9FCJLmH1SGfNPqhro7HMdoWjF7JPKoj3/HAQi\nIiIiIiIiIlIAp4MRERERERERESmAg0BERERERERERArgIBARERERERERkQI4CEREREREREREpAAO\nAhERERERERERKYCDQERERERERERECuAgEBERERERERGRAjgIRERERERERESkAA4CEREREREREREp\ngINAREREREREREQK4CAQEREREREREZECOAhERERERERERKQADgIRERERERERESmAg0BERERERERE\nRArgIBARERERERERkQI4CEREREREREREpAAOAhERERERERERKYCDQERERERERERECuAgEBERERER\nERGRAuKGegEodjU1NaG1tTXgY263G+np6YO8RESDg9knVTH7pDLmn1TF7JOq7Jp9DgJRvzQ1NeE7\nMy9H85eOgI+npaXhtddei9kVgygYZp9UxeyTyph/UhWzT6qyc/Y5CET90traiuYvHfjNKgOjM+Vj\nJxuA7/+oGa2trTG5UhD1htknVTH7pDLmn1TF7JOq7Jx9DgJRWEaM9GLUaHmfxwCA+KFYHKJBw+yT\nqph9UhnzT6pi9klVdsw+B4EoLDp88MIw3Rf4lDkiO2H2SVXMPqmM+SdVMfukKjtmn4NAFBYPfPAY\nhum+2F4piELB7JOqmH1SGfNPqmL2SVV2zD4HgSgsXvjgMY2MemN8pSAKBbNPqmL2SWXMP6mK2SdV\n2TH7QQeB2tvbceDAAWRmZsLpdA7mMlEU0HUdDQ0NmDhxIhITE4M+z2sYfiOjXiPIk2MI868uZp/Z\nVxWzz+yrTOX8M/tqUzn7APOvMpWzH3QQ6MCBA1iwYMFgLgtFocrKSlx66aVBH9dhQPebIxn7mH9i\n9klVzD6pTMX8M/sEqJl9gPknNbMfdBAoM7PrOmijPstHnNc1aAtE0cEb14lTuUd7chD0eUZ3d3R5\nX6xj/tXF7DP7qmL2mX2VqZx/Zl9tKmcfYP5VpnL2gw4CdZ8OF+d1cYVQWF+nRXoMh99K4TFie44k\nwPwTs8/sq4vZZ/ZVpmL+mX0C1Mw+wPyTmtlnY2gKiwcaOk2nx8V6t3SiUDD7pCpmn1TG/JOqmH1S\nlR2zz0EgCovX0AI0yortlYIoFMw+qYrZJ5Ux/6QqZp9UZcfscxCIwqLDAd00EhrrjbKIQsHsk6qY\nfVIZ80+qYvZJVXbMPgeBKCxdI6Pm+4ZmWYgGE7NPqmL2SWXMP6mK2SdV2TH7HASisHiMuACNsoZm\nWYgGE7NPqmL2SWXMP6mK2SdV2TH7HASisHgMDZ2mOZFdcyZjfM0g6oPV7Ou6jlWrVmHXrl3QNA3F\nxcWoqKhAUlJSr+/zj3/8A08//TR8Ph90XUdaWhrKy8tRUFAQqY9CZAm3+6Qy5p9UxeyTquyYfQ4C\nUVi8cPp1R/fCAOAdmgUiGiRWs19eXo6WlhZUVVUhPj4eFRUVKCsrw7p16+BwBG4ut3fv3p7nFBcX\nAwA2b96MW265BS+99BJycnIi+pmIQsHtPqmM+SdVMfukKjtmXxvqBaDY5oHz61PkzrrBOdSLRTTg\nrGR/27Zt2LFjB5YsWQKXywWHw4HFixdj9+7d2LJlS9D32LBhA6ZMmdIzAAQA8+bNg67reOONNyL+\nmf1s+X8AACAASURBVIhCwe0+qYz5J1Ux+6QqO2afg0AUFp/hgG66+WL8knlEobCS/crKSqSnp2PC\nhAk99+Xm5iInJweVlZVB38Pj8eDjjz+Gx+Ppuc/r9ULXdeTl5UXuwxBZwO0+qYz5J1Ux+6QqO2af\ng0AUFr9R0a9vRHYXavZbW1uxf/9+ZGdn+037ys/PR3V1NZqbmwO+x/z583Hs2DEsXboUut51McqN\nGzdi1qxZmD59euQ/FFEIuN2nSHFeMLbXWzRi/klVzD6pyo7Zj+2lpyHX1SjLabpviBaGaBCFmv36\n+nrouo6MjAy/x1JTU2EYBurq6pCWlub3+BVXXIEHH3wQy5cvx/Hjx3HjjTciJSUFy5cvj9jnILKK\n231SGfNPqmL2SVV2zD4HgSgsXiMOHtNK4Y3x0+OIQhFq9puamgAg4CCQ09n1+vb29qDv8/3vfx+N\njY04duwY7r33XixcuBA33HAD4uK4+aahwe0+qYz5J1Ux+6QqO2afRxEUFo+h+a0UXZfMI7K3ULOf\nmJjY9dhZfX26dXZ2AkDAs4C6PfbYY7j66qvxox/9CJMmTcKKFStQV1eHp556CprGGb00+Pqz3dd1\nHatWrcKuXbugaRqKi4tRUVGBpKSkXl/3j3/8A08//TR8Ph90XUdaWhrKy8tRUFAQ9ucg6g/u95Cq\nmH1SlR2zz0EgCosPGnRTaylfL62meCBAdhFq9rsbODc2Nvo91tjYCKfTiaysrIDvsWnTJrz99ttY\nsmQJAODWW2+FpmlYvnw5tm7dinnz5oX7MYgss7rdB4Dy8nK0tLSgqqoK8fHxqKioQFlZGdatW+fX\nK6vb3r17e57TfYW8zZs345ZbbsFLL72EnJycyHwgihjjiotEfSYrQdSdblNOTPvQ8Wd8onanJsun\n7z8Y3gJGAPd7KBo4L8wXtf7J0QF/T2afVNWf/Z5ozz9/RqaweAxnwFsw5eXlePfdd1FVVYVNmzah\nqakJZWVlMHoZTe0+EPjJT36C9evX48UXX8SMGTNwyy23oK6ubiA+FlGfQs1+amoqioqKUFNT4/dY\nbW0tiouL4Xa7A77Hhg0b/K4CtnDhQkycOBE7d+6MzAchssjqdn/btm3YsWMHlixZApfLBYfDgcWL\nF2P37t3YsmVL0Ndt2LABU6ZM6RkAAoB58+ZB13W88cYbEf1MRKHifg+pitknVVnNPhD9+ecgEIXF\n+/XpcWffvEbgWPFAgOzESvYXLFiAhoYGVFdX99xXU1OD+vp6lJaW9tzX3T+oW3JyMo4dO+b397Ky\nspCSkhKZD0JkkZXsA0BlZSXS09MxYcKEnvtyc3ORk5ODysrKoK/zeDz4+OOPxVRKr9cLXdf9BkeJ\nBgv3e0hVzD6pyup+Tyzkn4NAFBYPAoyMIvDIKA8EyE6sZH/OnDmYOnUq1qxZA8Mw4PV6sXLlSkyf\nPh2zZ88GAKxduxaTJ0/G9u3be15322234cMPP8TGjRt77tu/fz/27t2LH/zgBwP7AYmCsJL91tZW\n7N+/H9nZ2X7TvvLz81FdXY3m5uaAr50/fz6OHTuGpUuXQtd1AMDGjRsxa9YsTJ8+PbIfiihE3O8h\nVTH7pCor2QdiI//sCURh8fri4PHFme7zP82t+0CgsLAw4IHAW2+9hebm5oANcufPn49FixZh6dKl\nePjhh+F0OnkgQEMu1OwDgKZpWL16NVasWIG5c+dC0zRMmzYNZWVlPetDRkYG3G43hg0b1vO66dOn\nY/Xq1fjlL3+J3/zmN8jMzERiYiJeeOEFjBs3buA+HFEvrGS/vr4euq4HvDpeamoqDMNAXV1dwG3/\nFVdcgQcffBDLly/H8ePHceONNyIlJQXLly+PzAehPmkT5XambewwUTflyxx8eaEu/4DbK0pHnOz5\nY+hyf0BrcIl6lDNV1Kn7e1/ewcD9noHR+d3LRN10Qbyo20fI5/vi5L95fKv8N3bIqPnV3kRZx5ku\n0hnfKv+++Ud/Zwd65eyQr/fF9b58TtP1pvV4+XxdtteCw7SqJU0YKeqUIy3y/Q9UI1zMPqnKyn5P\nrOSfg0AUFm+AbuleQ/d7Hg8EyG5CzX635ORkLFu2LOjjJSUlKCkp8bt/xowZmDFjRv8XlCjCrGS/\ne4pjoG2/09n1N9rb2/0e6/b9738fjY2NOHbsGO69914sXLgQN9xwA+LiuPtCQ4P7PaQqZp9UZWW/\nJ1byz+lgFBaf4Qh4M4vEgcBdd92FkSNH4t5778XBgwfh9XqDPp9ooIWafSK7sZL9xMSun9vPPrW5\nW2dnJwAE3BHq9thjj2Hq1Kl4/PHH8b//+79Yv349fvzjH8Pn8wV9DdFA4n4PqYrZJ1VZ2e+Jlfzz\npzQKizdAd3RvgG7pkTgQuPrqq/GjH/0IkyZNwooVK1BXV4ennnoKmsaxTBp8oWafyG6sZL97Dntj\nY6PfY42NjXA6ncjKygr42k2bNuHtt9/GkiVLAAC33norNE3D8uXLsXXrVsybNy+cj0HUL9zvIVUx\n+6QqK/s9sZJ/DgJRWDwBTo/zBOiWzgMBsptQs0/WOb5dJGqtTTZf0A8dHszFIRMr2U9NTUVRURFq\namr8HqutrUVxcTHcbnfA127YsAHnnXeeuG/hwoV4+eWXsXPnTm77I8Bx2SRRN46T/xct58tfOjvy\n5a+X2aP+KervZh0V9cj4L+XrfbLPy/GOdFG/WXuBqL88JXsQZeSfJ2rv0WMYbNzvGRinvi2zkThN\nZuvKrM9EneSUB1hfdCaLOkGTUzU6fM5eHzdf6aexQ/49l7P3X+I1h+wPcrpdXsFTg3y8Q+/9EKzd\nKx/XPbJu+TJJ1IkfylrzyH5aCQd6fbuQMPsUjHm/TR/mCvLMLnFN8rvE995HEV+mSLKy3xMr+ecR\nC4VFN5zw+uRNDzAyGu6BgLkj+sKFCzFx4kTs3LkzMh+EyKJQs09kN1azv2DBAjQ0NKC6+pvGpDU1\nNaivr0dpaWnPfd2nUHdLTk7GsWPH/P7e/8/evYdHVZ77w/+uNZnJaSZHQgIhoEEOAnJQLEK7xSq7\nFrQigu8W6U/7aq211F5iZbu31L4Vj+xuqRbKroq+nhBRq9bNRrG1m1bUtkbxDMhJ5CABJQkJIcnM\nWuv3Byb1fmaSzMrMJDPr+X6ua/64ZyaZNcl3rXnmmfXcU15ejvz8/KjriXoDxz2kK2afdOVm3JMp\n+eckECXk+Mxo9CUWvhEgL3GTfSIvcZv9WbNmYfLkybjvvvvgOA4ikQiWLl2KqVOnYubMmQCAlStX\nYtKkSXjxxRc7fu6qq67CRx99hDVr1nRct2nTJtTU1ODyyy9P3RMk6gLHPaQrZp905Xbckwn55zsW\nSojlmIg4PnGxOtkp+EaAvMRN9om8xG32TdPEihUrEAqFMGfOHMydOxfV1dVYvnx5x9enFhcXIxgM\noqDgH8t/pk6dihUrVuCZZ57Bd77zHVxxxRV44IEH8Oijj2LMmDEpf55EsXDcQ7pi9klXbsc9mZB/\nrXsC+UbIted2XraoDUuuF7bf2wKSLMeApXRHV+t27W8E7rrrLsyZMwemaWLKlCmYP39+XG8E/uu/\n/guPP/44ysrKkJOTg0cffRQjR45M3ZMj6oKb7JM7TUPlabLNZbIvSM74fqIu/FB+kmJ/wGN1KvUk\n+3l5eVi8eHGnt8+ePRuzZ8+Ouv7ss8/G2Wef3bMNJThTxom6abDsG/L5WPl/Kxz7hahPLz0g6qlF\nH4t6ZPZ+UVf4mkWtDpFblEHz1nB/uT2t8pPOd4plXxM7JLe/L3DckxxqD5G2ItkzZ1aV7BEyt+jv\noi4xY389czuf0fUxyXLk46ktXMPyZljo+vepZwW0dLM83Kf0CFK1OPIt2qeRElG/3jhM1C9YY+X2\nbJM9luQ7nJ5h9jOXOe5kUbeVyp5XrSUyb5FspR9cocy3Ek+E1ZNUlFhkHZN16FPZMyj/XaQ1t+Oe\nTMi/1pNAlDjry3WR6nWd4RsB8gq32SfyCmafdMZxD+mK2Sdd9WTck+755yQQJSTimPApn36o37BA\n5EXMPumK2SedMf+kK2afdOXF7HMSiBJiOWb0zGiG7xRE8WD2SVfMPumM+SddMfukKy9m39uTQGfI\n9bFthXL94bEy+fRbC+Q/01DWCwdOOUPUwX1tovYflmvhjeZWUVvbo78qLtNFHBOmx2ZGieLB7KeO\nY8p11vWjbXmHYESUX5xSLOqBr04UdWB9TfI2jpj9NGJMlA2yG4bLflpfjJH7knOCbMxw1tBtor64\nVPZdOcEv+22VKP/mPEP2HfEZsvOI2nel1ZH7bhifi/qk/EOirimQvRsjIfn7+yJ1zH9y2HkyO5F+\nsivPN4Ky/9SJWfINWLaSNZ+R2v+B5djd3+kr7G56/kTfX/7+sCP/HuW+faJutGR/rLV+eSww3G1u\nXJj99OEbVi3qcEWhqBuHyP3jWJn8Px0rV3piFctjM7Lk7f48tWuWZPpk4GxbvvYc+0Lm1QrI/Tnn\nrFNF7dvwdpeP19u8mH1vTwJRytmOAVtpjKXWRF7E7JOumH3SGfNPumL2SVdezD4ngSghlm0iYptR\n1xF5HbNPumL2SWfMP+mK2SddeTH7nASihEQcA0bU6XGZPTNKFA9mn3TF7JPOmH/SFbNPuvJi9jN6\nEsgcO1LUTUPlesjDI+V6w5YyuV4xZ0ijqEuDsqfPsbBcr3y4Ua5nPHRA1rm1OaLOkr8OgYYKURfu\nbBG1+eomZBrLNmF4bGaUKB7MfurkfCHXnvuOyn5ulcO/EPWIUbWi/vNw2UekLH+SqPOe/Vuim6g1\nZr/3+EaPEPXRE+U459A4OYw7NkTuO+NGfCrqi8rfEvXpOfL2qiz5f8w25LjGhBz0dteHxVL6nPgM\n+fNFjuydeEKO7BHkZCl9UkJyXCa7XvQO5j85IrlyjJ6VK3uSlPnkGN1vyPunugeQyu3jdf3l0d3/\nRJZS55uWqBttuW/abfL+ZsRdT6J4MPu9Rz32Nw1Tev5UymN/Y7U8VpqV8k1oRbHcn75e8pmoi5Q3\nrUGfPDb7TaWfm6301XVkveNomai3F/QT9aFWWTcOlkfzIqQXL2Y/oyeBqO9ZToydIsMbZRHFg9kn\nXTH7pDPmn3TF7JOuvJh9TgJRQizHBBxv7RRE8XCbfcuysGzZMrz66qswTRNjx47FDTfcgNzc3E5/\n5qts28ZLL72EV155BRUVFRg+fDhmzpyZ0HMg6gke90lnzD/pitknXXkx+5wEooQ4DuAoayKd5J+B\nSpR23GZ/wYIFOHLkCFavXg2/348bbrgB8+fPx4MPPgjD6HpdcW1tLX7605+iqKgIt912G4qK0u1E\nWdIJj/ukM+afdMXsk668mP2MmgTyDasW9aHTikX9xXj53xg6Zo+op/TbKeqv5e8QdZEp10O2OHLt\n+aFIgai3tgwQ9d8OnyDqvQ3K2v0v8kV9tFKeAVDST+lb8Vz6962wbBPw2BpJoni4yf66deuwfv16\nPPvsswgEjve3ue666zBt2jQ888wzuPjiizt9nNraWsydOxdjxozBPffcA9P0/v6Vvf+IqPM+k2vL\nI+Pk32BygTyWjx2zV9QrL/+6fAAn84616YTH/dQxThst6gOny3FH/RjZ92Hw8P2inj7gQ1FPzd8i\n6pMDbaLOM2QfBrXvSqKi+qgog+Y8ZQK81Nckfz4o+1BYubI/WF9g/pPDypFZy8k91uX91X5UumlR\n3nHuPCZfF7P2y30jb7/cl5LxfpXZT53W6aeL+vNx8j3o0eHy2F09WI5zvlUq+7uNzpW3Dw0cFHWZ\nT+5vOYZMiPpftZW60Zb77xe2fE87OCD74OZnnSjq/x2YJ+rmA0FRF0+Qr4XOJvna1tu8mP3M3nrq\nc7ZtwFIutq33CzXpwU32V61ahaKiIowaNarjuqqqKlRWVmLVqlWdPkYkEsEPf/hDWJaFO+64Q4sJ\nIEp/PO6Tzph/0hWzT7ryYvb5joISYsGE5SgXxoo0EG/2m5qasGnTJgwcODBq2Vd1dTW2bNmChoaG\nmI+xevVqfPTRR7jyyisRDAZj3oeot/G4Tzpj/klXzD7pyovZz6jlYJR+LNuAo8yEZvrMKFE84s1+\nbW0tLMtCcXFx1G2hUAiO42Dfvn0oLCyMun3NmjUwTROFhYW4+eabsXnzZhQUFOC73/0uzj777OQ9\nGSIXenLcZ2N08gqOe0hXzD7pyovZz6hJIKtY6akzSP7xx0/YLurLBrwh6nGBA6Iu88mnb0bN6Mm1\n6K2BRlFPyZU9h6YXvCvqbW1yPeQbR04S9Uu5J4v6aJ0cDAfHjBS1/YFc258WHCOqURbU+iv4RqDn\nfKNHiLqtTO4PkXy5Ptew5M/n7Jf5td9LwzxlkjizX19fDwAxJ4F8vuP/s5aWlqjbDh48iG3btqGy\nshKVlZWYOXMmjh49ioULF+Kaa67BkiVLcOGFFybhiaQfa/M2URefIBth7x5bIusB/UQ9OV/+/IIR\nfxT1f/yfc0UdyT1D1AVP/DX+jdWRy+M+wMbonWk5/2ui/nysHJdkfa1O1BdUyWyfXfCRqE/NVvs+\nyJ4/2UZ8r7W9JUfpQVSWJfuBFYRkr8bWghxRy64SvYTjnqSwsuXfrDBXvg7mGXIMnmFvWRJmK118\n6m35/D89Kl8HA0eUprVvvp/8jWL2e0zt91Z/ckjUtd+QXXcmjJLH+gv6y/eYw5T3tFVZ8lgZUvqx\n5Zmyx5AJeSx1q58p32SUOUdFXWruErXaZ/eDItlX92CpfE9jFcgeV31+zk0Pxj3pnn+9jqiUdJZt\nwFAaY6kzpV/FNwLkFfFmPyfn+AttOByOuq2t7Xijv1hnAR04cPwFfuLEiZg4cSIAID8/H7fccgv+\n8pe/4O677/bsJBClN7fHfTZGJy/huId0xeyTrtxmH0j//HM0RQmxv2yMpV5iaX8jsHDhQgQCARiG\ngeuuuw6vvfYannnmmS4fp/2NQElJCX7961/zxYD6XLzZHzx4MACgrq4u6ra6ujr4fD6Ul5dH3ZaV\ndXyOXu0FVFZWhvHjx+PgwYMdZxkR9SY3x32AjdHJWzjuIV0x+6Qrt+OeTMg/R1SUEAcGbEdenE6+\nxpNvBMhL4s1+KBTC6NGjsWvXrqjbdu/ejbFjx8Zs+jxkyBAEAgHs378/6raysjIEAgGEQqGo24hS\nzc1xn43RyWs47iFdMfukKzfZBzIj/xm1HKytWK5tb6mQ6xEnFn0q6lOU9ZIDs9S18XJ9YnfyINcn\nFjhy/WY/U65frvLtFnVlljwToP4EuSbw77tlj6B+hXK9Zjq2n3JsA1BnQm0jalvb3wiMGDEi5huB\njRs3oqGhIeaymPY3AosWLdLqjYD9jfGi/ny0zEvjCfL+kQKZR6NN/p19rbInTeFpk0Vd+r7sGeTU\nfBDvpmop3uwDwLx583DTTTdhy5YtGDnyeK+vXbt2oba2Ftdff33H/err6ztm/PPz8zF9+nT84Q9/\niNo39uzZg7POOqujp5DX5e2WkwSFm2QPoE1Dq0St9mcb5lfOwhq1XpRLZsseQQB7BHXFTfZ1b4zu\nO+lEUTecKs/6OyCjhhPG7hX1/xkks3d6jhxXDFJGcUFDdsnxGen1BkrdHj/kMSzfaBN1tl+Oq8Jp\ncMjjuCc52oIyCyWBVlEHDDmm0Y0N+fwPROSHPjsOl4o6t1b2EEoFZj9+bedOFPX+b8j3nHlj5bjk\nh9VviXpa8ENRV2fJY6Ha4ycLvXvsN5X/ui9qeZM8lo/O3ifqUcWDRb0/JMd1x/rJ99yyY1DvczPu\nyZT8p9fogDJOvKfHxftGIBb1jcCcOXNwxRVX4E9/+lPSnw9RvNycGjpr1ixMnjwZ9913HxzHQSQS\nwdKlSzF16tSOJm8rV67EpEmT8OKLL3b83MKFC1FcXIzbbrsNlnV80nvt2rXYs2cP/vVf/zX1T5Io\nBjfZT7Qx+oABA1BZWYlbb70VjzzyCHJycnDNNdfg+eefT+IzIoofxz2kK2afdOVm3JMp+eckECXE\n+bJbunpR8Y0AeU282QcA0zSxYsUKhEIhzJkzB3PnzkV1dTWWL1/e8SlBcXExgsEgCgoKOn6urKwM\nTzzxBMLhMC666CLMnTsX69evx5o1a1BVVRXzsYhSzU32U9EY3e/34+67707KcyFyi+Me0hWzT7py\nM+7JlPxn1HIwSj+dnR6n4jckkdfEm/12eXl5WLx4cae3z549G7Nnz466vqKiAvfcc0+Pt5Mo2dxk\nP1WN0d98802xfJKot3DcQ7pi9klXbsY9mZL/jJoEiuTJxeBOjuwJVJLVJOpCU/5zspDcxeTqests\n5c9ZqJxnVe6T2zcgR/a5cJTNM1vl+s/Ur/Z1z3EQvRPE2FC+EXCv4STZA+jwOJn36uGy59X4YtlL\nwlJO9Pu8Vf4NPz6lTNS7quV63IqKr4k6Z+3fu9livcSbfUqc9dHHoi4rlavD3x09RNQflVeKelZQ\n9lGZkS/rxpP/Iup7vyP7zVgB2T+r+OE3utlib3OTfd0aoztTxom6dozs03B4khwUnjV6q6i/W/a6\nqMcFjoi60JS9Av1GGjTJSYCpvE75Dfn3yVH6YEQPqXsfxz3JEQ7Jv2HQL3sCZadjI8xe1OLI7G9r\nqxB10+fydbC0Vt4/FZj9f4icfZqovxgj+842nCz/H+NG7RT1FQNfFfX47IOi7mfKnjjZhjz293W/\nN/XxfcqxXH0PPCRLvpaNDsrlUH8MyL64dlZ6TVG4GfdkSv65HIwS4thGzItKtzcC5H3xZp/Ia9xm\nf968eTh06BC2bNnScV17Y/RLLrmk47r2U6iBfzRG/9vf/hb17WG6NUan9MJxD+mK2SdduRn3ZEr+\nOQlEiXGM2JcY+EaAPMVF9ok8xWX22RidPIXjHtIVs0+6cjnuyYT8cxKIEmN3comBbwTIU1xkn8hT\nXGafjdHJUzjuIV0x+6Qrl+OeTMh/ei2464464abUOaZcLe5X1yumeP1k9PpIuYFhZc5t3zG5pi9Q\nL+/v1HyQxK1LkVgzod18Q9Jdd92FOXPmwDRNTJkyBfPnz4/rjcBdd92Fiy66CHl5eejXr5/n3ggY\nE0aLurVQ/h3zyo+K+pv9ZZ+U6aH3RF3ma+vy8Q4MkOuXX6g8VdRrhsr1zoGxU0Rd+edmURuvvdPl\n43mOi+xTcpmvbhJ14QSZzYerZf1PI+Ra/HKffOm7KLhZ1L5x8pX9l5FvidoxZY+gsldrRW1tk4/n\nOT3Ivmcbo3/tFFHWTpJ9OlonN4r6ypFvivr80LuiHu6Xf8dsQ/aG6+s+EMlmKuMkvzKqzjJlbaTD\nRDvHPUkRVlZDVOTIfSW/l8fwfc1yZLibbdkH8pMW2bfRbJRnBPiPRH/bUNJpnP3wNDkmPjRBjqGt\nSbLnzWXD5Djlm8o4Y0xA5r3QlMf6TO/3pvbhLTLl/jvQL/vlmEqfX9ufZlMULsc9mZD/NPsLU8Zx\nAOV1C0YXzXE9+0aA9OMy+0SeweyTzjjuIV0x+6SrHox70j3/nASixNgGDBdfk03kGcw+6YrZJ50x\n/6QrZp905cHscxKIEmMb0TtBhu8URHFh9klXzD7pjPknXTH7pCsPZj+jJoGympXzsJSy0ZLrKW1H\nnqelrrdN9frisCPXN+6JyB5AWw+XiTq3NgPPp4/VGCsd1uxnoqyu89jSHBB1syXro46shxiyR1Y/\nn+xVUemT/6iqkjdE/a0z3hf1iiFni/pvQ6tFXTRe9mEZsOELUVsfboWnMPtpY8Cf5drynf0HiPrZ\ngeNE/f8Wyv5Z/XzytWNuwXZRNytr/5eF5b6Q1dJf1AVe7wmkcfZ9w+Rx7+A42dikcbzsy3HpcNkr\nTe0BdJJfvu7nmTJruslRmv7kZ8nedofly1zf0Dj/ydRSKrN/Up7srab29fQ6G/LvcciWb9F2NMme\nQHn75d/H3NgLfRk1zr6dLf/eLf3k/2tc+QFRn5Ynvx682i97BuUZ8mCm9kfLdOp77GxD5rm/T/ZE\nKimSfU/bQjny9510oqit7dFfv55SHsx+Rk0CUZrKwLkroqRg9klXzD7pjPknXTH7pCuPZZ+TQJQQ\nw4OnxxHFg9knXTH7pDPmn3TF7JOuvJh9TgJRYhxEz4x6bKaUKCZmn3TF7JPOmH/SFbNPuvJg9jNq\nEsjXInvsmE1yPeUnLaWibgnJxXoFqdmsDmrPoUY7IuoPjlWJ+vBe2SNo6I7W1GxYCnlxZrSvOG/K\nHjyhIZNEbeXKviWr274m6j8NGC7qcf32ifqCkk2irvQ1iLqfT27PKL9cn/v/Va4V9c7yErk9J58h\n6r9WjxR1xRvy+RS897morY93IJMw++nDfnezqKtKThX1w0o2Tx3/iagnZx8TddCQfVkuK5T7ZnCy\n7PuyxH+uqMN5k0Xd/9VDora2yp5DmUbn7DcPk+OMhqHy9m+O+FjU5xfIPh1qD6BcIx2a3KQPvxKj\noF+Oi6xA3+dM5/wnwnfyMFHbuUpfQv9hUfsNZVDicU22zPq2tgpRf/y57D2X3wd9RHXOfu5u2cOm\nYGexqN8qO0HUpdlyDB0oeVvU4wKyb2aJT/k7Kv/eVPexTTWfIZ9fia9Z1CcWyb/HllzZAwtG3+bM\ni9nPqEkgSkMebJRFFBdmn3TF7JPOmH/SFbNPuvJg9jkJRAkxHETvBBl+ehxRPJh90hWzTzpj/klX\nzD7pyovZ5yQQJcaDaySJ4sLsk66YfdIZ80+6YvZJVx7MfkZNAvkPy/WD2Z/niHpfi+yx0+jItXr9\nlP9WslcbtzqyB9DOSJ6oXzssmwfk1Mo/v79OrjfNhLPMDBsx1kj2yaZ4Tv4zfxN1aPwoUbe8ky/q\nxsoyUb9aIdePvzxkjKirhsiePJP77xL10JyDoj4t5xNRn5BVJ+rvl/9F1JO+LX/f/z9M9mX5fFO5\nqEs/lNtf+K7SMyjN+qi4zb5lWVi2bBleffVVmKaJsWPH4oYbbkCu0uupK7t378ZFF12ERYsWiuLU\nwwAAIABJREFU4aKLLurZhmsgsEdmEzUDRPmbsrNFXTXkeVGfmCVfHfr55L52cVBmsfVUv6iXZZ0l\naisg98XyPNlzyNn0ITKJTsd933D5ut1wgvxf+6rl6/aUQpmNQVmy31S2Iff3TO/zkCj1+avjsn7Z\nTaJuK0zxBsVBp/wnk50j+185ubLPZ6lP/q9NeHvfCDvy+R+2ZYjeOnqCqI8elK9DZQfDKdmuruic\nffuDLaIuy5FjascIifqP9eNE/cEoOQ6ZPUj2i/tG/lZRVyk9c0p8ctyQpRwt0/21RN2fQ4bsgVXo\nl70WLfl006AnEDyX/fRODKU9w459IfI6t9lfsGAB3nnnHaxevRpPPfUU6uvrMX/+fDhOfB8ltLa2\n4rrrrkNTU1P3dyZKIR73SWfMP+mK2SddeTH7nASihBhOjJ0iw0+PI4qHm+yvW7cO69evx8KFCxEI\nBGAYBq677jq89tpreOaZZ+J6vNtvvx2TJk3q/o5EKcbjPumM+SddMfukKy9mn5NAlBi7kwuR17nI\n/qpVq1BUVIRRo/6xpK+qqgqVlZVYtWpVtw/1+9//HgUFBfjmN7+ZhA0nSlAPjvuWZeGee+7B7Nmz\ncfHFF+PWW2/FsWPHuv4hxe7du3Haaafh2Wef7dl2EyUDxz2kK2afdOXB7GdUTyD7PbkeM+frk0W9\n7XA/UR8YINfPDvK1idpvJNYVyHLkf7/Blr9/49HRon5/Z6WoK7bKn7ff3ZzQ9vQFw2WjLPZF6Tn7\nnY9EHVBuL1Xq/icPE3VLlWymcKSqQtT/PVCuVw4H5T+yYNQXoh5ftl/UpxfIHkCn5OwR9fxhcv3v\n3ytOFPXLA+X66pYi2SOotETuz8Yb76IvxZv9pqYmbNq0CSNGjIChrGmurq7Gxo0b0dDQgMLC2M0u\ntm/fjrVr1+K3v/0tampqkrPxHmdtl1kc9EpQ1B8UniTq1YWni/qnpW+LWn2tKDBlP7pZIdnTJ3u8\n7NVwtzlN1Ad88n9dYcrXCuet9O4R5Pa4DxxfDnnkyBGsXr0afr8fN9xwA+bPn48HH3wwar+Ipa+W\nQzoB2QMoIqOEfgVHRV2WdUTU6ijDjho1Jrs7obf4Ddk3JaK8LvlOkq8j6r6fChz39JBP7udmtvzf\nFplyUtiE3Pe8Ru0JtM+SB5c3Dw8Rde4e+ZYtp7Ze1L3xfpTZ/wen5gNRVxyR/eMK9pSIum6PHHMv\nGyHHBc+fKHsIjS/dK+o5JW+KeliWfC0sNOW7gmxD5qWvewaZkPu/X3nZL1D65znq5vZ1T6AejHvS\nPf88E4gS43Ry6QT7opBnxJn92tpaWJaF4uLiqNtCoRAcx8G+fftiPkRzczNuueUW3H777fD5+GaR\n0oTL4z6XQ5KncNxDumL2SVcusw+kf/45CUSJidUkq5OPI/hGgDwlzuzX1x//tC7WJFD7xE5LS0vU\nbQCwePFi/OhHP0L//v1j3k7UJ1wc9wEuhySP4biHdMXsk65cjnsyIf+cBKKEuGmUxTcC5CXxZj8n\n5/jSoXA4+utc29qOLyGNtRTsySefxODBgzF58uSo24j6kpvjfvtyyIEDB8ZcDrllyxY0NDR0+ljt\nyyEXLFiQzKdA1GMc95CumH3SldvG0JmQ/4zqCaTKPyCn4PYeKhD1zjb56fm4wO6kPn6rExH11rB8\n/D8eHCnq0PvZoi56/7CoM7G/VMyvyLOjz5BjX5TeZ23eJmq/0nJK7SGk1r7hcn3zsWq5vnnTCfIn\nXj3hFFEXjpE9hMqDjaIekCt7Z5QNlOvbvzhTro9vLZY9gcrzThN11itvoTfFm/3BgwcDAOrq6qJ+\nR11dHXw+H8rLy6Nue+CBB/DZZ59h+fLlHde1n0K6aNEi/OxnP8Mf/vAHVFZWRv0sKf7+vihLh54h\n6hfHjhL1rELZE+hkv+zdoPYIKvfJ9d0z8j8WdfMYuVb/ft83RF1ry+PegJYRorY+3Ip0Em/2gfiX\nQ8Y69rcvh7z77rv7bDmk2SzP0stqlrd/9rnc7k0DThB1f5887vkM+fsKlY/ispQeQX3dxyHV1N6K\nlnJ72JF/j6iMBXq/bwzHPT1jB5RsZ6nH1UwcBcdPzXqzIz8Yer9Fjrk+qZVjrMJamTC1T2RvYPY7\nZ328Q9TZSl35iXxdL94q3zMeGSJ7Bv2xn+zT+Yex8j3lmUO2i/pbRbKX4CnZn4l6oPIammvIcUlf\nv9Ycs+X22Nnp9dVbbsY9mZL/jJ4EojQQ63S4GK/jmf5GgChKnNkPhUIYPXo0du2Kbli6e/dujB07\nFsFgMOq2lStXRp099P777+Omm27CT37yE5xzzjlcJkZ9I87sA1wOSR7EcQ/pitknXbkY92RK/r39\nEROlnOHEvqj4RoC8Jt7sA8C8efNw6NAhbNnyj2843LVrF2pra3HJJZd0XNe+nwDAiSeeiOHDh4vL\noEGDAADl5eUYPnw4/H5vf3sKpSc32edySPIajntIV8w+6crNuCdT8s9JIEqM3clFwTcC5DlxZh8A\nZs2ahcmTJ+O+++6D4ziIRCJYunQppk6dipkzZwI4fubPpEmT8OKLL/bO9hP1lIvsJ7Iccvny5Rg1\nalTH5Xvf+x6A48shR40a1em36hGlFMc9pCtmn3TlYtyTKfnP6OVguQeUtfqH8kT9YbPslTE1d6eo\ng8rivu7WQ4YduX651moT9eovpop6998GibrqnVZR2x9sQaZrb5QlxJgZZV+UzKOubw4odZlyf7W2\n/2mCqD8fLHsK7TxB7m/HqmSPLV+B3L+aK+X+V18n1w+XH+jdPirxZh8ATNPEihUrcNddd2HOnDkw\nTRNTpkzB/PnzO9YLFxcXIxgMoqCgIPYvoaQpWP1XUe8ZOkXUj5TL+kf9/iLqAT6ZPbVHUMiU9T/n\ny4Zc4ZHy9hV1Z4s6u15+elQol/r3OTfZz/TlkJGdn4i6cJc80oWDsh/U6oDsVbZtkNzGM4tlv6jx\nObJXYZVPNh0KmXKYlm3Is/9MyH4Dfd3XIVHqye9+Qx73rTwZvEiR/PvLv0ZqcNzTM2pPIL9fjolD\npvxfZ3qW1R5Aah/RWks+v7ePDBG1XZsj6twv+r5nErPfc+qYNFt5XVfH0Oa4k0Xd+JEcG24cLsfY\nL580WtSnj5CvueeWygc8O0/2FCrzydeaVPcMUo/1Jf6jonbUg3mcX6ueKm7GPZmS/4yeBKK+11mj\nLFWmvxEgUsWb/XZ5eXlYvHhxp7fPnj0bs2fP7vIxJ02ahK1b06tJMOnHbfbnzZuHm266CVu2bMHI\nkcebW7Yvh7z++us77ldfX4+ioiIAx5dDqtoHVO3LIYn6Asc9pCtmn3TlZtyTKfnP7Gl26nPsi0K6\ncpN9Ii9xm30uhyQv4biHdMXsk67cjnsyIf+cBKLEOJ1cYuAbAfIUF9kn8hSX2W9fDhkKhTBnzhzM\nnTsX1dXVWL58OZdDUubhuId0xeyTrlyOezIh/xm9HCzr80ZR5+/NF3XNF4NFvb9I9gwq98n1udnd\n/DmabLl++U/NJ4n65Q/keszKt+R5Yll/eqvL35+RYpwe53Ryehz7oujFfHWTqNUWaGptf2O8qBuH\nyPXw4aBcIJylNNZ3snp5TttF9im9Vb56TNTPlZ8u6tYz5Ccv3yp6X9TD/J93+fu/sOVrj+3IrPYb\n0CDqw6NLRZ33rYmi9r9c0+XjpVwPsu+V5ZDZ//OmqMtbZA+gunp5ivebVbKvw19PkMvcRlYeEPXY\nQtnseny+7Bk0OOuwfHylh1C+KY+TfqVLTrYhxzk+Q95uKp8NprrnkK2Moi3l9rAjO0cYttyeSFDu\nm71yjgDHPT3SVqiOseWY2srwD1G67wEk+xy+1XKCqDcdlH0+gp/KfS1vn3zP0yeY/V5jvyt7Cea/\nK28vOHmYqFsHyr/hx6Nkn8yakfI96/+cIvvk/j/lclzxjdw9oi4xZY+g6NeSJI/B1V9n9EbHty64\nHPdkQv4zehKI+l6sRlldnR7nlTcCRG6zT+QVzD7pjOMe0hWzT7rqybgn3fPPSSBKSKxGWVGNs4g8\niNknXTH7pDPmn3TF7JOuvJh9TgJRQry4UxDFg9knXTH7pDPmn3TF7JOuvJj9jJ4EsrbJ9Yy5p8uv\nTdv9meyrsLFCro8ckiV7lpQo6w/Dyur0bRG52vy52gmiLnhXrpcs+OCg3F54kI3or8jL8J2C+oa5\n8R1RF26Ut/tGyPXMdjBb1sr66ZRj9j3D/LN8LRiSI3vw/O9+2fdlXaXsX1U0uF7UpfmyT0vEli8u\nDcdkv6vmFvnaYeXIc4xbSuRLdZ9/Nwqz3yHrFdnrr+wVeXv/iWNE3TBM9gzaPUj2CNpcOUTUT/c/\nVdSV/WTWBodkj6AT874Qdb+sJlFXZ9eKusJ3RNQhUzZbCynnu+cofR/8UbXs4dOdsCNHRo3KvpJn\nyj4qdkj2WbGylcd39eg9xPz3yLFSmY2KQqWvp9nHPT9cUrPb6sivdt5vydv/euwEUT9/UL6HqN9V\nLOqKz2SofIflvtwn7ymY/bRhbd4m6ixlCFyuvBaVKX03t3wmewotGjFI1BeOkk2Izi2UvRBHBepE\nrfYMUvvNqcJK3WrLcY7SOhFOdh9PWXgw+xk9CUR9z0D0msjMehkn6hlmn3TF7JPOmH/SFbNPuvJi\n9jkJRAk5fnqcE3Udkdcx+6QrZp90xvyTrph90pUXs89JIEqIF9dIEsWD2SddMfukM+afdMXsk668\nmH1PTQIVbpXrixuGFor6tSFDRT0h9xNRj1TWNx625Ory5+tlX4gPt1eK+oQtcu26tXV71xvsAV7c\nKSg9pdv+xOx7V2B9jagHrZe3m2NHijpcmi/rvAJRWwG5uD2rQNYhpY2K/6j8tCm0W/YY6mvMfvyc\nmg9EXSCjBZkUwP4n2SfkaKXsH9VYOEDUNf0Givq1fvIfYWfLOqfsmKjLCmSfkZOLD4h6RJ7sIZRn\ntnZZlyo9iNQeQ/mGHCf5lBPqP430k/WxElGjTe47RqSb7+hNAea/Z8L58n89OCjH3EoLkD7XXc+f\nw7bsT7U/kivq15tHifq/PztF1Lu3VIi639vy71O4Wfb/srbv6maLU4/Zz1xq383Bx0aL+uhHsl/d\num1niPrFsTLPZw+RPYnmlLwp6qos2W8uW1k7Va/0ADKVtVam0jTI8bnrN5dsXsy+pyaBqA84MXaC\n3h+TEfU+Zp90xeyTzph/0hWzT7ryYPY5CUQJ8eLMKFE8mH3SFbNPOmP+SVfMPunKi9nnJBAlxnGO\nX9TriLyO2SddMfukM+afdMXsk648mH1PTQI5b30o6oKT5XrGD3bJHj7P58keP2cWbJX3PzZI1E99\nJO9f/JbsGRRY/0b8G+sRXpwZJYoHs68v+70tolZXqne3cj0vqVvT+5j91DFf3STqkHK7WptjZH+q\nSKnsS+IYshFDW6G8/Wi57APx50rZc+jlctn3BNmyT0puSPYEKsyXPYfUPg85WfL3FWfLfldN4WxR\nH2yS25fVIPcuf6N8vN7A/MfHN3qEqBtPlH+kU4L75P1T/IXLlqP0y1LWcjQ7sl/VYUtmfY8ls/ju\nsSGifqexStSvfXqiqJ1t8uf7fSy3r+Q92UPFfncz0g2z7x3qe+a8t+TtQz4dI+q6T+Srz/pRp4r6\nnfHyPfboks9EPTa4V9R+Q+5fm4/IHllKiy2ES2R/vN6ewPBi9j01CUS9z7ABZT/O+J2CKB7MPumK\n2SedMf+kK2afdOXF7HMSiBJi2A4M24m6jsjr3GbfsiwsW7YMr776KkzTxNixY3HDDTcgNze3058J\nh8N48MEH8dxzz2H//v0YPHgwrrrqKlx44YVJex5EbvG4Tzpj/klXzD7pyovZT7dvZKQM0356nHoh\n8jq32V+wYAHeeecdrF69Gk899RTq6+sxf/58OF2sKb7zzjvR0NCAJUuWYPny5cjNzcWNN96IRx99\nNAXPiCg+PO6Tzph/0hWzT7ryYvY9fSZQwQ65Vry5f76o/5gzXNSHq2Snhr2NRaI29sj1iKE9ynlh\nGjKc4xf1us7wbAjyCjfZX7duHdavX49nn30WgUAAAHDddddh2rRpeOaZZ3DxxRdH/UxtbS0qKirw\ngx/8oOO60047Deeddx7uvfdezJ07F36/P+rniFLN7XEf4LE/VewPZH+q7j7ZU//aal1+8jBRW0oP\noUiePOYcHSj7nBwrLpDbpxyiDsu7Y29ABsfpZlQa+kTW/oONou6NURnHPfEJFyv9qbLkH2mgv07U\nptK/Su3h053uevw022qPH9l/amfbQFGrfUHfqZf11v3lonb2yedbpPT8Ce2R/bByd9eL2tq8DemO\n2deHU/OBqItq5O2FU8aJ+tAe2dPnlRP7i/pPA2WPsKIC2Q+usVnuj6ZyMLdy5Ktbr/cE8uC4h2cC\nUULaT49TL53h2RDkFW6yv2rVKhQVFWHUqFEd11VVVaGyshKrVq2K+TNtbW247LLLxHXBYBBnnXUW\nmpqaUF9fH/PniFLN7XEf4LGfvIPjHtIVs0+68uK4h5NAlBgnxqlx3ZwNsXDhQgQCARiGgeuuuw6v\nvfYannnmmZg/0342xI033ojx48dj6tSpePjhh1FRUYF7770X4XA4dc+NqCtxZr+pqQmbNm3CwIED\nYSifdFZXV2PLli1oaGiI+rmqqirk5OREXZ+bm4tgMIiSkpJkPRMid1wc9wEe+8ljOO4hXTH7pCsP\njns4CUSJsZzYlxh4NgR5SpzZr62thWVZKC4ujrotFArBcRzs27cv6rbO1NTUYObMmfD5uvsicqIU\ncXHcB3jsJ4/huId0xeyTrjw47vF0TyDjjXdFXVp4uqg/K5I9gt4MDxG1fUz+eQoOyE/xcz87KurM\n7hHeM/F2S28/G2LEiBExz4bYuHEjGhoaUFhYKG6rqqqK+bg8G4L6WrzZbz9ox5oEap/IaWlpiesx\n3377bXz66ae4//773W4uUdK4+ZYMHvszS3d9SdRBY2E3tcqYMFrUdk6WUnc9uR3YJ8+atD7e0c0j\nJh/HPfGxlP+lo/R/MpWuqo1Kzx6fKXv6qJodef/9EZmlLUqPn52tskfJ64erRf3xgTJRW5/JPqG5\ntfJz836fyu3Pq5Xbm71XyerW7bJG5mH2qZ3xunyP3f91eXvJOaeJ+vBIuT81lcraypM5yt8vcxOo\n6/p4kGpeHPfwTCBKiBHj9LhYjbJ4NgR5TbzZb1/SFes0zra24y9q6gtBLG1tbbj11ltxxx13cCBE\nfSre7AM89pP3cNxDumL2SVdeHPd4+kwgSj3DcWAoDa7UGuDZEOQ98WZ/8ODBAIC6urqo2+rq6uDz\n+VBeXh51m+q2227DtGnTMG3atB5uMVFyxJt9gMd+8h6Oe0hXzD7pyovjHk4CUUIMy4GhTIUaMdZI\n8mwI8pp4sx8KhTB69Gjs2rUr6rbdu3dj7NixCAaDUbd91f333w+fz4f58+cnttFESRBv9gEe+8l7\nOO4hXTH7pCsvjnu0mgTK3f65qMvy5Prgw8dyRW0qC3aD++X6X+etD5O3cZnKBqCuibSj78azIchz\n4sw+AMybNw833XQTtmzZgpEjRwIAdu3ahdraWlx//fUd96uvr0dRUZH42UcffRTbt2/HkiVLxPW1\ntbVx7TNESeci+zz201c5m+S4yVBu7+5k97Too8JxT1yaK/yiziqQn3w3WnLM/bkl719ryXQcsApE\nXXNU9vTZcHCYqPcekp/C2wflt20Gd8uOGGV7ZbpCu5pEbTY0i9rathNdSYusJhuzT3HKeuUtUQ/Y\nL/fP1oFyfw4H5dE/u15OoKg9iHqdB8c97AlECTEcp6NZVsclxulxPBuCvCbe7APArFmzMHnyZNx3\n331wHAeRSARLly7F1KlTMXPmTADAypUrMWnSJLz44osdP/fQQw9h3bp1uOqqq7Bz507s2LEDW7du\nxQsvvICHH364N54mURQ32eexn7yG4x7SFbNPuvLiuIeTQJQQw3JiXmKZN28eDh06hC1btnRc1342\nxCWXXNJxXayvwGs/G+LnP/+5uL62tjZJz4TIHTfZN00TK1asQCgUwpw5czB37lxUV1dj+fLlHd8c\nUFxcjGAwiIKC45+O3HfffViyZAk2bdqE888/HzNmzMCMGTNwwQUXYOHChTj33HN77bkSfZWb7AM8\n9pO3cNxDumL2SVdeHPdwEogS43RyiYFnQ5CnuMg+AOTl5WHx4sX43e9+h6effhoLFixAIBDouH32\n7Nl466238PWvfx0AcPXVV2Pr1q2dXsaPH5/CJ0fUBZfZ57GfPIXjHtIVs0+68uC4R6ueQNZ2eVpW\nyC+fflaz7MXhKFNkObVyPXAX/3ttGLYNw7Cjroul/WyIu+66C3PmzIFpmpgyZQrmz5/f5dkQS5cu\nBQCcf/75Ub9zzZo1yXw6RHFzk30iL3GbfR77yUs47omPr03WkSbZ8+f1hpNE/e7RKlF/crRU1B/X\nlslfuEUupwh+Km8etDci6uzaRlGr/alU6hjfkz1+XGL2qaeszdtEnbVZ3p7uExJeHPek+9+c0p3t\nwFBfKtXGWV/RfjZEZ2bPno3Zs2d31FdffTWuvvrqhDeTKOlcZp/IM3qQfR77yTM47iFdMfukKw+O\nezgJRImxbUS1R+fZEKQDZp90xeyTzph/0hWzT7ryYPY5CUQJMSxEdUc3MnufIIoLs0+6YvZJZ8w/\n6YrZJ115MftaTwKp6xMDmzu545e40CMGx46eCXUyfK8gigezT7pi9klnzH9cgp/KPpp5u2QPnz/5\nRorabJBvSQp2ysacFbvC8vdtOyhq6+MdXW4Px/BJwOyTrjyYfa0ngSgJnBjt0R2+1JIGmH3SFbNP\nOmP+SVfMPunKg9nnJBAlxLAcGIZyelyG7xRE8WD2SVfMPumM+SddMfukKy9mn5NAlJhYjbKiaiIP\nYvZJV8w+6Yz5J10x+6QrD2afk0CUGNtB9E7gAGasOxN5CLNPumL2SWfMf3z++p4oq6xTRG1tlG9B\nfMdaRO3UfNDlr7cS2DTqIWafdOXB7HMSiBJjdTIzmsE7BVFcmH3SFbNPOmP+SVfMPunKg9nnJBAl\nxrYQ/XkMP58hDTD7pCtmn3TG/JOumH3SlQezz0kgSozjRHdHNzK7URZRXJh90hWzTzpj/klXzD7p\nyoPZ5yQQJcZ2AEc5PS7DdwqiuDD7pCtmn3TG/PeI8+b7olZXUfAvmAGYfdKVB7PPSSBKjGUBjnI6\nnJHZp8cRxYXZJ10x+6Qz5p90xeyTrjyYfU4CUWIc+8uvzfsKM7O/Mo8oLsw+6YrZJ50x/6QrZp90\n5cHsdzoJZFnHZ7ciWW29tjGUPtr/7+056Ixj2XBseR9HPV0uAzH/+mL2mX1dMfvMvs50zj+zrzed\nsw8w/zrTOfudTgIdOnQIAHCwamevbQyln0OHDmHIkCGd38F2jl+EztdIWpaFZcuW4dVXX4Vpmhg7\ndixuuOEG5Obmdrkdzc3N+I//+A98+OGHcBwHZ555JubPnw+fz+fi2cSP+Sdmn9nXVbKzD2RG/pl9\nAvQ89jP7BOiZfYD5Jz3HPZ1OAo0ZMwarVq1CWVlZynY6Sl+WZeHQoUMYM2ZM13e0rePrJOVPd3r3\nBQsW4MiRI1i9ejX8fj9uuOEGzJ8/Hw8++CAMw4j5M21tbbjiiiswZMgQPPXUU4hEIrjiiivw85//\nHLfffrvLZxYf5l9fzD6zr6tUZR/IjPwz+3rT+djP7OtN5+wDzL/OdB73dDoJlJOTg4kTJyb1wSiz\ndDkj+iXHsuAoO4XTyU6xbt06rF+/Hs8++ywCgQAA4LrrrsO0adPwzDPP4OKLL475c4888gjeffdd\nrFixAoZhwO/3Y/78+bj88stx3nnnYcqUKS6fWfeYf70x+8y+rpKdfSBz8s/sk67HfmafdM0+wPzr\nTtdxj/oNjUTuWA5g2col9ulxq1atQlFREUaNGtVxXVVVFSorK7Fq1apOH+KJJ57AySefjJKSko7r\nTj31VAQCgS5/jiilmH3SlYvsA8w/eQyP/aQrZp905cFxDyeBKDG21TE72jFLakfPjDY1NWHTpk0Y\nOHBg1Clw1dXV2LJlCxoaGqJ+bseOHdi/fz8GDhworg8EAhg0aBDefPNNOE7XazKJUoLZJ13FmX2A\n+ScP4rGfdMXsk648OO7hJBAlJGy0IGw0K5eWqPvV1tbCsiwUFxdH3RYKheA4Dvbt2xd12969ewGg\n059raGjAkSNHkvBMiNxh9klX8WYfYP7Je3jsJ10x+6QrL457Ou0JRNSVYDCIwsJCHMT2mLcXFhYi\nGAx21PX19QBiB7u9CVtLS/TO1D5T2t3PFRYWunwGRD3D7JOu3GYfYP7JO3jsJ10x+6QrL497OAlE\nPVJUVISXX34ZTU1NMW8PBoMoKirqqHNycgAA4XA46r5tbW0AEDPUXf1ca2trx7YQ9RZmn3TlNvsA\n80/ewWM/6YrZJ115edzDSSDqsaKiorjDOHjwYABAXV1d1G11dXXw+XwoLy/v9OfaZ1W/qr6+HiUl\nJcjOznaz2UQJY/ZJV26yDzD/5C089pOumH3SlVfHPewJRL0iFAph9OjR2LVrV9Rtu3fvxtixY6NO\npwOA4cOHo7S0FDt37hTXt7a24sCBAyn5qkiiZGL2SWfMP+mK2SddMfuks0zJPyeBqNfMmzcPhw4d\nwpYtWzqu27VrF2pra3HJJZd0XPfVGVDTNHHJJZfg/fffF9e/+eabsCxL/BxRumL2SWfMP+mK2Sdd\nMfuks0zIPyeBqNfMmjULkydPxn333QfHcRCJRLB06VJMnToVM2fOBACsXLkSkyZNwosvvtjxcz/4\nwQ9QXV2NBx54AADQ3NyMZcuW4ZJLLsHpp5/eJ8+FyA1mn3TG/JOumH3SFbNPOsuE/HMSiHqNaZpY\nsWIFQqEQ5syZg7lz56K6uhrLly+HYRgAjndEDwaDKCgo6Pi5nJwcPPLII9i7dy/+5V+EaUxsAAAg\nAElEQVT+BZdffjm+/e1v4xe/+EUfPRMid5h90hnzT7pi9klXzD7pLBPybziO4yT9txIRERERERER\nUVrhmUBERERERERERBrgJBARERERERERkQY4CUREREREREREpAFOAhERERERERERaYCTQERERERE\nREREGuAkEBERERERERGRBjgJRERERERERESkAU4CERERERERERFpgJNAREREREREREQa4CQQERER\nEREREZEGOAlERERERERERKQBTgIREREREREREWmAk0BERERERERERBrgJBARERERERERkQY4CURE\nREREREREpAFOAhERERERERERaYCTQEREREREREREGuAkEPWqF154Aeeffz7Gjx+P8847D88//3xc\nP/fFF1/g+uuvxyWXXII5c+bgscceS/GWEhEREREREXkLJ4Go1zz//PPYvHkz7rzzTixduhSRSAQ3\n3ngjXnnllS5/rr6+HpdeeimGDBmCJ598Eg8//DCeeOIJrFixope2nIiIiIiI4sUPfonSV1ZfbwBl\nrvr6ejQ1NcW8LRgMoqioSFyXn5+PG2+8saMuLy/HRRddhNdffx3nnHNOp49zzz33oL6+Hj/60Y86\nfvf3v/99/PznP8e5556LoUOHJuHZEMXPbfaJvILZJ50x/6Qrt9l//vnnsXXrVtx55504dOgQlixZ\nghtvvBGhUKjLMX/7B78zZszA0qVL0dTUhIsvvhiNjY0d7wOIepNXj/ucBKIeqa+vx7emfQ0NjUbM\n2wsLC/Hyyy+LHeOf//mfxX2qq6sBAGPHju30cY4dO4Znn30WU6dOhd/v77h+0qRJiEQiWLNmDW66\n6aZEngqRKz3JPpEXMPukM+afdNWT7PODX/ICLx/3OQlEPdLU1ISGRgOPLrNQUSZvO3AIuOzaBjQ1\nNXW5U7zxxhv49re/jQsuuKDT+/z9739Ha2srBg4cKK4fNGgQsrOz8fe//z2h50HkVjKyT5SJmH3S\nGfNPuupJ9vnBL3mBl4/7nASihJT2s9C/Ql4XdgDA1+XP/fnPf8Ytt9yCRYsWwTBiz64CwL59+wAA\nxcXFUbeFQiHs2bPH7SYTJUVPs0+U6Zh90hnzT7pKJPv84JcymReP+2wMTQmJwEbYscQlArvT+9u2\njUceeQQrVqzAgQMHcO211+KBBx7o9P4NDQ0AYk8C+Xw+tLa2Jv4kiHrAbfaJvILZJ50x/6Srnma/\n/YPf8847jx/8Ukby4nGfk0CUEBtOzEtnTNPE5ZdfjjVr1uDBBx9Ebm4uli1bhiNHjsS8f05ODgAg\nHA5H3dba2orCwsLkPBEil9xmn8grmH3SGfNPunKbfX7wS17hxeM+J4EoIRHYCCuXeGdGv/GNb+C7\n3/0uWltb8cknn8S8T1VVFYDjjbm+yrIsNDY2YsCAAQltP1FPJZJ9okzG7JPOmH/Sldvs84Nf8gov\nHvc77QnU0tKCDz74AGVlZfD5Mne9G/WMZVk4dOgQxowZ03FQjiXiOAg7jnJd/I9z+umn44EHHui0\nodbpp58Ov9+PnTt3iuv37t0Ly7Lw9a9/Pf4Hc4H511dvZT9dMfv6YvaZfZ3pnH9mX2+9kf32D34f\neOABfPLJJzEbRHf3we+oUaPiezCXmH996Xzc73QS6IMPPsC8efN6c1soDa1atQoTJ07s9PY2x0Gb\nslO0udgp9u3bh3HjxmHw4MEAAMdxcOTIkY7Z/sLCQpx33nnYsGEDbNuGaR4/ee2vf/0r/H4/Lrro\nIpfPKD7MP6U6++mK2Sdmn3SmY/6ZfQJSn/10/eCX+Scdj/udTgKVlR3/HrTwBd8BgsFe2yBKE01N\n8L/w3x056EwEBtSTNiOIbvrW1NSEFStW4NRTT8U555wDwzCwY8cO/O53v8N//ud/dtxv8eLFWLNm\nDZ544gmMHz8eALBw4UK89tprePLJJ3HppZfi8OHDWLlyJX7yk59gyJAhCT/VWNqft+/rF8DIZf51\n4hxrgvXaC0nLfqZpf97991QjKxLo462h3hTJasPBqp3aZ7/10vOAUH4fbw31usajyH7if7TMf8dz\nPvsCIC81Y56By94WtRGQry9GTrao914+LCXbQTE0NwF/Sv24J10/+G1/3taMC4B8jvm1crQJvnV6\njvk7nQTqOB0uGAQKQr21PZRmujstMuIYX35FnrxO1dbWhrfffhuPP/44BgwYgDFjxmDQoEG4//77\nUVpa2nG/0tJShEIh5ObmdlzXr18/rFq1CosXL8batWth2zbmz5+PCy+8MLEn14X2523kBmHkFaTs\ncSh9JSv7mab9eWdFApwE0pTu2UcoH04hxz26aU+wjvnveM55QRjB1Ix51NcTwycnfUxbLsVI1XZQ\ntPY4Jyv7mfbBb8fzzg8CIR77daTjcb/TSSCieFgwYMW4TlVSUoInn3yy29/34x//GD/+8Y+jrh8y\nZAgefPDBnm4mUdLFm30ir2H2SWfMf8/suXlKX28CJSje7GfaB79E3fHicZ+TQJSQiGPGaJSV2TsF\nUTyYfdIVs086Y/5JV/Fmnx/8ktd48bjPSSBKSNgxo06PU2siL2L2SVfMPumM+SddMfukKy9mn5NA\nlJCw44vqjp7pOwVRPJh90hWzTzpj/klXzD7pyovZ5yQQJSQCX4xu6UTex+yTrph90hnzT7pi9klX\nXsw+J4EoIZZjwFLWRFqOg3981wCRNzH7pCtmn3TG/JOumH3SlRezz0kgSsjxmVFDuc5B5s+PEnWN\n2SddMfukM+afdMXsk668mH1OAlFCwvAh7JjKdTYyeacgigezT7pi9klnzD/pitknXXkx+5wEooQc\nb5Sl7BQZ/pV5RPFg9klXzD7pjPknXTH7pCsvZp+TQJSQiOND2PEp11l9tDVEvYfZJ10x+6Qz5p90\nxeyTrryYfbP7uxB1LvzlTqFeiLyO2SddMfukM7f5r62txd13341Zs2a5epynn34aI0aMEJenn346\n0c0n6jEe+0lXXsw+zwSihNiOCUs5Pc52MrdTOlG83GTfcRysWbMGjz/+OD799FP0798f8+bNw/e+\n9z0YRmafTkr64XGfdOYm/5s2bcLGjRvx0EMPoby8PO7HsCwLjzzyCE488cSO67Kzs3H++ef3bKOJ\nkoDHftKVF7PPSSBKSNgxo2ZCwxm+UxDFw032V65ciZ07d+IXv/gFIpEIVq5cibvuugsHDhzAv//7\nv/fG5hIlTU+O+7W1tXj88cexceNGPPfcc64ez7ZtvPTSS3jllVdQUVGB4cOHY+bMma63m1Jv+A9q\nRG34ZE6MLDnsNHKyRb35l8NTs2FJ5Cb/EyZMwIQJE7BhwwbU1dXF/Rhr167FOeecgwULFiS0rUTJ\nxDE/6cqL2eckECUk9hrJzN4piOIRb/bb2tpw+PBh3HnnnR3XnX766bj44ovx6KOP4vvf/z7KyspS\nvr1EyeL2uN/TsyGA45NHP/3pT1FUVITbbrsNRUVFPdpmomTpybgnNzc37kkg27Zx//3349JLL0Vt\nba3rfYYoVTjmJ115MfvsCUQJCTtZMS9EXhdv9puamvD9739fXOfz+fDtb38btm1j3759vbXJREnh\n9rg/YcIEXHvttRg5cqSrx6mtrcXcuXNRUlKCX//615wAorTQk3GPm2W/69evx/bt27F48WKcddZZ\nuPrqq7Fr165EN5soYRzzk668mP3M3nrqcxFEnx4Xgd1HW0PUe+LNfklJScyfz83NhWmaqKqqSsn2\nEaVKT4/7bs6GiEQi+OEPfwjLsnDHHXfANPmZFaWHVI97hg8fjt/85jfYvn07XnrpJWzYsAE1NTV4\n4IEHcOqppybtcYjc4pifdOXF7HMSiBJiwYAFI+o6Iq9LNPs1NTU488wzUVpamuxNI0qpnmbfzdkQ\nq1evxkcffYRFixYhGAy63kZKjeFXvy2vcJRBsHJ6vGN18xW6LbI8eeHHok7HHkGpHvcMHToUQ4cO\nxbRp03D11Vfjsccewx133IHrr78eL7/8MgKBQNIeK53lHZBZCgfl3zicJ+8fCcr7R/JlNp18mcWs\n3Ih8vLxWUYdyZF2QLcMa9Mvbc3xhUWebXWffduTzabXlW7IWS9aNbTmi3rNhcJe/PxU45iddeTH7\nnASihETsLISVF66IndlrJInikUj29+3bhz//+c/43e9+l4pNI0qp3jjur1mzBqZporCwEDfffDM2\nb96MgoICfPe738XZZ5+d1McicqM3xz2GYeCyyy5DXV0dVqxYgZqaGkyZMiUlj0XUHY75SVdezD7P\nr6aERL7slv7VS8RhrMj7Esn+LbfcggULFmDo0KEp3kqi5Ev1cf/gwYPYtm0bBgwYgMrKStx66614\n5JFHkJOTg2uuuQbPP/980h6LyK2+GPdceeWVME0T9fX1KX0coq5wzE+68mL2M3vrqc+pO0T7hcjr\nepr9++67D/369cPll1/eC1tJlHypPu4fOHAAADBx4kRMnDgRAJCfn49bbrkFfr8fd999d9Iei8it\nvhj3BINBFBQUYNiwYSl9HKKucMxPuupJ9mtra3H33Xdj1qxZrh7r6aefxogRI8Tl6aefTmTzY+Jy\nMEqI5ZiIKDuBleEzo0Tx6En2165di/feew/33ntvKjeNKKVSfdzPyjo+NFF7AZWVlWH8+PF48803\nUV9fz28LS4GT/32nvKJN9jmx/cqwUen5010PILc9gtJRT/LvOA6cTr5OOJ4s79ixA2eccYanJ4Fy\nDsm/T1tI9tuI5Mv7h/Pl/a2g2gNI9vzJCbaJOpQnw1aS2yzrbKUOyLog65iogz7ZI8hvysdXqUtL\nmm3Z6+mY5Rd1vV82QQp9S27/Ry+nvn8Wx/zUzsqV+1/Je3J/Ld4s9xf/3i9Eve1HmfWlKG6zv2nT\nJmzcuBEPPfQQysvL438cy8IjjzyCE088seO67OxsnH/++e43uhvccykhYduMeelKus+MEsXDbfZf\nfvllPP/88/jVr37V8SYXOL70pbM3B0TpqCfHfTeGDBmCQCCA/fv3R91WVlaGQCCAUCiUtMcjcsNt\n/h3HweHDh9HY2Ii2NjkRsXLlSkyaNAkvvvgiAKCtrQ2LFi3CU089BevLCbO9e/fisccew+233566\nJ0UUh1Qf+4nSldvsT5gwAddeey1Gjhzp6nHWrl2Lc845By+99FLH5fe//z1yc3MTfQpRuOdSQhyY\nsB15cbqI1aZNm/DUU0/hoYceQkNDQ9yP89WZ0fbLyJEjUzIzShQPN9lft24d7r33Xlx33XXYs2cP\nduzYgW3btuGPf/wj7rnnHlffmkTU19we9zt+rpuzIdrl5+dj+vTp+Nvf/hb1OrFnzx6cddZZ8Pm4\nBIH6httj//Tp07Fjxw40NjZi+vTpWLVqVcftxcXFHUu9gONnwbW0tGDJkiWYPn06fvazn+H111/H\nzTffzG/Joz7X02M/UabrafbdTN7Yto37778f/fv3R21tbSKbGxcuB6OEhB0TWcrpcOEuTo+bMGEC\nJkyYgA0bNqCuri7ux2mfGV2wYEGPt5UomeLN/gsvvIAbb7wRtm1j9uzZUbcvXbo0ZdtIlApuj/tA\n9NkQX/2a65UrV+KXv/wl7rnnHkyfPh0AsHDhQtTU1OC2227DXXfdBZ/Ph7Vr12LPnj341a9+lfwn\nRRQnN/mfMWMGZsyY0envmj17tnhdME2TPa8obfXk2E/kBT3NvpsPedevX4/t27dj8eLFuO2223Dm\nmWfi3/7t38TSsGTiJBAlpKfrg3Nzc+OeBGqfGb300ktRW1vram0lUarEm/0LLrgAF1xwQW9tFlHK\nuT3ur1u3Dr/+9a+xa9cuAMD06dNxxRVXYN68eQCiz4YAji/7euKJJ3DXXXfhoosuQl5eHvr164c1\na9agqiqzegmkkxG/lX0azGa5PMnIl31HHEP2HTFNOaB1WmQfFJWjfoWuo/Rt6a5HUBpiX5TUiOQl\n1gMIQdmDJzcos1kclNkvVXoAlec0irosIOt+/iZRF/rkz4d8skdQwOg6221KhprtbFHXW3JfDGbJ\n55Prk2eGfdTloyUHs+9dv58rJ5+fapgo6+0TRO17r1DUOfUy71lH5GuHoyyFHbZij6jTvUdQb2R/\n+PDh+M1vfoPt27fjpZdewoYNG1BTU4MHHngAp556alIfC+AkECUobPvgs31R13UnnWdGieLR0+wT\nZTq32Xd7NkS7iooK3HPPPT3fUKIU4LGfdMXsk656I/tDhw7F0KFDMW3aNFx99dV47LHHcMcdd+D6\n66/Hyy+/LM6gTgZO31JCjs+MykuqZkYXLFiAESNGYMOGDZgzZw7efvvtpD4OkRu9kX2idMTsk86Y\nf9JVT7LPL4MhL+jt475hGLjssstwzTXX4LPPPkNNTU3SH4OvWpSQiG0iYvuUS3Jj1T4r+sMf/hDP\nPfccFi1ahKNHj+L666+P+qYNot7SG9knSkfMPumM+Sdduc0+vwyGvKKvjvtXXnklTNMUX56RLFov\nB7t35sOi/kuj/Bq3Nw7K5UZ79pWKOnuvX9TBT+XvD+0Jy5//lvf+3A4M2I4RdV2qtM+M1tXVYcWK\nFaipqcGUKVNS9nhEnent7BOlC2Y/fZRtlOMKf7Psk+I/KutISPYdyTLlINZUlmobyu1Os3K7oQyC\nW5UeQWHZp8VR26RkYI8g5j81IsqX6ETylB5AeUpW8mW2cvLlh4JqD6DyPNnjpyJX1gOz5Zus/v4j\nss6SdZGp9AQyZQ8UH5TtVzLS4sh996jSEyjPlPtSjiHfU/QFt9nnl8Gkj82X/UbUEchj7RstssfU\ntqP9Rd1cJ3fQwsPy92fXyXwaTXL/cCJyf4XSfy7d9dVxv71f4rBhw5L+u/nRBSVEPTWu/ZJqqZwZ\nJYpHX2WfqK8x+6Qz5p901dPsp/PXZBPFo6fZdxwHjuPEvC2e97A7duzAGWecwUkgSj+WbX55itw/\nLlYvnB6XyplRonj0VfaJ+hqzTzpj/klXPc1+T78M5qyzzsLVV1/d8c2SRH2lJ9l3HAeHDx9GY2Nj\nVPuSlStXYtKkSXjxxRcBAG1tbVi0aBGeeuopWF+eEbt371489thjuP3221PynPiqRQmJOEaMmdHu\nD/bpPDNKFI+eZp8o0zH7pDPmn3TVG9nnl8FQOnKb/XXr1mH69OnYsWMHGhsbMX36dKxatarj9uLi\n4o4TGgAgKysLLS0tWLJkCaZPn46f/exneP3113HzzTcjGAx29jAJ8V6Tmi789+ylon67tUrU9WF5\nuuLRNtnzB61yzsx3TP7zs1rkemVfq6yH//ag/H2fy/WxH/9sRPRGpznLNmEoM6FuZ0a/+pV3K1eu\nxC9/+Uvcc889mD59Otra2nDLLbdg3LhxmD17Nnw+X8pnRoni0ZPsE3kBs997sj+Tw7QspSdPW4G8\nv+NTeviYRpe12tLAp9RmNz2DkKV8OUPU71d6BCl3765H0PCr5Ru/j+87FX2N+U8NK0fpoZMtaydH\n6XeVI3uM5OXIrBVmyx49pdmyR0lZQPYEGuCXH0BWqLVP9gTKM+Tjh0zlPQAkNerNjuyhkq/0/AkY\n8idsZelJi6O8R+kFvZH93v6a7Ew15Gt7RX3JwDdF/Z3gDlH7jHxRf2EdE/XHbUNE/cmRElFnHZZ5\ny6mTec+ql/uf06Ic+xVObnaXt6cbt9mfMWMGZsyY0ents2fPxuzZsztq0zRx9913J76hLvBVixJi\nO0bMS2cyYWaUKB5us0/kFcw+6Yz5J131dvZ742uyieLhxeO+VmcCUfJZToyZ0S4aZWXCzChRPNxm\nn8grmH3SGfNPuuqr7F955ZX47W9/yy+DoT7jxeM+J4EoIZZjAo63dgqieDD7pCtmn3TG/JOu+ir7\n/DIY6mtePO57ahJo+9zfirrOkut/t0XkesbDEbmcqEHpCXSsVa47NVuUnkDKcsesFrl+2dci1wsb\nbXK9rx2Rt590ozzNcfuSiUh3lm0AUWskM/v0OKJ4MPups37/O329CV06d+D4vt6EPsXsJ4+RLft+\nOM1yWBYOKX1RlDGno/TocXxqrdxf6fHj+OTj+ZWePj6lx5DaRMBQ7h/1LUDdfStQRLndUv4eduwv\nkOhLzH9q2EqLGycg//fqvhLIVnryZMuGUwUB2ROoJHBU1P39ssdPaVaTrE15/xJT/v6Qkv0cQ+5L\nprKz2JA9VAJRDbHk82lRegblma1KrTTY6gU9zX53XwZTVFTU5c/r+GUw/iPy73pspMxzea7saVXp\nl31msw35fworedsfkXnddqxc1AfrQqIO1Cl5r5N59TXI/cVpVd4kZ8seQHZeZvV28uJxP7OnsKjP\n2V9+Rd5XLzYbJJIGmH3SFbNPOmP+SVc9yX66f002UTy8eNzP7K2nPmc5RswLkdcx+6QrZp90xvyT\nrtxmn18GQ17hxeO+p5aDUe9zYMBRdgJH/c5ZIg9i9klXzD7pjPknXbnNPr8MhrzCi8f9jJ4EUnsA\nWY5cb9ui1I12jqgbLNkDqL5V1m2tcoGyr0VZKy+XZ8Kn9AQyj8n1vFB6AiEs63Rc+94dyzbgKGsi\n7QxfI0kUD2Y/edK9B5BK3V7degT1JPu1tbV4/PHHsXHjRjz33HPdPkY4HMaDDz6I5557Dvv378fg\nwYNx1VVX4cILL0xo23vbCSccFHVTq+yL0KyMM1pMOQ6wTaVHkCmb/DjKKC6qJ5Cp1ur91R5B8v5Z\nWV33HPL55PaYSh3dI0g+nqH0jXCU5SKw5DguHfDYnxpR/auylD6bSu33yR4nOVlyTJ3vU3r4KIP2\nfKXHTpEp+4gWKrfnK/tSniH33Wyj67dUEcjtzYZ8PjmGvN1vqD2D+h6znzyD/lfm9WiFzFP9MCUf\n+TLPA3IaRF3ikz2t/JA7VIMt878jPEDU2xr7izpcL1+rQofl9gTqlGN18zFROmHZM8gMyR5DkaB8\nvvl7ZY6ODkqv98RezH5GTwJR33NsI2pNpLqTEHlRT7Lv9o0wUTpym/1NmzZh48aNeOihh1BeXt7p\n/b7qzjvvRHZ2NpYsWYKGhgYsW7YMN954I44cOYLLLrssoe0nSgTHPaQrZp905cXscxKIEmLZBgxl\nJ8j0nYIoHm6z35M3wkTpyG32J0yYgAkTJmDDhg2oq6vr9H7tamtrUVFRgR/84Acd15122mk477zz\ncO+992Lu3Lnw+/1d/Aai1OG4h3TF7JOuvJh9NoamhNjO8e7o4qKeb07kQW6zP2HCBFx77bUYOXJk\nL24lUfL19Lifm5vb7X2A498Qo57tEwwGcdZZZ6GpqQn19fU92m6iZOC4h3TF7JOuvJj9jDoT6L1/\n+bVyTUBU6nrbZmU5YaMtB6B14Tx5e5tc/2g1yz+PX+kJlKX0APK1yMc32uR6SEftAZSGa917Ir1W\nbepj0FrZa8JoletznaajXdZ2i1wf/NlPpyRx6/TQk+zn5ubGdTaETtSeOpnWI0hHPcl+VH+YTlRV\nVcW8Pjc3F8FgECUlJT149NQ4b/x7oj5myTOUGsKyF2HAlOOELJ8cd/h8clxwzJS3R3yOUis9goyu\ne/x01yPIjro/lFrt8aPUyv1N5f6G2oMo6nalMYzSMyhdcNyTOLUHEAyny9pQatOU+4qp3J5ldt1j\nJ8cIK7fLMbtP+X3Zyr7lU7LvU25X+5SqwlF9TA2lVvqBKXWzLd8D9RZmPz5Vf5Bjcl+LzFc4JF8r\nIvKlAlae/EsX58kx+4CA7AkUUvIcVpoW11oynzvbykT9aUORqANfyB00p07Zv+pkDy1HeU8BUznW\n58rXsnBQybdsGZSWvJb9jJoEovTj2Aagng5nGxneL52oez3NfrxvhInSVV8d92tqajBz5syoZsRE\nvYnjHtIVs0+68mL2OQlECbE72Sk4RCevY/ZJV32R/bff/r/s3XmYFNW9P/53Vy+zdc/Cqmxe0ACK\nkmCICCZilJsncKNcHFCURLkTg8Yx/hhl5IqGKAFDRFlEiaMDuagjAVETNSAa8yURTRQj7sEFEB2Q\nEZl96a26fn/gjH5Oz9LrdHfV+/U8rc/pququHt5dffp0nU+9jk8++QQPPPBAEp+FqGc89pNVMftk\nVWbMPgeBKC5GSANC6vnkmT1HkigSzD5ZVW9n3+/349e//jXuuOOOtJoKRtYUbf51XcfatWvx4osv\nQtM0jB07FgsWLOixRlZrayvuvPNOvPvuuzAMA+eeey5KS0t5JhylDPs9ZFVmzH5GDQI5bd1/8HkN\nOd+yRZk/eyzoFu16pSZQs1fOV7T5lPm/ynRHR1sPNYG8So0Wv1oTSK6fiQwDgDrt2WyTJtNEbo38\nQzee3le0nS1yuaNFmd/eJvNnb5KBHrr5oGgbLXK+b3XJqZHvrAUw+9ROrWGk1jgym97O/tKlSzFl\nyhRMmTIleU8SoZ9OeFG0vSFZ1yFLk221LolaEyjLIY/TWXa5vFGpe9LqkHVA/A6lroRd9nsMtQaP\nWvPHIdshpVdoqHUd1JpAas0g5fEdyvaa0g6bHhtWYyj9TraPNv9lZWVobGzEpk2b4HQ6sWDBApSW\nlmL9+vVdTg/2+/0oKSnBSSedhC1btiAYDKKkpASLFy/GsmXLEvZaUkr9myk1cdR2sj9e9R6uleNT\navg4lT3yQfaxVOp3FK8ht29VjiUtIfmdpF5XvrPoShGZXsB+z1dyD8t8Fu5Xakw1ye+AUP69DeVY\nrmfLx9Pz5GdBnxzZJx/olDWBsm3yH8anvH8OBWXNnw9aThDt+ro80XbXyu2zauXrsTW3iXZI+Y5r\ny5H5DHlk21cg32/+/PQOkhmzn9lDWJRyRuj4PEl5S/VeESUfs09W1ZvZf+CBB2C321FaWpqcJyCK\nUjT537ZtG3bs2IHy8nK4XC7YbDbMnz8fL730ErZu3drlc2zcuBFvvvkmFi5cCJvNBqfTidLSUmzd\nuhUvv/xykl4ZUffY7yGrMmP2OQhEcTEMW6c3IrNj9smqYs2+YRgwjM5/OuvssmU6NicAACAASURB\nVO8PPfQQPvroIyxevFjcX1NTE9uOEyVANPmvqqpCYWEhTjvttI77hg4disGDB6OqqqrL53j00Udx\n6qmniumPZ555JlwuV7fbESUT+z1kVWbMPgeBKD6GrfMbkdnFmP3uvggTZYQYsm8YBmpra9HU1AS/\nX55WXllZiQkTJmD79u0d923YsAHbtm3Dz372M+zfvx/79u3D+++/j6eeegr/93//l4xXRRSZCPPf\n3NyMPXv2YNCgQWHTvkaMGIG9e/eioaEhbLt9+/bh8OHDGDRokLjf5XJhyJAh2L17Nz9DKDXY5yer\nMmH2M6omkCpgyPmS6nzdppAsutegzKet88vlXp+cj6u1yTEyh5z+CIdXqQnkU2r8BOT8XyMo2+p5\nZPvumoCME0L4HMkMPz0uXTSfJPPU1l/No9qWNbMcLUptijY5H9fZImtkuZrl8zlaZXvQ3xpFW1Nq\nClX/aAAsJYbsq1+EXS5X9xtYlFpTR625k+5MXyMoyuxv27YN99xzDw4cOAAAmDp1KkpKSjBnzhwA\nQFFREdxuN/Lz8wEAFRUVWLlyJQDgRz/6Udjjbd68Oe6XECu3UhzQaZPHyWxN1kXI0oLdtnPscn2X\nstyp1Ahy2OUfukVpe+2yX6KrNYIc8nPCUEotqjV/wmsIyfVDSpHisO2VtkN5PE1p25QaRmptxbQQ\nYf5ramqg6zqKiorClnk8HhiGgUOHDqGgoEAsq66uBoAut9u/fz8aGxvDtss0SgmTsLZab8NQrswT\nUoqy6ko7GJLZDChh9xqyj6TW9/Jq6vpKH16pAaQp+6u+HLUGUL1SgOtYSH5HORKU/75fBD1yuS8F\n//4W7vPbfTJ/OV/IF+5SauZozfKzIpSr1AByKXmV//yw5cq89ctuFm2PpnwpVRzV5ePv98s++keN\n/eTz1cr9y66VeXXVyddjNLfIJwzJv4ctW37nCHpkjStfvvL6PcpnXXOanadiwuxn9CAQpQHDFvbB\nbMvwkVGiiESZ/Z6+CBNljCizP23aNEybNq3L5cXFxSguLu5oX3311bj66qvj30+iZIgw/+1THDsb\nzGm/wpfX6w1b1n52UE/bZfogEGUg9vnJqkyYfQ4CUXxCtuM39T4is4sy+z19ESbKGDzuk5VFmP/s\nL38JDwTCrxrVPiWys4Gc7rbz+XwAgMLCwrBlREnHYz9ZlQmzz0Egik+ok1N4uzk9Ttd1rF27Fi++\n+CI0TcPYsWOxYMEC5OTkdL0RgNbWVtx555149913YRgGzj33XJSWlnb8KkbU66LMPpFpMPtkZRHm\nf9iwYQCAurq6sGV1dXWw2+0YOHBgl9t1Viy9vr4effr0QVZWVtgyoqTjsZ+syoTZz+hBoJDy128J\nyfmLak2g2mCeaDcoNYECXvnncPrk89nVGkBeZf5jm/zVxvDJ+aFGQJ1PbAKdFcbq5vS4srIyNDY2\nYtOmTXA6nViwYAFKS0uxfv36sMKJ7fx+P0pKSnDSSSdhy5YtCAaDKCkpweLFi7Fs2bJEvpq0Mvy0\nz0S7Xqnp0+qVncCWNjmf3WiVeQ6rcdUq/972Vrm9U2k7WuTzuZplTaHCj5RaFkqNoaPfMlmnNcrs\nU+wyvUaQ6Vg4+2oNILVmj1p3RF2/p5pAWXa53KUpz6e2ledvUmoEtWnyuBtUagYFlB9SQkpNnpCj\nhxpBStkGQ93eaeu27XDI9dUfdjSlfdrSI6L93q0noNdFmH+Px4MxY8Z0TAH+uoMHD2Ls2LFwu91h\ny0aOHIm+ffti//794n6fz4cjR45g6tSp8e1/mrApXWLlrQFbQMlOQGbFH5TZaAvKPkuLLmucNOmy\nD9USku8N9TtDdkg9E0tO3ctW3tuqgPLm8CrHhmO6/E6i1gD6LCCnAx7yybO/PveFZyfpLHTsD3rk\nsTSvWv775RyV+XDUyho5Nr+Sn7CaQPLvFlR+C8/Old8hB2Y1iXa+JvPoVfJ2VMnXh21ywPlIfb5o\nZx2T2+cck29Ie518fSGf+iVZqQ+Xp3zH9sjvJAH59DDylPdTutUEMmH20+wvTBkn1MWtE9u2bcOO\nHTtQXl4Ol8sFm82G+fPn46WXXsLWrVu7fIqNGzfizTffxMKFC2Gz2eB0OlFaWoqtW7fi5ZdfTvxr\nIopEFNknMhVmn6wsivzPmTMHR48exd69ezvuO3DgAGpqajB79uyO+75+1o+maZg9ezbefvttcf/u\n3buh67rYjqhX8dhPVmXC7HMQiOJj2L6aJ9l+62JktKqqCoWFhTjttNM67hs6dCgGDx6MqqqqLp/i\n0Ucfxamnnoo+ffp03HfmmWfC5XJ1ux1RUkWRfSJTYfbJyqLI/4wZMzBx4kRUVFTAMAwEg0GsXLkS\nkydPxvTp0wEAlZWVmDBhArZv396x3bx58zBixAg8+OCDAI5PiV+7di1mz56N73znO8l/jUSd4bGf\nrMqE2c/o6WCUerYIL5nX3NyMPXv2YNSoUWHTvkaMGIFdu3ahoaEhrEjivn37cPjwYYwZM0bc73K5\nMGTIEOzevRuGYXQ5lYwoWSLNPpHZMPtkZdHkX9M0rFu3DsuXL8fMmTOhaRomTZqE0tLSjn5LUVER\n3G438vO/mh+RnZ2NjRs3YsmSJbj00ksRCoUwbdo0zJ07NymviSgSPPaTVcWS/XSvg5vRg0BeQ85X\nbFXm29brubIdkO0mn5wPbLTJP4daM8Wh1ARyeOX8RZtSAwgBpSaQLtffd9cEZDqbAcBQ7lTbAGpq\naqDreqeXPPV4PDAMA4cOHQobBKqurgbQ+aVSPR4P9u/fj8bGRlNcKvU3P98g2seCcr73F0E5gfaL\ngFxe45PL65SaV1+0yfWb1JpCrbLdptQEsrXK95e9RakxFPb+UWpsNctgOOX0YrT1z6yBvEizT4mX\naTWC1P1T9z/TWCn7C855VrTtSq9PsynHNaVOSLZN1oXI1eRxUa07kq3JtquHGkLZatsh1290yP1p\ndsrjvM8hj/NBu9w/Q63zoNQEUmsGhdTlSg2ikNKHVR8PPbQ1R+ovBhFt/nNzc7FkyZIulxcXF6O4\nuDjs/r59+2LNmjWx7WQGsCtd5pBf+bdW2rpf9jkCfpnVtoDMcnNAZr0+IPtEHrusmaK+d+1KFdie\n6n2p1PXVmkNqH68mIPuxR/yyT3fEq7RblKIqvcBKx35Hk8xb7lGZB9cxWZPH1twqH0CT2xtOmYdg\njpLvHPmHLMyVj9/P2SwfXslnkyHz/3Ggn2jva+4v2t5jMo+FSv16V53yHbZFvj61zq3mlu+nkEfW\n4PIWytfvz5evN8stawyNPvlT0X7v5RFIpViyn+51cDkdjOLTXihLvSna57V3NpjTPrLp9XrDljU0\nNMS0HVHSRZh9ItNh9snKmH+yKmafrCrK7GdCHVwOAlF8IiyUlZ19fEQ4EFCvtnB81BNAp2fzdLed\n78vK9IWFhWHLiJLOhEXiiCLC7JOVMf9kVcw+WVWU2c+EOrgcBKK42Izj8yTFrZPT44YNGwYAqKur\nC1tWV1cHu92OgQMHhi1r3+7rV8hoV19fjz59+iAry2SXHqeMEGn2icyG2ScrY/7Jqph9sqpost9e\nB3fQoEGd1sHdu3dvx0yXr2uvgzto0CBxv1oHN1EyqibQqX8oFe1/XnK3aLcYLtE+psv5trV+WROo\n1SfX17xyTMwupyeG1QTS1JpAfnm2SkiZL4lQ9/OHM1GkhbI8Hg/GjBmDAwcOhC07ePAgxo4dC7fb\nHbZs5MiR6Nu3L/bv3y/u9/l8OHLkCKZOnRrH3qeXm39XItovlsl8Nyj5aVKKKxxT5psfVWoIHQ16\nRPsLpX3EJ8/E+sIv5/ce88p2bat8vtawGkNyfrKh1AgKqynUotTgUqdXK2+nVGOBxPSRaTWCMp2V\nsq/W9VDrhKg1gtSaPj09Xpayfm5I1mHIsskDX66mLLfL5dlK26nJzw2HXfkcsSt1Gxyyn6MrbbWu\nhVrTx9CUmkBOta3UyVC3V9oOu/I54Uj9b5dWyn8yqX1sXXbJw2oChZSaQEG/zGKbX6kJ5Jd9kgan\n7LNkKZ0KTSnwEVKmenjt8vHVmiyq1pBSk0ipU1oXlH2qo36lT9Ym25+3ynZ9a/fFZZPBytk/Ok7m\nofAf8ku80SILXdrylBo5yrErkKvUBMqTx+aCLFnqQj32q2qV77wHfLIG0CcNctaEs06+f3KOyX9I\nR718fqNZKeRpyPVtuTKPwXyZf3+BTVkuX2/fXHlAyHV0/3p7WzTZz5Q6uKn/NKXMFkL4JfO6eFPM\nmTMHR48exd69ezvuO3DgAGpqajB79uyO+75+1o+maZg9ezbefvttcf/u3buh67rYjqhXRZF9IlNh\n9snKmH+yqiizr+s6Vq9ejeLiYsyaNQu//vWv0dbW1uPTtLa24rbbbsOsWbMwc+ZM3HPPPdB18/2Q\nThkkiuxnSh1cDgJRXMJOjfvy1pkZM2Zg4sSJqKiogGEYCAaDWLlyJSZPnozp06cDACorKzFhwgRs\n3769Y7t58+ZhxIgRePDBBwEc/3BYu3YtZs+eje985ztJf41EnYkm+0RmwuyTlTH/ZFXRZr+srAxv\nvPEGNm3ahC1btqC+vh6lpaXdTmlpvzpSW1sbtmzZgk2bNmH37t1YvHhxEl4RUWSiyX6m1MHlIBDF\nxYYv50l+/dbFupqmYd26dfB4PJg5cyYuu+wyjBgxAvfee2/HnMmioiK43W7k5381lSk7OxsbN25E\ndXU1Lr30Ulx55ZX44Q9/iNtuuy3pr4+oK9Fkn8hMmH2yMuafrCqa7GfC1ZGIIhVN9jOlDm5G1QRS\ntSojyU0hObddnW9b75fzFb1eZX6vV/5z2pUzrhxtcshP8ypFSnxy/qIRlMv33X02TCfK+cG5ublY\nsmRJl8uLi4tRXFwcdn/fvn2xZs2a2PYxQ31v1Y1Rrf/WjetEuzn0uWg3hQ6Ldm2SawqpNbiOeuV8\n5QavfL82t8kDW2urLBBgtMn91VplO6+6l8e0LTw3Pt2le40gdX/U/U17MWRf13WsXbsWL774IjRN\nw9ixY7FgwQLk5HRf1+LVV1/Ffffdh1AoBF3XUVBQgLKyMowcOTKulxApX0j2E5xKTZ1sm/zFTq0R\npNYNUdfXlW5kQJPdMrVmkEfpmOTaZb8jR6kbkafUVch1yONytkP2Uxoc8rjc4lDqOjjkcTngkMdh\nte6FWhMovAaQaPa4fciVBr9d8tgfk8IP5B/J71H63C7137r7tu6V4fG75HunySmzmu2QxxqH8t50\n2uR7O6S8N9XvGHalhpD6XlaPHQ1B+fy1St3FGq/sU9V55fp1zfK9621RXh96QRTZ7+nqSLNmzep0\nu56ujjRp0qQ4X0RifHjt0G6Xf2Pdp6Kt58p86spHn5Erj8UFWXLaXK4ma+Z4lXwdCco++IfNA0S7\nvl7mLa9W5jWrTn7WaE2yBpCu1L21uZQ+ep58Qb5CuX9++ZUC8MjHK8yRr1etd5dyUWQ/U+rgpsGn\nKWWysFFRg1cKIGtg9smqYsl+LNMCXnvtNZSWluLGG2/Eww8/jEcffRTnn38+fvKTn+DQoUMJflVE\nkeGxn6wq0uxnytWRiCIV7XE/E+rgchCI4tPZ/Ej+IkZWwOyTVUWZ/VinBfzhD3/A2WefjbFjx3bc\nN2vWLOi6jr/+9a8JfEFEUeCxn6wqwuxHenUkVU9XR2poaEBjY2PcL4MoalEe9zOhDi4HgSg+oS5u\nRGYXZfZjvUoGUdqJMvs9TQvoSiAQwAcffCCKJAaDQei63jF3nqjXsd9DVhVh9jPl6khEEYvyuJ8J\ndXAzuibQ+Y8tEO27LnpEtOsCcv5so0/OmNXblLn3PrUmkDzPy+5TawIpNYCUat5GIM3mMyZBe6Es\nFU/W7H1j7742ru1fLlsp2k2OZtGuD8kx4yZDzvetzZXzWz9XagbV6nL5FwFleUDOV/5crSGk1PQK\nqyk0VNaucPxDnYCcWNFmv6ysDI2Njdi0aROcTicWLFiA0tJSrF+/Pux0aUqsnmrupLpmUKbVCIom\n++3TAkaNGtXptIBdu3ahoaGh0ytlXHLJJSgpKcHixYuxdOlS2O12bN68GdOmTcPkyZMT82J6oNZd\nUGv0+A3ZjwivEST/Kk6b7BdkK3VJdENun9dDHQi1ToRbqRnUEJT9oBy7WiNIPp9ah6HeIeukNClt\nr1J3JeiQ+2coNX4Mh1LnRa35oy53GN0uB3r/stHs98TG7pd/IYfyXV4PqwEEpd19faigT74X25Rs\nNijZ1XqYw9em7IBab0vdPmTI/WvWZZ+kISD7MPU+pUZQm3yvNrXKPo63We6Prbn3v8JFmv1MuTpS\nMvVUM8iu/AaYlSfzVeRqlesrnxX1IZmXg75+ov1JkxxIsx1Takgdk/9qWcfkG9JolN8BDF15/7hl\nnz2Ur9QEKpDvT3+BfL4ct/zsKnDJP4hasyvVYjnup3sdXJ4JRHHhpVLJqqLJfqzTYYjSUTTZj3Va\nAACcc845+OUvf4k//vGPKCkpwZNPPom8vDwsW7YskS+HKCrs95BVRZr9TLk6ElGkzHjc5yAQxYen\nRZNVRZH9WKfDEKWlKLIf67SAdj/+8Y9x7bXXol+/fvjf//1fvPvuuwgGzX+WLaUx9nvIqiLMfrKu\njpQuVwYjCzLhcZ+DQBQXM46MEkUi0uzHepUMonQVzXE/1mkB7VasWIGJEyfi7rvvxs0334yHH34Y\n119/PUIhftBQarDfQ1YVTfYz4epIRJEy43E/o2sCqY4F5ahyvVITqNUv56rDJ8fAHMr8TIdaE6hN\nzoe0KTWBQn7Zyd23IrFVvNORzejkTcCJ8Rlp0qobEvp4v79utWi3GHI+clOWnO+uvn/VGkN1QTn/\nuCGo1AhS5tvXXSTf//ufOrmHPY5OpNmPdDpMd1+EKbnUGjyprhGU7qI57sc6LQAAtmzZgldeeQXl\n5eUAgLlz50LTNCxbtgyPP/44Zs2aFfNriFSrUhfEqdRFcNpkW1d+W1NrBLls6vbyrCZ7D3VKdE0O\nJOeG5NQIjyE7MrlKHROPUjPIbZd1GfIcSo0hpzzuHlPaDU6lNptDtgNKjaCQyy7bTvn3CqsJpNYM\nUrpxqcB+T2wcrTL7IYfMgtrnDqsBpGRBV2oC6V75eH6nDEuzXf6j9fRe84fk47m07C7WPC5oyP1p\nDcpjh1qXtNEr37vNrbIdaFVrAMn9yT4q270hmuzPmDEDTz/9NCoqKrBy5Urout7p1ZFWrFiB1atX\nY+rUqQCOXx3pueeew4MPPojy8vKkXh0plfQc5Q+3T/Z580/pvgB2rdJn/ritr2h/Xi+XZx2T+cyp\nlZ89WoOsQRRqbhFtm/LZY8uVfW5/ocyvP1+uHyyQz9cvV76+XIf8rFLfT6lmxuN+ev2FKeOYcWSU\nKBKRZj/e6TBE6Saa436s0wKA45eIV68CdsUVV+D000/H//t//y/u10EUC/Z7yKqiyX4mXB2JKFJm\nPO6b6kwgSgED4SOhGT4yShSRCLMf73QYorQT5XF/zpw5WLRoEfbu3YvRo0cD+GpawA03fHUGYn19\nvbjyS25uLj7++OOwxxs4cCDy8vLC7ifqFez3kFVFmf10vzoSUcRMeNznmUAUF5vR+Y3I7CLNfjzT\nYYjSUbTH/RkzZmDixImoqKiAYRgIBoOdTguYMGECtm/f3rHdz372M7z33nvYvHlzx3179uzBa6+9\nhiuvvDJpr4+oO+z3kFUx+2RVZsy+qc4EatBlDZCGgJx/2+qV82vtbXIMTJkqD2ebUhPIq1yRRKkB\nhE5+6Te9Tk6HMzL89DhKjP+5d35c2//q6kdEu4+9WbSbnHI+crNLvt+rHvvPuJ6/RxFmP57pMJQa\nqa4RpD6fuj8pF+Vxv31awPLlyzFz5kxomoZJkyahtLS022kBkydPxrp16/C73/0OjzzyCPr374/s\n7Gw89NBDHWcUJduW12QNijnf+adoZ4fk577XJuuQqDWBIMskhNUUckG2tR7ON8+zyToKfsg6IXma\nrPHTYpd1G9w91AhS2x6nbNc4PKJd55T9pEbluNzmUuqeOGU3VK0TY6g1gZQ6MYV75N+7flwv9MPY\n74lJWE0gpR6U7lL63DJq0LPkv71dRh8hpc6n7pRZ8mkyK03KNzj1+1xAqQlk7+G96NNllv263L5Z\nqQHU1ia/kwRa5f7ZWpSaSc1p8Ls9s99r/rTjbNlWll827e+iXd1SKNqBOnnszVN+h8z6QnkDNco+\nthGUx1ItS+bXcMvv3P4CmX+f3B1obvl4Rdmyfl2OXS5vUr7D55xaL9pt/1aeINlMmH1TDQJRChgI\nv0Reho+MEkUkiuxHOh2GKCPEcNyPdVrA+eefj/PPPz/6fSRKFvZ7yKqYfbIqE2Y/DYaVKZOZsVAW\nUSSiyX4k02GIMgWP+2RlzD9ZFbNPVmXG7HMQiOJixjcFUSQSfZUMokzB4z5ZGfNPVsXsk1WZMfum\nmg62bscPRHv4tw6JdsAnX67dp8wv9qo1gOS/rq1Nzlc0lJpARlCpGWQBnRXGyvRCWZQebq/4cap3\noVvRZr+n6TCUvlJdIyjdWPm4X7X77G6XLzjnWdEOGLKuh64WBVKoNYKylbamnn/ewxiyWjPIq8l+\nUL4mawIV2ltEu8CRL9r5Drl+nlKY5XOnrG92zCmv4qbWDGpyyNpuQYesixJyyb9fSK0RZO/9QXQr\n5z8emk/+29u98ndoR7b8t9TVPrpaI0hdLqODoEs+flCpN+Wzd/8VyB+Uyx12vYs129eXj+/1yx3y\n+WRbb5OP31MNIGeTfL1GCr7BMfvpY9O2c7tdrrwdkHNMfqd11smaPEZDo9zAkP+wthx5rA7ly7av\nQOY1UCCfz+1WPmtc8vntSpBag7JmVmurrEnU20d+M2afZwJRfEJd3IjMjtknq2L2ycqSmP+2tjZU\nVVXhBz/4Aaqrq6Pa9uOPP8Zpp52GUaNGddzKy8sTs2NEAI/9ZF0mzL6pzgSi3mczwk+Hy/SRUaJI\nMPtkVcw+WVmy8v/pp5/ihRdewO9//3scOXIk6u0rKiowdOhQMcV4zpw58e8Y0Zd47CerMmP2OQhE\ncTk+J9IIu4/I7Jh9sipmn6wsWfkfOnQo5s6di5qaGmzYsCGqbaurq1FdXY0dO3bEvyNEXeCxn6zK\njNk39SBQk0/OHwwp829dXjmj0NFTTSClBhACci78R78dH8tuZrTOCmNl+puCKBLMvnVZvUYQs9+1\nu176YVzbb51yn2g7lT+sU6kJ5OypJhBkPyVgyHahJgutNIVkHQa1ZlBfe7No93HIGkL5jgLRLnDK\n7WucHtE+qtQIanDJOhPeLLk/fqfsx4Wcso5Kb0h2/nOU2huReOCBBzB8+HB88MEHGDlyZOJ2JoE0\nv6ypY/eFlLbSB/fLtuaXYdeULrnatvllxQvDLrMSsHf/M74ektvbbDJ7hqHUMNKVmih+ub7eqtQA\nalNqIrWobeU7Sqvcv4As19UreOzPXF9806a0+4j28JvflxsoFy2x5crjkr9Ifsf2FSrvB488thfk\nyM8Cj/LZoCvvp3qfUi+uSVY5UmseJZsZs8+aQBSX9kJZ6i0RODee0lkys0+Uzph9srJk5z/aK0bW\n1NTgiSeewObNm3HhhRfioosuwq5duxK3Q0Rf4rGfrMqM2Tf1mUDUCzqZI6levCQWnBtPaS9J2SdK\ne8w+WVma5V/TNKxduxaHDx/G3//+d+zcuRNXXXUVFi1ahCuuuCJ1O0bmk2bZJ+o1Jsw+B4EoLsk6\nPY5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93Ws7Q+nn6NGjOOmkk7pcbughGCHlg1s9XS5Kqe4MAV/lX3/pqbheC2WuVGQ/HbRn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\nYRgN8P8DbSiYyDJQMy4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0de0e83b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ntest=10000\n",
    "np.random.seed()\n",
    "plt.viridis()\n",
    "n = 6\n",
    "f,axp=plt.subplots(5,n,figsize=(20, 15))\n",
    "from matplotlib import ticker\n",
    "tick_locator = ticker.MaxNLocator(nbins=5)\n",
    "\n",
    "\n",
    "for i in range(0,n):\n",
    "    j=int(round(np.random.rand()*ntest))\n",
    "    # display original intens\n",
    "    im=axp[1,i].imshow(real_intens_test[j].reshape(h, w))\n",
    "    axp[1,i].get_xaxis().set_visible(False)\n",
    "    axp[1,i].get_yaxis().set_visible(False)\n",
    "    cb=plt.colorbar(im, ax=axp[1,i])\n",
    "    cb.locator = tick_locator\n",
    "    cb.update_ticks()\n",
    "    \n",
    "    # display original phase\n",
    "    im=axp[3,i].imshow(real_phase_test[j].reshape(h, w)*2*np.pi-np.pi) #MAP BACK TO -pi to pi phases)\n",
    "    axp[3,i].get_xaxis().set_visible(False)\n",
    "    axp[3,i].get_yaxis().set_visible(False)\n",
    "    cb=plt.colorbar(im, ax=axp[3,i])\n",
    "    cb.locator = tick_locator\n",
    "    cb.update_ticks()\n",
    "\n",
    "    # display FT\n",
    "    im=axp[0,i].imshow(ft_test[j].reshape(h, w))\n",
    "#    im=axp[0,i].imshow((ft_test[j].reshape(h, w)))\n",
    "    axp[0,i].get_xaxis().set_visible(False)\n",
    "    axp[0,i].get_yaxis().set_visible(False)\n",
    "    cb=plt.colorbar(im, ax=axp[0,i])\n",
    "    cb.locator = tick_locator\n",
    "#    im.set_clim(2,5)\n",
    "    cb.update_ticks()\n",
    "    \n",
    "    # display intens predict\n",
    "    im=axp[2,i].imshow(preds_intens[j].reshape(h, w))\n",
    "    axp[2,i].get_xaxis().set_visible(False)\n",
    "    axp[2,i].get_yaxis().set_visible(False)\n",
    "    cb=plt.colorbar(im, ax=axp[2,i])\n",
    "    cb.locator = tick_locator\n",
    "    cb.update_ticks()\n",
    "    \n",
    "    # display reconstruction\n",
    "    im=axp[4,i].imshow(preds_phase3[j].reshape(h, w))\n",
    "    axp[4,i].get_xaxis().set_visible(False)\n",
    "    axp[4,i].get_yaxis().set_visible(False)\n",
    "    cb=plt.colorbar(im, ax=axp[4,i])\n",
    "    cb.locator = tick_locator\n",
    "    cb.update_ticks()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Test gold data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tifffile as tff\n",
    "ifile='summededit1crop.tif'\n",
    "#Read image\n",
    "im=tff.TiffFile(ifile)\n",
    "imarr=im.asarray()\n",
    "imarr=imarr.transpose(2,1,0) #I think the detector is rotated 90?\n",
    "nx,ny,nz=imarr.shape[0],imarr.shape[1], imarr.shape[2]\n",
    "print (\"Original number of X,Y and Z bins\", nx, ny, nz)\n",
    "\n",
    "#Take only middle slice along Z\n",
    "imarr=imarr[:,:,nz/2]**0.5\n",
    "# Crop the junk\n",
    "imarr=imarr[25:-25,25:-25]\n",
    "plt.imshow(np.log10(imarr))\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.ndimage.interpolation import zoom\n",
    "\n",
    "dims=imarr.shape\n",
    "imarr2=zoom(imarr,(h/float(dims[0]),w/float(dims[1])))\n",
    "\n",
    "imarr2=np.reshape(imarr2,(1,h,w,1)) #Reshape to format predictor needs\n",
    "print (np.max(imarr2))\n",
    "imarr2=imarr2/np.max(imarr2)*10000\n",
    "print (np.max(imarr2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.imshow(np.log10(imarr2.squeeze()))\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "str_model=load_model('v4_struct_weights/tmp_weights_struct/weights.10.hdf5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tmp_str=str_model.predict(imarr2)\n",
    "plt.imshow(tmp_str.squeeze())\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ph_model=load_model('v4_struct_weights/tmp_weights_phase/weights.10.hdf5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tmp_ph=ph_model.predict(imarr2)*2*np.pi-np.pi\n",
    "tmp_ph=np.where(tmp_str<0.3, 0, tmp_ph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.imshow(tmp_ph.squeeze())\n",
    "plt.colorbar()\n",
    "#plt.clim(0,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "i=500\n",
    "tmp_str=str_model.predict(ft_train[i].reshape(1,32,32,1))\n",
    "tmp_str=np.where(tmp_str<0.3,0,tmp_str)\n",
    "plt.imshow(tmp_str.squeeze())\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.imshow(real_intens_train[i].squeeze())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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