Full Code of radioML/examples for AI

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└── modulation_recognition/
    └── RML2016.10a_VTCNN2_example.ipynb

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FILE CONTENTS
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FILE: modulation_recognition/RML2016.10a_VTCNN2_example.ipynb
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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modulation Recognition Example: RML2016.10a Dataset + VT-CNN2 Mod-Rec Network\n",
    "\n",
    "This work is copyright DeepSig Inc. 2017.\n",
    "It is provided open source under the Create Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence\n",
    "https://creativecommons.org/licenses/by-nc/4.0/\n",
    "\n",
    "Use of this work, or derivitives inspired by this work is permitted for non-commercial usage only and with explicit citaiton of this original work.\n",
    "\n",
    "A more detailed description of this work can be found at\n",
    "https://arxiv.org/abs/1602.04105\n",
    "\n",
    "A more detailed description of the RML2016.10a dataset can be found at\n",
    "http://pubs.gnuradio.org/index.php/grcon/article/view/11\n",
    "\n",
    "Citation of this work is required in derivative works:\n",
    "\n",
    "```\n",
    "@article{convnetmodrec,\n",
    "  title={Convolutional Radio Modulation Recognition Networks},\n",
    "  author={O'Shea, Timothy J and Corgan, Johnathan and Clancy, T. Charles},\n",
    "  journal={arXiv preprint arXiv:1602.04105},\n",
    "  year={2016}\n",
    "}\n",
    "@article{rml_datasets,\n",
    "  title={Radio Machine Learning Dataset Generation with GNU Radio},\n",
    "  author={O'Shea, Timothy J and West, Nathan},\n",
    "  journal={Proceedings of the 6th GNU Radio Conference},\n",
    "  year={2016}\n",
    "}\n",
    "```\n",
    "\n",
    "The RML2016.10a dataset is used for this work (https://radioml.com/datasets/)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using gpu device 1: GeForce GTX TITAN X (CNMeM is disabled, cuDNN 5004)\n",
      "Using Theano backend.\n",
      "/usr/local/lib/python2.7/dist-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n",
      "  \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n"
     ]
    }
   ],
   "source": [
    "# Import all the things we need ---\n",
    "#   by setting env variables before Keras import you can set up which backend and which GPU it uses\n",
    "%matplotlib inline\n",
    "import os,random\n",
    "os.environ[\"KERAS_BACKEND\"] = \"theano\"\n",
    "#os.environ[\"KERAS_BACKEND\"] = \"tensorflow\"\n",
    "os.environ[\"THEANO_FLAGS\"]  = \"device=gpu%d\"%(1)\n",
    "import numpy as np\n",
    "import theano as th\n",
    "import theano.tensor as T\n",
    "from keras.utils import np_utils\n",
    "import keras.models as models\n",
    "from keras.layers.core import Reshape,Dense,Dropout,Activation,Flatten\n",
    "from keras.layers.noise import GaussianNoise\n",
    "from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D\n",
    "from keras.regularizers import *\n",
    "from keras.optimizers import adam\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import cPickle, random, sys, keras\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the dataset ...\n",
    "#  You will need to seperately download or generate this file\n",
    "Xd = cPickle.load(open(\"RML2016.10a_dict.dat\",'rb'))\n",
    "snrs,mods = map(lambda j: sorted(list(set(map(lambda x: x[j], Xd.keys())))), [1,0])\n",
    "X = []  \n",
    "lbl = []\n",
    "for mod in mods:\n",
    "    for snr in snrs:\n",
    "        X.append(Xd[(mod,snr)])\n",
    "        for i in range(Xd[(mod,snr)].shape[0]):  lbl.append((mod,snr))\n",
    "X = np.vstack(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:7: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
     ]
    }
   ],
   "source": [
    "# Partition the data\n",
    "#  into training and test sets of the form we can train/test on \n",
    "#  while keeping SNR and Mod labels handy for each\n",
    "np.random.seed(2016)\n",
    "n_examples = X.shape[0]\n",
    "n_train = n_examples * 0.5\n",
    "train_idx = np.random.choice(range(0,n_examples), size=n_train, replace=False)\n",
    "test_idx = list(set(range(0,n_examples))-set(train_idx))\n",
    "X_train = X[train_idx]\n",
    "X_test =  X[test_idx]\n",
    "def to_onehot(yy):\n",
    "    yy1 = np.zeros([len(yy), max(yy)+1])\n",
    "    yy1[np.arange(len(yy)),yy] = 1\n",
    "    return yy1\n",
    "Y_train = to_onehot(map(lambda x: mods.index(lbl[x][0]), train_idx))\n",
    "Y_test = to_onehot(map(lambda x: mods.index(lbl[x][0]), test_idx))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(110000, 2, 128) [2, 128]\n"
     ]
    }
   ],
   "source": [
    "in_shp = list(X_train.shape[1:])\n",
    "print X_train.shape, in_shp\n",
    "classes = mods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build the NN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "reshape_1 (Reshape)              (None, 1, 2, 128)     0           reshape_input_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "zeropadding2d_1 (ZeroPadding2D)  (None, 1, 2, 132)     0           reshape_1[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "conv1 (Convolution2D)            (None, 256, 2, 130)   1024        zeropadding2d_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)              (None, 256, 2, 130)   0           conv1[0][0]                      \n",
      "____________________________________________________________________________________________________\n",
      "zeropadding2d_2 (ZeroPadding2D)  (None, 256, 2, 134)   0           dropout_1[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "conv2 (Convolution2D)            (None, 80, 1, 132)    122960      zeropadding2d_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)              (None, 80, 1, 132)    0           conv2[0][0]                      \n",
      "____________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)              (None, 10560)         0           dropout_2[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "dense1 (Dense)                   (None, 256)           2703616     flatten_1[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "dropout_3 (Dropout)              (None, 256)           0           dense1[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "dense2 (Dense)                   (None, 11)            2827        dropout_3[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_1 (Activation)        (None, 11)            0           dense2[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "reshape_2 (Reshape)              (None, 11)            0           activation_1[0][0]               \n",
      "====================================================================================================\n",
      "Total params: 2830427\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Build VT-CNN2 Neural Net model using Keras primitives -- \n",
    "#  - Reshape [N,2,128] to [N,1,2,128] on input\n",
    "#  - Pass through 2 2DConv/ReLu layers\n",
    "#  - Pass through 2 Dense layers (ReLu and Softmax)\n",
    "#  - Perform categorical cross entropy optimization\n",
    "\n",
    "dr = 0.5 # dropout rate (%)\n",
    "model = models.Sequential()\n",
    "model.add(Reshape([1]+in_shp, input_shape=in_shp))\n",
    "model.add(ZeroPadding2D((0, 2)))\n",
    "model.add(Convolution2D(256, 1, 3, border_mode='valid', activation=\"relu\", name=\"conv1\", init='glorot_uniform'))\n",
    "model.add(Dropout(dr))\n",
    "model.add(ZeroPadding2D((0, 2)))\n",
    "model.add(Convolution2D(80, 2, 3, border_mode=\"valid\", activation=\"relu\", name=\"conv2\", init='glorot_uniform'))\n",
    "model.add(Dropout(dr))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(256, activation='relu', init='he_normal', name=\"dense1\"))\n",
    "model.add(Dropout(dr))\n",
    "model.add(Dense( len(classes), init='he_normal', name=\"dense2\" ))\n",
    "model.add(Activation('softmax'))\n",
    "model.add(Reshape([len(classes)]))\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up some params \n",
    "nb_epoch = 100     # number of epochs to train on\n",
    "batch_size = 1024  # training batch size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 110000 samples, validate on 110000 samples\n",
      "Epoch 1/100\n",
      "15s - loss: 2.2384 - val_loss: 2.1028\n",
      "Epoch 2/100\n",
      "15s - loss: 2.0282 - val_loss: 1.8806\n",
      "Epoch 3/100\n",
      "15s - loss: 1.8641 - val_loss: 1.7373\n",
      "Epoch 4/100\n",
      "16s - loss: 1.7378 - val_loss: 1.6276\n",
      "Epoch 5/100\n",
      "16s - loss: 1.6693 - val_loss: 1.5724\n",
      "Epoch 6/100\n",
      "16s - loss: 1.6102 - val_loss: 1.4985\n",
      "Epoch 7/100\n",
      "16s - loss: 1.5535 - val_loss: 1.4475\n",
      "Epoch 8/100\n",
      "16s - loss: 1.5115 - val_loss: 1.4226\n",
      "Epoch 9/100\n",
      "16s - loss: 1.4799 - val_loss: 1.4122\n",
      "Epoch 10/100\n",
      "16s - loss: 1.4569 - val_loss: 1.3884\n",
      "Epoch 11/100\n",
      "16s - loss: 1.4412 - val_loss: 1.3534\n",
      "Epoch 12/100\n",
      "16s - loss: 1.4264 - val_loss: 1.3395\n",
      "Epoch 13/100\n",
      "16s - loss: 1.4175 - val_loss: 1.3401\n",
      "Epoch 14/100\n",
      "16s - loss: 1.4080 - val_loss: 1.3408\n",
      "Epoch 15/100\n",
      "16s - loss: 1.4012 - val_loss: 1.3495\n",
      "Epoch 16/100\n",
      "16s - loss: 1.3988 - val_loss: 1.3250\n",
      "Epoch 17/100\n",
      "16s - loss: 1.3835 - val_loss: 1.3171\n",
      "Epoch 18/100\n",
      "16s - loss: 1.3775 - val_loss: 1.3095\n",
      "Epoch 19/100\n",
      "16s - loss: 1.3752 - val_loss: 1.3052\n",
      "Epoch 20/100\n",
      "16s - loss: 1.3743 - val_loss: 1.3056\n",
      "Epoch 21/100\n",
      "16s - loss: 1.3670 - val_loss: 1.3045\n",
      "Epoch 22/100\n",
      "16s - loss: 1.3643 - val_loss: 1.3231\n",
      "Epoch 23/100\n",
      "16s - loss: 1.3620 - val_loss: 1.3103\n",
      "Epoch 24/100\n",
      "16s - loss: 1.3541 - val_loss: 1.2933\n",
      "Epoch 25/100\n",
      "16s - loss: 1.3530 - val_loss: 1.3028\n",
      "Epoch 26/100\n",
      "16s - loss: 1.3479 - val_loss: 1.2945\n",
      "Epoch 27/100\n",
      "16s - loss: 1.3478 - val_loss: 1.2984\n",
      "Epoch 28/100\n",
      "16s - loss: 1.3428 - val_loss: 1.2893\n",
      "Epoch 29/100\n",
      "16s - loss: 1.3358 - val_loss: 1.3019\n",
      "Epoch 30/100\n",
      "16s - loss: 1.3333 - val_loss: 1.2903\n",
      "Epoch 31/100\n",
      "16s - loss: 1.3304 - val_loss: 1.2911\n",
      "Epoch 32/100\n",
      "16s - loss: 1.3247 - val_loss: 1.2825\n",
      "Epoch 33/100\n",
      "16s - loss: 1.3252 - val_loss: 1.2891\n",
      "Epoch 34/100\n",
      "16s - loss: 1.3207 - val_loss: 1.2846\n",
      "Epoch 35/100\n",
      "16s - loss: 1.3167 - val_loss: 1.2836\n",
      "Epoch 36/100\n",
      "16s - loss: 1.3186 - val_loss: 1.2789\n",
      "Epoch 37/100\n",
      "16s - loss: 1.3127 - val_loss: 1.2765\n",
      "Epoch 38/100\n",
      "16s - loss: 1.3101 - val_loss: 1.2802\n",
      "Epoch 39/100\n",
      "16s - loss: 1.3077 - val_loss: 1.2973\n",
      "Epoch 40/100\n",
      "16s - loss: 1.3033 - val_loss: 1.2996\n",
      "Epoch 41/100\n",
      "16s - loss: 1.3016 - val_loss: 1.2877\n",
      "Epoch 42/100\n",
      "16s - loss: 1.2993 - val_loss: 1.2769\n",
      "Epoch 43/100\n",
      "16s - loss: 1.2963 - val_loss: 1.3045\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/Keras-1.0.4-py2.7.egg/keras/models.py:391: UserWarning: The \"show_accuracy\" argument is deprecated, instead you should pass the \"accuracy\" metric to the model at compile time:\n",
      "`model.compile(optimizer, loss, metrics=[\"accuracy\"])`\n",
      "  warnings.warn('The \"show_accuracy\" argument is deprecated, '\n"
     ]
    }
   ],
   "source": [
    "# perform training ...\n",
    "#   - call the main training loop in keras for our network+dataset\n",
    "filepath = 'convmodrecnets_CNN2_0.5.wts.h5'\n",
    "history = model.fit(X_train,\n",
    "    Y_train,\n",
    "    batch_size=batch_size,\n",
    "    nb_epoch=nb_epoch,\n",
    "    show_accuracy=False,\n",
    "    verbose=2,\n",
    "    validation_data=(X_test, Y_test),\n",
    "    callbacks = [\n",
    "        keras.callbacks.ModelCheckpoint(filepath, monitor='val_loss', verbose=0, save_best_only=True, mode='auto'),\n",
    "        keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, verbose=0, mode='auto')\n",
    "    ])\n",
    "# we re-load the best weights once training is finished\n",
    "model.load_weights(filepath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate and Plot Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.27649493232\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/Keras-1.0.4-py2.7.egg/keras/models.py:432: UserWarning: The \"show_accuracy\" argument is deprecated, instead you should pass the \"accuracy\" metric to the model at compile time:\n",
      "`model.compile(optimizer, loss, metrics=[\"accuracy\"])`\n",
      "  warnings.warn('The \"show_accuracy\" argument is deprecated, '\n"
     ]
    }
   ],
   "source": [
    "# Show simple version of performance\n",
    "score = model.evaluate(X_test, Y_test, show_accuracy=True, verbose=0, batch_size=batch_size)\n",
    "print score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f089a6d7ad0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6s0VEJNyEbDCPic3BbrGzpZsBYEeNTsBmNVirYBYRkTARssFst9rJjRtFcX0p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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f089ab35510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show loss curves \n",
    "plt.figure()\n",
    "plt.title('Training performance')\n",
    "plt.plot(history.epoch, history.history['loss'], label='train loss+error')\n",
    "plt.plot(history.epoch, history.history['val_loss'], label='val_error')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues, labels=[]):\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(labels))\n",
    "    plt.xticks(tick_marks, labels, rotation=45)\n",
    "    plt.yticks(tick_marks, labels)\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/pymodules/python2.7/matplotlib/collections.py:548: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == 'face':\n"
     ]
    },
    {
     "data": {
      "image/png": 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ERES1GDs3JiQkyIPGxMbGon///vJzV69exSuvvIKqqir07dsXCxcubHBfbFYl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\nGBuvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f089a5c3c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot confusion matrix\n",
    "test_Y_hat = model.predict(X_test, batch_size=batch_size)\n",
    "conf = np.zeros([len(classes),len(classes)])\n",
    "confnorm = np.zeros([len(classes),len(classes)])\n",
    "for i in range(0,X_test.shape[0]):\n",
    "    j = list(Y_test[i,:]).index(1)\n",
    "    k = int(np.argmax(test_Y_hat[i,:]))\n",
    "    conf[j,k] = conf[j,k] + 1\n",
    "for i in range(0,len(classes)):\n",
    "    confnorm[i,:] = conf[i,:] / np.sum(conf[i,:])\n",
    "plot_confusion_matrix(confnorm, labels=classes)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall Accuracy:  0.095516925892\n",
      "Overall Accuracy:  0.0919164396004\n",
      "Overall Accuracy:  0.10003617945\n",
      "Overall Accuracy:  0.104474918802\n",
      "Overall Accuracy:  0.150045578851\n",
      "Overall Accuracy:  0.227652390261\n",
      "Overall Accuracy:  0.348704758405\n",
      "Overall Accuracy:  0.493401759531\n",
      "Overall Accuracy:  0.589463955638\n",
      "Overall Accuracy:  0.649310595065\n",
      "Overall Accuracy:  0.704197080292\n",
      "Overall Accuracy:  0.708833151581\n",
      "Overall Accuracy:  0.722834067548\n",
      "Overall Accuracy:  0.72761732852\n",
      "Overall Accuracy:  0.717583408476\n",
      "Overall Accuracy:  0.732786885246\n",
      "Overall Accuracy:  0.723881948217\n",
      "Overall Accuracy:  0.729470381989\n",
      "Overall Accuracy:  0.725067873303\n",
      "Overall Accuracy:  0.723852385239\n"
     ]
    },
    {
     "data": {
      "image/png": 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Hl/PRCiGEKIklBUed0Zw5Uspw4cIFOnbsiLu7OzqdjtzcXKpUqYK/vz/Nmzcn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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f089a5c3e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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k+oCTmqMQQgh9KNZTH5PgKIQQQh8V0CqjlQRHIYQQ+pCaoxBCCFGI1ByFEEKIQqyo5mg9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f089a435590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0f/zxB5MnTwagoKCA3r17s3HjxgrLbDSBFcz8IoQQFissAUcdv7PQ9JU1FqU/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\ne+ONN2jRogX+/v5VV3ghhBC3RbHwpad7ouZoNBopLS29ab