172 lines
528 KiB
Plaintext
172 lines
528 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"import librosa\n",
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"from librosa import display\n",
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"from parakeet.datasets import ljspeech\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"ljspeech_dataset = ljspeech.LJSpeech(\"/workspace/datasets/LJSpeech-1.1/\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"example = ljspeech_dataset[0] # mag, mel, text"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(array([[ -0.27462918, -6.2973747 , -48.616695 , ..., -48.616695 ,\n",
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" -48.616695 , -48.616695 ],\n",
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" [ -0.27177352, -6.2941885 , -48.616695 , ..., -48.616695 ,\n",
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" -48.616695 , -48.616695 ],\n",
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" [ -0.26818287, -6.2838745 , -48.616695 , ..., -48.616695 ,\n",
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" -48.616695 , -48.616695 ],\n",
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" ...,\n",
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" [ -0.2587558 , -6.2825537 , -48.616695 , ..., -48.616695 ,\n",
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" -48.616695 , -48.616695 ],\n",
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" [ -0.25943533, -6.2737536 , -48.616695 , ..., -48.616695 ,\n",
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" -48.616695 , -48.616695 ],\n",
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" [ -0.26199442, -6.2909927 , -48.616695 , ..., -48.616695 ,\n",
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" -48.616695 , -48.616695 ]], dtype=float32), array([[0.7972537 , 0.7370262 , 0.313833 , ..., 0.313833 , 0.313833 ,\n",
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" 0.313833 ],\n",
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" [0.7972823 , 0.7370581 , 0.313833 , ..., 0.313833 , 0.313833 ,\n",
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" 0.313833 ],\n",
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" [0.7973182 , 0.7371613 , 0.313833 , ..., 0.313833 , 0.313833 ,\n",
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" 0.313833 ],\n",
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" ...,\n",
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" [0.7974124 , 0.73717445, 0.313833 , ..., 0.313833 , 0.313833 ,\n",
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" 0.313833 ],\n",
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" [0.7974056 , 0.7372624 , 0.313833 , ..., 0.313833 , 0.313833 ,\n",
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" 0.313833 ],\n",
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" [0.7973801 , 0.73709005, 0.313833 , ..., 0.313833 , 0.313833 ,\n",
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" 0.313833 ]], dtype=float32), array([43, 45, 36, 41, 47, 36, 41, 34, 58, 64, 36, 41, 64, 47, 35, 32, 64,\n",
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" 42, 41, 39, 52, 64, 46, 32, 41, 46, 32, 64, 50, 36, 47, 35, 64, 50,\n",
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" 35, 36, 30, 35, 64, 50, 32, 64, 28, 45, 32, 64, 28, 47, 64, 43, 45,\n",
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" 32, 46, 32, 41, 47, 64, 30, 42, 41, 30, 32, 45, 41, 32, 31, 58, 64,\n",
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" 31, 36, 33, 33, 32, 45, 46, 64, 33, 45, 42, 40, 64, 40, 42, 46, 47,\n",
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" 64, 36, 33, 64, 41, 42, 47, 64, 33, 45, 42, 40, 64, 28, 39, 39, 64,\n",
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" 47, 35, 32, 64, 28, 45, 47, 46, 64, 28, 41, 31, 64, 30, 45, 28, 33,\n",
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" 47, 46, 64, 45, 32, 43, 45, 32, 46, 32, 41, 47, 32, 31, 64, 36, 41,\n",
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" 64, 47, 35, 32, 64, 32, 51, 35, 36, 29, 36, 47, 36, 42, 41, 60, 1]))\n"
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]
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}
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],
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"source": [
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"print(example)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.axes._subplots.AxesSubplot at 0x7f0f1fd73690>"
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]
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},
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"execution_count": 19,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAADrCAYAAABXYUzjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOy8V5Bl13Wm+R1/zj3Xe5M3b3pflVWoQhl4kARBEjSipJ6WF3sUreiH6el+mIeOnqeOiZ7omIl5GNN66VCHJkRFTLQoE6JEUY4gSLgCikABKF+ZWendzev9PW4ebiGBRFUBVZRGnJip/ynz3GP22Wfvf6/1r7W24Hkej/AIj/AIj/CPD/Fn3YBHeIRHeIT/v+IRAT/CIzzCI/yM8IiAH+ERHuERfkZ4RMCP8AiP8Ag/Izwi4Ed4hEd4hJ8RHhHwIzzCIzzCzwjyw5wcjUS8vpe8/80UBdd1cR3n792wB4UoiniA57oPdZ0ky6i6CnjgQafVeaDrZEXBtqyHb+g/InSfgSgJ2H2bfq8PgICAx/1TDgVRBNfDw0P3GXTbD9Yf/2+BP2TS61qomkKn1f0HH4Oqph725f8XoPl0BISH+s6CIPAgaasPet4/BkRRwvM8PO/e/CAIIoqm0O/2/h9tR6t288DzvMQnjz8UAedzWQKjv3PP33S/SXYiR3GjSKNUOfKbZhr0HpDgHhQLTx1n49YWsqJQ2tx96OuDiSiCKDC5OILneuysl9i+ufap15x4/gSNaoft5S069eZDPzM2lP6p2goP14dTp2dRdYV6pcX6leUHOn9mIY7PEFhabuHzKazeKj7Qtf/QEGWJ1EiOnaX1I8eNoB9RlmhXG3cttuF0Aqvf54VvHKfdcXnlu+/c1Vd/3zE4PD9OcWMPRVNpVRsAOJaF7jcxwwEC0QBrl5cf2BCQNRX7Z0TovnCQcDJCr9176PEoyhKu/dHiZkZDtMq1I+d8/p88wTuvLdFtth9ongRikUPOCMQidJqtu/pGUhScn8Lw+WR7P4mZc/OkMgGq5Q7vvfLeQ9//QfHanz93T3L5e0kQkqIc/p0spElmQ0QzsbvOMwJ+YkPpn/o5gnh3M7udPulCinQhwcjxycPjoix95v1mzs3z+HMz/PJvzpPJ+Di2EOArXxv51GtEWeLUyRD+oE40Ez/ymxH033W+ZhqY0dDh/4qhIyl3ty2UihFMRD+zzf5I6L59KMoS4fRgcT33lcd55tkk585GWDiZAQYEVViYuO+9e12LP/u9HyOJAqMjJlPjGtlCFFlTP7Nd/9CYOzfP+edGmDo9SzyfwRcOMnV6ln6nR6tcuyfBpUeSzJ6eIBQUScRknvnqybvOOf25Y5x4/sRnPj+STd71PcLpBIapcfLpOfJTOfSAj6HpAgDz56ZJ5uPIisTis8fR/SYw+CaJQva+z0mNZEmP5VEM/TPbBIPxM3V69r6/i7KELxx8oHvNn53k9BPDPPPi1AONveH5cU5+/iSz5xcwQ0efMXFshJlz83fGt8KTXztDJqXw0s/PcvzJmQdqz9yZcZ795jk+/0+eYP7sxD0XpgchX1lTCacTh/2umcYh+QYT0Xu+a74QIpNWMf0aJ54/8Y8+5n8qAs5MDDM8P04gFj48phsqPp9MMhc6cq5mGoTiIXRTRzONn6qR808sABzpnFQ2SDoXJJ40MUwNxdApLEyQGc8/UCeO5BVsR+DsTA9VBkmEwsLEkUXl48jPjKKrHnPzYYLRjwhXlCUi6RiKoR8hyF6rQ7/zkVuj+wwiiaN9o/tNbMtGlD590RBliaHxFImhuxe3D2GGTBafXaQwbJCN9AmbDlcufWTdTM0neeGfPnHPawNhH//m3z3JaLqPZXm4LiQSOuoDksOHeFAy+TToPoVwQCCZDTJ9Ik88l6BZbzOyMEY8nyGez9x1zeh4hJGCSbfnYTseuczd3/C1775FabdGcjR3/2f7TdKFJMqd8ZObHiGez2CGTE4/nkBRRWzLoVWusXZ5CYDr7ywzOx9l7liCYEhH9/sAcG2H4tr2fZ+VHYmRLsTwXJdEIfuZcyOWSdDrWgRikbt+M6Mh5p9YIJ4bLMKfNv5z0yPE4wbpuEgiIqD7jXsaEB9HcWOPpUurAAiicDhHFEMnFDGYmY1y5gvHmT07y7kTKuGAx+ZOn37f+dT+Pmy/qeB54PNJJBI6w/Pjh/MpNz0CwNkvn/7UBVQzDbITeeZOjaL7NAAmT07y7DfPsfDU8cO2f3KM1ut9bBseO64zOhY8Qv4fN6A+ic/qswfFT0XAcyeHmD+RITOaOjwWTfrRdYl2s39k5bd6fUanE4h3Ppxi6J/6Yp+EZhrYtkN2qnCkc07M66RTKtVyh8npCPnpYTZvrNFtdT/TtSvvVelb8FLiAhPqMsezRebTJR4/n+VLv3SWQCxy14SYXkiRCPSZzvVIpPxEsgMtPJpJEkuHiWUSjMxkDy2g7FSBSDrG7PkFZE0lko4yVAgfIZCRhRFkRcbwf8bky6UQZZGxiaOTL1HIEhtKM3dunvHZNL2ezVDCZbui4nnw+RcGg//EkxNMj8mMF2R0v3nYxg/x1RcCDAfKKJLLSA7GU21Mn8DofIGxE1Of2rYPkZkYZnim8Pe2IDIZH4bmMVrQee27b3H6fI6nP1dgfjFJYijGwcbOkfOnz8wRi0g8PtEiHfOIhaDTvVt//O3/7llOnh2iU2/f99ndZovSboVYetDPhYkk+ak0hl/n5b9ZJRZTmTsW5yu/9hRf/9bTnPvK47z484tsb3eoVi3mplSCsSDhdIJv/etn+eZvPXPP5zz+4ilOHvNj9R0UTWV4Mk0gGr7nuQDpsTxzjw0RSfjvkvcUQ2dycZRQWGf9yjLjJ6cZmi6QHsvf816O5bCz3eTqzS6rmzbl7eJdMkE8nzniMQmiSCKf4ObFG/Q7vUM91XNdfvSnbzJdcFmYGcxTyxGYSxR56WyXp8/6GZ25e8H8OIKJKKYp02r2abVsjo05dNtdvvFrZ3jmxSkiiYHFresymn5/xXTy5CT5sRidroXjDNoXjhpEoyq1UoPRuTy1vRKFuZFD8gylYjx3VuPMZJOY2SeXHIzjD6H7ji5Op77wGKe/eGrQ7/9AlvJDacAfYm2ljOnXSGUDtObHWb+yzMyUj9Fkl7mxEN/+dpXYUJp+p8fM6TF8hsTsYpZ3ml1sXaO6W7zvvUVZQlIUrE4XAFXXyQ4FWTgWY3syxet/8RYAuuJyfKRHPBzi93/3Mmc/N4NmqBQ3S5/Z/me+MM5srsWb3VMkpBa2K7NR8fHuxT3SQyFUQ6NRqmAE/YeD8/231vnV81C1wwwPRdnfTVDdPeDkE6OEQzKhsE63a6OZOkbQZPHxPB8qJwfbcbZurrO9tIHd6xNKxWhVG6RzQcYmItxerrK/dn+Na2wuSy7nY2W5fuR4NBXGcz32Ng7od0PEUwEmo0UC0RplL8Z2aSCV3HhvC8jhuR79bhczFMS2rMOFShZdIhxgmk0UMcVu3WC/aLF+fZNWrf7J5tz7u0kigbBBaiTL1o3VB7rmnu86BKlAmw+uy/zmv3qWzW2L0kGbZ84H0NQoxc0MsUyEG29dBSCTD1FIWqS0IoGYSaXvJxmUjnw7AMeFUqnHxIkR3v27yl3PnT2/wPHFKK4L771TRDF0BBG6bYvnnksxl65R7lr0bYl6R8Svu1y+VCObNTh7UqfbF4maFvMnMly6YJEK21y/2Sc5mmP/9tbhcwKxCOGwhk93sS2HTr1Jt2MRigfvWlw+xO7KBsfPDJPN+cn+2lO8/eOVQ+ta0VRicR8Bv8SLv/IkjZrF/m4d5z5ByHPPjvDMXANB6PKDD4KEEtFDHfhDLbbX7tKqNQ6v8VwXM2hw8vnj3HpvFRgsVqFElPxkGknwkCUP068xEasQEOtIikNAN/D7P52o/JEAoYCINupnashGljxOPzlK8aBPqdjG6tuMn5ym27XpdW10v4n1MQPrwznTafXQfQoz00GmJgNc0FXyQzqXL1f5yleHub7U49lvnuPKxTV0v49OvUk4EcGn2sTVCpvVIVw
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"display.specshow(example[0])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.axes._subplots.AxesSubplot at 0x7f0f20c61b90>"
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]
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},
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"execution_count": 20,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"display.specshow(example[1])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.7.4"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|