104 lines
3 KiB
Plaintext
104 lines
3 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": 1,
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"id": "3ae332a0",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np"
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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": 5,
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"id": "aa7bbab6",
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"sound_data = np.random.rand(100)"
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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": 6,
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"id": "626eafc7",
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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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"array([0.66709183, 0.55973494, 0.95416669, 0.60810949, 0.05188879,\n",
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" 0.58619063, 0.25555136, 0.72451477, 0.2646681 , 0.08694215,\n",
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" 0.75592186, 0.67261696, 0.62847452, 0.06232598, 0.20549438,\n",
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" 0.11718457, 0.25184725, 0.48625729, 0.8103058 , 0.18100915,\n",
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" 0.81113341, 0.62055231, 0.9046905 , 0.56664205, 0.73235338,\n",
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" 0.74382869, 0.64856368, 0.80644398, 0.46199345, 0.78516632,\n",
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" 0.91298397, 0.48290914, 0.20847714, 0.99162659, 0.26374781,\n",
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" 0.3602381 , 0.07173351, 0.8584085 , 0.32248766, 0.39167573,\n",
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" 0.67944923, 0.00930429, 0.21714217, 0.58810089, 0.17668711,\n",
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" 0.57444803, 0.25760187, 0.43785728, 0.39119371, 0.68268063,\n",
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" 0.95954499, 0.45934239, 0.03616905, 0.23896063, 0.61872801,\n",
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" 0.76332531, 0.96272817, 0.57169277, 0.50225193, 0.01361629,\n",
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" 0.15357459, 0.8057233 , 0.0642748 , 0.95013941, 0.38712684,\n",
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" 0.97231498, 0.20261775, 0.74184693, 0.26629893, 0.84672705,\n",
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" 0.67662718, 0.96055977, 0.64942314, 0.66487937, 0.86867536,\n",
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" 0.40815661, 0.1139344 , 0.95638066, 0.87436447, 0.18407227,\n",
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" 0.64457074, 0.19233097, 0.24012179, 0.90399279, 0.39093908,\n",
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" 0.26389161, 0.97537645, 0.14209784, 0.75261696, 0.10078122,\n",
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" 0.87468408, 0.77990102, 0.92983283, 0.45841805, 0.61470669,\n",
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" 0.87939755, 0.09266009, 0.41177209, 0.46973971, 0.43152144])"
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]
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},
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"execution_count": 6,
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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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"source": [
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"sound_data"
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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": null,
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"id": "ef55bee9",
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"metadata": {},
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"outputs": [],
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"source": [
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"synonyms = {\n",
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" 'hot': ['blazing', 'boiling', 'heated'],\n",
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" 'airplane': ['aircraft', 'airliner', \n",
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" 'cab', 'jet', 'plane'],\n",
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" 'beach': ['coast', 'shore', 'waterfront'],\n",
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" # ...\n",
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"}"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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