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November 27, 2015 08:36
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[Pandas] 信号データの呼び出し
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# Pandasで信号のロード" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"%matplotlib inline" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"import pandas as pd\n", | |
"import numpy as np" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 39, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"import os\n", | |
"import sys" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 40, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"pd.options.display.max_rows=5" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 41, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"# カレントパスの取得\n", | |
"BASE_DIR = os.path.dirname(sys.argv[0])\n", | |
"# パスの追加\n", | |
"sys.path.append('E:\\Python\\GolfClassification/')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 42, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"from FiSig.AudioManager import AudioManager" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 82, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"['./wav/audio1.wav', './wav/audio2.wav', './wav/audio3.wav']" | |
] | |
}, | |
"execution_count": 82, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"audiofile_path_s = ['./wav/audio1.wav', './wav/audio2.wav', './wav/audio3.wav']\n", | |
"audiofile_path_s" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 84, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"['audio1.wav', 'audio2.wav', 'audio3.wav']" | |
] | |
}, | |
"execution_count": 84, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"audiofile_basename_s = [os.path.basename(path) for path in audiofile_path_s]\n", | |
"audiofile_basename_s" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 86, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"['audio1', 'audio2', 'audio3']" | |
] | |
}, | |
"execution_count": 86, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"audioname_s = [os.path.splitext(basename)[0] for basename in audiofile_basename_s]\n", | |
"audioname_s" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 87, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"\n", | |
"-------------------------\n", | |
"ファイル名: audio1.wav\n", | |
"チャンネル数: 2\n", | |
"サンプル[Byte]: 2\n", | |
"サンプリング周波数: 48000\n", | |
"フレーム数: 24001\n", | |
"パラメータ: (2, 2, 48000, 24001, 'NONE', 'not compressed')\n", | |
"長さ(秒): 0.500020833333\n", | |
"振幅幅 32767\n", | |
"-------------------------\n", | |
"\n", | |
"\n", | |
"-------------------------\n", | |
"ファイル名: audio2.wav\n", | |
"チャンネル数: 2\n", | |
"サンプル[Byte]: 2\n", | |
"サンプリング周波数: 48000\n", | |
"フレーム数: 24001\n", | |
"パラメータ: (2, 2, 48000, 24001, 'NONE', 'not compressed')\n", | |
"長さ(秒): 0.500020833333\n", | |
"振幅幅 32767\n", | |
"-------------------------\n", | |
"\n", | |
"\n", | |
"-------------------------\n", | |
"ファイル名: audio3.wav\n", | |
"チャンネル数: 2\n", | |
"サンプル[Byte]: 2\n", | |
"サンプリング周波数: 48000\n", | |
"フレーム数: 24001\n", | |
"パラメータ: (2, 2, 48000, 24001, 'NONE', 'not compressed')\n", | |
"長さ(秒): 0.500020833333\n", | |
"振幅幅 32767\n", | |
"-------------------------\n", | |
"\n" | |
] | |
} | |
], | |
"source": [ | |
"audio_manager_s = [AudioManager(f) for f in audiofile_path_s]" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 88, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"[array([ -2.44148076e-04, -1.83111057e-04, -9.15555284e-05, ...,\n", | |
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]),\n", | |
" array([ -9.15555284e-05, -1.22074038e-04, -6.10370190e-05, ...,\n", | |
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]),\n", | |
" array([ 0.00012207, 0.00030519, 0.00030519, ..., 0. ,\n", | |
" 0. , 0. ])]" | |
] | |
}, | |
"execution_count": 88, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"audio_data_s = [am.getData(mode='norm') for am in audio_manager_s]\n", | |
"audio_data_s" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 89, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"fs = audio_manager_s[0].getFs()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 90, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"24001L" | |
] | |
}, | |
"execution_count": 90, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"N = audio_data_s[0].shape[0]\n", | |
"N" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## PandasのDataFrameを使ってみる" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 94, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"{'audio1': array([ -2.44148076e-04, -1.83111057e-04, -9.15555284e-05, ...,\n", | |
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]),\n", | |
" 'audio2': array([ -9.15555284e-05, -1.22074038e-04, -6.10370190e-05, ...,\n", | |
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]),\n", | |
" 'audio3': array([ 0.00012207, 0.00030519, 0.00030519, ..., 0. ,\n", | |
" 0. , 0. ])}" | |
] | |
}, | |
"execution_count": 94, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"table_dict = dict(zip(audioname_s, audio_data_s))\n", | |
"table_dict" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 95, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"(24001L,)" | |
] | |
}, | |
"execution_count": 95, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"trange = np.linspace(0, N/float(fs), N)\n", | |
"trange.shape" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 96, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/html": [ | |
"<div>\n", | |
"<table border=\"1\" class=\"dataframe\">\n", | |
" <thead>\n", | |
" <tr style=\"text-align: right;\">\n", | |
" <th></th>\n", | |
" <th>audio1</th>\n", | |
" <th>audio2</th>\n", | |
" <th>audio3</th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>0.000000</th>\n", | |
" <td>-0.000244</td>\n", | |
" <td>-0.000092</td>\n", | |
" <td>0.000122</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>0.000021</th>\n", | |
" <td>-0.000183</td>\n", | |
" <td>-0.000122</td>\n", | |
" <td>0.000305</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>...</th>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>0.500000</th>\n", | |
" <td>0.000000</td>\n", | |
" <td>0.000000</td>\n", | |
" <td>0.000000</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>0.500021</th>\n", | |
" <td>0.000000</td>\n", | |
" <td>0.000000</td>\n", | |
" <td>0.000000</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"<p>24001 rows × 3 columns</p>\n", | |
"</div>" | |
], | |
"text/plain": [ | |
" audio1 audio2 audio3\n", | |
"0.000000 -0.000244 -0.000092 0.000122\n", | |
"0.000021 -0.000183 -0.000122 0.000305\n", | |
"... ... ... ...\n", | |
"0.500000 0.000000 0.000000 0.000000\n", | |
"0.500021 0.000000 0.000000 0.000000\n", | |
"\n", | |
"[24001 rows x 3 columns]" | |
] | |
}, | |
"execution_count": 96, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"df = pd.DataFrame(table_dict, trange)\n", | |
"df" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 98, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/html": [ | |
"<div>\n", | |
"<table border=\"1\" class=\"dataframe\">\n", | |
" <thead>\n", | |
" <tr style=\"text-align: right;\">\n", | |
" <th></th>\n", | |
" <th>audio2</th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>0.000000</th>\n", | |
" <td>-0.000092</td>\n", | |
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" <tr>\n", | |
" <th>0.000021</th>\n", | |
" <td>-0.000122</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>...</th>\n", | |
" <td>...</td>\n", | |
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" <tr>\n", | |
" <th>0.500000</th>\n", | |
" <td>0.000000</td>\n", | |
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" <tr>\n", | |
" <th>0.500021</th>\n", | |
" <td>0.000000</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"<p>24001 rows × 1 columns</p>\n", | |
"</div>" | |
], | |
"text/plain": [ | |
" audio2\n", | |
"0.000000 -0.000092\n", | |
"0.000021 -0.000122\n", | |
"... ...\n", | |
"0.500000 0.000000\n", | |
"0.500021 0.000000\n", | |
"\n", | |
"[24001 rows x 1 columns]" | |
] | |
}, | |
"execution_count": 98, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"df[[1]]" | |
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{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": { | |
"collapsed": true | |
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{ | |
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} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python 2", | |
"language": "python", | |
"name": "python2" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 2 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython2", | |
"version": "2.7.10" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 0 | |
} |
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