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peace098beat/pandas-signalprocessing.ipynb

Created November 27, 2015 08:36
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[Pandas] 信号データの呼び出し
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pandas-signalprocessing.ipynb
{
"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",
" </tr>\n",
" <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",
" </tr>\n",
" <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>"
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"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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"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
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