{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ThorDelayAnalysis\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Common delay I/O\n",
    "---\n",
    "\\begin{equation}\n",
    "    \\displaystyle y(n) = \\sum_{m = 0}^{M} a^{m} x(n - md)\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Thor's delay\n",
    "---\n",
    "### Parameters\n",
    "![DelayPanel](Fig\\DelayPanel)\n",
    "\n",
    "|Name|Unit|Min val|Max val|\n",
    "|:--:|:--:|:--:|:--:|\n",
    "|TIME|ms|0|1000|\n",
    "|F.BACK|-|0|127|\n",
    "|RATE|Hz|0.14|18.2|\n",
    "|AMT|-|0|127|\n",
    "|D.WET|-|0|127|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rec IR\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create input inpulse train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0         1.0\n",
      "48000     1.0\n",
      "96000     1.0\n",
      "144000    1.0\n",
      "192000    1.0\n",
      "240000    1.0\n",
      "288000    1.0\n",
      "336000    1.0\n",
      "384000    1.0\n",
      "432000    1.0\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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ifWYO93hKAMzux41OJiJWMv6SEieffPIB3cYrFs3l9182n5/vfrqXU+vKndsf4/gj57Dk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\ngSBJKgwESRIA/weaYdclRoTWdAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x158e7086240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "\n",
    "#samplerate = 48 kHz, period = 1 sec, signal_length = 10 sec\n",
    "fs = 48000\n",
    "period = fs\n",
    "length = period * 10 #10 sec\n",
    "\n",
    "#Create IT and show\n",
    "inpulse_train = np.arange(length) % period < 1\n",
    "inpulse_train = inpulse_train.astype(np.float)\n",
    "\n",
    "ser = pd.Series(inpulse_train)\n",
    "ser = ser[inpulse_train > 0]\n",
    "print(ser)\n",
    "\n",
    "plt.plot(range(inpulse_train.size), inpulse_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0         0.999969\n",
      "48000     0.999969\n",
      "96000     0.999969\n",
      "144000    0.999969\n",
      "192000    0.999969\n",
      "240000    0.999969\n",
      "288000    0.999969\n",
      "336000    0.999969\n",
      "384000    0.999969\n",
      "432000    0.999969\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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ifWYO93hKAMzux41OJiJWMv6SEieffPIB3cYrFs3l9182n5/vfrqXU+vKndsf4/gj57Dk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\ngSBJKgwESRIA/weaYdclRoTWdAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x158e71d7550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import soundfile as sf\n",
    "\n",
    "#save as wavefile\n",
    "sf.write(\"ImpulseTrain_fs48kHz_p1sec_len10sec.wav\", inpulse_train, int(fs))\n",
    "\n",
    "#load wav and show\n",
    "inpulse_wav, _ = sf.read(\"ImpulseTrain_fs48kHz_p1sec_len10sec.wav\")\n",
    "\n",
    "ser = pd.Series(inpulse_wav)\n",
    "ser = ser[inpulse_wav > 0]\n",
    "print(ser)\n",
    "\n",
    "plt.plot(range(inpulse_wav.size), inpulse_wav)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IR rec conditions\n",
    "InputSignal: Impulse Train (freq. 1 Hz, fs 48 kHz, bit 16, len 10 sec, Mono)  \n",
    "OutSignal: Input(Mono) -> Thor Filter3 (L & R) -> Thor Delay -> Out (Stereo)\n",
    "\n",
    "![BackPanel](Fig\\BackPanel)\n",
    "![Looting](Fig\\Looting)\n",
    "\n",
    "## Analysis of \"TIME\" parameter dependence\n",
    "---\n",
    "### Mesurement conditions\n",
    "|Entry|TIME|F.BACK|RATE|AMT|D.WET|\n",
    "|:--:|:--:|:--:|:--:|:--:|:--:|\n",
    "|T_0|0|0|0.14|0|0|\n",
    "|T_1|0|0|0.14|0|127|\n",
    "|T_2|197|0|0.14|0|127|\n",
    "|T_3|402|0|0.14|0|127|\n",
    "|T_4|606|0|0.14|0|127|\n",
    "|T_5|803|0|0.14|0|127|\n",
    "\n",
    "![InpulseRec](Fig\\InpulseRec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>msec</th>\n",
       "      <th>sample</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T_1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T_2</th>\n",
       "      <td>197</td>\n",
       "      <td>9456.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T_3</th>\n",
       "      <td>402</td>\n",
       "      <td>19296.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T_4</th>\n",
       "      <td>606</td>\n",
       "      <td>29088.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T_5</th>\n",
       "      <td>803</td>\n",
       "      <td>38544.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     msec   sample\n",
       "T_1     0      0.0\n",
       "T_2   197   9456.0\n",
       "T_3   402  19296.0\n",
       "T_4   606  29088.0\n",
       "T_5   803  38544.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def msec_to_sample_fs48kHz(t_msec):\n",
    "    return t_msec / 1000.0 * 48000\n",
    "\n",
    "T_msec = [0, 197, 402, 606, 803]\n",
    "T_samp = [msec_to_sample_fs48kHz(x) for x in T_msec]\n",
    "\n",
    "index_label = [\"T_1\", \"T_2\", \"T_3\", \"T_4\", \"T_5\"]\n",
    "df = pd.DataFrame({\"msec\": T_msec, \"sample\": T_samp}, index = index_label)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis of IRs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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B5GHwz0EIycvD55g6UggEUbbz6PYq80QyclgIJfsYc1WUjhs3Tm1tbaUp1Jxz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x158855d7358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import soundfile as sf\n",
    "%matplotlib inline\n",
    "\n",
    "def extract_left_channel(stereo_wav, _):\n",
    "    return stereo_wav[:, 0] \n",
    "\n",
    "input_inpulse, fs = sf.read(\"ImpulseTrain_fs48kHz_p1sec_len10sec.wav\")\n",
    "T_data = []\n",
    "T_data.append(extract_left_channel(*sf.read(\"IR\\T_0.wav\")))\n",
    "T_data.append(extract_left_channel(*sf.read(\"IR\\T_1.wav\")))\n",
    "T_data.append(extract_left_channel(*sf.read(\"IR\\T_2.wav\")))\n",
    "T_data.append(extract_left_channel(*sf.read(\"IR\\T_3.wav\")))\n",
    "T_data.append(extract_left_channel(*sf.read(\"IR\\T_4.wav\")))\n",
    "T_data.append(extract_left_channel(*sf.read(\"IR\\T_5.wav\")))\n",
    "\n",
    "plt.figure(figsize = (16, 4))\n",
    "plt.plot(range(input_inpulse.size), input_inpulse, label = \"Input\")\n",
    "for num, data in enumerate(T_data):\n",
    "    label_name = \"T_\" + str(num)\n",
    "    plt.plot(range(data.size), data, label = label_name)\n",
    "plt.xlim(-fs, fs * 10)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0-1周期目は安定していない？  \n",
    "3周期目を取り出して、各IRの遅延サンプル時間を特定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0         0.999969\n",
      "4593      0.519989\n",
      "48005     0.971466\n",
      "48006     0.027710\n",
      "96002     0.999969\n",
      "144002    0.999969\n",
      "192002    0.999969\n",
      "240002    0.999969\n",
      "288002    0.999969\n",
      "336002    0.999969\n",
      "384002    0.999969\n",
      "432002    0.999969\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x158e7ba5e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>2</th>\n",
       "      <th>9448</th>\n",
       "      <th>9449</th>\n",
       "      <th>19275</th>\n",
       "      <th>19276</th>\n",
       "      <th>29102</th>\n",
       "      <th>29103</th>\n",
       "      <th>38551</th>\n",
       "      <th>38552</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.999969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.999969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.207031</td>\n",
       "      <td>0.792938</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.457031</td>\n",
       "      <td>0.542938</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.832001</td>\n",
       "      <td>0.167969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.976532</td>\n",
       "      <td>0.023438</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0         2         9448      9449      19275     19276     29102  \\\n",
       "0  0.999969       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN  0.999969       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN  0.207031  0.792938       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN  0.457031  0.542938       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN  0.832001   \n",
       "5       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "      29103     38551     38552  \n",
       "0       NaN       NaN       NaN  \n",
       "1       NaN       NaN       NaN  \n",
       "2       NaN       NaN       NaN  \n",
       "3       NaN       NaN       NaN  \n",
       "4  0.167969       NaN       NaN  \n",
       "5       NaN  0.976532  0.023438  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def extract_cycle(IR_list, cycle):\n",
    "    new_data_set = []\n",
    "    st = fs * cycle\n",
    "    en = st + fs\n",
    "    \n",
    "    for data in IR_list:\n",
    "        new_data_set.append(data[st : en])\n",
    "        \n",
    "    return new_data_set\n",
    "\n",
    "def extract_position_intensity(IR, threshold):\n",
    "    s = pd.Series(IR)\n",
    "    return s[IR > threshold] \n",
    "\n",
    "print(extract_position_intensity(T_data[1], 0))\n",
    "\n",
    "#第3周期のIRを抽出\n",
    "T_extract = extract_cycle(T_data, 3)\n",
    "\n",
    "for num, data in enumerate(T_extract):\n",
    "    label_name = \"T_\" + str(num)\n",
    "    plt.plot(range(data.size), data)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#0を除外して、整理\n",
    "T_ser = []\n",
    "for data in T_extract:\n",
    "    T_ser.append(extract_position_intensity(data, 0))\n",
    "\n",
    "df = pd.DataFrame(T_ser)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "右チャンネル出力についても同様に解析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4HFNHCoEgynYe3V5lnkhGDguhZB9jrorSCRMmqL29vTSFmnNO7e3tmjBhQuxQ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\nKEoBAAAAANFQlAIAAAAAovn/JuqXBxqfJ40AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x158855d7748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0         0.999969\n",
      "4593      0.540009\n",
      "48005     0.971466\n",
      "48006     0.027740\n",
      "96002     0.999969\n",
      "144002    0.999969\n",
      "192002    0.999969\n",
      "240002    0.999969\n",
      "288002    0.999969\n",
      "336002    0.999969\n",
      "384002    0.999969\n",
      "432002    0.999969\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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5yTXdtjclSdd+TJL3dO2XJ9k2y/HdmyQnJvlkkuuS7Enysq59w48/ybFJvpjky93Y/6Fr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\nDTMEJKlhhoAkNez/ASi91Y/hBNarAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x158854ed940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>2</th>\n",
       "      <th>9448</th>\n",
       "      <th>9449</th>\n",
       "      <th>19275</th>\n",
       "      <th>19276</th>\n",
       "      <th>29102</th>\n",
       "      <th>29103</th>\n",
       "      <th>38551</th>\n",
       "      <th>38552</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.999969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.999969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.207031</td>\n",
       "      <td>0.792938</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.457031</td>\n",
       "      <td>0.542938</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.832001</td>\n",
       "      <td>0.167969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.976532</td>\n",
       "      <td>0.023438</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0         2         9448      9449      19275     19276     29102  \\\n",
       "0  0.999969       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN  0.999969       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN  0.207031  0.792938       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN  0.457031  0.542938       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN  0.832001   \n",
       "5       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "      29103     38551     38552  \n",
       "0       NaN       NaN       NaN  \n",
       "1       NaN       NaN       NaN  \n",
       "2       NaN       NaN       NaN  \n",
       "3       NaN       NaN       NaN  \n",
       "4  0.167969       NaN       NaN  \n",
       "5       NaN  0.976532  0.023438  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import soundfile as sf\n",
    "%matplotlib inline\n",
    "\n",
    "def extract_right_channel(stereo_wav, _):\n",
    "    return stereo_wav[:, 1] \n",
    "\n",
    "input_inpulse, fs = sf.read(\"ImpulseTrain_fs48kHz_p1sec_len10sec.wav\")\n",
    "T_data = []\n",
    "T_data.append(extract_right_channel(*sf.read(\"IR\\T_0.wav\")))\n",
    "T_data.append(extract_right_channel(*sf.read(\"IR\\T_1.wav\")))\n",
    "T_data.append(extract_right_channel(*sf.read(\"IR\\T_2.wav\")))\n",
    "T_data.append(extract_right_channel(*sf.read(\"IR\\T_3.wav\")))\n",
    "T_data.append(extract_right_channel(*sf.read(\"IR\\T_4.wav\")))\n",
    "T_data.append(extract_right_channel(*sf.read(\"IR\\T_5.wav\")))\n",
    "\n",
    "plt.figure(figsize = (16, 4))\n",
    "plt.plot(range(input_inpulse.size), input_inpulse, label = \"Input\")\n",
    "for num, data in enumerate(T_data):\n",
    "    label_name = \"T_\" + str(num)\n",
    "    plt.plot(range(data.size), data, label = label_name)\n",
    "plt.xlim(-fs, fs * 10)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "def extract_cycle(IR_list, cycle):\n",
    "    new_data_set = []\n",
    "    st = fs * cycle\n",
    "    en = st + fs\n",
    "    \n",
    "    for data in IR_list:\n",
    "        new_data_set.append(data[st : en])\n",
    "        \n",
    "    return new_data_set\n",
    "\n",
    "def extract_position_intensity(IR, threshold):\n",
    "    s = pd.Series(IR)\n",
    "    return s[IR > threshold] \n",
    "\n",
    "print(extract_position_intensity(T_data[1], 0))\n",
    "\n",
    "#第3周期のIRを抽出\n",
    "T_extract = extract_cycle(T_data, 3)\n",
    "\n",
    "for num, data in enumerate(T_extract):\n",
    "    label_name = \"T_\" + str(num)\n",
    "    plt.plot(range(data.size), data)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#0を除外して、整理\n",
    "T_ser = []\n",
    "for data in T_extract:\n",
    "    T_ser.append(extract_position_intensity(data, 0))\n",
    "\n",
    "df = pd.DataFrame(T_ser)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Measured vs calced delay value\n",
    "左右チャンネルで同様の結果、この時点でステレオ性なし  \n",
    "2周期目以降のIRの遅延サンプル時間は安定  \n",
    "遅延時間については、計算値とややぶれあり\n",
    "\n",
    "\n",
    "|Entry|mesured|calced|\n",
    "|:--:|:--:|:--:|\n",
    "|T_1|2.0|0.0|\n",
    "|T_2|9448.5|9456.0|\n",
    "|T_3|19275.5|19296.0|\n",
    "|T_4|29102.5|29088.0|\n",
    "|T_5|38551.5|38544.0|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of \"F.Back\" parameter dependence\n",
    "---\n",
    "\n",
    "### Mesurement conditions\n",
    "|Entry|TIME|F.BACK|RATE|AMT|D.WET|\n",
    "|:--:|:--:|:--:|:--:|:--:|:--:|\n",
    "|F_0|197|0|0.14|0|127|\n",
    "|F_1|197|20|0.14|0|127|\n",
    "|F_2|197|40|0.14|0|127|\n",
    "|F_3|197|60|0.14|0|127|\n",
    "|F_4|197|80|0.14|0|127|\n",
    "|F_5|197|100|0.14|0|127|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis of IRs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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G6H6TH7JhH76GucVKKhHFPHcP7/+pP/fhb9EQvKQAsaaS+FEXcDbqQlZiG9TN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\nhBpEnLsgCEIN8v8BVdGfdFNQk4IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1588552e0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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       "      <td>0.106110</td>\n",
       "      <td>0.358490</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.065491</td>\n",
       "      <td>0.147522</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.060699</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.142639</td>\n",
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       "      8693      8694      9448      9449      18142     18143     18897  \\\n",
       "0       NaN       NaN  0.128906  0.871063       NaN       NaN       NaN   \n",
       "1       NaN       NaN  0.128906  0.871063       NaN       NaN       NaN   \n",
       "2       NaN       NaN  0.128906  0.871063       NaN       NaN  0.070740   \n",
       "3       NaN       NaN  0.128906  0.871063       NaN       NaN  0.106110   \n",
       "4       NaN       NaN  0.128906  0.871063       NaN       NaN  0.141449   \n",
       "5  0.117401  0.132233  0.128906  0.871063  0.093964  0.090698  0.176819   \n",
       "\n",
       "      18898     27591     27592     28346     28347     37040     37795  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1  0.119507       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2  0.238983       NaN       NaN       NaN  0.065582       NaN       NaN   \n",
       "3  0.358490       NaN       NaN  0.065491  0.147522       NaN       NaN   \n",
       "4  0.477966       NaN       NaN  0.116425  0.262268       NaN  0.085175   \n",
       "5  0.597473  0.073639  0.062225  0.181915  0.409790  0.056824  0.166382   \n",
       "\n",
       "      37796     47244     47245  \n",
       "0       NaN       NaN       NaN  \n",
       "1       NaN       NaN       NaN  \n",
       "2       NaN       NaN       NaN  \n",
       "3  0.060699       NaN       NaN  \n",
       "4  0.143921  0.058441  0.078979  \n",
       "5  0.281097  0.142639  0.192810  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import soundfile as sf\n",
    "%matplotlib inline\n",
    "\n",
    "def extract_left_channel(stereo_wav, _):\n",
    "    return stereo_wav[:, 0] \n",
    "\n",
    "input_inpulse, fs = sf.read(\"ImpulseTrain_fs48kHz_p1sec_len10sec.wav\")\n",
    "T_data = []\n",
    "T_data.append(extract_left_channel(*sf.read(\"IR\\F_0.wav\")))\n",
    "T_data.append(extract_left_channel(*sf.read(\"IR\\F_1.wav\")))\n",
    "T_data.append(extract_left_channel(*sf.read(\"IR\\F_2.wav\")))\n",
    "T_data.append(extract_left_channel(*sf.read(\"IR\\F_3.wav\")))\n",
    "T_data.append(extract_left_channel(*sf.read(\"IR\\F_4.wav\")))\n",
    "T_data.append(extract_left_channel(*sf.read(\"IR\\F_5.wav\")))\n",
    "\n",
    "#plt.figure(figsize = (16, 4))\n",
    "plt.plot(range(input_inpulse.size), input_inpulse, label = \"Input\")\n",
    "for num, data in enumerate(T_data):\n",
    "    label_name = \"T_\" + str(num)\n",
    "    plt.plot(range(data.size), data, label = label_name)\n",
    "plt.xlim(fs * 2, fs * 10)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "def extract_cycle(IR_list, cycle):\n",
    "    new_data_set = []\n",
    "    st = fs * cycle\n",
    "    en = st + fs\n",
    "    \n",
    "    for data in IR_list:\n",
    "        new_data_set.append(data[st : en])\n",
    "        \n",
    "    return new_data_set\n",
    "\n",
    "def extract_position_intensity(IR, threshold):\n",
    "    s = pd.Series(IR)\n",
    "    return s[IR > threshold] \n",
    "\n",
    "#第3周期のIRを抽出\n",
    "T_extract = extract_cycle(T_data, 3)\n",
    "\n",
    "for num, data in enumerate(T_extract):\n",
    "    label_name = \"T_\" + str(num)\n",
    "    plt.plot(range(data.size), data)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#0を除外して、整理\n",
    "T_ser = []\n",
    "for data in T_extract:\n",
    "    T_ser.append(extract_position_intensity(data, 0.05))\n",
    "\n",
    "df = pd.DataFrame(T_ser)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x15882480898>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dknQDjb5+oKoeBR5dMnak5/X/Y/F0jSRpBPgJVUlqIctdklrIcpekFrLcJamFLHdJaiHL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\nkOUuSS1kuUtSC1nuktRC/x+rpZbdgFDBDAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x158855595c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "x = np.array([0, 20, 40, 60, 80, 100]) / 127.0\n",
    "y = np.array([0, 0.12, 0.31, 0.46, 0.62, 0.77])\n",
    "\n",
    "plt.scatter(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}