Driver code draft 1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import mne\n",
    "import pandas as pd\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.set_printoptions(threshold=sys.maxsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting EDF parameters from F:\\PY\\Pro\\pro\\BTP_Seizure_Pred\\chb01_04.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-50-6b5873c81120>:1: RuntimeWarning: Channel names are not unique, found duplicates for: {'T8-P8'}. Applying running numbers for duplicates.\n",
      "  edf = mne.io.read_raw_edf('chb01_04.edf')\n"
     ]
    }
   ],
   "source": [
    "\n",
    "edf = mne.io.read_raw_edf('chb01_04.edf')\n",
    "header = ','.join(edf.ch_names)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 4 - 14 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 4.00\n",
      "- Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 3.00 Hz)\n",
      "- Upper passband edge: 14.00 Hz\n",
      "- Upper transition bandwidth: 3.50 Hz (-6 dB cutoff frequency: 15.75 Hz)\n",
      "- Filter length: 423 samples (1.652 sec)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#edf_f = edf.filter(l_freq = 4 , h_freq = 14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#np.savetxt('your_csv_file.csv', edf.get_data().T, delimiter=',', header=header)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#functions for calc of stat features\n",
    "def mean_variance(df):\n",
    "    variance_vals = np.var(df)\n",
    "    return np.mean(variance_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "seizure_start = 1467\n",
    "seizure_end = 1494\n",
    "#window function to work over data and calculate features\n",
    "def add_feature(edf_file,start,duration):\n",
    "    d = edf_file[:,256*start:256*(start+duration)]\n",
    "    row_to_add = []\n",
    "    mean_var = mean_variance(d[0])\n",
    "    \n",
    "    if start in range(seizure_start,(seizure_end,-1)):\n",
    "        t = 1\n",
    "    else:\n",
    "        t = 0\n",
    "    row_to_add.append(mean_var)\n",
    "    row_to_add.append(start)\n",
    "    row_to_add.append(t)\n",
    "    return row_to_add"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame() #columns = ['mean_var','start','target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#driver code\n",
    "start = temp = 1397\n",
    "duration = 10\n",
    "end = 1574\n",
    "while temp<end:\n",
    "    row = add_feature(edf,temp,duration)\n",
    "    df=df.append(pd.Series(row),ignore_index=True)\n",
    "    temp = temp + duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
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       "      <th>1</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>1.010236e-09</td>\n",
       "      <td>1437.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>5</th>\n",
       "      <td>1.088562e-09</td>\n",
       "      <td>1447.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.048230e-09</td>\n",
       "      <td>1457.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>7</th>\n",
       "      <td>1.247817e-08</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.114856e-08</td>\n",
       "      <td>1477.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.315812e-08</td>\n",
       "      <td>1487.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6.696728e-09</td>\n",
       "      <td>1497.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1507.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>3.628476e-09</td>\n",
       "      <td>1517.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>13</th>\n",
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       "      <td>1527.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.761706e-09</td>\n",
       "      <td>1537.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.319366e-09</td>\n",
       "      <td>1547.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.057838e-09</td>\n",
       "      <td>1557.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.199415e-09</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>0.0</td>\n",
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       "5   1.088562e-09  1447.0  0.0\n",
       "6   3.048230e-09  1457.0  0.0\n",
       "7   1.247817e-08  1467.0  1.0\n",
       "8   1.114856e-08  1477.0  1.0\n",
       "9   1.315812e-08  1487.0  1.0\n",
       "10  6.696728e-09  1497.0  0.0\n",
       "11  3.506317e-09  1507.0  0.0\n",
       "12  3.628476e-09  1517.0  0.0\n",
       "13  2.363630e-09  1527.0  0.0\n",
       "14  1.761706e-09  1537.0  0.0\n",
       "15  1.319366e-09  1547.0  0.0\n",
       "16  2.057838e-09  1557.0  0.0\n",
       "17  1.199415e-09  1567.0  0.0"
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     "execution_count": 72,
     "metadata": {},
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_f = pd.DataFrame()\n",
    "start = temp = 2956\n",
    "duration = 10\n",
    "end = 3076\n",
    "while temp<end:\n",
    "    row = add_feature(edf_f,temp,duration)\n",
    "    df_f=df_f.append(pd.Series(row),ignore_index=True)\n",
    "    temp = temp + duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
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       "      <th>4</th>\n",
       "      <td>1.965291e-09</td>\n",
       "      <td>2996.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.459066e-09</td>\n",
       "      <td>3006.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.538410e-09</td>\n",
       "      <td>3016.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.361507e-09</td>\n",
       "      <td>3026.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>3036.0</td>\n",
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       "      <td>5.261949e-10</td>\n",
       "      <td>3056.0</td>\n",
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       "      <th>11</th>\n",
       "      <td>4.212931e-10</td>\n",
       "      <td>3066.0</td>\n",
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       "3   3.188420e-10  2986.0\n",
       "4   1.965291e-09  2996.0\n",
       "5   2.459066e-09  3006.0\n",
       "6   2.538410e-09  3016.0\n",
       "7   1.361507e-09  3026.0\n",
       "8   9.248479e-10  3036.0\n",
       "9   9.684857e-10  3046.0\n",
       "10  5.261949e-10  3056.0\n",
       "11  4.212931e-10  3066.0"
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     "execution_count": 33,
     "metadata": {},
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    }
   ],
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    "df_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2100\n"
     ]
    }
   ],
   "source": [
    "f = 2095\n",
    "print(round(f,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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