Simple_Sin_Signal_for_ML.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "#%matplotlib notebook\n",
    "matplotlib.use('Qt4Agg')\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import integrate, interpolate, signal, optimize, stats\n",
    "\n",
    "import cPickle as pickle\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "import keras\n",
    "from keras.models import Sequential, load_model\n",
    "from keras.layers import Dense, Conv2D, MaxPool2D, Dropout, BatchNormalization, Flatten\n",
    "from keras.optimizers import Nadam, SGD\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "\n",
    "\n",
    "np.set_printoptions(edgeitems=30, linewidth=160)\n",
    "#edgeitems: Number of array items in summary at beginning and end of each dimension (default 3).\n",
    "#linewidth : The number of characters per line for the purpose of inserting line breaks (default 75)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This Part of The Code Reads The Noise Curve Files For The Selected Detectors  \n",
    "#help(lal.ArrivalTimeDiff)\n",
    "def ASDtxt(x):\n",
    "    return {\n",
    "        'LET':'./ASD/ET_D.txt',\n",
    "        'LCE':'./ASD/CE.txt',\n",
    "        'H1': './ASD/ligoII_NS.txt',\n",
    "        'L1': './ASD/ligoII_NS.txt',\n",
    "        'V1': './ASD/virgoII.txt',\n",
    "        'I2': './ASD/ligoII_NS.txt',\n",
    "        'KAGRA': './ASD/ligoII_NS.txt',\n",
    "        'ET_1': './ASD/ET_D.txt',\n",
    "        'ET_2': './ASD/ET_D.txt',\n",
    "        'ET_3': './ASD/ET_D.txt',\n",
    "        'A2': './ASD/ligoII_NS.txt',\n",
    "        'A2.5': './ASD/ligoII_NS.txt',\n",
    "    }[x]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading the noise curves.\n",
    "def readnos(detector, f_points):\n",
    "    nos_file = ASDtxt(detector)\n",
    "    f_str = []\n",
    "    ASD_str = []\n",
    "    file = open(nos_file, 'r')\n",
    "    readFile = file.readlines()\n",
    "    file.close()\n",
    "    f = []\n",
    "    ASD = []\n",
    "    \n",
    "    for line in readFile:\n",
    "        p = line.split()\n",
    "        f_str.append(float(p[0]))\n",
    "        ASD_str.append(float(p[1]))\n",
    "    f = np.log10(np.array(f_str))\n",
    "    ASD = np.log10(np.array(ASD_str))\n",
    "    nosinterpolate = interpolate.splrep(f, ASD, w=1.0*np.ones(len(ASD)), s=0)\n",
    "    \n",
    "    nos = interpolate.splev(np.log10(f_points), nosinterpolate, der = 0, ext = 3)\n",
    "    nos = 10**nos\n",
    "    \n",
    "    return nos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simsinsignal(t_samples, NO_sin, A_low, A_high, f_low, f_high, phase_low, phase_high, index):\n",
    "    \n",
    "    \n",
    "    \n",
    "    Amplitudes = np.random.uniform(A_low, A_high, NO_sin)\n",
    "    Frequencies = np.random.uniform(f_low, f_high, NO_sin)\n",
    "    Phases = np.random.uniform(phase_low, phase_high, NO_sin)\n",
    "    \n",
    "    if index == 0:\n",
    "        msg = 'Each signal generated will contain %s simple sinusoidal signal components.' %(NO_sin) \n",
    "        print(msg)\n",
    "        msg = 'The amplitudes are as follows:'\n",
    "        print(msg)\n",
    "        print(Amplitudes[0])\n",
    "        msg = 'The frequencies are as follows:'\n",
    "        print(msg)\n",
    "        print(Frequencies[0])\n",
    "        msg = 'The phases are as follows:'\n",
    "        print(msg)\n",
    "        print(Phases[0])\n",
    "        print('\\n')\n",
    "    \n",
    "    signal = np.zeros_like(t_samples)\n",
    "    \n",
    "    for i in range(NO_sin):\n",
    "        signal += Amplitudes[i] * np.sin(2.0 * np.pi * Frequencies[i] * t_samples + Phases[i])\n",
    "    return signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def noisegenerator(Tobs, det, SR, df, dt, ASD):\n",
    "    Ns = Tobs * SR\n",
    "    Nf = int(np.ceil(Ns /2) + 1)\n",
    "    \n",
    "    Amp = ASD\n",
    "    \n",
    "    real_nos = Amp * np.random.normal(0.0, 1.0, Nf)\n",
    "    img_nos = Amp * np.random.normal(0.0, 1.0, Nf)\n",
    "    \n",
    "    nos = real_nos + 1j * img_nos\n",
    "    \n",
    "    idx_1 = int(20 // df)\n",
    "    nos[0:idx_1] = 0\n",
    "    \n",
    "    \n",
    "    \n",
    "    nos_realization = np.fft.irfft(nos) * df\n",
    "    \n",
    "    return nos_realization\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#whole signal length always longer than the signal itself so that the signal can shift\n",
    "def shift_signal(nos_realization, SD, shift_percent, signal, shift_applied):\n",
    "    \n",
    "    Tobs_mid = len(nos_realization) // 2 + 1\n",
    "    \n",
    "    signal_mid = SD / 2\n",
    "    \n",
    "    signal_len = len(signal)\n",
    "    \n",
    "    \n",
    "    max_shift_allowed = Tobs * shift_percent\n",
    "    if shift_applied == True:\n",
    "        actual_shift = np.random.uniform(Tobs_mid - max_shift_allowed, max_shift_allowed + Tobs_mid) - signal_mid\n",
    "    else:\n",
    "        actual_shift = Tobs_mid - signal_mid\n",
    "        \n",
    "    \n",
    "    shifted_signal = nos_realization\n",
    "    \n",
    "    shifted_signal[actual_shift : actual_shift + signal_len] += signal\n",
    "    \n",
    "    return shifted_signal\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def signal_generator(NO_sin, No_signals, No_realizations, shift_applied, usingASD, Detectors, SR, Tobs, \n",
    "                     SD, seed, shift_percent, A_low, A_high, f_low, f_high, phase_low, phase_high):\n",
    "    \n",
    "    np.random.seed(seed)\n",
    "    msg = 'This generator uses seed = %s for random number generator in python.' % (seed)\n",
    "    print(msg)\n",
    "    \n",
    "    total_number_of_time_series = int(No_signals * No_realizations * 2)\n",
    "    msg = 'The No. of different signals in the data will be %s.' %(No_signals)\n",
    "    print(msg)\n",
    "\n",
    "    msg = 'The No. of nosie realizations is %s, of which the first %s will be mixed with signals later.' %(total_number_of_time_series , No_signals * No_realizations)\n",
    "    print(msg)\n",
    "    \n",
    "    N = Tobs * SR\n",
    "    dt = 1.0 / SR\n",
    "    ts = np.arange(int(N)) * dt\n",
    "    df = 1.0 / ts[-1]\n",
    "    \n",
    "    Nf = int(N//2) + 1\n",
    "    fs = np.arange(Nf) * df\n",
    "    \n",
    "  \n",
    "    \n",
    "    \n",
    "    msg = 'The total observation time is %s seconds, while the signal duration is %s seconds.' %(Tobs, SD)\n",
    "    print(msg)\n",
    "    \n",
    "    \n",
    "    msg = 'The No. of samples in the time domain is equal to round(%s) = %s. This means delta_t is %s seconds.' %(N, int(N), dt)\n",
    "    print(msg)\n",
    "    \n",
    "    msg = 'So the No. of samples in the frequency domain is equal to %s, with delta_f equal to %s Hz. The maximum frequency is %s Hz.' %(Nf, df, fs[-1])\n",
    "    print(msg)\n",
    "    \n",
    "    NW = len(Detectors)\n",
    "    \n",
    "    \n",
    "    ASDs = np.array([np.zeros_like(fs) for i in range(NW)])\n",
    "    \n",
    "    nos_realizations = np.array([[np.zeros_like(ts) for i in range(total_number_of_time_series)] for j in range(NW)]) \n",
    "    SNR = np.zeros(total_number_of_time_series)\n",
    "    \n",
    "    \"\"\"This part is for signal\"\"\"\n",
    "    N_signal = SD * SR\n",
    "    t_signal = np.arange(int(N_signal)) * dt\n",
    "    \n",
    "    \n",
    "    msg = 'The No. of samples in the time domain containing the signal is %s.' %(N_signal)\n",
    "    print(msg)\n",
    "    \n",
    "    signals = np.array([np.zeros_like(t_signal) for i in range(No_signals)])\n",
    "    \n",
    "    simulated_data = np.array([np.zeros_like(ts) for i in range(total_number_of_time_series)])\n",
    "\n",
    "    print('\\n')\n",
    "    msg = 'Generating noise realizations............'\n",
    "    print(msg)\n",
    "    print(total_number_of_time_series)\n",
    "    for i in range(NW):\n",
    "        ASDs[i] = readnos(Detectors[i], fs)\n",
    "        \n",
    "        if usingASD == True:\n",
    "            for j in range(total_number_of_time_series):\n",
    "                nos_realizations[i, j] = noisegenerator(Tobs, Detectors[i], SR, df, dt, ASDs[i])\n",
    "        else:\n",
    "            nos_realizations[i, j] = np.zeros_like(ts)\n",
    "            \n",
    "    msg = '%s noise realizations have been generated.' %(nos_realizations.shape[0] * nos_realizations.shape[1])\n",
    "    print(msg)\n",
    "    print('\\n')\n",
    "    msg = 'Generating signals..........'\n",
    "    print(msg)\n",
    "    \n",
    "    \n",
    "    for i in range(No_signals):\n",
    "     \n",
    "        \n",
    "        signals[i] = simsinsignal(ts, NO_sin, A_low, A_high, f_low, f_high, phase_low, phase_high, i)\n",
    "\n",
    "        for j in range(No_realizations):\n",
    "            count = i * No_realizations + j\n",
    "            if Tobs > SD:\n",
    "                simulated_data[count] = shift_signal(nos_realization[0, j], SD, shift_percent, signals[i],  shift_applied)\n",
    "            else:\n",
    "                temporary = nos_realizations[0, j] + signals[i]\n",
    "\n",
    "                temporary = np.fft.rfft(temporary) * dt\n",
    "                SNR[count] = np.sqrt(4.0 * sum(abs(temporary) ** 2 / ASDs[0] **2  * df))\n",
    "                temporary = temporary / ASDs[0]\n",
    "                \n",
    "                temporary = np.fft.irfft(temporary) * df\n",
    "                simulated_data[count] = temporary \n",
    "   \n",
    "    msg = '%s signals have been generated, of which the first %s should contain signals.' %(simulated_data.shape[0], No_realizations * No_signals)\n",
    "    print(msg)\n",
    "\n",
    "    for i in range(No_signals * No_realizations,  No_signals * No_realizations * 2):       \n",
    "        temporary = np.fft.rfft(nos_realizations[0,i]) * dt \n",
    "        SNR[i] = np.sqrt(4.0 * sum(abs(temporary) ** 2 / ASDs[0] **2  * df))\n",
    "        temporary = temporary / ASDs[0]\n",
    "        temporary = np.fft.irfft(temporary) * df \n",
    "        simulated_data[i] = temporary\n",
    "    \n",
    "    \n",
    "    return simulated_data, ts, signals, nos_realizations, ASDs, SNR\n",
    "                \n",
    "            \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This generator uses seed = 7 for random number generator in python.\n",
      "The No. of different signals in the data will be 20.\n",
      "The No. of nosie realizations is 10000, of which the first 5000 will be mixed with signals later.\n",
      "The total observation time is 0.25 seconds, while the signal duration is 0.25 seconds.\n",
      "The No. of samples in the time domain is equal to round(1024.0) = 1024. This means delta_t is 0.000244140625 seconds.\n",
      "So the No. of samples in the frequency domain is equal to 513, with delta_f equal to 4.0039100684261975 Hz. The maximum frequency is 2050.001955034213 Hz.\n",
      "The No. of samples in the time domain containing the signal is 1024.0.\n",
      "\n",
      "\n",
      "Generating noise realizations............\n",
      "10000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:20: RuntimeWarning: divide by zero encountered in log10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000 noise realizations have been generated.\n",
      "\n",
      "\n",
      "Generating signals..........\n",
      "Each signal generated will contain 1 simple sinusoidal signal components.\n",
      "The amplitudes are as follows:\n",
      "2e-23\n",
      "The frequencies are as follows:\n",
      "300.0012148969859\n",
      "The phases are as follows:\n",
      "0.0\n",
      "\n",
      "\n",
      "10000 signals have been generated, of which the first 5000 should contain signals.\n"
     ]
    }
   ],
   "source": [
    "# set random number generator\n",
    "\n",
    "\n",
    "#sampling rate, in Hz\n",
    "SR = 4096\n",
    "#Time series duration and signal duration, in seconds\n",
    "Tobs = 0.25\n",
    "SD = 0.25\n",
    "\n",
    "NO_sin = 1\n",
    "No_signals = 20\n",
    "No_realizations = 250\n",
    "shift_applied = False\n",
    "shift_percent = 0\n",
    "usingASD = True\n",
    "Detectors = ['H1']\n",
    "\n",
    "A_low = 2.0e-23\n",
    "A_high = 2.0e-23\n",
    "f_low =300.0\n",
    "f_high = 300.030\n",
    "phase_low = 0.0\n",
    "phase_high = 0.0\n",
    "seed = 7\n",
    "simulated_data, ts, signals, nos_realizations, ASD, SNR= signal_generator(NO_sin, No_signals, No_realizations, shift_applied, usingASD, Detectors, SR, Tobs, \n",
    "                     SD, seed, shift_percent, A_low, A_high, f_low, f_high, phase_low, phase_high)\n",
    "\n",
    "simulated_label = np.zeros(No_signals * No_realizations * 2)\n",
    "simulated_label[0:No_signals * No_realizations] = 1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_signal = simulated_data\n",
    "train_label = simulated_label\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = Tobs * SR\n",
    "dt = 1.0 / SR\n",
    "fontsize = 22\n",
    "ts = np.arange(int(N)) * dt\n",
    "\n",
    "plt.plot(ts, signals[0], linewidth = 2, color = 'b', label = 'Simple sin wave')\n",
    "plt.xlim([-0.025, 0.275])\n",
    "plt.xlabel('Time (s)', fontsize = fontsize)\n",
    "plt.ylabel('Strain', fontsize =fontsize)\n",
    "plt.legend(fontsize = fontsize)\n",
    "plt.grid()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "ax = plt.gca()\n",
    "for tick in ax.xaxis.get_major_ticks():\n",
    "    tick.label1.set_fontsize(fontsize)\n",
    "    tick.label1.set_fontweight('normal')\n",
    "for tick in ax.yaxis.get_major_ticks():\n",
    "    tick.label1.set_fontsize(fontsize)\n",
    "    tick.label1.set_fontweight('normal')\n",
    "text = ax.yaxis.get_offset_text()\n",
    "text.set_size(fontsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(ts, train_signal[0], linewidth = 2, color = 'b', label = r'The inverse fft of the sin wave in the frequency domain. SNR $\\simeq 2.8$')\n",
    "plt.plot(ts, train_signal[5000], linewidth = 2, color = 'r', label = r'The inverse fft of a noise realization')\n",
    "\n",
    "fontsize = 22\n",
    "plt.xlabel('Time (s)', fontsize = fontsize)\n",
    "plt.ylabel('Strain', fontsize =fontsize)\n",
    "\n",
    "\n",
    "plt.grid(which = 'both')\n",
    "\n",
    "ax = plt.gca()\n",
    "for tick in ax.xaxis.get_major_ticks():\n",
    "    tick.label1.set_fontsize(fontsize)\n",
    "    tick.label1.set_fontweight('normal')\n",
    "for tick in ax.yaxis.get_major_ticks():\n",
    "    tick.label1.set_fontsize(fontsize)\n",
    "    tick.label1.set_fontweight('normal')\n",
    "text = ax.yaxis.get_offset_text()\n",
    "text.set_size(fontsize)\n",
    "plt.legend(fontsize = fontsize, loc = 'upper right')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'fs' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-360-13cc52e7af25>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemporary\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mASDs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Amplitude Spectral Density (aLIGO)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myscale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'log'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxscale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'log'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'frequency (Hz)'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'fs' is not defined"
     ]
    }
   ],
   "source": [
    "plt.plot(fs, abs(temporary), )\n",
    "plt.plot(fs, ASDs[0], linewidth = 2, color = 'r', label = 'Amplitude Spectral Density (aLIGO)')\n",
    "plt.yscale('log')\n",
    "plt.xscale('log')\n",
    "plt.xlabel('frequency (Hz)', fontsize = fontsize)\n",
    "plt.ylabel('Strain', fontsize =fontsize)\n",
    "plt.grid(which = 'both')\n",
    "\n",
    "\n",
    "\n",
    "ax = plt.gca()\n",
    "for tick in ax.xaxis.get_major_ticks():\n",
    "    tick.label1.set_fontsize(fontsize)\n",
    "    tick.label1.set_fontweight('normal')\n",
    "for tick in ax.yaxis.get_major_ticks():\n",
    "    tick.label1.set_fontsize(fontsize)\n",
    "    tick.label1.set_fontweight('normal')\n",
    "text = ax.yaxis.get_offset_text()\n",
    "text.set_size(fontsize)\n",
    "plt.legend(fontsize = fontsize, loc = 'upper right')\n",
    "plt.show()\n",
    "\n",
    "dsfds\n",
    "                \n",
    "#plt.plot(train_signal[0])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_signal = simulated_data\n",
    "test_label = simulated_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_signal = simulated_data\n",
    "val_label = simulated_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def shuffle_data(train_signal, train_label, val_signal, val_label, test_signal, test_label,  shuffle_times):\n",
    "    \n",
    "    for i in range(shuffle_times):\n",
    "        state = np.random.randint(0,100)\n",
    "        train_signal, train_label = shuffle(train_signal, train_label, random_state=state)\n",
    "\n",
    "        state = np.random.randint(0,100)\n",
    "        test_signal, test_label = shuffle(test_signal, test_label, random_state=state)\n",
    "\n",
    "        state = np.random.randint(0,100)\n",
    "        val_signal, val_label = shuffle(val_signal, val_label, random_state=state)\n",
    "\n",
    "    return train_signal, train_label, val_signal, val_label, test_signal, test_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "shuffle_times = 10\n",
    "train_signal, train_label, val_signal, val_label, test_signal, test_label = shuffle_data(train_signal, train_label, val_signal, val_label, test_signal, test_label,  shuffle_times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "1.0\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "i = 16\n",
    "plt.plot(ts,train_signal[i])\n",
    "print(train_label[i])\n",
    "\n",
    "\n",
    "plt.plot(ts, test_signal[i])\n",
    "print(test_label[i])\n",
    "\n",
    "plt.plot(ts, val_signal[i])\n",
    "print(val_label[i])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "pathandname = \"./Data/data.pkl\"\n",
    "fp = open(pathandname,\"w\")\n",
    "pickle.dump([train_signal, train_label, val_signal, val_label, test_signal, test_label, ts], fp)\n",
    "fp.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./Data/data.pkl') as f:\n",
    "    train_signal, train_label, val_signal, val_label, test_signal, test_label, ts = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "checksignal(train_signal, train_label, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def checksignal(signal, label, reshaped):\n",
    "    iteration = signal.shape[0]\n",
    "    if reshaped == False:\n",
    "        zeros_check = np.zeros_like(signal[0])\n",
    "        for i in range(iteration):\n",
    "            if (np.array_equal(zeros_check, signal[i])) == True and label[i] != 0:\n",
    "                print(i)\n",
    "            elif (np.array_equal(zeros_check, signal[i])) == False and label[i] == 0:\n",
    "                print(i)\n",
    "                                \n",
    "    \n",
    "    elif reshaped == True:\n",
    "        zeros_check = np.zeros_like(signal[0][0])\n",
    "        for i in range(iteration):\n",
    "            \n",
    "            if (np.array_equal(zeros_check, signal[i][0])) == True and (np.array_equal(label[i], np.array([1, 0]))) == False:\n",
    "                print(i)\n",
    "            elif (np.array_equal(zeros_check, signal[i][0])) == False and (np.array_equal(label[i], np.array([1, 0]))) == True:\n",
    "                print(i)\n",
    "\n",
    "                                \n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Below is the CNN part of this code\"\"\" \n",
    "\n",
    "batch_size = 30      # number of time series per batch\n",
    "num_classes = 2      # signal or background\n",
    "epochs = 20          # number of full passes of the dataset\n",
    "outdir = './results' # directory to store results in\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "length = len(train_signal)\n",
    "signal_len = len(train_signal[0])\n",
    "\n",
    "train_signal = train_signal.reshape(length,1, signal_len)\n",
    "test_signal = test_signal.reshape(length,1, signal_len)\n",
    "val_signal = val_signal.reshape(length,1, signal_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "i = 4\n",
    "plt.plot(train_signal[i][0])\n",
    "print(train_label[i])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.set_image_data_format('channels_first')\n",
    "signal_len = len(train_signal[0][0])\n",
    "\n",
    "train_signal = train_signal.reshape(-1, 1, 1, signal_len)\n",
    "val_signal = val_signal.reshape(-1, 1, 1, signal_len)\n",
    "test_signal = test_signal.reshape(-1, 1, 1, signal_len)\n",
    "\n",
    "input_shape = train_signal.shape[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_label = keras.utils.to_categorical(train_label , num_classes)\n",
    "val_label = keras.utils.to_categorical(val_label, num_classes)\n",
    "test_label = keras.utils.to_categorical(test_label, num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5120.0"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5.0*1024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_5 (Conv2D)            (None, 16, 1, 961)        1040      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 16, 1, 240)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 16, 1, 225)        4112      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 16, 1, 56)         0         \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 896)               0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 32)                28704     \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 2)                 66        \n",
      "=================================================================\n",
      "Total params: 33,922\n",
      "Trainable params: 33,922\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()    # define the type of keras model\n",
    "\n",
    "# add the layers\n",
    "# conv1\n",
    "model.add(Conv2D(16, (1,64), activation='elu', input_shape=input_shape))\n",
    "# maxpool2\n",
    "model.add(MaxPool2D((1,4)))\n",
    "\n",
    "\n",
    "model.add(Conv2D(16, (1,16), activation='elu'))\n",
    "# maxpool2\n",
    "model.add(MaxPool2D((1,4)))\n",
    "\n",
    "\n",
    "# the input the fully connected layer must be 1-D vector\n",
    "model.add(Flatten())\n",
    "model.add(Dense(32, activation='elu'))\n",
    "model.add(Dropout(0.5))\n",
    "# add the output layer with softmax actiavtion for classication\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "# print a summary\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_5 (Conv2D)            (None, 8, 1, 961)         520       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 8, 1, 120)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 16, 1, 105)        2064      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 16, 1, 17)         0         \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 272)               0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 32)                8736      \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 2)                 66        \n",
      "=================================================================\n",
      "Total params: 11,386\n",
      "Trainable params: 11,386\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()    # define the type of keras model\n",
    "\n",
    "# add the layers\n",
    "# conv1\n",
    "model.add(Conv2D(8, (1,64), activation='elu', input_shape=input_shape))\n",
    "# maxpool2\n",
    "model.add(MaxPool2D((1,8)))\n",
    "# conv2\n",
    "model.add(Conv2D(16, (1,16), activation='elu'))\n",
    "# maxpool2\n",
    "model.add(MaxPool2D((1,6)))\n",
    "# the input the fully connected layer must be 1-D vector\n",
    "model.add(Flatten())\n",
    "model.add(Dense(32, activation='elu'))\n",
    "model.add(Dropout(0.5))\n",
    "# add the output layer with softmax actiavtion for classication\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "# print a summary\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=SGD(lr = 0.1),\n",
    "              metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "modelCheck = ModelCheckpoint('{0}/best_weights.hdf5'.format(outdir), monitor='val_acc', verbose=0, \n",
    "                save_best_only=True,save_weights_only=True, mode='auto', period=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 10000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "10000/10000 [==============================] - 2s 246us/step - loss: 0.5234 - acc: 0.6773 - val_loss: 0.0065 - val_acc: 1.0000\n",
      "Epoch 2/20\n",
      "10000/10000 [==============================] - 2s 220us/step - loss: 0.0040 - acc: 1.0000 - val_loss: 2.8644e-04 - val_acc: 1.0000\n",
      "Epoch 3/20\n",
      "10000/10000 [==============================] - 2s 216us/step - loss: 0.0011 - acc: 1.0000 - val_loss: 1.0303e-04 - val_acc: 1.0000\n",
      "Epoch 4/20\n",
      "10000/10000 [==============================] - 2s 215us/step - loss: 6.8878e-04 - acc: 1.0000 - val_loss: 4.7736e-05 - val_acc: 1.0000\n",
      "Epoch 5/20\n",
      "10000/10000 [==============================] - 2s 223us/step - loss: 4.8201e-04 - acc: 1.0000 - val_loss: 2.8308e-05 - val_acc: 1.0000\n",
      "Epoch 6/20\n",
      "10000/10000 [==============================] - 2s 221us/step - loss: 3.6795e-04 - acc: 1.0000 - val_loss: 1.8900e-05 - val_acc: 1.0000\n",
      "Epoch 7/20\n",
      "10000/10000 [==============================] - 2s 226us/step - loss: 3.5598e-04 - acc: 1.0000 - val_loss: 1.3084e-05 - val_acc: 1.0000\n",
      "Epoch 8/20\n",
      "10000/10000 [==============================] - 2s 224us/step - loss: 2.4088e-04 - acc: 1.0000 - val_loss: 9.6244e-06 - val_acc: 1.0000\n",
      "Epoch 9/20\n",
      "10000/10000 [==============================] - 2s 231us/step - loss: 2.3670e-04 - acc: 1.0000 - val_loss: 7.6864e-06 - val_acc: 1.0000\n",
      "Epoch 10/20\n",
      "10000/10000 [==============================] - 2s 223us/step - loss: 1.9760e-04 - acc: 1.0000 - val_loss: 5.8807e-06 - val_acc: 1.0000\n",
      "Epoch 11/20\n",
      "10000/10000 [==============================] - 2s 225us/step - loss: 1.8987e-04 - acc: 1.0000 - val_loss: 5.1467e-06 - val_acc: 1.0000\n",
      "Epoch 12/20\n",
      "10000/10000 [==============================] - 2s 226us/step - loss: 1.5977e-04 - acc: 1.0000 - val_loss: 3.8264e-06 - val_acc: 1.0000\n",
      "Epoch 13/20\n",
      "10000/10000 [==============================] - 2s 231us/step - loss: 1.2648e-04 - acc: 1.0000 - val_loss: 3.7782e-06 - val_acc: 1.0000\n",
      "Epoch 14/20\n",
      "10000/10000 [==============================] - 2s 236us/step - loss: 1.1972e-04 - acc: 1.0000 - val_loss: 2.9083e-06 - val_acc: 1.0000\n",
      "Epoch 15/20\n",
      "10000/10000 [==============================] - 2s 228us/step - loss: 1.3134e-04 - acc: 1.0000 - val_loss: 2.4305e-06 - val_acc: 1.0000\n",
      "Epoch 16/20\n",
      "10000/10000 [==============================] - 2s 222us/step - loss: 8.7838e-05 - acc: 1.0000 - val_loss: 2.0689e-06 - val_acc: 1.0000\n",
      "Epoch 17/20\n",
      "10000/10000 [==============================] - 2s 220us/step - loss: 9.9688e-05 - acc: 1.0000 - val_loss: 1.8153e-06 - val_acc: 1.0000\n",
      "Epoch 18/20\n",
      "10000/10000 [==============================] - 2s 223us/step - loss: 9.1620e-05 - acc: 1.0000 - val_loss: 1.6916e-06 - val_acc: 1.0000\n",
      "Epoch 19/20\n",
      "10000/10000 [==============================] - 2s 227us/step - loss: 8.3808e-05 - acc: 1.0000 - val_loss: 1.4400e-06 - val_acc: 1.0000\n",
      "Epoch 20/20\n",
      "10000/10000 [==============================] - 2s 222us/step - loss: 8.7516e-05 - acc: 1.0000 - val_loss: 1.3463e-06 - val_acc: 1.0000\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_signal, train_label,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=epochs,\n",
    "                    verbose=1,\n",
    "                    validation_data=(val_signal, val_label),\n",
    "                    callbacks = [modelCheck]\n",
    "                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 72us/step\n",
      "('Test loss:', 0.006506993394810707)\n",
      "('Test accuracy:', 1.0)\n"
     ]
    }
   ],
   "source": [
    "# load the best model\n",
    "model.load_weights('{0}/best_weights.hdf5'.format(outdir))\n",
    "# evaluate\n",
    "eval_results = model.evaluate(test_signal, test_label,\n",
    "                              sample_weight=None,\n",
    "                              batch_size=batch_size, verbose=1)\n",
    "print('Test loss:', eval_results[0])\n",
    "print('Test accuracy:', eval_results[1])\n",
    "signal_preds = model.predict(test_signal)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.00696066 0.9930393 ]\n",
      " [0.9937889  0.00621109]\n",
      " [0.00678684 0.9932132 ]\n",
      " [0.0065595  0.99344057]\n",
      " [0.00677403 0.993226  ]\n",
      " [0.00680905 0.99319094]\n",
      " [0.00675936 0.9932406 ]\n",
      " [0.99375385 0.00624612]\n",
      " [0.00656023 0.99343973]\n",
      " [0.99382174 0.00617828]\n",
      " [0.00683852 0.99316144]\n",
      " [0.9937302  0.00626982]\n",
      " [0.9937412  0.00625882]\n",
      " [0.00665454 0.9933455 ]\n",
      " [0.00680777 0.99319226]\n",
      " [0.00671431 0.9932857 ]\n",
      " [0.00676572 0.9932342 ]\n",
      " [0.9937138  0.00628616]\n",
      " [0.9937814  0.00621859]\n",
      " [0.0068589  0.9931411 ]\n",
      " [0.0067991  0.99320096]\n",
      " [0.00669436 0.9933056 ]\n",
      " [0.9938253  0.00617476]\n",
      " [0.00667201 0.993328  ]\n",
      " [0.993749   0.00625103]\n",
      " [0.9938036  0.00619632]\n",
      " [0.00678366 0.99321634]\n",
      " [0.00675233 0.9932476 ]\n",
      " [0.9937908  0.00620924]\n",
      " [0.00676055 0.9932394 ]\n",
      " ...\n",
      " [0.9937761  0.00622393]\n",
      " [0.9937808  0.00621917]\n",
      " [0.9937622  0.00623776]\n",
      " [0.00664625 0.9933537 ]\n",
      " [0.00682054 0.99317944]\n",
      " [0.00677661 0.9932234 ]\n",
      " [0.00686715 0.9931329 ]\n",
      " [0.00660854 0.9933915 ]\n",
      " [0.9937261  0.00627399]\n",
      " [0.00666693 0.99333304]\n",
      " [0.00673412 0.99326587]\n",
      " [0.9938046  0.00619547]\n",
      " [0.9936913  0.0063087 ]\n",
      " [0.99378246 0.00621761]\n",
      " [0.00666705 0.9933329 ]\n",
      " [0.0067839  0.9932161 ]\n",
      " [0.00666289 0.99333715]\n",
      " [0.99376315 0.00623687]\n",
      " [0.99375206 0.00624795]\n",
      " [0.00687265 0.99312735]\n",
      " [0.9936998  0.00630019]\n",
      " [0.00685419 0.9931458 ]\n",
      " [0.00671486 0.9932852 ]\n",
      " [0.00681299 0.99318695]\n",
      " [0.00676241 0.9932376 ]\n",
      " [0.00681685 0.9931832 ]\n",
      " [0.00676578 0.9932342 ]\n",
      " [0.9937431  0.00625692]\n",
      " [0.9938261  0.00617384]\n",
      " [0.9937663  0.00623363]]\n"
     ]
    }
   ],
   "source": [
    "signal_preds = model.predict(test_signal)\n",
    "print(signal_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig , axs = plt.subplots(2,1, sharex = True)\n",
    "axs = axs.ravel()\n",
    "fontsize = 22\n",
    "# plot history\n",
    "axs[0].plot(history.history['loss'], label = 'loss', linewidth = 2, color = 'b')\n",
    "axs[0].plot(history.history['val_loss'], label = 'val loss', linewidth = 2, color = 'r')\n",
    "axs[1].plot(history.history['acc'], label = 'acc', linewidth = 2, color = 'b')\n",
    "axs[1].plot(history.history['val_acc'], label = 'val_acc',  linewidth = 2, color = 'r')\n",
    "# set labels\n",
    "axs[0].set_ylabel('Loss', fontsize = fontsize)\n",
    "axs[1].set_xlabel('Epoch', fontsize = fontsize)\n",
    "axs[1].set_ylabel('Acc', fontsize = fontsize)\n",
    "# legends\n",
    "axs[0].legend( fontsize = fontsize)\n",
    "axs[1].legend(fontsize = fontsize)\n",
    "# grids\n",
    "axs[0].grid()\n",
    "axs[1].grid()\n",
    "\n",
    "for tick in axs[0].xaxis.get_major_ticks():\n",
    "    tick.label1.set_fontsize(fontsize)\n",
    "    tick.label1.set_fontweight('normal')\n",
    "for tick in axs[0].yaxis.get_major_ticks():\n",
    "    tick.label1.set_fontsize(fontsize)\n",
    "    tick.label1.set_fontweight('normal')\n",
    "                \n",
    "\n",
    "for tick in axs[1].xaxis.get_major_ticks():\n",
    "    tick.label1.set_fontsize(fontsize)\n",
    "    tick.label1.set_fontweight('normal')\n",
    "for tick in axs[1].yaxis.get_major_ticks():\n",
    "    tick.label1.set_fontsize(fontsize)\n",
    "    tick.label1.set_fontweight('normal')\n",
    "        \n",
    "        \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "i = 4000\n",
    "plt.plot(abs(np.fft.rfft(train_signal[i]) * dt /ASD))\n",
    "plt.show()\n",
    "print(train_label[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"638.888905813665\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_signal[i][0][0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 143us/step\n",
      "('Test loss:', 0.6934680640697479)\n",
      "('Test accuracy:', 0.5)\n"
     ]
    }
   ],
   "source": [
    "# load the best model\n",
    "model.load_weights('{0}/best_weights.hdf5'.format(outdir))\n",
    "# evaluate\n",
    "eval_results = model.evaluate(test_signal, test_label,\n",
    "                              sample_weight=None,\n",
    "                              batch_size=batch_size, verbose=1)\n",
    "print('Test loss:', eval_results[0])\n",
    "print('Test accuracy:', eval_results[1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_preds = model.predict(test_signal)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_10 (Dense)             (None, 1, 1, 40)          32840     \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 1, 1, 12)          492       \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 12)                0         \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, 2)                 26        \n",
      "=================================================================\n",
      "Total params: 33,358\n",
      "Trainable params: 33,358\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()    # define the type of keras model\n",
    "\n",
    "# add the layers\n",
    "# conv1\n",
    "model.add(Dense(40, activation ='sigmoid', input_shape=input_shape))\n",
    "# add the output layer with softmax actiavtion for classication\n",
    "model.add(Dense(12, activation ='sigmoid'))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "# print a summary\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 30 samples, validate on 30 samples\n",
      "Epoch 1/20\n",
      "30/30 [==============================] - 0s 9ms/step - loss: 0.6941 - acc: 0.5000 - val_loss: 0.6935 - val_acc: 0.5000\n",
      "Epoch 2/20\n",
      "30/30 [==============================] - 0s 267us/step - loss: 0.6994 - acc: 0.5000 - val_loss: 0.6939 - val_acc: 0.5000\n",
      "Epoch 3/20\n",
      "30/30 [==============================] - 0s 265us/step - loss: 0.6950 - acc: 0.5000 - val_loss: 0.6932 - val_acc: 0.5000\n",
      "Epoch 4/20\n",
      "30/30 [==============================] - 0s 333us/step - loss: 0.6932 - acc: 0.5000 - val_loss: 0.6932 - val_acc: 0.5000\n",
      "Epoch 5/20\n",
      "30/30 [==============================] - 0s 308us/step - loss: 0.6932 - acc: 0.5000 - val_loss: 0.6931 - val_acc: 0.5000\n",
      "Epoch 6/20\n",
      "30/30 [==============================] - 0s 305us/step - loss: 0.6947 - acc: 0.5000 - val_loss: 0.6935 - val_acc: 0.5000\n",
      "Epoch 7/20\n",
      "30/30 [==============================] - 0s 367us/step - loss: 0.6994 - acc: 0.5000 - val_loss: 0.6939 - val_acc: 0.5000\n",
      "Epoch 8/20\n",
      "30/30 [==============================] - 0s 298us/step - loss: 0.6958 - acc: 0.5000 - val_loss: 0.6945 - val_acc: 0.5000\n",
      "Epoch 9/20\n",
      "30/30 [==============================] - 0s 411us/step - loss: 0.7101 - acc: 0.3000 - val_loss: 0.6997 - val_acc: 0.5000\n",
      "Epoch 10/20\n",
      "30/30 [==============================] - 0s 290us/step - loss: 0.6988 - acc: 0.5000 - val_loss: 0.6955 - val_acc: 0.5000\n",
      "Epoch 11/20\n",
      "30/30 [==============================] - 0s 374us/step - loss: 0.6952 - acc: 0.5000 - val_loss: 0.6940 - val_acc: 0.5000\n",
      "Epoch 12/20\n",
      "30/30 [==============================] - 0s 315us/step - loss: 0.6951 - acc: 0.5000 - val_loss: 0.6932 - val_acc: 0.5000\n",
      "Epoch 13/20\n",
      "30/30 [==============================] - 0s 288us/step - loss: 0.6947 - acc: 0.5000 - val_loss: 0.6935 - val_acc: 0.5000\n",
      "Epoch 14/20\n",
      "30/30 [==============================] - 0s 357us/step - loss: 0.6953 - acc: 0.4333 - val_loss: 0.6941 - val_acc: 0.5000\n",
      "Epoch 15/20\n",
      "30/30 [==============================] - 0s 374us/step - loss: 0.6960 - acc: 0.5000 - val_loss: 0.6946 - val_acc: 0.5000\n",
      "Epoch 16/20\n",
      "30/30 [==============================] - 0s 270us/step - loss: 0.6955 - acc: 0.5000 - val_loss: 0.6932 - val_acc: 0.5000\n",
      "Epoch 17/20\n",
      "30/30 [==============================] - 0s 366us/step - loss: 0.6948 - acc: 0.4333 - val_loss: 0.6936 - val_acc: 0.5000\n",
      "Epoch 18/20\n",
      "30/30 [==============================] - 0s 385us/step - loss: 0.6949 - acc: 0.5000 - val_loss: 0.6932 - val_acc: 0.5000\n",
      "Epoch 19/20\n",
      "30/30 [==============================] - 0s 365us/step - loss: 0.6997 - acc: 0.3667 - val_loss: 0.6950 - val_acc: 0.5000\n",
      "Epoch 20/20\n",
      "30/30 [==============================] - 0s 298us/step - loss: 0.6958 - acc: 0.5000 - val_loss: 0.6932 - val_acc: 0.5000\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=SGD(lr = 0.1),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(train_signal, train_label,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=epochs,\n",
    "                    verbose=1,\n",
    "                    validation_data=(val_signal, val_label),\n",
    "                    callbacks = [modelCheck]\n",
    "                   )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_noise(fs,T_obs,psd):\n",
    "    \"\"\"\n",
    "    Generates noise from a psd\n",
    "    \"\"\"\n",
    "    np.random.seed(2)\n",
    "    N = T_obs * fs          # the total number of time samples\n",
    "    Nf = int(np.ceil(N / 2) + 1)\n",
    "    dt = 1 / fs             # the sampling time (sec)\n",
    "    df = 1 / T_obs\n",
    "\n",
    "    amp = np.sqrt(0.25*T_obs*psd)\n",
    "    \n",
    "    idx = np.argwhere(psd==0.0)\n",
    "    amp[idx] = 0.0\n",
    "    re = amp*np.random.normal(0,1,Nf)\n",
    "    im = amp*np.random.normal(0,1,Nf)\n",
    "    re[0] = 0.0\n",
    "    im[0] = 0.0\n",
    "    idx_1 = int(20 // df)\n",
    "    re[0:idx_1] = 0\n",
    "    im[0:idx_1] = 0\n",
    "    x = N*np.fft.irfft(re + 1j*im)*df\n",
    "\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(100)\n",
    "y = np.sin(x)\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
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