XES_timescans_TT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "import jungfrau_utils as ju\n",
    "import numpy as np\n",
    "import json, h5py\n",
    "import os, glob\n",
    "import time\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import cm\n",
    "from scipy.optimize import curve_fit\n",
    "from IPython.display import clear_output, display\n",
    "import matplotlib.colors as colors\n",
    "import matplotlib.ticker as ticker\n",
    "from ipyfilechooser import FileChooser\n",
    "from scipy.stats.stats import pearsonr\n",
    "\n",
    "from alvra_tools.load_data import *\n",
    "from alvra_tools.channels import *\n",
    "from alvra_tools.utils import *\n",
    "from alvra_tools.XES_functions import *\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 runs: [176]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['/sf/alvra/data/p20537/raw/run0176-FePhen_RXES_250fs/meta/scan.json']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pgroup='p20537'\n",
    "#runlist = [790]\n",
    "runlist = np.arange(176, 177, 1)\n",
    "\n",
    "print ('{} runs: {}'.format(len(runlist),runlist))\n",
    "\n",
    "#############################################################\n",
    "from itertools import chain\n",
    "jsonlist = []\n",
    "for run in runlist:\n",
    "    file = glob.glob('/sf/alvra/data/{}/raw/*{:04d}*/meta/scan.json'.format(pgroup, run))\n",
    "    jsonlist.append(file)\n",
    "jsonlist = itertools.chain.from_iterable(jsonlist)\n",
    "jsonlist = sorted(jsonlist, key = os.path.getmtime)\n",
    "jsonlist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It took 21.99 seconds to process this file\n",
      "\n",
      "Job done! It took 2768.84 seconds to process 121 file(s)\n",
      "------------------------------\n",
      "Timezero found in file = 199.379920000045 mm\n",
      "Processed run run0176-FePhen_RXES_250fs\n"
     ]
    }
   ],
   "source": [
    "roi1 = (5050, 5200, 130, 290) #signal CRY2 low\n",
    "roi2 = (5050, 5200, 310, 370) #signan CRY2 up\n",
    "\n",
    "roi3 = (8400, 8540, 125, 225) #signal CRY1 low\n",
    "roi4 = (8400, 8540, 250, 350) #signal CRY1 up\n",
    "\n",
    "threshold_low = 3\n",
    "threshold_high = 50\n",
    "nshots = None\n",
    "nsteps = None\n",
    "\n",
    "reducedir='../Claudio/Reduced_TT/'\n",
    "\n",
    "TT = TT_PSEN126\n",
    "Timezero_mm = 100.02694\n",
    "channel_delay_motor = channel_delay_topas\n",
    "\n",
    "#####################################################################################\n",
    "from sfdata import SFScanInfo\n",
    "\n",
    "for json in jsonlist:\n",
    "\n",
    "    scan = SFScanInfo(json)\n",
    "    try:\n",
    "        Timezero_mm = get_timezero_NBS(json)\n",
    "        flag = True\n",
    "    except:\n",
    "        Timezero_mm = Timezero_mm\n",
    "\n",
    "    Delays_fs_scan, Delays_corr_scan, \\\n",
    "    spectrum_roi1_ON, spectrum_roi2_ON, spectrum_roi3_ON, spectrum_roi4_ON, \\\n",
    "    spectrum_roi1_OFF, spectrum_roi2_OFF, spectrum_roi3_OFF, spectrum_roi4_OFF, Delay_fs, Delay_mm = \\\n",
    "    XES_delayscan_TT_4ROIs(scan, pgroup, TT, channel_delay_motor, Timezero_mm, roi1, roi2, roi3, roi4, threshold_low, threshold_high, nshots, nsteps=nsteps)\n",
    "\n",
    "    print ('------------------------------')\n",
    "    if flag:\n",
    "        print ('Timezero found in file = {} mm'.format(Timezero_mm))\n",
    "    else:\n",
    "        print ('Timezero = {} mm'.format(Timezero_mm))\n",
    "\n",
    "    if scan.parameters['units'] == ['nS']:\n",
    "        Delay_fs = - scan.values\n",
    "    #    Delay_fs = Delay_fs - 0.17\n",
    "    else:\n",
    "        Delay_fs = scan.values\n",
    "        Delay_fs = Delay_fs - mm2fs(Timezero_mm, 0)\n",
    "        \n",
    "    run_name = json.split('/')[-3]\n",
    "    os.makedirs(reducedir+run_name, exist_ok=True)\n",
    "\n",
    "    np.save(reducedir+run_name+'/delays_corr.npy', Delays_corr_scan)\n",
    "    np.save(reducedir+run_name+'/delays_stage.npy', Delays_fs_scan)\n",
    "    np.save(reducedir+run_name+'/Delay_readbacks.npy', Delay_fs)\n",
    "\n",
    "    for i, spectrum in enumerate([spectrum_roi1_ON, spectrum_roi2_ON, spectrum_roi3_ON, spectrum_roi4_ON]):\n",
    "        np.save(reducedir+run_name+'/spectrum_roi{}_ON.npy'.format(i+1), spectrum)\n",
    "\n",
    "    for i, spectrum in enumerate([spectrum_roi1_OFF, spectrum_roi2_OFF, spectrum_roi3_OFF, spectrum_roi4_OFF]):\n",
    "        np.save(reducedir+run_name+'/spectrum_roi{}_OFF.npy'.format(i+1), spectrum)\n",
    "    \n",
    "    print ('Processed run {}'.format(json.split('/')[-3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the data for the last run of runlist.\n",
    "## For averaging more runs use script XES_timescans_plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(35155, 120)\n",
      "(5860, 120)\n",
      "(35155, 120)\n",
      "(5860, 120)\n",
      "(35155, 140)\n",
      "(5860, 140)\n",
      "(35155, 140)\n",
      "(5860, 140)\n"
     ]
    }
   ],
   "source": [
    "roi_array = [spectrum_roi1_ON, spectrum_roi1_OFF, \n",
    "             spectrum_roi2_ON, spectrum_roi2_OFF,\n",
    "             spectrum_roi3_ON, spectrum_roi3_OFF,\n",
    "             spectrum_roi4_ON, spectrum_roi4_OFF,]\n",
    "\n",
    "for index in range(len(roi_array)):\n",
    "    roi_array[index] = np.asarray(list(itertools.chain.from_iterable(roi_array[index])))\n",
    "    print (np.shape(roi_array[index]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "binsize = 50\n",
    "\n",
    "binList = np.arange(Delays_fs_scan[0] - 100, Delays_fs_scan[-1] + 100, binsize)\n",
    "bin_centres = (binList[:-1] + binList[1:])/2\n",
    "Delay_fs_TT = np.arange(Delays_fs_scan[0] -100 + binsize/2, Delays_fs_scan[-1] + 100 - binsize/2, binsize)\n",
    "\n",
    "spectrum_roi1_on_rebin = np.zeros((len(bin_centres), roi1[1]-roi1[0]))\n",
    "spectrum_roi2_on_rebin = np.zeros((len(bin_centres), roi2[1]-roi2[0]))\n",
    "spectrum_roi3_on_rebin = np.zeros((len(bin_centres), roi3[1]-roi3[0]))\n",
    "spectrum_roi4_on_rebin = np.zeros((len(bin_centres), roi4[1]-roi4[0]))\n",
    "\n",
    "for i in range(len(bin_centres)):\n",
    "    cond1 = Delays_corr_scan > binList[i]\n",
    "    cond2 = Delays_corr_scan < binList[i+1]\n",
    "\n",
    "    idx = np.where(cond1*cond2)[0]\n",
    "\n",
    "    Delay_fs_TT[i] = np.average(Delays_corr_scan[idx])\n",
    "    \n",
    "    spectrum_roi1_on_rebin[i] = np.average(roi_array[0][idx,:], axis=0)\n",
    "    spectrum_roi2_on_rebin[i] = np.average(roi_array[2][idx,:], axis=0)\n",
    "    spectrum_roi3_on_rebin[i] = np.average(roi_array[4][idx,:], axis=0)\n",
    "    spectrum_roi4_on_rebin[i] = np.average(roi_array[6][idx,:], axis=0)\n",
    "    \n",
    "    #spectrum_roi1_off_rebin[i] = np.average(spectrum_roi1_OFF[idx,:], axis=0)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1d0113a739b64dbb9f2cd61c1675183c",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=1200.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xaxis12 = np.arange(0,roi1[1]-roi1[0])\n",
    "xaxis34 = np.arange(0,roi3[1]-roi3[0])\n",
    "\n",
    "plt.figure(figsize=(12,4))\n",
    "plt.suptitle(run_name)\n",
    "plt.subplot(141)\n",
    "plt.title('ROI1 - Ka')\n",
    "plt.pcolormesh(Delay_fs_TT, xaxis12, np.transpose(spectrum_roi1_on_rebin-np.average(roi_array[1],axis=0)))\n",
    "\n",
    "plt.subplot(142)\n",
    "plt.title('ROI2 - Ka')\n",
    "plt.pcolormesh(Delay_fs_TT, xaxis12, np.transpose(spectrum_roi2_on_rebin-np.average(roi_array[3],axis=0)))\n",
    "\n",
    "plt.subplot(143)\n",
    "plt.title('ROI3 - Kb')\n",
    "plt.pcolormesh(Delay_fs_TT, xaxis34, np.transpose(spectrum_roi3_on_rebin-np.average(roi_array[5],axis=0)))\n",
    "\n",
    "plt.subplot(144)\n",
    "plt.title('ROI4 - Kb')\n",
    "plt.pcolormesh(Delay_fs_TT, xaxis34, np.transpose(spectrum_roi4_on_rebin-np.average(roi_array[7],axis=0)))\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Extract_edgepos(sig, back, delays):\n",
    "    edgePos = []\n",
    "    Delays_stage = []\n",
    "    for k in range(0, len(sig)):\n",
    "        # background subtraction\n",
    "        sig2 = np.nan_to_num(sig[k] / back[k]) / background_from_fit\n",
    "\n",
    "        # interpolate to get evenly sampled in frequency space\n",
    "        sig3inter = interp1d(nus, sig2, kind='cubic')\n",
    "        sig4 = np.pad(sig3inter(nus_new), (0,len(sig3inter(nus_new))), 'constant', constant_values=0)\n",
    "        sig4power = np.abs(np.fft.fft(sig4))**2\n",
    "\n",
    "        # Fourier transform, filter, inverse fourier transform, take the real part, and do a moving average\n",
    "        sig4fft = np.fft.fft(sig4)\n",
    "        sig4filtered = sig4fft * filter\n",
    "        sig4inverse = np.fft.ifft(sig4filtered)\n",
    "        sig4invreal = 2 * np.real(sig4inverse)\n",
    "\n",
    "        sig4inter = interp1d(nus_new, sig4invreal[0:2047], kind='cubic')\n",
    "        sig5 = sig4inter(nus)\n",
    "        sig5gaussO0 = gaussian_filter1d(sig5, 50)\n",
    "        sig5gaussO1 = gaussian_filter1d(sig5, 50, order = 1)\n",
    "        sig6 = np.roll(convolve1d(sig5gaussO0,filter3), -100)\n",
    "\n",
    "        peak2 = np.argmax(sig5gaussO1 - psen_peak_background) \n",
    "\n",
    "        #if (peak2 > 500): \n",
    "        edgePos.append(peak2)\n",
    "        Delays_stage.append(np.round(np.mean(delays),4))\n",
    "        #sigtraces.append(sig6)\n",
    "        #peaktraces.append(sig5gaussO1)\n",
    "        \n",
    "        #Pump_probe_all.append(Laser_diff)\n",
    "        #edgeSig.append(sig5)\n",
    "    return edgePos, Delays_stage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import optimize\n",
    "from scipy.interpolate import interp1d\n",
    "from scipy import signal\n",
    "from scipy.signal import savgol_filter\n",
    "from scipy.signal import find_peaks\n",
    "from scipy.ndimage import gaussian_filter1d\n",
    "from scipy.stats import binned_statistic, binned_statistic_2d\n",
    "from scipy.ndimage import gaussian_filter1d\n",
    "from scipy.ndimage import convolve1d\n",
    "from scipy.special import erf\n",
    "\n",
    "lambdas = 467.55 + 0.07219*np.arange(0,2047)      #calibration new spec 23-9-2020\n",
    "nus = 299792458 / (lambdas * 10**-9)\n",
    "nus_new = np.linspace(nus[0], nus[-1], num=2047, endpoint=True)\n",
    "#filter = np.concatenate((np.ones(5), 1-signal.tukey(65), np.ones(1430), np.zeros(1500)))\n",
    "filter = np.concatenate((np.ones(50),signal.tukey(40)[20:40], np.zeros(1977), np.zeros(2047)))\n",
    "filter2 = np.concatenate((signal.tukey(40)[20:40], np.zeros(2027), np.zeros(2047))) # fourier filter for 5um SiN\n",
    "filter3 = np.concatenate((np.zeros(100), np.ones(100)))\n",
    "pixelNum = np.arange(0,2047)\n",
    "\n",
    "#######################################################################################################3\n",
    "\n",
    "background_from_fit = np.loadtxt('/sf/alvra/data/p18938/scratch/2021-02-27-19:01-psen-background.txt')\n",
    "psen_peak_background = np.loadtxt('/sf/alvra/data/p18938/scratch/2021-02-27-19:01-psen-peak-background.txt')\n",
    "\n",
    "px2fs = 1.993008440575784      # new spectrometer, it is ~ 2 px to fs\n",
    "p0 = 1024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/sf/alvra/data/p18938/raw/scan_info/Febpy_XES_timescan_NOPA_loop_1.json']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datafiles = []\n",
    "for file in glob.glob('/sf/alvra/data/p18938/raw/scan_info/Febpy_XES_timescan_NOPA_loop*'):\n",
    "    datafiles.append(file)\n",
    "datafiles = sorted(datafiles, key = os.path.getmtime)\n",
    "patch = datafiles[2:3]\n",
    "patch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It took 51.4 to process this file\n",
      "\n",
      "Job done! It took 51.45 seconds to process all the files\n"
     ]
    }
   ],
   "source": [
    "channel_list_pp = [channel_Events, 'JF02T09V02',\n",
    "                  channel_PSEN_signal, channel_PSEN_bkg, channel_delay_topas, channel_Izero117]\n",
    "channel_list_all = channel_list_pp\n",
    "\n",
    "roi1 = (7740, 7900, 220, 300) #signal 1\n",
    "roi2 = (7740, 7900, 400, 480) #bkg 1\n",
    "\n",
    "roi3 = (5080, 5220, 180, 280) #signal 2\n",
    "roi4 = (5080, 5220, 300, 400) #bkg 2\n",
    "\n",
    "Timezero = 215.498720\n",
    "binsize = 10 # in fs\n",
    "threshold_low = 5\n",
    "#threshold_high = 10\n",
    "\n",
    "#outdir = '/sf/alvra/data/p18938/res/Claudio/'\n",
    "\n",
    "####################################################################################################3\n",
    "clock = Clock()\n",
    "from sfdata import SFScanInfo\n",
    "\n",
    "for json_file in patch:\n",
    "\n",
    "    scan = SFScanInfo(json_file)\n",
    "    Delay_mm = scan.readbacks\n",
    "\n",
    "    XAS_roi1 = []\n",
    "    XAS_roi1_on = []\n",
    "    #Error_XAS_roi1 = []\n",
    "    XAS_roi2 = []\n",
    "    XAS_roi2_on = []\n",
    "    #Error_XAS_roi2 = []\n",
    "    RIXS_transient_roi1 = []\n",
    "    RIXS_transient_roi2 = []\n",
    "    Error_transient_roi1 = []\n",
    "    Error_transient_roi2 = []\n",
    "    RIXS_unpumped_roi1 = []\n",
    "    RIXS_unpumped_roi2 = []\n",
    "    Error_unpumped_roi1 = []\n",
    "    Error_unpumped_roi2 = []\n",
    "    RIXS_pumped_roi1 = []\n",
    "    RIXS_pumped_roi2 = []\n",
    "    Error_pumped_roi1 = []\n",
    "    Error_pumped_roi2 = []\n",
    "    XAS_transient_roi1=[]\n",
    "    XES_transient_roi1 =[]\n",
    "    XAS_transient_roi2=[]\n",
    "    XES_transient_roi2 =[]\n",
    "    \n",
    "    total_transient_roi1 = 0\n",
    "    total_s_roi1_OFF = 0\n",
    "    total_s_roi1_ON = 0\n",
    "    total_binNumber = []\n",
    "\n",
    "    for i, step in enumerate([scan[0]]):\n",
    "        check_files_and_data(step)\n",
    "        \n",
    "        filename = scan.files[i][0].split('/')[-1].split('.')[0]\n",
    "        print ('Step {} of {}: Processing {}'.format(i+1, len(scan.files), filename))\n",
    "\n",
    "        spectrum_ON_roi1 = 0\n",
    "        spectrum_ON_roi2 = 0\n",
    "        spectrum_ON_roi3 = 0\n",
    "        spectrum_ON_roi4 = 0\n",
    "        spectrum_OFF_roi1 = 0\n",
    "        spectrum_OFF_roi2 = 0\n",
    "        spectrum_OFF_roi3 = 0\n",
    "        spectrum_OFF_roi4 = 0\n",
    "\n",
    "        resultsPP, results, _, _ = load_data_compact_laser_pump_JF(channel_list_pp, channel_list_all, step, roi1, roi2, roi3, roi4)\n",
    "        \n",
    "        Izero_unpump = resultsPP[channel_Izero117].unpump\n",
    "        Izero_pump = resultsPP[channel_Izero117].pump\n",
    "        \n",
    "        sig_pump = resultsPP[channel_PSEN_signal].pump\n",
    "        sig_unpump = resultsPP[channel_PSEN_signal].unpump\n",
    "        back_pump = resultsPP[channel_PSEN_bkg].pump\n",
    "        delay_shot = resultsPP[channel_delay_topas].pump\n",
    "        \n",
    "        delay_shot_fs = (delay_shot - Timezero)*2/(3e8*1e3*1e-15)\n",
    "        \n",
    "        edgePos, Delays_stage = Extract_edgepos(sig_pump, back_pump, delay_shot_fs)\n",
    "        Delays_corr = Delays_stage + ((p0 - np.array(edgePos))*px2fs)\n",
    "        \n",
    "        images_pump_roi1 = resultsPP['JFroi1'].pump# / Izero_pump[:,None, None]\n",
    "        images_unpump_roi1 = resultsPP['JFroi1'].unpump# / Izero_unpump[:,None, None]\n",
    "        images_pump_roi2 = resultsPP['JFroi2'].pump# / Izero_pump[:,None, None]\n",
    "        images_unpump_roi2 = resultsPP['JFroi2'].unpump# / Izero_unpump[:,None, None]\n",
    "        images_pump_roi3 = resultsPP['JFroi3'].pump# / Izero_pump[:,None, None]\n",
    "        images_unpump_roi3 = resultsPP['JFroi3'].unpump# / Izero_unpump[:,None, None]\n",
    "        images_pump_roi4 = resultsPP['JFroi4'].pump# / Izero_pump[:,None, None]\n",
    "        images_unpump_roi4 = resultsPP['JFroi4'].unpump# / Izero_unpump[:,None, None]\n",
    "\n",
    "     #   Delay_mm_single = resultsPP[channel_delay_topas].pump[0]\n",
    "\n",
    "        images_pump_roi1_thr = images_pump_roi1.copy()\n",
    "        images_pump_roi1_thr[images_pump_roi1 < threshold_low] = 0\n",
    "        #images_pump_roi1_thr[images_pump_roi1 > threshold_high] = 0\n",
    "        images_unpump_roi1_thr = images_unpump_roi1.copy()\n",
    "        images_unpump_roi1_thr[images_unpump_roi1 < threshold_low] = 0\n",
    "        #images_unpump_roi1_thr[images_unpump_roi1 > threshold_high] = 0\n",
    "\n",
    "        images_pump_roi2_thr = images_pump_roi2.copy()\n",
    "        images_pump_roi2_thr[images_pump_roi2 < threshold_low] = 0\n",
    "        #images_pump_roi2_thr[images_pump_roi2 > threshold_high] = 0\n",
    "        images_unpump_roi2_thr = images_unpump_roi2.copy()\n",
    "        images_unpump_roi2_thr[images_unpump_roi2 < threshold_low] = 0\n",
    "        #images_unpump_roi2_thr[images_unpump_roi2 > threshold_high] = 0\n",
    "\n",
    "        images_pump_roi3_thr = images_pump_roi3.copy()\n",
    "        images_pump_roi3_thr[images_pump_roi3 < threshold_low] = 0\n",
    "        #images_pump_roi3_thr[images_pump_roi3 > threshold_high] = 0\n",
    "        images_unpump_roi3_thr = images_unpump_roi3.copy()\n",
    "        images_unpump_roi3_thr[images_unpump_roi3 < threshold_low] = 0\n",
    "        #images_unpump_roi3_thr[images_unpump_roi3 > threshold_high] = 0\n",
    "\n",
    "        images_pump_roi4_thr = images_pump_roi4.copy()\n",
    "        images_pump_roi4_thr[images_pump_roi4 < threshold_low] = 0\n",
    "        #images_pump_roi4_thr[images_pump_roi4 > threshold_high] = 0\n",
    "        images_unpump_roi4_thr = images_unpump_roi4.copy()\n",
    "        images_unpump_roi4_thr[images_unpump_roi4 < threshold_low] = 0\n",
    "        #images_unpump_roi4_thr[images_unpump_roi4 > threshold_high] = 0\n",
    "        \n",
    "        images_pump_roi1_thr   = images_pump_roi1_thr #/ Izero_pump[:,None, None]\n",
    "        images_unpump_roi1_thr = images_unpump_roi1_thr #/ Izero_unpump[:,None, None]\n",
    "        images_pump_roi2_thr   = images_pump_roi2_thr #/ Izero_pump[:,None, None]\n",
    "        images_unpump_roi2_thr = images_unpump_roi2_thr #/ Izero_unpump[:,None, None]\n",
    "        images_pump_roi3_thr   = images_pump_roi3_thr #/ Izero_pump[:,None, None]\n",
    "        images_unpump_roi3_thr = images_unpump_roi3_thr #/ Izero_unpump[:,None, None]\n",
    "        images_pump_roi4_thr   = images_pump_roi4_thr #/ Izero_pump[:,None, None]\n",
    "        images_unpump_roi4_thr = images_unpump_roi4_thr #/ Izero_unpump[:,None, None]\n",
    "        \n",
    "        spectrum_ON_roi1 = images_pump_roi1_thr.sum(axis = 1)#sum(axis = 0)\n",
    "        spectrum_OFF_roi1 = images_unpump_roi1_thr.sum(axis = 1)#.sum(axis = 0)\n",
    "        spectrum_ON_roi2 = images_pump_roi2_thr.sum(axis = 1)#.sum(axis = 0)\n",
    "        spectrum_OFF_roi2 = images_unpump_roi2_thr.sum(axis = 1)#.sum(axis = 0)\n",
    "        \n",
    "        ######################### ROI 1 ##############################################\n",
    "        XES_transient_roi1_ON = spectrum_ON_roi1 - spectrum_ON_roi2\n",
    "        XES_transient_roi1_OFF = spectrum_OFF_roi1 - spectrum_OFF_roi2\n",
    "        \n",
    "        XES_transient_roi1_ON_tolist = ([XES_transient_roi1_ON[:,i] for i in range(XES_transient_roi1_ON.shape[1])])\n",
    "        XES_transient_roi1_OFF_tolist = ([XES_transient_roi1_OFF[:,i] for i in range(XES_transient_roi1_OFF.shape[1])])\n",
    "#         XES_transient_roi1_shot = (spectrum_ON_roi1 - spectrum_ON_roi2) - (spectrum_OFF_roi1 - spectrum_OFF_roi2)\n",
    "#         XES_transient_roi1_tolist = ([XES_transient_roi1_shot[:,i] for i in range(XES_transient_roi1_shot.shape[1])])\n",
    "        #XES_transient_roi1_all.append(XES_transient_roi1_shot)\n",
    "        \n",
    "        #XES_transient_roi1_all = list(itertools.chain.from_iterable(XES_transient_roi1_all))\n",
    " \n",
    "        binList = np.arange(-500, 1000, binsize)\n",
    "        Delays_rebinned = np.arange(-500 + binsize/2, 1000 - binsize/2, binsize)\n",
    "\n",
    "#         s_roi1, binEdges, binNumber = binned_statistic(Delays_corr, XES_transient_roi1_tolist, statistic='sum', bins=binList)\n",
    "#         total_s_roi1 += s_roi1\n",
    "\n",
    "        s_roi1_ON, binEdges, binNumber = binned_statistic(Delays_corr, XES_transient_roi1_ON_tolist, statistic='mean', bins=binList)\n",
    "        s_roi1_OFF, binEdges, binNumber = binned_statistic(Delays_corr, XES_transient_roi1_OFF_tolist, statistic='mean', bins=binList)\n",
    "\n",
    "        total_s_roi1_ON += np.nan_to_num(s_roi1_ON)\n",
    "        total_s_roi1_OFF += np.nan_to_num(s_roi1_OFF)\n",
    "        total_binNumber.append(binNumber)\n",
    "        \n",
    "        clear_output(wait=True)\n",
    "        print (\"It took {} to process this file\".format(clock.tick()))\n",
    "        \n",
    "        break\n",
    "\n",
    "    total_transient_roi1 = total_s_roi1_ON - total_s_roi1_OFF\n",
    "    \n",
    "#     filename_h5 = outdir + json_file.split('/')[-1].split('.')[0] + \"_TT_export.h5\" \n",
    "#     with h5py.File(filename_h5, \"w\") as file2write:\n",
    "#         file2write.create_dataset(\"RIXS_pumped_roi1_TT\", data = total_s_roi1_ON)\n",
    "#         file2write.create_dataset(\"RIXS_unpumped_roi1_TT\", data = total_s_roi1_OFF)\n",
    "#         file2write.create_dataset(\"RIXS_transient_roi1_TT\", data = total_transient_roi1)\n",
    "#         #file2write.create_dataset(\"RIXS_pumped_roi3\", data = RIXS_pumped_roi3)\n",
    "#         #file2write.create_dataset(\"RIXS_unpumped_roi3\", data = RIXS_unpumped_roi3)\n",
    "#         #file2write.create_dataset(\"RIXS_transient_roi3\", data = RIXS_transient_roi3)   \n",
    "#         file2write.create_dataset(\"Delay_rebin\", data = Delays_rebinned)\n",
    "#         #file2write.create_dataset(\"binNumber\", data = total_binNumber)\n",
    "        \n",
    "#     print(filename_h5)\n",
    "    \n",
    "    #RIXS_transient_roi1.append(total_s_roi1)\n",
    "\n",
    "    #XES_abs_roi1 = np.abs(total_s_roi1)\n",
    "\n",
    "#    XAS_transient_roi1.append(np.sum(XES_abs_roi1[50:80]))\n",
    "#    XAS_transient_roi1_2.append(np.abs(np.sum(total_s_roi1[55:63])) + np.abs(np.sum(total_s_roi1[64:75])))\n",
    "    \n",
    "        ######################### ROI 2 ##############################################\n",
    "\n",
    "#         XAS_roi2.append(np.abs(spectrum_OFF_roi2.sum()))\n",
    "#         XAS_roi2_on.append(np.abs(spectrum_ON_roi2.sum()))\n",
    "#         RIXS_unpumped_roi2.append(spectrum_OFF_roi2)\n",
    "#         Error_unpumped_roi2.append(np.sqrt(spectrum_OFF_roi2))\n",
    "#         RIXS_pumped_roi2.append(spectrum_ON_roi2)\n",
    "#         Error_pumped_roi2.append(np.sqrt(spectrum_ON_roi2))\n",
    "#         RIXS_transient_roi2.append(spectrum_ON_roi2 - spectrum_OFF_roi2)\n",
    "#         Error_transient_roi2.append(np.sqrt((np.sqrt(spectrum_ON_roi2))**2 + (np.sqrt(spectrum_OFF_roi2))**2))\n",
    "\n",
    "#         XES_transient_roi2.append(np.abs(spectrum_ON_roi2 - spectrum_OFF_roi2))\n",
    "#     #    XES_1D.append(np.sum(XES_transient_roi2, axis = 1))\n",
    "\n",
    "#         XAS_transient_roi2.append(np.abs(np.sum(RIXS_transient_roi2)))\n",
    "        #clear_output(wait=True)\n",
    "        \n",
    "    \n",
    "# Energy_eV = np.array(Energy_eV)\n",
    "# Delay_mm = np.array(Delay_mm)\n",
    "# XAS_roi1 = np.array(XAS_roi1)\n",
    "# XAS_roi1_on = np.array(XAS_roi1_on)\n",
    "# RIXS_unpumped_roi1 = np.array(RIXS_unpumped_roi1)\n",
    "# Error_unpumped_roi1 = np.array(Error_unpumped_roi1)\n",
    "# RIXS_pumped_roi1 = np.array(RIXS_pumped_roi1)\n",
    "# Error_pumped_roi1 = np.array(Error_pumped_roi1)\n",
    "# RIXS_transient_roi1 = np.array(RIXS_transient_roi1)\n",
    "# Error_transient_roi1 = np.array(Error_transient_roi1)\n",
    "# XAS_transient_roi1=np.array(XAS_transient_roi1)\n",
    "# XES_transient_roi1=np.array(XES_transient_roi1)\n",
    "# #XES_1D=np.array(XES_1D)\n",
    "# XAS_roi2 = np.array(XAS_roi2)\n",
    "# XAS_roi2_on = np.array(XAS_roi2_on)\n",
    "# RIXS_unpumped_roi2 = np.array(RIXS_unpumped_roi2)\n",
    "# Error_unpumped_roi2 = np.array(Error_unpumped_roi2)\n",
    "# RIXS_pumped_roi2 = np.array(RIXS_pumped_roi2)\n",
    "# Error_pumped_roi2 = np.array(Error_pumped_roi2)\n",
    "# RIXS_transient_roi2 = np.array(RIXS_transient_roi2)\n",
    "# Error_transient_roi2 = np.array(Error_transient_roi2)\n",
    "# XAS_transient_roi2=np.array(XAS_transient_roi2)\n",
    "# XES_transient_roi2=np.array(XES_transient_roi2)\n",
    "# #XES_1D=np.array(XES_1D)\n",
    "    \n",
    "print (\"\\nJob done! It took\", clock.tock(), \"seconds to process all the files\")\n",
    "\n",
    "    \n",
    "-7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(985, 160)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images_pump_roi1_thr.sum(axis = 1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(985, 160)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XES_transient_roi1_ON.shape\n",
    "#len(XES_transient_roi1_ON_tolist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.       ,  0.       ,  0.       ,  0.       ,  0.       ,\n",
       "        0.       ,  0.       ,  0.       ,  0.       ,  0.       ,\n",
       "        0.       ,  0.       ,  0.       ,  0.       ,  0.       ,\n",
       "        0.       ,  0.       ,  0.       ,  0.       ,  0.       ,\n",
       "        0.       ,  0.       ,  0.       ,  0.       ,  0.       ,\n",
       "        0.       ,  0.       ,  0.       ,  0.       ,  0.       ,\n",
       "        0.       ,  0.       ,  0.       ,  0.       ,  5.7101936,\n",
       "        0.       ,  0.       ,  0.       ,  0.       ,  0.       ,\n",
       "        6.428775 ,  0.       ,  0.       ,  0.       ,  0.       ,\n",
       "        0.       ,  0.       ,  0.       ,  0.       ,  0.       ,\n",
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       "        0.       ,  0.       ,  0.       ,  0.       ,  0.       ],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XES_transient_roi1_ON_tolist[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(160,)\n",
      "(985,)\n",
      "(985, 160)\n",
      "(150,)\n"
     ]
    }
   ],
   "source": [
    "print (pixel.shape)\n",
    "print (Delays_corr.shape)\n",
    "print (XES_transient_roi1_ON.shape)\n",
    "print (binList.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/sf/alvra/anaconda/alvra-analysis/lib/python3.6/site-packages/numpy/core/_asarray.py:136: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  return array(a, dtype, copy=False, order=order, subok=True)\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "The number of `values` elements must match the length of each `sample` dimension.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-79-fadd5034e37a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbinned_statistic_2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDelays_corr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpixel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mXES_transient_roi1_ON\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatistic\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'mean'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m150\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m160\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[0;32m/sf/alvra/anaconda/alvra-analysis/lib/python3.6/site-packages/scipy/stats/_binned_statistic.py\u001b[0m in \u001b[0;36mbinned_statistic_2d\u001b[0;34m(x, y, values, statistic, bins, range, expand_binnumbers)\u001b[0m\n\u001b[1;32m    348\u001b[0m     medians, edges, binnumbers = binned_statistic_dd(\n\u001b[1;32m    349\u001b[0m         \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatistic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 350\u001b[0;31m         expand_binnumbers=expand_binnumbers)\n\u001b[0m\u001b[1;32m    351\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    352\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mBinnedStatistic2dResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmedians\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medges\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medges\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbinnumbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/sf/alvra/anaconda/alvra-analysis/lib/python3.6/site-packages/scipy/stats/_binned_statistic.py\u001b[0m in \u001b[0;36mbinned_statistic_dd\u001b[0;34m(sample, values, statistic, bins, range, expand_binnumbers, binned_statistic_result)\u001b[0m\n\u001b[1;32m    552\u001b[0m     \u001b[0;31m# Make sure `values` match `sample`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    553\u001b[0m     \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatistic\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'count'\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mVlen\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mDlen\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[0;32m--> 554\u001b[0;31m         raise AttributeError('The number of `values` elements must match the '\n\u001b[0m\u001b[1;32m    555\u001b[0m                              'length of each `sample` dimension.')\n\u001b[1;32m    556\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: The number of `values` elements must match the length of each `sample` dimension."
     ]
    }
   ],
   "source": [
    "binned_statistic_2d(Delays_corr, pixel, XES_transient_roi1_ON, statistic='mean', bins=[150,160])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(985,)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Delays_corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(985, 160)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(XES_transient_roi1_ON)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_roi1_ON, binEdges, binNumber = binned_statistic(Delays_corr, XES_transient_roi1_ON_tolist, statistic='mean', bins=binList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(160, 149)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_s_roi1_ON.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\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": [
    "plt.figure()\n",
    "#plt.plot(images_pump_roi1_thr.sum(axis = 1)[0]- images_pump_roi2_thr.sum(axis = 1)[0])\n",
    "plt.plot(XES_transient_roi1_ON_tolist[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\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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\" 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\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>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(binNumber)\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = ([XES_transient_roi1_shot[:,i] for i in range(XES_transient_roi1_shot.shape[1])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\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": {
      "text/html": [
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(total_s_roi1.T, extent=[roi1[0], roi1[1], binList[-1], binList[0]], aspect='auto')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(160, 149)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_roi1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:alvra-analysis]",
   "language": "python",
   "name": "conda-env-alvra-analysis-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}