Spike Analysis 500ms.ipynb

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    "### Analyzing Spike Recordings\n",
    "#### Methods from Tong-Wey\n",
    "- The amplifier's high pass filter is set at 10 Hz and low pass filter at 3 kHz.\n",
    "- Nerve cell recordings from  were acquired at 10kHz. \n",
    "- The raw spikes were recorded in a .abf format on Clampex 10.4 by Molecular Devices. The files have be converted into a .atf format, which can be opened with Microsoft Excel. "
   ]
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   "source": [
    "#### Initialize variables"
   ]
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     "text": [
      "Wed Oct 19 10:49:20 SGT 2016\r\n"
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   "source": [
    "!date\n",
    "datadir = '/Users/josesho/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/'\n",
    "atfdir = datadir+'atf/raw/'\n",
    "# Sample rate. MUST BE SAME ACROSS ALL NERVE RECORDINGS.\n",
    "fs = 10000.0       \n",
    "\n",
    "# Here we implement Tracey et al. 2003 bandpass settings.\n",
    "lowcut = 100.0     # discard frequencies lower than this.\n",
    "highcut = 1500.0   # discard frequencies higher than this.\n",
    "order = 5         # order of the Butterworth filter (not exactly sure what this means, \n",
    "                   # but the higher the order, the \"tighter\" the cutoff.\n",
    "lightCol = 'lightgreen' # color of light pulse, for plotting on spike plots and raster plots.\n",
    "dpi = 250 # dpi of figures.\n",
    "\n",
    "# Define the datetimes and timedeltas here.\n",
    "import datetime \n",
    "starttime = datetime.datetime(2016, 7, 1, 0, 0, 3, 156100) # Assigns arbitrary date for easy parsing.\n",
    "#### READ THIS FROM TONG-WEY'S SPREADSHEET; NOT IMPLEMENTED YET! ####\n",
    "# 0.1561 seconds = 156100 microseconds.\n",
    "pulse = datetime.timedelta(milliseconds = 500)\n",
    "pulseInterval = datetime.timedelta(seconds = 2)"
   ]
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   "metadata": {},
   "source": [
    "#### Load Libraries and Custom Commands"
   ]
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    "import seaborn as sb\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from numpy import fft\n",
    "from scipy import signal\n",
    "import quickspikes as qs\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "import bootstrapContrast as bs\n",
    "#%matplotlib inline\n",
    "sb.set_style('ticks')\n",
    "plt.rcParams['svg.fonttype'] = 'none' # Ensures the text in figures are not lines but text.\n",
    "mpl.rcParams['agg.path.chunksize'] = 10000 \n",
    "# Mitigates this error: https://github.com/matplotlib/matplotlib/issues/5907\n",
    "\n",
    "def convert_time(a):\n",
    "    sec = int(a) # get the second\n",
    "    temp_us = round(a, 6) # round to 6 decimal places.\n",
    "    us = str(a).split(\".\")[-1]\n",
    "    mult = 6 - len(us)\n",
    "    # extract the numbers after the decimal place. \n",
    "    # we need to add enough zeros so that we get\n",
    "    # microseconds (1 millionth of a second).\n",
    "    return datetime.datetime(2016, 7, 1, 0, 0, sec, int(us)*(10**mult))\n",
    "\n",
    "def stateChange(data):\n",
    "    import numpy as np\n",
    "    '''Get points where light turned on/off.'''\n",
    "    return np.split(data, np.where(np.diff(data) > stepsize)[0]+1)\n",
    "\n",
    "def butter_bandpass(lowcut, highcut, fs, order=5):\n",
    "    '''Bandpass filter. Taken from http://scipy.github.io/old-wiki/pages/Cookbook/ButterworthBandpass'''\n",
    "    from scipy.signal import butter\n",
    "    nyq = 0.5 * fs\n",
    "    low = lowcut / nyq\n",
    "    high = highcut / nyq\n",
    "    b, a = butter(order, [low, high], btype='band', analog=False)\n",
    "    return b, a\n",
    "\n",
    "def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):\n",
    "    '''Applies custom Butterworth bandpass filter to timeseries.'''\n",
    "    from scipy.signal import lfilter\n",
    "    b, a = butter_bandpass(lowcut, highcut, fs, order=order)\n",
    "    y = lfilter(b, a, data)\n",
    "    return y\n",
    "\n",
    "def plotSpectrum(y, Fs, ax):\n",
    "    \"\"\" Plots a Single-Sided Amplitude Spectrum of y(t)\n",
    "    Modified from: http://glowingpython.blogspot.sg/2011/08/how-to-plot-frequency-spectrum-with.html \"\"\"\n",
    "    import numpy as np\n",
    "    from numpy import fft\n",
    "    n = len(y) # length of the signal\n",
    "    k = np.arange(n)\n",
    "    T = n/Fs\n",
    "    frq = k/T # two sides frequency range\n",
    "    frq = frq[ np.arange( int(n/2) ) ] # one side frequency range\n",
    "\n",
    "    Y = fft.fft(y)/n # fft computing and normalization\n",
    "    Y = Y[ np.arange( int(n/2) ) ]\n",
    "\n",
    "    ax.plot(frq, abs(Y),'r') # plotting the spectrum\n",
    "    ax.set_xlabel('Freq (Hz)', fontsize = 12)\n",
    "    ax.set_ylabel('|Y(freq)|', fontsize = 12)\n",
    "\n",
    "def plotSpectrogram(y, Fs, ax, colmap = 'plasma', ylim = (0,2000)):\n",
    "    \"\"\" Plots a Spectrogram of `y`, given the sampling frequency `fs`.\n",
    "    Modified from: http://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.spectrogram.html \"\"\"\n",
    "    from scipy import signal\n",
    "    import matplotlib.pyplot as plt\n",
    "    cmap = plt.get_cmap(colmap)            # For more colormaps, see \n",
    "                                           # http://matplotlib.org/examples/color/colormaps_reference.html\n",
    "    # (y,x,z) of spectrogram\n",
    "    f, t, Sxx = signal.spectrogram(y, Fs)\n",
    "    ax.pcolormesh(t, f, Sxx, cmap = cmap, axes = ax)\n",
    "    ax.set_ylim(ylim)\n",
    "    ax.set_ylabel('Frequency [Hz]', fontsize = 12)\n",
    "    ax.set_xlabel('Time [sec]', fontsize = 12)\n",
    "    \n",
    "def to_seconds(t, pos):\n",
    "    '''Converts 10kHz timestamp into seconds.'''\n",
    "    sec = str(t/10000)\n",
    "    return sec\n",
    "\n",
    "# I need to give the timeseries itself AND the lighton protocol.\n",
    "def bandpassFilter(series, \n",
    "                   plot = False,\n",
    "                   fname = 'bandpassFilter', \n",
    "                   titlefs = 15,\n",
    "                   fs = 10000.,\n",
    "                   lowcut = 100.,\n",
    "                   highcut = 2000.,\n",
    "                   order = 5,   \n",
    "                   plotsize = (18, 12)):\n",
    "    '''Takes a pandas Series and bandpass filters it with a Butterworth filter.\n",
    "    It can produce two things: a pandas Series of the bandpass filtered data, \n",
    "    and a before-and-after plot with a frequency spectrum and spectrogram (if plot = True).\n",
    "    '''\n",
    "    import matplotlib.pyplot as plt\n",
    "    from matplotlib.ticker import FuncFormatter\n",
    "    secFmt = FuncFormatter(to_seconds)\n",
    "    \n",
    "    # Parse the lighton series. Get the indexes where LED > 0.\n",
    "    # Use these indexes later to 'color in' the illumination epochs.\n",
    "    \n",
    "    # Bandpass filter raw trace according to Tracey et al. 2003 settings: 100Hz – 2kHz.\n",
    "    # Keep a pandas DataFrame version and a np.array version.\n",
    "    # This is because you cannot pipe a pandas DataFrame to the spike detector.\n",
    "    bandpass_orig = butter_bandpass_filter(series, lowcut, highcut, fs, order)\n",
    "    bandpass = pd.Series( bandpass_orig )\n",
    "    bandpass.index = series.index # Set index of bandpass trace.\n",
    "    \n",
    "    # Create figure. \n",
    "    fig, axarr = plt.subplots(nrows = 3, ncols = 2, figsize = plotsize)\n",
    "    \n",
    "    if plot is True:\n",
    "        # Plot the raw trace.\n",
    "        series.plot( ax = axarr[0,0], figsize = plotsize, color = 'k' )\n",
    "        axarr[0,0].set_title( fname + \" Raw Trace\", fontsize = titlefs )\n",
    "        axarr[0,0].yaxis.set_major_formatter(secFmt) # Make sure the x-axis is in seconds.\n",
    "        axarr[0,0].set_ylabel('mV', fontsize = 12)\n",
    "        axarr[0,0].set_ylabel('Time [sec]', fontsize = 12)\n",
    "\n",
    "        # Plot spectrogram of raw trace.\n",
    "        plotSpectrogram( y = series, Fs = fs, ax = axarr[1,0])\n",
    "        axarr[1,0].set_title( fname + \" Raw Spectrogram\", fontsize = titlefs )\n",
    "\n",
    "        # Plot frequency spectrum of raw trace.\n",
    "        plotSpectrum( y = series, Fs = fs, ax = axarr[2,0] )\n",
    "        axarr[2,0].set_title( fname + \" Raw Frequency Spectrum\", fontsize = titlefs )\n",
    "\n",
    "        # Plot the bandpass trace.\n",
    "        bandpass.plot( ax = axarr[0,1], figsize = plotsize, color = 'k' )\n",
    "        axarr[0,1].set_title( fname + \" Bandpass Trace \" + str(lowcut) + \"Hz–\" + str(highcut) + \"Hz\", \n",
    "                             fontsize = titlefs )\n",
    "        axarr[0,1].yaxis.set_major_formatter(secFmt)\n",
    "        axarr[0,1].set_ylabel('mV')\n",
    "\n",
    "        # Plot spectrogram of bandpass trace.\n",
    "        plotSpectrogram( y = bandpass, Fs = fs, ax = axarr[1,1])\n",
    "        axarr[1,1].set_title( fname + \" Bandpass Spectrogram\", fontsize = titlefs )\n",
    "\n",
    "        # Plot frequency spectrum of bandpass trace.\n",
    "        plotSpectrum( y = bandpass, Fs = fs, ax = axarr[2,1] )\n",
    "        axarr[2,1].set_title( fname + \" Bandpass Frequency Spectrum\", fontsize = titlefs )\n",
    "\n",
    "        # Aesthetic tweaks\n",
    "        for arr in axarr:\n",
    "            for a in arr:\n",
    "                sb.despine(ax = a)\n",
    "        fig.tight_layout()\n",
    "        return fig, bandpass_orig\n",
    "    \n",
    "    else: # If we are not plotting anything.\n",
    "        return bandpass_orig"
   ]
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    "# # Read in led protocols. \n",
    "# # These protocol files are large (>10MB) and will take about 30sec each to read in.\n",
    "# # Instead I will load the pickled datasets. If these change, rerun the lines below.\n",
    "# led1pro = pd.read_excel('/Users/josesho/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/LED1 protocol.xlsx',\n",
    "#                        index_col = 'Time (s)')\n",
    "# led30pro = pd.read_excel('/Users/josesho/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/30s_pulse/LED 30s protocol.xlsx',\n",
    "#                        index_col = 'Time (s)')\n",
    "\n",
    "# # Read in experiment info.\n",
    "# exptInfo = pd.read_excel('/Users/josesho/Google Drive/ACC Lab/ACR/Larval nerve ephys/Expt Info Spreadsheets/Expt Info Ephys GtACR1 all data.xlsx')\n",
    "\n",
    "# # Pickle the data.\n",
    "# led1pro.to_pickle('/Users/josesho/Google Drive/ACC Lab/ACR/Larval nerve ephys/Expt Info Spreadsheets/led1pro.pickle')\n",
    "# led30pro.to_pickle('/Users/josesho/Google Drive/ACC Lab/ACR/Larval nerve ephys/Expt Info Spreadsheets/led30pro.pickle')\n",
    "# exptInfo.to_pickle('/Users/josesho/Google Drive/ACC Lab/ACR/Larval nerve ephys/Expt Info Spreadsheets/exptInfo.pickle')\n",
    "\n",
    "# Read in pickled data. Very fast.\n",
    "led1pro = pd.read_pickle('/Users/josesho/Google Drive/ACC Lab/ACR/Larval nerve ephys/Expt Info Spreadsheets/led1pro.pickle')\n",
    "led30pro = pd.read_pickle('/Users/josesho/Google Drive/ACC Lab/ACR/Larval nerve ephys/Expt Info Spreadsheets/led30pro.pickle')\n",
    "exptInfo = pd.read_pickle('/Users/josesho/Google Drive/ACC Lab/ACR/Larval nerve ephys/Expt Info Spreadsheets/exptInfo.pickle')\n",
    "\n",
    "# Identify light change indexes, add into a dictionary.\n",
    "lightChangeProtocols = {'LED1' : np.where(np.diff(led1pro['LED1']) != 0)[0]+1,\n",
    "                        'LED 30s' : np.where(np.diff(led30pro['LED 30s']) != 0)[0]+1\n",
    "                       }"
   ]
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      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16717001.atf\n",
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      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16925066.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16925066.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16925066.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16925076.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16925076.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16925076.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16925077.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16925077.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16925077.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16925078.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16925078.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16925078.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16926000.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16926000.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16926000.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16926001.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16926001.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16926001.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16926002.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16926002.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16926002.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16926012.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16926012.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16926012.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16926013.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16926013.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16926013.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16926014.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16926014.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16926014.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16926024.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16926024.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16926024.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16926025.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16926025.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16926025.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16926026.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16926026.atf\n",
      "/Users/jacuzzijo/Google Drive/ACC Lab/ACR/Larval nerve ephys/data/500ms_pulse/atf/raw/16926026.atf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jacuzzijo/anaconda/lib/python3.4/site-packages/matplotlib/pyplot.py:516: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  max_open_warning, RuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "# This cell will read all the raw ATF files and bandpass filter them.\n",
    "# All the bandpass filtered signals are stored in binary .npy format.\n",
    "# If you have already done this, do not run this cell unless you want to change the bandpass settings.\n",
    "\n",
    "# Basic variables to initialise.\n",
    "makePlot = False # if we want to produce a plot. If we don't and only want the bandpass filtered signal, set to False.\n",
    "\n",
    "for subdir, dirs, files in os.walk(atfdir): # loop thru the subdirectories in the ATF_files folder. \n",
    "    for file in files:\n",
    "        if (len(file.split('.')) > 1) and (file.split('.')[1] == 'atf'):\n",
    "            # if and only if the file extension is `.atf`...\n",
    "            fname = os.path.join(subdir, file) # pathname of .atf file to read in.\n",
    "            dfname = file.split('.')[0]        # dfname to print in plots.\n",
    "            df = pd.read_csv(fname,\n",
    "                 sep='\\t',\n",
    "                 skiprows = range(0,10),       # Skips the first 10 rows.\n",
    "                 index_col = 'Time (s)'\n",
    "                ) \n",
    "            # Get the relevant row from experiment info for reference and details. \n",
    "            tempExptInfo = exptInfo[ exptInfo['Filename'] == int(dfname) ]\n",
    "            # Get illumination distance.\n",
    "            illumDist = str(tempExptInfo.iloc[0]['Illumination_mm'])\n",
    "            # Get Genotype_LarvaID_abdominal segment\n",
    "            genoLarvaID = tempExptInfo.iloc[0]['Genotype_Larva']\n",
    "            abSeg = str(tempExptInfo.iloc[0]['Abdominal segment'])\n",
    "            # Get light epoch from exptInfo.\n",
    "            protocol = tempExptInfo.iloc[0]['Protocol']\n",
    "\n",
    "            for ix, c in enumerate(df.columns):\n",
    "                # Parse trace title.\n",
    "                name = dfname + '_' + df.columns[ix]\n",
    "                # Make a nice title for entire figure.\n",
    "                title = name + \"_\" + protocol + \"_\" + illumDist + \"mm_\" + genoLarvaID + \"_\" + abSeg\n",
    "                \n",
    "                # obtain bandpass filtered trace and plot basic frequency spectrum and spectrogram if so desired.\n",
    "                if makePlot is True:\n",
    "                    figTrace, bp = bandpassFilter(df[c], # the timeseries\n",
    "                                                  plot = makePlot,\n",
    "                                                  fname = name,       \n",
    "                                                  fs = fs,\n",
    "                                                  lowcut = lowcut,\n",
    "                                                  highcut = highcut,\n",
    "                                                  order = order\n",
    "                                                  )\n",
    "                    # for each lightEpoch, color in the raw traces (axes 0 and 1, the top-row plots.)\n",
    "                    for ax in [0,1]:\n",
    "                        for idx in np.arange(0, len(lightChangeIdx), 2):\n",
    "                            figTrace.get_axes()[ax].axvspan(xmin = lightChangeIdx[idx]/fs, \n",
    "                                                            xmax = lightChangeIdx[idx+1]/fs, \n",
    "                                                            ymin = -1, \n",
    "                                                            ymax = 1, \n",
    "                                                            alpha = 0.2, \n",
    "                                                            color = 'grey')\n",
    "                    # Add the title to the _bottom_ of the plot.\n",
    "                    figTrace.suptitle(title, x = 0.5, y = 0.015, fontsize = 15)\n",
    "                    # Save the figure.\n",
    "                    figTrace.savefig(datadir + \"/bandpassPlots/\" + title + '.' + fileformat, format = fileformat, dpi = dpi)\n",
    "                    # closes the figure to save memory.\n",
    "                    plt.close(figTrace)\n",
    "\n",
    "                else: # if we only want the bandpass filtered trace.\n",
    "                    bp = bandpassFilter(df[c], \n",
    "                                        plot = makePlot,\n",
    "                                        fname = name,    \n",
    "                                        fs = fs,\n",
    "                                        lowcut = lowcut,\n",
    "                                        highcut = highcut,\n",
    "                                        order = order\n",
    "                                       )\n",
    "                    \n",
    "                # Save mean and std of bandpass signal for later spike detection.\n",
    "                bp_mean = bp.mean()\n",
    "                bp_std = bp.std()\n",
    "                # Save bandpass filtered trace as .npy\n",
    "                np.save(datadir + \"/atf/bandpass_filtered/\" + title + '_bandpassFiltered_' + str(int(lowcut)) + '-' + str(int(highcut)) + 'Hz.npy',\n",
    "                       bp)\n",
    "                # if processing jammed up, we can identify the file.\n",
    "                print(fname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-10-13T14:49:32.594233",
     "start_time": "2016-10-13T14:49:32.356691"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spike detection settings:\n",
      "Scaled Threshold (in SD) = 2.58\n",
      "Window Size = 500\n",
      "16717001_Trace #1 (mV)_LED1_280mm_nSyb-Gal4>UAS-dGtACR1_larva3_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16717001_Trace #2 (mV)_LED1_280mm_nSyb-Gal4>UAS-dGtACR1_larva3_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16717001_Trace #3 (mV)_LED1_280mm_nSyb-Gal4>UAS-dGtACR1_larva3_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16717002_Trace #1 (mV)_LED1_280mm_nSyb-Gal4>UAS-dGtACR1_larva2_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16717002_Trace #2 (mV)_LED1_280mm_nSyb-Gal4>UAS-dGtACR1_larva2_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16717002_Trace #3 (mV)_LED1_280mm_nSyb-Gal4>UAS-dGtACR1_larva2_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16719022_Trace #1 (mV)_LED1_280mm_nSyb-Gal4>UAS-dGtACR1_larva1_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16719022_Trace #2 (mV)_LED1_280mm_nSyb-Gal4>UAS-dGtACR1_larva1_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16719022_Trace #3 (mV)_LED1_280mm_nSyb-Gal4>UAS-dGtACR1_larva1_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16719023_Trace #1 (mV)_LED1_280mm_nSyb-Gal4>UAS-dGtACR1_larva1_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16719023_Trace #2 (mV)_LED1_280mm_nSyb-Gal4>UAS-dGtACR1_larva1_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16719023_Trace #3 (mV)_LED1_280mm_nSyb-Gal4>UAS-dGtACR1_larva1_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16719043_Trace #1 (mV)_LED1_280mm_UAS-dGtACR1_larva2_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16719043_Trace #2 (mV)_LED1_280mm_UAS-dGtACR1_larva2_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16719043_Trace #3 (mV)_LED1_280mm_UAS-dGtACR1_larva2_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16719044_Trace #1 (mV)_LED1_280mm_UAS-dGtACR1_larva3_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16719044_Trace #2 (mV)_LED1_280mm_UAS-dGtACR1_larva3_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16719044_Trace #3 (mV)_LED1_280mm_UAS-dGtACR1_larva3_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16719045_Trace #1 (mV)_LED1_280mm_UAS-dGtACR1_larva1_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16719045_Trace #2 (mV)_LED1_280mm_UAS-dGtACR1_larva1_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16719045_Trace #3 (mV)_LED1_280mm_UAS-dGtACR1_larva1_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925004_Trace #1 (mV)_LED1_280mm_UAS-dGtACR1_larva4_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925004_Trace #2 (mV)_LED1_280mm_UAS-dGtACR1_larva4_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925004_Trace #3 (mV)_LED1_280mm_UAS-dGtACR1_larva4_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925005_Trace #1 (mV)_LED1_330mm_UAS-dGtACR1_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925005_Trace #2 (mV)_LED1_330mm_UAS-dGtACR1_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925005_Trace #3 (mV)_LED1_330mm_UAS-dGtACR1_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925007_Trace #1 (mV)_LED1_220mm_UAS-dGtACR1_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925007_Trace #2 (mV)_LED1_220mm_UAS-dGtACR1_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925007_Trace #3 (mV)_LED1_220mm_UAS-dGtACR1_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925008_Trace #1 (mV)_LED1_110mm_UAS-dGtACR1_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925008_Trace #2 (mV)_LED1_110mm_UAS-dGtACR1_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925008_Trace #3 (mV)_LED1_110mm_UAS-dGtACR1_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925028_Trace #1 (mV)_LED1_330mm_UAS-dGtACR1_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925028_Trace #2 (mV)_LED1_330mm_UAS-dGtACR1_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925028_Trace #3 (mV)_LED1_330mm_UAS-dGtACR1_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925029_Trace #1 (mV)_LED1_220mm_UAS-dGtACR1_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925029_Trace #2 (mV)_LED1_220mm_UAS-dGtACR1_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925029_Trace #3 (mV)_LED1_220mm_UAS-dGtACR1_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925030_Trace #1 (mV)_LED1_110mm_UAS-dGtACR1_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925030_Trace #2 (mV)_LED1_110mm_UAS-dGtACR1_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925030_Trace #3 (mV)_LED1_110mm_UAS-dGtACR1_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925040_Trace #1 (mV)_LED1_330mm_UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925040_Trace #2 (mV)_LED1_330mm_UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925040_Trace #3 (mV)_LED1_330mm_UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925041_Trace #1 (mV)_LED1_220mm_UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925041_Trace #2 (mV)_LED1_220mm_UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925041_Trace #3 (mV)_LED1_220mm_UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925042_Trace #1 (mV)_LED1_110mm_UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925042_Trace #2 (mV)_LED1_110mm_UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925042_Trace #3 (mV)_LED1_110mm_UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925052_Trace #1 (mV)_LED1_330mm_nSyb-Gal4>UAS-dGtACR1_larva1_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925052_Trace #2 (mV)_LED1_330mm_nSyb-Gal4>UAS-dGtACR1_larva1_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925052_Trace #3 (mV)_LED1_330mm_nSyb-Gal4>UAS-dGtACR1_larva1_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925053_Trace #1 (mV)_LED1_220mm_nSyb-Gal4>UAS-dGtACR1_larva1_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925053_Trace #2 (mV)_LED1_220mm_nSyb-Gal4>UAS-dGtACR1_larva1_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925053_Trace #3 (mV)_LED1_220mm_nSyb-Gal4>UAS-dGtACR1_larva1_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925054_Trace #1 (mV)_LED1_110mm_nSyb-Gal4>UAS-dGtACR1_larva1_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925054_Trace #2 (mV)_LED1_110mm_nSyb-Gal4>UAS-dGtACR1_larva1_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925054_Trace #3 (mV)_LED1_110mm_nSyb-Gal4>UAS-dGtACR1_larva1_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925064_Trace #1 (mV)_LED1_330mm_nSyb-Gal4>UAS-dGtACR1_larva2_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925064_Trace #2 (mV)_LED1_330mm_nSyb-Gal4>UAS-dGtACR1_larva2_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925064_Trace #3 (mV)_LED1_330mm_nSyb-Gal4>UAS-dGtACR1_larva2_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925065_Trace #1 (mV)_LED1_220mm_nSyb-Gal4>UAS-dGtACR1_larva2_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925065_Trace #2 (mV)_LED1_220mm_nSyb-Gal4>UAS-dGtACR1_larva2_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925065_Trace #3 (mV)_LED1_220mm_nSyb-Gal4>UAS-dGtACR1_larva2_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925066_Trace #1 (mV)_LED1_110mm_nSyb-Gal4>UAS-dGtACR1_larva2_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925066_Trace #2 (mV)_LED1_110mm_nSyb-Gal4>UAS-dGtACR1_larva2_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925066_Trace #3 (mV)_LED1_110mm_nSyb-Gal4>UAS-dGtACR1_larva2_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925076_Trace #1 (mV)_LED1_330mm_nSyb-Gal4>UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925076_Trace #2 (mV)_LED1_330mm_nSyb-Gal4>UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925076_Trace #3 (mV)_LED1_330mm_nSyb-Gal4>UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925077_Trace #1 (mV)_LED1_220mm_nSyb-Gal4>UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925077_Trace #2 (mV)_LED1_220mm_nSyb-Gal4>UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925077_Trace #3 (mV)_LED1_220mm_nSyb-Gal4>UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925078_Trace #1 (mV)_LED1_110mm_nSyb-Gal4>UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925078_Trace #2 (mV)_LED1_110mm_nSyb-Gal4>UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16925078_Trace #3 (mV)_LED1_110mm_nSyb-Gal4>UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16926000_Trace #1 (mV)_LED1_330mm_nSyb-Gal4_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16926000_Trace #2 (mV)_LED1_330mm_nSyb-Gal4_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16926000_Trace #3 (mV)_LED1_330mm_nSyb-Gal4_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16926001_Trace #1 (mV)_LED1_220mm_nSyb-Gal4_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16926001_Trace #2 (mV)_LED1_220mm_nSyb-Gal4_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16926001_Trace #3 (mV)_LED1_220mm_nSyb-Gal4_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16926002_Trace #1 (mV)_LED1_110mm_nSyb-Gal4_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16926002_Trace #2 (mV)_LED1_110mm_nSyb-Gal4_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16926002_Trace #3 (mV)_LED1_110mm_nSyb-Gal4_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16926012_Trace #1 (mV)_LED1_330mm_nSyb-Gal4_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16926012_Trace #2 (mV)_LED1_330mm_nSyb-Gal4_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16926012_Trace #3 (mV)_LED1_330mm_nSyb-Gal4_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16926013_Trace #1 (mV)_LED1_220mm_nSyb-Gal4_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16926013_Trace #2 (mV)_LED1_220mm_nSyb-Gal4_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16926013_Trace #3 (mV)_LED1_220mm_nSyb-Gal4_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16926014_Trace #1 (mV)_LED1_110mm_nSyb-Gal4_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16926014_Trace #2 (mV)_LED1_110mm_nSyb-Gal4_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16926014_Trace #3 (mV)_LED1_110mm_nSyb-Gal4_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16926024_Trace #1 (mV)_LED1_330mm_nSyb-Gal4_larva3_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16926024_Trace #2 (mV)_LED1_330mm_nSyb-Gal4_larva3_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16926024_Trace #3 (mV)_LED1_330mm_nSyb-Gal4_larva3_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16926025_Trace #1 (mV)_LED1_220mm_nSyb-Gal4_larva3_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16926025_Trace #2 (mV)_LED1_220mm_nSyb-Gal4_larva3_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16926025_Trace #3 (mV)_LED1_220mm_nSyb-Gal4_larva3_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16926026_Trace #1 (mV)_LED1_110mm_nSyb-Gal4_larva3_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16926026_Trace #2 (mV)_LED1_110mm_nSyb-Gal4_larva3_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n",
      "16926026_Trace #3 (mV)_LED1_110mm_nSyb-Gal4_larva3_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58\n"
     ]
    }
   ],
   "source": [
    "# This cell will read stored bandpass signals and run spike detection on them.\n",
    "# All the bandpass filtered signals are stored in binary .npy format.\n",
    "# The detected spike IDs (indices corresponding to the index of the corresponding raw ATF file) are stored as .npy format.\n",
    "# If you have already done this, do not run UNLESS you want to tweak the spike detection settings. \n",
    "\n",
    "# The spike detector settings.\n",
    "threshold = 2.58 # This is the scaled threshold, in SD of the signal. A spike must cross this threshold to count.\n",
    "window = 500    # A spike cannot be longer than this window. Default of 500 will look for signals below 200Hz. \n",
    "\n",
    "print(\"Spike detection settings:\")\n",
    "print(\"Scaled Threshold (in SD) =\", threshold)\n",
    "print(\"Window Size =\", window)\n",
    "\n",
    "bpdir = datadir + \"/atf/bandpass_filtered/\"\n",
    "for subdir, dirs, files in os.walk(bpdir): # loop thru the subdirectories in the folder with the bandpass filtered data. \n",
    "    for file in files:\n",
    "        if (len(file.split('.')) > 1) and (file.split('.')[1] == 'npy'):\n",
    "            # if and only if the file extension is `.npy`...\n",
    "            signalname = file.split('.')[0]\n",
    "            atfname = file.split('_')[0]\n",
    "            signal = np.load(bpdir+file) # Read in the pickled bandpass signal.\n",
    "            \n",
    "            # Perform spike detection ACROSS ENTIRE BANDPASS SIGNAL.\n",
    "            # For more info see https://github.com/melizalab/quickspikes\n",
    "            d = qs.detector(threshold, window)\n",
    "            d.scale_thresh(np.mean(signal), np.std(signal))\n",
    "            spikes_idx = d.send(signal)\n",
    "            \n",
    "            # Turn the list generated into a numpy array.\n",
    "            spikes_idx = np.array(spikes_idx)\n",
    "            # Create filename.\n",
    "            signalname = signalname + \"_spikes--WindowSize\" + str(window) + \"--ScaledThresholdSD\" + str(threshold)\n",
    "            # Save as binary .npy format. \n",
    "            np.save(datadir+'threshold_'+str(threshold)+'_SD/spikes/npy_files/'+signalname+\".npy\", spikes_idx)\n",
    "            # to see where (if) the code gums up.\n",
    "            print(signalname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-10-13T14:59:43.848805",
     "start_time": "2016-10-13T14:49:32.595936"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16717001_Trace #1 (mV)_LED1_280mm_nSyb-Gal4>UAS-dGtACR1_larva3_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16717001_Trace #2 (mV)_LED1_280mm_nSyb-Gal4>UAS-dGtACR1_larva3_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16717001_Trace #3 (mV)_LED1_280mm_nSyb-Gal4>UAS-dGtACR1_larva3_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16717002_Trace #1 (mV)_LED1_280mm_nSyb-Gal4>UAS-dGtACR1_larva2_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16717002_Trace #2 (mV)_LED1_280mm_nSyb-Gal4>UAS-dGtACR1_larva2_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16717002_Trace #3 (mV)_LED1_280mm_nSyb-Gal4>UAS-dGtACR1_larva2_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16719022_Trace #1 (mV)_LED1_280mm_nSyb-Gal4>UAS-dGtACR1_larva1_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16719022_Trace #2 (mV)_LED1_280mm_nSyb-Gal4>UAS-dGtACR1_larva1_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16719022_Trace #3 (mV)_LED1_280mm_nSyb-Gal4>UAS-dGtACR1_larva1_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16719023_Trace #1 (mV)_LED1_280mm_nSyb-Gal4>UAS-dGtACR1_larva1_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16719023_Trace #2 (mV)_LED1_280mm_nSyb-Gal4>UAS-dGtACR1_larva1_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16719023_Trace #3 (mV)_LED1_280mm_nSyb-Gal4>UAS-dGtACR1_larva1_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16719043_Trace #1 (mV)_LED1_280mm_UAS-dGtACR1_larva2_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16719043_Trace #2 (mV)_LED1_280mm_UAS-dGtACR1_larva2_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16719043_Trace #3 (mV)_LED1_280mm_UAS-dGtACR1_larva2_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16719044_Trace #1 (mV)_LED1_280mm_UAS-dGtACR1_larva3_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16719044_Trace #2 (mV)_LED1_280mm_UAS-dGtACR1_larva3_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16719044_Trace #3 (mV)_LED1_280mm_UAS-dGtACR1_larva3_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16719045_Trace #1 (mV)_LED1_280mm_UAS-dGtACR1_larva1_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16719045_Trace #2 (mV)_LED1_280mm_UAS-dGtACR1_larva1_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16719045_Trace #3 (mV)_LED1_280mm_UAS-dGtACR1_larva1_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925004_Trace #1 (mV)_LED1_280mm_UAS-dGtACR1_larva4_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925004_Trace #2 (mV)_LED1_280mm_UAS-dGtACR1_larva4_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925004_Trace #3 (mV)_LED1_280mm_UAS-dGtACR1_larva4_segmentUnknown_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925005_Trace #1 (mV)_LED1_330mm_UAS-dGtACR1_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925005_Trace #2 (mV)_LED1_330mm_UAS-dGtACR1_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925005_Trace #3 (mV)_LED1_330mm_UAS-dGtACR1_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925007_Trace #1 (mV)_LED1_220mm_UAS-dGtACR1_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925007_Trace #2 (mV)_LED1_220mm_UAS-dGtACR1_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925007_Trace #3 (mV)_LED1_220mm_UAS-dGtACR1_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925008_Trace #1 (mV)_LED1_110mm_UAS-dGtACR1_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925008_Trace #2 (mV)_LED1_110mm_UAS-dGtACR1_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925008_Trace #3 (mV)_LED1_110mm_UAS-dGtACR1_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925028_Trace #1 (mV)_LED1_330mm_UAS-dGtACR1_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925028_Trace #2 (mV)_LED1_330mm_UAS-dGtACR1_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925028_Trace #3 (mV)_LED1_330mm_UAS-dGtACR1_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925029_Trace #1 (mV)_LED1_220mm_UAS-dGtACR1_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925029_Trace #2 (mV)_LED1_220mm_UAS-dGtACR1_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925029_Trace #3 (mV)_LED1_220mm_UAS-dGtACR1_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925030_Trace #1 (mV)_LED1_110mm_UAS-dGtACR1_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925030_Trace #2 (mV)_LED1_110mm_UAS-dGtACR1_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925030_Trace #3 (mV)_LED1_110mm_UAS-dGtACR1_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925040_Trace #1 (mV)_LED1_330mm_UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925040_Trace #2 (mV)_LED1_330mm_UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925040_Trace #3 (mV)_LED1_330mm_UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925041_Trace #1 (mV)_LED1_220mm_UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925041_Trace #2 (mV)_LED1_220mm_UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925041_Trace #3 (mV)_LED1_220mm_UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925042_Trace #1 (mV)_LED1_110mm_UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925042_Trace #2 (mV)_LED1_110mm_UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925042_Trace #3 (mV)_LED1_110mm_UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925052_Trace #1 (mV)_LED1_330mm_nSyb-Gal4>UAS-dGtACR1_larva1_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925052_Trace #2 (mV)_LED1_330mm_nSyb-Gal4>UAS-dGtACR1_larva1_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925052_Trace #3 (mV)_LED1_330mm_nSyb-Gal4>UAS-dGtACR1_larva1_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925053_Trace #1 (mV)_LED1_220mm_nSyb-Gal4>UAS-dGtACR1_larva1_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925053_Trace #2 (mV)_LED1_220mm_nSyb-Gal4>UAS-dGtACR1_larva1_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925053_Trace #3 (mV)_LED1_220mm_nSyb-Gal4>UAS-dGtACR1_larva1_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925054_Trace #1 (mV)_LED1_110mm_nSyb-Gal4>UAS-dGtACR1_larva1_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925054_Trace #2 (mV)_LED1_110mm_nSyb-Gal4>UAS-dGtACR1_larva1_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925054_Trace #3 (mV)_LED1_110mm_nSyb-Gal4>UAS-dGtACR1_larva1_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925064_Trace #1 (mV)_LED1_330mm_nSyb-Gal4>UAS-dGtACR1_larva2_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925064_Trace #2 (mV)_LED1_330mm_nSyb-Gal4>UAS-dGtACR1_larva2_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925064_Trace #3 (mV)_LED1_330mm_nSyb-Gal4>UAS-dGtACR1_larva2_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925065_Trace #1 (mV)_LED1_220mm_nSyb-Gal4>UAS-dGtACR1_larva2_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925065_Trace #2 (mV)_LED1_220mm_nSyb-Gal4>UAS-dGtACR1_larva2_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925065_Trace #3 (mV)_LED1_220mm_nSyb-Gal4>UAS-dGtACR1_larva2_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925066_Trace #1 (mV)_LED1_110mm_nSyb-Gal4>UAS-dGtACR1_larva2_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925066_Trace #2 (mV)_LED1_110mm_nSyb-Gal4>UAS-dGtACR1_larva2_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925066_Trace #3 (mV)_LED1_110mm_nSyb-Gal4>UAS-dGtACR1_larva2_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925076_Trace #1 (mV)_LED1_330mm_nSyb-Gal4>UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925076_Trace #2 (mV)_LED1_330mm_nSyb-Gal4>UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925076_Trace #3 (mV)_LED1_330mm_nSyb-Gal4>UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925077_Trace #1 (mV)_LED1_220mm_nSyb-Gal4>UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925077_Trace #2 (mV)_LED1_220mm_nSyb-Gal4>UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925077_Trace #3 (mV)_LED1_220mm_nSyb-Gal4>UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925078_Trace #1 (mV)_LED1_110mm_nSyb-Gal4>UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925078_Trace #2 (mV)_LED1_110mm_nSyb-Gal4>UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16925078_Trace #3 (mV)_LED1_110mm_nSyb-Gal4>UAS-dGtACR1_larva3_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16926000_Trace #1 (mV)_LED1_330mm_nSyb-Gal4_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16926000_Trace #2 (mV)_LED1_330mm_nSyb-Gal4_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16926000_Trace #3 (mV)_LED1_330mm_nSyb-Gal4_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16926001_Trace #1 (mV)_LED1_220mm_nSyb-Gal4_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16926001_Trace #2 (mV)_LED1_220mm_nSyb-Gal4_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16926001_Trace #3 (mV)_LED1_220mm_nSyb-Gal4_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16926002_Trace #1 (mV)_LED1_110mm_nSyb-Gal4_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16926002_Trace #2 (mV)_LED1_110mm_nSyb-Gal4_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16926002_Trace #3 (mV)_LED1_110mm_nSyb-Gal4_larva1_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16926012_Trace #1 (mV)_LED1_330mm_nSyb-Gal4_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16926012_Trace #2 (mV)_LED1_330mm_nSyb-Gal4_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16926012_Trace #3 (mV)_LED1_330mm_nSyb-Gal4_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16926013_Trace #1 (mV)_LED1_220mm_nSyb-Gal4_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16926013_Trace #2 (mV)_LED1_220mm_nSyb-Gal4_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16926013_Trace #3 (mV)_LED1_220mm_nSyb-Gal4_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16926014_Trace #1 (mV)_LED1_110mm_nSyb-Gal4_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16926014_Trace #2 (mV)_LED1_110mm_nSyb-Gal4_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16926014_Trace #3 (mV)_LED1_110mm_nSyb-Gal4_larva2_seg4_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16926024_Trace #1 (mV)_LED1_330mm_nSyb-Gal4_larva3_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16926024_Trace #2 (mV)_LED1_330mm_nSyb-Gal4_larva3_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16926024_Trace #3 (mV)_LED1_330mm_nSyb-Gal4_larva3_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16926025_Trace #1 (mV)_LED1_220mm_nSyb-Gal4_larva3_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16926025_Trace #2 (mV)_LED1_220mm_nSyb-Gal4_larva3_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16926025_Trace #3 (mV)_LED1_220mm_nSyb-Gal4_larva3_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16926026_Trace #1 (mV)_LED1_110mm_nSyb-Gal4_larva3_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16926026_Trace #2 (mV)_LED1_110mm_nSyb-Gal4_larva3_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n",
      "16926026_Trace #3 (mV)_LED1_110mm_nSyb-Gal4_larva3_seg3_bandpassFiltered_100-1500Hz_spikes--WindowSize500--ScaledThresholdSD2.58.npy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jacuzzijo/anaconda/lib/python3.4/site-packages/matplotlib/pyplot.py:516: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  max_open_warning, RuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "# This cell will read all spike IDs and create spike-rug plots for each bandpass filtered trace.\n",
    "# It will create spike-rug plots for each trace as well.\n",
    "# All the spike IDs are stored in binary .npy format.\n",
    "# Line below turns off interactive mode for matplotlib.\n",
    "# See http://matplotlib.org/faq/usage_faq.html#non-interactive-example \n",
    "plt.ioff()\n",
    "# Load patches for plotting light on pulses.\n",
    "import matplotlib.patches as patches\n",
    "\n",
    "# Define the correct directories.\n",
    "threshold = 2.58 # This is the scaled threshold, in SD of the signal.\n",
    "                 # Change as appropriate for plotting and analysis.\n",
    "bpdir = datadir + \"atf/bandpass_filtered/\"\n",
    "spikedir = datadir + 'threshold_'+str(threshold)+'_SD/spikes/npy_files/'\n",
    "\n",
    "# In prep to make sure the x-axis is properly formatted.\n",
    "secFmt = FuncFormatter(to_seconds)\n",
    "\n",
    "# Define the before and after padding, in terms of multiples of the light pulse.\n",
    "# For 500ms we set this as twice the pulse length, i.e. 1s.\n",
    "# This CANNOT be less than 0.\n",
    "beforePad = 2 \n",
    "afterPad = 2\n",
    "\n",
    "for subdir, dirs, files in os.walk(spikedir):\n",
    "    for file in files:\n",
    "        if (len(file.split('.')) > 1) and (file.split('.')[-1] == 'npy'):\n",
    "            # If and only if the file extension is `.npy`...\n",
    "            # Read in the correct bandpass signal.\n",
    "            bandpassFilename = bpdir + file.split('_spikes')[0] +'.npy'\n",
    "            print(file)\n",
    "            bandpass = pd.Series(np.load(bandpassFilename)) # make sure is a pandas Series.\n",
    "            # Read in the spike indicies.\n",
    "            spike_idx = np.load(spikedir+file)\n",
    "            \n",
    "            # Read in the correct protocol from the filename.\n",
    "            protocol = file.split('_')[2]\n",
    "            # Get light epochs from exptInfo.\n",
    "            lightChangeIdx = lightChangeProtocols[ protocol ]\n",
    "            \n",
    "            # Make spike-rug plot for entire trace.\n",
    "            figSpikeRug, axx = plt.subplots(1, figsize = (15,7))\n",
    "            pd.Series(bandpass).plot( ax = axx, color = 'k' )\n",
    "\n",
    "            for spike in spike_idx:\n",
    "                axx.axvline(x = spike, \n",
    "                            ymax = 0.05, # the height of the rugtick will be this fraction of the plot.\n",
    "                            color = 'k', \n",
    "                            linewidth = 0.5,\n",
    "                            alpha = 0.5)\n",
    "\n",
    "            # Prepare y-axis for plotting of lightOn pulse rectangles.\n",
    "            # First, get the tick interval.\n",
    "            tickInt = axx.yaxis.get_ticklocs()[1] - axx.yaxis.get_ticklocs()[0]\n",
    "            # Then, get the current view interval.\n",
    "            minY, maxY = axx.yaxis.get_view_interval()\n",
    "            # Make sure, after adjustment, the y-axis is symmetrical about zero.\n",
    "            halfY = max([minY - tickInt, maxY + tickInt])\n",
    "            axx.yaxis.set_view_interval(-halfY, halfY)\n",
    "            \n",
    "            # Draw red line to indicate mean\n",
    "            axx.axhline(y = np.mean(bandpass), lw = 0.5, color = 'red')\n",
    "            # Draw red line to indicate the spike threshold.\n",
    "            # Threshold should have been defined in cell above; double-check with filename...\n",
    "            axx.axhline(y = threshold * np.std(bandpass), lw = 0.5, color = 'red')\n",
    "\n",
    "            # Aesthetic tweaks.\n",
    "            axx.set_ylabel('mV', fontsize = 12)\n",
    "            axx.set_xlabel('Time [sec]', fontsize = 12)\n",
    "            axx.set_title(file, fontsize = 9)\n",
    "            # Make sure the x-axis is in seconds.\n",
    "            axx.xaxis.set_major_formatter(secFmt)\n",
    "            \n",
    "            # Loop thru each light pulse and make each light pulse its own spikerug plot.\n",
    "            for n, idx in enumerate(np.arange(0, len(lightChangeIdx), 2)):\n",
    "                pulseNum = str(n+1)\n",
    "                # For each pulse, extract the pulse length\n",
    "                pulseLengthIdx = lightChangeIdx[idx+1] - lightChangeIdx[idx]\n",
    "                pulseLengthSec = pulseLengthIdx/fs\n",
    "                \n",
    "                # Draw the rectangle for this pulse.\n",
    "                axx.add_patch(\n",
    "                    patches.Rectangle(\n",
    "                        ( lightChangeIdx[idx], maxY ),   # xy coords of left most corner of rect\n",
    "                        pulseLengthIdx,       # width of rect.\n",
    "                        tickInt/5,            # height of rect\n",
    "                        color = lightCol\n",
    "                        )\n",
    "                    )\n",
    "                \n",
    "                # Zoom in and save the figure.\n",
    "                axx.set_xlim( lightChangeIdx[idx] - (pulseLengthIdx*beforePad),\n",
    "                             lightChangeIdx[idx+1] + (pulseLengthIdx*afterPad) )\n",
    "                sb.despine(ax = axx)\n",
    "                for f in ['svg', 'png']:\n",
    "                    figSpikeRug.savefig(datadir + 'threshold_'+str(threshold)+'_SD/spikes/plots/pulses/'+file+'_pulseNum'+pulseNum+'.'+f,\n",
    "                                       format = f, dpi = dpi)\n",
    "            \n",
    "            # Now, reset the view interval, and save the entire span of the spikerug plot.\n",
    "            axx.set_xlim( 0, len(bandpass) )\n",
    "            for f in ['svg', 'png']:\n",
    "                figSpikeRug.savefig(datadir + 'threshold_'+str(threshold)+'_SD/spikes/plots/wholeTrace/'+file+'.'+f,\n",
    "                                   format = f, dpi = dpi)\n",
    "            plt.close(figSpikeRug)\n",
    "            \n",
    "#                 # Then extract the limits of the desired timespan from the bandpass.\n",
    "#                 bandpassWindow = bandpass[ lightChangeIdx[idx] - (pulseLengthIdx*pulsePad): \n",
    "#                                           lightChangeIdx[idx+1] + (pulseLengthIdx*pulsePad) ]\n",
    "#                 # Extract the spikes\n",
    "#                 spikes_in_window = spikes_idx[ (spikes_idx >= lightChangeIdx[idx] - (pulseLengthIdx*pulsePad)) \n",
    "#                                               & (spikes_idx <= lightChangeIdx[idx+1] + (pulseLengthIdx*pulsePad))\n",
    "#                                              ]\n",
    "\n",
    "#                 before = bandpass[ lightChangeIdx[idx] - (pulseLengthIdx*pulsePad): lightChangeIdx[idx] ]\n",
    "#                 during = bandpass[ lightChangeIdx[idx] : lightChangeIdx[idx+1] ]\n",
    "#                 after = bandpass[ lightChangeIdx[idx+1] : lightChangeIdx[idx+1] + (pulseLengthIdx*pulsePad) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-10-13T15:27:24.394853",
     "start_time": "2016-10-13T15:27:22.257427"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# This cell will read all spikeIDs and create a dataframe for contrast plots, \n",
    "# and another one for raster plots.\n",
    "spikeList = list()\n",
    "rasterList = list()\n",
    "\n",
    "# Define the before and after padding, in terms of multiples of the light pulse.\n",
    "# This CANNOT be less than 0.\n",
    "beforePad = 1\n",
    "afterPad = 1\n",
    "\n",
    "# Define the correct directories.\n",
    "threshold = 2.58 # This is the scaled threshold, in SD of the signal.\n",
    "                 # Change as appropriate for plotting and analysis.\n",
    "spikedir = datadir + 'threshold_'+str(threshold)+'_SD/spikes/npy_files/'\n",
    "\n",
    "for subdir, dirs, files in os.walk(spikedir):\n",
    "    for file in files:\n",
    "        if (len(file.split('.')) > 1) and (file.split('.')[-1] == 'npy'):\n",
    "            # If and only if the file extension is `.npy`...\n",
    "            # Read in the file.\n",
    "            spike_idx = np.load(spikedir+file)\n",
    "            # Get the ATF filename and the trace number.\n",
    "            atfname = file.split('_')[0]\n",
    "            trace = file.split('_')[1]\n",
    "            # Get bandpass filter info and spike detection info.\n",
    "            allInfo = file.split('_bandpass')[-1].split('.')[0]\n",
    "            # Get the relevant row from experiment info for reference and details. \n",
    "            tempExptInfo = exptInfo[ exptInfo['Filename'] == int(atfname) ]\n",
    "\n",
    "            # Read in the correct protocol from the filename.\n",
    "            protocol = file.split('_')[2]\n",
    "            # Get light epochs from exptInfo.\n",
    "            lightChangeIdx = lightChangeProtocols[ protocol ]\n",
    "            \n",
    "            # Cycle thru each light pulse and collect the spikes that fall \n",
    "            # before, during, and after each epoch.\n",
    "            for n, idx in enumerate(np.arange(0, len(lightChangeIdx), 2)):\n",
    "                pulseNum = str(n+1)\n",
    "                # For each pulse, extract the pulse length\n",
    "                pulseLengthIdx = lightChangeIdx[idx+1] - lightChangeIdx[idx]\n",
    "                pulseLengthSec = pulseLengthIdx/fs\n",
    "                \n",
    "                # define a rasterEpoch, which spans from start of before to end of after.\n",
    "                rasterEpoch = spike_idx[ (spike_idx >= lightChangeIdx[idx] - (pulseLengthIdx*beforePad*2)) & \n",
    "                                        (spike_idx < lightChangeIdx[idx+1]  + (pulseLengthIdx*afterPad*2)) ]\n",
    "                \n",
    "                # shift rasterEpoch so that 0 is the start of the window, for all pulses.\n",
    "#                 rasterEpoch = rasterEpoch - lightChangeIdx[idx]\n",
    "                if len(rasterEpoch) > 0:\n",
    "                    rasterEpoch = rasterEpoch - int( lightChangeIdx[idx] - (pulseLengthIdx*beforePad*2) ) \n",
    "                # Save into pandas dataframe.\n",
    "                rasters = pd.Series( { 'traceName': atfname,\n",
    "                                      'traceNum': trace,\n",
    "                                      'pulseNum' : str(n+1),\n",
    "                                      'illumination_mm': tempExptInfo.iloc[0][\"Illumination_mm\"],\n",
    "                                     \n",
    "                                      'spikeIndices' : rasterEpoch,\n",
    "                                      'spikeCount': len(rasterEpoch),\n",
    "                                     \n",
    "                                      'genotype': tempExptInfo.iloc[0][\"Genotype\"],\n",
    "                                      'larva': tempExptInfo.iloc[0][\"Larva\"],\n",
    "                                      'nerve': tempExptInfo.iloc[0][\"Nerve\"],\n",
    "                                      'abdominalSegment': tempExptInfo.iloc[0][\"Abdominal segment\"],\n",
    "                                      'sampleRate' : fs,\n",
    "                                      'pulseLengthSec' : pulseLengthSec,\n",
    "                                      'pulseStartIdx' : 0,\n",
    "                                      'pulseEndIdx' : pulseLengthIdx,\n",
    "                                      'illuminationProtocol' : tempExptInfo.iloc[0][\"Protocol\"],\n",
    "                                         \n",
    "                                      'processingSettings': allInfo\n",
    "                                       } )\n",
    "                rasterList.append(rasters)\n",
    "                \n",
    "                # Define the before, during, and after epochs.\n",
    "                bef = spike_idx[ (spike_idx >= lightChangeIdx[idx] - (pulseLengthIdx*beforePad)) & \n",
    "                                   (spike_idx < lightChangeIdx[idx]) ]\n",
    "                dur = spike_idx[ (spike_idx >= lightChangeIdx[idx]) & \n",
    "                                   (spike_idx < lightChangeIdx[idx+1]) ]\n",
    "                aft = spike_idx[ (spike_idx >= lightChangeIdx[idx+1]) & \n",
    "                                   (spike_idx < lightChangeIdx[idx+1]  + (pulseLengthIdx*afterPad)) ]\n",
    "                epochs = {'before': bef,\n",
    "                          'during': dur,\n",
    "                          'after': aft}\n",
    "                # Loop thru the epochs and compute the spike count and spike frequency.\n",
    "                for epoch in epochs.keys():\n",
    "                    spikes = pd.Series( { 'traceName': atfname,\n",
    "                                         'traceNum': trace,\n",
    "                                         'pulseNum' : str(n+1),\n",
    "                                         'epoch': epoch,\n",
    "                                         'illumination_mm': tempExptInfo.iloc[0][\"Illumination_mm\"],\n",
    "                                         \n",
    "                                         'spikeIndices' : epochs[epoch],\n",
    "                                         'spikeCount': len(epochs[epoch]),\n",
    "                                         \n",
    "                                         'genotype': tempExptInfo.iloc[0][\"Genotype\"],\n",
    "                                         'larva': tempExptInfo.iloc[0][\"Larva\"],\n",
    "                                         'nerve': tempExptInfo.iloc[0][\"Nerve\"],\n",
    "                                         'abdominalSegment': tempExptInfo.iloc[0][\"Abdominal segment\"],\n",
    "                                         'pulseLength' : pulseLengthSec,\n",
    "                                         'illuminationProtocol' : tempExptInfo.iloc[0][\"Protocol\"],\n",
    "                                         \n",
    "                                         'processingSettings': allInfo\n",
    "                                       } )\n",
    "                    spikeList.append(spikes)\n",
    "\n",
    "rasterList = pd.DataFrame(rasterList)\n",
    "rasterList['tickLabel'] = rasterList['traceName'] + \" \" + rasterList['genotype'] + \" \" + rasterList['traceNum'] + \" \" + rasterList['pulseNum'] + \" \" + rasterList['illumination_mm'].astype(str) + 'mm'\n",
    "# Create parental status column.\n",
    "rasterList['status'] = np.where(rasterList['genotype'] == 'nSyb-Gal4>UAS-dGtACR1', \n",
    "                                'offspring', 'parent')\n",
    "rasterList.to_csv(datadir + 'threshold_'+str(threshold)+'_SD/rasters_'+str(threshold)+'_SD.csv')\n",
    "rasterList.to_pickle(datadir + 'threshold_'+str(threshold)+'_SD/rasters_'+str(threshold)+'_SD.pickle')\n",
    "\n",
    "spikeList = pd.DataFrame(spikeList)\n",
    "spikeList['spikeFrequency (Hz)'] = spikeList['spikeCount'] / spikeList['pulseLength']\n",
    "spikeList.to_csv(datadir + 'threshold_'+str(threshold)+'_SD/spikeCounts_'+str(threshold)+'_SD.csv')\n",
    "spikeList.to_pickle(datadir + 'threshold_'+str(threshold)+'_SD/spikeCounts_'+str(threshold)+'_SD.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-10-13T16:29:11.090100",
     "start_time": "2016-10-13T16:29:11.085412"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Input in this cell all the indices (from the figures above) that you want to remove from analysis.\n",
    "# We are focussing on threshold SD = 1.96 first.\n",
    "dropList = [129, 39, 38, 138, 108, 109, 110, 142, 143, 132, 139,   # UAS-dGtACR1\n",
    "            289, 290, 295, 294, 293, 292, 296, 288, 291,           # nSyb-Gal4\n",
    "            297, 298, 299, 300, 301, 302, 303, 304, 305, \n",
    "            158, 153, 154, 155, 0,  1, 2, 3, 4, 5,                 # Mutants\n",
    "            10, 11, 12, 13, 14, 15, 165, 166, 167, 161\n",
    "           ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-10-14T10:36:54.048517",
     "start_time": "2016-10-14T10:36:05.842615"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jacuzzijo/anaconda/lib/python3.4/site-packages/matplotlib/tight_layout.py:222: UserWarning: tight_layout : falling back to Agg renderer\n",
      "  warnings.warn(\"tight_layout : falling back to Agg renderer\")\n"
     ]
    }
   ],
   "source": [
    "# This cell will read the spike counts, and create stacked and summed raster plots.\n",
    "# The spike counts are stored in pickle format.\n",
    "# Line below turns off interactive mode for matplotlib.\n",
    "# See http://matplotlib.org/faq/usage_faq.html#non-interactive-example \n",
    "plt.ioff()\n",
    "fsize = (20,20)\n",
    "stackRasterRowSpan = 15 \n",
    "summedRasterRowSpan = 5 # These two variables should add up to fsize[0].\n",
    "maxY_Hz = 0.1\n",
    "\n",
    "# Define the correct directories.\n",
    "threshold = 2.58 # This is the scaled threshold, in SD of the signal.\n",
    "                 # Change as appropriate for plotting and analysis.\n",
    "workdir = datadir + 'threshold_'+str(threshold)+'_SD/'\n",
    "writedir = datadir + 'threshold_'+str(threshold)+'_SD/rasters/'\n",
    "\n",
    "secFmt = FuncFormatter(to_seconds) # to format the x-axis of the spike zoom ins\n",
    "\n",
    "# Read in the pickle. \n",
    "# Below variables are hardcoded for ease, for now.\n",
    "rollingAvBinSize = 1000 # this is the window size. Divide by fs to get this in seconds.\n",
    "pulseLengthSec = 0.5\n",
    "pulseLengthIdx = 5000\n",
    "# Define the before and after padding, in terms of multiples of the light pulse.\n",
    "# This CANNOT be less than 0.\n",
    "beforePad = 1\n",
    "afterPad = 1\n",
    "rasterSpanIdx = pulseLengthIdx + beforePad*pulseLengthIdx*2 + afterPad*pulseLengthIdx*2\n",
    "rasters = pd.read_pickle(workdir+'rasters_'+str(threshold)+'_SD.pickle')\n",
    "# Comment out the line below if you DO NOT want to drop any data.\n",
    "rasters = rasters.drop(dropList, axis = 0)\n",
    "\n",
    "for status in rasters['status'].unique():\n",
    "    # Filter the data and sort it.\n",
    "    tempdf = rasters[(rasters['status'] == status) &\n",
    "                     (rasters['illumination_mm'] != 110)].sort_values( by = ['genotype',\n",
    "                                                                            'illumination_mm'] \n",
    "                                                                     )\n",
    "    # Calculate summed Rasters\n",
    "    summedRasters = list()\n",
    "    # loop thru each row of tempIdx and pull out the spike indices for each combined epoch.\n",
    "    for i in tempdf.index:\n",
    "        spikeIdx = tempdf.ix[i,'spikeIndices']\n",
    "        # Make a temp Series, assign all values as 0.\n",
    "        spikePos = pd.Series( np.repeat(0, rasterSpanIdx) )\n",
    "        spikePos[spikeIdx] = 1\n",
    "        # Calculate spike frequency in rolling windows.\n",
    "        spikeFreq = spikePos.rolling(rollingAvBinSize).sum() / (rollingAvBinSize/fs)\n",
    "        # Save this Series.\n",
    "        summedRasters.append(spikeFreq)\n",
    "    # Turn the list into a nice DataFrame.\n",
    "    summedRasters = pd.DataFrame(summedRasters)\n",
    "    summedRasters = summedRasters.mean()\n",
    "    \n",
    "    # Initialize figure.\n",
    "    fig = plt.figure(figsize = fsize)\n",
    "    axarr = list()\n",
    "    axarr.append( plt.subplot2grid(fsize,\n",
    "                                   (0, 0), \n",
    "                                   rowspan = stackRasterRowSpan, \n",
    "                                   colspan = fsize[1] )\n",
    "                )\n",
    "    axarr.append( plt.subplot2grid(fsize,\n",
    "                                   (stackRasterRowSpan, 0), \n",
    "                                   rowspan = summedRasterRowSpan, \n",
    "                                   colspan = fsize[1] )\n",
    "                )\n",
    "\n",
    "    # Calculate the height of each raster.\n",
    "    ymin = 1/len(tempdf)\n",
    "    # Make sure each tick location matches the number of pulses we are plotting.\n",
    "    yticklocs = list()\n",
    "    yindex = list()\n",
    "    #yticklocs = ( np.array( range( 1, len(tempdf)+1) ) / ( len(tempdf)+1 ) )# + ymin/2\n",
    "    # Loop thru each spike index and plot it.\n",
    "    for i, raster in enumerate(tempdf['spikeIndices']):\n",
    "        for spike in raster:\n",
    "            axarr[0].axvline(x = spike,\n",
    "                             linewidth = 0.5,\n",
    "                             ymin = ymin * (i),     # both ymin and ymax are in terms of figure fractional height.\n",
    "                             ymax = ymin * (i+1), \n",
    "                             color = 'k', \n",
    "                             alpha = 0.4)\n",
    "        yindex.append(i)\n",
    "        yticklocs.append((ymin * i) + ymin/2)\n",
    "\n",
    "    # make sure the xlimit spans over all the epochs.\n",
    "    axarr[0].set_xlim(0, rasterSpanIdx)\n",
    "    # Set the y-tick locations.\n",
    "    axarr[0].set_yticks(yticklocs)\n",
    "    # Set the ytick labels and font size.\n",
    "    axarr[0].set_yticklabels(tempdf['tickLabel'].index.astype(str) +'--'+ tempdf['tickLabel'],\n",
    "                             fontsize = 10)\n",
    "    # remove the xticklabels to reduce clutter. \n",
    "    axarr[0].set_xticklabels([]) \n",
    "    # Add title.\n",
    "    axarr[0].set_title(status, fontsize = 25, fontweight = 'bold')\n",
    "\n",
    "    # # Plot summed raster.\n",
    "    summedRasters.plot( ax = axarr[1], color = 'k' )\n",
    "    \n",
    "    import matplotlib.patches as patches\n",
    "    # indicate the LightOn pulse with a rectangle at the top of the plot.\n",
    "    summedRasterMaxY = axarr[1].yaxis.get_view_interval()[1]\n",
    "    axarr[1].add_patch(\n",
    "        patches.Rectangle(\n",
    "            ( (pulseLengthSec*beforePad*2)*fs, (maxY_Hz/20)*19),   # xy coords of left most corner of rect\n",
    "            pulseLengthSec*fs,    # width of rect.\n",
    "            maxY_Hz/20,               # height of rect.\n",
    "            color = lightCol\n",
    "        )\n",
    "    )\n",
    "    \n",
    "    # Make sure the x-axis is in seconds, and label it as such.\n",
    "    axarr[1].xaxis.set_major_formatter(secFmt)\n",
    "    axarr[1].set_xlabel('Time (s)', fontsize = 18)\n",
    "    # Make sure the y-axis of the summed raster plot starts from 0, and label it appropriately.\n",
    "    axarr[1].yaxis.set_view_interval(0, maxY_Hz)\n",
    "    axarr[1].set_ylabel('Average Spike Frequency (Hz)', fontsize = 18)\n",
    "    # Despine all the axis.\n",
    "    for a in axarr:\n",
    "        sb.despine(ax = a)\n",
    "    # Make sure nothing overlaps.\n",
    "    fig.tight_layout()\n",
    "    # Use the lines below if you DID NOT DROP any data.\n",
    "#     fig.savefig(writedir+genotype+'_rasterplot.png', format = 'png', dpi = 250)\n",
    "#     fig.savefig(writedir+genotype+'_rasterplot.svg', format = 'svg', dpi = 250)\n",
    "    # Use the lines below if you DID drop data.\n",
    "    fig.savefig(writedir+status+'_dataDrop_rasterplot.png', format = 'png', dpi = 250)\n",
    "    fig.savefig(writedir+status+'_dataDrop_rasterplot.svg', format = 'svg', dpi = 250)\n",
    "    plt.close(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-10-14T21:59:48.446638",
     "start_time": "2016-10-14T21:59:48.182808"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "115\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "print(len(tempdf['illumination_mm'].astype(str)))\n",
    "print(len( axarr[1].yaxis.get_ticklocs()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-10-13T15:24:49.456849",
     "start_time": "2016-10-13T15:24:26.725220"
    },
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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A9cnzViSCykbAhh3XlTKzJYjAf1P5OXe/2907jn8HuNTdtwHWJIZevlj2rAaiJyJLbD1V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\nXIoYht2SqIkHsWr2fmBqTxKidDa/BLB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qRugf4HsbsL9eYa32uL/yDL1q6XsoqfL7Cqml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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125234320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IjeabKfb+O/EePTERejawe2zbHsAc/EF1ppm1A98Afh5C2C51vu8Ck2LvvBzn4vlAHy6z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\nwLvgh0ivx3B16zXwjV+xv+xi/nExB2+0QR5tqCv5c46+ixSTjEfDYR9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eo4B/l1weH+xd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\nVCGTPEmSpAqZ5EmSJFXIJE+SJKlC/wP1gtOZqFYJIQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x133ef5e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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wonPuJ5knnHNR/Hpoj+aqYtJ5JOPRucDrwGWRWKJHK+7/B7CIYDxekOj9Gdg9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12379d470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8UuCzwJOAHHA58A5332fvy4nQgqUiU0+xRUSk8epaJ8/d+4FLkkdDmdl7gVcC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\n8s81yMhj5eeh0SrBXas62xn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2QuWoVsJUTiDHSiIXEyplJxIqZb8hdMXeRwMTyHpV/L8e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OZgVdv3JVyf/iKZ7mdlvNdCf9DtDgZLndF4sZUXS0lZLMZFuRRX5ZXW15Wrl6rS2l4Zx0fyKFod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\nAATo/wLc0Gm+zhh/nwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1248db898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cB3xijPmPMWZQVlqp8ioO/W+AfwOTvSDqme/2tEYc+rOA14Df5LstKjMaW5RS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x127951b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WFl/d4/riscU550qgkCRvMzC3j+MnAFsOrDluJKpvbekA7g03wnDuycDrgKPa\nGhpfwHr57gPWetLn8nhscc65Eigkyfsd8F4RuQ7YEO7LAojI+cC7gJ8WtXVuRKpvbdmJ9b7cBtDW\n0DgdOAW4BJjb1tD4LCHpq29teWHIGuqGC48tzjlXAoXMyZuMfWgfBTwMLARuByYAJwFPA2eq6qaS\ntLRMfN7M/r55+SULJuxJfyETRV2ZiFQ2QVcmiroyCboyEV2ZRNSVSdCZiaLOdJLOTBR1pZN0pBNR\nVyoZdaSSdKaTUUdnddSZStB58JbUxLkbOudN3ZE6sirNhL01iWdemFz16CNH1j26bnrNTiCNbWuV\n7uH7nn5+8ba0aYn3Eo4wHlucc640CiqGLCJjgY8BFwFHYtsMPQ1cD1ylqlsPpDEi8gMgoarvjN33\nGqwivgCrgE+o6p9ix6cD3wPOAzqBa4BPqup+e18OhAfi/X3tU5ckq9IsTGSytckMdclMtjaRoS7K\nUpPIZmujLDVRltoom61JZMn9bDey1XaM6nBfdZTNVkdQTZYqIKpKZ8dVp7KTqlPZiUAyVRXt7qyK\ndnXUJPakk6TTiSiTTpDN2NdMOhFlM8kom0qQSScj0skom06QTdlX0skok05EqXSSTDoRpdNJMim7\nL51KRplUFelUMsqkk1GmKxll0knSRFH8V87Sf6KZf+srES3ksQNOaAdwboYRkvh6bHHOueIbNjte\niMjngE8D/5sLxCJyPDaP60rgt8BbsQ+CU1T1sXDObdgH2vuweT0/A36oqp8ZTDs8EA+ttobGamwO\n1kJgPpaoPIYt8nga+/CvAarzvvZ0X/xY7ufeHpfL8rJZIBsRZSFBBNmIZDYik4miVDaiKxORziSi\nVO5r2pLNVDpBOp2MMqlklE4lyaSTUborGWVSVVGmqypKd1ZH6a6qKNtVFWU7q6N0Z3WU7ahOZDtq\noszemkS2szoik4wS2GrlZOzW38/9nbtPBjsIEYUlpZuXNi352QG+ZtF4bBm+FjUvngFcCKwFngOe\nBbaPhAsT50aCQoZrDx3Ieaq6ppAGiMjhwI+xD/Y9wF9igfgHwDGq+srY+bcAq1T13SJyFjasc3ju\ndUXk34BvA9NVteBSHh6Ih5e2hsax2JDdQqzHZSdWlHkFsLpcizjaGhqTFDexzH9M7vtE3kvnJ2gZ\noCvcOsOtq5ev8eN9Hevpazr+t13UvDhi4Illx9KmJbm5df3y2DJ6LWpenMT+feZgifQhwKRweDOW\n+L14W9q3+s/1AAAgAElEQVS0ZOdQtNO5kaqQhRdPM7BNxJMFtuFsYA3wJqA579jLerhvGdAUvn8p\n8Exe8F8GTMRWd95TYFvcMBMKLd8ZbrQ1NE7GEr6LgHltDY0bsIRvRX1ry7oStiPXUzWkuy60NTRG\nDC6xnNDDY/pKOqvaGhpffN0P7t+UiN6Tx0008csCfq2n8dgyKn3w2o0HYRdv9wJ/yl1YhIuKaVji\nNxd4BTB3UfPi8dj/lQ3s2/u3dmnTEi/K7lyeQpK8z7F/IE4CM4F/xnpYPltoA1T1l2AfCCKSf3gu\n9kaOex672uvrOOEcD8QVJmypdku45bZcWwi8o62hcSawmu6kr+K2XwsfgrlkasiEZDM3dJ6fNKYK\nfDqPLaPXFqz49Ruxi7Y1wD0fhHvrW1s2hWP3xx+wqHlxAjiY7t6/1wJzFjUvHoNdiK1n3x7AtUub\nlnSU6fdxblgZcJKnqlf0dkxEJmA9LUcXoU1xY9m/56QDqOvtuKqmRCQbO8dVsNB7dyNwY0g8DseS\nvv9qa2icSPf2aw/69mvFE5LNVLgd0N/VY8voFQqrLwOWhffvocBpwOVtDY1jsJ107sF20kkBLG1a\nkgHWhdu98edb1Ly4Crs4yPUALsQSwBqst3kd1vP3HJbEP7+0aYnv0OMqViE9eb1S1Z0i8iPgMuAL\nxXjOoB2ozbuvFtjd23ERqcKGknbTBxG5gh56B6qrq58ZZFvdEAuJx1Ph9pvY9msLgYvCvLqHsaTv\nEd9+bfjz2DJ6hPfvM+H2m7aGxhpsJ52zsZ767VhSd3dvUzOWNi1J0d2Dt4+Q6M3Ckr9DgbOA2SEx\n7KB7+Dd3W7+0aUm6qL+kc2VWlCQvqMHmUBTTs9ibMm423cMozwLn93Ac9h9q2UfoPbgi//4oipYV\n2EY3TPWx/drpwNvaGhr3YtuvrQAer29tGVRpDFdyHltGofrWlk5sBfR9AG0NjQcBLwHeHqZmPI71\n8j0wkF76pU1LOulOIvexqHlxHd3Dv0cB5wCzwtBwO/v2/j0HbAw9is4NawNO8vpYAVeLTUT+KPaB\nWUy3Y2+2+BX8ucDfY8e/JCJzVDUXeF8J7ChBW9wI18f2a68F3t/W0LgF+0BZATzn26+Vh8cWNxBh\nd5ybgZvD0O7R2NDuRWGR0P1Y0vd4oe/dpU1L9gJPhts+FjUvHoclgIcAxwOvAWaEBHAHeSuAgRe8\nBIwbLgopoZKh9xVwETZ/5bWq+vdezhnIa9wKPB4rczAf+0D+EnAd8BZs2Gahqmo4py206/3YXIyf\nAt9V1c8Ppg1e5mD0amtonIYN7S7EAvpzhKSvvrVl81C2rZJ5bHEHKpRaOhnrpT8KW3xxNyXeOnFR\n8+KJdM//y90Owv7fbmHf3j+vAejKrpAk7wp6DsS51Uy/V9UD+iAMdaqeyKtKfz7wFawK/j+Ay1T1\n1tjxGcAS7OpqJ/DjwRYrBQ/EzoSegjnAqVjSdxDwBJb0PRD253VF4LHFFVtbQ+NsrJfvJVjdvUew\nXr6HwzBwSYUSMJOxi8XcMPDccB94DUBXJsNmx4vhwgOx60lbQ2MC6yFYiPUYjAEexZK+lWEo2Lle\neWwZGmEB1vFY0rcAm2N3L5b0PVvuaRk91ADM3SZgxc69BqArmqLveJGv0Cr1Q80DsRuIsP3a8VjS\ntwALzg9hSZ/myj24/nlsceXU1tA4Cevhewm2ynY1lvCtGOoe+h5qAOZuXgPQDUqx5uT1SlULrVI/\npDwQ7y/MOzkCeNDnk/QszAk6EUv6jgV2YRPB76OM26+NRB5b3FCJ1dY8DXvv1gAPYknfP8JON8NC\nDzUA52LJYLwG4Iu9f3gNQEdhSd4lWFmAacDPsU3j92JDWG/Dtvv5DtYV/iJVvbJ4zS09D8T7C1sJ\nXYLtMfk34OalTUt2DG2rhrew/dop2AfH4cBGbNXufaXcfm0k8tjihotQZukkLOk7Fls8cQ9Wm2/j\nULatL3k1AHO32VgFjfwagGuxGoA+2jAKFJLkXY1tPXOWqq7POzYBuAP4m6q+r+itLCMPxL0LgeQc\nrOTIZuB64FHv3etfqOuVW7k7C9uvNbf92tYhbNqQ89jihqu2hsYZ2LDuacB0QLFVuw/Wt7YM6T7W\nA5VXAzDX+zcLyNUAjPf+eQ3AClNIkrcJ+LKqfrWX4x8EPquqU4vYvrLzQDwwi5oXHwFciA3j/hX4\ni08OHpgwRDSP7qRvIvbhsYIBFnatJB5b3EgQFl8JlvCdjM2Ruw/r6XtqJE7JiNUAjPcAzsASwHgN\nwLVYIug1AEeYQpK8rcA3VPVzvRz/IvAuVT2oiO0rOw/EhVnUvHgsViT2PKyS/PVLm5Y8MbStGlnC\ndmu57ddOwoZYctuvPVzp2695bHEjUVtD43hsSsbp2MXuc1jCd299a8u2oWxbMeTVAMwVg87VANzK\nvr1/zwHbPAEcfgpJ8lqAVwGvV9Xb8469Bvgt8CtVfUfRW1lGHogHJ5QFEKABmxx8M3Crr/wqXJgX\nNB9L+k7A5tRU7PZrHlvcSBerq3k6VltzPHahlqvNVzHz3wqoAZjr/fMagEOokCTvcOBObF7CQ9j2\nLxFwDFZK4lHgHFXdUpqmlocH4gO3qHnxBKyA7LnYJPo/LG1aMqLKXQwnYfu1k7Ck72jsKnpFuJW9\nzlexeWxxlSaUWJqPDe2egK22z9XmWzvS37O9yasBmOv9m4slvVn2rQGYKwI9qqanlFtBxZBFZBpw\nOfDPWH0hsE2iW4CvqeqI/8fyQFw84Q1/EjZ3bwJwI3C7r+o6MGGj9vj2a2vpXsSxaSjbNlgeW1wl\na2tonEL3Ao7Z2IXMvdh7dvdQtq1cQg3AGexfBNprAJaQ73iRxwPx/toaGsdgb841g70CXdS8eApw\nPlCP1Y+7YWnTkvV9P8r1p4/t11YA9w91cVfXzWOLgxffs0dhSd9CbJHDA1gv36pKm44xELEagPHe\nvzlALVYD8Hks8Xt8adOSFUPVzpGo4CRPRBYAr8f+Ib4N7Abmq+pNxW9e+Xkg3l9bQ+NU4B1YD8tD\nwN+xXR0KTvjC1dxp2P+hBNa7t9yX7BdH7APkVPbdfm0Ftv3asC374LHFjUbhIvpkLOkTrKbmPcA9\n9a0tB7RncyXIqwE4bmnTkj8PcZNGlEKHa78DvAebL5PFVlROAX6NfVhfrKrD9kNkIDwQ7y8sBDge\n+zc/Erv6nIcNp92Nbf7dhW3tlY7d8n9OxxPDRc2LZwCvw5K+5cCNS5uWvFCWX2qUiO3beSq2/VoW\nq+i/AqvoPyyGzj22OGdCTc3c0O5UbF7z3fge2W4QCll48QHgm8BVQCv2ofxq7MPiM8CHgStHWhX6\nfB6I9xfmgF2A9bwlY1/nYL1Gc4DtWAmVddgHdTLv3Nz3ifznz0Qkto1Pzto+ITkvypKesjP11MRd\nmY1R703K0nMiuV9SSe9JZ78JaZEf1+vzlHMSdth+bQGW9MW3X1vBENX68tjiXM9CeaXjsITvRGyl\nfa423zOVuoDDFU8hSd6jwEpVbRKRg4BNwKtV9ZZw/OfA6ap6bMlaWwYeiAsX2//xZVjysAm4Dbir\nvrWlva/H5lvUvHguNmQ3HxsW/lP+Fmrh9ZL0nkj2diz/1te5fT2uWK+Z+z4/n839PNAAHjH4hLSG\n7nkw07Bk/UngKawUwmAS2Z31rS1PDrDtHltGsbaGxhpgSn1ry4ahbstIEFban4olffOwC+t7sO0S\nfatJt5+qAs49AvhWH8dvw7YmcqNMuJp8Ktx+1tbQOAdL+L7Y1tC4G/u/ccdAFgEsbVryHPCD2BZq\nn13UvPgFrIfn0aVNS7Lh9VLh5ugx8R1oApr/83RsePeU8P0GbCHHU9iQfBU2GbqvRHYHligOlMeW\n0Wsi8M62hsaDgVXYitP7C704HC1CDF0GLAvv+UOx2nyfbGtorMPmTN8DPDZcpmK4oVVIT95zwDWq\n+plerra/CbxRVeeWrLVl4FfbxdXW0DgNeCm2qjaD7UN6e31ry4Dn3oUt1C7A5gP6FmplEj5EDqO7\nXMsk7IP4PmzvzqKUfvDY4sKWYcdgc9FOwWLF/VjC8oQPS/Yv9IqeiPXyHQdsI9Tmq29tWTeUbXND\np5Ak7zvAW7AP7A1YIH6Vqt4qIudjVel/qqqLS9XYcvBAXDptDY2TgLOxhG8sNvfqtoEGoEXNi8dg\nOyOcB6zBtlB7vETNdXnC/KBjsITvZKAaWInNnXukvrWlczDP67HF5QtzRxdiSd9R2Fzf3JZhI7oo\ndrmEudSnhdvB2EK5e/Ce0lGlkCRvMjZschS2XctC4HasyO1JwNPAmao6Ioux5nggLo8QxE8HXo7N\nBbsX+//1dH9X7bEt1C7Eltb/GbjFC2eWV1h1fQLd2691YfW+lte3tjw10Ofx2DJ6hdXfU4HNfdWH\nC1NAcitOJ2Ir+u9mFOztXAyhV/4Y7O+X6yl9APsbek9pBSu0hMpY4GPARdjQWRILwNcDV6nq1hK0\nsaw8EJdfGGY4FZvHV1AtvrCF2nnAK4F/YFuoPVPaFruehA3bTwLm1re2NBfyWI8to1NbQ+MMrAbn\ndGyeZye288E6rADu+vB1Zy4WhMTwBCxhWYDVU8xtGfacJyz9CxfZp2B/w6Owv/PdeE9pxSmkJ+9t\nwO2qWtHDYx6I9xfmy0wBttW3tqRL/FpJbF7Jy7HeusewXp6Vfb126N07Eevdmwj8EbjNt1Arv7aG\nxvH1rS27Bnq+x5bRKz+2hN7hmeE2G+upn4W9p8EW9eQSwHXh1o6990/HCuY+hSV8Kwr5fziatTU0\nzqZ7aHcCVkD9Hizuek/pCFZIkrcDuFpVP1/aJg0tD8T7Cyvf3okF42S4exfwAlZmI//r9mJcTYch\nhuOwHr4FWLmAv2PBu9fAs6h58WRsD9SX0r2Fmk88LpGwGftJwBnYylytb2359kAf77Fl9BpEbMlg\nyV88CZyJlQLKYLtFpIHJ4dhebO7vPVjxb99Zpx+xAuqns29P6b3As95TOrIUkuStA76uqleXtklD\nywNx/0LyNRabS3dQD18n013rbTs9B+sXgF0DDRiDqcUXevdyW6hVATcAdy1tWlLS3sjRoK2hcTqW\n1J2B9bI8hH2YPlboB6nHFpdzgLFlK5bo1WL/J2djPXvHYQsPJmJx40GgDRslWOe9fX1ra2icTHdt\nvkOwntJ7sYvtsu6N3dbQWDect2YcjgpJ8v4F+AHwVezDNXfFtA9VXVPMBpabB+LiCQF7Ij0H62nY\nsABYYN5Gz8ng5p6SuFgtvjOwK80+a/H5FmoHJlZ5/0xsaGwbcBeWZB/Q/poeW1yhBhlbdgDjgaOx\neWhTsNgRjz3xeYDrgA0+XNktdrF9GrZAqhpLmoveUxp7rQVYcfxZ2CKRbxbrNUaDQpK8duwftI/d\npkBVk30dH+48EJdfmJczmf0Dde42JpyapucewQyWgJxBP7X4FjUvrgLOwhK+DuAPwH1Lm5b4EESe\nUPLmdCyxOxibp7OcIs/T8djiSmUAsWUO9n/7ICwerMUSvF1AHdarmMYWhGxk/yRwy2gevgxzKE/G\nVj4fi8Xje7DafBsH8VzHYgndAmAcsBpbcb8SWD+a/9aDVUiSdwUD2GbJ95d0pRIrt9Db1XtNuE0P\n56XoHppRLABtyVWCX9S8eA42lHsi3VuobS/jrzSshCvno7Ak+BTsQ+9urCTK86V6XY8tbqiF2DKX\n7tGBI7AFHS9g8/rALjZrsQvJCLswqQrHt2HDmGvpXhCyrr61ZVQVbQ+rpU/Dkr7pWMWDe4AH6ltb\nOvLOnUx3L90x2N/1H1hC90i5h4IrVUElVEYDD8QjX7ginIoF7XqsJ2oGFrC3Y0M0YEF8c1eSbasO\nq5u5ZmbNCe11iXU1XdkWeXrvnZd+99qKn6QdSim8BPsbzcMKpt6FzbfxuS9F5LFl5AgXPHPpTljG\nYcnHA1hP3hT2vcichc1Xm4j1/tVgJWEyWJzZiJUEehJ7jz0PbKzkrcdCL+qx2N/wJCxJ3oYlyOPD\n97leulU+LF4avSZ5IvJ14Beqen95mzS0PBBXpl5q8d2NDfm+2Bu4ZULymK0Tk2cRMWvC7sxzU7en\n1lRl2Ervi0d2jKQhhNh+l2dgwTfCJlHfxQAKUReDxxY30oQV5POx6QvHAzvp3oFjbQ/nj8UuNHOx\nZV64HYoND4/HksEUdtG5AdvF52lsr2gF1oyk2BIXekaPobunbjKW6HZg8yWnAs9hMfi++taWbUPU\n1IrXV5KXAd6qqtfG7hsHfAf4iqr+ozxNLC8PxJVvILX4whZqrySbfc249syG+U/uvePMlbt3sf8Q\n8US655LlLx6Jf79nqAJ2bN7MmVjgfRabW3fvUKws9NjiRrq2hsapdO/AMQtLzHIrTgc0RBsuuMZj\nSd8x2BDx4dg8wXhtwC4sjqzF3rtPYb2B6xji2JLT1tA4AUt+F2Dzo5PYPtcrsV1Jtuad31tP6T3Y\nUG3F9nCWW6FJ3kFYNn5ebvPwSuOBeHQZSC2+Rc2Lj8WKLM8GbqaHLdTC80xi34nd8YRwfDg1jZV6\n6LEOWLGGSNsaGmfSXeJkHFYvcDkD2EWk1Dy2uEoSm8ua2zIsgb3f7qUI77ewk8wx4XYkcBgWiyZi\ni0NSwB5s9fB6rEdwNXkXmkWMLRGWmOZ66Y7AeiMfwRK1x/Ln3w3gOeM9pSfQ3VN6T089pW7gBpPk\nbQJe7YHYVZr+avEtal48HngNtoWaAtcXuoVa6EWcTO91wHIriXNX7z0lgy/kz18JwyPzsd66+aHt\ny4G786+ih5rHFlfJ2hoax2A956dhidkGuhOWopZtCjFrGtbzNwdLuI7AksBx2OKQFLawqYvukjHr\nGHhsSYbnXBBu07FkcmW4PV3sItN5PaWzsZ7S3C4mo2oxy4HyJC+PB2KX01stvm/964xdWLC7EOu9\nuwnbQq1oE4fDlW18Tk/861RsYnct3QViE1jieRfWg7AB2FrqbegGw2OLG03aGhpn0T0sOYXuLcMe\nqm9t6Szxa9dgO4LMit3mYAvRxmALQzqwRBBsmDWDxZSJWKLYhc0VXInFllWUMbbk9ZROr29t+VY5\nXrdSeJKXxwOx60lbQ+M0bJu0emK1+L71rzPSwPlYMvgA8IdSbaEWgt2xWG/dyVgtr/uwq1zYf6h4\nKt1bRe2m962itpVzCNdjixutQo/7cVjCdyKWYN2HLUAo60KL2JzA47GySSdjPXZ1dO8msjZ830F3\n6Zg69t2GbtjEFrc/T/LyeCB2/QlFgs/GEr6xwPJUktu+1zRjLnABNkRyA7D8QLdQCxOacyVO5mJ1\npO7C6k4NqBcgBPMx9L1VVCKcvoP9F43kvg54G7q+eGxxzrQ1NE6ke8uww7A5wbkVp0WvExdiwWF0\nz6ebg72/V2LlTJ7ILXoIJVCm0b1HcO42HUv2Ouge9m3HCkZn2XdburLGFre//pK8v2MreXJqgTcB\nf8b+cfNlVfXtxW5kOXkgdoUIpRJOx1bqTgPu/ce82kf+fNbEBdkoOh1LyG5c2rRkQFt/xeYFnokF\n/zQ2tLMceK6UgTC89gR6TwZzq/2y9L14pM85Mx5bnNtfLAE7DXvv19G9ZdhjgxkeDcO1x2JJ3QnY\n+/tpuuvTrRtsTGlraKxj/6Hg2XQvMttO984g67DFFGlsiktJYovbX39JXqGyvvWQG63ya/FlIlbe\ndsr4XQ/ImAVEUSdwPT1soRYmap+CDZkcia2MW45dzQ+7oBau8HsK1Lmh4q31rS1f6e3xHltcTpiv\n9gm6P9w3YvNJc183AdtHYy9PKH20ALuIPA77++QWcKzv5TGTsB66+Vh5KLC5urlSJjtK3e7Qjly1\ngVl5t5nYSEca+7eNJ4HrgC0cQGxx++sryTtsME+oqgWtNhxuPBC7YsivxbdjbGLtnSeNq1t1WN1B\nmUT099e2bX9Qnuk4Hgvg1cAKrNfviUr/QPPY4vKFpGAKtiDg4PA19/2kcFonlhjslwgWWrJjJArz\ngk8LtxnYAoinsaHTY7BewO3su4tESRd2DFaIjzPYPwk8CPt92tk3+cttE7e7xyd0vfJtzfJ4IN5f\nuNr+eOyuCAu47eG2N3br7b69efe3j5aCl+EDbAHwpiy8dG9NNHXjlKrUM7NrH9g8per/PTuzZnl+\n756rPB5bDkzoKZ/Ovolg7mtNOG0HlvzlJ4JbRvrFU1i0cTTWS7cAWyRRg10kbgT+hvX0DYsLxRD3\narD5wHWx25gB3BefNjIBm+c3DkvoX1fWX2SEKzjJE5EF2KbuhwLfwlbWzFfVm4rfvPLzQNy/2Js3\n/00a/9rb9/H7qsNT5v8nzCWQ+YlhX0nkPvcNhwSyraFxOt0FiSdiW6ktx9r40j210SvWTaue8cSh\ntZ3PHlzzq91jkzcsbVoyaq9UPba4AxGbUxrvBcx9nYItAEhjc7ziSeBGYMNwmxrR1tA4DptHl9tF\nopruXSQeidfcC+eegvXyHYkNg96LDe32Wiezj1jeUxLWV0zPn0oRYXG9g34u9vs5Pixi+UhWUJIn\nIt8B3kP3P+B52Jvn18CNwMWqWtRNzUXkOKySdpbu7aOywMtU9Q4ReQ3wZWz+wSrgE6r6p8G+ngfi\nodXP1V8hSWRv87c66D+w9Hm8twnQYQjiOGzRxIlY0dG7sGLKPS68aGtonNNRHb1yx7jkRVsmJeds\nnFL14Kap1d//2gd/PKr2dfXY4soh9IYdhCV+09k3ERwbTmtn317AXCK4uZQJR1tD4wy6Cw4fEdqR\n20XiCbrLlwwkFua2Sjs8/LyN7kUM+XNiOzmAJIxRNCozEg04yRORDwDfBK4CWrEeiVdjc4k+A3wY\nuFJVryxmA0VkEban5Xy6AzHYf1jBagxdCfwWeCvwMeAUVX1sMK/ngbhy5SWQB5JEJmJPmys2OhNb\nIboOeBKbK7OHAoavgXHPzai+OJ2MFnVWReM7aqI/jt+T+cFbfnDd8yX4cwwbHlvccBJWzOfPDZyB\nJYVJuheJ5CeCW7CEqb8RjDosoTwEm0d3CDbvsBNLxjZhc+viuhhgwtXDfZ1Y0rgQSyB3E3r5KPGK\nfTf0CknyHgVWqmpTTzWtROTnwOmqemwxGygin8OurM/t4dgPgGNU9ZWx+24BVqnquwfzeh6IXV9i\n1dfPwoZHOrC6VsuxID/QOSe9fQAkgGwqwfjdYxInZuGw6nS2szrFYzWp7EPY+67g4WvsartowTys\n/Iv3hOQ+FKdj82YGvALOY8voFVtd24UlOFvD1y2xn7diRXUPeHFF6MkbzByx+Pu0NtzGYPPE6sLX\nWiwJTGNxYWv4PTaH76uw4eTZ4fvV2CKJB4FnsfdpqtRJV1tD42SsCsDpWJ281VgMu7++tWVXKV/b\nlV9VAecegc2T6c1twBsPrDk9mg/0duX8MqA5775lQFMJ2uFGqXBlnytIPA94HBuGXdrDpt97sQ+n\noljUvDia/kLnaXM2pd4za1Pny6ZvTW2ZuDuzLJnlH1iPU/wDaAq9J5G1ofRJ7gMk13OVoedexiq6\nP8jGYLWvJoTny/UqrMfm/jyHzTfciCV4ha7o89gyStW3tqwDPhi28ZtC977OM7B/n1zpjKmh1FAS\n+7/ZiSVSXeGWwv4vZ9h3+B32/T+fov+esO1Y71xvF01d/SViIXmtx+LGsdh7aVd4/M7Q5jQ2dHxC\n+H03ABvbGhpLukikvrVlG/BX4K/hovUIbC7fBSEJfgDr6ftHsfekdeVXSJK3Gau435sTsKuWYpsP\n1InIndgH7MPAJ1X1ntCetXnnP491fzs3KCHwHYotmDgN+3C4F5sf9nQ5hzfCqtu7gbsXNS+eXNuZ\ned0h6zsvkqc7zpi7sXN7XWd2OVZYWAfarpC0xucizaV7T8sarDdxTwZ2paro6KxKpLqqox0d1dHO\nVFVUm40Yn7Wk7yTg7CibHZPIRBOq05nx1ansuD9csmjnBb9YelwBv6bHllHqF4vf/Kpp21LXZscl\nsplElM5EdGUiOlNVUXs6GbV3VUW7u6qinZ1V0a50MtqZSbAnE7EnypKuTmWj6lSW6lQ2WZ3KJqpT\n2arqVLa6KpWtrUpnaxKQjTJkIthLlhcSWV5IZLMvJNJsTnT3GG7Faq8Net/pWLzIzaebg/1/XQn8\nDHg8f85aeMxE9u0FPzZ8nRKO52rJxWsGbgA2FmuRSIgZT4bbr0KB45OAVwKL2xoaN2PDuvfWt7Zs\nLMZruvIqZLj2O8BbsP07c//hXqWqt4rI+di8lZ+q6uJiNU5E6rCrnzuxLv0O4P3YVf2p2JvoUlX9\nZewxlwA/VtWa/Z+xfz6kMjqF4ceTsd66Y7Dhk+VYcBtWQxiLmhdHwKmJdLZh3rrOOQsf27Nj9qau\n2sg2Pr8DWLe3Ojq4oyY6JJOIDsnC7ESWWdmI8ekE1Z3VUWZPXbJ959jE3h3jk13bxyfTWycmszvG\nJzOZRBQPCBlsXuFu7H24G9g9dXsqI0/vnT5rc9fMSbvSc6pS2QSwOoKHarqyDyQzrCmkB8Bjy+iW\n23YvlWBKqio6qKsqmp6JmA4cFMHUKMvUKMukbERNJiKRSUR0VkV7OmuiPe21ib3ttYn2XWMTHbvG\nJju2j090bp1Y1bW3NpGbf1td05kZM649M2F8e2ZiXUdm/JiOzLi6jmxtbWemtq4zW1fTlalLZLvn\n2XZWRR2d1Yn2zupoz96aaGdHTWJXe120c3ddcseusYkdXVXsmbeu66A5GztnTt2enlXTla3bWxut\n3zYh+eSamTWrnpxb+3w2EXVivY2ddPc2dsa+dgJdS5uW9Po+Cb1quV7N/NXCY8Jpe+h5kcgLxVgM\nERaDnI71SE7Der7vAR4cDbUJK0EhSd5kbNjkKOyKdyFwOzaEcxI20fxMVd1UzAaKyHigQ1W7ws8R\nNjR0K/DvwIdV9Sex898BXK2qUwbzeh6IR4+2hsaZdJc4GQfcjyV2A+4VK4VFzYtrsInZ40O7xgHj\nogjoNt8AACAASURBVEx2/OSd6elTdqRnjm9Pzxi7NzO9pis7M5Hh8GQqM2VcR6Zrwu5MuiqdTbbX\nJHZumVz1zPPTqh7fPqFq/daJyU17xiS30/0BkxvuStE93PXiB2Pua3VXpvbw5zsPmbm5a97knelD\nazuzUzuro84tk5Kb1h9UveXZmTXb4h+o2OjA2qVNSwqZk+exZZRa1Lw4V4NzN1bjbic2XNrb950f\nvHZjEluokBvenRK75X4eE3uZXeT13OX9vCN3UZLrYWuvjaamktGMdCKalshk5lSlmZ9MZ49MZK23\nO5NgZyqZ2NFRxZaO2sT2jppE+97aaO/uMcn23WMSHTvGJTq3j092bptQlUpVRVV0vz+q2fc9Fl/E\n1ZMs+yaJ8cSxc9yedDR9a2rMlJ3psZN2pceN25MeN7YjO6G2MzMukSGKsqTSSbamEtHGVFW0saM6\nWt9el1i/YWrVOp1Xt6WzJpF73nR/tTpD9YBjsdGNE0MbVmBJ32pfwDE8FVpCZSy2wuwirBZPEgvA\n1wNXqWqv9XiKSUSasTfI0cB1qvqF2LH/Bhap6vx+nuMK4LP5969evfqZzs7OeUVtsBsWwpXxfKy3\nbj7WY7QcuLuvWlIDFXrYatg3OZuIfSBNCt9PpHt+24TwfRWQqEplq8fvSddN2JOpHb87XTW+PVM9\ndm+meuzeTG0yk43Iku2qjva21yb27B5jt51jk3t2jEu27xyX6CCKZgHzyGbT07emNh75bEft9K2p\nGXtrE+3PHVz93FNza5/tqEnkNhLv8UNjxpauusPXds6csaVr1sTd6ZlRhuze2sSaHeOTT6yZWb1q\n1aF1z2eSUVf+43JflzYtKXh/TfDYMpqF903uvZK7TaD7PZP7PjcnNCeNJXDbsSRwR/6tuiuz44K/\nb08dsqFrIt1J4FT2TQ4n0p1spcJtXDg2AUsEV2JTNu6rb23ZmWtAGN6c3Mvz5r7PTYvKhHbFk8z8\nBSZ74slS+NvkJ4a5rz3dFz9Wk8hka6bsSE+asiM1bcLuzJRxezNT6joyk2s7M5Oq04zJQjKdJLun\nLrF3T12iPRdTdo5L7tk+PtmeqoriPY25C8JOoGvcnnR01LMdM2Zv6po9YXd66t7axKYNU6seX3VY\n3aqtk6q20UuMIK83M/+rF4YvrqLueCEiE1W1aHvjichC7Kr6Fap6f7gvgXUZL8VW8h2pqufFHnMr\n8Jiqvmcwr+lX2/sLV9sfwYLpfqvect8vbVoyLLbQiQXG6voHds2Yt7bjjDEd2dMjsgftrks++dzB\n1Y/cPX/cmr21iST7BsVc8jUu9jV3G8u+K+7GhMdEeS+fpvvKu4tcXb5stn3Cnkxq0q50NGlnOjFx\ndzo5fk+melx7ura2K1sTZUkDnZ1V0QudNdELe2sSm3aPSWzaNiG56fnpNRu2T0jupo+AGQ+Mi5oX\nzwYuwK62bwtbqJ2E9VjuxnrN7giPPwY4PtymYvPjHg23x+OLKMKQ9gS6E9fc9/m37fWtLVcX+M/W\nJ48tlembl19y0pQd6R93VUXtHTXR7s7qaHdHTWLn3ppoZ3tdYueuMYnt28cnt3XWJHKlQOL/71NY\nApV7D9ey/+rYseGWoLs+XBchCaxKZXcd+WzHuHnrOqZN25qaVduRGZfIsjHK8lRdV/aZRJY97J+8\n1dK9mCPXS9hb0rYzl7SFhU8T6bnnMff9+Nifp52+eyC3F2lINn+lfO7rdMJOIlnYnonY1Fkdbdlb\nm9i6ZWJy67Mza7Y/fNSYPelkVJ1IZ2tOeKr90MOe7zxp0q70CVGWsdsnJNesmVnz5KNH1K3vqk7E\nezPzezVr8u7LxdR47cqc5wsZJXAF1slT1W/3cbwJ+LqqzilW40QkidWq6gTeh31AfRz4Z6zbeCZ2\ndfUl4DpsXs9lwEJV1cG8pgfini1qXvx+rPcrF0hr8265AsQR3avY4vNRckE5hSVCWSzwJsLj4t/n\n/5x/LD+x4sVzstnEtG2putmbusZM35qq66qKMuumVe99fvr/z96bR1t2nfWBv2/vfaY73zfVrCqV\nhmcZ2cYGG4ywAzjLARMkN0WqjBN3EjcBKnEbkobu1avJYsrUHVYaHILoXsFkmUZEylJ3m27HBDCx\no6htMJFtCcu6mkqq6c33vTudce/99R/nnHfve/Wq9F6pNJT8fmvtde654773nvOd3/6G3+cko4os\nq+4ExqK7k6Ncqe4UHpkMcZZjkwB5iVXNoak0RqZSC21QiW2lEtvAT2yFivkmrohDT4RhIMJhIMJB\nVUS9mgxDX6Sgnb7Sy4ZAngR+HMxpu28WT15M3OML6YnWwBwAgQcV0V1tqqVBVQwJRK5mx02t52p2\nnYxdmvitjSCTKUpTJx+JQ0niijT2KI1dkUaeSENfpLFHGYieeejM/f9qN5Pcty3f3Pj9j5x2BGNa\nSyqJxbRgnibGtLBoE/MUAI+JhBUQWlKSuBTGrogiXyQjXyTDikj6VZlt1GU2rAgLoh29Xl5inSPL\n2czcejbX7psZYjgbdRkuT6lwYcaJCxtR2hkH44rdUhalzE8dgrlfjWzSGJqsNTTcGhhuDQxqoVVe\nZituxjVpuCIYVlhYYo6I0RUWa2JMBidJ4ca2RVWAq4ejy9ul6LtBvgi/Fim8LhmlIoTdxFbNwJII\ntpHbiLJIpMwLXEVOjk8iX0j2MO7A8YbW/Xy9YS8kzwL4Hzudzv+87f4TAH4DwPcDWOp0Oodu5ATn\n5+cPAfhfkCvgVwE8CuAfdDqdJ4vHf6B4/DYATwH47zqdzn+83s/bN8RX4uf/6d9638yG/lS/KqNe\nTYbrDTnq12TIRAZj0lYSpFJqIMOYIJSkysFWgljKHWjkCcRh8dpJElUSw8nWO97k/ILYipMXk/ax\npXSmFtrKMBAXFmecJ5+61X8y9kQP2woHihHtNiywS5X8MgF60tAt4QYlQF9lXoR85T/pVStvzyLP\ncbsVwBEtUI984YcekTJ4vj4yf+YaDIrvUFaMfgW5l+8cijyoq3X3uJHYty372C0mjvlrEZ8Gxran\nlAIqw75D5F7qryLPJ7s8WVl7+sGzk9797WHR2sRnNSc+q3zvWjFKz2G5QNUAtDRsKrGlSmxVEFsR\nJBaV2IogYeklVvkZu8JyGTbmTFKUOhRmDg0TRwxjjwaRJ/qhL/r9mtwYVsSQKS/wUJr19IZ2p/ra\nbQ1MUAttUC0WmW7GFUdzXRp2c8LJhoChsFgjxhrt7IHs7+XcL2xk2Vd4OxEMAPgMNCxhyggEiSsu\nr7bk018/GTz3whEvNYomf7/qxO9YxdboSfehM/ffu9t57WNvJO83APwEgH/U6XR+vlgJ/yyAn0N+\nEv0mgJ/rdDrblbpvKuwb4p3xez/+oYN+yvOO5juk5ZNkccRIcsNAjLoN2V+cdkaX5px0oy4d0OYJ\nO3myVpAfJ9sTjRljUliGOkvyN+l5K1fSEZjXD69k5vYLSfPgajajDA8jT3x5adp55L/cVflG4onh\nHghc2e9yJwI3ha2r1O0E7oZIGZTVhdg59Lk9RynAOHzByC9aA+QXoSbyCrgGcpL2deRCq4/d8+mH\nuwBw+sGzPoDvBfBXkEuE/P5DZ+7vFLIq7wLw3uI9/hw54XvFJWP2bcs+bgSKcOgJjKVMDiInL88j\n70QzwJUFGy0UIUnkdmeAnb1g5e3hXs6HiXzDnXIMr55vyGyqkdX1kUVjZKgWWVELjazE1vMT63oZ\nB06WkzYmkBFkUoei2KUo8kQSBiIZBSIZVGXar8psoya1KQtAmB0v5UotsvVKbCtBbIMgsV6QWM9L\n2StIYQBAMUFaAiWu0IlLOvKEiV2ykS9s7AkbegKRL6AVleRYFaPU5Ny05U5mUQst10JjDq5l7tya\nDlpDEziaKVM0TBzRDwNaSB3RjV1aiT2xOArEhW5TvbjSUotWUg/AxkNn7t+v6t0D9lp48csA/icA\nv428wuZu5BIEf6/T6Xz1FZnhq4x9Q3wlipy8n0F+8o4PGOZsdl3LA2van9nQ1dbA1P3E+oIRpQ5d\niD06N6jIZ88d8TrnD7lrAEbb8/YmihWuqThfDU1j/sXkTYdWszcHsT26UZfh+YNu/4XDbpK6YpJE\nehgbbUjDaS00aX1ks8bImMbI2PrIUi0y0s04U5pjJtrQEita0WKmaDFxxKVBVVx86lZ/cb2hor0U\nE1wjb217DlsVW8POIbYmje9UWThATohbGOfR3Vl833MY59Jd2s2F6PSDZ+eR5+4dA/CHAD730Jn7\n40fvO+UilxF5D3L9r8exRy2+vWLftuxjryjOtbuQE7pvQX5OPY+8QvsJAEt7OV4ntOuuVURRw/i8\nLYXPr0oKr9eLX3gUa7j6om8GRWgbQEVpDoLEVoLYekFilZ+wChKrvIw9L7WOl7JDzBJEwhIodYhj\nV3DskUkcYWKPdOwJU5C4KHUoBFEEIBaGk0psbTWynA8jKrFVlZiln1rlJ9Z1DEtiGGIYJgwzRaPY\nFVHoiyT0RTqsiGRQlVmvKrNBVVgWmxXHrpfY4PhievjQcnpwqm+mtSQd+iI0AiZIbFCNueamNpAW\nkgm9e3/noWsWPu1jK/ZceFH0mfxfkTP0n+x0Or/1SkzstcLNYIiJqPT8vGo4+K23yMWvnt8V2fnA\nsePOj9x627Fp37/Vl/K4EuIEARUGhpm1L8bGvLAaR+f+9VNPXvjS8tLV3jP6z/f+8GHklbDvQr5K\nfAx5p4ln7/n0w1wY5Tau1JCaA9BkAJkijAKR9Goy6zYlrzUVrbaUXG9IP3NESb485ARWosjHE4aN\nn1oKElZ+YilILAcJw0+sdDN23cy6XsrCy1gKy0YwdFE8ERuBgRXUNwI9I2nDCqxrSV0tqZu4tDas\nyJXnj7irYSDja+lkAUDRCeAO5BezNxffr4sxoXt6h64be8LpB89WAbwfwPsAPA3g/3nozP3nis+X\nyAs43ou8n+s3kHv4nrjRodx927KPa+HR+061kZP/tyBf3Bjkx+MTAJ6crHp9leZz1cpaBlpGYEYr\nqmaKfC1JxS6ZyBNZGAgzDAQPK5KHFSGGFSmGgZCJSx6Itrc6dK7y8Tv1sh0hXyyWKTQG45xCIPeu\nKbIsK7GtVvPqfb8SWy+IrahFliuxpSC20suYpOVMGiTC8kBYrDiGF92Ml3YI724WgBQ2uYKdQ+nl\nfnPiO5ZpOwJjT6DAeNGeIf+P/yyT+NJy21k4/Vv/9oYVYH0z4Kokb35+/pZrvO4jAH4ZwKeQNxDf\n9Eh0Op3zN3KCrzZuBkNMRJWPfvSj76vVaq9KNevgkDk5Omp+khganOfeFbfzAgWLjIAMTJoYKSxS\nYqRgaLJIySIFyFZiI2d7WXNqpJvN0LSqsWkQQ6SKhkNfrg48ueZodmqDZO5AlAFKroS+OBd5tORp\n9oOY225m247mNjEEAdYS1q2gJSuwrGWuA7VRl4t/+pbqeq+u7Hc8PjTf+Rfh1fLWttxmwMkUecVw\nYpdM5AsOfUGjQMjQFyr0hYw9kUQepbEnRqEn1q2kIba2PipzCQ225hLu1A+zTJwGmNEemODQSjY1\n19WN1tDUiTkbVuTiWlOevzjnnltpq2UWtL0d0463X4pAbkfhVb0bwL3IL1qfBfCFh87cnwGbBvwu\n5B6+twB4EbmH77G9dAzYty37eCkUx9oxjEndMeSEovTSPX0jcl1PP3jWQ36sT+bYlSHUMkesjrFu\nZVmxO3k+X4uIxQBishwHic2qkdXVyHIttFyNLSqRoWpshZ+w9FIrpUWqDCfScE8ZLDqaV+gVrKwt\nfgPCOG/xiuEndqoWmgNBzDN+attBbD0/Zc9PrB8kloKESRnW0nIqLGIAPWl4pAwnrubUzdgKC0X5\n72eR28Y+ijaIyG1WqdkpsZUYHkSeE3sYufdy/Z5PP/y2G/G9v1lwLZJXxtKv+tpiu+U5nU5H7vDc\nmwY3gyEmosrHP/7x90xNTb0quQkWFv1GfFBL6xnJniX2rbS+FexZYo8JLudbL9+yy9i8zwXBwWbx\nBRMAIstUiVnNrKf+ictpc24jbQYJ52FWBixxbBwRhx71+lW51m3K5UFV9VNFCQtoL2Xlp9b1E/bc\njANX28DRHDiafWngMIEYICYgU5RlDmWpI9LUoSRx8mrQOCdqaeiJNPJFuk0TastPjq0r4/J2GWou\n5RsmK43LUXwjGExW5gEDP7Hx8YXUP7KcNqd7ZsZNrZs6Yq1XE+cuz7kXO8e95cwRAmOZiDLfxZ3Y\nOhPDw9ZEcQIAZrZWGwnAovA2MiMBc8TMERgJM8dsy2FjIhLKd94ulLjbGvtkvB7+h/XnV873LnSj\n5//kG3Eltvjn3/FdJ45Uq/d4Qr7dMq8OsuzR/+uF57/8qWc61/Qs3nHHHUNch2155pln6ld5fsQ3\nUgvqFcJNYlte9SgBAHzf4SPqo/N33THl+d/iSflmAlqG+UJk9Nefd/Uzn36zGjnT1ZbynbpwZEMo\n2SRBNRJUI6I6CaqAUAOoQoQAoACUkzGiLR4xApGlcTV72cu2XCCFE2PyfO1PbHsYp1J0AYQ3Wtvt\n9VJZu8O8NqMnlnAgdukWLekoE44y0bQR7BpJXqqIE0dQ5pAyklxLcASDHM2u0iwFI5OWUwaGYPSV\n5czJWLua2c1b0wlpMBR5f+zzyPOHLyIPwe+3V9sDrkXyfgHXNsQ7otPp/OLLnNNripvEEL+qJG+v\nIGZU4jhojsKpSpK2vDRru1q3lTEtaUxTWm5Ja6eJuWoFxakSK5Erl1NHZlZAGmuqQRqHHou2YNSF\nRV1YroEgjSAduzQaVuSgXxWjtZYajioyiV3KQl8kkS+SVJEBkcWV3q2dmpFf676kNN7FaldeZYhr\n7AsAUlh27ngxufNAN/uWxsjc6aV8OFMk1ppqtDitooVZx4SBLJ9PyL2B5cWmXOmWFcelNE1J+rYn\nPcuJ28pkxlMJ3SKkAFMRFqFifgTBGN/GpGQNQXJ+24cocgg5V8fP/2gQOC+OqYVGHVnJnINrmRP6\nIrk4aP+1qx0fX/ziF/8GrsO2vPvd7/7d7fcNh0P3k5/85OeY+Yb08nwlcbPYllczSiCno/fc3gt/\nGgA26tKsNlWy2lRJ4onSEw7ki5MMjIQYCRhpsY3JUkwWMVmEZBGTQUiGRiJDJDIayQRDFdNIRogE\nxE11vOwWRUpF6Ym8GimsTLwkxM45hOvI7V6pmzdZJTuL3DZw8bzJNmrltn81IlkUdh0FcNQQjmlF\nJy3hiJFUzSSJxKMsdogTV4hMwWWg6li0HM0+8eaizySuOPdjv/q7V7Ut+7gSN1QM+Y2Am8UQv5Yk\nz8ky1RqGrXoUTVfiZM7LsoOOMTPS2CliDqgo0ODNkCULwagQc43AypJYz5S8lCh1wUo50lKMtJRh\nptQoUTJcNKm5dP4bn4/+2l3dUUVuIU+Hl9Pg7mejE+2BOeGlfNzRfIwYvpYII19c6tXkwuVZZ6Fz\n3O8mnnCws4jxZD6Ij6tr/5UeyJJ4lQMYh2PtDlvrx1YeXMuqs+u6Pt3TdWmYRoEcrLXU2tK0Wllp\nqQ0WBFzFa4Wx9MPkXCa9hu7E6wzGhLCUihmU23SUxPXYP+G6bgrAEMMCxEUOoSUmC8BSfjG1xGQJ\nMGCyxPn9FiwTVx9KfP12I/kEMUJY9AnQxJQIK9aEpQ1lqFcbiaWpXuupvR5X14Nut+t94hOfeORm\nuGjv25YrYVnjhWPds6lrZ5kgicgD4DJtHvOb5xvlC4wk31JKJekDNO0onXkljLYycbNL0lGviKzR\n6xnCMtWH2p/umfpUzzQbI1OvRrYeJLbqaPaEhSQwMYiNhDaCtJZIY0+Gw4CGsS/j2KUk8kUceiIZ\nBSIeVUSSOsIAgJtZOdUztVZf1xojW6uFplaJbZUAMoL0sCKGg6oc9KpyuN6Uw42aDAsbuCtIw1IZ\ndhJXPLFbDc59jNut7GMfV0UlXnjPbQvrP+NmXAY5rBGkjUCWOCIe1EQS+ioZBiKKPZGliiI/Y6fV\n1/XmSNczRdnSlNu9eMB7tl9TMbDJaA7tcIqz0YqSe970bdKVCbaFSC/PuebynFuGS4fIc3RsY2ic\nw8vp1MyGfscti+nUm16I22SBxKWNYUUurjfk4uUZ5/LijHOeBe0Uet3NfjkYE2FSP7HBm87FJw6u\nZbc3RuY2J+O51KF4vaE6CzPO8tdvq62HgVTYmo+3ResPY3Fmg6t7H3e6HSH36k3m/tUxsYqXjjxk\ngNsSpQ0ACKaILA2lpaGwYiAsho6WfWEosYIdrUwtdeyslnbWSp5msCSmRBqxEsTunzqp+Ix2bDv2\n9N0EZLWh98W51dpTu73Q7mMfJQQpnLw4d/+1nsNgJK4JYj9rpI5pasc0tLQtI23DCm4ycbXUWiKm\noTCiJy31lRE9lcm+m8meHzt9L5XRenfd+8Qn7r8pFgV7wYR+4E5FaFMYy1Cdx1YP3AqApV/78FyI\nHaIUM+uZc/Ji2m4NzFRzlLX92M76KR93jD0qDQ5LyzPEaAIQDFgjqVcUmC2Fvni8V5XLg5oc9Ksy\n7DZluNZUiZW0Xdz+WpGRzdtGkjSSNh46c/+/fqV/zzcSrkry5ufnDYCPdDqdB4r9l8rRAwDudDr7\nxPENhtCdfeSJW4mXptwk8alqBdes5LolrjFxAABOZun4QuodXU4bzZGuRa66vFH3H3nyeOMvyAar\nXqaSuRVgbhct5nPvzMs3xIXhmwOyk8iV129FLg1iAVxALrfwPPLm2ruuzHv0vlOzxXvdhbzKTwB4\nDnnl728DWLze/JfTD55V2FqoMbmdJHFz2+6b9Fa6E29JoPw8J5BhtpkFfFZ2OiXUmLjKee6SAzAI\nlCH3msTEFBLTQFjqCUt9wQjJIgWRCGLnYrPvP2WkdTaa8TufvXX1+71UPT6zVv2zSuwOr/UdH3jg\ngc8dPXr0n7z3ve/9XLH/J9hF+PbDH/7w+3b9Q+7jDQMCwU9V5KcqQk5MdgSDEflZLfF0TgaVbUR+\ndmJUTRpWcNMSB/aYlT/8qR/7q6cfPLuGPJ+ui3HYssxrKwsAdkrF2FOaxnW8bsfXKs1Oa6DrjaGt\n16KxBy5IuCosKxxxkSlKI0+EoS+iYUWEw4oMezUZDioiZkFlh6EyBLv9fNuMREjDdqqng6meqbWG\nplYfmXqQ2Jq0LFOXdL8uN4aB6PZq8qn1puouTalu5oiMcm0/1eqboDEyftH5Z2Z6Qx87spxV3MxW\nHc0+AUQMy4A2kvpaUj9T1Esd2khc6o98sdqvyd7lWWd9o6FibF1gR7s+cPYB4NqevE8hv3BN7u/H\ndr8J4drBial++JdnB1FsiCIjRagFjYLExs1hFtRDPQNQul53X3zhYPVPv3yXPzKKAyu4akV6jxFJ\nk4k9ACCQFTYnDtKIXr7aFn0vVb0gdvqOlruu0nwpFERrqRhfLO8v1NmPISdq7wJw5tH7TtWRG5AX\nMCZ/ZTXnZH/XaeSr3yeRS4l8crIV0cvFQ2fu1xgne18XCo2tAyhyYKy2J8CYZbADIkdY6olMXJRW\nDIWhgTQiAkFYwS4Tu1YUxTME15L1rEDdSHsgc/g2eBxwXkjjllsAgsE2U+btw2ryE8RYu+uZg1ft\n71qtVv+w0WhcntzHvm35psTl2d5be634Y2DExAiJERJoREwZAENMGvnqQwIgxl4CfOODajLPAnnV\nxQzyln/bi6UCjHPPJs/FAbZqV/aBzfzUq3n8TfGcyceviBYIw+bE5bR6dDlttwamVYls28/slNI8\nLQ3qBDAxUktYYUIHhCVLtGQJC0xY+IHf+3d7Dq0XwtEHit/g6MQIinldRl7oUI5L93z64Ss+5+UU\n6nzn3AH5wRMn2weCoFlznFZNyLYjREsSnRBEbUHUoomIBwODzNrz9CH6N9fzedeJm6Ko61rYz8nb\nhv28mR3ARlSytbc6mW5N9eOT9Sg74aVmNl85ylHsiRiAKy17wrJnBSQTZJHkT1qSziQZrUhnikyq\nBKcO2UzBpo7gxCFOHSESl2TqEKfERit7WShZdplYQV5ltYTc+CwgF1a+0RVtFQDfAeAeAG9Hrp5P\nyCu7HkdOFL+KvBXSnuRJbjSKQpBZ5Ib5GMbGuobcSC+hMNDr51ZW59cO3zpbm35FdMQYDCNYZo52\nY083U8fUDqzVn30lPms79nPybixebduSSu0sHOx/j5E8Y6SdtoLbTNxgwAVBFtXgGTEiMCLJFBNT\nT1gxEkyhsBQKSyNpRKiMGCktRk4mQy9V4U4Lxr0cL4VXfQo5IZye2JYixGXFboK8V+vaDtv1n3pg\n2cHWdl+TxQylwHxZzFCOMqTau96oQBHJmMFWElfaiHIBXFatXkBO5PZ0Hr1ahTrEjFqmA9dateZ7\nr4oe4hulSOe6Qqvz8/PfhvwCaAA80+l0vn4jJ7WP1xemBmHjjksb307Mfuy6z642pv/40uz0eaaX\nXlMTM4Ik8f00870kC2pD7UtrAoKpE2wNbGoEWxWWawSuCLYBG1sVVgfsyNu1xFu0JD9zSMWuUIlL\nTuqQkzqC/uE/+9tWK8oyhShTFBZjkDqim7i0YgV1cY0K2rm1jN7ybHT4wJq+pRaZ40rCI+CCNHic\ngH+LPIxrChHWMtz7YQCHCwO6gCLcW2y7N7IjREHkprDVQB9Bnm/HyMnvBeRG+k8AXHzozP1XeACJ\nqPLmj3/8yLU+i5jhZNpxjFbKWKWMcaS1UhnrCGuVzIcjmKXIt0owK7LFltkptsoIET1+2/WRvCee\neOLOXq93kIjs1NTUxbvuuuuF63mffdwcOLgeVd/V6cnYdZ4e+f5Kt15bGQVBBABGWBlW0mbk6ZnU\n1dNa2ikj7bSV3NbCtgFAWIyIKQaQEpMB2ILIs8QVJt7UryOQJUZoD5n0g5/86O2nHzy7iq0dZjY7\nzRTe9NKrXpKuHfHofafk5Rl1aFiRtwO4nRjfJi0fkwYHAG4ZQf4zxzwT+SKKXdpIHFqIfHFx8KVR\nYQAAIABJREFUGMgXlqfUI4knlpETwo3rWbQWdqiFrQu9o8jzcbl479Ib93kAF2+0aHStVktfpUVB\nwsgN4j52j722NfsQgH+OXJhwsirwaQAf63Q6n7vhM3yVsb/avhLKGFmJk6BfrVx3CHEv2L7aLgyZ\nj7xtUdkPtwqgGrnUHgXiYOqIWSMxY4mmrcA0A20WCJhIMiDIslGGrZuxcDUrZaCMQBx5Ym1QFcsr\nbbXRr8os8oWIXUGJRzp2RWYkTZ4gFiVhZI4OrGnv0GpWn9nQzebQtP3UBmCkqSMuRh690K/J558/\n4j138aDbxZhcpqUxf/S+U7TcVvIr88FMGMjjmcQtTHQLCEeIeVoZCDfjYSW2a7XQrLX7ZmNuPVuv\nD60WW/XwdmqovrmfGuOHvn9USVFq++0Ey0SaAQ0ibYk0AG2JMibSnG9NuW+L21aQtkJklkhbIbQR\npBPHCZfbrV1kX47xyCOPfN+lS5d+0lo7PTFHVkpdvPXWW3/tne9852O7OVZez9i3LVdCGSOPLa/e\nWkmSWS/Ts47Ws4I5YABGyG7qqJXYdVaGvr/SbdRWI8/bnFfiaC+spFOJp2cyZae0MtNG8HSZGiIs\nDaQRq8qIrpPJrtQiinshLtRWn2wcabvYuY9sA6XzgxlBzNweaNvuG9EeGNEYGrcaGT9I2FOGtTIc\nSYMFZfiS4M3UkJIYjiYXfacfPFvBlV7BctvCuK93H9u8grdeTJJ3fn3kHVjTbTEmctPF89dRhFQx\n9spdtwdwL3itlR5eSdxMtuVa2LUnb35+/ocBPADgWeR9TJ9BnrtwJ4CzAD4zPz//vk6n8+grMdF9\nvHbo1XXtuePxd4OTrJDM0MTjrWDKyJImJi2YtLDQwoqMmIw0lAlLWlqhpRaZtGT2WoVZGKvSI7e6\nm9c8et+pKRStwBiYNwKVUSC63abqnT+o4kFVBMrgkDJ8QBpuVSPbbveN6yfMfmq1n1q4GUMZNkpD\nK8NGMGAJxARrBLEVsJYgrCBYAWEECQb7fmK+PYjwPbPrunrHi7FPgDKCkDpkY5fwqS98iBJHEJ/0\nlRXgQ2taM3RMQEjMfWHRI0YfOTEkAFVikJaoLMw4rcVphMJiJCyPHM1rbsajILGDWmRH0m5q6G1u\nT/3xH6iP/MRPvCKGWAsroyBrJK5uZo5paGWbAMSxy/jCbt/j0Ucffc+FCxd+Tkp5aXZ29v5arXaJ\nmcVwODy6vr5+3zPPPPPPpJT/4B3veMdf3Oj57+O1RW5bnNuF9UJp6JI04hllxMhNRHRoLXZm15N6\nNUlnD6xv3Hp0dW1WMLsMWC1lN1VqJfLc5WHgr67V60+lrrMZnmUwwiBrRH42lbp6Ovb0cVO101nd\nzlTd+ncAiKXhlbluNjyynKWHVrP+XFePqpEd0rioCYYQJR71BhUZbtRluDitkoWZQC/NOGQFTfai\nvgt5/9wSEYD+rz14didvYR95FKAPIJpY9FUM4eioIu5MHLoToHcT8xGtKIg8gaVpR3dO+LpXk8Nu\nUw36VbEAou0h4h7ycx/3vGL/2j5uJuwlXPtzAP4LgPd0OlsV7efn538DwP8H4B8B+N4bN719vB7g\nxc6wGnpPMrG0xA4LVpbYYYKygn2TV9kqBhSK+8f7UEXYRILZldY6wkA4loU0xbAgaVlIy0IYiOoB\nK/7hL33ko//4l/5mpgxbZQClmZVhKFNuQdIwpGFSmslPueVq21QaLWHZh0JsQetMWGfiDQFK/YRn\nji6l7VsWUiMYMeW6ch0UhIiBNHZJjgLhhoEI1lrCjzwRxK6oJC7VMkWuJbCwDDfjkZfx0Et5VIlt\nVIlt7KfWWhLVTFE7c2jWCMwA1BLMUhg2ldia5sjC2bDS0YBgpEXLokuGcDF16PKgIlaGVZn1q9L2\napK7TWnWGwrDQAgWW/pa1pB7Aa4ly8IA6Ht/5CNYTbO5DbkWEiMlpqTYpsSUCkuJsJSK/HY+DKVW\nsKOlDay0gZFcM9I2jOCmFdxgyruTEMiU1bfSiJ7Sou8nak9evMuXL3/EcZynP/CBD3y8Wq1uye3p\n9/uf/oM/+INfP3fu3H/zjne84+/v+eDdx+saXuwMK5H7rJa2YqStaqVbkeCqrXNleY6rTDLgzcJx\ngJhSpTFq97Wa6utj7UF6W2MUBfOXVqvKsBUWiRFy0ZBcyJQzZIIGyDhGx642ISdpWI8iKCXd2KUT\nw4rMejVpl9tKPHXC9zbqUm3UZZg5ooc8D7jMAV4AsPDQmftfsk90kWbhY2t7sDqAZhDbE7Pr2dHm\n0B6thuZgNbZTn/rCj1YJgDnm6dAXa6EvFocVcXm9ob6+NKX+wErqYVz0McC46KOFrR7Bt07sV08/\neBbIU6q6GJPALTmDD525/w3ngdvHVuyF5L0JwH+/neABQKfTGc3Pz/8WgH98w2a2j9cNZnph9duf\nXrmrzL2iIveKAFXcLrstXBWFCJxlopiBMgSoGdCbt4kyS6RDk4nqsP+kX/Hi1CGbOITEFRy7hMQh\nFgZTbmqP+qk95mZ80AriQVUsrrbcP3/uqLewOOuETHS18OX20GZ1YorA1ipPRp5UPUAeBimr6arI\nDek0ck92C2PZElO8ZoTcKD+FsXFdAbAcxHbtzhfj/skLsTjQ1XOuxm2ScTJI+d1Bag7MbRiFsahx\nWLzfpPuTi1G2Xupi3IJpNDGGAEZfGK2h+7Y3v0scqCaZsm7qmlbqmGmtbNtKO2eFLYgb6kzwAZbF\n52kAGkyGwIaYSjHahEC2/FuZ2LcCxGQCI+2UVrbR7lUuY5fIsuyWubm5/207wQOARqMRT01N/fuV\nlZUf2+377ePmQeLpauhnJ4sIgRGWImnEYDNCwGTIUibyPtiaCcIKuBbaj51kul+hqUyK1jAgx9Xc\nclNzWFpztzLaJSQkmCEYggnpyBfRoCnWzjn+V9K6e269IV84f9B9IXXFBgoP20Nn7k8B4PSDZ2sA\nDhXjGPIq/EOnHzzrY5zrtp0Erjx05n4zUWgxWRR1GLmNTJCHVb+Oca7c0j2fflgX5LDslTtJEGeQ\n5wRP3lcu6Bh5GklJ/vrFe5a3hxi3X/SR5+qdREEMi969QO7136lwpCSDr7h4NIOhlXUyx/iZMr5W\nNjDSBkawZwX7VnBADH10obUfLdwD9kLyziEnelfDYeQH/T7eYAg9L1qvVb9hpNBG5ENLmWkptZZC\nZ1JqrZTeTSHGbtDtdr1PPPC7jzBzWFS8zmMsYdJEblCfAPAQgGdvVKPuEkVVXWncjwO4BbnB9pAb\n1BXk6QpfQk6w+sjzabYTSx9jUeKTyCt2G5EvGl+br9S/Nl/JPZz5e24KHJPlQbtvMLuh2+2ePtwc\nmoaXscOEdBiIbr8mV9YacqVfkyNpYSqxrVRiW3MzvtXRXJOGa9KiJi3XpUG1bQ43pnVvyrnQA3Hu\nQZQGqbSIwRgRU08wrSotniYWQyNEZKSItZRRJmWcOipKlYpj140jz42tEJtE2AgrM8e4eV9j6xrJ\nbt5RY/dQSi1EUXTL1R5P03RGCNHdy3vu4+aAm8moEjvPjyMEUGDr1qNsqhbqZiXRjSA2DT81DS+1\nNWXZY0BoSRz5Igl9kfZrUo8CNxtU5MYokEt2ax4tOO+9p5Rhp95Lq40wu70V89uOLme1288nFQJk\npogHFZH9wnN/KxlUZVSvyeGgKkIQDZATpVJDrwdgQ2mmg2vZ9FRP31Mf2YN+ag/7Kc/9zhd+VPAR\nN8sULaaOOJc69Ezi0lcWZ5x/c/GA271WcUXxWCnVsutFUiGZVBLDyRzDw9g537DUjk6Rk7g+8kXh\npJbeEYy1N+unHzxbFrHEmPAIfuDX//og3DBHXSdZUVqkxrFepqxfRAA8Izgw0gb5QpB9S/BZcMDE\nQd7VhAUmlW6YMmLEgikipljYfCstRU4m1txM9nb7u+wjx15I3v8A4N/Nz893ANzf6XQ2L6zz8/On\nAXwMeeXhPt5g2Giy/9wJ58REeC8VFqmwNpEWiTScSmNTZUTqZDKVVpiXftcrQcw4sL5x4MTa+u1/\n9IF73/7ofacOI1/5PoVcl+4z93z64Y0b8Z22a8lhXJnmI/fGLWBcufolAJdfqdBGsZrelGpgQbPd\nlpruttRMcb8HIFCanZkNHUz19KHDq3p+/nzSEBZupijtV2W/V5fdhRm5tNZSS0bS8yjyGNNRYuqx\nd7vjuZRL27DDgAJYOtr6bmZ9x3DbzewBN7OO0iwdbZSjtXS1VcqwcjTLdshqdsCSGJYYXJA5y0SZ\nlpQYQYmRFKdKLndrxz+52+9/7Nix//3cuXO/8Md//Mfn3/ve9/6+67qbx88jjzzyPb1e7786fvz4\nL9/QH30frwtUkuHMLc+u3VuJre+l1heWmUCZEdTPlOxmUi6mjvv12HVXutXqcuL6oysWkxaojPJx\nLViyYm2wXvkPG1/78m1/+c0aEx7+A6tZ8/hCeuTocnY0iJODfmoPq8zOgOioEXC1IrJ5yzXfSBKJ\nQzSqCNuvSntpzqF+TepeVWqjiJEv3A4jtynvxbiPtDj94Nmyo8068gXiBsaizGX3msm82sle1dd6\nLC1e30fukEkBmGuRytMPnnUxJn+T8i5lv9sp5IvUOq6MiggAqExVZb9l6n3aAIhR6BtmxBhRnsax\nIa3oCUvrSou1IJOrfux0XS0jN1WxYOJND54ymwtFI61rBbuFF89lYjdzbNtIriG3y/vYJfZC8n4K\n+YH4qwB+cX5+/jnkB9KtyC+WGsCvzs/P/+rEa7jT6dx2oya7j9cGTiYTN1NLVrBriV2tbINpLJhb\nbMf7YLk9tgjk+TSEMhcMaTWyOLSaNGbXs2ZrkLWVYRG7cnWtLRafPuR99oW3tf/PzBEhJuRP9pJM\nvE1LbnJUkXvPNrXkkHsGLz105v5XrZJqO7nbYSuLpw4AnNOK+oszTro445Q/qQIQVENzeGZDH5vq\nmfbh5ey2emjucjSnRtBS7NHzXdbPrVXVBTRrz3mpE+6m8MUAMAqIt1kIS1YYycpIK41gxwqjlNEV\nN9NVT+tAaVMTuaHfNS5fvnxKCNFfXl7+2MMPP/y3lVKXiSjTWh+y1rYBmAsXLnzswQcf/NjEy/jM\nmTN/fS+fs4/XHwx5/bVG5WvPHVFxr6a0VnC32ZY6E2aY+C1MI5cx3JVtGS9IkRBTKpgSspSmgcXc\n4cPLyElW5GR29L4/HdROLKTkZVymQUiM0x2WkIcty1QCHzkRKqvAuxhXtl4AcPGT905Hg5rcHnKd\nHLPICeAB5NfPuzFOG8mQE7WN4nO7yCMH68VjupjjZNSghrHiQDCx9YuohIvcVpTpKpNndSnOvEVe\nqvj+pTj8cGI/Ll6jAFRNaqacTMwLVygm9hnwmdgHoWIFHzHSnshgnKLrTtnNg1C2h2SkBEoojyj0\nhaUNwUWHHUujzZxhpkRp0XMy2b/KobSPq2AvJE8hl0p5etv9nWLs4w0KR8v0wEr9yZfzHtJoOdPb\nONYIR7cHSXrCMfZ4qijp19yl5Zb3ja+faHVjX5AV7KZpWh1U1BHHEcewrcVXYbSArblzZSsvD2Mj\n5yA3iF3kaQQLyI3xXyA3mJMGLQYQP3Tm/uvyQG7HhIjqTsRtBuP8vTJcUua+LCHP1VkD0L2ePJgy\nr0canjlxKblzumfe7A34nSeHo2/1e6O6lnBGgYx7VRn2ampto+Yspa5az0VlxVAaCpURQ2FJM0GA\nIJhYMiDyfRYMllbAscS+FdZnAY+JA0vkMSlPMKKTL+JP9zBt6ThOSbY34TjOBeyv2t/QWGva2uqM\nvJMYBtAWgCGGJsCAYQmkyWIoQBZ5Nb8Bym3eEYOYDUAMMDERFSFAwYT8WCUbBLGZbg2zZjBIpr49\nxD31pzdajuaKFZCRJ7LH7wiiXk2GG/W8cjXyRblQOYDcmzWptbkE4EWyHM9uaHlkKavPbOhvaQ7N\nd1diO336D9cDaVkDWCTGeWX4BWXxHLZp1BV2ogaggtxmzSAnkCeKcRjAbcjtRg253ZDICVJJ+FIU\nIWQUOn/I7UcZZp7M0y2JqsS4YKuCMUGsFMMrntNA7tEr26uZ4jM3u3WQFJItGuX9xDQSTEMGE4HA\nAFEeMM/tNZFiwCFAWbIOkNsOBipWcMtI3AZAMSBp3M9bgxETKBSGLs+sV//J3o6yb27smuR1Op39\nqtl97Brt/qA52+ufqMXJcTfLjgIQqVKXRr7/4uXp9mNrzXp3MuwyPRGE7Xa73u984pOb+kQFcWni\nyhY8pS7mAnIit4axsTMYG61yBADeVmzL/U0SWRjdyQ5I5aqzNKhZcV+5KvYwJpc+xu2QDHKjOmlo\nQ+QXijK3rDScVeTG9DZs7V1JRXXcdcFIwnO3+PY5wJhU9/zUeVYqmQnD3B7oamuQNQ+updN3XIxu\nk4b9TJHo1aTZqElebyhabwoYReX3t2BogDJilOGYmEDDosdtQkwxMWJpqS+12JXMTYlTp07tV81+\nk8LRMnK0uMyAAEEyIJEvKiQLeEURkGBiify8IwCSCSrv9gVZtDwTXmKdxshUGqHxq6HxapHx/dQ6\nDFDiCjuoiGzQFPbSkWDQbzhp7FLZ4ixAfg5eDYT8nCzPh3wOguTylCOXpxyx7TkCzCJIWNZHRtZD\nq2qhEbXQiq/9zN8gABz6gt9cEWZYkXpQEXpQlalWVBIovW1kyL15ufcrtzcOxraozB8ui6WSiVGG\nefPzePyeMcbksCwiWUFuS0sb2gMwvNZi80bp5FliMtIqI60ygh0rrbLESgvrZo6pp46e1Ypn97sf\n7h3X1fGixPz8vAPg/cgPms9N5unt440DZQaztWT93kyS1YpMlg+bOsJmiiyD0RzqZmuo282hnvYy\nW4ldEV+eUxsrbb+33K4PtCKA0ATwrYz0HcCa2N6PkgHJxBVzq62e+t0fjwvx0LIvYopxD8mSOJWV\n3m3kq+0de0Nu2y/7SUbbHnOxlRBOCi83MTbuZSijj3Hla2lMGVtzV8rwSPl+ZSVcuZ0suBhg7Fks\nCWGMrR6ECOOLSXnRU9i6Gt8+KiA4TPAtscMKWG05abfpnidGp0hujv3YZge7aeXoUla/+7m4UY1M\nSxkmS9QLfWd1rekMFqfdaHHagXa4vl1KBQRFTIaYImXFy1bUT5JEPvbYY+8UQpi3v/3tj03m6e3j\njQNphVFaLufHzlhzs6ys3a65WQszObcetZqjpF1Jsik307MqD+nDEkWpUiuJ46yEfmWlV62uXG5X\ne5OLyeHCmvdHz/2nP3vbR94tcOU5M7lfFjCUhQtlmLMcZW/b0sYAk+cmkYp8EpEvzPL05rlOACRZ\nNo2Rcdp947b7unrLoq1WI1ORBooJGPkiGVZl2KuKpFeXyUZdZVoRYbwITIs5lDYmxZhgyuK+sgdv\nGSbFxJxL4liSRmDrAnOStMpti81yEWsBmB954Md5gVNnkRZt0YLOAmACaYxtnC3zd8eDCk/t5n2l\nHebSS5tX9W8+pomJhaX1ax1P+7gSexFD9gD8GoCTnU7n/cX+F5F7RgDgG/Pz89/X6XSu2gJmHzcn\nGDIhpn4lYamGRlUS3ajGZtpLbdsxtk4MMoJCS4i0ECMIWq+NjJ1dNzz/YuITw2Wi1BIlRpCOPBKx\nRyryyI98ChKXgkyRsUQxsVg1iX2G48HnD8J56u7n4kuu5jwfb48K7oUH8KVU5suQQB/5ivYyrpQR\nGL7cPrlFocdOZGwKOUltFPM5WNwunzdJ5gTGRrGcT2l0y9BNuU2QE+F1NqxFBjgsQ1HkKhGj1MRL\nhBWpsJSuNYJkoypSZUSqtEicTKatYVibGgwPHViLDx9f7B9VxswQAC1kN3adZ0LfW+jWawurjWAY\nVnQjcXWTBe9p8RiGofO5z33uv03T9NCpU6d+NgxD5zOf+cy/yrLsNgB48cUXz7/nPe/5+4cOHboh\nRTf7eP1AWNLCUjKpuam08RqDrN0cZe1qZFqVWDf91FaRV8FiUJHJ81PS9KqKN+qeGQYEJnJAm4sq\nh4kVMFSgoZz4OGIGvfU7vzPEmKhYjL1dk2NyMVdKkZSSRSHGC81y0bm9fWIEINur3Xj0vlMKuQ3Y\n3srQLeY1WRB2CXkf7StyYCe0+iajFVXkIeByHEAeHm5hTFy3L1zL36pESSQJAKyxys3EISElQBAA\nK0s8SYSB3FGXECMSTDGYQpEXZmgCZcJSkre/zhfKTCRLzy0TS86dAZKJlbB0U3efeC2wF2P88wB+\nHEBZNfdfA/hW5MTvqwD+BYBfAvCTN3KC+3jt4aeU3bKoe36anRCWg0zJlcgLvnZppvriUru1YKTc\nIpmRKeOMKul07GezqWNmDZlZATvnaNtwMzZBjF4lxno95EFzwGF9xImyVglrXcHay9Ks0hrEJypK\n34axofIeve9U2fYHRkCkivzMIS92hUxcUrErnNglN3GFqxXZb5MwlmgIypOXibEqLC85mv+iPrKX\nT15MlrycQCbXIpCnHzyrCt2s7av+Cq70AJT77sRblJ670VXGBeygbwcguV5yefrBs6L47fzu8yvt\naefYe/yGByutVxbQWMGukVyzZDwuKtgYZeI7PBA7DCaABCMQQLAZrmoMzS3TfX1naxAFzeGwcseC\ndSzBjgI57NWcC6mo/cvdzvXzn//83xwMBn+1Vqt9FgC+9KUvvT/LstsbjcbDtVrt2YWFhb/753/+\n5x/9oR/6oX9xPb/FPl6/UHY0ffuF9Q9WEtPyE1MnwMkUiUFF2EFV8kpbmkHVtcNAmNzfnxMO4twb\nlRdVICZLIYEisggFUygsDYUVI2loRDz2CiWjWC56G4/VDjRCjL1r12wLiK36mh5yL9/Rbc/ZsZqp\n8IKVodKXrpz98NzkY30AXwPwZQBZbWT4zvNJ89BKNtMcmnf4if0rruaZz//wKUmMkBgXBOM8ARd/\nKieBi/d8+uFo25SeuNp/UURODmKsD1iOsiiki22pMevnVrPb6Mh3+Y3KFbaFCa4l9qywLhNXLKHK\nxDWWtm2IfQAOExwGysKMMgSde+8YhkBx/t0oJEaotFi42vz3sTP2QvJOA/itTqfzd4r9U8hj9j/b\n6XT0/Pz8SQA/hn2S9wYEYRAELz57+OAjk83Dh9VkKvbDN6WOmdXKzBrJMwx2CJRJQ6tKyxU3VUtB\n7D9RG3nd7dIqQy8fmx0YkbfJWsm6sy80zTONo+0atnreppEbAiBfYV6QhteC2PZbAzOcXdejIytp\nOP9ikrqaXUsIUocasSuamaKmkWhYorcyoc5AbWVKbVak/d6Pf8ixglzOQ5sEAAywkaTfLyhlQsSE\nUTEGnBOxPoA+AT0wFqXFhpvxej00662+6XmaIwDpdXogJfI8wZ0uNDtdhLZfpDbva986U41SO5+6\nMRjsgFB2JylznLZgLFoFEIQFIxV58rMmkAbDhIG0YeDi4gGAi6dKw2Kqn9VrodW0h+DqYDD43lqt\n9u/vvffeXwGAjY2Nv0REo/e///2/6bqu+exnP3uo1+v94F5+w33cHGAmhIFcWZ7yn+xXnEWQ6ist\nBk4m+16qRpWRTBo9ke21FeLV0O1q77c/8cBXXu1+pMU5/VLn7E7EMigfG1al89hdFRd3bT42Qk4O\nXTe1/lTPfEtroL+rMbK1emjqQWIrz//dHyUjSA8rYjCoymGvKofrDbm+UZc9FvRSkiwLAM5P3O8h\nD18fRl4R3G7fOjs71OZgJMJEWFqTRqw6Wqz4sVqoxO5aEDmDvfx3lqzIlHW0sp6Wxk1c09SOaRhp\nG0ZyvQgJ72MP2AvJO4o8PIv5+fkKgL8E4P+dyMM7jzzstI83GNaaCk/d5s9pGb3ZyNEsE3sEMsLQ\nmjJixc3kSn3odaqhu+ZouaN8hiUrRpWkGXu6mam8x+nV2mTZxIwCpzqNIlQK4HkA30BufHxMeM6M\npOqwKueGVVm5eNCtfuWuSultKlHm8pWhlwVc2RliciQohI3rI+NNb+h6kNi6l3LdzbgmLdekRUVY\nrkqDimBuC4sjgtkniwCEYBSIIPSFJyx8YnZf/IkPCRDlyeRUVP0RrBZkjCRtJLSWpLUkoyXpd0ho\nIyk1khItKdYSUaYo1JKizKEwVRRGnghTl0ImmjTQcfEdtxjvweUNMZcdXqxXa6G0pIURWlrSSt+4\ni+fLgTFmtl6vfx0ABoOBF8fx24Ig+GKZh+f7/vL6+nr9tZ3lPl4JKNu4DDT+ZW0I1Iav9WxeORQe\n+TIM+qri0ftOVVGEfhk4woRbLOEgCygtKExdWh4FctBtyJXzB9215455oRW0nXRWim0Z0tUA1q02\nSzJVqXBJWOK6cbIDmYN6GMS1DWafGC4xSDC0sJwKi0RaJMIiVQaxNMiKx6y0ZKVlKw2sm8HKmI0Y\nwErLLA2stGBArA/8V/sXvLmxF5K3hNyVCwDfj5zVf2bi8bdiDyrd+7iJQAylxaAaus9XR+6ql6lN\nQ8VgRH5Wiz3d7LbC27WyTS1NwyieMcJOMaGKvI8tlfpVyKszi4RaYobl/FMgGexaxXWhxLuQ57WU\nuSElkZmsOitz0QbIK8LKwoftq+QA+Qp0M9y707csXr/5WYOqTAdVOUmYJkVHe7gy/DI5z2s9Zn7q\ngWWJbfIw2Jo/U+7vdN8MxrIxk/MvkWEiN2iUZWaj0T3muMPQCkqNEIkVIjVCpFqKVAuZaiWTTMo0\ndVSaKCfNHJXdqA4mLwUhRDdN0ykAePzxx98FwGm3218qHx+NRiellGuvymT2sY9d4tH7Tk0WQ5RF\nCzvd3mn/Wo9d631e1usIkMSQgrEBC+mAgyDl+ebQvvPwSla/+7m4jty2AOOWjsOJsYWkpsao0Pdm\nlZSlh20EYMggA4LlvMjCGgmRuPBTh7zUQSV1KEgVGkZAsSAmi0QwRtIgkpZCYRFJg8QKghFERkBY\nCZlJ2t6fex8vgb2QvP8I4Kfn5+djAH8P+Z/5f8/Pz7cAfBR5vt5v3vgp7uO1Ruxk1UGNOLU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M1e8JsWQiiqoKIKyiuFogryVdUYb3d2N/O835RQKJ7wTtfiOeWKgVv/JHXgBHLrjUvOB9WfHLVa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\nAAASRMgDA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x128938390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "writedir = datadir + 'threshold_'+str(threshold)+'_SD/contrastPlots/'\n",
    "\n",
    "# Create color palette.\n",
    "myPal = dict( zip( spikes_dropped['genotype'].unique(), \n",
    "          sb.color_palette(n_colors = len(spikes_dropped['genotype'].unique()) )\n",
    "                 )\n",
    "            )\n",
    "# Plot 500ms data.\n",
    "%matplotlib inline\n",
    "contrastDict500ms = dict()\n",
    "figureDict500ms = dict()\n",
    "\n",
    "for illum in spikes_dropped['illumination_mm'].unique():\n",
    "    for status in spikes_dropped['status'].unique():\n",
    "        tempdf = spikes_dropped[ (spikes_dropped['status'] == status) & \n",
    "                                (spikes_dropped['illumination_mm'] == illum) ]\n",
    "        \n",
    "        fig, con = bs.pairedcontrast( tempdf, \n",
    "                                     figsize = (10,8),\n",
    "                                     x = 'epoch', y = 'spikeFrequency (Hz)',\n",
    "                                     idcol = 'traceNameNumPulse',\n",
    "                                     floatContrast = False,\n",
    "                                     summaryBar = True,\n",
    "                                     showAllYAxes = False,\n",
    "                                     pal = myPal,\n",
    "                                     swarmYlim = (0,200),\n",
    "                                     contrastYlim = (-175,50),\n",
    "                                     violinWidth = 0.002,\n",
    "                                     barWidth = 0.002,\n",
    "                                     beforeAfterSpacer = 0.005,\n",
    "                                     summaryMarkerSize = 7,\n",
    "                                     hue = 'genotype',\n",
    "                                     idx = (('before', 'during'),\n",
    "                                           ('during', 'after')) )\n",
    "        # Aesthetic tweaks\n",
    "        conKey = str(status)+' '+str(illum)+' mm'\n",
    "        fig.suptitle(conKey, fontsize = 17)\n",
    "        figureDict500ms[conKey] = fig\n",
    "        # save the contrast information.\n",
    "        contrastDict500ms[conKey] = con\n",
    "        # Save each figure in SUPERHIGHRES png and svg YAS\n",
    "        for f in ['png', 'svg']:\n",
    "            fig.savefig(writedir+conKey+'.'+f, format = f, dpi = 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  },
  "toc": {
   "toc_cell": false,
   "toc_number_sections": true,
   "toc_threshold": 6,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}