{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "#import matplotlib.gridspec as gridspec\n",
    "from matplotlib.colors import LinearSegmentedColormap, ListedColormap\n",
    "import matplotlib.font_manager\n",
    "import random \n",
    "from matplotlib import rcParams, cm\n",
    "import scipy.stats as ss\n",
    "import h5py\n",
    "import glob\n",
    "import seaborn as sns\n",
    "import time\n",
    "import os\n",
    "import itertools\n",
    "import timeit\n",
    "import networkx as nx\n",
    "import collections \n",
    "import scipy.io\n",
    "from matplotlib import gridspec \n",
    "#For ROI Maps\n",
    "from scipy.io import loadmat\n",
    "from skimage import measure\n",
    "from scipy import ndimage\n",
    "\n",
    "#For Fitting Baselines\n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "#For Clustering\n",
    "from sklearn.cluster.bicluster import SpectralBiclustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "#matplotlib.font_manager._rebuild()\n",
    "#Set matplotlib fonts for figures\n",
    "rcParams['font.family'] = 'Arial'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Last day of training/extinction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeAliFilenames(mouse_numbers, mouse_days, mouse_sessions):\n",
    "    #Input lists of mouse numbers, days, and sessions to permute to give out directories and dictionary for filename\n",
    "    directories = []\n",
    "    filenames = {}\n",
    "    for m_num in mouse_numbers:\n",
    "        for m_day in mouse_days:\n",
    "            for m_sess in mouse_sessions:\n",
    "                if ((m_day != 2) | (m_sess != 2)): #Skip d2_s2.  Only do d5_s2\n",
    "                    mouse_dir = 'ali{}_d{}_s{}/'.format(m_num, m_day, m_sess)\n",
    "                    directories.append(mouse_dir)\n",
    "                    if m_day == 2:\n",
    "                        filenames[mouse_dir] = 'trace_kyleFinalwDoughnut_AllBGs_BinaryVideo.hdf5'\n",
    "                         \n",
    "                    else:\n",
    "                        filenames[mouse_dir] = 'trace_matched_final_BinaryVideo.hdf5'\n",
    "    return directories, filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeAliFilenames_modified(mouse_numbers, mouse_days, mouse_sessions):\n",
    "    #Input lists of mouse numbers, days, and sessions to permute to give out directories and dictionary for filename\n",
    "    directories = []\n",
    "    filenames = {}\n",
    "    for m_num in mouse_numbers:\n",
    "        for m_day in mouse_days:\n",
    "            for m_sess in mouse_sessions:\n",
    "                if ((m_day != 2) | (m_sess != 2)): #Skip d2_s2.  Only do d5_s2\n",
    "                    mouse_dir = 'ali{}_d{}_s{}/'.format(m_num, m_day, m_sess)\n",
    "                    directories.append(mouse_dir)\n",
    "                    filenames[mouse_dir] = 'binary_traces_final.h5'\n",
    "                   \n",
    "    return directories, filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeRebeccaFilenames(mouse_numbers, mouse_days, mouse_sessions):\n",
    "    #Input lists of mouse numbers, days, and sessions to permute to give out directories and dictionary for filename\n",
    "    directories = []\n",
    "    filenames = {}\n",
    "    for m_num in mouse_numbers:\n",
    "        for m_day in mouse_days:\n",
    "            for m_sess in mouse_sessions:\n",
    "                mouse_dir = '{}/{}_d{}_s{}/'.format(m_num, m_num, m_day, m_sess)\n",
    "                directories.append(mouse_dir)\n",
    "                if m_day == 1:\n",
    "                    filenames[mouse_dir] = 'trace_kyleFinal_BinaryVideo.hdf5'\n",
    "                else:\n",
    "                    filenames[mouse_dir] = 'trace_final_BinaryVideo.hdf5'\n",
    "                #filenames[mouse_dir] = 'trace_final_BinaryVideo.hdf5'\n",
    "    return directories, filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeRebeccaFilenames_modified(mouse_numbers, mouse_days, mouse_sessions):\n",
    "    #Input lists of mouse numbers, days, and sessions to permute to give out directories and dictionary for filename\n",
    "    directories = []\n",
    "    filenames = {}\n",
    "    for m_num in mouse_numbers:\n",
    "        for m_day in mouse_days:\n",
    "            for m_sess in mouse_sessions:\n",
    "                mouse_dir = '{}/{}_d{}_s{}/'.format(m_num, m_num, m_day, m_sess)\n",
    "                directories.append(mouse_dir)\n",
    "                filenames[mouse_dir] = 'binary_traces_final.h5'\n",
    "    return directories, filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "def combineFilenames(base, dirs, fnDict):\n",
    "    #Combine Filenames into list for multiple directories with a common base.  fnDict is a dictionary of the filename for each directory\n",
    "    outFiles = []\n",
    "    for d in dirs:\n",
    "        outFiles.append(os.path.join(base+d+fnDict[d]))\n",
    "    return outFiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali24_d2_s1/trace_kyleFinalwDoughnut_AllBGs_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali24_d5_s1/trace_matched_final_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali24_d5_s2/trace_matched_final_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali25_d2_s1/trace_kyleFinalwDoughnut_AllBGs_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali25_d5_s1/trace_matched_final_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali25_d5_s2/trace_matched_final_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali26_d2_s1/trace_kyleFinalwDoughnut_AllBGs_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali26_d5_s1/trace_matched_final_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali26_d5_s2/trace_matched_final_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff-Rebecca/2982/2982_d1_s1/trace_kyleFinal_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff-Rebecca/2982/2982_d9_s1/trace_final_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff-Rebecca/4540/4540_d1_s1/trace_kyleFinal_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff-Rebecca/4540/4540_d9_s1/trace_final_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff_Rebecca2/2712/2712_d1_s1/trace_kyleFinal_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff_Rebecca2/2712/2712_d5_s1/trace_final_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff_Rebecca2/2713/2713_d1_s1/trace_kyleFinal_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff_Rebecca2/2713/2713_d5_s1/trace_final_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff_Rebecca2/2714/2714_d1_s1/trace_kyleFinal_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff_Rebecca2/2714/2714_d5_s1/trace_final_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff_Rebecca2/8089/8089_d1_s1/trace_kyleFinal_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff_Rebecca2/8089/8089_d5_s1/trace_final_BinaryVideo.hdf5']\n"
     ]
    }
   ],
   "source": [
    "#Initialize Input Values\n",
    "#Ali Mice\n",
    "aliBase = '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/'\n",
    "aliMice, aliDays, aliSessions = ([24, 25, 26], [2,5], [1,2])\n",
    "aliFolders, aliFilenames = makeAliFilenames(aliMice, aliDays, aliSessions)\n",
    "aliFiles = combineFilenames(aliBase, aliFolders, aliFilenames)\n",
    "#Rebecca Mice\n",
    "rBase = '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff-Rebecca/'\n",
    "\n",
    "rMice, rDays, rSessions = ([2982,4540], [1,9], [1])\n",
    "rFolders, rFilenames = makeRebeccaFilenames(rMice, rDays, rSessions)\n",
    "rFiles = combineFilenames(rBase, rFolders, rFilenames)\n",
    "#New Rebecca Mice\n",
    "nrBase = '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff_Rebecca2/'\n",
    "nrMice, nrDays, nrSessions = ([2712, 2713,2714,8089], [1,5], [1]) #Also have 2 as full imaging session.  Drop 2714 because of data quality\n",
    "nrFolders, nrFilenames = makeRebeccaFilenames(nrMice, nrDays, nrSessions)\n",
    "nrFiles = combineFilenames(nrBase, nrFolders, nrFilenames)\n",
    "#Combine All Filenames into one List\n",
    "allFiles = aliFiles + rFiles + nrFiles\n",
    "print(allFiles)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load behavior data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadFiles(allFiles, fieldName, whichData=None, multiIndex=False, fixedSize=None):\n",
    "    #Get File Inputs\n",
    "    firstf = allFiles[0]\n",
    "    firstdir = firstf.split('/')[-2]\n",
    "    f = h5py.File(firstf, 'r')\n",
    "    if multiIndex: #Adjust indexing for first DataFrame if using a multi-index\n",
    "        first_array = np.array(f[fieldName]).T\n",
    "        index_iterables = [[firstdir], range(first_array.shape[1])]\n",
    "        index = pd.MultiIndex.from_product(index_iterables, names=['mouse','cell_num'])\n",
    "        df = pd.DataFrame(data=first_array, columns=index)\n",
    "    else:\n",
    "        if fixedSize == None:\n",
    "            df = pd.DataFrame(data=np.array(f[fieldName]).T, columns=[firstdir])\n",
    "        else:\n",
    "            df = pd.DataFrame(data=np.array(f[fieldName]).T[:(fixedSize)], columns=[firstdir])\n",
    "    \n",
    "    df.index.name='Time'\n",
    "    df_size = df.shape[0] #Determine Size of DataFrame\n",
    "    for f in allFiles[1:]: #Loop through remaining files and add to DataFrame\n",
    "        fdir = f.split('/')[-2] #Current Filename/Dir\n",
    "        f = h5py.File(f, 'r')\n",
    "        if multiIndex: #Iteratively add multiIndexed DataFrames\n",
    "            data_arr = np.array(f[fieldName]).T\n",
    "            index_iterables = [[fdir], range(data_arr.shape[1])]\n",
    "            index = pd.MultiIndex.from_product(index_iterables, names=['mouse', 'cell_num'])\n",
    "            df = df.join(pd.DataFrame(data=data_arr, columns=index))\n",
    "        else:\n",
    "            #print(f)\n",
    "            #print(df.shape)\n",
    "            #print(df_size)\n",
    "            #print(np.array(f[fieldName]).T.shape)\n",
    "            try: #Standard Loading\n",
    "                df.loc[:,fdir] = np.array(f[fieldName]).T \n",
    "            except ValueError: #Exception cases where behavior was different size from other behavior sessions\n",
    "                if (whichData == 'Ali'): #Ali26_d5_s2 and Ali25_d5_s2 are one frame short\n",
    "                    df.loc[:,fdir] = np.insert(np.array(f[fieldName]).T, 0, np.array(f[fieldName]).T.mean())\n",
    "                elif np.array(f[fieldName]).T.shape[0]<df_size:\n",
    "                    temp = np.array(f[fieldName]).T\n",
    "                    df.loc[:,fdir] = np.append(temp, temp.mean().reshape(1,-1), axis=0)\n",
    "                elif (whichData == 'Rebecca'): #2982_d9_s1 is one frame longer\n",
    "                    new_size = np.array(f[fieldName]).shape[1]\n",
    "                    diff_size = abs(new_size-df_size)\n",
    "                    df.loc[:,fdir] = np.array(f[fieldName]).T[:-diff_size]\n",
    "    drop_index = df.index[df.isna().any(axis='columns')]\n",
    "    df = df.drop(drop_index)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "aliSounds = loadFiles(aliFiles, 'binSounds', whichData='Ali')\n",
    "rSounds = loadFiles(rFiles, 'binSounds', whichData='Rebecca')\n",
    "nrSounds = loadFiles(nrFiles, 'binSounds', whichData='Rebecca', fixedSize=39500)\n",
    "sound_traces = pd.concat([aliSounds,rSounds,nrSounds], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "aliPuffs = loadFiles(aliFiles, 'binPuffs', whichData='Ali')\n",
    "rPuffs = loadFiles(rFiles, 'binPuffs', whichData='Rebecca')\n",
    "nrPuffs = loadFiles(nrFiles, 'binPuffs', whichData='Rebecca', fixedSize=39500)\n",
    "puff_traces = pd.concat([aliPuffs,rPuffs,nrPuffs], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "aliTrials = loadFiles(aliFiles, 'binTrials', whichData='Ali')\n",
    "rTrials = loadFiles(rFiles, 'binTrials', whichData='Rebecca')\n",
    "nrTrials = loadFiles(nrFiles, 'binTrials', whichData='Rebecca', fixedSize=39500)\n",
    "trial_traces = pd.concat([aliTrials,rTrials,nrTrials], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "aliEyeTraces = loadFiles(aliFiles, 'eyeTrace', whichData='Ali')\n",
    "rEyeTraces = loadFiles(rFiles, 'eyeTrace', whichData='Rebecca')\n",
    "nrEyeTraces = loadFiles(nrFiles, 'eyeTrace', whichData='Rebecca', fixedSize=39500)\n",
    "eye_traces = pd.concat([aliEyeTraces,rEyeTraces,nrEyeTraces], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def linear_func(x, a, b):\n",
    "    return a*x + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def curve_fit_wrapper(y, x, func):\n",
    "    #Wrap curve_fit to match Pandas DataFrame Use and return only parameters, not covariance\n",
    "    return curve_fit(func, x, y)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_line_wrapper(params, x, func):\n",
    "    #Apply Funcion in a way that works with Pandas DataFrame\n",
    "    return func(x, *params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pick_thresh_idx_wrapper(bool_arr):\n",
    "    return bool_arr.index[bool_arr == True][-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calcThresholds(trace_df, drop_thresh, pct_fit, make_plots):\n",
    "    #Calculate thresholds for each trace by removing linearized value\n",
    "    #Sort each trace\n",
    "    sorted_traces = trace_df.apply(np.sort, axis=0)\n",
    "    #Calculate indexes for use\n",
    "    Ntot = sorted_traces.shape[0]\n",
    "    Nhalf = np.floor(Ntot/2).astype(int)\n",
    "    Nfit = np.floor(Ntot * pct_fit).astype(int)\n",
    "    fit_start = np.floor((Ntot-Nfit)/2).astype(int)\n",
    "    fit_end = Ntot-fit_start\n",
    "    #Generate X Data and Fit Line\n",
    "    x_tot = np.arange(0, Ntot)\n",
    "    fitted_params = sorted_traces.loc[fit_start:fit_end,:].apply(curve_fit_wrapper, args=(x_tot[fit_start:(fit_end+1)], linear_func)) #Add 1 to fit end because of np/pd differences in indexing\n",
    "    fit_lines = fitted_params.apply(generate_line_wrapper, args=(x_tot, linear_func))\n",
    "    #Adjust traces by fit line and determine thresholds\n",
    "    shift_traces = sorted_traces.subtract(fit_lines)\n",
    "    half_thresh = (shift_traces < drop_thresh).loc[:Nhalf,:]\n",
    "    thresh_idxs = half_thresh.apply(pick_thresh_idx_wrapper)\n",
    "    pick_thresh = []\n",
    "    for idx, col in zip(thresh_idxs, thresh_idxs.index):\n",
    "        pick_thresh.append(sorted_traces.loc[idx, col])\n",
    "    if make_plots: #Make Plots of Interest\n",
    "        #Fixed Plot Parameters\n",
    "        maxWidth = 3.\n",
    "        #Dynamic Parameters\n",
    "        Nplots = sorted_traces.shape[1]\n",
    "        layoutR, layoutC = [np.ceil(Nplots/maxWidth).astype(int), int(min(Nplots, maxWidth))]\n",
    "        all_ax = sorted_traces.plot(subplots=True, figsize=(9,layoutR*5), layout=(layoutR, layoutC), legend=False)\n",
    "        step=0\n",
    "        for ax_row in all_ax:\n",
    "            for sel_ax in ax_row:\n",
    "                fit_lines.iloc[:,step].plot(ax=sel_ax, legend=True, linestyle='--', color='k')\n",
    "                step+=1\n",
    "    return pd.DataFrame(data=np.array(pick_thresh).reshape(1,-1), columns=thresh_idxs.index.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def findPulses(inputTrace):\n",
    "    #Find location of onset and offset of pulses\n",
    "    #inputTrace = 1-D Numpy Array Trace of values assumed to be similar to a train of ttl pulses, where there is a clear onset and offset for each pulse.\n",
    "    #locationTrace = Pseudo-logical 1-D trace, with 1s at pulse onset, and -1s at pulse offset, and 0s everywhere else\n",
    "    \n",
    "    shiftedInput = inputTrace - inputTrace.mean() #Shift to have mean 0 and baseline below 0\n",
    "    changes = np.sign(shiftedInput) #Find where sign changes\n",
    "    locationTrace = np.zeros(inputTrace.shape); #Initialize Output\n",
    "    #locationTrace = np.zeros((behav_df.shape[0]+1,behav_df.shape[1]))\n",
    "    diffArray = np.diff(changes)\n",
    "    locationTrace[np.insert(diffArray==2,0,0)] = 1 #Set onsets to 1\n",
    "    locationTrace[np.insert(diffArray==-2,0,0)] = -1 #Set offsets to -1\n",
    "    return locationTrace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeBehavTrialBlocks(trace_df, behav_df):\n",
    "    #Make Blocks of Trials for plain index behavior, and return df with those trial blocks removed\n",
    "    #Pre-defined Values\n",
    "    tot_block_time = 201 #201 timepoints is about 10 seconds\n",
    "    before_sound_time = 40 #40 timepoints before the puff 2 seconds\n",
    "    after_sound_time = tot_block_time - before_sound_time - 1 #Time after sound.  Subtract 1 as index between before sound and after sound is included as \"spot\"\n",
    "    #Shorten behav_df to length of trace_df\n",
    "    behav_length = behav_df.shape[0]\n",
    "    trace_length = trace_df.shape[0]\n",
    "    if behav_length > trace_length:\n",
    "        behav_df = behav_df.loc[:(trace_length-1),:]\n",
    "    #Make Trial Blocks for Each Mouse\n",
    "    trace_blocks=[]\n",
    "    trace_noBlocks_df = trace_df.copy()\n",
    "    for mouse in trace_df:\n",
    "        soundPulses = findPulses(behav_df.loc[:,mouse])\n",
    "        sound_on_inds = behav_df.index[soundPulses == 1]\n",
    "        if sound_on_inds[-1]+after_sound_time > trace_df.loc[:,mouse].shape[0]: #Get rid of last pulse on at end of recording\n",
    "            sound_on_inds = sound_on_inds[:-1]\n",
    "        cur_block = np.zeros((sound_on_inds.shape[0], tot_block_time))\n",
    "        for idx, spot in enumerate(sound_on_inds):\n",
    "            cur_block[idx,:] = trace_df.loc[(spot-before_sound_time):(spot+after_sound_time),mouse].values\n",
    "            trace_noBlocks_df.loc[:, mouse] = trace_noBlocks_df.loc[:, mouse].drop(range((spot-before_sound_time), (spot+after_sound_time)))\n",
    "        trace_blocks.append(cur_block)\n",
    "    return np.stack(trace_blocks), trace_noBlocks_df.dropna().reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def norm_MA_block(trace_df, windSize, sounds):\n",
    "    #Normalize traces, take moving window of windSize, and make trial blocks\n",
    "    norm_df = (trace_df - trace_df.mean(axis=0)) / trace_df.mean(axis=0)\n",
    "    wind_df = norm_df.subtract(norm_df.rolling(windSize).mean())\n",
    "    blocks, allButBlocks = makeBehavTrialBlocks(wind_df, sounds)\n",
    "    return blocks, allButBlocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "aliEyeBlocks, aliAllButEyeBlocks = norm_MA_block(aliEyeTraces, 20, aliSounds)\n",
    "rEyeBlocks, rAllButEyeBlocks = norm_MA_block(rEyeTraces, 20, rSounds)\n",
    "nrEyeBlocks, nrAllButEyeBlocks = norm_MA_block(nrEyeTraces, 20, nrSounds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ali24_d5_s1</th>\n",
       "      <th>ali24_d5_s2</th>\n",
       "      <th>ali25_d5_s1</th>\n",
       "      <th>ali25_d5_s2</th>\n",
       "      <th>ali26_d5_s1</th>\n",
       "      <th>ali26_d5_s2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.083445</td>\n",
       "      <td>-0.049202</td>\n",
       "      <td>-0.106046</td>\n",
       "      <td>-0.044256</td>\n",
       "      <td>-0.046424</td>\n",
       "      <td>-0.060169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ali24_d5_s1  ali24_d5_s2  ali25_d5_s1  ali25_d5_s2  ali26_d5_s1  \\\n",
       "0    -0.083445    -0.049202    -0.106046    -0.044256    -0.046424   \n",
       "\n",
       "   ali26_d5_s2  \n",
       "0    -0.060169  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aliEyeThresholds = calcThresholds(aliAllButEyeBlocks, -0.02, 0.95, 0)\n",
    "aliEyeThresholds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2982_d9_s1</th>\n",
       "      <th>4540_d9_s1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.07293</td>\n",
       "      <td>-0.117255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2982_d9_s1  4540_d9_s1\n",
       "0    -0.07293   -0.117255"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rEyeThresholds = calcThresholds(rAllButEyeBlocks, -0.02, 0.95, 0)\n",
    "rEyeThresholds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2712_d5_s1</th>\n",
       "      <th>2713_d5_s1</th>\n",
       "      <th>2714_d5_s1</th>\n",
       "      <th>8089_d5_s1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.09615</td>\n",
       "      <td>-0.106935</td>\n",
       "      <td>-0.115779</td>\n",
       "      <td>-0.082427</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2712_d5_s1  2713_d5_s1  2714_d5_s1  8089_d5_s1\n",
       "0    -0.09615   -0.106935   -0.115779   -0.082427"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nrEyeThresholds = calcThresholds(nrAllButEyeBlocks, -0.02, 0.95, 0)\n",
    "nrEyeThresholds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def binaryBehaviorScoring(blocks, thresholds, make_plots):\n",
    "    #Take blocks (trial by trial blocks as np.array) and use thresholds (pandas df) to determine values\n",
    "    #Fixed Parameters\n",
    "    start=40\n",
    "    end=52\n",
    "    #Thresholding\n",
    "    blockMins = blocks[:,:,start:end].min(axis=2)\n",
    "    binScoring = pd.DataFrame(columns=thresholds.columns)\n",
    "    for idx, mouse in enumerate(thresholds):\n",
    "        binScoring.loc[:,mouse] = blockMins[idx,:] < thresholds.loc[0,mouse]\n",
    "    if make_plots:\n",
    "        #Fixed Plot Parameters\n",
    "        maxWidth = 2.\n",
    "        endView = 60\n",
    "        cmax = .1\n",
    "        cmin = -.1\n",
    "        #Dynamic Parameters\n",
    "        Nplots = thresholds.shape[1]\n",
    "        layoutR, layoutC = [np.ceil(Nplots/maxWidth).astype(int), int(min(Nplots, maxWidth))]\n",
    "        fig, all_ax = plt.subplots(figsize=(9, layoutR*4), nrows=layoutR, ncols=layoutC)\n",
    "        fig.tight_layout()\n",
    "        try: #Reshape if only one row of axes indexes\n",
    "            all_ax.shape[1]\n",
    "        except:\n",
    "            all_ax = all_ax.reshape(1,-1)\n",
    "        step=0\n",
    "        for ax_row in all_ax:\n",
    "            for sel_ax in ax_row:\n",
    "                if step == Nplots:\n",
    "                    sel_ax.set_axis_off()\n",
    "                else:\n",
    "                    sns.heatmap(blocks[step,:,:endView], ax=sel_ax, vmin=cmin, vmax=cmax, cmap=plt.get_cmap(\"bwr\"))\n",
    "                    sel_ax.axvline(x=start, linestyle='-.', color='k')\n",
    "                    sel_ax.axvline(x=end, linestyle='--', color='k')\n",
    "                    sel_ax.set_title(thresholds.columns[step])\n",
    "                    step+=1\n",
    "    return binScoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x864 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "aliBehaviorScoring = binaryBehaviorScoring(aliEyeBlocks, aliEyeThresholds, 1)\n",
    "rBehaviorScoring = binaryBehaviorScoring(rEyeBlocks, rEyeThresholds, 1)\n",
    "nrBehaviorScoring = binaryBehaviorScoring(nrEyeBlocks, nrEyeThresholds, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load traces to make hdf5 files "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def linear_func(x, a, b):\n",
    "    return a*x + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def curve_fit_wrapper(y, x, func):\n",
    "    #Wrap curve_fit to match Pandas DataFrame Use and return only parameters, not covariance\n",
    "    return curve_fit(func, x, y)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calcBaselineDistributions(df, regress_thresh=0.05, pct_datapoints=0.5):\n",
    "    #Take DataFrame of traces, fit linear baseline, and calculate calcium threshold from devaition of residuals from that fit\n",
    "    #Number of points for linear fit (Default is 0.5, or first half of sorted data)\n",
    "    N_linfit = int(np.floor(df.shape[0]*pct_datapoints))\n",
    "    #Calculate Distribution Fits and Generate Parameters and Outputs Matrix\n",
    "    dist_series = df.apply(lambda x: ss.probplot(x)[0], axis=0)\n",
    "    distributions = pd.DataFrame(data=[dist_series[item] for item in dist_series])\n",
    "    #Rename Columns/Rows, since doesn't work at creation time for some reason...\n",
    "    distributions.columns = ['Theoretical', 'Measured']\n",
    "    distributions.index = dist_series.columns\n",
    "    param_series = distributions.apply(lambda x: curve_fit(linear_func, x['Theoretical'][:N_linfit], x['Measured'][:N_linfit]), axis=1)\n",
    "    distributions[['Parameters','Covariance']] = pd.DataFrame(data=[item for item in param_series], index=distributions.index, columns=['Parameters', 'Covariance'])\n",
    "    distributions['Line'] = distributions.apply(lambda x: linear_func(x['Theoretical'], *x['Parameters']), axis=1)\n",
    "    distributions['Difference'] = distributions.apply(lambda x: (x['Measured'] - x['Line']) / (np.nanmax(x['Measured']) - np.nanmin(x['Measured'])), axis=1)\n",
    "    distributions['Deviation_idx'] = distributions.apply(lambda x: np.argmax(x['Difference'][N_linfit:] > regress_thresh)+N_linfit, axis=1)\n",
    "    distributions['Ca_thresh'] = distributions.apply(lambda x: x['Measured'][x['Deviation_idx']], axis=1)\n",
    "    \n",
    "    return distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scaleTraces(df):\n",
    "    #Take DataFrame of traces, subtract off linear baseline and scale by percentage of maximum values for cell\n",
    "    distribution_calculations = calcBaselineDistributions(df)\n",
    "    #Get list of values into DataFrames/Series for scaling\n",
    "    scale_vals = distribution_calculations.loc[:,('Parameters', 'Ca_thresh')].transpose(copy=True)\n",
    "    baseline_list = []\n",
    "    for row in scale_vals.loc['Parameters',:]:\n",
    "        baseline_list.append(row[1])\n",
    "    scale_vals.loc['Baseline',:] = pd.Series(baseline_list, index=scale_vals.columns)\n",
    "    scale_vals.loc['Unit_Val',:] = scale_vals.loc['Ca_thresh',:] - scale_vals.loc['Baseline',:]\n",
    "    #Adjust by subtracting baseline and dividing by scaling unit\n",
    "    scaled_df = (df - scale_vals.loc['Baseline',:]) / scale_vals.loc['Unit_Val',:]\n",
    "    \n",
    "    return scaled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadTraces(files, whichData):\n",
    "    #Load all the trace data and normalize traces with baseline adjustments\n",
    "    #Load and doughnut subtract traces\n",
    "    rawTraces = loadFiles(files, 'trace', whichData=whichData, multiIndex=True)\n",
    "    BGTraces = loadFiles(files, 'BG10trace', whichData=whichData, multiIndex=True)\n",
    "    traces = rawTraces.subtract(BGTraces)\n",
    "    #Adjust Baseline Calculations\n",
    "    scaled_traces= scaleTraces(traces)\n",
    "    #Load Color Info TODO: Add Loading Color Values to loadFiles\n",
    "    #colors = loadFiles(files, 'color', whichData)\n",
    "    \n",
    "    return scaled_traces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "aliTraces = loadTraces(aliFiles, 'Ali')\n",
    "rTraces = loadTraces(rFiles, 'Rebecca')\n",
    "nrTraces = loadTraces(nrFiles, 'Rebecca')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Convert to hdf5 files for Rebecca's mice to run event detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to hdf5 files - r mice\n",
    "traces= nrTraces['2713_d5_s1']\n",
    "f1 = h5py.File(\"traces_2713_n.h5\", \"w\")\n",
    "dset1 = f1.create_dataset(\"/traces\", data=traces)\n",
    "list(f1.keys())\n",
    "f1.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function to make trial blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def findPulses(inputTrace):\n",
    "    #Find location of onset and offset of pulses\n",
    "    #inputTrace = 1-D Numpy Array Trace of values assumed to be similar to a train of ttl pulses, where there is a clear onset and offset for each pulse.\n",
    "    #locationTrace = Pseudo-logical 1-D trace, with 1s at pulse onset, and -1s at pulse offset, and 0s everywhere else\n",
    "    \n",
    "    shiftedInput = inputTrace - inputTrace.mean() #Shift to have mean 0 and baseline below 0\n",
    "    changes = np.sign(shiftedInput) #Find where sign changes\n",
    "    locationTrace = np.zeros(inputTrace.shape); #Initialize Output\n",
    "    #locationTrace = np.zeros((behav_df.shape[0]+1,behav_df.shape[1]))\n",
    "    diffArray = np.diff(changes)\n",
    "    locationTrace[np.insert(diffArray==2,0,0)] = 1 #Set onsets to 1\n",
    "    locationTrace[np.insert(diffArray==-2,0,0)] = -1 #Set offsets to -1\n",
    "    return locationTrace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeTraceTrialBlocks(trace_df, behav_df):\n",
    "    #Make Blocks of Trials for Traces (with MultiIndex), and return df with those trial blocks removed\n",
    "    #Pre-defined Values\n",
    "    tot_block_time = 201 #201 timepoints is about 10 seconds\n",
    "    before_sound_time = 40 #40 timepoints before the puff 2 seconds\n",
    "    after_sound_time = tot_block_time - before_sound_time - 1 #Time after sound.  Subtract 1 as index between before sound and after sound is included as \"spot\"\n",
    "    #Shorten behav_df to length of trace_df\n",
    "    behav_length = behav_df.shape[0]\n",
    "    trace_length = trace_df.shape[0]\n",
    "    if behav_length > trace_length:\n",
    "        behav_df = behav_df.loc[:(trace_length-1),:]\n",
    "    #Initialize Lists\n",
    "    trace_blocks=[]\n",
    "    trace_noBlocks=[]\n",
    "    for mouse, cell_df in trace_df.groupby(level='mouse', axis=1):\n",
    "        print(mouse) #Output Tracking\n",
    "        #Shape Behavior Data\n",
    "        soundPulses = findPulses(behav_df.loc[:,mouse])\n",
    "        sound_on_inds = behav_df.index[soundPulses == 1]\n",
    "        if sound_on_inds[-1]+after_sound_time > trace_df.loc[:,mouse].shape[0]: #Get rid of last pulse on at end of recording\n",
    "            sound_on_inds = sound_on_inds[:-1]\n",
    "        #Setup Numpy Arrays for Faster Loops\n",
    "        cur_block = np.zeros((tot_block_time, sound_on_inds.shape[0], cell_df.shape[1]))\n",
    "        cur_noBlock = cell_df.copy()\n",
    "        cur_blockDrop = []\n",
    "        for trial_idx, spot in enumerate(sound_on_inds):\n",
    "            cur_block[:, trial_idx, :] = cell_df.loc[(spot-before_sound_time):(spot+after_sound_time), :].values\n",
    "            cur_blockDrop.append(range((spot-before_sound_time), (spot+after_sound_time)))\n",
    "        #Drop Indexes from Current Values and Save Dataframe\n",
    "        flat_blockDrop = [value for sublist in cur_blockDrop for value in sublist]\n",
    "        trace_noBlocks.append(cur_noBlock.drop(flat_blockDrop).reset_index(drop=True))\n",
    "        #Make DataFrame for Current Cell Case\n",
    "        mIdx = pd.MultiIndex.from_product([[mouse], range(sound_on_inds.shape[0]), range(cell_df.shape[1])], names=['mouse', 'trial', 'cell_num'])\n",
    "        cur_df = pd.DataFrame(data=cur_block.reshape(tot_block_time,-1), columns=mIdx)\n",
    "        trace_blocks.append(cur_df)\n",
    "    #Convert Lists into Concatenated DataFrame\n",
    "    trace_noBlocks_df = pd.concat(trace_noBlocks, axis=1)\n",
    "    trace_blocks_df = pd.concat(trace_blocks, axis=1)\n",
    "    trace_blocks_df.index.name = 'Time'\n",
    "    return trace_blocks_df, trace_noBlocks_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function to match cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadMatchedIndexes(files):\n",
    "    #Function to load matched cell indexes and output them in a DataFrame\n",
    "    allInds=[]\n",
    "    for f in files:\n",
    "        name = f.split('/')[-2]\n",
    "        if ('d5' in name):\n",
    "            f_open = h5py.File(f,'r')\n",
    "            mIdx = pd.MultiIndex.from_tuples([(name,'lastTraining')], names=['mouse','session'])\n",
    "            cellIndexes = pd.DataFrame(data=np.array(f_open['lastTraining_d5_s1_index']), columns=mIdx)\n",
    "            cellIndexes[name,'firstExtinction'] = np.array(f_open['firstExtinction_d5_s2_index'])\n",
    "            allInds.append(cellIndexes)\n",
    "    return pd.concat(allInds, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def matchDF(traces, match_df):\n",
    "    #Function to compare match values in a dataframe and output only those matched in appropriate sessions\n",
    "    #Includes re-sorting to ensure column match at cell-level\n",
    "    mice = traces.columns.unique('mouse')\n",
    "    matchedInds=[]\n",
    "    for mouse in mice:\n",
    "        print(mouse)\n",
    "        if ('d5' in mouse): #If a d5 Session with Matching\n",
    "            match_sess = match_df[mouse].dropna()\n",
    "            if ('s1' in mouse):\n",
    "                search_col = 'firstExtinction'\n",
    "                sel_col = 'lastTraining'\n",
    "                sel_inds = (match_sess.loc[(match_sess[search_col] != -1), sel_col] - 1).values.astype(int)\n",
    "                trace_sess = traces[mouse]\n",
    "                out_sess = trace_sess.loc[:,sel_inds]\n",
    "            elif ('s2' in mouse):\n",
    "                search_col = 'lastTraining'\n",
    "                sel_col = 'firstExtinction'\n",
    "                #Number Magic because Pandas Dataframes with MultiIndexing doesn't sort correctly... :(\n",
    "                sel_inds_wrongSorting = (match_sess.loc[(match_sess[search_col] != -1), sel_col] - 1).values.astype(int)\n",
    "                sel_inds_toSort = (match_sess.loc[(match_sess[search_col] != -1), search_col] - 1).values.astype(int)\n",
    "                sorting_inds = np.argsort(sel_inds_toSort)\n",
    "                sel_inds = sel_inds_wrongSorting[sorting_inds]\n",
    "                #corr_sorting_multi_cols = pd.MultiIndex.from_product([sel_inds], names=['cell_num'])\n",
    "                trace_sess = traces[mouse]\n",
    "                out_sess = trace_sess.loc[:,sel_inds]\n",
    "            mIdx = pd.MultiIndex.from_product([[mouse],range(sel_inds.shape[0])], names=['mouse','cell_num'])\n",
    "            out_sess.columns = mIdx\n",
    "            matchedInds.append(out_sess)\n",
    "        else: #No d5 Session with Matching\n",
    "            noChange = traces[mouse]\n",
    "            noChange.columns = pd.MultiIndex.from_product([[mouse],noChange.columns], names=['mouse','cell_num'])\n",
    "            matchedInds.append(noChange)\n",
    "    return pd.concat(matchedInds, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def matchCells(files, traces):\n",
    "    #Function to cell index matches and output a DataFrame with those cell indexes matched\n",
    "    #Load Matched Indexes\n",
    "    matchInds = loadMatchedIndexes(files)\n",
    "    \n",
    "    #Match Indexes and Output\n",
    "    matchedTraces = matchDF(traces, matchInds)\n",
    "        \n",
    "    return matchedTraces, matchInds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ali24_d5_s1\n",
      "ali24_d5_s2\n",
      "ali25_d5_s1\n",
      "ali25_d5_s2\n",
      "ali26_d5_s1\n",
      "ali26_d5_s2\n"
     ]
    }
   ],
   "source": [
    "aliMatched, aliMatchInds = matchCells(aliFiles, aliTraces)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cell_num</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>311</th>\n",
       "      <th>312</th>\n",
       "      <th>313</th>\n",
       "      <th>314</th>\n",
       "      <th>315</th>\n",
       "      <th>316</th>\n",
       "      <th>317</th>\n",
       "      <th>318</th>\n",
       "      <th>319</th>\n",
       "      <th>320</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.134247</td>\n",
       "      <td>2.560634</td>\n",
       "      <td>0.648526</td>\n",
       "      <td>-0.010525</td>\n",
       "      <td>1.263843</td>\n",
       "      <td>0.883854</td>\n",
       "      <td>-0.097179</td>\n",
       "      <td>0.050932</td>\n",
       "      <td>0.367342</td>\n",
       "      <td>0.271267</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.432709</td>\n",
       "      <td>2.607987</td>\n",
       "      <td>0.818500</td>\n",
       "      <td>-0.457522</td>\n",
       "      <td>1.947966</td>\n",
       "      <td>-0.557557</td>\n",
       "      <td>-0.184207</td>\n",
       "      <td>-0.178068</td>\n",
       "      <td>-0.034590</td>\n",
       "      <td>-0.064788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.026904</td>\n",
       "      <td>1.694838</td>\n",
       "      <td>0.614711</td>\n",
       "      <td>0.001805</td>\n",
       "      <td>1.029390</td>\n",
       "      <td>0.720393</td>\n",
       "      <td>-0.139837</td>\n",
       "      <td>0.516313</td>\n",
       "      <td>0.213910</td>\n",
       "      <td>0.496811</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.731785</td>\n",
       "      <td>2.447858</td>\n",
       "      <td>0.924479</td>\n",
       "      <td>-0.392479</td>\n",
       "      <td>1.999042</td>\n",
       "      <td>-0.528550</td>\n",
       "      <td>-0.107367</td>\n",
       "      <td>0.020739</td>\n",
       "      <td>0.044920</td>\n",
       "      <td>0.043649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.118428</td>\n",
       "      <td>1.894202</td>\n",
       "      <td>0.782734</td>\n",
       "      <td>0.315057</td>\n",
       "      <td>0.699401</td>\n",
       "      <td>0.763207</td>\n",
       "      <td>-0.207204</td>\n",
       "      <td>0.333901</td>\n",
       "      <td>0.311776</td>\n",
       "      <td>0.512857</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.373491</td>\n",
       "      <td>2.577815</td>\n",
       "      <td>0.820217</td>\n",
       "      <td>-0.075433</td>\n",
       "      <td>1.889641</td>\n",
       "      <td>-0.635769</td>\n",
       "      <td>-0.271914</td>\n",
       "      <td>-0.073472</td>\n",
       "      <td>-0.064119</td>\n",
       "      <td>-0.019100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.007620</td>\n",
       "      <td>1.474720</td>\n",
       "      <td>0.369188</td>\n",
       "      <td>0.119869</td>\n",
       "      <td>0.355084</td>\n",
       "      <td>0.629528</td>\n",
       "      <td>-0.238108</td>\n",
       "      <td>-0.110650</td>\n",
       "      <td>-0.087634</td>\n",
       "      <td>0.324159</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.220021</td>\n",
       "      <td>2.115447</td>\n",
       "      <td>0.773415</td>\n",
       "      <td>-0.061379</td>\n",
       "      <td>1.423951</td>\n",
       "      <td>-0.461160</td>\n",
       "      <td>-0.188779</td>\n",
       "      <td>-0.015428</td>\n",
       "      <td>-0.053141</td>\n",
       "      <td>-0.062171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.352272</td>\n",
       "      <td>2.110743</td>\n",
       "      <td>0.501358</td>\n",
       "      <td>0.196372</td>\n",
       "      <td>0.612778</td>\n",
       "      <td>0.781227</td>\n",
       "      <td>-0.111757</td>\n",
       "      <td>-0.188460</td>\n",
       "      <td>0.473016</td>\n",
       "      <td>0.436990</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.062958</td>\n",
       "      <td>2.089172</td>\n",
       "      <td>0.797337</td>\n",
       "      <td>0.273510</td>\n",
       "      <td>1.135293</td>\n",
       "      <td>-0.469409</td>\n",
       "      <td>-0.267668</td>\n",
       "      <td>0.294436</td>\n",
       "      <td>0.016269</td>\n",
       "      <td>-0.171535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26477</th>\n",
       "      <td>-0.234812</td>\n",
       "      <td>2.832761</td>\n",
       "      <td>0.319006</td>\n",
       "      <td>0.232698</td>\n",
       "      <td>0.549048</td>\n",
       "      <td>-0.474531</td>\n",
       "      <td>-0.207775</td>\n",
       "      <td>-0.566180</td>\n",
       "      <td>-0.408151</td>\n",
       "      <td>0.273293</td>\n",
       "      <td>...</td>\n",
       "      <td>0.621530</td>\n",
       "      <td>-1.219931</td>\n",
       "      <td>-0.815103</td>\n",
       "      <td>-0.873928</td>\n",
       "      <td>-1.389977</td>\n",
       "      <td>0.313987</td>\n",
       "      <td>-0.038933</td>\n",
       "      <td>0.486957</td>\n",
       "      <td>0.062265</td>\n",
       "      <td>2.504184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26478</th>\n",
       "      <td>-0.153354</td>\n",
       "      <td>2.853344</td>\n",
       "      <td>0.204058</td>\n",
       "      <td>0.146408</td>\n",
       "      <td>0.665331</td>\n",
       "      <td>-0.794330</td>\n",
       "      <td>-0.278018</td>\n",
       "      <td>-0.207080</td>\n",
       "      <td>-0.317338</td>\n",
       "      <td>0.187191</td>\n",
       "      <td>...</td>\n",
       "      <td>0.649858</td>\n",
       "      <td>-1.545909</td>\n",
       "      <td>-0.850827</td>\n",
       "      <td>-1.125449</td>\n",
       "      <td>-1.444812</td>\n",
       "      <td>0.296799</td>\n",
       "      <td>-0.153601</td>\n",
       "      <td>0.178629</td>\n",
       "      <td>0.166806</td>\n",
       "      <td>2.284881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26479</th>\n",
       "      <td>-0.138794</td>\n",
       "      <td>3.008380</td>\n",
       "      <td>0.341852</td>\n",
       "      <td>0.214725</td>\n",
       "      <td>0.622193</td>\n",
       "      <td>-0.896566</td>\n",
       "      <td>-0.272724</td>\n",
       "      <td>-0.376924</td>\n",
       "      <td>-0.300546</td>\n",
       "      <td>0.171462</td>\n",
       "      <td>...</td>\n",
       "      <td>0.548290</td>\n",
       "      <td>-1.455468</td>\n",
       "      <td>-0.894209</td>\n",
       "      <td>-0.782700</td>\n",
       "      <td>-1.263288</td>\n",
       "      <td>0.275779</td>\n",
       "      <td>-0.117846</td>\n",
       "      <td>0.323920</td>\n",
       "      <td>0.150866</td>\n",
       "      <td>2.460256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26480</th>\n",
       "      <td>-0.136290</td>\n",
       "      <td>2.672342</td>\n",
       "      <td>0.063163</td>\n",
       "      <td>0.179449</td>\n",
       "      <td>0.542604</td>\n",
       "      <td>-0.893162</td>\n",
       "      <td>-0.195765</td>\n",
       "      <td>-0.293273</td>\n",
       "      <td>-0.524704</td>\n",
       "      <td>0.288852</td>\n",
       "      <td>...</td>\n",
       "      <td>0.555935</td>\n",
       "      <td>-1.490353</td>\n",
       "      <td>-1.063435</td>\n",
       "      <td>-1.231206</td>\n",
       "      <td>-0.908329</td>\n",
       "      <td>0.352009</td>\n",
       "      <td>0.016224</td>\n",
       "      <td>-0.152298</td>\n",
       "      <td>-0.021373</td>\n",
       "      <td>2.143723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26481</th>\n",
       "      <td>-0.359421</td>\n",
       "      <td>2.482719</td>\n",
       "      <td>0.334731</td>\n",
       "      <td>0.225794</td>\n",
       "      <td>0.966381</td>\n",
       "      <td>-0.734828</td>\n",
       "      <td>-0.104436</td>\n",
       "      <td>-0.223133</td>\n",
       "      <td>-0.100770</td>\n",
       "      <td>0.303338</td>\n",
       "      <td>...</td>\n",
       "      <td>0.379342</td>\n",
       "      <td>-1.522041</td>\n",
       "      <td>-0.711056</td>\n",
       "      <td>-0.689076</td>\n",
       "      <td>-1.202724</td>\n",
       "      <td>0.222462</td>\n",
       "      <td>-0.079842</td>\n",
       "      <td>0.251764</td>\n",
       "      <td>0.060860</td>\n",
       "      <td>2.212505</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26482 rows × 321 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "cell_num       0         1         2         3         4         5    \\\n",
       "Time                                                                   \n",
       "0         0.134247  2.560634  0.648526 -0.010525  1.263843  0.883854   \n",
       "1         0.026904  1.694838  0.614711  0.001805  1.029390  0.720393   \n",
       "2         0.118428  1.894202  0.782734  0.315057  0.699401  0.763207   \n",
       "3        -0.007620  1.474720  0.369188  0.119869  0.355084  0.629528   \n",
       "4         0.352272  2.110743  0.501358  0.196372  0.612778  0.781227   \n",
       "...            ...       ...       ...       ...       ...       ...   \n",
       "26477    -0.234812  2.832761  0.319006  0.232698  0.549048 -0.474531   \n",
       "26478    -0.153354  2.853344  0.204058  0.146408  0.665331 -0.794330   \n",
       "26479    -0.138794  3.008380  0.341852  0.214725  0.622193 -0.896566   \n",
       "26480    -0.136290  2.672342  0.063163  0.179449  0.542604 -0.893162   \n",
       "26481    -0.359421  2.482719  0.334731  0.225794  0.966381 -0.734828   \n",
       "\n",
       "cell_num       6         7         8         9    ...       311       312  \\\n",
       "Time                                              ...                       \n",
       "0        -0.097179  0.050932  0.367342  0.271267  ... -0.432709  2.607987   \n",
       "1        -0.139837  0.516313  0.213910  0.496811  ... -0.731785  2.447858   \n",
       "2        -0.207204  0.333901  0.311776  0.512857  ... -0.373491  2.577815   \n",
       "3        -0.238108 -0.110650 -0.087634  0.324159  ... -0.220021  2.115447   \n",
       "4        -0.111757 -0.188460  0.473016  0.436990  ... -0.062958  2.089172   \n",
       "...            ...       ...       ...       ...  ...       ...       ...   \n",
       "26477    -0.207775 -0.566180 -0.408151  0.273293  ...  0.621530 -1.219931   \n",
       "26478    -0.278018 -0.207080 -0.317338  0.187191  ...  0.649858 -1.545909   \n",
       "26479    -0.272724 -0.376924 -0.300546  0.171462  ...  0.548290 -1.455468   \n",
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       "26481    -0.104436 -0.223133 -0.100770  0.303338  ...  0.379342 -1.522041   \n",
       "\n",
       "cell_num       313       314       315       316       317       318  \\\n",
       "Time                                                                   \n",
       "0         0.818500 -0.457522  1.947966 -0.557557 -0.184207 -0.178068   \n",
       "1         0.924479 -0.392479  1.999042 -0.528550 -0.107367  0.020739   \n",
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       "3         0.773415 -0.061379  1.423951 -0.461160 -0.188779 -0.015428   \n",
       "4         0.797337  0.273510  1.135293 -0.469409 -0.267668  0.294436   \n",
       "...            ...       ...       ...       ...       ...       ...   \n",
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       "\n",
       "cell_num       319       320  \n",
       "Time                          \n",
       "0        -0.034590 -0.064788  \n",
       "1         0.044920  0.043649  \n",
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       "...            ...       ...  \n",
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       "\n",
       "[26482 rows x 321 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aliMatched['ali24_d5_s2']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading binary traces after event detection ( event onsets as 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali24_d5_s1/binary_traces_final.h5', '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali24_d5_s2/binary_traces_final.h5', '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali25_d5_s1/binary_traces_final.h5', '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali25_d5_s2/binary_traces_final.h5', '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali26_d5_s1/binary_traces_final.h5', '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali26_d5_s2/binary_traces_final.h5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff-Rebecca/2982/2982_d9_s1/binary_traces_final.h5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff-Rebecca/4540/4540_d9_s1/binary_traces_final.h5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff_Rebecca2/2712/2712_d5_s1/binary_traces_final.h5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff_Rebecca2/2713/2713_d5_s1/binary_traces_final.h5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff_Rebecca2/2714/2714_d5_s1/binary_traces_final.h5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff_Rebecca2/8089/8089_d5_s1/binary_traces_final.h5']\n"
     ]
    }
   ],
   "source": [
    "#Initialize Input Values\n",
    "#Ali Mice\n",
    "aliBase = '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/'\n",
    "aliMice, aliDays, aliSessions = ([24,25,26], [5], [1,2])\n",
    "aliFolders, aliFilenames = makeAliFilenames_modified(aliMice, aliDays, aliSessions)\n",
    "bin_aliFiles = combineFilenames(aliBase, aliFolders, aliFilenames)\n",
    "#Rebecca Mice\n",
    "rBase = '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff-Rebecca/'\n",
    "rMice, rDays, rSessions = ([2982,4540], [9], [1])\n",
    "rFolders, rFilenames = makeRebeccaFilenames_modified(rMice, rDays, rSessions)\n",
    "bin_rFiles = combineFilenames(rBase, rFolders, rFilenames)\n",
    "#New Rebecca Mice\n",
    "nrBase = '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff_Rebecca2/'\n",
    "nrMice, nrDays, nrSessions = ([2712, 2713,2714,8089], [5], [1]) #Also have 2 as full imaging session.  Drop 2714 because of data quality\n",
    "nrFolders, nrFilenames = makeRebeccaFilenames_modified(nrMice, nrDays, nrSessions)\n",
    "bin_nrFiles = combineFilenames(nrBase, nrFolders, nrFilenames)\n",
    "#Combine All Filenames into one List\n",
    "bin_allFiles = bin_aliFiles + bin_rFiles + bin_nrFiles\n",
    "print(bin_allFiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadbinaryTraces(files, whichData):\n",
    "    #Load all the trace data and normalize traces with baseline adjustments\n",
    "    #Load and doughnut subtract traces\n",
    "    binTraces = loadFiles(files, 'onset_binary_trace', whichData=whichData, multiIndex=True)\n",
    "    \n",
    "    return binTraces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "ali_binary_traces=loadbinaryTraces(bin_aliFiles, 'Ali')\n",
    "r_binary_traces=loadbinaryTraces(bin_rFiles, 'Rebecca')\n",
    "nr_binary_traces=loadbinaryTraces(bin_nrFiles, 'Rebecca')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1.])"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(ali_binary_traces['ali24_d5_s2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
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       "cell_num  0    1    2    3    4    5    6    7    8    9    ...  311  312  \\\n",
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       "26478     0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "26479     0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "26480     0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "26481     0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "\n",
       "cell_num  313  314  315  316  317  318  319  320  \n",
       "Time                                              \n",
       "0         0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "1         0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2         0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "3         0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "4         0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "...       ...  ...  ...  ...  ...  ...  ...  ...  \n",
       "26477     0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "26478     0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "26479     0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "26480     0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "26481     0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[26482 rows x 321 columns]"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ali_binary_traces['ali24_d5_s2']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make trial blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ali Binary Blocks\n",
      "ali24_d5_s1\n",
      "ali24_d5_s2\n",
      "ali25_d5_s1\n",
      "ali25_d5_s2\n",
      "ali26_d5_s1\n",
      "ali26_d5_s2\n",
      "Rebecca Binary Blocks\n",
      "2982_d9_s1\n",
      "4540_d9_s1\n",
      "New Rebecca Binary Blocks\n",
      "2712_d5_s1\n",
      "2713_d5_s1\n",
      "2714_d5_s1\n",
      "8089_d5_s1\n"
     ]
    }
   ],
   "source": [
    "print('Ali Binary Blocks')\n",
    "bin_aliTraceBlocks, bin_aliTraceNoBlocks = makeTraceTrialBlocks(ali_binary_traces, aliSounds)\n",
    "#bin_aliTraceBlocks, bin_aliTraceNoBlocks = makeTraceTrialBlocks(aliMatched, aliSounds)\n",
    "print('Rebecca Binary Blocks')\n",
    "bin_rTraceBlocks, bin_rTraceNoBlocks = makeTraceTrialBlocks(r_binary_traces, rSounds) \n",
    "print('New Rebecca Binary Blocks')\n",
    "bin_nrTraceBlocks, bin_nrTraceNoBlocks = makeTraceTrialBlocks(nr_binary_traces, nrSounds) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(321,)\n",
      "(200,)\n",
      "(437,)\n",
      "(329,)\n",
      "(341,)\n",
      "(186,)\n",
      "(170,)\n",
      "(243,)\n",
      "(105,)\n"
     ]
    }
   ],
   "source": [
    "print(bin_aliTraceBlocks['ali24_d5_s1'].columns.unique('cell_num').shape)\n",
    "print(bin_aliTraceBlocks['ali25_d5_s1'].columns.unique('cell_num').shape)\n",
    "print(bin_aliTraceBlocks['ali26_d5_s1'].columns.unique('cell_num').shape)\n",
    "print(bin_rTraceBlocks['2982_d9_s1'].columns.unique('cell_num').shape)\n",
    "print(bin_rTraceBlocks['4540_d9_s1'].columns.unique('cell_num').shape)\n",
    "print(bin_nrTraceBlocks['2712_d5_s1'].columns.unique('cell_num').shape)\n",
    "print(bin_nrTraceBlocks['2713_d5_s1'].columns.unique('cell_num').shape)\n",
    "print(bin_nrTraceBlocks['2714_d5_s1'].columns.unique('cell_num').shape)\n",
    "print(bin_nrTraceBlocks['8089_d5_s1'].columns.unique('cell_num').shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201 rows × 12840 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "trial      0                                                ...   39       \\\n",
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       "Time                                                        ...             \n",
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       "\n",
       "trial                                             \n",
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       "Time                                              \n",
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       "197       0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "198       0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "199       0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "200       0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[201 rows x 12840 columns]"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bin_aliTraceBlocks['ali24_d5_s1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "104183.0"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_events_lastday=np.sum(np.sum(bin_aliTraceNoBlocks['ali24_d5_s1']))+np.sum(np.sum(bin_aliTraceNoBlocks['ali25_d5_s1']))+np.sum(np.sum(bin_aliTraceNoBlocks['ali26_d5_s1']))+np.sum(np.sum(bin_aliTraceNoBlocks['ali24_d5_s2']))+np.sum(np.sum(bin_aliTraceNoBlocks['ali25_d5_s2']))+np.sum(np.sum(bin_aliTraceNoBlocks['ali26_d5_s2']))+np.sum(np.sum(bin_rTraceNoBlocks['2982_d9_s1']))+np.sum(np.sum(bin_rTraceNoBlocks['4540_d9_s1']))+np.sum(np.sum(bin_nrTraceNoBlocks['2712_d5_s1']))+np.sum(np.sum(bin_nrTraceNoBlocks['2713_d5_s1']))+np.sum(np.sum(bin_nrTraceNoBlocks['2714_d5_s1']))+np.sum(np.sum(bin_nrTraceNoBlocks['8089_d5_s1']))\n",
    "n_events_lastday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a function to output p and its mean \n",
    "def baseline_firing_rate(mouse_df):\n",
    "    cells = mouse_df.columns.unique('cell_num')\n",
    "    Ncells = cells.shape[0]\n",
    "    out= np.zeros((Ncells,1))\n",
    "    for cell in cells:\n",
    "        out[cell]=np.array(np.where(mouse_df.loc[:,cell]==1)).size\n",
    "    # Time in seconds    \n",
    "    t= mouse_df.shape[0]/20  \n",
    "    p=out/t*20\n",
    "    return p,np.mean(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_prob_histogram(data):\n",
    "    n_bins= np.arange(min(data), max(data)+0.5, 0.2)\n",
    "    plt.hist(data,n_bins,align='left')\n",
    "    plt.xlabel('Number of baseline events')\n",
    "    plt.ylabel('Number of cells')\n",
    "    plt.title('Baseline firing rate')\n",
    "    plt.xticks(np.arange(min(data),max(data)+0.5,0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5483486332243446\n",
      "0.31807939023936493\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,mean_firing_rate_ali24_CS=baseline_firing_rate(bin_aliTraceNoBlocks['ali24_d5_s1'])\n",
    "print(mean_firing_rate_ali24_CS)\n",
    "print(1.5*np.std(p))\n",
    "plot_prob_histogram(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.519896263468944\n",
      "0.3008344474047015\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,mean_firing_rate_ali24=baseline_firing_rate(bin_aliTraceNoBlocks['ali24_d5_s2'])\n",
    "print(mean_firing_rate_ali24)\n",
    "print(1.5*np.std(p))\n",
    "plot_prob_histogram(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3568877827074992\n",
      "0.29275256063609995\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,mean_firing_rate_ali25_CS=baseline_firing_rate(bin_aliTraceNoBlocks['ali25_d5_s1'])\n",
    "print(mean_firing_rate_ali25_CS)\n",
    "print(1.5*np.std(p))\n",
    "plot_prob_histogram(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6474974599567097\n",
      "0.40932634715066607\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,mean_firing_rate_ali26_CS=baseline_firing_rate(bin_aliTraceNoBlocks['ali26_d5_s1'])\n",
    "print(mean_firing_rate_ali26_CS)\n",
    "print(1.5*np.std(p))\n",
    "plot_prob_histogram(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5594473611494888\n",
      "0.5052874898219994\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,mean_firing_rate_2982_CS=baseline_firing_rate(bin_rTraceNoBlocks['2982_d9_s1'])\n",
    "print(mean_firing_rate_2982_CS)\n",
    "print(1.5*np.std(p))\n",
    "plot_prob_histogram(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5078965609170888\n",
      "0.3107999379142329\n"
     ]
    },
    {
     "data": {
      "image/png": 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uqpZW1dKFCxfOIAxJ0tqm6j56Zc/0j4AXttNjwKNnUOcqYFVVXdLOn06TFH6UZFFV3ZJkETNrjUiS1sOkSaGqXjeICqvqv5LclORXq+p6YA/g2va1DDi6fT9zEPVLkiY37YnmJLsAb6G5VLQrP8Ohs98CnJxkC5pzFK+j6co6LcmhwI3Aq2awfUnSeujn6qMvACfQnEt4cENUWlXfAZZOsGqPDbF9SdL66Scp3FtVHx14JJKkkesnKXwkyZHAucB94wur6oqBRSVJGol+ksLTgdcCL+ah7qNq5yVJc0g/SeF/AU/oHT5bkjQ39XNH878DCwYdiCRp9PppKWwPfDfJZfziOYWZXJIqSZqF+kkKRw48CknSrNDP8xQuGkYgkqTR6+eO5rt46JnMWwCbA/dU1daDDEySNHz9tBS26p1Psh+w28AikiSNTD9XH/2CqvoC3qMgSXNSP91H+/fMbkIzZtGMntEsSZqd+rn6qPe5CmuAlcC+A4lGkjRS/ZxTGMhzFSRJs8+kSSHJe6b4XFXV+wcQjyRphKZqKdwzwbJHAocCjwFMCpI0x0z1OM5jx6eTbAW8jeYJaacCx072OUnSxmvKcwpJtgXeDrwGOAl4VlXdPozAJEnDN9U5hQ8B+wPHAU+vqruHFpUkaSSmunntT4DHA+8Gbk6yun3dlWT1cMKTJA3TVOcU1vluZ0nSxs0vfklSZ2RJIcmmSb6d5Evt/C5JLkny/SSfS7LFqGKTpPlqlC2FtwHX9cwfA3y4qpYAt9PcDyFJGqKRJIUkOwJ7A8e386EZefX0tshJwH6jiE2S5rNRtRT+BvhT4MF2/jHAHVW1pp1fBewwisAkaT4belJI8grg1qq6vHfxBEUnHJ47yWFJlidZPjY2NpAYJWm+GkVL4fnAPklW0gyZ8WKalsOCJOOXyO4I3DzRh6vquKpaWlVLFy5cOIx4JWneGHpSqKojqmrHqloMHAh8vapeA1wAHNAWWwacOezYJGm+m033KbwTeHuSFTTnGE4YcTySNO/08+S1gamqC4EL2+kbgN1GGY8kzXezqaUgSRoxk4IkqWNSkCR1TAqSpI5JQZLUMSlIkjomBUlSx6QgSeqYFCRJHZOCJKljUpAkdUwKkqSOSUGS1DEpSJI6JgVJUsekIEnqmBQkSR2TgiSpY1KQJHVMCpKkzmajDmBUFh9+9qhD0Bw3W//GVh6996hD0CxmS0GS1DEpSJI6Q08KSXZKckGS65Jck+Rt7fJtk5yX5Pvt+6OHHZskzXejaCmsAf6kqp4KPAd4c5JdgcOB86tqCXB+Oy9JGqKhJ4WquqWqrmin7wKuA3YA9gVOaoudBOw37Ngkab4b6TmFJIuB3wAuAbavqlugSRzAYyf5zGFJlidZPjY2NqxQJWleGFlSSPIo4J+BP6qq1f1+rqqOq6qlVbV04cKFgwtQkuahkSSFJJvTJISTq+qMdvGPkixq1y8Cbh1FbJI0n43i6qMAJwDXVdVf96w6C1jWTi8Dzhx2bJI0343ijubnA68FrkrynXbZu4CjgdOSHArcCLxqBLFJ0rw29KRQVd8AMsnqPYYZiyTpF3lHsySpY1KQJHVMCpKkjklBktQxKUiSOiYFSVLHpCBJ6pgUJEkdk4IkqWNSkCR1TAqSpI5JQZLUMSlIkjomBUlSx6QgSeqYFCRJHZOCJKljUpAkdUwKkqSOSUGS1DEpSJI6JgVJUsekIEnqbDbqANaW5GXAR4BNgeOr6ugRhyTNKYsPP3vUIUxo5dF7jzoEMctaCkk2Bf4e2AvYFTgoya6jjUqS5o9ZlRSA3YAVVXVDVd0PnArsO+KYJGnemG3dRzsAN/XMrwJ+s7dAksOAw9rZu5NcP6TYprId8ONRBzGPefxHZ4Md+xyzIbYyr8zk2O882YrZlhQywbL6hZmq44DjhhNOf5Isr6qlo45jvvL4j47HfnQGdexnW/fRKmCnnvkdgZtHFIskzTuzLSlcBixJskuSLYADgbNGHJMkzRuzqvuoqtYk+UPgqzSXpH6yqq4ZcVj9mFXdWfOQx390PPajM5Bjn6qavpQkaV6Ybd1HkqQRMilIkjomhXWQ5GVJrk+yIsnhE6zfMsnn2vWXJFk8/Cjnpj6O/SFJxpJ8p339/ijinIuSfDLJrUmunmR9kny0/be5Msmzhh3jXNXHsd89yZ09f/fvmWmdJoU+9TkEx6HA7VX1JODDgLfjbADrMPzJ56rqme3r+KEGObedCLxsivV7AUva12HAx4cQ03xxIlMfe4B/6fm7f99MKzQp9K+fITj2BU5qp08H9kgy0Q15WjcOfzJCVXUxcNsURfYFPl2NbwELkiwaTnRzWx/HfoMzKfRvoiE4dpisTFWtAe4EHjOU6Oa2fo49wP9uuy9OT7LTBOs1GP3++2gwnpvk35N8OcnTZroxk0L/ph2Co88yWnf9HNcvAour6hnA13ioxabB8+9+dK4Adq6qXwf+FvjCTDdoUuhfP0NwdGWSbAZsw5CbfnPUtMe+qn5SVfe1s/8I/I8hxSaHpxmZqlpdVXe30+cAmyfZbibbNCn0r58hOM4ClrXTBwBfL+8O3BCmPfZr9WHvA1w3xPjmu7OAg9urkJ4D3FlVt4w6qPkgyePGz1sm2Y3mO/0nM9nmrBrmYjabbAiOJO8DllfVWcAJwD8lWUHTQjhwdBHPHX0e+7cm2QdYQ3PsDxlZwHNMklOA3YHtkqwCjgQ2B6iqTwDnAC8HVgA/BV43mkjnnj6O/QHAm5KsAX4GHDjTH6IOcyFJ6th9JEnqmBQkSR2TgiSpY1KQJHVMCpKkjklBG1SSSnJsz/w7khy1gbZ9YpIDNsS2pqnnVUmuS3LBWst3T/KlAdZ7SJK/a6ffmOTgQdW1ISXZb5IBCrURMiloQ7sP2H+md1VuaO1Iq/06FPiDqnrRoOKZTlV9oqo+Par619F+NKPXag4wKWhDW0Pz7Ng/XnvF2r/0k9zdvu+e5KIkpyX5XpKjk7wmyaVJrkryxJ7N7JnkX9pyr2g/v2mSDyW5rB0Q7w09270gyWeBqyaI56B2+1cnOaZd9h7gt4BPJPnQBPu3dZLPJ7k2ySeSbNJ+7uNJlie5Jsl7e+o4ui17ZZK/apctTPLPbbyXJXn+BLEdleQd7fSFSY5pj8f3kvzPqfZ7gm39XvvZ7yT5h/Zzb0rywZ4yhyT528nKj/97JflAO/jat5Jsn+R5NHeQf6gt/8Qkb+3Z51MnikmzWFX58rXBXsDdwNbASpqxn94BHNWuOxE4oLds+747cAewCNgS+CHw3nbd24C/6fn8V2h+zCyhGXPnYTRj+L+7LbMlsBzYpd3uPcAuE8T5eOBGYCHNnf1fB/Zr110ILJ3gM7sD9wJPoLmz+rzx/QG2bd83bT//DGBb4Hoeukl0Qfv+WeC32ulfAa5rpw8B/q6dPgp4R088x7bTLwe+1k5PuN9rxfxUmsECN2/nPwYc3O73ip5yX6ZJhhOWb6cLeGU7/cGeutf+d70Z2LJ3n31tPC+HudAGV1Wrk3waeCvNrff9uKza8XKS/Adwbrv8KqC3G+e0qnoQ+H6SG4CnAC8FntHTCtmGJmncD1xaVT+YoL5nAxdW1Vhb58nAC5h+lMlLq+qG9jOn0HyRng68OslhNAlmEU13yrU0SeT4JGcD4+cj9gR2zUOP2tg6yVbT1HtG+345sLidnmy/e/d3D5rBAS9r63s4cGtVjSW5Ic1YRd8HfhX4JvDmicq327q/Zx8uB14ySaxXAicn+QIbYNRODZdJQYPyNzTD+n6qZ9ka2i7LNN84W/Ssu69n+sGe+Qf5xb/TtcdlKZqhm99SVV/tXZFkd5qWwkTW9+FHv1R/kl1oWkTPrqrbk5wIPKyaMZt2o/liPhD4Q+DFNMfguVX1CwkzUz+Pafx4/JyHjseE+72WACdV1RETrPsc8Grgu8Dnq6raf5fJyj9Q7c//teJY2940CXYf4M+SPK2a54toI+A5BQ1EVd0GnEZz0nbcSh4a0npf2oG91tGrkmzSnmd4Ak33zFdpBgXbHCDJk5M8cprtXAK8MMl2bZ/5QcBFfdS/W5rRWjcBfgf4Bk132T3AnUm2p3k8JUkeBWxTzZDGfwQ8s93GuTQJgrbcM1k//ez3+cABSR7bltk2yc7tujNoThIfRJMgpis/mbuArdrymwA7VdUFwJ8CC4BHref+aQRsKWiQjqXny4/mOQdnJrmU5stnsl/xU7me5st7e+CNVXVvkuNpulSuaH/pjtF82U2qqm5JcgRwAc2v6XOq6sw+6v834Gjg6cDFNL+wH0zybeAa4AaabhhovijPTPKwto7xk+9vBf4+yZU0/wcvBt7Yz86vZdr9rqprk7wbOLf9wn6ApovoP9tWzbXArlV16XTlp4jjVOAfk7yVpkV0QpJt2n3+cFXdsR77phFxlFRJUsfuI0lSx6QgSeqYFCRJHZOCJKljUpAkdUwKkqSOSUGS1PlvOjBRt3E0m90AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,mean_firing_rate_4540_CS=baseline_firing_rate(bin_rTraceNoBlocks['4540_d9_s1'])\n",
    "print(mean_firing_rate_4540_CS)\n",
    "print(1.5*np.std(p))\n",
    "plot_prob_histogram(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8028444436102661\n",
      "0.34162975551101477\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,mean_firing_rate_2712_CS=baseline_firing_rate(bin_nrTraceNoBlocks['2712_d5_s1'])\n",
    "print(mean_firing_rate_2712_CS)\n",
    "print(1.5*np.std(p))\n",
    "plot_prob_histogram(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7230209440771108\n",
      "0.35519228501045697\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,mean_firing_rate_2713_CS=baseline_firing_rate(bin_nrTraceNoBlocks['2713_d5_s1'])\n",
    "print(mean_firing_rate_2713_CS)\n",
    "print(1.5*np.std(p))\n",
    "plot_prob_histogram(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4486508076537613\n",
      "0.2665663560807462\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,mean_firing_rate_2714_CS=baseline_firing_rate(bin_nrTraceNoBlocks['2714_d5_s1'])\n",
    "print(mean_firing_rate_2714_CS)\n",
    "print(1.5*np.std(p))\n",
    "plot_prob_histogram(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6652363116234531\n",
      "0.248870561422956\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEWCAYAAABhffzLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAaJklEQVR4nO3debhdZX328e+dECYhCZRDjEAIQ1Doq2IbEcHKbGVOucBCUYJN3wBOqKUaKFootQYQsYMWIyDpKxBSZAYZjAmIA5AwhCEyGCNg8pIgSRMiU8ivf6znNJuTPaxzkrX3OXnuz3Xta695/fZeyb3Xefbaz1JEYGZm+RjU6QLMzKy9HPxmZplx8JuZZcbBb2aWGQe/mVlmHPxmZplx8NsGR9LJku6tGX9Z0s4V7GeEpHskrZB0kaSzJF3aZPlRqZbB67sWs97YqNMF2IZN0gJgBPAm8Abwc+DUiHiuXTVExBYVbXoi8CIwNEr8ICYingWqqqVXJI0GfgMMiYhVna3G2s1n/NYOR6bwHQm8APxbh+tZX3YEnigT+pLaepLlvyqsGQe/tU1EvApcC+zRPU3S4ZIekrRc0nOSzqmZt6mkH0j6vaRlkh6QNCLNGybpMkmLJP1O0j81CjtJIWnXNHyFpG9LujU10dwnaZeaZd8l6S5JL0l6UtLHGmzzCmA88KXUfHOwpHMk/SDNH532O0HSs8BPaqZtlJaZJek8ST9LtdwpaZuafZwk6bfp9X9F0gJJBzeqR9J/SLpN0krggGbvLXBPel6W6v9g2s5fS5onaamkOyTtWG9/NrA5+K1tJG0O/CXwy5rJK4GTgOHA4cBpksaleeOBYcAOwB8BpwKvpHlTgVXArsD7gI8Af1OylBOAc4GtgGeAr6X63gbcBVwFbJuW+46kP+65gYg4GbgSuCAitoiIHzfY137A7sCfN5j/V8An0/42Bs5ItewBfAc4keIvpWHAdi1e11+l17IlcC/N39sPp+fhqf5fpHlnAccAXcBPgatb7NMGIAe/tcMNkpYBy4FDgAu7Z0TErIh4NCJWR8RciqDZL81+gyLwd42INyNiTkQsT2f9hwKfj4iVEbEYuBg4vmQ910XE/alt+0pgzzT9CGBBRHw/IlZFxIPAD4Fj1+G1n5NqfKXB/O9HxFNp/vSaWo4Fbo6IeyPideCrQKsmpRsj4mfpvXy1xXtbzynA1yNiXnpv/hnY02f9Gx4Hv7XDuIgYDmwCfAa4W9LbASR9QNJMSUsk/TfFWX13c8f/A+4ApklaKOkCSUMo2taHAItSE9Ay4LsUZ81l/P+a4T+w5gvXHYEPdG8zbfdE4O19feFAqy+xG9Xyjtp1I+IPwO97s68W7209OwL/UvPaXwJE6780bIBx8FvbpLP26yiu8PlQmnwVcBOwQ0QMAy6hCBsi4o2IODci9gD2oTgjP4ki4F4DtomI4ekxNCLWapLppeeAu2u22d0Mcto6bLOv3d8uArbvHpG0GcVfP73ZV8P3tkFdzwGn9Hj9m0XEz/v0CqzfcvBb26hwNEXb+rw0eUvgpYh4VdJeFO3U3csfIOnd6Uvb5RRNP29GxCLgTuAiSUMlDZK0i6RmzRhl3ALsJukTkoakx/sl7b6O2+2La4EjJe0jaWOK7yTUYp2eGr63wBJgNVD7+4ZLgDO7v9NIX6Af1+dXYP2Wg9/a4WZJL1OE99eA8RHxeJr3KeAfJa2gaMeeXrPe2ykCcDnFB8XdwA/SvJMovgx9Alialhu5LkVGxAqKL4mPBxZSNMOcT9FE1Vbp/fksMI3i7H8FsJjiL52yGr63qenoa8DPUtPO3hFxPcXrnSZpOfAYxXcptoGRb8Ri1v9J2gJYBoyJiN90uh4b2HzGb9ZPSTpS0ubpMtNvAI8CCzpblW0IHPxm/dfRFE1OC4ExwPFlfiVs1oqbeszMMuMzfjOzzAyI3jm32WabGD16dKfLMDMbUObMmfNiRHT1nD4ggn/06NHMnj2702WYmQ0okn5bb7qbeszMMuPgNzPLjIPfzCwzDn4zs8w4+M3MMuPgNzPLjIPfzCwzDn4zs8w4+M3MMjMgfrlr/dPoSbd2uoTSFkw+vNMlmPUbPuM3M8uMg9/MLDMOfjOzzDj4zcwy4+A3M8uMg9/MLDMOfjOzzDj4zcwy4+A3M8uMg9/MLDMOfjOzzDj4zcwy4+A3M8uMg9/MLDOVdsssaQGwAngTWBURYyVtDVwDjAYWAB+LiKVV1mFmZmu044z/gIjYMyLGpvFJwIyIGAPMSONmZtYmnWjqORqYmoanAuM6UIOZWbaqDv4A7pQ0R9LENG1ERCwCSM/bVlyDmZnVqPrWi/tGxEJJ2wJ3SfpV2RXTB8VEgFGjRlVVn5lZdio944+Ihel5MXA9sBfwgqSRAOl5cYN1p0TE2IgY29XVVWWZZmZZqSz4Jb1N0pbdw8BHgMeAm4DxabHxwI1V1WBmZmursqlnBHC9pO79XBURt0t6AJguaQLwLHBchTWYmVkPlQV/RMwH3ltn+u+Bg6rar5mZNedf7pqZZcbBb2aWGQe/mVlmHPxmZplx8JuZZcbBb2aWGQe/mVlmHPxmZplx8JuZZcbBb2aWGQe/mVlmHPxmZplx8JuZZcbBb2aWGQe/mVlmHPxmZplx8JuZZcbBb2aWGQe/mVlmHPxmZplx8JuZZcbBb2aWGQe/mVlmHPxmZplx8JuZZcbBb2aWGQe/mVlmHPxmZplx8JuZZaby4Jc0WNJDkm5J4ztJuk/S05KukbRx1TWYmdka7TjjPx2YVzN+PnBxRIwBlgIT2lCDmZkllQa/pO2Bw4FL07iAA4Fr0yJTgXFV1mBmZm9V9Rn/t4AvAavT+B8ByyJiVRp/Htiu3oqSJkqaLWn2kiVLKi7TzCwflQW/pCOAxRExp3ZynUWj3voRMSUixkbE2K6urkpqNDPL0UYVbntf4ChJhwGbAkMp/gIYLmmjdNa/PbCwwhrMzKyHys74I+LMiNg+IkYDxwM/iYgTgZnAsWmx8cCNVdVgZmZr68R1/F8GvijpGYo2/8s6UIOZWbZaBr+kCyQNlTRE0gxJL0r6eG92EhGzIuKINDw/IvaKiF0j4riIeK2vxZuZWe+VOeP/SEQsB46guApnN+DvKq3KzMwqUyb4h6Tnw4CrI+KlCusxM7OKlbmq52ZJvwJeAT4lqQt4tdqyzMysKi3P+CNiEvBBYGxEvAGsBI6uujAzM6tGwzN+ScfUmVY7el0VBZmZWbWaNfUc2WRe4OA3MxuQGgZ/RHyynYWYmVl7NGvq+WKzFSPim+u/HDMzq1qzpp4t21aFmZm1TbOmnnPbWYiZmbVHmS4bdktdNTyWxt8j6ezqSzMzsyqU+eXu94AzgTcAImIuRW+bZmY2AJUJ/s0j4v4e01bVXdLMzPq9MsH/oqRdSHfKknQssKjSqszMrDJl+ur5NDAFeJek3wG/AXrVLbOZmfUfLYM/IuYDB0t6GzAoIlZUX5aZmVWlzFU9/yxpeESsjIgVkraS9E/tKM7MzNa/Mk09h0bEWd0jEbE03UDdl3RWYPSkWztdgplt4Mp8uTtY0ibdI5I2AzZpsryZmfVjZc74fwDMkPR9iit7/hqYWmlVZmZWmTJf7l4gaS5wMCDgvIi4o/LKzMysEmXO+ImI24HbK67FzMzaoEwbv5mZbUAc/GZmmWkY/JJmpOfz21eOmZlVrVkb/0hJ+wFHSZpG8cXu/4qIByutzMzMKtEs+L8KTAK2B3reZjGAA6sqyszMqtPsDlzXAtdK+kpEnNfGmszMrEJlruM/T9JRwIfTpFkRcUu1ZZmZWVXKdNL2deB04In0OD1Na7XeppLul/SIpMclnZum7yTpPklPS7pG0sbr+iLMzKy8MpdzHg4cEhGXR8TlwEfTtFZeAw6MiPcCewIflbQ3cD5wcUSMAZYCE/pWupmZ9UXZ6/iH1wwPK7NCFF5Oo0PSo/tL4WvT9KnAuJI1mJnZelCmy4avAw9JmklxSeeHKW6+3pKkwcAcYFfg28CvgWUR0X3P3ueB7RqsOxGYCDBq1KgyuzMzsxJanvFHxNXA3sB16fHBiJhWZuMR8WZE7ElxSehewO71Fmuw7pSIGBsRY7u6usrszszMSijbSdsi4Ka+7iQilkmaRfEBMlzSRumsf3tgYV+3a2ZmvVdZXz2SuiQNT8ObUXTrPA+YCRybFhsP3FhVDWZmtrZSZ/x9NBKYmtr5BwHTI+IWSU8A09J9ex8CLquwBjMz66Fp8EsaBMyNiP/T2w1HxFzgfXWmz6do7zczsw5o2tQTEauBRyT5shozsw1EmaaekcDjku4HVnZPjIijKqvKzMwqUyb4z628CjMza5synbTdLWlHYExE/FjS5sDg6kszM7MqlOmk7f9SdLHw3TRpO+CGKosyM7PqlLmO/9PAvsBygIh4Gti2yqLMzKw6ZYL/tYh4vXtE0kY06GbBzMz6vzLBf7eks4DNJB0C/Bdwc7VlmZlZVcoE/yRgCfAocApwG3B2lUWZmVl1ylzVs1rSVOA+iiaeJyPCTT1mZgNUy+CXdDhwCUVf+gJ2knRKRPyo6uLMzGz9K/MDrouAAyLiGQBJuwC3Ag5+M7MBqEwb/+Lu0E/mA4srqsfMzCrW8Ixf0jFp8HFJtwHTKdr4jwMeaENtZmZWgWZNPUfWDL8A7JeGlwBbVVaRmZlVqmHwR8Qn21mImZm1R5mrenYCPguMrl3e3TKbmQ1MZa7quYHi9og3A6urLcfMzKpWJvhfjYh/rbwSMzNrizLB/y+S/gG4E3ite2JEPFhZVWZmVpkywf9u4BPAgaxp6ok0bmZmA0yZ4P8LYOfarpnNzGzgKvPL3UeA4VUXYmZm7VHmjH8E8CtJD/DWNn5fzmlmNgCVCf5/qLwKMzNrmzL98d/djkLMzKw9yvxydwVr7rG7MTAEWBkRQ6sszMzMqlHmjH/L2nFJ44C9KqvIzMwqVeaqnreIiBsocQ2/pB0kzZQ0T9Ljkk5P07eWdJekp9Oze/o0M2ujMk09x9SMDgLGsqbpp5lVwN9GxIOStgTmSLoLOBmYERGTJU2iuJn7l3tduZmZ9UmZq3pq++VfBSwAjm61UkQsAhal4RWS5gHbpXX3T4tNBWbh4Dcza5sybfzr3C+/pNHA+4D7gBHpQ4GIWCRp2wbrTAQmAowaNWpdSzAzs6TZrRe/2mS9iIjzyuxA0hbAD4HPR8RySaUKi4gpwBSAsWPHlmlaMjOzEpp9ubuyzgNgAiWbZiQNoQj9KyPiujT5BUkj0/yR+MbtZmZt1ezWixd1D6cvZ08HPglMAy5qtF7NOqK4gcu8iPhmzaybgPHA5PR8Y58qNzOzPmnaxi9pa+CLwIkUX8T+SUQsLbntfSm6c35U0sNp2lkUgT9d0gTgWeC4vhRuZmZ906yN/0LgGIp29ndHxMu92XBE3As0atA/qDfbMjOz9adZG//fAu8AzgYWSlqeHiskLW9PeWZmtr41a+Pv9a96zcys/3O4m5llxsFvZpYZB7+ZWWYc/GZmmXHwm5llxsFvZpYZB7+ZWWYc/GZmmXHwm5llxsFvZpYZB7+ZWWYc/GZmmXHwm5llxsFvZpYZB7+ZWWYc/GZmmXHwm5llxsFvZpYZB7+ZWWYc/GZmmXHwm5llxsFvZpYZB7+ZWWYc/GZmmXHwm5llxsFvZpYZB7+ZWWYqC35Jl0taLOmxmmlbS7pL0tPpeauq9m9mZvVVecZ/BfDRHtMmATMiYgwwI42bmVkbVRb8EXEP8FKPyUcDU9PwVGBcVfs3M7P62t3GPyIiFgGk520bLShpoqTZkmYvWbKkbQWamW3o+u2XuxExJSLGRsTYrq6uTpdjZrbBaHfwvyBpJEB6Xtzm/ZuZZa/dwX8TMD4NjwdubPP+zcyyV+XlnFcDvwDeKel5SROAycAhkp4GDknjZmbWRhtVteGIOKHBrIOq2qeZmbXWb7/cNTOzajj4zcwy4+A3M8uMg9/MLDMOfjOzzDj4zcwy4+A3M8uMg9/MLDMOfjOzzFT2y10z67vRk27tdAmlLZh8eKdLsF7yGb+ZWWYc/GZmmXHwm5llZoNv4x9IbaVWHf87MFvDZ/xmZplx8JuZZcbBb2aWGQe/mVlmHPxmZplx8JuZZcbBb2aWGQe/mVlmHPxmZplx8JuZZcbBb2aWGQe/mVlmHPxmZplx8JuZZcbBb2aWmY4Ev6SPSnpS0jOSJnWiBjOzXLU9+CUNBr4NHArsAZwgaY9212FmlqtOnPHvBTwTEfMj4nVgGnB0B+owM8tSJ269uB3wXM3488AHei4kaSIwMY2+LOnJNtTWG9sAL3a6CCvNx6siOr+yTfuYrbsd603sRPCrzrRYa0LEFGBK9eX0jaTZETG203VYOT5eA4+PWXU60dTzPLBDzfj2wMIO1GFmlqVOBP8DwBhJO0naGDgeuKkDdZiZZantTT0RsUrSZ4A7gMHA5RHxeLvrWA/6bTOU1eXjNfD4mFVEEWs1r5uZ2QbMv9w1M8uMg9/MLDPZBn+rbiMkfVHSE5LmSpohaceaebdLWibplh7rHCTpQUkPS7pX0q6ttmXllThmF6f3/mFJT0laVjPPx6zNJF0uabGkxxrMPzG9v3Ml/VzSe1utK2lPSb9Mx2u2pL1abcvqiIjsHhRfKv8a2BnYGHgE2KPHMgcAm6fh04BrauYdBBwJ3NJjnaeA3dPwp4ArWm3Lj/V3zHos/1mKCwd8zDp3zD4M/AnwWIP5+wBbpeFDgftarQvcCRyahg8DZrXalh9rP3I942/ZbUREzIyIP6TRX1L83qB73gxgRZ3tBjA0DQ8j/T6h2bastN529XECcHX3iI9Z+0XEPcBLTeb/PCKWptGe/8cardvoeDXclq2tE7/c7Q9KdRtRYwLwoxLb/RvgNkmvAMuBvddhW/ZWpY9ZapbZCfhJie36mPUPZd/jzwN3SPoGRVP1PuuwrWzlesZfqtsIAEkfB8YCF5bY7heAwyJie+D7wDfXYVv2VqWPGcWPAq+NiDdLbNfHrMMkHUAR1l8usfhpwBciYgeKY3fZOmwrW7kGf6luIyQdDPw9cFREvNZsg5K6gPdGxH1p0jXUnI30ZltWV2+6+jiemmaeRnzMOk/Se4BLgaMj4vclVhkPXJeG/4uiCbCv28pWrsHfstsISe8Dvkvxn35xiW0uBYZJ2i2NHwLM6+O2bG2luvqQ9E5gK+AXJbbpY9ZBkkZRhPgnIuKpkqstBPZLwwcCT6/DtvLV6W+XO/WguCLgKYorRf4+TftHiv/oAD8GXgAeTo+batb9KbAEeIXiTPTP0/S/AB6luOJkFrBzq235sf6OWRo/B5hcZ10fs/Yfr6uBRcAb6T2fAJwKnJrmX0rx4dv9Hs9utm6a/iFgTjpe9wF/2mpbfqz9cJcNZmaZybWpx8wsWw5+M7PMOPjNzDLj4Dczy4yD38wsMw5+6xNJIemimvEzJJ2znrZ9haRj18e2WuznOEnzJM3sMX3/nr14ruf9nizp39PwqZJOqmpf65OkcZL26HQdtu4c/NZXrwHHSNqm04XUkjS4F4tPAD4VEQdUVU8rEXFJRPxnp/bfS+MAB/8GwMFvfbWK4p6oX+g5o+cZu6SX0/P+ku6WND31lz859aN+v6RHJe1Ss5mDJf00LXdEWn+wpAslPZD6XT+lZrszJV1F8WOsnvWckLb/mKTz07SvUvwY6BJJ9frhGSrp+tQn/yWSBqX1/iP1A/+4pHNr9jG5pv/+b6RpXZJ+mOp9QNK+dWo7R9IZaXiWpPPT+/GUpD9r9rrrbOvjad2HJX03rXeapAtqljlZ0r81Wr77eEn6mqRHVPR9P0LSPsBRwIVp+V0kfa7mNU+rV5P1U53+BZkfA/MBvEzRPe4Ciu5xzwDOSfOuAI6tXTY97w8sA0YCmwC/A85N804HvlWz/u0UJyZjKH65uSkwETg7LbMJMJuiF879gZXATnXqfAfwLNBF0RvtT4Bxad4sYGyddfYHXqXo+38wcFf36wG2Ts+D0/rvAbYGnmTNPayHp+ergA+l4VHAvDR8MvDvafgc4Iyaei5Kw4cBP07DdV93j5p3B24GhqTx7wAnpdf9TM1yP6L4wKu7fBoO4Mg0fEHNvnse14XAJrWv2Y+B8ci1W2ZbDyJiuaT/BD5H0RVCGQ9ExCIASb+muLEGFGfqtU0u0yNiNfC0pPnAu4CPAO+p+WtiGMUHw+vA/RHxmzr7ez/FzTqWpH1eSXGTjxta1Hl/RMxP61xNEZbXAh+TNJHiQ2QkRdPHExQfFJdKuhXo/n7gYGAP6X87Fh0qacsW++3ugGwOMDoNN3rdta/3IOBPgQfS/jYDFkfEEknzJe1N0a/NO4GfAZ+ut3za1us1r2EORR9G9cwFrpR0A63fT+tHHPy2rr4FPEjRpXG3VaRmRBWpsnHNvNpeLlfXjK/mrf8ee/YlEhRdM382Iu6onSFpf4oz/nrqdedcxlr7l7QTxV8274+IpZKuADaNiFUqbgF4EEXncZ+h6EBsEPDBiHjLh2LNB0E93e/Hm6x5P+q+7h4ETI2IM+vMuwb4GPAr4PqIiHRcGi3/RqTT+B519HQ4xYfoUcBXJP1xRKxqUqP1E27jt3USES8B0ym+KO22gOJsEoq7ZA3pw6aPkzQotfvvTNGUcgdwmqQhAJJ2k/S2Ftu5D9hP0japDfsE4O4S+99LRU+gg4C/BO6laNpaCfy3pBEUt/hD0hbAsIi4jeJGIXumbdxJ8SFAWm5P+qbM654BHCtp27TM1lpzn+DrKL6YPYHiQ6DV8o2sALZMyw8CdoiImcCXgOHAFn18fdZmPuO39eEiagIO+B5wo6T7KQKm0dl4M09SBPQIit4cX5V0KUXzx4PpjHUJRaA1FBGLJJ0JzKQ4K74tIm4ssf9fAJOBdwP3UJwpr5b0EPA4MJ+iyQSKMLxR0qZpH91feH8O+LakuRT/1+6h6J2yt1q+7oh4QtLZwJ0plN+gaM75bfrr5AmKexTf32r5JnVMA74n6XMUf9lcJmlYes0XR8SyJutaP+LeOc3MMuOmHjOzzDj4zcwy4+A3M8uMg9/MLDMOfjOzzDj4zcwy4+A3M8vM/wDmYnwKeNRduQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,mean_firing_rate_8089_CS=baseline_firing_rate(bin_nrTraceNoBlocks['8089_d5_s1'])\n",
    "print(mean_firing_rate_8089_CS)\n",
    "print(1.5*np.std(p))\n",
    "plot_prob_histogram(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First training session "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading binary traces for first sessions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeAliFilenames_modified(mouse_numbers, mouse_days, mouse_sessions):\n",
    "    #Input lists of mouse numbers, days, and sessions to permute to give out directories and dictionary for filename\n",
    "    directories = []\n",
    "    filenames = {}\n",
    "    for m_num in mouse_numbers:\n",
    "        for m_day in mouse_days:\n",
    "            for m_sess in mouse_sessions:\n",
    "                if ((m_day != 2) | (m_sess != 2)): #Skip d2_s2.  Only do d5_s2\n",
    "                    mouse_dir = 'ali{}_d{}_s{}/'.format(m_num, m_day, m_sess)\n",
    "                    directories.append(mouse_dir)\n",
    "                    filenames[mouse_dir] = 'binary_traces.h5'\n",
    "                   \n",
    "    return directories, filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeRebeccaFilenames_modified(mouse_numbers, mouse_days, mouse_sessions):\n",
    "    #Input lists of mouse numbers, days, and sessions to permute to give out directories and dictionary for filename\n",
    "    directories = []\n",
    "    filenames = {}\n",
    "    for m_num in mouse_numbers:\n",
    "        for m_day in mouse_days:\n",
    "            for m_sess in mouse_sessions:\n",
    "                mouse_dir = '{}/{}_d{}_s{}/'.format(m_num, m_num, m_day, m_sess)\n",
    "                directories.append(mouse_dir)\n",
    "                filenames[mouse_dir] = 'binary_traces.h5'\n",
    "    return directories, filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali24_d2_s1/binary_traces.h5', '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali25_d2_s1/binary_traces.h5', '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali26_d2_s1/binary_traces.h5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff-Rebecca/2982/2982_d1_s1/binary_traces.h5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff-Rebecca/4540/4540_d1_s1/binary_traces.h5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff_Rebecca2/2712/2712_d1_s1/binary_traces.h5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff_Rebecca2/2713/2713_d1_s1/binary_traces.h5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff_Rebecca2/2714/2714_d1_s1/binary_traces.h5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff_Rebecca2/8089/8089_d1_s1/binary_traces.h5']\n"
     ]
    }
   ],
   "source": [
    "#Initialize Input Values\n",
    "#Ali Mice\n",
    "aliBase = '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/'\n",
    "aliMice, aliDays, aliSessions = ([24,25,26], [2], [1])\n",
    "aliFolders, aliFilenames = makeAliFilenames_modified(aliMice, aliDays, aliSessions)\n",
    "bin_aliFiles = combineFilenames(aliBase, aliFolders, aliFilenames)\n",
    "#Rebecca Mice\n",
    "rBase = '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff-Rebecca/'\n",
    "rMice, rDays, rSessions = ([2982,4540], [1], [1])\n",
    "rFolders, rFilenames = makeRebeccaFilenames_modified(rMice, rDays, rSessions)\n",
    "bin_rFiles = combineFilenames(rBase, rFolders, rFilenames)\n",
    "#New Rebecca Mice\n",
    "nrBase = '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff_Rebecca2/'\n",
    "nrMice, nrDays, nrSessions = ([2712,2713,2714,8089], [1], [1]) #Also have 2 as full imaging session.  Drop 2714 because of data quality\n",
    "nrFolders, nrFilenames = makeRebeccaFilenames_modified(nrMice, nrDays, nrSessions)\n",
    "bin_nrFiles = combineFilenames(nrBase, nrFolders, nrFilenames)\n",
    "#Combine All Filenames into one List\n",
    "bin_allFiles = bin_aliFiles + bin_rFiles + bin_nrFiles\n",
    "print(bin_allFiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_ali_binary_traces=loadbinaryTraces(bin_aliFiles, 'Ali')\n",
    "first_r_binary_traces=loadbinaryTraces(bin_rFiles, 'Rebecca')\n",
    "first_nr_binary_traces=loadbinaryTraces(bin_nrFiles, 'Rebecca')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "mouse    ali24_d2_s1                                               ...  \\\n",
       "cell_num         0    1    2    3    4    5    6    7    8    9    ...   \n",
       "Time                                                               ...   \n",
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       "\n",
       "mouse    ali26_d2_s1                                               \n",
       "cell_num         746  747  748  749  750  751  752  753  754  755  \n",
       "Time                                                               \n",
       "0                0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
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       "\n",
       "[26482 rows x 2379 columns]"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_ali_binary_traces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ali Binary Blocks\n",
      "ali24_d2_s1\n",
      "ali25_d2_s1\n",
      "ali26_d2_s1\n",
      "Rebecca Binary Blocks\n",
      "2982_d1_s1\n",
      "4540_d1_s1\n",
      "New Rebecca Binary Blocks\n",
      "2712_d1_s1\n",
      "2713_d1_s1\n",
      "2714_d1_s1\n",
      "8089_d1_s1\n"
     ]
    }
   ],
   "source": [
    "print('Ali Binary Blocks')\n",
    "bin_aliTraceBlocks, bin_aliTraceNoBlocks = makeTraceTrialBlocks(first_ali_binary_traces, aliSounds)\n",
    "#bin_aliTraceBlocks, bin_aliTraceNoBlocks = makeTraceTrialBlocks(aliMatched, aliSounds)\n",
    "print('Rebecca Binary Blocks')\n",
    "bin_rTraceBlocks, bin_rTraceNoBlocks = makeTraceTrialBlocks(first_r_binary_traces, rSounds) \n",
    "print('New Rebecca Binary Blocks')\n",
    "bin_nrTraceBlocks, bin_nrTraceNoBlocks = makeTraceTrialBlocks(first_nr_binary_traces, nrSounds) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(862,)\n",
      "(761,)\n",
      "(756,)\n",
      "(115,)\n",
      "(187,)\n",
      "(113,)\n",
      "(143,)\n",
      "(234,)\n",
      "(70,)\n"
     ]
    }
   ],
   "source": [
    "print(bin_aliTraceBlocks['ali24_d2_s1'].columns.unique('cell_num').shape)\n",
    "print(bin_aliTraceBlocks['ali25_d2_s1'].columns.unique('cell_num').shape)\n",
    "print(bin_aliTraceBlocks['ali26_d2_s1'].columns.unique('cell_num').shape)\n",
    "print(bin_rTraceBlocks['2982_d1_s1'].columns.unique('cell_num').shape)\n",
    "print(bin_rTraceBlocks['4540_d1_s1'].columns.unique('cell_num').shape)\n",
    "print(bin_nrTraceBlocks['2712_d1_s1'].columns.unique('cell_num').shape)\n",
    "print(bin_nrTraceBlocks['2713_d1_s1'].columns.unique('cell_num').shape)\n",
    "print(bin_nrTraceBlocks['2714_d1_s1'].columns.unique('cell_num').shape)\n",
    "print(bin_nrTraceBlocks['8089_d1_s1'].columns.unique('cell_num').shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79320.0"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_events_firstday=np.sum(np.sum(bin_aliTraceNoBlocks['ali24_d2_s1']))+np.sum(np.sum(bin_aliTraceNoBlocks['ali25_d2_s1']))+np.sum(np.sum(bin_aliTraceNoBlocks['ali26_d2_s1']))+np.sum(np.sum(bin_rTraceNoBlocks['2982_d1_s1']))+np.sum(np.sum(bin_rTraceNoBlocks['4540_d1_s1']))+np.sum(np.sum(bin_nrTraceNoBlocks['2712_d1_s1']))+np.sum(np.sum(bin_nrTraceNoBlocks['2713_d1_s1']))+np.sum(np.sum(bin_nrTraceNoBlocks['2714_d1_s1']))+np.sum(np.sum(bin_nrTraceNoBlocks['8089_d1_s1']))\n",
    "n_events_firstday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4292632123912624\n",
      "0.2623381718765102\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,mean_firing_rate_ali24_FS=baseline_firing_rate(bin_aliTraceNoBlocks['ali24_d2_s1'])\n",
    "print(mean_firing_rate_ali24_FS)\n",
    "print(1.5*np.std(p))\n",
    "plot_prob_histogram(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3720777583644619\n",
      "0.2191603358132384\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,mean_firing_rate_ali25_FS=baseline_firing_rate(bin_aliTraceNoBlocks['ali25_d2_s1'])\n",
    "print(mean_firing_rate_ali25_FS)\n",
    "print(1.5*np.std(p))\n",
    "plot_prob_histogram(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4156196018548578\n",
      "0.30518112423134447\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,mean_firing_rate_ali26_FS=baseline_firing_rate(bin_aliTraceNoBlocks['ali26_d2_s1'])\n",
    "print(mean_firing_rate_ali26_FS)\n",
    "print(1.5*np.std(p))\n",
    "plot_prob_histogram(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.583209486166008\n",
      "0.3861046232276966\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,mean_firing_rate_2982_FS=baseline_firing_rate(bin_rTraceNoBlocks['2982_d1_s1'])\n",
    "print(mean_firing_rate_2982_FS)\n",
    "print(1.5*np.std(p))\n",
    "plot_prob_histogram(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5892075838599903\n",
      "0.32040926747720927\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,mean_firing_rate_4540_FS=baseline_firing_rate(bin_rTraceNoBlocks['4540_d1_s1'])\n",
    "print(mean_firing_rate_4540_FS)\n",
    "print(1.5*np.std(p))\n",
    "plot_prob_histogram(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8371020410396258\n",
      "0.3143561348250756\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,mean_firing_rate_2712_FS=baseline_firing_rate(bin_nrTraceNoBlocks['2712_d1_s1'])\n",
    "print(mean_firing_rate_2712_FS)\n",
    "print(1.5*np.std(p))\n",
    "plot_prob_histogram(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6284271748470446\n",
      "0.31297574643720216\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,mean_firing_rate_2713_FS=baseline_firing_rate(bin_nrTraceNoBlocks['2713_d1_s1'])\n",
    "print(mean_firing_rate_2713_FS)\n",
    "print(1.5*np.std(p))\n",
    "plot_prob_histogram(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.40461455785649386\n",
      "0.32646647687355607\n"
     ]
    },
    {
     "data": {
      "image/png": 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llrQTcD+wY1pvVkR8MW1vBjAcWAycWXllsJmZFSvPr3puA64ju1p3Uw+2/SrwkYh4WdJg4AFJPwL+Drg6ImZImgpMBK7pYd1mZtZLeYL/lYj4155uOF3k9XIaHZweAXwE+ESaPh24FAe/mVnT5An+r0v6InAP2Vk8ABGxuNGKkgYCi4CDgW+QXQS2NiI2pkVWknXzXG3dScAkgP32850fzcy2lTzB/07gTLIz9a6mnq4z97oi4g3gMEnDgFuBQ6otVmPdacA0gI6ODncRYWa2jeQJ/j8HDtyaL2AjYq2k+cD7gWGSBqWz/n2AVb3drpmZ9VyeK3d/AQzr6YYltaUzfSTtDBwLLAXmAePTYhOA2T3dtpmZ9V6eM/6RwC8lLeDNbfx1f84JjAKmp3b+AcDMiPihpCXADElfBh4m+8WQmZk1SZ7g/2JvNhwRjwLvrjJ9OXB4b7ZpZmZbL09//Pc1oxAzM2uOPFfurmfzL292IPs9/oaIGFJkYWZmVow8Z/y7V45LGoebaszM+q08v+p5k4i4jRy/4Tczs74pT1PPKRWjA4AOalx0ZWZmfV+eX/VU9su/EVgBjC2kGjMzK1yeNn73y29mth2pd+vFL9RZLyLiSwXUY2ZmBat3xr+hyrRdyfrP3xNw8JuZ9UP1br14VdewpN2BC4FzyO6edVWt9czMrG+r28YvaTjZHbPOILtpynsi4qVmFGZmZsWo18Z/JXAKWZ/474yIl2sta2Zm/Ue9C7j+HngrcAmwStK69FgvaV1zyjMzs22tXht/j6/qNTOzvs/hbmZWMg5+M7OScfCbmZWMg9/MrGQc/GZmJePgNzMrmcKCX9K+kuZJWirpCUkXpunDJd0raVl63qOoGszMbEtFnvFvBP4+Ig4B3g+cL+lQYDIwNyJGA3PTuJmZNUlhwR8RqyNicRpeDywF9ia7icv0tNh0YFxRNZiZ2Zaa0sYvqR14N/AgMDIiVkP24QDsVWOdSZIWSlrY2dnZjDLNzEqh8OCXtBvwA+AzEZG7j5+ImBYRHRHR0dbWVlyBZmYlU2jwSxpMFvo3RsQtafJzkkal+aOANUXWYGZmb1bkr3oEXAcsjYh/qZg1B5iQhicAs4uqwczMttTwZutb4UPAmcBjkh5J0z4PTAFmSpoIPA2cWmANZmbWTWHBHxEPAKox+5ii9mtmZvX5yl0zs5Jx8JuZlYyD38ysZBz8ZmYl4+A3MysZB7+ZWck4+M3MSsbBb2ZWMg5+M7OScfCbmZWMg9/MrGQc/GZmJePgNzMrGQe/mVnJOPjNzErGwW9mVjIOfjOzknHwm5mVTJH33DWzXmqffEerS8htxZQxrS7Beshn/GZmJVNY8Ev6jqQ1kh6vmDZc0r2SlqXnPYrav5mZVVfkGf8NwMe6TZsMzI2I0cDcNG5mZk1UWPBHxP3Ai90mjwWmp+HpwLii9m9mZtU1u41/ZESsBkjPezV5/2Zmpddnv9yVNEnSQkkLOzs7W12Omdl2o9nB/5ykUQDpeU2tBSNiWkR0RERHW1tb0wo0M9veNTv45wAT0vAEYHaT929mVnpF/pzzZuBnwNslrZQ0EZgCHCdpGXBcGjczsyYq7MrdiDi9xqxjitqnmZk11me/3DUzs2I4+M3MSsbBb2ZWMg5+M7OScfCbmZWMg9/MrGQc/GZmJePgNzMrGQe/mVnJOPjNzErGwW9mVjIOfjOzknHwm5mVjIPfzKxkHPxmZiXj4DczKxkHv5lZyTj4zcxKxsFvZlYyDn4zs5Jx8JuZlUxLgl/SxyQ9KekpSZNbUYOZWVk1PfglDQS+ARwPHAqcLunQZtdhZlZWrTjjPxx4KiKWR8RrwAxgbAvqMDMrpUEt2OfewDMV4yuB93VfSNIkYFIafVnSk02orSdGAM+3ugjLze9XQXR5YZv2e7b19q82sRXBryrTYosJEdOAacWX0zuSFkZER6vrsHz8fvU/fs+K04qmnpXAvhXj+wCrWlCHmVkptSL4FwCjJR0gaQfgNGBOC+owMyulpjf1RMRGSZ8C7gYGAt+JiCeaXcc20Geboawqv1/9j9+zgihii+Z1MzPbjvnKXTOzknHwm5mVjIO/gUbdS0jaUdL30/wHJbU3v0rrkuP9OltSp6RH0uNvWlGnZSR9R9IaSY/XmC9J/5rez0clvafZNW6PHPx15OxeYiLwUkQcDFwNFHc5i9XVg+5Avh8Rh6XHtU0t0rq7AfhYnfnHA6PTYxJwTRNq2u45+OvL073EWGB6Gp4FHCOp2kVqVjx3B9LPRMT9wIt1FhkLfDcyPweGSRrVnOq2Xw7++qp1L7F3rWUiYiPwe2DPplRn3eV5vwD+IjUbzJK0b5X51nfkfU+tBxz89eXpXiJXFxTWFHnei9uB9oh4F/BjNv+1Zn2T/38VwMFfX57uJf5nGUmDgKHU/9PVitPw/YqIFyLi1TT6beB/Nak26x138VIAB399ebqXmANMSMPjgZ+Er4prlYbvV7f24ZOBpU2sz3puDnBW+nXP+4HfR8TqVhfV37Wid85+o1b3EpL+EVgYEXOA64D/kPQU2Zn+aa2ruNxyvl8XSDoZ2Ej2fp3dsoINSTcDRwEjJK0EvggMBoiIqcCdwAnAU8AfgHNaU+n2xV02mJmVjJt6zMxKxsFvZlYyDn4zs5Jx8JuZlYyD38ysZBz81iuSQtJVFeMXSbp0G237Bknjt8W2GuznVElLJc3rNv0oST8scL9nS/r3NHyepLOK2te2JGlcjU7vrJ9x8FtvvQqcImlEqwuplHrozGsi8LcRcXRR9TQSEVMj4rut2n8PjSPr9dT6OQe/9dZGsnui/t/uM7qfsUt6OT0fJek+STMl/UrSFElnSHpI0mOSDqrYzLGS/istd2Jaf6CkKyUtSJ2snVux3XmSbgIeq1LP6Wn7j0u6PE37AvBhYKqkK6sc3xBJt0paImmqpAFpvWskLZT0hKTLKvYxJS37qKR/TtPaJP0g1btA0oeq1HappIvS8HxJl6fX41eS/ne9466yrb9K6z4i6VtpvU9KuqJimbMl/Vut5bveL0lfkfQLST+XNFLSB8mudL4yLX+QpAsqjnlGtZqsj4oIP/zo8QN4GRgCrCDrn+gi4NI07wZgfOWy6fkoYC0wCtgReBa4LM27EPhaxfp3kZ2YjCbrr2Unsv7YL0nL7AgsBA5I290AHFClzrcCTwNtZFeq/wQYl+bNBzqqrHMU8ApwINkVwPd2HQ8wPD0PTOu/CxgOPMnmCyKHpeebgA+n4f2ApWn4bODf0/ClwEUV9VyVhk8AfpyGqx53t5oPIeuAbnAa/yZwVjrupyqW+xHZB17V5dNwACel4Ssq9t39fV0F7Fh5zH70j4e7bLBei4h1kr4LXAD8MedqCyL1tSLp18A9afpjQGWTy8yI2AQsk7QceAfwUeBdFX9NDCX7YHgNeCgiflNlf+8F5kdEZ9rnjcARwG0N6nwoIpandW4mC8tZwMclTSL7EBlF1vSxhOyD4lpJdwBd3w8cCxyqzbdnGCJp9wb7vSU9LwLa03Ct46483mPIOpxbkPa3M7AmIjolLU/93CwD3g78FDi/2vJpW69VHMMi4LgatT4K3CjpNhq/ntaHOPhta30NWAxcXzFtI6kZUVmq7FAx79WK4U0V45t487/H7n2JBFkXvZ+OiLsrZ0g6iuyMv5re3hRni/1LOoDsL5v3RsRLkm4Adoqsj6DDycL3NOBTwEfIXoMPRMSbPhRV/z49Xa/HG2x+PaoedzcCpkfExVXmfR/4OPBL4NaIiPS+1Fr+9Uin8d3q6G4M2YfoycA/SPqTyO5JYX2c2/htq0TEi8BMsi9Ku6xgc3fHY0mdbvXQqZIGpHb/A8maUu4GPilpMICkt0natcF2HgSOlDQitWGfDtyXY/+HK+vlcwDwl8ADZE1bG4DfSxpJdltAJO0GDI2IO4HPAIelbdxD9iFAWu4weifPcc8FxkvaKy0zXNL+ad4tZF/Mnk72IdBo+VrWA7un5QcA+0bEPOCzwDBgt14enzWZz/htW7iKioAj6+d+tqSHyAKm1tl4PU+SBfRI4LyIeEXStWTNH4vTGWsnWaDVFBGrJV0MzCM7K74zImbn2P/PgCnAO4H7yc6UN0l6GHgCWE7WZAJZGM6WtFPaR9cX3hcA35D0KNn/tfuB8/IcfDcNjzsilki6BLgnhfLrZM05v01/nSwBDo2IhxotX6eOGcC3JV1A9pfNdZKGpmO+OiLW9uLYrAXcO6eZWcm4qcfMrGQc/GZmJePgNzMrGQe/mVnJOPjNzErGwW9mVjIOfjOzkvn/UgMkeJyaOu0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,mean_firing_rate_2714_FS=baseline_firing_rate(bin_nrTraceNoBlocks['2714_d1_s1'])\n",
    "print(mean_firing_rate_2714_FS)\n",
    "print(1.5*np.std(p))\n",
    "plot_prob_histogram(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6745180344220308\n",
      "0.22905237421158683\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,mean_firing_rate_8089_FS=baseline_firing_rate(bin_nrTraceNoBlocks['8089_d1_s1'])\n",
    "print(mean_firing_rate_8089_FS)\n",
    "print(1.5*np.std(p))\n",
    "plot_prob_histogram(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Day before extinction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeAliFilenames(mouse_numbers, mouse_days, mouse_sessions):\n",
    "    #Input lists of mouse numbers, days, and sessions to permute to give out directories and dictionary for filename\n",
    "    directories = []\n",
    "    filenames = {}\n",
    "    for m_num in mouse_numbers:\n",
    "        for m_day in mouse_days:\n",
    "            for m_sess in mouse_sessions:\n",
    "                if ((m_day != 2) | (m_sess != 2)): #Skip d2_s2.  Only do d5_s2\n",
    "                    mouse_dir = 'ali{}_d{}_s{}/'.format(m_num, m_day, m_sess)\n",
    "                    directories.append(mouse_dir)\n",
    "                    filenames[mouse_dir] = 'trace_matched_final_BinaryVideo.hdf5'\n",
    "                   \n",
    "    return directories, filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeRebeccaFilenames(mouse_numbers, mouse_days, mouse_sessions):\n",
    "    #Input lists of mouse numbers, days, and sessions to permute to give out directories and dictionary for filename\n",
    "    directories = []\n",
    "    filenames = {}\n",
    "    for m_num in mouse_numbers:\n",
    "        for m_day in mouse_days:\n",
    "            for m_sess in mouse_sessions:\n",
    "                mouse_dir = '{}/{}_d{}_s{}/'.format(m_num, m_num, m_day, m_sess)\n",
    "                directories.append(mouse_dir)\n",
    "                filenames[mouse_dir] = 'trace_final_BinaryVideo.hdf5'\n",
    "    return directories, filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeAliFilenames_modified(mouse_numbers, mouse_days, mouse_sessions):\n",
    "    #Input lists of mouse numbers, days, and sessions to permute to give out directories and dictionary for filename\n",
    "    directories = []\n",
    "    filenames = {}\n",
    "    for m_num in mouse_numbers:\n",
    "        for m_day in mouse_days:\n",
    "            for m_sess in mouse_sessions:\n",
    "                if ((m_day != 2) | (m_sess != 2)): #Skip d2_s2.  Only do d5_s2\n",
    "                    mouse_dir = 'ali{}_d{}_s{}/'.format(m_num, m_day, m_sess)\n",
    "                    directories.append(mouse_dir)\n",
    "                    filenames[mouse_dir] = 'binary_traces_final.h5'\n",
    "                   \n",
    "    return directories, filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeRebeccaFilenames_modified(mouse_numbers, mouse_days, mouse_sessions):\n",
    "    #Input lists of mouse numbers, days, and sessions to permute to give out directories and dictionary for filename\n",
    "    directories = []\n",
    "    filenames = {}\n",
    "    for m_num in mouse_numbers:\n",
    "        for m_day in mouse_days:\n",
    "            for m_sess in mouse_sessions:\n",
    "                mouse_dir = '{}/{}_d{}_s{}/'.format(m_num, m_num, m_day, m_sess)\n",
    "                directories.append(mouse_dir)\n",
    "                filenames[mouse_dir] = 'binary_traces_final.h5'\n",
    "    return directories, filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali24_d4_s1/binary_traces_final.h5', '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali24_d4_s2/binary_traces_final.h5', '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali25_d4_s1/binary_traces_final.h5', '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali25_d4_s2/binary_traces_final.h5', '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali26_d4_s1/binary_traces_final.h5', '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali26_d4_s2/binary_traces_final.h5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff-Rebecca/2982/2982_d8_s1/binary_traces_final.h5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff-Rebecca/4540/4540_d8_s1/binary_traces_final.h5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff_Rebecca2/2712/2712_d4_s1/binary_traces_final.h5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff_Rebecca2/2713/2713_d4_s1/binary_traces_final.h5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff_Rebecca2/2714/2714_d4_s1/binary_traces_final.h5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff_Rebecca2/8089/8089_d4_s1/binary_traces_final.h5']\n"
     ]
    }
   ],
   "source": [
    "#Initialize Input Values\n",
    "#Ali Mice\n",
    "aliBase = '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/'\n",
    "aliMice, aliDays, aliSessions = ([24,25,26], [4], [1,2])\n",
    "aliFolders, aliFilenames = makeAliFilenames_modified(aliMice, aliDays, aliSessions)\n",
    "bin_aliFiles = combineFilenames(aliBase, aliFolders, aliFilenames)\n",
    "#Rebecca Mice\n",
    "rBase = '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff-Rebecca/'\n",
    "rMice, rDays, rSessions = ([2982,4540], [8], [1])\n",
    "rFolders, rFilenames = makeRebeccaFilenames_modified(rMice, rDays, rSessions)\n",
    "bin_rFiles = combineFilenames(rBase, rFolders, rFilenames)\n",
    "#New Rebecca Mice\n",
    "nrBase = '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff_Rebecca2/'\n",
    "nrMice, nrDays, nrSessions = ([2712, 2713,2714,8089], [4], [1]) #Also have 2 as full imaging session.  Drop 2714 because of data quality\n",
    "nrFolders, nrFilenames = makeRebeccaFilenames_modified(nrMice, nrDays, nrSessions)\n",
    "bin_nrFiles = combineFilenames(nrBase, nrFolders, nrFilenames)\n",
    "#Combine All Filenames into one List\n",
    "bin_allFiles = bin_aliFiles + bin_rFiles + bin_nrFiles\n",
    "print(bin_allFiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "ali_binary_traces=loadbinaryTraces(bin_aliFiles, 'Ali')\n",
    "r_binary_traces=loadbinaryTraces(bin_rFiles, 'Rebecca')\n",
    "nr_binary_traces=loadbinaryTraces(bin_nrFiles, 'Rebecca')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali24_d4_s1/trace_matched_final_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali24_d4_s2/trace_matched_final_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali25_d4_s1/trace_matched_final_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali25_d4_s2/trace_matched_final_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali26_d4_s1/trace_matched_final_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/ali26_d4_s2/trace_matched_final_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff-Rebecca/2982/2982_d8_s1/trace_final_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff-Rebecca/4540/4540_d8_s1/trace_final_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff_Rebecca2/2712/2712_d4_s1/trace_final_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff_Rebecca2/2713/2713_d4_s1/trace_final_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff_Rebecca2/2714/2714_d4_s1/trace_final_BinaryVideo.hdf5', '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff_Rebecca2/8089/8089_d4_s1/trace_final_BinaryVideo.hdf5']\n"
     ]
    }
   ],
   "source": [
    "#Initialize Input Values\n",
    "#Ali Mice\n",
    "aliBase = '/home/hanlabadmins/eng_handata/Kyle_Hansen/AliEyeBlink/'\n",
    "aliMice, aliDays, aliSessions = ([24,25,26], [4], [1,2])\n",
    "aliFolders, aliFilenames = makeAliFilenames(aliMice, aliDays, aliSessions)\n",
    "aliFiles = combineFilenames(aliBase, aliFolders, aliFilenames)\n",
    "#Rebecca Mice\n",
    "rBase = '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff-Rebecca/'\n",
    "\n",
    "rMice, rDays, rSessions = ([2982,4540], [8], [1])\n",
    "rFolders, rFilenames = makeRebeccaFilenames(rMice, rDays, rSessions)\n",
    "rFiles = combineFilenames(rBase, rFolders, rFilenames)\n",
    "#New Rebecca Mic_\n",
    "nrBase = '/home/hanlabadmins/eng_handata/eng_research_handata2/Kyle_Hansen/TonePuff_Rebecca2/'\n",
    "nrMice, nrDays, nrSessions = ([2712, 2713,2714,8089], [4], [1]) #Also have 2 as full imaging session.  Drop 2714 because of data quality\n",
    "nrFolders, nrFilenames = makeRebeccaFilenames(nrMice, nrDays, nrSessions)\n",
    "nrFiles = combineFilenames(nrBase, nrFolders, nrFilenames)\n",
    "#Combine All Filenames into one List\n",
    "allFiles = aliFiles + rFiles + nrFiles\n",
    "print(allFiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "aliSounds = loadFiles(aliFiles, 'binSounds', whichData='Ali')\n",
    "rSounds = loadFiles(rFiles, 'binSounds', whichData='Rebecca')\n",
    "nrSounds = loadFiles(nrFiles, 'binSounds', whichData='Rebecca', fixedSize=39500)\n",
    "sound_traces = pd.concat([aliSounds,rSounds,nrSounds], axis=1)\n",
    "#sound_traces = pd.concat([rSounds,nrSounds], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ali Binary Blocks\n",
      "ali24_d4_s1\n",
      "ali24_d4_s2\n",
      "ali25_d4_s1\n",
      "ali25_d4_s2\n",
      "ali26_d4_s1\n",
      "ali26_d4_s2\n",
      "Rebecca Binary Blocks\n",
      "2982_d8_s1\n",
      "4540_d8_s1\n",
      "New Rebecca Binary Blocks\n",
      "2712_d4_s1\n",
      "2713_d4_s1\n",
      "2714_d4_s1\n",
      "8089_d4_s1\n"
     ]
    }
   ],
   "source": [
    "print('Ali Binary Blocks')\n",
    "bin_aliTraceBlocks, bin_aliTraceNoBlocks = makeTraceTrialBlocks(ali_binary_traces, aliSounds) \n",
    "print('Rebecca Binary Blocks')\n",
    "bin_rTraceBlocks, bin_rTraceNoBlocks = makeTraceTrialBlocks(r_binary_traces, rSounds) \n",
    "print('New Rebecca Binary Blocks')\n",
    "bin_nrTraceBlocks, bin_nrTraceNoBlocks = makeTraceTrialBlocks(nr_binary_traces, nrSounds) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "113833.0"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_events_lateday=np.sum(np.sum(bin_aliTraceNoBlocks['ali24_d4_s1']))+np.sum(np.sum(bin_aliTraceNoBlocks['ali25_d4_s1']))+np.sum(np.sum(bin_aliTraceNoBlocks['ali26_d4_s1']))+np.sum(np.sum(bin_aliTraceNoBlocks['ali24_d4_s2']))+np.sum(np.sum(bin_aliTraceNoBlocks['ali25_d4_s2']))+np.sum(np.sum(bin_aliTraceNoBlocks['ali26_d4_s2']))+np.sum(np.sum(bin_rTraceNoBlocks['2982_d8_s1']))+np.sum(np.sum(bin_rTraceNoBlocks['4540_d8_s1']))+np.sum(np.sum(bin_nrTraceNoBlocks['2712_d4_s1']))+np.sum(np.sum(bin_nrTraceNoBlocks['2713_d4_s1']))+np.sum(np.sum(bin_nrTraceNoBlocks['2714_d4_s1']))+np.sum(np.sum(bin_nrTraceNoBlocks['8089_d4_s1']))\n",
    "n_events_lateday"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baseline firing rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.46340540171724665\n",
      "0.3443132904875368\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,mean_firing_rate_ali24_DB=baseline_firing_rate(bin_aliTraceNoBlocks['ali24_d4_s1'])\n",
    "print(mean_firing_rate_ali24_DB)\n",
    "print(1.5*np.std(p))\n",
    "plot_prob_histogram(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3552232994435681\n",
      "0.3280642139323471\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,mean_firing_rate_ali25_DB=baseline_firing_rate(bin_aliTraceNoBlocks['ali25_d4_s1'])\n",
    "print(mean_firing_rate_ali25_DB)\n",
    "print(1.5*np.std(p))\n",
    "plot_prob_histogram(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5888124895808047\n",
      "0.3504081879973942\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAb4ElEQVR4nO3deZwdVZn/8c+XEJA9YJoYgdCAQYXRiU4bxQWCoMMiBPkBJsPIIjMBBcFRZiYwyCLDyCLDuIJhMTBCAsMaBAVEFnFkSQBDICAhRAjJkGZN2Eny/P6o05Wiud1d6e57q5fv+/W6r1t16lSdpypwn65TVacUEZiZmQGsUXUAZmbWdzgpmJlZzknBzMxyTgpmZpZzUjAzs5yTgpmZ5ZwUbNCQdIikuwrzr0jaug7tjJB0p6Rlks6WdLykCzqpPyrFMqS3YzFbXWtWHYANTpIWACOAFcDbwP8CR0TE042KISLWr9OmJwHPARtGiQeBIuIpoF6xrBZJzcCTwNCIWF5tNFYFnylYlfZKP8wjgWeBH1ccT2/ZEnikTEKQ1NA/zHw2Yl1xUrDKRcQbwJXAdm1lkvaU9ICkpZKelnRyYdl7JP1S0vOSXpJ0n6QRadlGki6UtFjSM5L+vaMfQkkh6QNpeqqkn0q6IXX73CNpm0LdD0m6RdILkh6TdEAH25wKHAz8S+oS2lXSyZJ+mZY3p3YPk/QU8LtC2Zqpzu2STpX0hxTLzZKGF9o4SNJf0v5/V9ICSbt2FI+kcyXdKOlVYOfOji1wZ/p+KcW/Q9rO1yTNlfSipJskbVmrPev/nBSscpLWBb4C3F0ofhU4CBgG7Al8XdI+adnBwEbAFsB7gSOA19Oyi4HlwAeAjwFfBP6hZCgTgVOAjYF5wGkpvvWAW4DLgE1TvZ9J2r79BiLiEOBS4MyIWD8ifttBWzsBHwb+toPlfwccmtpbCzg2xbId8DPgQLIzrI2AzbrYr79L+7IBcBedH9sd0/ewFP8f07LjgX2BJuD3wLQu2rR+yknBqnStpJeApcAXgLPaFkTE7RHxUESsjIjZZD9CO6XFb5Mlgw9ExIqImBURS9PZwu7AtyLi1YhYApwDTCgZz9URcW/qS78UGJPKvwQsiIhfRMTyiLgfuArYrwf7fnKK8fUOlv8iIv6cll9RiGU/4PqIuCsi3gJOBLrqprouIv6QjuUbXRzbWg4Hvh8Rc9Ox+Q9gjM8WBiYnBavSPhExDFgbOAq4Q9L7ACR9UtJtklolvUx2NtDWhfLfwE3AdEmLJJ0paShZX/5QYHHqVnoJ+DnZX9tl/F9h+jVWXfzdEvhk2zbTdg8E3tfdHQe6uqDeUSzvL64bEa8Bz69OW10c21q2BH5Y2PcXANH1GYr1Q04KVrn01/7VZHcifTYVXwbMALaIiI2A88h+iIiItyPilIjYDvg02V/yB5H9+L0JDI+IYemzYUS8q5tnNT0N3FHYZlvXytd7sM3uDk+8GNi8bUbSOmRnTavTVofHtoO4ngYOb7f/60TE/3ZrD6xPc1Kwyikznqwvf24q3gB4ISLekDSWrF+8rf7Okj6SLiAvJetOWhERi4GbgbMlbShpDUnbSOqsa6SMXwHbSvqqpKHp8wlJH+7hdrvjSmAvSZ+WtBbZNRB1sU57HR5boBVYCRSf3zgPOK7tGkq6mL9/t/fA+jQnBavS9ZJeIfthPw04OCIeTsu+AXxP0jKyfvMrCuu9j+zHcSlZErkD+GVadhDZhdlHgBdTvZE9CTIilpFdsJ4ALCLr2jmDrNurodLx+SYwneysYRmwhOwMqawOj23qjjoN+EPqLvpURFxDtr/TJS0F5pBdu7EBSH7Jjln/JWl94CVgdEQ8WXU81v/5TMGsn5G0l6R1062yPwAeAhZUG5UNFE4KZv3PeLJurEXAaGBCmaenzcpw95GZmeV8pmBmZrl+PUrq8OHDo7m5ueowzMz6lVmzZj0XEU21lvXrpNDc3MzMmTOrDsPMrF+R9JeOlrn7yMzMck4KZmaWc1IwM7Ock4KZmeWcFMzMLOekYGZmOScFMzPLOSmYmVnOScHMzHL9+olmM4DmyTdUHUJNC07fs+oQzFabzxTMzCznpGBmZjknBTMzyzkpmJlZzknBzMxyTgpmZpZzUjAzs5yTgpmZ5ZwUzMws56RgZmY5JwUzM8s5KZiZWa5uSUHSRZKWSJpTKLtc0oPps0DSg6m8WdLrhWXn1SsuMzPrWD1HSZ0K/AS4pK0gIr7SNi3pbODlQv0nImJMHeMxM7Mu1C0pRMSdkpprLZMk4ADg8/Vq38zMVl9V1xQ+BzwbEY8XyraS9ICkOyR9rqMVJU2SNFPSzNbW1vpHamY2iFSVFCYC0wrzi4FREfEx4NvAZZI2rLViREyJiJaIaGlqampAqGZmg0fDk4KkNYF9gcvbyiLizYh4Pk3PAp4Atm10bGZmg10VZwq7Ao9GxMK2AklNkoak6a2B0cD8CmIzMxvU6nlL6jTgj8AHJS2UdFhaNIF3dh0B7AjMlvQn4ErgiIh4oV6xmZlZbfW8+2hiB+WH1Ci7CriqXrGYmVk5fqLZzMxyTgpmZpZzUjAzs5yTgpmZ5ZwUzMws56RgZmY5JwUzM8s5KZiZWc5JwczMck4KZmaWc1IwM7Ock4KZmeWcFMzMLOekYGZmOScFMzPLOSmYmVnOScHMzHJ1e/OaDTzNk2+oOgQzq7N6vqP5IklLJM0plJ0s6RlJD6bPHoVlx0maJ+kxSX9br7jMzKxj9ew+mgrsVqP8nIgYkz43AkjaDpgAbJ/W+ZmkIXWMzczMaqhbUoiIO4EXSlYfD0yPiDcj4klgHjC2XrGZmVltVVxoPkrS7NS9tHEq2wx4ulBnYSp7F0mTJM2UNLO1tbXesZqZDSqNTgrnAtsAY4DFwNmpXDXqRq0NRMSUiGiJiJampqb6RGlmNkg1NClExLMRsSIiVgLns6qLaCGwRaHq5sCiRsZmZmYNTgqSRhZmvwy03Zk0A5ggaW1JWwGjgXsbGZuZmdXxOQVJ04BxwHBJC4GTgHGSxpB1DS0ADgeIiIclXQE8AiwHjoyIFfWKzczMaqtbUoiIiTWKL+yk/mnAafWKx8zMuuZhLszMLOekYGZmOScFMzPLOSmYmVnOScHMzHJOCmZmlnNSMDOznJOCmZnlnBTMzCznpGBmZjknBTMzyzkpmJlZrsukIOlMSRtKGirpVknPSfr7RgRnZmaNVeZM4YsRsRT4EtnLcLYF/rmuUZmZWSXKJIWh6XsPYFpEvFDHeMzMrEJl3qdwvaRHgdeBb0hqAt6ob1hmZlaFLs8UImIysAPQEhFvA68C4+sdmJmZNV6HZwqS9q1RVpy9uh4BmZlZdTrrPtqrk2VBF0lB0kVkF6eXRMRfpbKz0nbfAp4ADo2IlyQ1A3OBx9Lqd0fEEWV2wMzMek+HSSEiDu3htqcCPwEuKZTdAhwXEcslnQEcB/xrWvZERIzpYZtmZtYDnXUffbuzFSPiP7tYfmc6AyiW3VyYvRvYr+sQzcysUTrrPtqgzm1/Dbi8ML+VpAeApcAJEfH7OrdvZmbtdNZ9dEq9GpX0b8By4NJUtBgYFRHPS/ob4FpJ26eH5tqvOwmYBDBq1Kh6hWhmNiiVGeZi2zS8xZw0/1FJJ3S3QUkHk12APjAiAiAi3oyI59P0LLKL0NvWWj8ipkRES0S0NDU1dTcMMzOrocwTzeeTXRB+GyAiZgMTutOYpN3ILizvHRGvFcqbJA1J01sDo4H53WnDzMy6r8wTzetGxL3tnlFY3tVKkqYB44DhkhYCJ5Ell7WBW9L22m493RH4nqTlwArgCA+nYWbWeGWSwnOStiF7NgFJ+5FdA+hUREysUXxhB3WvAq4qEYuZmdVRmaRwJDAF+JCkZ4AnAQ+dbWY2AHWZFCJiPrCrpPWANSJiWf3DMjOzKpS5++g/JA2LiFcjYpmkjSX9eyOCMzOzxipz99HuEfFS20xEvEj2bgUzMxtgyiSFIZLWbpuRtA7ZHURmZjbAlLnQ/EvgVkm/ILsD6WvAxXWNyszMKlHmQvOZkmYDuwICTo2Im+oemZmZNVyZMwUi4jfAb+oci5mZVazMNQUzMxsknBTMzCzXYVKQdGv6PqNx4ZiZWZU6u6YwUtJOwN6SppNdZM5FxP11jczMzBqus6RwIjAZ2Bxo/+rNAD5fr6DMzKwanb157UrgSknfjYhTGxiTmZlVpMxzCqdK2pvsnQcAt0fEr+ob1uDWPPmGqkMws0GqzIB43weOAR5Jn2NSmZmZDTBlHl7bExgTESsBJF0MPED2FjUzMxtAyj6nMKwwvVE9AjEzs+qVOVP4PvCApNvIbkvdEZ8lmJkNSF2eKUTENOBTwNXps0NETC+zcUkXSVoiaU6hbBNJt0h6PH1vnMol6UeS5kmaLenj3dslMzPrrlLdRxGxOCJmRMR1EfF/q7H9qcBu7comA7dGxGjg1jQPsDswOn0mAeeuRjtmZtYL6jr2UUTcCbzQrng8q97HcDGwT6H8ksjcDQyTNLKe8ZmZ2TtVMSDeiIhYDNkZCLBpKt8MeLpQb2EqewdJkyTNlDSztbW17sGamQ0mnSYFSWsUrwfUmWqUxbsKIqZEREtEtDQ1NTUgLDOzwaPTpJCeTfiTpFG92Oazbd1C6XtJKl8IbFGotzmwqBfbNTOzLpTpPhoJPCzpVkkz2j49aHMGcHCaPhi4rlB+ULoL6VPAy23dTGZm1hhlnlM4pbsblzQNGAcMl7QQOAk4HbhC0mHAU8D+qfqNwB7APOA14NDutmtmZt1TZkC8OyRtCYyOiN9KWhcYUmbjETGxg0W71KgbwJFltmtmZvVRZkC8fwSuBH6eijYDrq1nUGZmVo0y3UdHAmOBewAi4nFJm3a+ipn11SHQF5y+Z9UhWB9W5kLzmxHxVtuMpDWpcauomZn1f2WSwh2SjgfWkfQF4H+A6+sblpmZVaFMUpgMtAIPAYeT3SV0Qj2DMjOzapS5+2hlerHOPWTdRo+lO4XMzGyA6TIpSNoTOA94gmwoiq0kHR4Rv653cGZm1lhl7j46G9g5IuYBSNoGuAFwUjAzG2DKXFNY0pYQkvmsGq/IzMwGkA7PFCTtmyYflnQjcAXZNYX9gfsaEJuZmTVYZ91HexWmnwV2StOtwMZ1i8jMzCrTYVKICA9IZ2Y2yJS5+2gr4JtAc7F+ROxdv7DMzKwKZe4+uha4kOwp5pX1DcfMzKpUJim8ERE/qnskZmZWuTJJ4YeSTgJuBt5sK4yI++sWlZmZVaJMUvgI8FXg86zqPoo0b2ZmA0iZpPBlYOvi8NlmZjYwlUkKfwKG0UtPMUv6IHB5oWhr4MTUxj+SPQcBcHxE3NgbbZqZWTllksII4FFJ9/HOawrduiU1Ih4DxgBIGgI8A1wDHAqcExE/6M52zcys58okhZPq2P4uwBMR8RdJdWzGzMzKKPM+hTvq2P4EYFph/ihJBwEzge9ExIvtV5A0CZgEMGrUqDqGZmY2+HQ5SqqkZZKWps8bklZIWtrThiWtBexN9npPgHOBbci6lhaTDdn9LhExJSJaIqKlqampp2GYmVlBmTOFDYrzkvYBxvZC27sD90fEs6mdZwttnA/8qhfaMDOz1VDmfQrvEBHX0jvPKEyk0HUkaWRh2ZeBOb3QhpmZrYYyA+LtW5hdA2ghe3it2yStC3wBOLxQfKakMWnbC9otMzOzBihz91HxvQrLyX6wx/ek0Yh4DXhvu7Kv9mSbZmbWc2WuKfi9CmZmg0Rnr+M8sZP1IiJOrUM8ZmZWoc7OFF6tUbYecBhZ14+TgpnZANPZ6zjz5wQkbQAcQzYUxXQ6eIbAzMz6t06vKUjaBPg2cCBwMfDxWk8Zm5nZwNDZNYWzgH2BKcBHIuKVhkVlZmaV6Ozhte8A7wdOABYVhrpY1hvDXJiZWd/T2TWF1X7a2czM+jf/8JuZWc5JwczMck4KZmaWc1IwM7Ock4KZmeWcFMzMLOekYGZmOScFMzPLOSmYmVnOScHMzHJlXsdZF5IWAMuAFcDyiGhJo7JeDjSTvfbzAI/KambWOFWfKewcEWMioiXNTwZujYjRwK1p3szMGqTqpNDeeLL3NpC+96kwFjOzQafKpBDAzZJmSZqUykZExGKA9L1p+5UkTZI0U9LM1tbWBoZrZjbwVXZNAfhMRCyStClwi6RHy6wUEVPIXvxDS0tL1DNAM7PBprIzhYhYlL6XANcAY4FnJY0ESN9LqorPzGwwqiQpSFpP0gZt08AXgTnADODgVO1g4Loq4jMzG6yq6j4aAVwjqS2GyyLiN5LuA66QdBjwFLB/RfGZmQ1KlSSFiJgP/HWN8ueBXRofkZmZQd+7JdXMzCrkpGBmZjknBTMzyzkpmJlZzknBzMxyTgpmZpZzUjAzs5yTgpmZ5ZwUzMws56RgZmY5JwUzM8s5KZiZWc5JwczMck4KZmaWc1IwM7Ock4KZmeWcFMzMLOekYGZmuYYnBUlbSLpN0lxJD0s6JpWfLOkZSQ+mzx6Njs3MbLCr4h3Ny4HvRMT9kjYAZkm6JS07JyJ+0KhAmiff0KimzMz6hYYnhYhYDCxO08skzQU2a3QcZmb2bpVeU5DUDHwMuCcVHSVptqSLJG1cWWBmZoNUZUlB0vrAVcC3ImIpcC6wDTCG7Ezi7A7WmyRppqSZra2tDYvXzGwwqCQpSBpKlhAujYirASLi2YhYERErgfOBsbXWjYgpEdESES1NTU2NC9rMbBCo4u4jARcCcyPiPwvlIwvVvgzMaXRsZmaDXRV3H30G+CrwkKQHU9nxwERJY4AAFgCHVxCbmdmgVsXdR3cBqrHoxkbHYmZm7+Qnms3MLOekYGZmuSquKZhZhfrqk/wLTt+z6hAMnymYmVmBk4KZmeWcFMzMLOekYGZmOScFMzPLOSmYmVnOScHMzHJOCmZmlnNSMDOznJOCmZnlnBTMzCznpGBmZjknBTMzyzkpmJlZzknBzMxyTgpmZpbrcy/ZkbQb8ENgCHBBRJxecUhm1gB++U/f0KfOFCQNAX4K7A5sB0yUtF21UZmZDR59KikAY4F5ETE/It4CpgPjK47JzGzQ6GvdR5sBTxfmFwKfLFaQNAmYlGZfkfRYg2LrynDguaqDGKR87Ksz4I+9zqg6gg715Nhv2dGCvpYUVKMs3jETMQWY0phwypM0MyJaqo5jMPKxr46PfXXqdez7WvfRQmCLwvzmwKKKYjEzG3T6WlK4DxgtaStJawETgBkVx2RmNmj0qe6jiFgu6SjgJrJbUi+KiIcrDqusPtelNYj42FfHx746dTn2ioiua5mZ2aDQ17qPzMysQk4KZmaWc1JYDZJ2k/SYpHmSJtdYvraky9PyeyQ1Nz7KganEsT9EUqukB9PnH6qIcyCSdJGkJZLmdLBckn6U/m1mS/p4o2McqEoc+3GSXi78d39iT9t0Uiip5BAchwEvRsQHgHOAvvvYSz+yGsOfXB4RY9LngoYGObBNBXbrZPnuwOj0mQSc24CYBoupdH7sAX5f+O/+ez1t0EmhvDJDcIwHLk7TVwK7SKr1QJ6tHg9/UqGIuBN4oZMq44FLInM3MEzSyMZEN7CVOPa9zkmhvFpDcGzWUZ2IWA68DLy3IdENbGWOPcD/S90XV0raosZyq4+y/z5WHztI+pOkX0vavqcbc1Ior8shOErWsdVX5rheDzRHxEeB37LqjM3qz//dV+d+YMuI+Gvgx8C1Pd2gk0J5ZYbgyOtIWhPYiAaf+g1QXR77iHg+It5Ms+cDf9Og2MzD01QmIpZGxCtp+kZgqKThPdmmk0J5ZYbgmAEcnKb3A34XfjqwN3R57Nv1Ye8NzG1gfIPdDOCgdBfSp4CXI2Jx1UENBpLe13bdUtJYst/053uyzT41zEVf1tEQHJK+B8yMiBnAhcB/S5pHdoYwobqIB46Sx/5oSXsDy8mO/SGVBTzASJoGjAOGS1oInAQMBYiI84AbgT2AecBrwKHVRDrwlDj2+wFfl7QceB2Y0NM/RD3MhZmZ5dx9ZGZmOScFMzPLOSmYmVnOScHMzHJOCmZmlnNSsF4lKSSdXZg/VtLJvbTtqZL2641tddHO/pLmSrqtXfk4Sb+qY7uHSPpJmj5C0kH1aqs3SdqngwEKrR9yUrDe9iawb0+fquxtaaTVsg4DvhERO9crnq5ExHkRcUlV7a+mfchGr7UBwEnBettysnfH/lP7Be3/0pf0SvoeJ+kOSVdI+rOk0yUdKOleSQ9J2qawmV0l/T7V+1Jaf4iksyTdlwbEO7yw3dskXQY8VCOeiWn7cySdkcpOBD4LnCfprBr7t6GkayQ9Iuk8SWuk9c6VNFPSw5JOKbRxeqo7W9IPUlmTpKtSvPdJ+kyN2E6WdGyavl3SGel4/FnS5zrb7xrb+vu07oOSfp7W+7qkMwt1DpH0447qt/17STotDb52t6QRkj5N9gT5Wan+NpKOLuzz9FoxWR8WEf7402sf4BVgQ2AB2dhPxwInp2VTgf2KddP3OOAlYCSwNvAMcEpadgzwX4X1f0P2x8xosjF33kM2hv8Jqc7awExgq7TdV4GtasT5fuApoInsyf7fAfukZbcDLTXWGQe8AWxN9mT1LW37A2ySvoek9T8KbAI8xqqHRIel78uAz6bpUcDcNH0I8JM0fTJwbCGes9P0HsBv03TN/W4X84fJBgscmuZ/BhyU9nteod6vyZJhzfppOoC90vSZhbbb/7suAtYu7rM//efjYS6s10XEUkmXAEeTPXpfxn2RxsuR9ARwcyp/CCh241wRESuBxyXNBz4EfBH4aOEsZCOypPEWcG9EPFmjvU8At0dEa2rzUmBHuh5l8t6ImJ/WmUb2Q3olcICkSWQJZiRZd8ojZEnkAkk3AG3XI3YFttOqV21sKGmDLtq9On3PAprTdEf7XdzfXcgGB7wvtbcOsCQiWiXNVzZW0ePAB4E/AEfWqp+29VZhH2YBX+gg1tnApZKupRdG7bTGclKwevkvsmF9f1EoW07qslT2i7NWYdmbhemVhfmVvPO/0/bjsgTZ0M3fjIibigskjSM7U6iluy8/elf7krYiOyP6RES8KGkq8J7IxmwaS/bDPAE4Cvg82THYISLekTDV+fuY2o7HClYdj5r73Y6AiyPiuBrLLgcOAB4FromISP8uHdV/O9Kf/+3iaG9PsgS7N/BdSdtH9n4R6wd8TcHqIiJeAK4gu2jbZgGrhrQeTxrYazXtL2mNdJ1ha7LumZvIBgUbCiBpW0nrdbGde4CdJA1PfeYTgTtKtD9W2WitawBfAe4i6y57FXhZ0giy11MiaX1go8iGNP4WMCZt42ayBEGqN4buKbPftwL7Sdo01dlE0pZp2dVkF4knkiWIrup3ZBmwQaq/BrBFRNwG/AswDFi/m/tnFfCZgtXT2RR+/Mjec3CdpHvJfnw6+iu+M4+R/XiPAI6IiDckXUDWpXJ/+ku3lezHrkMRsVjSccBtZH9N3xgR15Vo/4/A6cBHgDvJ/sJeKekB4GFgPlk3DGQ/lNdJek9qo+3i+9HATyXNJvt/8E7giDI7306X+x0Rj0g6Abg5/WC/TdZF9Jd0VvMIsF1E3NtV/U7imA6cL+losjOiCyVtlPb5nIh4qRv7ZhXxKKlmZpZz95GZmeWcFMzMLOekYGZmOScFMzPLOSmYmVnOScHMzHJOCmZmlvv/YuzSMoi5SWEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,mean_firing_rate_ali26_DB=baseline_firing_rate(bin_aliTraceNoBlocks['ali26_d4_s1'])\n",
    "print(mean_firing_rate_ali26_DB)\n",
    "print(1.5*np.std(p))\n",
    "plot_prob_histogram(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6111495116453795\n",
      "0.48890838764187233\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,mean_firing_rate_2982_DB=baseline_firing_rate(bin_rTraceNoBlocks['2982_d8_s1'])\n",
    "print(mean_firing_rate_2982_DB)\n",
    "print(1.5*np.std(p))\n",
    "plot_prob_histogram(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5296147919876734\n",
      "0.31050783077817146\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,mean_firing_rate_4540_DB=baseline_firing_rate(bin_rTraceNoBlocks['4540_d8_s1'])\n",
    "print(mean_firing_rate_4540_DB)\n",
    "print(1.5*np.std(p))\n",
    "plot_prob_histogram(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7900748663101604\n",
      "0.36902009216227943\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,mean_firing_rate_2712_DB=baseline_firing_rate(bin_nrTraceNoBlocks['2712_d4_s1'])\n",
    "print(mean_firing_rate_2712_DB)\n",
    "print(1.5*np.std(p))\n",
    "plot_prob_histogram(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7033981191222571\n",
      "0.3391274959301994\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,mean_firing_rate_2713_DB=baseline_firing_rate(bin_nrTraceNoBlocks['2713_d4_s1'])\n",
    "print(mean_firing_rate_2713_DB)\n",
    "print(1.5*np.std(p))\n",
    "plot_prob_histogram(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.44217008797653956\n",
      "0.2875195934527317\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,mean_firing_rate_2714_DB=baseline_firing_rate(bin_nrTraceNoBlocks['2714_d4_s1'])\n",
    "print(mean_firing_rate_2714_DB)\n",
    "print(1.5*np.std(p))\n",
    "plot_prob_histogram(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6967511177347244\n",
      "0.30947000653062384\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEWCAYAAABhffzLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAYxUlEQVR4nO3de/xu9Zz38de72hU6bGlLKu0kQx5M5t5yvAlxIx3u7phiVKa5cz7cxk15OBRjVKbBGCSiDEp3UlFU0gGD2oWOUhJSo01lV5S2Pvcfa/3sq93vsPbhun6H9Xo+HtfjWqdrrc9a7d7X+n3Xtb4rVYUkqT/Wmu4CJEmjZfBLUs8Y/JLUMwa/JPWMwS9JPWPwS1LPGPyac5Lsn+Q7A+N3JHnkELazWZILktye5Mgk70jy6UmWf0Rby9pruhZpZawz3QVobktyPbAZ8GfgHuA/gVdX1a9GVUNVbTCkVR8I/BbYqDrcEFNVvwSGVctKSbIQ+Dkwr6qWTW81GjXP+DUKu7bhuznwG+Cj01zPmrI1cGWX0E8y0pMs/6rQZAx+jUxV3QWcBGw/Ni3JLkl+mGRpkl8lOWRg3vpJPp/kd0luS3JRks3aeRsnOSbJTUl+neSfJgq7JJXkUe3wsUk+luT0tonmB0m2HVj2MUnOTnJLkquTvHSCdR4L7Ae8rW2+2TnJIUk+385f2G73gCS/BL41MG2ddpnzkrwvyXfbWs5KsunANvZN8ot2/9+V5PokO09UT5JPJDkjyZ3Asyc7tsAF7fttbf1Pbdfz90muSnJrkjOTbD3e9jS7GfwamSQPBP4W+P7A5DuBfYH5wC7Aa5Ls0c7bD9gY2Ap4CPBq4I/tvOOAZcCjgCcCzwf+oWMp+wCHAg8GrgXe39b3IOBs4IvAQ9vlPp7kcSuuoKr2B74AHFFVG1TVNyfY1rOAxwL/Y4L5LwNe2W5vXeCtbS3bAx8HXk7zl9LGwBZT7NfL2n3ZEPgOkx/bZ7bv89v6v9fOewewJ7AA+DZw/BTb1Cxk8GsUTklyG7AUeB7wwbEZVXVeVV1WVfdW1aU0QfOsdvY9NIH/qKr6c1VdXFVL27P+FwJvrqo7q+pm4EPA3h3rObmqLmzbtr8A7NBOfzFwfVV9tqqWVdUlwJeBvVZj3w9pa/zjBPM/W1U/beefOFDLXsBXq+o7VfUn4N3AVE1Kp1bVd9tjedcUx3Y8rwI+UFVXtcfmn4EdPOufewx+jcIeVTUfWA94PXB+kocBJHlyknOTLEnye5qz+rHmjv8AzgROSHJjkiOSzKNpW58H3NQ2Ad0GfJLmrLmL/xoY/gPLL7huDTx5bJ3tel8OPGxVdxyY6iL2RLU8fPCzVfUH4Hcrs60pju14tgY+MrDvtwBh6r80NMsY/BqZ9qz9ZJpf+DyjnfxF4DRgq6raGDiKJmyoqnuq6tCq2h54Gs0Z+b40AXc3sGlVzW9fG1XV/ZpkVtKvgPMH1jnWDPKa1VjnqnZ/exOw5dhIkgfQ/PWzMtua8NhOUNevgFetsP8PqKr/XKU90Ixl8Gtk0tidpm39qnbyhsAtVXVXkh1p2qnHln92kse3F22X0jT9/LmqbgLOAo5MslGStZJsm2SyZowuvgY8OskrksxrX09K8tjVXO+qOAnYNcnTkqxLc00iU3xmRRMeW2AJcC8weH/DUcDBY9c02gvoL1nlPdCMZfBrFL6a5A6a8H4/sF9VXdHOey3w3iS307RjnzjwuYfRBOBSmi+K84HPt/P2pbkYeiVwa7vc5qtTZFXdTnOReG/gRppmmMNpmqhGqj0+bwBOoDn7vx24meYvna4mPLZt09H7ge+2TTtPqaqv0OzvCUmWApfTXEvRHBMfxCLNfEk2AG4Dtquqn093PZrdPOOXZqgkuyZ5YPsz038BLgOun96qNBcY/NLMtTtNk9ONwHbA3l3uEpamYlOPJPWMZ/yS1DOzonfOTTfdtBYuXDjdZUjSrHLxxRf/tqoWrDh9VgT/woULWbx48XSXIUmzSpJfjDfdph5J6hmDX5J6xuCXpJ4x+CWpZwx+SeoZg1+Sesbgl6SeMfglqWcMfknqmVlx567mloUHnT7dJQBw/WG7THcJ0rTwjF+Sesbgl6SeMfglqWcMfknqGYNfknrG4JeknjH4JalnDH5J6hmDX5J6xuCXpJ4x+CWpZwx+SeoZg1+Sesbgl6SeMfglqWcMfknqGYNfknrG4JeknjH4JalnDH5J6hmDX5J6xuCXpJ4x+CWpZ4Ye/EnWTvLDJF9rx7dJ8oMk1yT5UpJ1h12DJGm5UZzxvwm4amD8cOBDVbUdcCtwwAhqkCS1hhr8SbYEdgE+3Y4HeA5wUrvIccAew6xBknRfwz7j/zDwNuDedvwhwG1VtawdvwHYYrwPJjkwyeIki5csWTLkMiWpP4YW/EleDNxcVRcPTh5n0Rrv81V1dFUtqqpFCxYsGEqNktRH6wxx3U8HdkvyImB9YCOavwDmJ1mnPevfErhxiDVIklYwtDP+qjq4qrasqoXA3sC3qurlwLnAXu1i+wGnDqsGSdL9Tcfv+N8OvCXJtTRt/sdMQw2S1FvDbOr5i6o6DzivHb4O2HEU25Uk3Z937kpSzxj8ktQzBr8k9YzBL0k9Y/BLUs8Y/JLUMwa/JPWMwS9JPWPwS1LPGPyS1DMGvyT1jMEvST1j8EtSzxj8ktQzBr8k9YzBL0k9Y/BLUs8Y/JLUMwa/JPWMwS9JPWPwS1LPGPyS1DMGvyT1jMEvST1j8EtSzxj8ktQzBr8k9YzBL0k9M2XwJzkiyUZJ5iU5J8lvk/zdKIqTJK15Xc74n19VS4EXAzcAjwb+71CrkiQNTZfgn9e+vwg4vqpuGWI9kqQhW6fDMl9N8hPgj8BrkywA7hpuWZKkYZnyjL+qDgKeCiyqqnuAO4Hdh12YJGk4JjzjT7LnONMGR08eRkGSpOGarKln10nmFQb/rLTwoNOnuwRJ02zC4K+qV46yEEnSaEzW1POWyT5YVf862fwk6wMXAOu12zmpqt6TZBvgBGAT4BLgFVX1p5UtXJK0aia7uLvhFK+p3A08p6r+GtgBeEGSpwCHAx+qqu2AW4EDVr18SdLKmqyp59DVWXFVFXBHOzqvfRXwHOBl7fTjgEOAT6zOtiRJ3XXpsuHRbVcNl7fjT0jyzi4rT7J2kh8BNwNnAz8DbquqZe0iNwBbTPDZA5MsTrJ4yZIlXTYnSeqgy527nwIOBu4BqKpLgb27rLyq/lxVOwBbAjsCjx1vsQk+e3RVLaqqRQsWLOiyOUlSB12C/4FVdeEK05aNu+QEquo24DzgKcD8JGNNTFsCN67MuiRJq6dL8P82yba0Z+ZJ9gJumupDSRYkmd8OPwDYGbgKOBfYq11sP+DUVahbkrSKuvTV8zrgaOAxSX4N/Bzo0i3z5sBxSdam+YI5saq+luRK4IQk/wT8EDhm1UqXVs9MuZnt+sN2me4S1DNTBn9VXQfsnORBwFpVdXuXFbfXAp44wfp2XNlCJUlrRpdf9fxzkvlVdWdV3Z7kwe3ZuiRpFurSxv/C9uIsAFV1K03f/JKkWahL8K+dZL2xkfZC7XqTLC9JmsG6XNz9PHBOks/S/LLn72nuuJUkzUJdLu4ekeRSmp9jBnhfVZ059MokSUPR5YyfqvoG8I0h1yJJGoEubfySpDnE4Jeknpkw+JOc074fPrpyJEnDNlkb/+ZJngXsluQEmgu7f1FVlwy1MknSUEwW/O8GDqLpQXPFxyyOPVBFkjTLTPYErpOAk5K8q6reN8KaJElD1OV3/O9LshvwzHbSeVX1teGWJUkali6dtH0AeBNwZft6UztNkjQLdbmBaxdgh6q6FyDJcTT96B88zMIkScPR9Xf88weGNx5GIZKk0ehyxv8B4IdJzqX5Secz8WxfkmatLhd3j09yHvAkmuB/e1X917ALkyQNR9dO2m4CThtyLZKkEbCvHknqGYNfknpm0uBPslaSy0dVjCRp+CYN/va3+z9O8ogR1SNJGrIuF3c3B65IciFw59jEqtptaFVJkoamS/AfOvQqJEkj0+V3/Ocn2RrYrqq+meSBwNrDL02SNAxdOmn738BJwCfbSVsApwyzKEnS8HT5OefrgKcDSwGq6hrgocMsSpI0PF2C/+6q+tPYSJJ1aJ7AJUmahboE//lJ3gE8IMnzgP8HfHW4ZUmShqVL8B8ELAEuA14FnAG8c5hFSZKGp8uveu5tH77yA5omnquryqYeSZqlpgz+JLsARwE/o+mWeZskr6qqrw+7OEnSmtflBq4jgWdX1bUASbYFTgcMfkmahbq08d88Fvqt64Cbh1SPJGnIJjzjT7JnO3hFkjOAE2na+F8CXDSC2iRJQzBZU8+uA8O/AZ7VDi8BHjzVipNsBXwOeBhwL3B0VX0kySbAl4CFwPXAS6vq1pWuXJK0SiYM/qp65Wquexnwj1V1SZINgYuTnA3sD5xTVYclOYjm56JvX81tSZI66vKrnm2AN9Ccof9l+am6ZW6f03tTO3x7kqto+vnZHdipXew44DwMfkkamS6/6jkFOIbmbt17V2UjSRYCT6S5F2Cz9kuBqropybj9/iQ5EDgQ4BGP8DkwkrSmdAn+u6rq31Z1A0k2AL4MvLmqlibp9LmqOho4GmDRokXeMCZJa0iX4P9IkvcAZwF3j02sqkum+mCSeTSh/4WqOrmd/Jskm7dn+5vjT0MlaaS6BP/jgVcAz2F5U0+14xNKc2p/DHBVVf3rwKzTgP2Aw9r3U1eyZknSaugS/P8TeORg18wdPZ3mC+OyJD9qp72DJvBPTHIA8Eua+wIkSSPSJfh/DMxnJZtkquo7NH37jOe5K7MuSdKa0yX4NwN+kuQi7tvGP+nPOSVJM1OX4H/P0KuQJI1Ml/74zx9FIZKk0ehy5+7tLH/G7rrAPODOqtpomIVJkoajyxn/hoPjSfYAdhxaRZKkoerSH/99VNUpTPEbfknSzNWlqWfPgdG1gEUsb/qRJM0yXX7VM9gv/zKaPvR3H0o1kqSh69LGv7r98kuSZpDJHr347kk+V1X1viHUI0kassnO+O8cZ9qDgAOAhwAGvyTNQpM9evHIseH20YlvAl4JnAAcOdHnJEkz26Rt/O2D0d8CvJzmMYl/44PRJWl2m6yN/4PAnjRPwXp8Vd0xsqokSUMz2Q1c/wg8HHgncGOSpe3r9iRLR1OeJGlNm6yNf6Xv6pUkzXyGuyT1jMEvST1j8EtSzxj8ktQzBr8k9YzBL0k9Y/BLUs8Y/JLUMwa/JPWMwS9JPWPwS1LPGPyS1DMGvyT1jMEvST1j8EtSzxj8ktQzBr8k9YzBL0k9Y/BLUs8Y/JLUM0ML/iSfSXJzkssHpm2S5Owk17TvDx7W9iVJ4xvmGf+xwAtWmHYQcE5VbQec045LkkZoaMFfVRcAt6wweXfguHb4OGCPYW1fkjS+Ubfxb1ZVNwG07w+daMEkByZZnGTxkiVLRlagJM11M/biblUdXVWLqmrRggULprscSZozRh38v0myOUD7fvOIty9JvTfq4D8N2K8d3g84dcTbl6TeG+bPOY8Hvgf8VZIbkhwAHAY8L8k1wPPacUnSCK0zrBVX1T4TzHrusLYpSZrajL24K0kaDoNfknrG4JeknjH4JalnDH5J6hmDX5J6xuCXpJ4Z2u/4JXWz8KDTp7sEAK4/bJfpLkEj4hm/JPWMwS9JPWPwS1LPGPyS1DNe3B2RmXIBT5I845eknjH4JalnDH5J6hmDX5J6xuCXpJ4x+CWpZwx+SeoZg1+Sesbgl6SeMfglqWcMfknqGYNfknrG4JeknjH4JalnDH5J6hmDX5J6xuCXpJ6Z80/g8slXknRfnvFLUs8Y/JLUMwa/JPXMnG/jl9TNTLkedv1hu0x3CXOeZ/yS1DPTEvxJXpDk6iTXJjloOmqQpL4aefAnWRv4GPBCYHtgnyTbj7oOSeqr6Tjj3xG4tqquq6o/AScAu09DHZLUS9NxcXcL4FcD4zcAT15xoSQHAge2o3ckuXoEtU1kU+C307j9ucbjuebNmWOaw6e7gr+YC8d06/EmTkfwZ5xpdb8JVUcDRw+/nKklWVxVi6a7jrnC47nmeUzXvLl8TKejqecGYKuB8S2BG6ehDknqpekI/ouA7ZJsk2RdYG/gtGmoQ5J6aeRNPVW1LMnrgTOBtYHPVNUVo65jJc2IJqc5xOO55nlM17w5e0xTdb/mdUnSHOadu5LUMwa/JPWMwT9gqq4kkqyX5Evt/B8kWTj6KmePDsdz/yRLkvyoff3DdNQ5WyT5TJKbk1w+wfwk+bf2eF+a5G9GXeNs0+GY7pTk9wP/Rt896hqHweBvdexK4gDg1qp6FPAhYObcajLDrETXHF+qqh3a16dHWuTscyzwgknmvxDYrn0dCHxiBDXNdscy+TEF+PbAv9H3jqCmoTP4l+vSlcTuwHHt8EnAc5OMd0Oa7JpjjauqC4BbJllkd+Bz1fg+MD/J5qOpbnbqcEznJIN/ufG6kthiomWqahnwe+AhI6lu9ulyPAH+V9sscVKSrcaZr+66HnOtnKcm+XGSryd53HQXsyYY/Mt16UqiU3cTArodq68CC6vqCcA3Wf7XlFaN/z7XvEuAravqr4GPAqdMcz1rhMG/XJeuJP6yTJJ1gI3p4Z+JHU15PKvqd1V1dzv6KeC/jai2ucruUNawqlpaVXe0w2cA85JsOs1lrTaDf7kuXUmcBuzXDu8FfKu8A24iUx7PFdqfdwOuGmF9c9FpwL7tr3ueAvy+qm6a7qJmsyQPG7uOl2RHmsz83fRWtfp85m5roq4kkrwXWFxVpwHHAP+R5FqaM/29p6/ima3j8Xxjkt2AZTTHc/9pK3gWSHI8sBOwaZIbgPcA8wCq6ijgDOBFwLXAH4BXTk+ls0eHY7oX8Joky4A/AnvPhZM9u2yQpJ6xqUeSesbgl6SeMfglqWcMfknqGYNfknrG4NcqSVJJjhwYf2uSQ9bQuo9NsteaWNcU23lJkquSnLvC9J2SfG2I290/yb+3w69Osu+wtrUmJdljgo72NMsY/FpVdwN7zrS7GNteQbs6AHhtVT17WPVMpaqOqqrPTdf2V9IeND2tapYz+LWqltE8k/T/rDhjxTP2JHe07zslOT/JiUl+muSwJC9PcmGSy5JsO7CanZN8u13uxe3n107ywSQXtR27vWpgvecm+SJw2Tj17NOu//Ikh7fT3g08AzgqyQfH2b+NknwlyZVJjkqyVvu5TyRZnOSKJIcObOOwdtlLk/xLO21Bki+39V6U5Onj1HZIkre2w+clObw9Hj9N8t8n2+9x1vV37Wd/lOST7edek+SIgWX2T/LRiZYf+++V5P1tx2TfT7JZkqfR3F39wXb5bZO8cWCfTxivJs1QVeXL10q/gDuAjYDrafoseitwSDvvWGCvwWXb952A24DNgfWAXwOHtvPeBHx44PPfoDkx2Y6mD5r1afqYf2e7zHrAYmCbdr13AtuMU+fDgV8CC2juVP8WsEc77zxg0Tif2Qm4C3gkzV3HZ4/tD7BJ+752+/knAJsAV7P8hsj57fsXgWe0w48ArmqH9wf+vR0+BHjrQD1HtsMvAr7ZDo+73yvU/FiaTu/mteMfB/Zt9/vageW+TvOFN+7y7XABu7bDRwxse8X/rjcC6w3us6/Z8bLLBq2yqlqa5HPAG2luZ+/iomr7j0nyM+CsdvplwGCTy4lVdS9wTZLrgMcAzweeMPDXxMY0Xwx/Ai6sqp+Ps70nAedV1ZJ2m18AnsnUvSxeWFXXtZ85niYsTwJemuRAmi+RzWmaPq6k+aL4dJLTgbHrAzsD22f5Ixs2SrLhFNs9uX2/GFjYDk+034P7+1yaTu4uarf3AODmqlqS5Lo0ffdcA/wV8F3gdeMt367rTwP7cDHwvAlqvRT4QpJTmCO9VvaFwa/V9WGarms/OzBtGW0zYppUWXdg3t0Dw/cOjN/Lff89rtiXSNF0O/yGqjpzcEaSnWjO+Mezqg/Kud/2k2xD85fNk6rq1iTHAutX0y/RjjThuzfweuA5NMfgqVV1ny/FTP7snrHj8WeWH49x93sFAY6rqoPHmfcl4KXAT4CvVFW1/10mWv6eak/jV6hjRbvQfInuBrwryeOqeU6FZjjb+LVaquoW4ESaC6Vjrmd5F8u703Z6tZJekmSttt3/kTRNKWfSdJg1DyDJo5M8aIr1/AB4VpJN2zbsfYDzO2x/xzQ9i64F/C3wHZqmrTuB3yfZjOZRhyTZANi4mm573wzs0K7jLJovAdrldmDVdNnvc4C9kjy0XWaTJFu3806muTC7D82XwFTLT+R2YMN2+bWArarqXOBtwHxgg1XcP42YZ/xaE45kIOBo+tY/NcmFNAEz0dn4ZK6mCejNgFdX1V1JPk3T/HFJe8a6hCbQJlRVNyU5GDiX5qz4jKo6tcP2vwccBjweuIDmTPneJD8ErgCuo2kygSYMT02yfruNsQvebwQ+luRSmv/XLgBe3WXnVzDlflfVlUneCZzVhvI9NM05v2j/OrkS2L6qLpxq+UnqOAH4VJI30vxlc0ySjdt9/lBV3bYK+6ZpYO+cktQzNvVIUs8Y/JLUMwa/JPWMwS9JPWPwS1LPGPyS1DMGvyT1zP8H2dpoNGBwBIoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,mean_firing_rate_8089_DB=baseline_firing_rate(bin_nrTraceNoBlocks['8089_d4_s1'])\n",
    "print(mean_firing_rate_8089_DB)\n",
    "print(1.5*np.std(p))\n",
    "plot_prob_histogram(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overall baseline firing rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_firing_24=np.mean([mean_firing_rate_ali24_FS,mean_firing_rate_ali24_CS,mean_firing_rate_ali24_DB])\n",
    "mean_firing_25=np.mean([mean_firing_rate_ali25_FS,mean_firing_rate_ali25_CS,mean_firing_rate_ali25_DB])\n",
    "mean_firing_26=np.mean([mean_firing_rate_ali26_FS,mean_firing_rate_ali26_CS,mean_firing_rate_ali26_DB])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_firing_2982=np.mean([mean_firing_rate_2982_FS,mean_firing_rate_2982_CS,mean_firing_rate_2982_DB])\n",
    "mean_firing_4540=np.mean([mean_firing_rate_4540_FS,mean_firing_rate_4540_CS,mean_firing_rate_4540_DB])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_firing_2712=np.mean([mean_firing_rate_2712_FS,mean_firing_rate_2712_CS,mean_firing_rate_2712_DB])\n",
    "mean_firing_2713=np.mean([mean_firing_rate_2713_FS,mean_firing_rate_2713_CS,mean_firing_rate_2713_DB])\n",
    "mean_firing_2714=np.mean([mean_firing_rate_2714_FS,mean_firing_rate_2714_CS,mean_firing_rate_2714_DB])\n",
    "mean_firing_8089=np.mean([mean_firing_rate_8089_FS,mean_firing_rate_8089_CS,mean_firing_rate_8089_DB])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "baseline_firing_rate_list= [mean_firing_24,mean_firing_25,mean_firing_26,mean_firing_2982,mean_firing_4540,mean_firing_2712,mean_firing_2713,mean_firing_2714,mean_firing_8089]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.4410172473328537,\n",
       " 1.0841888405155293,\n",
       " 1.6519295513923722,\n",
       " 1.753806358960876,\n",
       " 1.6267189367647523,\n",
       " 2.430021350960052,\n",
       " 2.0548462380464123,\n",
       " 1.2954354534867947,\n",
       " 2.0365054637802085]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## To find event rate/min \n",
    "multiplied_list = [element * 3 for element in baseline_firing_rate_list]\n",
    "multiplied_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_baseline_firing_rate = np.mean(multiplied_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.7082743823599837"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_baseline_firing_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3932783426501954"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(multiplied_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0285"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1.71/60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "297336.0"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## NUmber of events!! \n",
    "\n",
    "n_events_firstday+n_events_lateday+n_events_lastday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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