delay_remapping.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# delay_remapping.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# General\n",
    "import sys\n",
    "import os\n",
    "import os.path as op\n",
    "from time import time\n",
    "import warnings\n",
    "from collections import OrderedDict as od\n",
    "from importlib import reload\n",
    "from glob import glob\n",
    "import itertools\n",
    "import h5py\n",
    "\n",
    "# Scientific\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.options.display.max_rows = 200\n",
    "pd.options.display.max_columns = 999\n",
    "import scipy.io as sio\n",
    "\n",
    "# Stats\n",
    "import scipy as sp\n",
    "import scipy.stats as stats\n",
    "from scipy.spatial.distance import mahalanobis\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "import random\n",
    "from sklearn.preprocessing import minmax_scale\n",
    "from sklearn.decomposition import PCA\n",
    "from astropy.convolution import Gaussian2DKernel\n",
    "from astropy.convolution import convolve\n",
    "\n",
    "# Plots\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from matplotlib_venn import venn2\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "from matplotlib.lines import Line2D\n",
    "import matplotlib.patches as patches\n",
    "mpl.rcParams['grid.linewidth'] = 0.1\n",
    "mpl.rcParams['grid.alpha'] = 0.75\n",
    "mpl.rcParams['lines.linewidth'] = 1\n",
    "mpl.rcParams['lines.markersize'] = 3\n",
    "mpl.rcParams['xtick.labelsize'] = 12\n",
    "mpl.rcParams['ytick.labelsize'] = 12\n",
    "mpl.rcParams['xtick.major.width'] = 0.8\n",
    "mpl.rcParams['ytick.major.width'] = 0.8\n",
    "colors = ['1f77b4', 'd62728', '2ca02c', 'ff7f0e', '9467bd', \n",
    "          '8c564b', 'e377c2', '7f7f7f', 'bcbd22', '17becf']\n",
    "mpl.rcParams['axes.prop_cycle'] = mpl.cycler('color', colors)\n",
    "mpl.rcParams['axes.spines.right'] = False\n",
    "mpl.rcParams['axes.spines.top'] = False\n",
    "mpl.rcParams['axes.formatter.offset_threshold'] = 2\n",
    "mpl.rcParams['axes.labelsize'] = 14\n",
    "mpl.rcParams['axes.labelpad'] = 8\n",
    "mpl.rcParams['axes.titlesize'] = 16\n",
    "mpl.rcParams['axes.grid'] = False\n",
    "mpl.rcParams['axes.axisbelow'] = True\n",
    "mpl.rcParams['legend.loc'] = 'upper right'\n",
    "mpl.rcParams['legend.fontsize'] = 14\n",
    "mpl.rcParams['legend.frameon'] = False\n",
    "mpl.rcParams['figure.dpi'] = 300\n",
    "mpl.rcParams['figure.titlesize'] = 16\n",
    "mpl.rcParams['figure.figsize'] = (10, 4) \n",
    "mpl.rcParams['figure.subplot.wspace'] = 0.25 \n",
    "mpl.rcParams['figure.subplot.hspace'] = 0.25 \n",
    "mpl.rcParams['font.sans-serif'] = ['Helvetica']\n",
    "mpl.rcParams['savefig.format'] = 'pdf'\n",
    "mpl.rcParams['pdf.fonttype'] = 42\n",
    "\n",
    "# Personal\n",
    "sys.path.append('/home1/dscho/code/general')\n",
    "sys.path.append('/home1/dscho/code/projects/manning_replication')\n",
    "sys.path.append('/home1/dscho/code/projects')\n",
    "import data_io as dio\n",
    "import array_operations as aop\n",
    "from eeg_plotting import plot_trace, plot_trace2\n",
    "from time_cells import spike_sorting, spike_preproc, events_preproc, time_bin_analysis, time_cell_plots, remapping\n",
    "\n",
    "font = {'tick': 12,\n",
    "        'label': 14,\n",
    "        'annot': 12,\n",
    "        'fig': 16}\n",
    "\n",
    "# Colors\n",
    "n = 4\n",
    "c = 2\n",
    "colors = [sns.color_palette('Blues', n)[c], \n",
    "          sns.color_palette('Reds', n)[c], \n",
    "          sns.color_palette('Greens', n)[c],\n",
    "          sns.color_palette('Purples', n)[c],\n",
    "          sns.color_palette('Oranges', n)[c],\n",
    "          sns.color_palette('Greys', n)[c],\n",
    "          sns.color_palette('YlOrBr', n+3)[c],\n",
    "          'k']\n",
    "cmap = sns.palettes.blend_palette((colors[0], \n",
    "                                   'w',\n",
    "                                   colors[1]), 501)\n",
    "\n",
    "colws = od([('1', 6.55),\n",
    "            ('2-1/2', 3.15),\n",
    "            ('2-1/3', 2.1),\n",
    "            ('2-2/3', 4.2),\n",
    "            ('3', 2.083),\n",
    "            ('4', 1.525),\n",
    "            ('5', 1.19),\n",
    "            ('6', 0.967),\n",
    "            (1, 2.05),\n",
    "            (2, 3.125),\n",
    "            (3, 6.45),\n",
    "            ('nat1w', 3.50394),\n",
    "            ('nat2w', 7.20472),\n",
    "            ('natl', 9.72441)])\n",
    "\n",
    "proj_dir = '/home1/dscho/projects/time_cells'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 subjects, 10 sessions\n"
     ]
    }
   ],
   "source": [
    "# Get sessions.\n",
    "sessions = np.unique([op.basename(f).split('-')[0] \n",
    "                      for f in glob(op.join(proj_dir, 'analysis', 'events', '*.pkl'))])\n",
    "print('{} subjects, {} sessions'.format(len(np.unique([x.split('_')[0] for x in sessions])), len(sessions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Delay1: {'r': 0.35, 'd': 0.7, 'a': 0.3848451000647496, 'c': 2.199114857512855}\n",
      "Delay2: {'r': 0.32294045974535385, 'd': 0.6458809194907077, 'a': 0.3276383960010706, 'c': 2.029094751765828}\n"
     ]
    }
   ],
   "source": [
    "circle1 = circit(0.7, 'd')\n",
    "circle2 = circit(circle1['a'], 'a', scale=63/74)\n",
    "\n",
    "print('Delay1: {}'.format(circle1))\n",
    "print('Delay2: {}'.format(circle2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ols_pairs: (6720, 24)\n",
      "Done in 0.8s\n"
     ]
    }
   ],
   "source": [
    "load_single_file = True\n",
    "\n",
    "start_time = time()\n",
    "\n",
    "# Load all time OLS result files.\n",
    "filename = op.join(proj_dir, 'analysis', 'unit_to_behav', 'tmp',\n",
    "                   'ols-time_bin-model_pairs-420units.pkl')\n",
    "if load_single_file and op.exists(filename):\n",
    "    ols_pairs = dio.open_pickle(filename)\n",
    "else:\n",
    "    ols_pairs_files = glob(op.join(proj_dir, 'analysis', 'unit_to_behav', '*-time_bin-model_pairs.pkl'))\n",
    "    print('Found OLS outputs for {} neurons'.format(len(ols_pairs_files)))\n",
    "\n",
    "    warnings.filterwarnings('ignore')\n",
    "    bad_files = []\n",
    "    ols_pairs = pd.DataFrame([])\n",
    "    for filename in ols_pairs_files:\n",
    "        try:\n",
    "            ols_pairs = pd.concat((ols_pairs, dio.open_pickle(filename)))\n",
    "        except:\n",
    "            bad_files.append(filename)\n",
    "    ols_pairs = ols_pairs.sort_values(['subj_sess', 'neuron']).reset_index(drop=True)\n",
    "    warnings.resetwarnings()\n",
    "\n",
    "    # Restrict dataframe rows to navigation periods.\n",
    "    keep_cols = ['subj_sess', 'neuron', 'gameState', 'testvar', 'full', 'llf_full', 'lr', 'z_lr', 'emp_pval']\n",
    "    # ols_pairs = ols_pairs.query(\"(gameState==['Encoding', 'Retrieval'])\")[keep_cols].reset_index(drop=True)\n",
    "    ols_pairs = ols_pairs[keep_cols].reset_index(drop=True)\n",
    "\n",
    "    # Organize categorical columns.\n",
    "    test_vars = ['time', 'place', 'head_direc', 'gold_in_view', 'base_in_view']\n",
    "    test_var_cat = pd.CategoricalDtype(test_vars, ordered=True)\n",
    "    ols_pairs['testvar'] = ols_pairs['testvar'].astype(test_var_cat)\n",
    "\n",
    "    full_models = ['time', 'time_place', 'full']\n",
    "    full_model_cat = pd.CategoricalDtype(full_models, ordered=True)\n",
    "    ols_pairs['full'] = ols_pairs['full'].astype(full_model_cat)\n",
    "\n",
    "    # Add new columns.\n",
    "    roi_map = spike_preproc.roi_mapping(n=3)\n",
    "    val_map = od([('hem', []),\n",
    "                  ('roi', []),\n",
    "                  ('roi_gen', []),\n",
    "                  ('spike_mat'        , []),\n",
    "                  ('mean_frs'         , []),\n",
    "                  ('sem_frs'          , []),\n",
    "                  ('fr_mean'          , []),\n",
    "                  ('fr_max'           , []),\n",
    "                  ('fr_max_ind'       , []),\n",
    "                  ('sparsity'         , [])])\n",
    "    for idx, row in ols_pairs.iterrows():\n",
    "        if 'event_spikes' not in dir():\n",
    "            event_spikes = time_bin_analysis.load_event_spikes(row['subj_sess'], verbose=False)\n",
    "        elif event_spikes.subj_sess != row['subj_sess']:\n",
    "            event_spikes = time_bin_analysis.load_event_spikes(row['subj_sess'], verbose=False)\n",
    "        hemroi = spike_preproc.roi_lookup(row['subj_sess'], row['neuron'].split('-')[0])\n",
    "        hem = hemroi[0]\n",
    "        roi = hemroi[1:]\n",
    "        roi_gen = roi_map.get(roi, np.nan)\n",
    "        spike_mat = event_spikes.get_spike_mat(row['neuron'], row['gameState'])\n",
    "        mean_frs = time_bin_analysis.get_mean_frs(spike_mat) * 2\n",
    "        sem_frs = time_bin_analysis.get_sem_frs(spike_mat) * 2\n",
    "        fr_mean = np.mean(mean_frs.values)\n",
    "        fr_max = np.max(mean_frs.values)\n",
    "        fr_max_ind = np.argmax(mean_frs.values)\n",
    "        sparsity = time_bin_analysis.get_sparsity(spike_mat)\n",
    "\n",
    "        val_map['hem'].append(hem)\n",
    "        val_map['roi'].append(roi)\n",
    "        val_map['roi_gen'].append(roi_gen)\n",
    "        val_map['spike_mat'].append(spike_mat.values.tolist())\n",
    "        val_map['mean_frs'].append(mean_frs.tolist())\n",
    "        val_map['sem_frs'].append(sem_frs.tolist())\n",
    "        val_map['fr_mean'].append(fr_mean)\n",
    "        val_map['fr_max'].append(fr_max)\n",
    "        val_map['fr_max_ind'].append(fr_max_ind)\n",
    "        val_map['sparsity'].append(sparsity)\n",
    "\n",
    "    ols_pairs.insert(0, 'subj', ols_pairs['subj_sess'].apply(lambda x: x.split('_')[0]))\n",
    "    ols_pairs.insert(2, 'subj_sess_unit', ols_pairs.apply(lambda x: '{}-{}'.format(x['subj_sess'], x['neuron']), axis=1))\n",
    "    ols_pairs.insert(4, 'hem', val_map['hem'])\n",
    "    ols_pairs.insert(5, 'roi', val_map['roi'])\n",
    "    ols_pairs.insert(6, 'roi_gen', val_map['roi_gen'])\n",
    "    for col_name in val_map:\n",
    "        if col_name not in ols_pairs:\n",
    "            ols_pairs[col_name] = val_map[col_name]\n",
    "    roi_gen_cat = ['Hippocampus', 'MTL', 'Cortex']\n",
    "    ols_pairs['roi_gen'] = ols_pairs['roi_gen'].astype(pd.CategoricalDtype(roi_gen_cat, ordered=True))\n",
    "    ols_pairs.insert(ols_pairs.columns.tolist().index('roi_gen')+1, 'roi_gen2', ols_pairs['roi_gen'].tolist())\n",
    "    ols_pairs.loc[ols_pairs['roi_gen']=='Hippocampus', 'roi_gen2'] = 'MTL'\n",
    "    ols_pairs['roi_gen2'] = ols_pairs['roi_gen2'].astype(pd.CategoricalDtype(['MTL', 'Cortex'], ordered=True))\n",
    "\n",
    "    # Test significance.\n",
    "    alpha = 0.05\n",
    "    ols_pairs['sig'] = False\n",
    "    ols_pairs.loc[((ols_pairs['emp_pval']<alpha)), 'sig'] = True\n",
    "    ols_pairs['sig_holm'] = ols_pairs.groupby(['subj_sess_unit', 'gameState', 'full'])['emp_pval'].transform(lambda x: sm.stats.multipletests(x, alpha, method='holm')[0])\n",
    "\n",
    "    print('{} bad files'.format(len(bad_files)))\n",
    "    \n",
    "print('ols_pairs:', ols_pairs.shape)\n",
    "\n",
    "print('Done in {:.1f}s'.format(time() - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_time_field(v,\n",
    "                    z_thresh=2):\n",
    "    \"\"\"Return the time field given a Z-scored firing rate vector.\n",
    "    \n",
    "    The time field is given by contiguous time bins surrounding\n",
    "    the maximum Z-scored firing rate that are greater than\n",
    "    z_thresh. If the maximum firing rate is less than z_thresh,\n",
    "    a v-length vector of zeros is returned.\n",
    "    \"\"\"\n",
    "    time_field = np.zeros(len(v))\n",
    "    peak = v.argmax()\n",
    "    \n",
    "    # Check that the firing rate peak > z_thresh.\n",
    "    if v[peak]<=z_thresh:\n",
    "        return time_field\n",
    "    else:\n",
    "        time_field[peak] = 1\n",
    "    \n",
    "    # Move backwards from the peak firing rate.\n",
    "    for iBin in range(peak, -1, -1):\n",
    "        if v[iBin]>z_thresh:\n",
    "            time_field[iBin] = 1\n",
    "        else:\n",
    "            break\n",
    "    \n",
    "    # Move forwards from the peak firing rate.\n",
    "    for iBin in range(peak, len(time_field)):\n",
    "        if v[iBin]>z_thresh:\n",
    "            time_field[iBin] = 1\n",
    "        else:\n",
    "            break\n",
    "    \n",
    "    return time_field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(117, 420, 0.2785714285714286)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_cells = ols_pairs['subj_sess_unit'].unique()\n",
    "delay_time_cells = ols_pairs.query(\"(sig==True) & (gameState==['Delay1', 'Delay2'])\")['subj_sess_unit'].unique()\n",
    "delay_time_cells.size, all_cells.size, delay_time_cells.size / all_cells.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home1/dscho/code/general/array_operations.py:25: RuntimeWarning: invalid value encountered in true_divide\n",
      "  return np.dot(x, y) / (np.linalg.norm(x) * np.linalg.norm(y))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading saved Events file\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home1/dscho/code/general/array_operations.py:25: RuntimeWarning: invalid value encountered in true_divide\n",
      "  return np.dot(x, y) / (np.linalg.norm(x) * np.linalg.norm(y))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time_remapping: (117, 16)\n",
      "Dont in 33.9s\n"
     ]
    }
   ],
   "source": [
    "# Determine which delay time cells significantly remapped between Delay 1 and 2\n",
    "# by comparing the mean difference in firing rate cosine similarity between\n",
    "# within-delay vs. between-delay trial pairs.\n",
    "n_perm = 1000\n",
    "alpha = 0.05\n",
    "\n",
    "start_time = time()\n",
    "\n",
    "all_cells = ols_pairs['subj_sess_unit'].unique()\n",
    "delay_time_cells = ols_pairs.query(\"(gameState==['Delay1', 'Delay2']) & (sig==True)\")['subj_sess_unit'].unique()\n",
    "grp = (ols_pairs.query(\"(gameState==['Delay1', 'Delay2'])\")\n",
    "       .sort_values(['subj_sess', 'neuron', 'gameState'])\n",
    "       .query(\"(subj_sess_unit=={})\".format(list(delay_time_cells)))\n",
    "       .groupby(['subj_sess_unit']))\n",
    "\n",
    "time_remapping = []\n",
    "for neuron, _df in grp:\n",
    "    assert len(_df) == 2\n",
    "    assert _df.iloc[0]['gameState'] == 'Delay1'\n",
    "    assert _df.iloc[1]['gameState'] == 'Delay2'\n",
    "    \n",
    "    subj_sess = _df.iloc[0]['subj_sess']    \n",
    "    nsig = _df['sig'].sum()\n",
    "    assert nsig in [0, 1, 2]\n",
    "    \n",
    "    if 'event_spikes' not in dir():\n",
    "        event_spikes = time_bin_analysis.load_event_spikes(subj_sess, verbose=False)\n",
    "    elif event_spikes.subj_sess != subj_sess:\n",
    "        event_spikes = time_bin_analysis.load_event_spikes(subj_sess, verbose=False)\n",
    "    \n",
    "    \n",
    "    spike_mats = od([])\n",
    "    for _, row in _df.iterrows():\n",
    "        spike_mats[row['gameState']] = event_spikes.get_spike_mat(row['neuron'], row['gameState'], column='time_step')\n",
    "    \n",
    "    # Get Delay1 to Delay2 firing vector sims.\n",
    "    pairwise_sims = remapping.trial_pair_remapping(spike_mats)\n",
    "    mean_sims = {k : np.mean(v) for k, v in pairwise_sims.items()}\n",
    "    mean_sims['within'] = np.max((mean_sims['Delay1'], mean_sims['Delay2']))\n",
    "    asym = mean_sims['within'] - mean_sims['between']\n",
    "    \n",
    "    # Compute the null distribution.\n",
    "    pairwise_sims_vec = np.concatenate([pairwise_sims['Delay1'],\n",
    "                                        pairwise_sims['Delay2'],\n",
    "                                        pairwise_sims['between']]) # cond x trial_pair\n",
    "    divs = np.cumsum((len(pairwise_sims['Delay1']), len(pairwise_sims['Delay2']), len(pairwise_sims['between'])))[:2]\n",
    "    asyms_null = []\n",
    "    for iPerm in range(n_perm):\n",
    "        np.random.shuffle(pairwise_sims_vec)\n",
    "        mean_sims_null = dict(zip(['Delay1', 'Delay2', 'between'], \n",
    "                                  [np.mean(x) for x in np.split(pairwise_sims_vec, divs)]))\n",
    "        mean_sims_null['within'] = np.max((mean_sims_null['Delay1'], mean_sims_null['Delay2']))\n",
    "        asyms_null.append(mean_sims_null['within'] - mean_sims_null['between'])\n",
    "    asyms_null = np.array(asyms_null)\n",
    "    \n",
    "    asym_z = (asym - np.mean(asyms_null)) / np.std(asyms_null)\n",
    "    if asym_z > 0:\n",
    "        asym_z_sign = 1\n",
    "    elif asym_z == 0:\n",
    "        asym_z_sign = 0\n",
    "    else:\n",
    "        asym_z_sign = -1\n",
    "    pval_ind = np.sum(np.abs(asyms_null) >= np.abs(asym))\n",
    "    pval = (1 + pval_ind) / (1 + n_perm)\n",
    "    \n",
    "    # Figure out which trial phase (Delay 1 or Delay 2) has stronger time coding.\n",
    "    # (If only one trial phase is significant, take that one. Otherwise, take whichever\n",
    "    # has the highest info score). Then add time coding info for the stronger trial phase.\n",
    "    z_lr_diff = np.abs(np.diff(_df['z_lr']))[0]\n",
    "    if nsig == 1:\n",
    "        row = _df.query(\"(sig==True)\").iloc[0]\n",
    "    else:\n",
    "        row = _df.sort_values('z_lr', ascending=False).iloc[0]\n",
    "    game_state = row['gameState']\n",
    "    z_lr = row['z_lr']\n",
    "    fr_max_ind = row['fr_max_ind']\n",
    "    time_remapping.append([neuron, subj_sess, nsig, z_lr_diff, game_state, z_lr,\n",
    "                           fr_max_ind, asym, asym_z, asym_z_sign, pval_ind, pval])\n",
    "        \n",
    "time_remapping = pd.DataFrame(time_remapping, columns=['neuron', 'subj_sess', 'nsig', 'z_lr_diff', 'gameState', 'z_lr', \n",
    "                                                         'fr_max_ind', 'asym', 'asym_z', 'asym_z_sign', 'pval_ind', 'pval'])\n",
    "time_remapping['sig'] = time_remapping['pval'] < alpha\n",
    "time_remapping['asym_z_cat'] = 0\n",
    "time_remapping.loc[(time_remapping['sig']==True) & (time_remapping['asym_z_sign']==-1), 'asym_z_cat'] = 1\n",
    "time_remapping.loc[(time_remapping['sig']==True) & (time_remapping['asym_z_sign']==1), 'asym_z_cat'] = 2\n",
    "time_remapping.loc[(time_remapping['asym_z_cat']==1), 'sig'] = False\n",
    "time_remapping['dummy'] = 0\n",
    "unit_to_roi_map = ols_pairs[['subj_sess_unit', 'roi_gen']].drop_duplicates().set_index('subj_sess_unit')['roi_gen'].to_dict()\n",
    "time_remapping.insert(2, 'roi_gen', time_remapping['neuron'].apply(lambda x: unit_to_roi_map[x]))\n",
    "\n",
    "print('time_remapping: {}'.format(time_remapping.shape))\n",
    "print('Dont in {:.1f}s'.format(time() - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.06217327666849489, 0.5054551909161639)\n"
     ]
    },
    {
     "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>neuron</th>\n",
       "      <th>z_lr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asym_z_cat</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67</td>\n",
       "      <td>3.6 ± 0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2.2 ± 0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>48</td>\n",
       "      <td>3.9 ± 0.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            neuron       z_lr\n",
       "asym_z_cat                   \n",
       "0               67  3.6 ± 0.3\n",
       "1                2  2.2 ± 0.1\n",
       "2               48  3.9 ± 0.3"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(stats.pearsonr(time_remapping['z_lr'], time_remapping['asym_z']))\n",
    "time_remapping.groupby('asym_z_cat').agg({'neuron': len, 'z_lr': lambda x: '{:.1f} ± {:.1f}'.format(np.mean(x), stats.sem(x))})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "roi_gen\n",
       "Cortex         21/54; 38.9%\n",
       "Hippocampus    12/20; 60.0%\n",
       "MTL            15/43; 34.9%\n",
       "Name: asym_z_cat, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_remapping.groupby('roi_gen')['asym_z_cat'].apply(lambda x: '{}/{}; {:.1%}'.format(np.sum(x==2), len(x), np.mean(x==2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.4333554376657824,\n",
       " 0.4883720730670287,\n",
       " 2,\n",
       " array([[ 9.30909091, 16.87272727, 21.81818182],\n",
       "        [22.69090909, 41.12727273, 53.18181818]]))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctab = [[12, 15, 21],\n",
    "        [20, 43, 54]]\n",
    "stats.chi2_contingency(ctab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "_fr_max_ind\n",
       "0.0    16/41; 39.0%\n",
       "1.0     8/22; 36.4%\n",
       "2.0      2/5; 40.0%\n",
       "3.0     2/10; 20.0%\n",
       "4.0      3/5; 60.0%\n",
       "5.0      3/6; 50.0%\n",
       "6.0      2/4; 50.0%\n",
       "7.0       0/6; 0.0%\n",
       "8.0      4/7; 57.1%\n",
       "9.0     8/11; 72.7%\n",
       "Name: asym_z_cat, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_remapping['_fr_max_ind'] = np.floor(time_remapping['fr_max_ind'] / 2)\n",
    "time_remapping.groupby('_fr_max_ind')['asym_z_cat'].apply(lambda x: '{}/{}; {:.1%}'.format(np.sum(x==2), len(x), np.mean(x==2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>neuron</th>\n",
       "      <th colspan=\"3\" halign=\"left\">fr_max_ind</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>len</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>sem</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asym_z_cat</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>67</td>\n",
       "      <td>5.582090</td>\n",
       "      <td>5.944565</td>\n",
       "      <td>0.726244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.535534</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>48</td>\n",
       "      <td>7.145833</td>\n",
       "      <td>7.255195</td>\n",
       "      <td>1.047197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           neuron fr_max_ind                    \n",
       "              len       mean       std       sem\n",
       "asym_z_cat                                      \n",
       "0              67   5.582090  5.944565  0.726244\n",
       "1               2   3.500000  3.535534  2.500000\n",
       "2              48   7.145833  7.255195  1.047197"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_remapping.groupby(['asym_z_cat']).agg({'neuron': len, 'fr_max_ind': [np.mean, np.std, stats.sem]})"
   ]
  },
  {
   "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>neuron</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nsig</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97/420 (23.1%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20/420 (4.8%)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              neuron\n",
       "nsig                \n",
       "1     97/420 (23.1%)\n",
       "2      20/420 (4.8%)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_remapping.groupby('nsig').agg({'neuron': lambda x: '{}/{} ({:.1%})'.format(len(x), all_cells.size, len(x)/all_cells.size)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 615x615 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "savefig = 0\n",
    "overwrite = 0\n",
    "dpi = 1200\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(colws[1], colws[1]), dpi=300)\n",
    "#ax = np.ravel(ax)\n",
    "\n",
    "font = {'tick': 8, 'label': 8, 'annot': 8, 'fig': 10}\n",
    "_cmap = ['#bebebe', '#296eb4', '#e10600']\n",
    "\n",
    "i = 0\n",
    "ax = sns.violinplot(x='dummy', y='asym_z', data=time_remapping,\n",
    "                    inner=None, color='k',  saturation=100, zorder=0, ax=ax)\n",
    "\n",
    "poly = ax.collections[0]\n",
    "poly.set_alpha(0.1)\n",
    "poly.set_linewidth(0)\n",
    "ax = sns.swarmplot(x='dummy', y='asym_z', hue='asym_z_cat', data=time_remapping,\n",
    "                   palette=_cmap, size=4, \n",
    "                   edgecolor='k', linewidth=0.4, zorder=2, ax=ax)\n",
    "ax.get_legend().remove()\n",
    "ax.tick_params(axis='y', which='both', length=2.5, pad=2, labelsize=font['tick'])\n",
    "ax.set_xticks([])\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('Remapping score (Z)', fontsize=font['label'], labelpad=2)\n",
    "plt.hlines(0, -10, 10, color='k', linewidth=0.8, linestyle='--', zorder=1)\n",
    "\n",
    "if savefig:\n",
    "    n_neurons = time_remapping['neuron'].unique().size\n",
    "    filename = op.join(proj_dir, 'figs', 'plot_remapping',\n",
    "                       'delay_time_cell_remapping_scores-{}units.pdf'\n",
    "                       .format(n_neurons))\n",
    "    if overwrite or not op.exists(filename):\n",
    "        fig.savefig(filename, format='pdf', bbox_inches='tight', pad_inches=0)\n",
    "        fig.savefig(filename.replace('pdf', 'png'), format='png', dpi=dpi, bbox_inches='tight', pad_inches=0)\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 615x615 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(colws[1], colws[1]), dpi=300)\n",
    "#ax = np.ravel(ax)\n",
    "\n",
    "font = {'tick': 8, 'label': 8, 'annot': 8, 'fig': 10}\n",
    "_cmap = ['#bebebe', '#296eb4', '#e10600']\n",
    "_df = time_remapping.query(\"(nsig==2)\")\n",
    "\n",
    "i = 0\n",
    "ax = sns.violinplot(x='dummy', y='asym_z', data=_df,\n",
    "                    inner=None, color='k',  saturation=100, zorder=0, ax=ax)\n",
    "\n",
    "poly = ax.collections[0]\n",
    "poly.set_alpha(0.1)\n",
    "poly.set_linewidth(0)\n",
    "ax = sns.swarmplot(x='dummy', y='asym_z', hue='asym_z_cat', data=_df,\n",
    "                   palette=_cmap, size=4, \n",
    "                   edgecolor='k', linewidth=0.4, zorder=2, ax=ax)\n",
    "ax.get_legend().remove()\n",
    "ax.tick_params(axis='y', which='both', length=2.5, pad=2, labelsize=font['tick'])\n",
    "ax.set_xticks([])\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('Remapping score (Z)', fontsize=font['label'], labelpad=2)\n",
    "plt.hlines(0, -10, 10, color='k', linewidth=0.8, linestyle='--', zorder=1)\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sig\n",
      "False    69\n",
      "True     48\n",
      "dtype: int64\n",
      "1.50 ± 0.22, n = 117\n",
      "Ttest_1sampResult(statistic=6.900324301241092, pvalue=2.918553772932742e-10)\n"
     ]
    }
   ],
   "source": [
    "print(time_remapping.sort_values('asym_z', ascending=False).groupby('sig').size())\n",
    "print('{:.2f} ± {:.2f}, n = {}'.format(np.mean(time_remapping['asym_z']), stats.sem(time_remapping['asym_z']), len(time_remapping['asym_z'])))\n",
    "print(stats.ttest_1samp(time_remapping['asym_z'], 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3780x4725 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the mean timecourse for all Delay1 and Delay2 neurons.\n",
    "savefig = 1\n",
    "overwrite = 1\n",
    "remapped = True\n",
    "figsize = (colws['2-1/2'], colws['2-1/2'] * 1.25)\n",
    "#figsize = (colws['1'], colws['1']*(grid_shp[0]/grid_shp[1]))\n",
    "dpi = 1200\n",
    "font = {'tick': 10, 'label': 10, 'annot': 10, 'fig': 12}\n",
    "xticks = np.arange(0, 21, 5)\n",
    "xticklabels = [0, 2.5, 5, 7.5, 10]\n",
    "plot_title = False\n",
    "ax_labels = {0: 'Delay1', 1: 'Delay2'}\n",
    "add_cbar = False\n",
    "vmin = 0\n",
    "vmax = 1\n",
    "\n",
    "plt.close()\n",
    "fig, ax = plt.subplots(2, 2, figsize=figsize, dpi=dpi)\n",
    "ax = np.ravel(ax)\n",
    "\n",
    "n_neurons = time_remapping['neuron'].unique().size\n",
    "for ii, remapped in enumerate([False, True]):\n",
    "    iax = ii * 2\n",
    "    \n",
    "    # Sort neurons that we'll plot firing rates over time for.\n",
    "    _neurons = time_remapping.query(\"(sig=={})\".format(remapped)).groupby('gameState')['neuron'].apply(list)\n",
    "    sbt_neurons = []\n",
    "    for game_state in ['Delay1', 'Delay2']:\n",
    "        qry = \"(gameState=='{}') & (testvar=='time') & (subj_sess_unit=={})\".format(game_state, _neurons[game_state])\n",
    "        sbt_neurons.extend(ols_pairs.query(qry).sort_values(['fr_max_ind', 'sparsity'])['subj_sess_unit'].tolist())\n",
    "    # _neurons = time_remapping.query(\"(sig=={})\".format(remapped))['neuron'].tolist()\n",
    "    # qry = \"(gameState=='Delay1') & (testvar=='time') & (subj_sess_unit=={})\".format(_neurons)\n",
    "    # sbt_neurons = ols_pairs.query(qry).sort_values(['fr_max_ind', 'sparsity'])['subj_sess_unit'].tolist()\n",
    "    spikes_by_time = od([('Delay1', []), ('Delay2', [])])\n",
    "    for neuron in sbt_neurons:\n",
    "        for game_state in ['Delay1', 'Delay2']:\n",
    "            qry = \"(gameState=='{}') & (testvar=='time') & (subj_sess_unit=='{}')\".format(game_state, neuron)\n",
    "            _df = ols_pairs.query(qry)#\n",
    "            assert len(_df) == 1\n",
    "            spikes_by_time[game_state].append(_df.iloc[0]['mean_frs'])\n",
    "\n",
    "    fr_mat = np.concatenate((np.array(spikes_by_time['Delay1']), np.array(spikes_by_time['Delay2'])), axis=1)\n",
    "    time_bins = int(fr_mat.shape[1] / 2)\n",
    "    fr_mat = minmax_scale(fr_mat, axis=1)\n",
    "    fr_mat = [fr_mat[:, :time_bins], fr_mat[:, time_bins:]]\n",
    "\n",
    "    # Plot the firing rate heatmaps.\n",
    "    for imat in range(2):\n",
    "        ax[iax] = sns.heatmap(fr_mat[imat], vmin=vmin, vmax=vmax, cmap='rocket',\n",
    "                              square=False, cbar=add_cbar, ax=ax[iax])\n",
    "        ax[iax].tick_params(axis='x', which='both', length=2.5, pad=2)\n",
    "        ax[iax].tick_params(axis='y', which='both', length=0, pad=2)\n",
    "        ax[iax].set_xticks(xticks)\n",
    "        ax[iax].set_yticks([])\n",
    "        if iax in [0, 2]:\n",
    "            ax[iax].set_xticklabels(xticklabels, fontsize=font['tick'], rotation=0)\n",
    "            ax[iax].set_xlabel('Time (s)', fontsize=font['label'], labelpad=4)\n",
    "            ax[iax].set_ylabel('Unit 1..{}'.format(fr_mat[0].shape[0]), fontsize=font['label'], labelpad=5)\n",
    "        else:\n",
    "            ax[iax].set_xticklabels([], fontsize=font['tick'], rotation=0)\n",
    "        if plot_title:\n",
    "            ax[iax].set_title(ax_labels[i], pad=6, fontsize=font['fig'])\n",
    "\n",
    "        if add_cbar:\n",
    "            cbar = ax[iax].collections[0].colorbar\n",
    "            cbar.ax.tick_params(labelsize=font['tick'])\n",
    "            cbar.set_ticks([0.0, 0.2, 0.4, 0.6, 0.8, 1.0])\n",
    "            cbar.set_ticklabels([0.0, 0.2, 0.4, 0.6, 0.8, 1.0])\n",
    "            cbar.set_label('Norm. firing rate', fontsize=font['label'], labelpad=2)\n",
    "        \n",
    "        iax += 1\n",
    "\n",
    "fig.tight_layout(w_pad=0)\n",
    "\n",
    "if savefig:\n",
    "    filename = op.join(proj_dir, 'figs', 'plot_remapping',\n",
    "                       'delay_time_cell_remapping-{}units-mean_fr_over_time.pdf'\n",
    "                       .format(n_neurons))\n",
    "    if overwrite or not op.exists(filename):\n",
    "        fig.savefig(filename, format='pdf', bbox_inches='tight', pad_inches=0)\n",
    "        fig.savefig(filename.replace('pdf', 'png'), format='png', dpi=dpi, bbox_inches='tight', pad_inches=0)\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_bins = 20\n",
    "\n",
    "spikes_by_time = {'sig': [], 'ns': []}\n",
    "for sig in [True, False]:\n",
    "    neurons = time_remapping.query(\"(sig=={})\".format(sig))['neuron'].tolist()\n",
    "    for iUnit in range(len(neurons)):\n",
    "        subj_sess, chan, unit = neurons[iUnit].split('-')\n",
    "        neuron = dio.open_pickle(op.join(proj_dir, 'analysis', 'spikes', '{}-CSC{}-unit{}-spikes.pkl'.format(subj_sess, chan, unit)))\n",
    "\n",
    "        # Load events, firing rate vectors, and time bin indices.\n",
    "        if ('events' not in locals()) and ('events' not in globals()):\n",
    "            events = goldmine_behavior.Events(subj_sess)\n",
    "        elif subj_sess != events.event_times.iloc[0]['subj_sess']:\n",
    "            events = goldmine_behavior.Events(subj_sess)\n",
    "        if subj_sess == 'U518_ses1':\n",
    "            events.event_times = events.event_times.loc[events.event_times['trial']!=31].reset_index(drop=True)\n",
    "\n",
    "        sum_spikes = []\n",
    "        for game_state in ['Delay1', 'Delay2']:\n",
    "            twise_spikes = (events.event_times.query(\"(gameState=='{}')\".format(game_state))['time']\n",
    "                                              .apply(lambda x: spike_preproc.apply_spike_times_to_fr(x, neuron['spike_times'], sigma=0)).tolist())\n",
    "            sum_spikes.append(np.sum([[np.nansum(x) for x in np.array_split(x, time_bins)]\n",
    "                                      for x in twise_spikes], axis=0)) # time_bin; sum over trials\n",
    "        k = 'sig' if sig else 'ns'\n",
    "        spikes_by_time[k].append(np.concatenate(sum_spikes)) # time_bin; Delay 1 cat Delay 2 time bins\n",
    "    spikes_by_time[k] = np.array(spikes_by_time[k])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 615x559.02 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.close()\n",
    "fig, ax = plt.subplots(1, 2, figsize=(colws[1], 0.22 * 8.47), dpi=300)\n",
    "ax = np.ravel(ax)\n",
    "\n",
    "font = {'tick': 8, 'label': 8, 'annot': 8, 'fig': 10}\n",
    "\n",
    "xsort = np.argsort(np.argmax(spikes_by_time['ns'], axis=1))\n",
    "fr_mat = minmax_scale(spikes_by_time['ns'], axis=1)[xsort, :]\n",
    "fr_mat = [fr_mat[:, :time_bins], fr_mat[:, time_bins:]]\n",
    "\n",
    "# Plot the firing rate heatmaps.\n",
    "ax_labels = {0: 'Delay 1', 1: 'Delay 2'}\n",
    "add_cbar = False\n",
    "vmin = 0\n",
    "vmax = 1\n",
    "for i in range(2):\n",
    "    ax[i] = sns.heatmap(fr_mat[i], vmin=vmin, vmax=vmax, cmap='rocket',\n",
    "                        square=False, cbar=add_cbar, ax=ax[i])\n",
    "    ax[i].tick_params(axis='x', which='both', length=2.5, pad=2)\n",
    "    ax[i].tick_params(axis='y', which='both', length=0, pad=2)\n",
    "    ax[i].set_xticks(np.arange(0, fr_mat[i].shape[1]+1, 10))\n",
    "    ax[i].set_yticks([])\n",
    "    if i == 0:\n",
    "        ax[i].set_xticklabels(np.arange(0, (fr_mat[i].shape[1]+1)/2, 5, dtype=np.int), \n",
    "                              fontsize=font['tick'], rotation=0)\n",
    "        ax[i].set_xlabel('Time (s)', fontsize=font['label'], labelpad=2)\n",
    "        ax[i].set_ylabel('Unit 1..{}'.format(fr_mat[0].shape[0]), fontsize=font['label'], labelpad=5)\n",
    "    else:\n",
    "        ax[i].set_xticklabels([], fontsize=font['tick'], rotation=0)\n",
    "    ax[i].set_title(ax_labels[i], pad=6, fontsize=font['fig'])\n",
    "\n",
    "    if add_cbar:\n",
    "        cbar = ax[i].collections[0].colorbar\n",
    "        cbar.ax.tick_params(labelsize=font['tick'])\n",
    "        cbar.set_ticks([0.0, 0.2, 0.4, 0.6, 0.8, 1.0])\n",
    "        cbar.set_ticklabels([0.0, 0.2, 0.4, 0.6, 0.8, 1.0])\n",
    "        cbar.set_label('Norm. firing rate', fontsize=font['label'], labelpad=2)\n",
    "        \n",
    "fig.tight_layout(w_pad=0)\n",
    "\n",
    "if True:\n",
    "    fig.savefig(op.join(proj_dir, 'figs', 'delay_time_cells_delay1_delay2_firing_ns-remapping.pdf'), \n",
    "                format='pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [],
   "source": [
    "iUnit = 0\n",
    "game_state = 'Delay1'\n",
    "time_bins = 20\n",
    "\n",
    "# subj_sess, chan, unit = sig_cells[('delay', 'time')][iUnit].split('-')\n",
    "#subj_sess, chan, unit = 'U518_ses0', 4, 1\n",
    "# subj_sess, chan, unit = 'U518_ses1', 38, 1\n",
    "# subj_sess, chan, unit = 'U530_ses0', 13, 1\n",
    "# subj_sess, chan, unit = 'U527_ses0', 58, 2\n",
    "subj_sess, chan, unit = 'U527_ses0', 45, 2\n",
    "neuron = dio.open_pickle(op.join(proj_dir, 'analysis', 'spikes', '{}-CSC{}-unit{}-spikes.pkl'.format(subj_sess, chan, unit)))\n",
    "\n",
    "# Load events, firing rate vectors, and time bin indices.\n",
    "if ('events' not in locals()) and ('events' not in globals()):\n",
    "    events = goldmine_behavior.Events(subj_sess)\n",
    "elif subj_sess != events.event_times.iloc[0]['subj_sess']:\n",
    "    events = goldmine_behavior.Events(subj_sess)\n",
    "if subj_sess == 'U518_ses1':\n",
    "    events.event_times = events.event_times.loc[events.event_times['trial']!=31].reset_index(drop=True)\n",
    "\n",
    "twise_spikes_by_time = od([])\n",
    "for game_state in ['Delay1', 'Delay2']:\n",
    "    twise_spikes = (events.event_times.query(\"(gameState=='{}')\".format(game_state))['time']\n",
    "                                      .apply(lambda x: spike_preproc.apply_spike_times_to_fr(x, neuron['spike_times'], sigma=0)).tolist())\n",
    "    twise_spikes_by_time[game_state] = np.array([[np.nansum(x) for x in np.array_split(x, time_bins)] for x in twise_spikes]) # trial x time_bin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "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>neuron</th>\n",
       "      <th>subj_sess</th>\n",
       "      <th>subj</th>\n",
       "      <th>sess</th>\n",
       "      <th>chan</th>\n",
       "      <th>unit</th>\n",
       "      <th>fr</th>\n",
       "      <th>hemroi</th>\n",
       "      <th>roi</th>\n",
       "      <th>fr_cat</th>\n",
       "      <th>game_state</th>\n",
       "      <th>obs_fr</th>\n",
       "      <th>obs_fr_max_ind</th>\n",
       "      <th>obs_fr_max</th>\n",
       "      <th>obs_fr_z</th>\n",
       "      <th>obs_fr_z_max_ind</th>\n",
       "      <th>obs_fr_z_max</th>\n",
       "      <th>obs_fr_z_abs_max_ind</th>\n",
       "      <th>obs_fr_z_abs_max</th>\n",
       "      <th>obs_fr_z_abs_max_sign</th>\n",
       "      <th>obs_fr_z_abs_max_above_thresh</th>\n",
       "      <th>time_field</th>\n",
       "      <th>time_field_size</th>\n",
       "      <th>obs_info</th>\n",
       "      <th>obs_info_z</th>\n",
       "      <th>pval_ind</th>\n",
       "      <th>pval</th>\n",
       "      <th>sig</th>\n",
       "      <th>sig_fdr</th>\n",
       "      <th>crit_sig</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1560</th>\n",
       "      <td>U527_ses0-45-2</td>\n",
       "      <td>U527_ses0</td>\n",
       "      <td>U527</td>\n",
       "      <td>ses0</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>0.623313</td>\n",
       "      <td>REC</td>\n",
       "      <td>EC</td>\n",
       "      <td>fr_by_time</td>\n",
       "      <td>Delay1</td>\n",
       "      <td>[1.3321504884948367, 1.3625752452272857, 1.424...</td>\n",
       "      <td>2</td>\n",
       "      <td>1.424392</td>\n",
       "      <td>[1.7997669388035749, 2.3823625812482057, 2.774...</td>\n",
       "      <td>2</td>\n",
       "      <td>2.774921</td>\n",
       "      <td>2</td>\n",
       "      <td>2.774921</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>[0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.091119</td>\n",
       "      <td>4.498725</td>\n",
       "      <td>2</td>\n",
       "      <td>0.002997</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1561</th>\n",
       "      <td>U527_ses0-45-2</td>\n",
       "      <td>U527_ses0</td>\n",
       "      <td>U527</td>\n",
       "      <td>ses0</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>0.623313</td>\n",
       "      <td>REC</td>\n",
       "      <td>EC</td>\n",
       "      <td>fr_by_time</td>\n",
       "      <td>Delay2</td>\n",
       "      <td>[0.6201093450964343, 0.5308581171096035, 0.409...</td>\n",
       "      <td>11</td>\n",
       "      <td>0.868433</td>\n",
       "      <td>[-0.0022321816474061037, -0.5634069610126852, ...</td>\n",
       "      <td>11</td>\n",
       "      <td>1.463078</td>\n",
       "      <td>3</td>\n",
       "      <td>-1.686330</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.043149</td>\n",
       "      <td>-0.221273</td>\n",
       "      <td>506</td>\n",
       "      <td>0.506494</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              neuron  subj_sess  subj  sess  chan  unit        fr hemroi roi  \\\n",
       "1560  U527_ses0-45-2  U527_ses0  U527  ses0    45     2  0.623313    REC  EC   \n",
       "1561  U527_ses0-45-2  U527_ses0  U527  ses0    45     2  0.623313    REC  EC   \n",
       "\n",
       "          fr_cat game_state  \\\n",
       "1560  fr_by_time     Delay1   \n",
       "1561  fr_by_time     Delay2   \n",
       "\n",
       "                                                 obs_fr  obs_fr_max_ind  \\\n",
       "1560  [1.3321504884948367, 1.3625752452272857, 1.424...               2   \n",
       "1561  [0.6201093450964343, 0.5308581171096035, 0.409...              11   \n",
       "\n",
       "      obs_fr_max                                           obs_fr_z  \\\n",
       "1560    1.424392  [1.7997669388035749, 2.3823625812482057, 2.774...   \n",
       "1561    0.868433  [-0.0022321816474061037, -0.5634069610126852, ...   \n",
       "\n",
       "      obs_fr_z_max_ind  obs_fr_z_max  obs_fr_z_abs_max_ind  obs_fr_z_abs_max  \\\n",
       "1560                 2      2.774921                     2          2.774921   \n",
       "1561                11      1.463078                     3         -1.686330   \n",
       "\n",
       "      obs_fr_z_abs_max_sign  obs_fr_z_abs_max_above_thresh  \\\n",
       "1560                      1                           True   \n",
       "1561                     -1                          False   \n",
       "\n",
       "                                             time_field  time_field_size  \\\n",
       "1560  [0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...              4.0   \n",
       "1561  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...              0.0   \n",
       "\n",
       "      obs_info  obs_info_z  pval_ind      pval    sig  sig_fdr  crit_sig  \n",
       "1560  0.091119    4.498725         2  0.002997   True     True      True  \n",
       "1561  0.043149   -0.221273       506  0.506494  False    False     False  "
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_fr.query(\"(subj_sess=='{}') & (chan=={}) & (unit=={}) & (game_state==['Delay1', 'Delay2'])\".format(subj_sess, chan, unit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Delay1 0.021166272546616308 0.009238363533473364\n",
      "Delay2 -0.009927669393152084 0.009360664550492932\n",
      "Ttest_indResult(statistic=2.3642377191915394, pvalue=0.018223029665099543)\n",
      "Done in 0.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home1/dscho/anaconda3/envs/memlab/lib/python3.6/site-packages/scipy/stats/stats.py:3010: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  r = r_num / r_den\n"
     ]
    }
   ],
   "source": [
    "start_time = time()\n",
    "\n",
    "# Identify all within-trial and between-trial pairs.\n",
    "assert twise_spikes_by_time['Delay1'].shape[0] == twise_spikes_by_time['Delay2'].shape[0]\n",
    "n_trials = twise_spikes_by_time['Delay1'].shape[0]\n",
    "\n",
    "within_pairs = [(x, y) for x in range(n_trials) for y in range(n_trials) if (x<y)]\n",
    "between_pairs = [(x, y) for x in range(n_trials) for y in range(n_trials) if (x<=y)]\n",
    "\n",
    "# Calculate the Pearson correlation between firing over time vectors for each trial pair.\n",
    "distances = od([('Delay1', []),\n",
    "                ('Delay2', []),\n",
    "                ('between', [])])\n",
    "\n",
    "for iPair in range(len(within_pairs)):\n",
    "    t1, t2 = within_pairs[iPair]\n",
    "    distances['Delay1'].append(stats.pearsonr(twise_spikes_by_time['Delay1'][t1, :],\n",
    "                                             twise_spikes_by_time['Delay1'][t2, :])[0])\n",
    "    distances['Delay2'].append(stats.pearsonr(twise_spikes_by_time['Delay2'][t1, :],\n",
    "                                             twise_spikes_by_time['Delay2'][t2, :])[0])\n",
    "    \n",
    "for iPair in range(len(between_pairs)):\n",
    "    t1, t2 = between_pairs[iPair]\n",
    "    distances['between'].append(stats.pearsonr(twise_spikes_by_time['Delay1'][t1, :],\n",
    "                                               twise_spikes_by_time['Delay2'][t2, :])[0])\n",
    "    \n",
    "distances['Delay1'] = np.array(distances['Delay1'])[np.isfinite(distances['Delay1'])]\n",
    "distances['Delay2'] = np.array(distances['Delay2'])[np.isfinite(distances['Delay2'])]\n",
    "distances['between'] = np.array(distances['between'])[np.isfinite(distances['between'])]\n",
    "\n",
    "print('Delay1', np.mean(distances['Delay1']), stats.sem(distances['Delay1']))\n",
    "print('Delay2', np.mean(distances['Delay2']), stats.sem(distances['Delay2']))\n",
    "print(stats.ttest_ind(distances['Delay1'], distances['Delay2'], equal_var=False))\n",
    "\n",
    "print('Done in {:.1f}s'.format(time() - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Delay1 6.470626985535602 0.06321649796399922\n",
      "Delay2 5.566419082836474 0.08017988773811696\n",
      "Ttest_indResult(statistic=8.855790831619018, pvalue=2.9488370844402827e-18)\n",
      "Done in 0.0s\n"
     ]
    }
   ],
   "source": [
    "start_time = time()\n",
    "\n",
    "# Calculate the inverse covariance matrix across all Delay 1 and Delay 2 trials.\n",
    "spike_mat = np.concatenate((twise_spikes_by_time['Delay1'], \n",
    "                            twise_spikes_by_time['Delay2']), axis=0) # trial x time_bin\n",
    "inv_covmat = sp.linalg.inv(np.cov(spike_mat.T)) # time_bin x time_bin\n",
    "\n",
    "# Identify all within-trial and between-trial pairs.\n",
    "assert twise_spikes_by_time['Delay1'].shape[0] == twise_spikes_by_time['Delay2'].shape[0]\n",
    "n_trials = twise_spikes_by_time['Delay1'].shape[0]\n",
    "\n",
    "within_pairs = [(x, y) for x in range(n_trials) for y in range(n_trials) if (x<y)]\n",
    "between_pairs = [(x, y) for x in range(n_trials) for y in range(n_trials) if (x<=y)]\n",
    "\n",
    "# Calculate the Mahalanobis distance between firing over time vectors for each trial pair.\n",
    "distances = od([('Delay1', []),\n",
    "                ('Delay2', []),\n",
    "                ('between', [])])\n",
    "\n",
    "for iPair in range(len(within_pairs)):\n",
    "    t1, t2 = within_pairs[iPair]\n",
    "    distances['Delay1'].append(mahalanobis(twise_spikes_by_time['Delay1'][t1, :],\n",
    "                                           twise_spikes_by_time['Delay1'][t2, :],\n",
    "                                           inv_covmat))\n",
    "    distances['Delay2'].append(mahalanobis(twise_spikes_by_time['Delay2'][t1, :],\n",
    "                                           twise_spikes_by_time['Delay2'][t2, :],\n",
    "                                           inv_covmat))\n",
    "    \n",
    "for iPair in range(len(between_pairs)):\n",
    "    t1, t2 = between_pairs[iPair]\n",
    "    distances['between'].append(mahalanobis(twise_spikes_by_time['Delay1'][t1, :],\n",
    "                                            twise_spikes_by_time['Delay2'][t2, :],\n",
    "                                            inv_covmat))\n",
    "    \n",
    "print('Delay1', np.mean(distances['Delay1']), stats.sem(distances['Delay1']))\n",
    "print('Delay2', np.mean(distances['Delay2']), stats.sem(distances['Delay2']))\n",
    "print(stats.ttest_ind(distances['Delay1'], distances['Delay2'], equal_var=False))\n",
    "\n",
    "print('Done in {:.1f}s'.format(time() - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.heatmap(np.cov(spike_mat.T))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3000x900 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(10, 3), dpi=300)\n",
    "ax = np.ravel(ax)\n",
    "\n",
    "i = 0\n",
    "sns.heatmap(twise_spikes_by_time['Delay1'], ax=ax[i])\n",
    "ax[i].set_title('Delay 1')\n",
    "\n",
    "i = 1\n",
    "sns.heatmap(twise_spikes_by_time['Delay2'], ax=ax[i])\n",
    "ax[i].set_title('Delay 2')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Delay1 0.2410552361312156 0.007688825582608449\n",
      "Delay2 0.14736137858424547 0.008339319350092496\n",
      "between 0.169295772781115 0.007878699874328036\n",
      "Delay1 vs. between\n",
      "Ttest_indResult(statistic=6.51841931967042, pvalue=1.0179188977127066e-10)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3000x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 3, figsize=(10, 3), dpi=300)\n",
    "ax = np.ravel(ax)\n",
    "\n",
    "print('Delay1', np.mean(distances['Delay1']), stats.sem(distances['Delay1']))\n",
    "print('Delay2', np.mean(distances['Delay2']), stats.sem(distances['Delay2']))\n",
    "print('between', np.mean(distances['between']), stats.sem(distances['between']))\n",
    "if np.mean(distances['Delay1']) > np.mean(distances['Delay2']):\n",
    "    print('Delay1 vs. between')\n",
    "    print(stats.ttest_ind(distances['Delay1'], distances['between'], equal_var=False))\n",
    "else:\n",
    "    print('Delay2 vs. between')\n",
    "    print(stats.ttest_ind(distances['Delay2'], distances['between'], equal_var=False))\n",
    "\n",
    "i = 0\n",
    "ax[i].hist(distances['Delay1'])\n",
    "ax[i].set_title('Delay1')\n",
    "\n",
    "i = 1\n",
    "ax[i].hist(distances['Delay2'])\n",
    "ax[i].set_title('Delay2')\n",
    "\n",
    "i = 2\n",
    "ax[i].hist(distances['between'])\n",
    "ax[i].set_title('Between')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "memlab",
   "language": "python",
   "name": "memlab"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}