phase_locking_gold_encoding.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f8743a13-8ab3-4061-ae26-ccb1279f0c33",
   "metadata": {},
   "outputs": [],
   "source": [
    "# General\n",
    "import sys\n",
    "import os\n",
    "import os.path as op\n",
    "import glob\n",
    "from copy import deepcopy\n",
    "from time import time\n",
    "from collections import OrderedDict as od\n",
    "from importlib import reload\n",
    "from glob import glob\n",
    "import itertools\n",
    "import warnings\n",
    "from cluster_helper.cluster import cluster_view\n",
    "\n",
    "# Scientific\n",
    "import numpy as np\n",
    "import xarray\n",
    "import pandas as pd\n",
    "pd.options.display.max_rows = 200\n",
    "pd.options.display.max_columns = 999\n",
    "from scipy import signal\n",
    "from scipy.io import loadmat\n",
    "import h5py\n",
    "\n",
    "# Stats\n",
    "import scipy.stats as stats\n",
    "import statsmodels.api as sm\n",
    "import astropy.stats.circstats as circstats\n",
    "import pycircstat\n",
    "import random\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# Plots\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.style.use('/home1/dscho/code/general/preferred.mplstyle')\n",
    "import seaborn as sns\n",
    "from matplotlib.lines import Line2D\n",
    "import matplotlib.path as mpath\n",
    "import matplotlib.patches as patches\n",
    "from matplotlib.backends.backend_pdf import PdfPages\n",
    "\n",
    "# Neuro\n",
    "import mne\n",
    "from fooof import FOOOF, FOOOFGroup\n",
    "from fooof.objs.utils import combine_fooofs\n",
    "#import neurodsp\n",
    "\n",
    "# Personal\n",
    "sys.path.append('/home1/dscho/code/general')\n",
    "sys.path.append('/home1/dscho/code/projects')\n",
    "from formatting import *\n",
    "from helper_funcs import *\n",
    "import data_io as dio\n",
    "import array_operations as aop\n",
    "from eeg_plotting import plot_trace, plot_trace2\n",
    "from unit_activity_and_hpc_theta import phase_locking as phlock\n",
    "from time_cells import spike_sorting, spike_preproc, eeg_preproc, events_preproc, events_proc, spectral_analysis, time_bin_analysis\n",
    "from phase_locking import eeg_preproc, spectral_analysis, unit_lfp_comparison\n",
    "\n",
    "# Colors\n",
    "colors, palettes = get_colors()\n",
    "co = colors\n",
    "mpl.rcParams['axes.prop_cycle'] = mpl.cycler('color', palettes['economist'])\n",
    "\n",
    "colws = od([(1, 1.555118),\n",
    "            (2, 3.169291),\n",
    "            (3, 6.456693)])\n",
    "\n",
    "proj_dir = '/home1/dscho/projects/unit_activity_and_hpc_theta'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "416bec46-149e-4504-9014-4a342a3bd260",
   "metadata": {},
   "outputs": [],
   "source": [
    "from phase_locking.unit_lfp_comparison import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c07a4696-6776-46bd-ade3-2277fee787b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def unit_to_lfp_phase_locking_osc2mask(unit,\n",
    "                                       expmt='goldmine',\n",
    "                                       game_states=['Encoding', 'Retrieval'],\n",
    "                                       freqs=np.arange(1, 31),\n",
    "                                       n_rois=6,\n",
    "                                       n_perm=1000,\n",
    "                                       alpha=0.05,\n",
    "                                       data_dir='/home1/dscho/projects/unit_activity_and_hpc_theta/data2/goldmine/nav',\n",
    "                                       output_dir=None,\n",
    "                                       save_output=True,\n",
    "                                       overwrite=False,\n",
    "                                       verbose=True):\n",
    "    \"\"\"Calculate unit's phase-locking to hippocampal oscillations when\n",
    "    local oscillations are present versus absent.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    unit : dict or series\n",
    "        Contains the spike times vector and identifying info for a unit.\n",
    "    freqs : array\n",
    "        Frequencies at which spike-phase relations are analyzed.\n",
    "    n_rois : int\n",
    "        Number of meta-regions to assign for categorization.\n",
    "    n_perm : int\n",
    "        Number of permutations drawn to construst the null distribution.\n",
    "        For each permutation, spike times are circ-shifted at random\n",
    "        within each event, and phase-locking values at each frequency\n",
    "        are recalculcated across events.\n",
    "    alpha : float\n",
    "        Defines the significance threshold for the phase-locking\n",
    "        empirical p-value.\n",
    "    data_dir : str\n",
    "        Filepath to the location where saved inputs are stored.\n",
    "    output_dir : str | None\n",
    "        Filepath to the location where the output file is saved.\n",
    "    save_output : bool\n",
    "        Output is saved only if True.\n",
    "    overwrite : bool\n",
    "        If False and saved output already exists, it is simply returned\n",
    "        at the top of this function. Otherwise phase-locking is\n",
    "        calculated and, if save_output is True, any existing output file\n",
    "        is overwritten.\n",
    "    verbose : bool\n",
    "        If True, some info is printed to the standard output.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    pl_mrls : dataframe\n",
    "        Each row corresponds to one unit -> LFPs from one microwire\n",
    "        bundle.\n",
    "    \"\"\"\n",
    "    timer = Timer()\n",
    "\n",
    "    # Load the output file if it exists.\n",
    "    output_b = '{}-{}-{}.pkl'.format(unit['subj_sess'], unit['chan'], unit['unit'])\n",
    "    if output_dir is None:\n",
    "        output_dir = op.join(data_dir, 'phase_locking', 'osc2mask')\n",
    "    output_f = op.join(output_dir, output_b)\n",
    "    if op.exists(output_f) and not overwrite:\n",
    "        if verbose:\n",
    "            print('Loading saved output: {}'.format(output_f))\n",
    "        return dio.open_pickle(output_f)\n",
    "\n",
    "    # Process the local LFP and all hippocampal ROIs.\n",
    "    mont = spike_preproc.get_montage(unit['subj_sess'])\n",
    "    roi_map = spike_preproc.roi_mapping(n=n_rois)\n",
    "    hpc_rois = ['AH', 'MH', 'PH']\n",
    "    lfp_hemrois = np.unique([hemroi for hemroi in mont.keys() if hemroi[1:] in hpc_rois])\n",
    "    pl_mrls = []\n",
    "    for lfp_hemroi in lfp_hemrois:\n",
    "        # ------------------------------------\n",
    "        # Data loading\n",
    "\n",
    "        # Determine which regions we're looking at.\n",
    "        lfp_roi_gen = roi_map[lfp_hemroi[1:]]\n",
    "        if unit['roi_gen'] == 'HPC':\n",
    "            if lfp_roi_gen == 'HPC':\n",
    "                if unit['hemroi'] == lfp_hemroi:\n",
    "                    edge = 'hpc-local'\n",
    "                else:\n",
    "                    edge = 'hpc-hpc'\n",
    "            else:\n",
    "                edge = 'hpc-ctx'\n",
    "        else:\n",
    "            if lfp_roi_gen != 'HPC':\n",
    "                if unit['hemroi'] == lfp_hemroi:\n",
    "                    edge = 'ctx-local'\n",
    "                else:\n",
    "                    edge = 'ctx-ctx'\n",
    "            else:\n",
    "                edge = 'ctx-hpc'\n",
    "        same_hem = unit['hem'] == lfp_hemroi[0]\n",
    "        same_roi_gen = unit['roi_gen'] == lfp_roi_gen\n",
    "\n",
    "        # Load event_times.\n",
    "        expmt = 'goldmine'\n",
    "        game_states = ['Encoding']\n",
    "        event_times = load_event_times(unit['subj_sess'],\n",
    "                                       expmt=expmt,\n",
    "                                       game_states=game_states,\n",
    "                                       data_dir=data_dir)\n",
    "\n",
    "        # Calculate spike times relative to the start of each event.\n",
    "        event_time_spikes = load_event_time_spikes(event_times,\n",
    "                                                   unit['spike_times'])\n",
    "\n",
    "        # Load phase values for each event.\n",
    "        basename_lfp = '{}-{}.pkl'.format(unit['subj_sess'], lfp_hemroi)\n",
    "        phase = dio.open_pickle(op.join(data_dir, 'spectral', 'phase', basename_lfp))\n",
    "        if (phase.buffer > 0) & (not phase.clip_buffer):\n",
    "            phase = phase.loc[:, :, :, phase.buffer:phase.time.size-phase.buffer-1]\n",
    "            phase.attrs['clip_buffer'] = True\n",
    "        lfp_chans = [chan for chan in phase.chan.values if chan not in [unit['chan']]]\n",
    "        if unit['chan'] in phase.chan:\n",
    "            phase = phase.loc[{'chan': lfp_chans}]\n",
    "        if not np.array_equal(phase.freq.values, freqs):\n",
    "            phase = phase.loc[{'freq': [freq for freq in phase.freq.values if freq in freqs]}]\n",
    "        if np.nanmax(phase.values) > np.pi:\n",
    "            phase -= np.pi # convert values back to -π to π, with -π being the trough.\n",
    "\n",
    "        # Load the P-episode mask for each event.\n",
    "        roi_keys = ['unit', 'lfp']\n",
    "        osc_mask = {}\n",
    "        for key in roi_keys:\n",
    "            hemroi = lfp_hemroi if key=='lfp' else unit['hemroi']\n",
    "            basename = '{}-{}.pkl'.format(unit['subj_sess'], hemroi)\n",
    "            osc_mask[key] = dio.open_pickle(op.join(data_dir, 'p_episode', basename))\n",
    "            if (osc_mask[key].buffer > 0) & (not osc_mask[key].clip_buffer):\n",
    "                osc_mask[key] = osc_mask[key].loc[:, :, :, osc_mask[key].buffer:osc_mask[key].time.size-osc_mask[key].buffer-1]\n",
    "                osc_mask[key].attrs['clip_buffer'] = True\n",
    "            if unit['chan'] in osc_mask[key].chan:\n",
    "                osc_mask[key] = osc_mask[key].loc[{'chan': [chan for chan in osc_mask[key].chan.values\n",
    "                                                            if chan not in [unit['chan']]]}]\n",
    "            if not np.array_equal(osc_mask[key].freq.values, freqs):\n",
    "                osc_mask[key] = osc_mask[key].loc[{'freq': [freq for freq in osc_mask[key].freq.values\n",
    "                                                            if freq in freqs]}]\n",
    "        osc_mask['lfp_and_unit'] = osc_mask['lfp'].copy(data=osc_mask['lfp'].values & np.any(osc_mask['unit'].values, axis=1)[:, None, :, :])\n",
    "        osc_mask['lfp_not_unit'] = osc_mask['lfp'].copy(data=osc_mask['lfp'].values & ~np.any(osc_mask['unit'].values, axis=1)[:, None, :, :])\n",
    "        \n",
    "        mask_keys = ['lfp_and_unit', 'lfp_not_unit']\n",
    "        for key in mask_keys:\n",
    "            # Ensure that phase, oscillation mask, and spike_times are all in the same event order.\n",
    "            assert np.array_equal(phase.event.values, osc_mask[key].event.values)\n",
    "            assert np.array_equal(phase.event.values,\n",
    "                                  np.array(event_time_spikes.apply(lambda x: (x['gameState'],\n",
    "                                                                              x['trial']),\n",
    "                                                                   axis=1).values))\n",
    "\n",
    "            # Calculate P-episode at each frequency, across channels and events.\n",
    "            peps = 100 * osc_mask[key].mean(dim=('event', 'chan', 'time')).values\n",
    "            max_pep = np.nanmax(peps)\n",
    "            max_pep_freq = np.nanargmax(peps)\n",
    "\n",
    "            # ------------------------------------\n",
    "            # Calculate real phase-locking\n",
    "\n",
    "            # Get a masked array of spike phases, across events, during active\n",
    "            # oscillations at each channel and frequency.\n",
    "            event_dur = phase.time.size\n",
    "            spike_phases = np.ma.concatenate(event_time_spikes.apply(\n",
    "                lambda x: get_spike_phases(x['spike_times'],\n",
    "                                           phase.values[x['event_idx'], :, :, :],\n",
    "                                           mask=np.invert(osc_mask[key].values[x['event_idx'], :, :, :]),\n",
    "                                           event_dur=event_dur,\n",
    "                                           circshift=False),\n",
    "                axis=1).tolist(), axis=-1) # chan x freq x spike\n",
    "\n",
    "            # Calculate mean resultant lengths for each channel and frequency.\n",
    "            mrls = np.nanmean([[circstats.circmoment(spike_phases[iChan, iFreq, :].compressed())[1]\n",
    "                                for iFreq in range(spike_phases.shape[1])]\n",
    "                               for iChan in range(spike_phases.shape[0])], axis=0).astype(np.float32) # (freq,)\n",
    "\n",
    "            # Log how many spikes were counted per frequency,\n",
    "            # taking the mean across channels.\n",
    "            n_spikes_mask = np.nanmean([[spike_phases[iChan, iFreq, :].flatten().compressed().size\n",
    "                                         for iFreq in range(spike_phases.shape[1])]\n",
    "                                        for iChan in range(spike_phases.shape[0])], axis=0).astype(np.float32) # (freq,)\n",
    "\n",
    "            # Get the preferred phase at each frequency, across channels.\n",
    "            pref_phases = np.array([circstats.circmoment(spike_phases[:, iFreq, :].flatten().compressed())[0]\n",
    "                                    for iFreq in range(spike_phases.shape[1])]).astype(np.float32) # (freq,)\n",
    "\n",
    "            # ------------------------------------\n",
    "            # Calculate null phase-locking\n",
    "            mrls_null = []\n",
    "            n_spikes_mask_null = []\n",
    "            for iPerm in range(n_perm):\n",
    "                # Get a masked array of spike phases, across events, during active\n",
    "                # oscillations at each channel and frequency.\n",
    "                _spike_phases_null = np.ma.concatenate(event_time_spikes.apply(\n",
    "                    lambda x: get_spike_phases(x['spike_times'],\n",
    "                                               phase[x['event_idx'], :, :, :],\n",
    "                                               mask=np.invert(osc_mask[key][x['event_idx'], :, :, :]),\n",
    "                                               event_dur=event_dur,\n",
    "                                               circshift=True),\n",
    "                    axis=1).tolist(), axis=-1) # chan x freq x spike\n",
    "\n",
    "                # Calculate mean resultant lengths for each channel and frequency.\n",
    "                _mrls_null = np.nanmean([[circstats.circmoment(_spike_phases_null[iChan, iFreq, :].compressed())[1]\n",
    "                                          for iFreq in range(_spike_phases_null.shape[1])]\n",
    "                                         for iChan in range(_spike_phases_null.shape[0])], axis=0).astype(np.float32)\n",
    "                mrls_null.append(_mrls_null.tolist())\n",
    "\n",
    "                _n_spikes_mask_null = np.nanmean([[_spike_phases_null[iChan, iFreq, :].flatten().compressed().size\n",
    "                                                   for iFreq in range(_spike_phases_null.shape[1])]\n",
    "                                                  for iChan in range(_spike_phases_null.shape[0])], axis=0).astype(np.float32)\n",
    "                n_spikes_mask_null.append(_n_spikes_mask_null)\n",
    "\n",
    "            mrls_null = np.array(mrls_null) # (perm, freq)\n",
    "            n_spikes_mask_null = np.array(n_spikes_mask_null) # (perm, freq)\n",
    "\n",
    "            # ------------------------------------\n",
    "            # Statistics\n",
    "\n",
    "            # Z-score MRLs against the null distribution,\n",
    "            # and calculate empirical P-values.\n",
    "            mean_mrls_null = np.nanmean(mrls_null, axis=0)\n",
    "            std_mrls_null = np.nanstd(mrls_null, axis=0)\n",
    "            z_mrls = (mrls - mean_mrls_null) / std_mrls_null # (freq,)\n",
    "            z_mrls_null = (mrls_null - mean_mrls_null[None, :]) / std_mrls_null[None, :] # (perm, freq)\n",
    "            max_z_mrl = np.nanmax(z_mrls)\n",
    "            max_z_mrl_freq = freqs[np.nanargmax(z_mrls)]\n",
    "            pval = (1 + np.nansum(np.nanmax(z_mrls_null, axis=1) >= max_z_mrl)) / (1 + n_perm)\n",
    "            sig = pval < alpha\n",
    "\n",
    "            mean_n_spikes_mask_null = np.nanmean(n_spikes_mask_null, axis=0)\n",
    "            std_n_spikes_mask_null = np.nanstd(n_spikes_mask_null, axis=0)\n",
    "            z_n_spikes_mask = (n_spikes_mask - mean_n_spikes_mask_null) / std_n_spikes_mask_null\n",
    "\n",
    "            # ------------------------------------\n",
    "            # Add results to dataframe.\n",
    "            pl_mrls.append([unit['subj'],\n",
    "                            unit['subj_sess'],\n",
    "                            '{}-{}'.format(unit['chan'], unit['unit']),\n",
    "                            unit['hemroi'],\n",
    "                            unit['roi_gen'],\n",
    "                            unit['n_spikes'],\n",
    "                            unit['fr'],\n",
    "                            lfp_hemroi,\n",
    "                            lfp_roi_gen,\n",
    "                            lfp_chans,\n",
    "                            edge,\n",
    "                            same_hem,\n",
    "                            same_roi_gen,\n",
    "                            key,\n",
    "                            peps,\n",
    "                            max_pep,\n",
    "                            max_pep_freq,\n",
    "                            n_spikes_mask,\n",
    "                            n_spikes_mask_null,\n",
    "                            mrls,\n",
    "                            mrls_null,\n",
    "                            pref_phases,\n",
    "                            z_n_spikes_mask,\n",
    "                            z_mrls,\n",
    "                            max_z_mrl,\n",
    "                            max_z_mrl_freq,\n",
    "                            pval,\n",
    "                            sig])\n",
    "\n",
    "    # Create the output dataframe.\n",
    "    cols = ['subj', 'subj_sess', 'unit', 'unit_hemroi', 'unit_roi_gen', 'n_spikes', 'fr',\n",
    "            'lfp_hemroi', 'lfp_roi_gen', 'lfp_chans', 'edge', 'same_hem', 'same_roi_gen',\n",
    "            'mask', 'peps', 'max_pep', 'max_pep_freq', 'n_spikes_mask', 'n_spikes_mask_null',\n",
    "            'mrls', 'mrls_null', 'pref_phases',\n",
    "            'z_n_spikes_mask', 'z_mrls', 'max_z_mrl', 'max_z_mrl_freq', 'pval', 'sig']\n",
    "    pl_mrls = pd.DataFrame(pl_mrls, columns=cols)\n",
    "\n",
    "    # Save the output.\n",
    "    if save_output:\n",
    "        dio.save_pickle(pl_mrls, output_f, verbose)\n",
    "    \n",
    "    if verbose:\n",
    "        print('pl_mrls: {}'.format(pl_mrls.shape))\n",
    "        print(timer)\n",
    "        \n",
    "    return pl_mrls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06e293ea-c596-4b52-ba8f-cd192f938ecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gold_view_dur(gold_id,\n",
    "                  event_spikes,\n",
    "                  timebin_dur=500):\n",
    "    \"\"\"Return gold view duration in ms.\"\"\"\n",
    "    giv = event_spikes.event_spikes.query(\"(gameState=='Encoding') & (gold_in_view==1)\")\n",
    "    dur = timebin_dur * np.sum(giv['gold_in_view_ids'].apply(lambda x: np.isin(gold_id, x)))\n",
    "    return dur\n",
    "\n",
    "\n",
    "def dig_attempts(gold_id,\n",
    "                 event_spikes):\n",
    "    \"\"\"Return how many digs were attempted.\"\"\"\n",
    "    all_digs = event_spikes.event_spikes.query(\"(gameState=='Retrieval') & (dig_performed==1)\")\n",
    "    n_digs = np.sum(all_digs['dig_ids'].apply(lambda x: np.isin(gold_id, x)))\n",
    "    return n_digs\n",
    "\n",
    "\n",
    "def gold_dug(gold_id,\n",
    "             event_spikes):\n",
    "    \"\"\"Return whether or not gold was dug.\"\"\"\n",
    "    all_digs = event_spikes.event_spikes.query(\"(gameState=='Retrieval') & (dig_success==1)\")\n",
    "    n_success = np.sum(all_digs['dig_ids'].apply(lambda x: np.isin(gold_id, x)))\n",
    "    if n_success > 0:\n",
    "        n_success = 1\n",
    "    else:\n",
    "        n_success = 0\n",
    "    return n_success\n",
    "\n",
    "\n",
    "for subj_sess in sessions:\n",
    "    events = events_proc.load_events(subj_sess, verbose=0)\n",
    "    evsp = time_bin_analysis.load_event_spikes(subj_sess, verbose=0)\n",
    "    \n",
    "    # Keep only nav intervals.\n",
    "    game_states = ['Encoding', 'Retrieval']\n",
    "    evsp.event_spikes = evsp.event_spikes.query(\"(gameState=={})\".format(game_states))  \n",
    "\n",
    "    # ---------------------------------------\n",
    "    # Find all contiguous gold view events during Encoding.\n",
    "    timebin_size = 500 # ms\n",
    "    giv = evsp.event_spikes.query(\"(gameState=='Encoding') & (gold_in_view==True)\")\n",
    "    gold_views = []\n",
    "    for _, gold in events.gold_spawned.iterrows():\n",
    "        idx = giv.iloc[np.where(giv['gold_in_view_ids'].apply(lambda x: np.isin(gold['gold_id'], x)))[0]].index.tolist()\n",
    "        if len(idx) > 0:\n",
    "            gold_view_bounds = np.where(np.concatenate(([False], np.diff(idx) > 1)))[0]\n",
    "            for iView, _idx in enumerate(np.split(idx, gold_view_bounds)):\n",
    "                gold_views.append([gold['trial'],\n",
    "                                   gold['gold_id'],\n",
    "                                   iView+1,\n",
    "                                   timebin_size * len(_idx),\n",
    "                                   giv.loc[_idx, 'time_bin'].tolist(),\n",
    "                                   dig_attempts(gold['gold_id'], evsp),\n",
    "                                   gold_dug(gold['gold_id'], evsp),\n",
    "                                   _idx])\n",
    "    gold_views = pd.DataFrame(gold_views, columns=['trial', 'gold_id', 'view', 'dur',\n",
    "                                                   'time_bins', 'digs', 'dig_success', 'idx'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "012fd6bd-7d39-4751-b0c0-1739d5e52318",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 subjects, 12 sessions\n"
     ]
    }
   ],
   "source": [
    "# Get sessions.\n",
    "sessions = np.unique([op.basename(f).split('-')[0] \n",
    "                      for f in glob(op.join('/data7', 'goldmine', '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": 227,
   "id": "ae81739a-144f-416e-9f04-15a31b629a4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U518_ses0: 36 trials 40 gold spawned (1-2/trial, mean 1.1), 27/40 (67.5%) viewed, 13/27 (48.1%) dug, 34.2% digging accuracy\n",
      "U518_ses1: 34 trials 58 gold spawned (1-4/trial, mean 1.7), 46/58 (79.3%) viewed, 32/46 (69.6%) dug, 56.1% digging accuracy\n",
      "U521_ses0: 35 trials 39 gold spawned (1-2/trial, mean 1.1), 19/39 (48.7%) viewed,  9/19 (47.4%) dug, 25.7% digging accuracy\n",
      "U526_ses0: 36 trials 59 gold spawned (1-3/trial, mean 1.6), 44/59 (74.6%) viewed, 32/44 (72.7%) dug, 71.1% digging accuracy\n",
      "U527_ses0: 36 trials 71 gold spawned (1-3/trial, mean 2.0), 63/71 (88.7%) viewed, 46/63 (73.0%) dug, 63.9% digging accuracy\n",
      "U530_ses0: 36 trials 38 gold spawned (1-2/trial, mean 1.1), 19/38 (50.0%) viewed, 10/19 (52.6%) dug, 55.6% digging accuracy\n",
      "U530_ses1: 36 trials 48 gold spawned (1-2/trial, mean 1.3), 37/48 (77.1%) viewed, 20/37 (54.1%) dug, 48.8% digging accuracy\n",
      "U532_ses0: 33 trials 65 gold spawned (1-4/trial, mean 2.0), 56/65 (86.2%) viewed, 44/56 (78.6%) dug, 41.5% digging accuracy\n",
      "U536_ses0: 36 trials 58 gold spawned (1-3/trial, mean 1.6), 44/58 (75.9%) viewed, 32/44 (72.7%) dug, 57.1% digging accuracy\n",
      "U537_ses0: 36 trials 42 gold spawned (1-2/trial, mean 1.2), 22/42 (52.4%) viewed, 15/22 (68.2%) dug, 29.4% digging accuracy\n",
      "U539_ses0: 34 trials 38 gold spawned (1-2/trial, mean 1.1), 28/38 (73.7%) viewed, 11/28 (39.3%) dug, 18.3% digging accuracy\n",
      "U540_ses0: 36 trials 48 gold spawned (1-3/trial, mean 1.3), 27/48 (56.2%) viewed, 18/27 (66.7%) dug, 58.1% digging accuracy\n",
      "\n",
      "Across sessions\n",
      "---------------\n",
      "gold_spawned: mean = 50.3 (range 38-71)\n",
      "gold_viewed: mean = 36.0 (range 19-63)\n",
      "gold_dug: mean = 23.5 (range 9-46)\n",
      "dig_acc: mean = 46.7% (range 18.3%-71.1%)\n"
     ]
    }
   ],
   "source": [
    "# How many golds were spawned, viewed, and dug in each session?\n",
    "n_trials = []\n",
    "gold_spawned = []\n",
    "min_gold = []\n",
    "max_gold = []\n",
    "mean_gold = []\n",
    "gold_viewed = []\n",
    "gold_dug = []\n",
    "dig_acc = []\n",
    "for subj_sess in sessions:\n",
    "    events = events_proc.load_events(subj_sess, verbose=0)\n",
    "    n_trials.append(events.gold_spawned['trial'].unique().size)\n",
    "    gold_spawned.append(events.gold_spawned['gold_id'].unique().size)\n",
    "    gold_per_trial = events.gold_spawned.groupby('trial').size()\n",
    "    min_gold.append(np.min(gold_per_trial))\n",
    "    max_gold.append(np.max(gold_per_trial))\n",
    "    mean_gold.append(np.mean(gold_per_trial))\n",
    "    gold_viewed.append(events.gold_events.query(\"(gameState=='Encoding') & (in_view==True)\")['gold_id'].unique().size)\n",
    "    gold_dug.append(len(events.dig_events.query(\"(dig_success==True)\")))\n",
    "    dig_acc.append(events.dig_events['dig_success'].mean())\n",
    "    print('{}: {} trials {} gold spawned ({}-{}/trial, mean {:.1f}), {}/{} ({:.1%}) viewed, {:>2}/{} ({:.1%}) dug, {:.1%} digging accuracy'\n",
    "          .format(subj_sess, n_trials[-1], gold_spawned[-1], min_gold[-1], max_gold[-1], mean_gold[-1],\n",
    "                  gold_viewed[-1], gold_spawned[-1], gold_viewed[-1] / gold_spawned[-1],\n",
    "                  gold_dug[-1], gold_viewed[-1], gold_dug[-1] / gold_viewed[-1], dig_acc[-1]))\n",
    "\n",
    "print('', 'Across sessions', '-' * 15,\n",
    "      'gold_spawned: mean = {:.1f} (range {}-{})'.format(np.mean(gold_spawned), np.min(gold_spawned), np.max(gold_spawned)),\n",
    "      'gold_viewed: mean = {:.1f} (range {}-{})'.format(np.mean(gold_viewed), np.min(gold_viewed), np.max(gold_viewed)),\n",
    "      'gold_dug: mean = {:.1f} (range {}-{})'.format(np.mean(gold_dug), np.min(gold_dug), np.max(gold_dug)),\n",
    "      'dig_acc: mean = {:.1%} (range {:.1%}-{:.1%})'.format(np.mean(dig_acc), np.min(dig_acc), np.max(dig_acc)), sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "19df1415-6664-426a-9c5b-81d0433894bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 subjects, 12 sessions\n",
      "spikes: (448, 10)\n",
      "Ran in 30.6s\n"
     ]
    }
   ],
   "source": [
    "# Load spikes.\n",
    "fr_thresh = 0.2\n",
    "nspike_thresh = 0 # 400\n",
    "n_rois = 6\n",
    "expmt = 'goldmine'\n",
    "game_states = ['Encoding', 'Retrieval']\n",
    "spikes = unit_lfp_comparison.load_all_unit_spikes(fr_thresh=fr_thresh,\n",
    "                                                  nspike_thresh=nspike_thresh,\n",
    "                                                  n_rois=n_rois,\n",
    "                                                  expmt=expmt,\n",
    "                                                  game_states=game_states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "95cb616b-e71d-4b15-9b67-e54791a480fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def _get_gold_views(gold_df,\n",
    "                    events):\n",
    "    \"\"\"Return start, stop times for each gold view interval.\"\"\"\n",
    "    gold_df = gold_df.reset_index(drop=True)\n",
    "    trial = gold_df.iloc[0]['trial']\n",
    "    gold_id = gold_df.iloc[0]['gold_id']\n",
    "    event_start, event_stop = (events.event_times.query(\"(trial=={}) & (gameState=='Encoding')\".format(trial))\n",
    "                                                 .iloc[0]['time_bins'][[0, -1]])\n",
    "    \n",
    "    giv = gold_df.query(\"(in_view==True)\").index\n",
    "    if len(giv) > 0:\n",
    "        _gold_df = gold_df.loc[giv[0]:].copy()\n",
    "    else:\n",
    "        gold_views = pd.DataFrame([], columns=['subj', 'subj_sess', 'trial', 'gold_id', 'view',\n",
    "                                               'digs', 'dig_success', 'start_time', 'stop_time', 'dur'])\n",
    "        return gold_views\n",
    "\n",
    "    _gold_df['view_change'] = [False] + list(_gold_df['in_view'][:-1].values != _gold_df['in_view'][1:].values)\n",
    "    _gold_df.query(\"(view_change==True)\")\n",
    "\n",
    "    gv_idx = np.concatenate(([_gold_df.index.values[0]], _gold_df.query(\"(view_change==True)\").index.values))\n",
    "#     gv_idx[1::2] -= 1\n",
    "    gv_times = gold_df.loc[gv_idx, 'time'].tolist()\n",
    "    if len(gv_times) % 2 == 1:\n",
    "        gv_times.append(event_stop)\n",
    "    gold_views = pd.DataFrame(np.vstack((gv_times[::2], gv_times[1::2])).T,\n",
    "                              columns=['start_time', 'stop_time'])\n",
    "    gold_views -= event_start\n",
    "    gold_views.insert(0, 'subj', events.subj_sess.split('_')[0])\n",
    "    gold_views.insert(1, 'subj_sess', events.subj_sess)\n",
    "    gold_views.insert(2, 'trial', trial)\n",
    "    gold_views.insert(3, 'gold_id', gold_id)\n",
    "    gold_views.insert(4, 'view', np.arange(len(gold_views))+1)\n",
    "    gold_views.insert(5, 'digs', len(events.dig_events.query(\"(gold_id=='{}')\".format(gold_id))))\n",
    "    gold_views.insert(6, 'dig_success', len(events.dig_events.query(\"(gold_id=='{}') & (dig_success==True)\".format(gold_id))) > 0)\n",
    "    gold_views['dur'] = gold_views['stop_time'] - gold_views['start_time']\n",
    "    return gold_views"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "f3eb5994-62aa-4aaa-9149-8d86e1656168",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gold_views: (663, 10)\n",
      "Ran in 0.0s\n"
     ]
    }
   ],
   "source": [
    "# Save all gold view events for each session.\n",
    "output_dir = '/home1/dscho/projects/unit_activity_and_hpc_theta/data2/goldmine/nav/events'\n",
    "save_output = True\n",
    "overwrite = False\n",
    "\n",
    "# -----------------------\n",
    "timer = Timer()\n",
    "\n",
    "gold_views = []\n",
    "for subj_sess in sessions:\n",
    "    output_f = op.join(output_dir, '{}-gold_views.pkl'.format(subj_sess))\n",
    "    if op.exists(output_f) and not overwrite:\n",
    "        gold_views.append(dio.open_pickle(output_f))\n",
    "        continue\n",
    "    events = events_proc.load_events(subj_sess, verbose=False)\n",
    "    gold_views.append(pd.concat([_get_gold_views(df, events)\n",
    "                                 for _, df in events.gold_events.query(\"(gameState=='Encoding')\")\n",
    "                                                                .groupby(['trial', 'gold_id'])])\n",
    "                        .reset_index(drop=True))\n",
    "    if save_output:\n",
    "        dio.save_pickle(gold_views[-1], output_f)\n",
    "gold_views = pd.concat(gold_views).reset_index(drop=True)\n",
    "\n",
    "print('gold_views: {}'.format(gold_views.shape))\n",
    "print(timer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "cdbb46ef-ca81-4ba8-9573-26e32a4e7b87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved /home1/dscho/projects/unit_activity_and_hpc_theta/data2/goldmine/nav/events/U518_ses0-gold_view_mask.pkl\n",
      "Saved /home1/dscho/projects/unit_activity_and_hpc_theta/data2/goldmine/nav/events/U518_ses1-gold_view_mask.pkl\n",
      "Saved /home1/dscho/projects/unit_activity_and_hpc_theta/data2/goldmine/nav/events/U521_ses0-gold_view_mask.pkl\n",
      "Saved /home1/dscho/projects/unit_activity_and_hpc_theta/data2/goldmine/nav/events/U526_ses0-gold_view_mask.pkl\n",
      "Saved /home1/dscho/projects/unit_activity_and_hpc_theta/data2/goldmine/nav/events/U527_ses0-gold_view_mask.pkl\n",
      "Saved /home1/dscho/projects/unit_activity_and_hpc_theta/data2/goldmine/nav/events/U530_ses0-gold_view_mask.pkl\n",
      "Saved /home1/dscho/projects/unit_activity_and_hpc_theta/data2/goldmine/nav/events/U530_ses1-gold_view_mask.pkl\n",
      "Saved /home1/dscho/projects/unit_activity_and_hpc_theta/data2/goldmine/nav/events/U532_ses0-gold_view_mask.pkl\n",
      "Saved /home1/dscho/projects/unit_activity_and_hpc_theta/data2/goldmine/nav/events/U536_ses0-gold_view_mask.pkl\n",
      "Saved /home1/dscho/projects/unit_activity_and_hpc_theta/data2/goldmine/nav/events/U537_ses0-gold_view_mask.pkl\n",
      "Saved /home1/dscho/projects/unit_activity_and_hpc_theta/data2/goldmine/nav/events/U539_ses0-gold_view_mask.pkl\n",
      "Saved /home1/dscho/projects/unit_activity_and_hpc_theta/data2/goldmine/nav/events/U540_ses0-gold_view_mask.pkl\n",
      "Ran in 3.1s\n"
     ]
    }
   ],
   "source": [
    "# Save gold view masks for each session.\n",
    "expmt = 'goldmine'\n",
    "game_states = ['Encoding']\n",
    "event_dur = 30000\n",
    "data_dir='/home1/dscho/projects/unit_activity_and_hpc_theta/data2/goldmine/nav'\n",
    "save_output = True\n",
    "overwrite = False\n",
    "\n",
    "# -----------------------\n",
    "timer = Timer()\n",
    "\n",
    "for subj_sess in sessions:\n",
    "    # Load the output file if it exists.\n",
    "    output_f = op.join(data_dir, 'events', '{}-gold_view_mask.pkl'.format(subj_sess))\n",
    "    if op.exists(output_f) and not overwrite:\n",
    "        gold_view_mask = dio.open_pickle(output_f)\n",
    "        continue\n",
    "    \n",
    "    # Load event_times.\n",
    "    event_times = load_event_times(subj_sess,\n",
    "                                   expmt=expmt,\n",
    "                                   game_states=game_states,\n",
    "                                   data_dir=data_dir)\n",
    "    \n",
    "    # Load gold views.\n",
    "    gold_views = dio.open_pickle(op.join(data_dir, 'events', '{}-gold_views.pkl'.format(subj_sess)))\n",
    "    n_trials = event_times.query(\"(gameState=={})\".format(game_states))['trial'].unique().size\n",
    "    \n",
    "    # Create masks of gold view times for golds that were later dug,\n",
    "    # or not dug, respectively.\n",
    "    trial_to_iTrial = {v:k for (k,v) in event_times.query(\"(gameState=={})\".format(game_states))\n",
    "                                                   .reset_index()['trial']\n",
    "                                                   .to_dict().items()}\n",
    "    gold_view_mask = {'dug': np.zeros([n_trials, event_dur], dtype=bool),\n",
    "                      'not_dug': np.zeros([n_trials, event_dur], dtype=bool)}\n",
    "    for key in ['dug', 'not_dug']:\n",
    "        dig_success = True if (key=='dug') else False\n",
    "        qry = \"(dig_success=={})\".format(dig_success)\n",
    "        for (trial, df) in gold_views.query(qry).groupby('trial'):\n",
    "            iTrial = trial_to_iTrial[trial]\n",
    "            for (_, (start_time, stop_time)) in df[['start_time', 'stop_time']].iterrows():\n",
    "                gold_view_mask[key][iTrial, start_time:stop_time] = 1\n",
    "\n",
    "    if save_output:\n",
    "        dio.save_pickle(gold_view_mask, output_f)\n",
    "        \n",
    "print(timer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "9ec9667e-f821-4378-b44e-7a9fc3810499",
   "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>golds_viewed</th>\n",
       "      <th>views_per_gold</th>\n",
       "      <th>dur_per_gold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dig_success</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>13.10 ± 0.90</td>\n",
       "      <td>1.34 ± 0.07</td>\n",
       "      <td>2879.04 ± 210.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>23.65 ± 4.18</td>\n",
       "      <td>1.56 ± 0.08</td>\n",
       "      <td>5579.37 ± 727.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             golds_viewed views_per_gold      dur_per_gold\n",
       "dig_success                                               \n",
       "False        13.10 ± 0.90    1.34 ± 0.07  2879.04 ± 210.78\n",
       "True         23.65 ± 4.18    1.56 ± 0.08  5579.37 ± 727.51"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many total golds were viewed,\n",
    "# how many times was each gold viewed,\n",
    "# and how long was each gold viewed,\n",
    "# for golds that were dug vs. not dug,\n",
    "# across subjects?\n",
    "gold_view_agg = (gold_views\n",
    "                 .groupby(['subj', 'subj_sess', 'gold_id', 'dig_success'])\n",
    "                 .agg({'view': len,\n",
    "                       'dur': np.sum})\n",
    "                 .reset_index()\n",
    "                 .groupby(['subj', 'subj_sess', 'dig_success'])\n",
    "                 .agg({'gold_id': len,\n",
    "                       'view': np.mean,\n",
    "                       'dur': np.mean})\n",
    "                 .groupby(['subj', 'dig_success'])\n",
    "                 .agg({'gold_id': np.mean,\n",
    "                       'view': np.mean,\n",
    "                       'dur': np.mean})\n",
    "                 .groupby(['dig_success'])\n",
    "                 .agg({'gold_id': mean_sem,\n",
    "                       'view': mean_sem,\n",
    "                       'dur': mean_sem})\n",
    "                 .rename(columns={'gold_id': 'golds_viewed',\n",
    "                                  'view': 'views_per_gold',\n",
    "                                  'dur': 'dur_per_gold'}))\n",
    "gold_view_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "8359823b-bb5d-4eaf-b6e5-c688d692f661",
   "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>golds_viewed</th>\n",
       "      <th>views_per_gold</th>\n",
       "      <th>dur_per_gold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dig_success</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>12.75 (10.50, 14.38)</td>\n",
       "      <td>1.00 (1.00, 1.00)</td>\n",
       "      <td>2625.00 (2390.94, 3188.00)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>19.50 (12.62, 32.00)</td>\n",
       "      <td>1.25 (1.00, 1.50)</td>\n",
       "      <td>3562.75 (3250.25, 4187.00)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     golds_viewed     views_per_gold  \\\n",
       "dig_success                                            \n",
       "False        12.75 (10.50, 14.38)  1.00 (1.00, 1.00)   \n",
       "True         19.50 (12.62, 32.00)  1.25 (1.00, 1.50)   \n",
       "\n",
       "                           dur_per_gold  \n",
       "dig_success                              \n",
       "False        2625.00 (2390.94, 3188.00)  \n",
       "True         3562.75 (3250.25, 4187.00)  "
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many total golds were viewed,\n",
    "# how many times was each gold viewed,\n",
    "# and how long was each gold viewed,\n",
    "# for golds that were dug vs. not dug,\n",
    "# across subjects?\n",
    "gold_view_agg = (gold_views\n",
    "                 .groupby(['subj', 'subj_sess', 'gold_id', 'dig_success'])\n",
    "                 .agg({'view': len,\n",
    "                       'dur': np.sum})\n",
    "                 .reset_index()\n",
    "                 .groupby(['subj', 'subj_sess', 'dig_success'])\n",
    "                 .agg({'gold_id': len,\n",
    "                       'view': np.median,\n",
    "                       'dur': np.median})\n",
    "                 .groupby(['subj', 'dig_success'])\n",
    "                 .agg({'gold_id': np.median,\n",
    "                       'view': np.median,\n",
    "                       'dur': np.median})\n",
    "                 .groupby(['dig_success'])\n",
    "                 .agg({'gold_id': median_q,\n",
    "                       'view': median_q,\n",
    "                       'dur': median_q})\n",
    "                 .rename(columns={'gold_id': 'golds_viewed',\n",
    "                                  'view': 'views_per_gold',\n",
    "                                  'dur': 'dur_per_gold'}))\n",
    "gold_view_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "id": "0bad432e-2fc2-45b8-8e95-b01a30ad6bba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading saved Events file\n"
     ]
    }
   ],
   "source": [
    "# Load event_times.\n",
    "subj_sess = 'U530_ses1'\n",
    "unit = spikes.query(\"(subj_sess=='{}')\".format(subj_sess)).iloc[0]\n",
    "data_dir='/home1/dscho/projects/unit_activity_and_hpc_theta/data2/goldmine/nav'\n",
    "expmt = 'goldmine'\n",
    "game_states = ['Encoding']\n",
    "events = events_proc.load_events(subj_sess)\n",
    "event_times = load_event_times(unit['subj_sess'],\n",
    "                               expmt=expmt,\n",
    "                               game_states=game_states,\n",
    "                               data_dir=data_dir)\n",
    "\n",
    "# Calculate spike times relative to the start of each event.\n",
    "event_time_spikes = load_event_time_spikes(event_times,\n",
    "                                           unit['spike_times'])\n",
    "\n",
    "gold_views = dio.open_pickle(op.join(data_dir, 'events', '{}-gold_views.pkl'.format(subj_sess)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "id": "55b22ebc-4276-434a-8267-6adab5fa8f34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------\n",
      "U530_ses1\n",
      "36 trials completed, 36 good\n",
      "\n",
      "48 gold spawned\n",
      "43 gold viewed (90%)\n",
      "20 gold dug (42%)\n",
      "41 dig attempts (49% accuracy)\n",
      "\n",
      "dataframe sizes:\n",
      "\tevents: (76325, 8)\n",
      "\tevent_times: (216, 10)\n",
      "\tpositions: (47745, 15)\n",
      "\tbase_events: (9548, 7)\n",
      "\tgold_events: (11665, 8)\n",
      "\tgold_spawned: (48, 8)\n",
      "\tdig_events: (41, 8)\n",
      "\tevents_behav: (6210, 20)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(events)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "id": "1a61d74e-c8dc-4ca0-8a32-bc31bca5c49a",
   "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>subj</th>\n",
       "      <th>subj_sess</th>\n",
       "      <th>trial</th>\n",
       "      <th>gold_id</th>\n",
       "      <th>view</th>\n",
       "      <th>digs</th>\n",
       "      <th>dig_success</th>\n",
       "      <th>start_time</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>dur</th>\n",
       "      <th>spike_times</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>U530</td>\n",
       "      <td>U530_ses1</td>\n",
       "      <td>1</td>\n",
       "      <td>gold0001</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>12882</td>\n",
       "      <td>17133</td>\n",
       "      <td>4251</td>\n",
       "      <td>[164, 213, 422, 503, 764, 777, 1475, 1627, 172...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U530</td>\n",
       "      <td>U530_ses1</td>\n",
       "      <td>3</td>\n",
       "      <td>gold0003</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>10399</td>\n",
       "      <td>14399</td>\n",
       "      <td>4000</td>\n",
       "      <td>[129, 137, 531, 725, 732, 863, 1774, 2033, 224...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U530</td>\n",
       "      <td>U530_ses1</td>\n",
       "      <td>4</td>\n",
       "      <td>gold0005</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>28866</td>\n",
       "      <td>29115</td>\n",
       "      <td>249</td>\n",
       "      <td>[264, 522, 941, 1080, 1130, 1528, 1931, 2113, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>U530</td>\n",
       "      <td>U530_ses1</td>\n",
       "      <td>4</td>\n",
       "      <td>gold0005</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>29366</td>\n",
       "      <td>30000</td>\n",
       "      <td>634</td>\n",
       "      <td>[264, 522, 941, 1080, 1130, 1528, 1931, 2113, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>U530</td>\n",
       "      <td>U530_ses1</td>\n",
       "      <td>5</td>\n",
       "      <td>gold0007</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>11150</td>\n",
       "      <td>11401</td>\n",
       "      <td>251</td>\n",
       "      <td>[270, 382, 601, 809, 1215, 1413, 2154, 2269, 2...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subj  subj_sess trial   gold_id view digs dig_success start_time stop_time  \\\n",
       "0  U530  U530_ses1     1  gold0001    1    1       False      12882     17133   \n",
       "1  U530  U530_ses1     3  gold0003    1    1        True      10399     14399   \n",
       "2  U530  U530_ses1     4  gold0005    1    0       False      28866     29115   \n",
       "3  U530  U530_ses1     4  gold0005    2    0       False      29366     30000   \n",
       "4  U530  U530_ses1     5  gold0007    1    1        True      11150     11401   \n",
       "\n",
       "    dur                                        spike_times  \n",
       "0  4251  [164, 213, 422, 503, 764, 777, 1475, 1627, 172...  \n",
       "1  4000  [129, 137, 531, 725, 732, 863, 1774, 2033, 224...  \n",
       "2   249  [264, 522, 941, 1080, 1130, 1528, 1931, 2113, ...  \n",
       "3   634  [264, 522, 941, 1080, 1130, 1528, 1931, 2113, ...  \n",
       "4   251  [270, 382, 601, 809, 1215, 1413, 2154, 2269, 2...  "
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gold_view_spikes = pd.merge(gold_views.query(\"(subj_sess=='{}')\".format(subj_sess)),\n",
    "                            event_time_spikes[['trial', 'spike_times']],\n",
    "                            how='left',\n",
    "                            on='trial').reset_index(drop=True)\n",
    "gold_view_spikes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "id": "8ea02127-297c-497e-8576-182ed4a59f59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U530_ses1\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 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>gold_id</th>\n",
       "      <th colspan=\"2\" halign=\"left\">view</th>\n",
       "      <th colspan=\"2\" halign=\"left\">dur</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>len</th>\n",
       "      <th>mean_sem</th>\n",
       "      <th>median_q</th>\n",
       "      <th>mean_sem</th>\n",
       "      <th>median_q</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dig_success</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>False</th>\n",
       "      <td>18</td>\n",
       "      <td>1.17 ± 0.09</td>\n",
       "      <td>1.00 (1.00, 1.00)</td>\n",
       "      <td>2830.56 ± 338.05</td>\n",
       "      <td>2625.50 (1750.00, 3686.75)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>19</td>\n",
       "      <td>1.32 ± 0.11</td>\n",
       "      <td>1.00 (1.00, 2.00)</td>\n",
       "      <td>3180.79 ± 302.32</td>\n",
       "      <td>3250.00 (2001.00, 4250.00)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            gold_id         view                                  dur  \\\n",
       "                len     mean_sem           median_q          mean_sem   \n",
       "dig_success                                                             \n",
       "False            18  1.17 ± 0.09  1.00 (1.00, 1.00)  2830.56 ± 338.05   \n",
       "True             19  1.32 ± 0.11  1.00 (1.00, 2.00)  3180.79 ± 302.32   \n",
       "\n",
       "                                         \n",
       "                               median_q  \n",
       "dig_success                              \n",
       "False        2625.50 (1750.00, 3686.75)  \n",
       "True         3250.00 (2001.00, 4250.00)  "
      ]
     },
     "execution_count": 416,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(subj_sess)\n",
    "\n",
    "(gold_view_spikes\n",
    " .groupby(['dig_success', 'gold_id'])\n",
    " .agg({'view': len,\n",
    "       'dur': np.sum})\n",
    " .reset_index()\n",
    " .groupby(['dig_success'])\n",
    " .agg({'gold_id': len,\n",
    "       'view': [mean_sem, median_q],\n",
    "       'dur': [mean_sem, median_q]}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "id": "aa909c82-08ec-4ede-92b0-2899685c3f43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 7.0s\n"
     ]
    }
   ],
   "source": [
    "# unit_to_lfp_phase_locking_gold_view\n",
    "subj_sess = 'U527_ses0'\n",
    "unit = spikes.query(\"(subj_sess=='{}')\".format(subj_sess)).iloc[0]\n",
    "freqs=np.arange(1, 31)\n",
    "n_rois=6\n",
    "min_spikes = 20\n",
    "n_samp_iter=10\n",
    "n_perm=1000\n",
    "alpha=0.05\n",
    "data_dir='/home1/dscho/projects/unit_activity_and_hpc_theta/data2/goldmine/nav'\n",
    "output_dir=None\n",
    "save_output=True\n",
    "overwrite=False\n",
    "verbose=True\n",
    "                                        \n",
    "# ----------------------------------\n",
    "timer = Timer()\n",
    "\n",
    "# Load the output file if it exists.\n",
    "output_b = '{}-{}-{}.pkl'.format(unit['subj_sess'], unit['chan'], unit['unit'])\n",
    "if output_dir is None:\n",
    "    output_dir = op.join(data_dir, 'phase_locking', 'gold_view')\n",
    "output_f = op.join(output_dir, output_b)\n",
    "if op.exists(output_f) and not overwrite:\n",
    "    if verbose:\n",
    "        print('Loading saved output: {}'.format(output_f))\n",
    "    pl_mrls = dio.open_pickle(output_f)\n",
    "    raise AssertionError('Output already saved: {}'.format(output_f))\n",
    "\n",
    "# Hard-coded params.\n",
    "expmt = 'goldmine'\n",
    "game_states = ['Encoding']\n",
    "gold_keys = ['dug', 'not_dug']\n",
    "\n",
    "# Process the local LFP and all hippocampal ROIs.\n",
    "mont = spike_preproc.get_montage(unit['subj_sess'])\n",
    "roi_map = spike_preproc.roi_mapping(n=n_rois)\n",
    "hpc_rois = ['AH', 'MH', 'PH']\n",
    "lfp_hemrois = np.unique([unit['hemroi']] + [hemroi for hemroi in mont.keys()\n",
    "                                            if np.all((np.isin(hemroi[1:], hpc_rois),\n",
    "                                                       hemroi[0]==unit['hemroi'][0]))])\n",
    "pl_mrls = []\n",
    "for lfp_hemroi in lfp_hemrois:\n",
    "    # ------------------------------------\n",
    "    # Data loading\n",
    "\n",
    "    # Determine which regions we're looking at.\n",
    "    lfp_roi_gen = roi_map[lfp_hemroi[1:]]\n",
    "    if unit['roi_gen'] == 'HPC':\n",
    "        if lfp_roi_gen == 'HPC':\n",
    "            if unit['hemroi'] == lfp_hemroi:\n",
    "                edge = 'hpc-local'\n",
    "            else:\n",
    "                edge = 'hpc-hpc'\n",
    "        else:\n",
    "            edge = 'hpc-ctx'\n",
    "    else:\n",
    "        if lfp_roi_gen != 'HPC':\n",
    "            if unit['hemroi'] == lfp_hemroi:\n",
    "                edge = 'ctx-local'\n",
    "            else:\n",
    "                edge = 'ctx-ctx'\n",
    "        else:\n",
    "            edge = 'ctx-hpc'\n",
    "    same_hem = unit['hem'] == lfp_hemroi[0]\n",
    "    same_roi_gen = unit['roi_gen'] == lfp_roi_gen\n",
    "\n",
    "    # Load event_times.\n",
    "    event_times = load_event_times(unit['subj_sess'],\n",
    "                                   expmt=expmt,\n",
    "                                   game_states=game_states,\n",
    "                                   data_dir=data_dir)\n",
    "\n",
    "    # Calculate spike times relative to the start of each event.\n",
    "    event_time_spikes = load_event_time_spikes(event_times,\n",
    "                                               unit['spike_times'])\n",
    "\n",
    "    # Load phase values for each event.\n",
    "    basename_lfp = '{}-{}.pkl'.format(unit['subj_sess'], lfp_hemroi)\n",
    "    phase = dio.open_pickle(op.join(data_dir, 'spectral', 'phase', basename_lfp))\n",
    "    if (phase.buffer > 0) & (not phase.clip_buffer):\n",
    "        phase = phase.loc[:, :, :, phase.buffer:phase.time.size-phase.buffer-1]\n",
    "        phase.attrs['clip_buffer'] = True\n",
    "    lfp_chans = [chan for chan in phase.chan.values if chan not in [unit['chan']]]\n",
    "    if unit['chan'] in phase.chan:\n",
    "        phase = phase.loc[{'chan': lfp_chans}]\n",
    "    if not np.array_equal(phase.freq.values, freqs):\n",
    "        phase = phase.loc[{'freq': [freq for freq in phase.freq.values if freq in freqs]}]\n",
    "    if np.nanmax(phase.values) > np.pi:\n",
    "        phase -= np.pi  # convert values back to -π to π, with -π being the trough\n",
    "\n",
    "    # Load the P-episode mask for each event.\n",
    "    osc_mask = dio.open_pickle(op.join(data_dir, 'p_episode', basename_lfp))\n",
    "    if (osc_mask.buffer > 0) & (not osc_mask.clip_buffer):\n",
    "        osc_mask = osc_mask.loc[:, :, :, osc_mask.buffer:osc_mask.time.size-osc_mask.buffer-1]\n",
    "        osc_mask.attrs['clip_buffer'] = True\n",
    "    if unit['chan'] in osc_mask.chan:\n",
    "        osc_mask = osc_mask.loc[{'chan': [chan for chan in osc_mask.chan.values\n",
    "                                          if chan not in [unit['chan']]]}]\n",
    "    if not np.array_equal(osc_mask.freq.values, freqs):\n",
    "        osc_mask = osc_mask.loc[{'freq': [freq for freq in osc_mask.freq.values\n",
    "                                          if freq in freqs]}]\n",
    "    osc_mask = osc_mask.sel(gameState='Encoding')  # [trial, chan, freq, time]\n",
    "\n",
    "    # Load the gold-view mask for each event.        \n",
    "    basename = '{}-gold_view_mask.pkl'.format(unit['subj_sess'])\n",
    "    gold_mask = dio.open_pickle(op.join(data_dir, 'events', basename))  # [gold_key][trial, time]\n",
    "\n",
    "    # Combine gold view and oscillatory masks.\n",
    "#     osc_gold_mask = {key: (gold_mask[key][:, None, None, :] & osc_mask.values)\n",
    "#                      for key in gold_keys}  # [gold_key][trial, chan, freq, time]\n",
    "    osc_gold_mask = {key: gold_mask[key][:, None, None, :]\n",
    "                     for key in gold_keys}  # [gold_key][trial, chan, freq, time]\n",
    "\n",
    "    # Calculate P-episode at each frequency, across channels and events.\n",
    "    peps = {key: 100 * np.nanmean(osc_gold_mask[key], axis=(0, 1, 3))\n",
    "            for key in gold_keys}\n",
    "    max_pep = {key: np.nanmax(peps[key]) for key in gold_keys}\n",
    "    max_pep_freq = {key: np.nanargmax(peps[key]) for key in gold_keys}\n",
    "\n",
    "    # Get gold view spike phases at each freq. across events,\n",
    "    # for later dug and not dug golds.\n",
    "    event_dur = phase.time.size\n",
    "    spike_phases = {\n",
    "        key: np.ma.concatenate(\n",
    "            event_time_spikes.apply(\n",
    "                lambda x: get_spike_phases(x['spike_times'],\n",
    "                                           phase.values[x['event_idx'], :, :, :],\n",
    "                                           mask=np.invert(osc_gold_mask[key][x['event_idx'], :, :, :]),\n",
    "                                           event_dur=event_dur,\n",
    "                                           circshift=False),\n",
    "                axis=1).tolist(), axis=-1)\n",
    "        for key in gold_keys}  # [gold_key][chan, freq, spike]\n",
    "    \n",
    "    # Find the minimum number of spikes at each channel and frequency,\n",
    "    # between dug and not dug golds.\n",
    "    n_freq = phase.freq.size\n",
    "    n_chan = phase.chan.size\n",
    "    samp_spikes = np.nanmin([[[spike_phases[key][iChan, iFreq, :].compressed().size\n",
    "                               for iFreq in range(n_freq)]\n",
    "                              for iChan in range(n_chan)]\n",
    "                             for key in gold_keys], axis=0)  # [chan, freq]\n",
    "    \n",
    "    # Calculate mean resultant lengths for each channel and frequency.\n",
    "    mrls = {key: np.empty([n_chan, n_freq], dtype=np.float32)\n",
    "            for key in gold_keys}\n",
    "    for key in gold_keys:\n",
    "        for iChan in range(n_chan):\n",
    "            _mrls = []\n",
    "            for iFreq in range(n_freq):\n",
    "                phi = spike_phases[key][iChan, iFreq, :].compressed()\n",
    "                if samp_spikes[iChan, iFreq] < min_spikes:\n",
    "                    mrls[key][iChan, iFreq] = np.nan\n",
    "                elif phi.size == samp_spikes[iChan, iFreq]:\n",
    "                    mrls[key][iChan, iFreq] = circstats.circmoment(phi)[1]\n",
    "                else:\n",
    "                    mrls[key][iChan, iFreq] = np.nanmean(\n",
    "                        [circstats.circmoment(np.random.choice(phi,\n",
    "                                                               size=samp_spikes[iChan, iFreq],\n",
    "                                                               replace=False))[1]\n",
    "                         for _ in range(n_samp_iter)]\n",
    "                    )\n",
    "    mrls = {key: np.nanmean(mrls[key], axis=0)\n",
    "            for key in gold_keys}  # [gold_key][freq]\n",
    "    \n",
    "    break\n",
    "    \n",
    "print(timer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "id": "dc920715-6839-4a99-aacf-6efa6b452c1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['LMH'], dtype='<U3'),\n",
       " subj                                                        U527\n",
       " subj_sess                                              U527_ses0\n",
       " chan                                                           9\n",
       " unit                                                           2\n",
       " hemroi                                                       LMH\n",
       " hem                                                            L\n",
       " roi_gen                                                      HPC\n",
       " n_spikes                                                    5088\n",
       " fr                                                       2.35556\n",
       " spike_times    [1306, 4844, 6573, 6594, 6879, 7408, 7491, 110...\n",
       " Name: 126, dtype: object)"
      ]
     },
     "execution_count": 460,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lfp_hemrois, unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "id": "7d976bc3-10f8-47fb-b320-bb238c5618d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[367 367 367 367 367 367 367 367 367 367 367 367 367 367 367 367 367 367\n",
      "  367 367 367 367 367 367 367 367 367 367 367 367]\n",
      " [367 367 367 367 367 367 367 367 367 367 367 367 367 367 367 367 367 367\n",
      "  367 367 367 367 367 367 367 367 367 367 367 367]\n",
      " [367 367 367 367 367 367 367 367 367 367 367 367 367 367 367 367 367 367\n",
      "  367 367 367 367 367 367 367 367 367 367 367 367]\n",
      " [367 367 367 367 367 367 367 367 367 367 367 367 367 367 367 367 367 367\n",
      "  367 367 367 367 367 367 367 367 367 367 367 367]\n",
      " [367 367 367 367 367 367 367 367 367 367 367 367 367 367 367 367 367 367\n",
      "  367 367 367 367 367 367 367 367 367 367 367 367]\n",
      " [367 367 367 367 367 367 367 367 367 367 367 367 367 367 367 367 367 367\n",
      "  367 367 367 367 367 367 367 367 367 367 367 367]\n",
      " [367 367 367 367 367 367 367 367 367 367 367 367 367 367 367 367 367 367\n",
      "  367 367 367 367 367 367 367 367 367 367 367 367]]\n",
      "\n",
      "[[109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109\n",
      "  109 109 109 109 109 109 109 109 109 109 109 109]\n",
      " [109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109\n",
      "  109 109 109 109 109 109 109 109 109 109 109 109]\n",
      " [109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109\n",
      "  109 109 109 109 109 109 109 109 109 109 109 109]\n",
      " [109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109\n",
      "  109 109 109 109 109 109 109 109 109 109 109 109]\n",
      " [109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109\n",
      "  109 109 109 109 109 109 109 109 109 109 109 109]\n",
      " [109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109\n",
      "  109 109 109 109 109 109 109 109 109 109 109 109]\n",
      " [109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109\n",
      "  109 109 109 109 109 109 109 109 109 109 109 109]]\n",
      "\n",
      "[[109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109\n",
      "  109 109 109 109 109 109 109 109 109 109 109 109]\n",
      " [109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109\n",
      "  109 109 109 109 109 109 109 109 109 109 109 109]\n",
      " [109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109\n",
      "  109 109 109 109 109 109 109 109 109 109 109 109]\n",
      " [109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109\n",
      "  109 109 109 109 109 109 109 109 109 109 109 109]\n",
      " [109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109\n",
      "  109 109 109 109 109 109 109 109 109 109 109 109]\n",
      " [109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109\n",
      "  109 109 109 109 109 109 109 109 109 109 109 109]\n",
      " [109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109\n",
      "  109 109 109 109 109 109 109 109 109 109 109 109]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(np.array([[spike_phases['dug'][iChan, iFreq, :].compressed().size\n",
    "                        for iFreq in range(n_freq)]\n",
    "                       for iChan in range(n_chan)]), end='\\n'*2)\n",
    "\n",
    "print(np.array([[spike_phases['not_dug'][iChan, iFreq, :].compressed().size\n",
    "                        for iFreq in range(n_freq)]\n",
    "                       for iChan in range(n_chan)]), end='\\n'*2)\n",
    "\n",
    "print(samp_spikes, end='\\n'*2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "id": "4f9d5cf5-f7af-406f-9e07-4b8672b45e6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "osc_mask %\n",
      "----------\n",
      "['0.28' '5.26' '8.40' '9.49' '9.03' '7.46' '6.23' '5.67' '5.19' '4.64']\n",
      "['4.27' '4.26' '4.27' '4.29' '4.25' '4.37' '4.46' '4.47' '4.34' '4.19']\n",
      "['3.99' '3.71' '3.48' '3.30' '3.06' '2.80' '2.51' '2.27' '2.04' '1.80']\n"
     ]
    }
   ],
   "source": [
    "vals = np.array(['{:.2f}'.format(x*100)\n",
    "                 for x in np.mean(osc_mask.values, axis=(0, 1, 3))])\n",
    "print('osc_mask %', '-' * len('osc_mask %'),\n",
    "      vals[:10], vals[10:20], vals[20:],\n",
    "      sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "id": "35dfba25-de62-4b62-a338-42f45bd37bc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gold view %\n",
      "-----------\n",
      "dug: 14.25%\n",
      "not_dug: 3.41%\n"
     ]
    }
   ],
   "source": [
    "print('gold view %', '-' * len('gold view %'), sep='\\n')\n",
    "for key in gold_keys:\n",
    "    print('{}: {:.2%}'.format(key, np.mean(gold_mask[key])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "id": "1eff4fd5-4760-4310-a094-dab8645399b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "osc_gold_mask %\n",
      "---------------\n",
      "\n",
      "dug\n",
      "---\n",
      "['14.25']\n",
      "[]\n",
      "[]\n",
      "\n",
      "not_dug\n",
      "-------\n",
      "['3.41']\n",
      "[]\n",
      "[]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('osc_gold_mask %', '-' * len('osc_gold_mask %'), sep='\\n', end='\\n'*2)\n",
    "for key in gold_keys:\n",
    "    vals = np.array(['{:.2f}'.format(x) for x in peps[key]])\n",
    "    print(key, '-' * len(key),\n",
    "          vals[:10], vals[10:20], vals[20:],\n",
    "          sep='\\n', end='\\n'*2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "id": "464d79e1-b13a-444d-a595-1038c4539074",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_spikes | osc, gold in view\n",
      "----------------------------\n",
      "\n",
      "gold dug\n",
      "--------\n",
      "['367' '367' '367' '367' '367' '367' '367' '367' '367' '367']\n",
      "['367' '367' '367' '367' '367' '367' '367' '367' '367' '367']\n",
      "['367' '367' '367' '367' '367' '367' '367' '367' '367' '367']\n",
      "\n",
      "gold not dug\n",
      "------------\n",
      "['109' '109' '109' '109' '109' '109' '109' '109' '109' '109']\n",
      "['109' '109' '109' '109' '109' '109' '109' '109' '109' '109']\n",
      "['109' '109' '109' '109' '109' '109' '109' '109' '109' '109']\n",
      "\n",
      "min(dug, not dug)\n",
      "-----------------\n",
      "['109' '109' '109' '109' '109' '109' '109' '109' '109' '109']\n",
      "['109' '109' '109' '109' '109' '109' '109' '109' '109' '109']\n",
      "['109' '109' '109' '109' '109' '109' '109' '109' '109' '109']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('n_spikes | osc, gold in view', '-' * len('n_spikes | osc, gold in view'),\n",
    "      sep='\\n', end='\\n'*2)\n",
    "for key in gold_keys:\n",
    "    vals = np.nanmean([[spike_phases[key][iChan, iFreq, :].compressed().size\n",
    "                        for iFreq in range(n_freq)]\n",
    "                       for iChan in range(n_chan)], axis=0)\n",
    "    vals = np.array(['{:>2.0f}'.format(x) for x in vals])\n",
    "    print('gold {}'.format(key.replace('_', ' ')), '-' * len('gold {}'.format(key)),\n",
    "          vals[:10], vals[10:20], vals[20:],\n",
    "          sep='\\n', end='\\n'*2)\n",
    "    \n",
    "vals = np.array(['{:>2.0f}'.format(x) for x in np.nanmean(samp_spikes, axis=0)])\n",
    "print('min(dug, not dug)', '-' * len('min(dug, not dug)'),\n",
    "      vals[:10], vals[10:20], vals[20:],\n",
    "      sep='\\n', end='\\n'*2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "id": "163b54f4-99a7-4d90-a180-c4ee615fbbc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MRL (dug - not_dug)\n",
      "-------------------\n",
      "['-0.03' '0.05' '0.07' '0.07' '0.02' '0.01' '0.00' '0.03' '0.02' '0.02']\n",
      "['0.04' '0.02' '0.02' '0.02' '0.01' '0.01' '-0.01' '-0.01' '-0.02' '-0.02']\n",
      "['-0.02' '-0.02' '-0.03' '-0.01' '-0.00' '-0.01' '-0.01' '-0.01' '0.01'\n",
      " '0.00']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "vals = np.array(['{:.2f}'.format(x) for x in mrls['dug'] - mrls['not_dug']])\n",
    "print('MRL (dug - not_dug)', '-' * len('MRL (dug - not_dug)'),\n",
    "      vals[:10], vals[10:20], vals[20:],\n",
    "      sep='\\n', end='\\n'*2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c00d587-e80e-479a-824e-2c6c768f876b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "80a7fcca-ffd5-48b4-b4a5-d0a37abcd33b",
   "metadata": {},
   "outputs": [
    {
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eBgB9J5VKhfFVNptVOp3mREEAAAAAACARW0UOsRWAnkFsBQAAAACAO41GIwyvCoVCT5/oAAAAsJ2stbp582Z4+tXy57ffftv1tDUZY3TkyJEwvhodHVU+n9fu3btdTwOAvmKMaTv9KpvNKplMup4FAAAAAAC2H7FVxBBbAegZxFYAAAAAAPSOarWqUqmkSqWicrmsSqWiarUqXk8EAAB4V6FQaIuvZmZmdPnyZTWbTdfT1vTEE08on8+3RVgHDx6U53mupwFA34jFYmF4tRxi+b7vehYAAAAAAOguYquIIbYC0DOIrQAAAAAA6G3WWlWrVVUqlVUfvfxLxQAAANulVqvp8uXLmpmZCSOs2dlZLS0tuZ62pmw2q3w+3xZhHT9+nHAAANYhkUi0xVeZTEaxWMz1LAAAAAAAsHWIrSKG2ApAzyC2AgAAAACgf9Xr9Y4RVq1Wcz0NAADAqVarpWvXroWnXy1/3L592/W0NcXjcR07dkwjIyNhgJXP57Vjxw7X0wCgb6TT6TC+ymazSqfTMobfywIAAAAAoE/xQ33EEFsB6BnEVgAAAAAADJ5Wq9UxwqpUKuK1SQAAEGW3bt0KT75aDrDm5+d7+p+RDh06FMZXo6OjyufzeuKJJ4gHAOAReJ6ndDodxlfZbFbJZNL1LAAAAAAA8Gh4ETRiiK0A9AxiKwAAAAAAosNaq1qt1jHCajQarucBAAA4USqV9MYbb2hmZiaMsN544w3V63XX09b02GOPKZ/Pt0VYw8PDisVirqcBQM+LxWJt8VUmk5Hv+65nAQAAAACA1YitIobYCkDPILYCAAAAAACS1Gg0OkZY1WrV9TQAAIBt12g09Oabb4anXy1HWIVCwfW0NaVSKZ08ebItwjpx4oRSqZTraQDQ8xKJRFt8lc1m5Xme61kAAAAAAEQdsVXEEFsB6BnEVgAAAAAA4GFarZaq1WoYX5XL5TDCarVarucBAABsG2ut3nnnnTDAWv64ceOG62lr8jxPR48e1cjISBhg5fN57dq1y/U0AOh5yWRSqVRq1edEIuF6GgAAAAAAUUFsFTHEVgB6BrEVAAAAAADYqFqtFsZXD340Gg3X0wAAALbNvXv3NDs7G55+NTMzozfffLOnw/R9+/aFAdZyhHXgwAEZw+8uAMB78TxvVYC1/HU8Hnc9DwAAAACAQcILlhFDbAWgZxBbAQAAAACArdZoNFYFWMunYQEAAERBpVLR3NycZmZmwgjr0qVLqlQqrqetKZfLKZ/Ph/HV6Oiojh49SjgAAOsQj8dXBVjLnz3Pcz0PAAAAAIB+Q2wVMcRWAHoGsRUAAAAAANgurVZL1Wq1Y4jVyyc/AAAAbIVms6n5+fnw9KvlCOvu3buup60pkUjo+PHjbRFWPp9XJpNxPQ0A+k4ikVgVYKVSKSUSCU4WBAAAAACgM35gjhhiKwA9g9gKAAAAAAD0glqt1jHCqtfrrqcBAAB0jbVWt27dCgOs5Y+3337b9bQ1GWM0PDwcxlcjIyMaGRnRnj17XE8DgL5kjFkVYC1/7fu+63kAAAAAALhEbBUxxFYAegaxFQAAAAAA6GXNZrNjhFWtVsXrrAAAYFAVi0XNzs6Gp1/NzMzo8uXLajQarqetac+ePWF4tRxiHT58WJ7nuZ4GAH0rFoutCrCWP8diMdfzAAAAAADoNmKriCG2AtAziK0AAAAAAEA/staqWq22BVjlclmVSkWtVsv1PAAAgC1Xr9d1+fJlzczMhBHW7OysSqWS62lrymQyOnnyZBhfnThxQsPDwxoaGnI9DQD6nu/7qwKs5c/G8LtoAAAAAICBwA+4EUNsBaBnEFsBAAAAAIBBU6/X2+Kr5Y96ve56GgAAwJZqtVpaWFgIT79ajrAWFxddT3uonTt36vDhwxoeHtbw8HD49ZEjR7Rr1y4iAQDYBGOMEolEW4C1/HUikXA9DwAAAACA9eCFwoghtgLQM4itAAAAAABAVDSbzbb4avmjWq2K12wBAMAguXPnThheLX9cvXq1L/6ZJ5vNroqwlj/27NlDiAUAm+B53qoAa/nrWCzmeh4AAAAAACvxYmDEEFsB6BnEVgAAAAAAIOqstapWqx1DrGaz6XoeAADAliiXy7p06VJbhDU3N6dqtep62iNLp9OrIqzlr5944gl5nud6IgD0rXg83vE0rGQyyd+vAAAAAABXiK0ihtgKQM8gtgIAAAAAAFhbvV7vGGHVajXX0wAAADat0Wjorbfe0szMTFuEFQSB62nrlkwmO0ZYw8PD2rt3Lye2AMAmJBKJjiFWIpHgxEEAAAAAQDfxQ2fEEFsB6BnEVgAAAAAAAOvXarU6RliVSkW8/gsAAPqZtVY3btwIw6vlCOv69euup22Y7/s6dOhQGF89GGTt379f8Xjc9UQA6EvGmFUB1vLX/N0KAAAAANgCxFYRQ2wFoGcQWwEAAAAAAGwda61qtdqqAKtcLqvZbLqeBwAAsGFBELSdfjU7O6srV670/T/jxGKxthDrwROxDh48SCwAABsUi8U6noaVSqXkeZ7reQAAAACA/kBsFTHEVgB6BrEVAAAAAADA9mg0GmF49WCIVavVXE8DAADYkGq1qsuXL4cB1szMjC5duqRyuex62paIxWLav3//qhOxlkOsRCLheiIA9CXf9zuGWMlkUsbwe3QAAAAAgBA/JEYMsRWAnkFsBQAAAAAA4Far1Vp1EtbyB68lAwCAftNsNnXt2rXw9KvlCOvOnTuup20pY8yqEGv5VKxDhw4plUq5nggAfccYo0QiseokrFQqJd/3Xc8DAAAAAGw/YquIIbYC0DOIrQAAAAAAAHqTtVa1Wq1jhNVoNFzPAwAAWJcgCHTt2jXNz89rfn4+/PratWu6ffu263lbbt++fWF8tTLISqfTrucBQN/xPK/jaVipVEqxWMz1PAAAAABAdxBbRQyxFYCeQWwFAAAAAADQfxqNRscIq1qtup4GAACwbsViUdeuXesYYy0uLrqet+Uef/zxtpOwjhw5osOHD+vw4cPasWOH63kA0Hfi8XjHECuZTMrzPNfzAAAAAAAbR2wVMcRWAHoGsRUAAAAAAMDgaLVaqlarHUOsVqvleh4AAMC6lcvljhHW/Py8bty44Xreltu9e3fHE7GGh4eVy+VczwOAvpNIJFadhJVKpeT7vozhd/YAAAAAoMfxg1vEEFsB6BnEVgAAAAAAANFQq9VWBVjlclmNRsP1NAAAgA2pVCpaWFjQ1atXVwVZ169f16C9Lz80NBSGVytPxRoaGiIaAIB1MMZ0PA0rlUopHo+7ngcAAAAAeBcveEUMsRWAnkFsBQAAAAAAEG2NRkPValXlcrktxKpWq66nAQAAbFitVtPCwoKuXbumq1evtp2Kdf36dTWbTdcTt1Qul2uLsJa/PnLkiB577DFCLABYh1gstmaI5Xme63kAAAAAECW8qBUxxFYAegaxFQAAAAAAADqx1q46CWv5o9VquZ4HAACwYY1GQ9evX9f8/PyqU7HefvvtgQuxstnsqghr+evHH3+cEAsA1sH3/VUBViqVUiKR4O9TAAAAANh6/KAVMcRWAHoGsRUAAAAAAADWq1ardYyw6vW662kAAACb0mg0dOPGjfAkrAdPxbp27drA/fNOKpVaFWAt/9979+7lBBcAeETGGCWTyY6nYfm+73oeAAAAAPQrYquIIbYC0DOIrQAAAAAAALBVms1mxwirWq2K18UBAEC/azabWlxcDE/BevDj2rVrqlarriduqWQyqUOHDnU8EWvfvn2KxWKuJwJAX/A8b1WAtfyZv0sBAAAA4KGIrSKG2ApAzyC2AgAAAAAAQLdZa1WtVjuGWM1m0/U8AACATWu1Wrp161ZbfPVgjFUul11P3FK+74ch1spTsfbv3694PO56IgD0hXg8virEWv7aGH6nEAAAAEDk8YNRxBBbAegZxFYAAAAAAABwqV6vd4ywarWa62kAAABbwlqr27dvrwqwlj9KpZLriVsqFovp0KFDbRHW8tcHDx6U7/uuJwJAX0gmk6sCrFQqpUQi4XoaAAAAAGwXYquIIbYC0DOIrQAAAAAAANCLms1meBpWuVwOI6xqtSpeYwcAAIPCWqt79+6tOhHr6tWrmp+fV6FQcD1xS3mepwMHDqyKsJZDrGQy6XoiAPQ8z/PC8GrlZ04WBAAAADBgiK0ihtgKQM8gtgIAAAAAAEA/sdaGEdbKj2az6XoeAADAlrp//35bhLX89dWrV3Xv3j3X87aUMUb79u0L46sHg6zDhw8rlUq5nggAPS8Wi606CWv5a8/zXM8DAAAAgPUitooYYisAPYPYCgAAAAAAAIOiXq93jLBqtZrraQAAAFuuUCisirCWv759+7breVtu7969bSdhPRhjZTIZ1/MAoOclEolVAVYqlVIikZAx/P4iAAAAgJ7EDysRQ2wFoGcQWwEAAAAAAGDQtVqtjhFWpVIRr9cDAIBBVCqVwvhqZZB18+bN975An9mzZ8+qCGs5xNqxY4freQDQ04wxYXi18rPv+67nAQAAAIg2YquIIbYC0DOIrQAAAAAAABBV1lrVarWOEVaj0XA9DwAAoCsqlUpbgPVgkHXjxo2Bi9Efe+yxMLxaGWPt3LnT9TwA6GmxWGxVgLX8dSwWcz0PAAAAwOAjtooYYisAPYPYCgAAAAAAAFit0Wh0jLCq1arraQAAAF1TrVa1sLCgq1evtkVY165d0/Xr19VqtVxP3FJDQ0MdI6zh4WENDQ3JGH6fBwDW4vv+qgBr+TN/fwIAAADYIvxwETHEVgB6BrEVAAAAAAAA8OharZaq1WoYX5XL5TDCGrRfPgYAAHhQvV7XwsJCxxOxFhYW1Gw2XU/cUjt27FjzRKzdu3cTEgDAQ6w8DWv5cyKRcD0NAAAAQH/hBZiIIbYC0DOIrQAAAAAAAPD/s3f/wZLne13fX5/uPj92ZnZndmdm2b27d1G4iF4QlB+KUlgqEMtIEkkoCzDGmIgIRDGmrlqiJcZASkTjj4JKRdFLqaVEUilT8iMlKKBGoQSxFBAISmZn7/6YmZ2d2ZnZ86v7kz9m+9zz49tn+pzT53y/3f14VJ3qnm+f/pxPn+np6Z7Tz3kzG3sjrL0fOzs7bW8NAOBM7ezs5I033sirr76aGzdu7EZYr776al577bWFez504cKFfRHW+Pwrr7ySa9euCbEAJuj1eocCrPH5wWDQ9vYAAIDu8Y8sS0ZsBXSG2AoAAAAAztbOzk5jhLW5udn21gAAztxwOMybb765byLWeCrWzZs3s7W11fYWZ2ptbe1QhDX+eP7559Pr9dreIkAnDQaDQwHW+NRjJwAALC2x1ZIRWwGdIbYCAAAAgHaMRqOJ07BGo1Hb2wMAOHOj0ShvvfXW7iSsg1OxFi1OX11dzcsvv9w4FeuFF15Iv99ve4sAnbS6unoowFpfX8/q6qppggAAsNg84V8yYiugM8RWAAAAANA9W1tbjRHW9vZ221sDADgXtdbcvn173ySsGzdu7J5/9L715GUAACAASURBVOhR21ucqcFgkJdeeqlxKtaLL76YwWDQ9hYBOqeUcijAGp9fWVlpe3sAAMDpia2WjNgK6AyxFQAAAADMj+Fw2BhhbW5uxs8eAIBlUWvN22+/vRte7Z2KdePGjTx8+LDtLc5Uv9/PBz7wgUMR1ssvv5yXXnpJUADQoN/vHwqwxqcmCQIAwNwQWy0ZsRXQGWIrAAAAAJh/tdZsbm7uxlfvvffe7vnRaNT29gAAzk2tNffu3dudiLV3KtbNmzdz7969trc4U71eLy+88ELjRKyXXnopa2trbW8RoHNWVlYOBVjj01K8lxMAADrEE/QlI7YCOkNsBQAAAACLbXt7e198Nf7Y3t5ue2sAAOfu3r17u9Owxqfjj7t377a9vZkqpeT555/fja/2frz88stZX19ve4sAnVJKyerq6r4Aa3x+dXW17e0BAMAyElstGbEV0BliKwAAAABYTsPh8FCAtbGxkc3Nzfg5BgCwjB48eHAowBr/+vbt221vb+auX79+KMAan168eLHt7QF0Sq/XOxRgjc/3+/22twcAAItKbLVkxFZAZ4itAAAAAIC9aq3Z3NxsDLGGw2Hb2wMAaMWjR4/2hVh7p2O9+eabbW9v5q5evZqXX345r7zyym6ENf64dOlS29sD6JTBYLAvwNp72uv12t4eAADMM7HVkhFbAZ0htgIAAAAAprW9vd0YYW1tbbW9NQCA1mxsbOTmzZuNMdYbb7yxcFNDr1y5sjsB65VXXtk3Fevy5cttbw+gU1ZXVw9NwlpbW8vq6mpK8b5RAAB4Ak+al4zYCugMsRUAAAAAcFqj0ahxGtbm5qZpWADAUtva2srHPvax3LhxY1+E9eqrr+b111/PaDRqe4sz9cwzzxyahDX+uHLlirAA4H2llEMB1vj8YDBoe3sAANAV/iFhyYitgM4QWwEAAAAAZ2nvNKy9Qdbm5mbbWwMAaNX29nZef/313Lhx49BUrNdee23hovWLFy/uhlfjIOuVV17Jyy+/nKtXrwqxAN7X7/cbp2Gtr6+n1+u1vT0AADhP/rFgyYitgM4QWwEAAAAAbai1TpyGtbOz0/b2AABatbOzkzfeeCM3b948NBXrtddey/b2dttbnKmnnnpqX4Q1Pv/KK6/k2rVr4gKA962srDSGWGtra6JVAAAWkSe5S0ZsBXSG2AoAAAAA6JqdnZ2J07D8jAUAWHbD4TBvvfXW7iSsvR+vvfbawk0QXVtbOxRhjadiPf/880IsgCSllKyurh6ahLW+vp6VlZW2twcAACcltloyYiugM8RWAAAAAMC8qLVma2urcRrWok13AAA4idFolFu3bu1Owjo4FWtjY6PtLc7U6upqXnrppX0x1jjIeuGFFzIYDNreIkDrer1e4zSs9fX19Pv9trcHAABHEVstGbEV0BliKwAAAABgEQyHw4nTsEajUdvbAwBoXa01d+7caZyIdfPmzTx8+LDtLc7UYDDIBz7wgUMTsT74wQ/mAx/4gBALII8fK5tCrLW1NZMDAQDoArHVkhFbAZ0htgIAAAAAFt2kaVhbW1ttbw0AoBNqrbl79+6++OrGjRu7pw8ePGh7izPV7/fz4osvHoqwxiHW6upq21sEaN3q6uqhSVjr6+tZWVlJKd7zCgDAufDEc8mIrYDOEFsBAAAAAMtqNBo1TsPa2NgwDQsA4H211ty7dy83b95snIp17969trc4U71eLy+88EI+4RM+IdeuXcu1a9dy/fr1XL9+fff8tWvXcvHiRbEBsJRKKY3TsNbX100NBABg1rzwXjJiK6AzxFYAAAAAAIdtb283TsPa3Nxse2sAAJ1y//79fSHW3vNvv/1229s7M+vr6/viq73n956KsoBl0u/3J4ZYvV6v7e0BADB/vKBeMmIroDPEVgAAAAAA0xuNRrtTsA5OwxoOh21vDwCgUx48eJCbN282xli3bt1qe3vnoinKapqWJcoCFt3KysqhAGt9fT2rq6se/wAAmMQTxSUjtgI6Q2wFAAAAADAbOzs7E6dh+dkQAMB+77333sSJWG+++Wbb2zt3a2trhyZjNU3LunTpkigBWCillKytrTVOw1pZWWl7ewAAtMsL4CUjtgI6Q2wFAAAAAHC2aq0Tp2Ht7Oy0vT0AgM7Z2NjIa6+9dijCunnzZl5//fWlDtknRVkHp2WJsoBF0Ov1DgVY49N+v9/29gAAOHte2C4ZsRXQGWIrAAAAAID27OzsHAqwxr/28yQAgMO2trbysY99bDfA2htkvf766xkOh21vsRPW1tYaJ2MdPCbKAubVYDA4FGKNz3tcAwBYGJ7YLRmxFdAZYisAAAAAgO6ptWZra6txGtb29nbb2wMA6KSdnZ28/vrruXHjxqGpWK+99poQq0FTlHX16tV9vxZlAfNmbW3tUIC1vr6e1dXVtrcGAMDxeCG6ZMRWQGeIrQAAAAAA5stwODwUYI2jrNFo1Pb2AAA6aWdnJ2+++ea+iVjjIOvmzZuC9icYR1lHTcm6du1ann76aVEW0Fm9Xm83vDp4OhgM2t4eAACHeYG5ZMRWQGeIrQAAAAAAFsd4GtbBiVhbW1ttbw0AoLOGw2Fu3bqVV199NTdu3Mgbb7yR27dv737cunUr77zzTtvbnAuToqyDYZYoC+iafr9/aBLW+Hyv12t7ewAAy8oLxyUjtgI6Q2wFAAAAALD4RqPRxGlYw+Gw7e0BAHTe9vZ27ty5k1u3bu0GWOPT8XFR1vT2RllHTcsSZQFdsLq6eijAWl9fz+rqqscoAICz5cnWkhFbAZ0htgIAAAAAWG7b29uN07A2Nzfb3hoAwNwZR1kHg6y9Udbt27dz9+7dtrc6F1ZXV3P9+vVcvXo1169fnzgt65lnnhE8AOeulJK1tbWsra1ldXU1Kysrhz4Gg4HHJwCAk/NEasmIrYDOEFsBAAAAANCk1jpxGtbOzk7b2wMAmGuToqzbt2/vfty6dUuUNaWDUdakaVmiLKANg8GgMcQ6+NHr9dreKgBA13gBt2TEVkBniK0AAAAAADiunZ2didOw/BwMAGB29kZZk8IsUdb0VldXD03GapqWJcoC2tDr9aaKsgaDQdtbBQA4L16YLRmxFdAZYisAAAAAAGal1pqtra3GaVjb29ttbw8AYGFNE2Xdvn07b7/9dttbnQtNUVbTtCxRFtCGUsrU07I8RgEAc86TmSUjtgI6Q2wFAAAAAMB5GA6HE6dhjUajtrcHALAUDkZZk8IsUdZ0DkZZk8Ksy5cvCx6AVgwGg6nCrH6/3/ZWAQCaeCG1ZMRWQGeIrQAAAAAAaNukaVhbW1ttbw0AYCnt7Ozkzp07+wKspijr7t278T6oJ1tZWdkNsK5evZrr16+LsoBO6fV6U0VZg8HA4xQAcJ488VgyYiugM8RWAAAAAAB01Wg0apyGtbGxYRoWAEAHNEVZTWGWKGs6TVFW07QsURbQpnF09aQwq9frtb1VAGD+eeGzZMRWQGeIrQAAAAAAmEfb29uN07A2Nzfb3hoAAAeMo6yDk7H2Rll37tzJ22+/LcqawjjK2vvRNC1LlAW0qd/vTxVmDQaDtrcKAHSXFzRLRmwFdIbYCgAAAACARTIajXanYB2chjUcDtveHgAAR9jZ2cnbb7+dW7duTZyWdfv2bVHWlFZWVvZNyDo4JUuUBXRBKWVfeHVUmOWxCgCWjr/8l4zYCugMsRUAAAAAAMtiZ2dn3xSsnZ2dxg8AALptb5S1dzLWwUhLlDWdJ0VZ41NRFtC2vTHWUWFWv99ve6sAwGx4AbJkxFZAZ4itAAAAAABgv0kR1qQPE7MAALppHGUdnIy1N9ISZU1vMBjsC7AmTcsSZQFt6/V6++KrSWHWYDDweAUA3eYv6iUjtgI6Q2wFAAAAAACnU2ttjLC2t7cnBlp+XggA0B07Ozu5e/fuoclYB8MsUdZ0xlHWpDBrfOzy5cvp9XptbxdYck0BVlOY5fEKAFohtloyYiugM8RWAAAAAABw/obD4bEnaAEA0K69UdZR07Lu3r2b0WjU9nY7bzAY5OrVq/smYzVNyxJlAV3Q7/ennpYFAMyM2GrJiK2AzhBbAQAAAABA99VaGwOto6ZneYMvAEA7xlHWOL6aNC3r7bff9pxtCv1+/1CAdTDSEmUBXVFKmXpaVinePw4AT+AvyyUjtgI6Q2wFAAAAAACLaTQaHXt6lp9jAgCcn4NR1qRpWaKs6YyjrKOmZF27di1XrlwRZQGd0BRhNR3r9/ttbxUA2iK2WjJiK6AzxFYAAAAAAMDYcadnDYfDtrcMALDwhsNh7t69uy/IapqWJcqazqQo62CYJcoCuqLX6zVOxjoYZg0GA9OyAFg0/mJbMmIroDPEVgAAAAAAwEnVWo81OWt7e9v0LACAM9IUZTVNyxJlTWdvlHXUtCxRFtAlTVFWU5jlcQuAOSG2WjJiK6AzxFYAAAAAAMB5Go1GU0/OGn8AADA74yjrYJB1cFrWnTt3RFlTGEdZV69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\n",
      "text/plain": [
       "<Figure size 3870x1548 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sizes = [1, 2, 3, 5, 10, 20, 30, 50, 100, 250, 500, 1000]\n",
    "mrls = np.array([[circstats.circmoment(np.random.uniform(-np.pi, np.pi, size))[1]\n",
    "                  for size in sizes]\n",
    "                 for ii in range(1000)])  # [iter, size]\n",
    "plt.close()\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "ax = np.ravel(ax)\n",
    "\n",
    "iax = 0\n",
    "_ax = ax[iax]\n",
    "_means = np.nanmean(mrls, axis=0)\n",
    "_stds = np.nanstd(mrls, axis=0)\n",
    "xvals = sizes\n",
    "_ax.fill_between(xvals, _means + _stds, _means - _stds, lw=0, color='k', alpha=0.2)\n",
    "_ax.plot(xvals, _means, lw=1, color='k')\n",
    "_ax.set_xscale('log')\n",
    "_ax.set_xlim([1, 1000])\n",
    "_ax.set_xticks(xvals)\n",
    "_ax.set_xticklabels(xvals)\n",
    "_ax.set_xlabel('Spikes sampled')\n",
    "_ax.set_ylim([0, 1])\n",
    "_ax.set_yticks(np.linspace(0, 1, 6))\n",
    "_ax.set_ylabel('MRL')\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "4c4f443c-9de7-4d0c-aafe-4c38ee654f3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "noise_n: [1, 2, 3, 5, 10, 20, 30, 50, 100, 250, 500, 1000]\n",
      "signal_mult: 2\n",
      "signal_prop: 1\n",
      "n_samp_iter = 1\n",
      "n_perm: 1000\n",
      "Ran in 4.0s\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3870x1548 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "noise_n = [1, 2, 3, 5, 10, 20, 30, 50, 100, 250, 500, 1000]\n",
    "signal_mult = 2\n",
    "signal_prop = 0.2\n",
    "n_samp_iter = 1\n",
    "n_perm = 1000\n",
    "lw = 1.2\n",
    "\n",
    "timer = Timer()\n",
    "\n",
    "mrls1 = []\n",
    "mrls2 = []\n",
    "for ii in range(n_perm):\n",
    "    v1 = [np.random.uniform(-np.pi, np.pi, size)\n",
    "          for size in noise_n]\n",
    "    v2 = [np.concatenate((np.random.uniform(-np.pi, np.pi, int(size * signal_mult * (1-signal_prop))),\n",
    "                          np.random.uniform(0, np.pi/3, (size * signal_mult) - int(size * signal_mult * (1-signal_prop)))))\n",
    "          for size in noise_n]\n",
    "    mrls1.append([circstats.circmoment(v1[iSize])[1]\n",
    "                  for iSize in range(len(noise_n))])\n",
    "    mrls2.append(np.nanmean([[circstats.circmoment(np.random.choice(v2[iSize], noise_n[iSize], replace=False))[1]\n",
    "                              for iSize in range(len(noise_n))]\n",
    "                             for ii in range(n_samp_iter)], axis=0))\n",
    "mrls1 = np.array(mrls1)\n",
    "mrls2 = np.array(mrls2)\n",
    "\n",
    "plt.close()\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "ax = np.ravel(ax)\n",
    "\n",
    "iax = 0\n",
    "_ax = ax[iax]\n",
    "\n",
    "xvals = sizes\n",
    "\n",
    "# Plot MRL for all spikes from the signal vector.\n",
    "_ax.plot(xvals,\n",
    "         [np.mean([circstats.circmoment(np.concatenate((np.random.uniform(-np.pi, np.pi, int(size * signal_mult * (1-signal_prop))),\n",
    "                                                        np.random.uniform(0, np.pi/3, (size * signal_mult) - int(size * signal_mult * (1-signal_prop))))))[1]\n",
    "                   for _ in range(100)])\n",
    "          for size in noise_n], lw=0.8, ls='dotted', color=colors['r'])\n",
    "\n",
    "# Plot noise by no. spikes.\n",
    "_means = np.nanmean(mrls1, axis=0)\n",
    "_stds = np.nanstd(mrls1, axis=0)\n",
    "_ax.fill_between(xvals, _means + _stds, _means - _stds, lw=0, color=colors['b'], alpha=0.2)\n",
    "_ax.plot(xvals, _means, lw=lw, color=colors['b'])\n",
    "\n",
    "# Plot signal by no. spikes sampled.\n",
    "_means = np.nanmean(mrls2, axis=0)\n",
    "_stds = np.nanstd(mrls2, axis=0)\n",
    "_ax.fill_between(xvals, _means + _stds, _means - _stds, lw=0, color=colors['r'], alpha=0.2)\n",
    "_ax.plot(xvals, _means, lw=lw, color=colors['r'])\n",
    "\n",
    "_ax.set_xscale('log')\n",
    "_ax.set_xlim([1, 1000])\n",
    "_ax.set_xticks(xvals)\n",
    "_ax.set_xticklabels(xvals)\n",
    "_ax.set_xlabel('Spikes sampled (no replacement)')\n",
    "_ax.set_ylim([0, 1])\n",
    "_ax.set_yticks(np.linspace(0, 1, 6))\n",
    "_ax.set_ylabel('MRL')\n",
    "\n",
    "print('noise_n: {}'.format(noise_n),\n",
    "      'signal_mult: {}'.format(signal_mult),\n",
    "      'signal_prop: {}'.format(signal_prop),\n",
    "      'n_samp_iter = {}'.format(n_samp_iter),\n",
    "      'n_perm: {}'.format(n_perm),\n",
    "      sep='\\n')\n",
    "print(timer)\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "2d638ea0-eca9-4718-a6f2-f753f6789845",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "noise_n: 10\n",
      "signal_n: 80\n",
      "signal_prop: 0.2\n",
      "n_samp_iter = 10\n",
      "n_perm: 1000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3870x1548 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "noise_n = 30\n",
    "signal_n = 80\n",
    "signal_prop = 0.2\n",
    "n_samp_iter = 10\n",
    "n_perm = 1000\n",
    "\n",
    "mrls1 = []\n",
    "mrls2 = []\n",
    "for ii in range(n_perm):\n",
    "    v1 = np.random.uniform(-np.pi, np.pi, noise_n)\n",
    "    v2 = np.concatenate((np.random.uniform(-np.pi, np.pi, int(signal_n * (1-signal_prop))),\n",
    "                         np.random.uniform(0, np.pi/3, int(signal_n * signal_prop))))\n",
    "    mrls1.append([np.nanmean([circstats.circmoment(np.random.choice(v1, size=size, replace=True))[1]\n",
    "                              for _ in range(n_samp_iter)])\n",
    "                  for size in sizes])\n",
    "    mrls2.append([np.nanmean([circstats.circmoment(np.random.choice(v2, size=size, replace=True))[1]\n",
    "                              for _ in range(n_samp_iter)])\n",
    "                  for size in sizes])\n",
    "mrls1 = np.array(mrls1)\n",
    "mrls2 = np.array(mrls2)\n",
    "\n",
    "plt.close()\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "ax = np.ravel(ax)\n",
    "\n",
    "iax = 0\n",
    "_ax = ax[iax]\n",
    "\n",
    "xvals = sizes\n",
    "\n",
    "# Plot true MRL.\n",
    "_ax.axhline(np.mean([circstats.circmoment(np.concatenate((np.random.uniform(-np.pi, np.pi, int(signal_n * (1-signal_prop))),\n",
    "                                                          np.random.uniform(0, np.pi/3, int(signal_n * signal_prop)))))[1]\n",
    "                     for _ in range(1000)]), lw=0.8, ls='dotted', color='k')\n",
    "\n",
    "# Plot noise.\n",
    "_means = np.nanmean(mrls1, axis=0)\n",
    "_stds = np.nanstd(mrls1, axis=0)\n",
    "_ax.fill_between(xvals, _means + _stds, _means - _stds, lw=0, color=colors['b'], alpha=0.2)\n",
    "_ax.plot(xvals, _means, lw=1.2, color=colors['b'])\n",
    "\n",
    "# Plot signal.\n",
    "_means = np.nanmean(mrls2, axis=0)\n",
    "_stds = np.nanstd(mrls2, axis=0)\n",
    "_ax.fill_between(xvals, _means + _stds, _means - _stds, lw=0, color=colors['r'], alpha=0.2)\n",
    "_ax.plot(xvals, _means, lw=1.2, color=colors['r'])\n",
    "\n",
    "_ax.set_xscale('log')\n",
    "_ax.set_xlim([1, 1000])\n",
    "_ax.set_xticks(xvals)\n",
    "_ax.set_xticklabels(xvals)\n",
    "_ax.set_xlabel('spikes sampled w/ replacement')\n",
    "_ax.set_ylim([0, 1])\n",
    "_ax.set_yticks(np.linspace(0, 1, 6))\n",
    "_ax.set_ylabel('MRL')\n",
    "\n",
    "print('noise_n: {}'.format(noise_n),\n",
    "      'signal_n: {}'.format(signal_n),\n",
    "      'signal_prop: {}'.format(signal_prop),\n",
    "      'n_samp_iter = {}'.format(n_samp_iter),\n",
    "      'n_perm: {}'.format(n_perm),\n",
    "      sep='\\n')\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "26b125ce-4667-433b-9ede-13ffe8ee6355",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the minimum number of spikes at each channel and frequency,\n",
    "# between dug and not dug golds.\n",
    "samp_spikes = np.nanmin([[[spike_phases[key][iChan, iFreq, :].compressed().size\n",
    "                           for iFreq in range(n_freq)]\n",
    "                          for iChan in range(n_chan)]\n",
    "                         for key in gold_keys], axis=0)  # [chan, freq]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "068534d0-20b0-4732-a120-e9dd5034e0c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 0.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home1/dscho/anaconda3/envs/memlab/lib/python3.6/site-packages/ipykernel_launcher.py:31: RuntimeWarning: Mean of empty slice\n"
     ]
    }
   ],
   "source": [
    "timer = Timer()\n",
    "\n",
    "# Calculate mean resultant lengths for each channel and frequency.\n",
    "n_freq = phase.freq.size\n",
    "n_chan = phase.chan.size\n",
    "mrls = {key: np.empty([n_chan, n_freq], dtype=np.float32)\n",
    "        for key in gold_keys}\n",
    "for key in gold_keys:\n",
    "    for iChan in range(n_chan):\n",
    "        _mrls = []\n",
    "        for iFreq in range(n_freq):\n",
    "            phi = spike_phases[key][iChan, iFreq, :].compressed()\n",
    "            if samp_spikes[iChan, iFreq] < 2:\n",
    "                mrls[key][iChan, iFreq] = np.nan\n",
    "            elif phi.size == samp_spikes[iChan, iFreq]:\n",
    "                mrls[key][iChan, iFreq] = circstats.circmoment(phi)[1]\n",
    "            else:\n",
    "                mrls[key][iChan, iFreq] = np.nanmean(\n",
    "                    [circstats.circmoment(np.random.choice(phi,\n",
    "                                                           size=samp_spikes[iChan, iFreq],\n",
    "                                                           replace=False))[1]\n",
    "                     for _ in range(n_samp_iter)]\n",
    "                )\n",
    "mrls = {key: np.nanmean(mrls[key], axis=0)\n",
    "        for key in gold_keys}  # [gold_key][freq]\n",
    "                \n",
    "print(timer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64cba038-c814-46bc-9d7a-530391b1ee2c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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