{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "463b03ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os, h5py\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import decomposition\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b5a09f6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6095"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source  = '/home/sobolev/nevermind/Andrey/data'\n",
    "\n",
    "session = '009266_hippoSIT_2023-04-26_08-20-17'  # sample session with some PPC units\n",
    "#session = '009266_hippoSIT_2023-04-28_09-04-09'\n",
    "#session = '009266_hippoSIT_2023-05-04_09-11-06'  # coma session with baseline AEPs\n",
    "animal      = session.split('_')[0]\n",
    "sessionpath = os.path.join(source, animal, session)\n",
    "aeps_file = os.path.join(sessionpath, 'AEPs.h5')\n",
    "\n",
    "with h5py.File(aeps_file, 'r') as f:\n",
    "    aeps = np.array(f['aeps'])\n",
    "    aeps_events = np.array(f['aeps_events'])\n",
    "    \n",
    "t_N1 = 20  # all in ms\n",
    "N1_l, N1_r = 15, 25\n",
    "P1_l, P1_r = 30, 75\n",
    "\n",
    "len(aeps)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfc39b53",
   "metadata": {},
   "source": [
    "## Phase / amplitude space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aaf72be2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# do bandpass 3-15 and 50 - 80 and look at waveforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a07f8909",
   "metadata": {},
   "outputs": [],
   "source": [
    "# if all good do hilbert and try to look at the phases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6d93eea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a56eead9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8f5c31df",
   "metadata": {},
   "source": [
    "### Active listening - unit correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "95396069",
   "metadata": {},
   "outputs": [],
   "source": [
    "# putative active listening periods\n",
    "idxs_active = np.where(i_rate > thresh)[0]\n",
    "\n",
    "def get_event_tl_idx(t_event):\n",
    "    return (np.abs(tl[:, 0] - t_event)).argmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bb6e6749",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter tgt / bgr events\n",
    "sound_events = events[(events[:, 1] == 1) | (events[:, 1] == 2)]\n",
    "# get active / passive aeps\n",
    "aeps_al_idxs = np.array([i for i, event in enumerate(sound_events) if get_event_tl_idx(event[0]) in idxs_active])\n",
    "aeps_pl_idxs = np.array([i for i, event in enumerate(sound_events) if get_event_tl_idx(event[0]) not in idxs_active])\n",
    "aeps_al = aeps[aeps_al_idxs]\n",
    "aeps_pl = aeps[aeps_pl_idxs]"
   ]
  }
 ],
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