amgen/exp/ProcessExp.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment: Flashed gratings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import scipy\n",
    "import scipy.stats as sts\n",
    "import pickle as pkl\n",
    "import ipywidgets as widgets\n",
    "from mpl_toolkits import mplot3d\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from scipy import sparse\n",
    "import sys\n",
    "from matplotlib import cm\n",
    "sys.path.insert(0, '../model/')\n",
    "\n",
    "# color maps\n",
    "cmap_hot = cm.get_cmap('hot')\n",
    "cmap_viridis = cm.get_cmap('viridis')\n",
    "cmap_jet = cm.get_cmap('jet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "data_folder = \"../data/\"\n",
    "stim_file = \"Stiminfo_PVCre_2021_0012_s06_e14.csv\"\n",
    "spike_times_file = \"Spiketimes_PVCre_2021_0012_s06_e14.npy\"\n",
    "\n",
    "key_symbol = {'pair':'$(\\\\theta,\\phi)$', 'orientation':'$\\\\theta$', 'phase':'$\\phi$'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['data_folder', 'stim_file', 'spike_times_file', 'key_symbol', 'stim', 'num_trial', 'stim_val', 'trial_stim_id', 'key_list', 'stim_id_trial', 'num_stim', 'key', 'pair_id', 'trial_pair_id', 'pair_val', 'pair_trial_id', 'spike_times', 'num_unit', 'num_spike', 'spike_count_rate', 'avg_firing_rate', 'firing_rate', 'stim_num_trial', 'stim_hist', 'optimal_avg_firing_rate', 'latest_spike_time', 'dt', 'exp_time', 'B_stim', 'trials', 't_on', 't_off', 'B_spike', 'trial_range', 'units', 'orientation', 'phase', 'trial_spike_times', 'delta_t_on', 'delta_t_off', 'subseq_trials', 'subseq_spike_times', 'subseq_spike_times_locked', 'time_range', 'unit_id_2', 'p1', 'p2', 'delta_t', 'time_range_smoothing', 'period_1_dist', 'period_2_dist', 'period_3_dist', 'num_units', 'max_p_1', 'max_p_2', 'max_p_3', 'delta_2', 'delta_3', 't_p1', 't_p2', 'subseq_spike_times_locked_2', 'B_spike_fixed', 'B_stim_original', 'B_spike_smooth', 'B_stim_total', 'R', 'R_pair_smoothed', 'Rp', 'firing_rates_noise_red', 'firing_rates_flat', 'firing_rates_in_trials', 'avg_firing_rates'])\n"
     ]
    }
   ],
   "source": [
    "file = open(\"dump.pkl\", 'rb')\n",
    "pair_id = None\n",
    "data = pkl.load(file)\n",
    "print(data.keys())\n",
    "for key, value in data.items():\n",
    "    globals()[key] = value\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load the stimulus data\n",
    "**Stimulus data layout:**\n",
    "```\n",
    "stim_val = {'pair': [(or1, ph1), (or2, ph2), ...], 'orientation': [...], 'phase': [...]}\n",
    "stim_id_trial = {'pair': [id1, id2, ...], 'phase': [..], ...}   # corresponding trials\n",
    "pair_trial_id[orientation_id][phase_id] = [Trial ids]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stimulus DataFrame\n",
    "stim = pd.read_csv(data_folder + stim_file)\n",
    "num_trial = len(stim)\n",
    "stim_val = {}\n",
    "trial_stim_id = {}\n",
    "# 50 trials per orientation-phase pair\n",
    "stim_val['pair'], trial_stim_id['pair'] = np.unique(stim[['grat_orientation', 'grat_phase']], return_inverse=True, axis=0) \n",
    "# 1000 trials per orientation\n",
    "stim_val['orientation'], trial_stim_id['orientation'] = np.unique(stim['grat_orientation'], return_inverse=True) \n",
    "# 1000 trials per phase\n",
    "stim_val['phase'], trial_stim_id['phase'] = np.unique(stim['grat_phase'], return_inverse=True) \n",
    "key_list = ['pair', 'orientation', 'phase']\n",
    "stim_id_trial = {}\n",
    "num_stim = {}\n",
    "for key in key_list:\n",
    "    stim_id_trial[key] = [np.where(trial_stim_id[key] == i)[0] for i in range(len(stim_val[key]))]\n",
    "    num_stim[key] = len(stim_val[key])\n",
    "\n",
    "def pair_id(i):\n",
    "    return (int(i//num_stim['orientation']), i%num_stim['orientation'])\n",
    "\n",
    "trial_pair_id = np.array([pair_id(i) for i in trial_stim_id['pair']], dtype=object)\n",
    "pair_val = stim_val['pair'].reshape(num_stim['orientation'], num_stim['phase'],2)\n",
    "\n",
    "# for each (orientation_id = i, phase_id = j) find the trial indices\n",
    "pair_trial_id = np.ndarray((num_stim['orientation'], num_stim['phase']), dtype=object)\n",
    "for i in range(num_stim['pair']):\n",
    "    pair_trial_id[pair_id(i)] = stim_id_trial['pair'][i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Unnamed: 0</th>\n",
       "      <th>grat_orientation</th>\n",
       "      <th>grat_phase</th>\n",
       "      <th>stimvals</th>\n",
       "      <th>stim_ontime</th>\n",
       "      <th>stim_offtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>102</td>\n",
       "      <td>2.5005</td>\n",
       "      <td>2.5839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6195</td>\n",
       "      <td>2.7028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>162.0</td>\n",
       "      <td>288.0</td>\n",
       "      <td>376</td>\n",
       "      <td>2.7030</td>\n",
       "      <td>2.7863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.7865</td>\n",
       "      <td>2.8699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>108.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>242</td>\n",
       "      <td>2.8700</td>\n",
       "      <td>2.9534</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  grat_orientation  grat_phase  stimvals  stim_ontime  \\\n",
       "0           0              45.0        36.0       102       2.5005   \n",
       "1           1               0.0        18.0         1       2.6195   \n",
       "2           2             162.0       288.0       376       2.7030   \n",
       "3           3               0.0        54.0         3       2.7865   \n",
       "4           4             108.0        36.0       242       2.8700   \n",
       "\n",
       "   stim_offtime  \n",
       "0        2.5839  \n",
       "1        2.7028  \n",
       "2        2.7863  \n",
       "3        2.8699  \n",
       "4        2.9534  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stim.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pair, orientation, phase\n",
      "(400, 2)\n",
      "(20,) (20,)\n"
     ]
    }
   ],
   "source": [
    "print(', '.join(stim_val.keys()))\n",
    "print(stim_val['pair'].shape)\n",
    "print(stim_val['orientation'].shape, stim_val['phase'].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   82   482  1021  1421  1821  2183  2645  3045  3445  3643  4357  4757\n",
      "  5014  5493  5893  6293  6693  6965  7365  7652  8052  8513  9116  9203\n",
      "  9603 10003 10628 10849 11512 11912 12312 12535 12947 13347 13666 14248\n",
      " 14682 14857 15257 15657 16393 16793 17193 17262 17906 18146 18546 19050\n",
      " 19450 19904]\n"
     ]
    }
   ],
   "source": [
    "print(stim_id_trial['pair'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   82,   482,  1021,  1421,  1821,  2183,  2645,  3045,  3445,\n",
       "        3643,  4357,  4757,  5014,  5493,  5893,  6293,  6693,  6965,\n",
       "        7365,  7652,  8052,  8513,  9116,  9203,  9603, 10003, 10628,\n",
       "       10849, 11512, 11912, 12312, 12535, 12947, 13347, 13666, 14248,\n",
       "       14682, 14857, 15257, 15657, 16393, 16793, 17193, 17262, 17906,\n",
       "       18146, 18546, 19050, 19450, 19904])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pair_trial_id[0][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Stimulus data layout:**\n",
    "```\n",
    "stim_val = {'pair': [(or1, ph1), (or2, ph2), ...], 'orientation': [...], 'phase': [...]}\n",
    "stim_id_trial = {'pair': [id1, id2, ...], 'phase': [..], ...}   # corresponding trials\n",
    "pair_trial_id[orientation_id][phase_id] = [Trial ids]\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load the spike data and correlations\n",
    "```\n",
    "Loaded: spike_count_rate, avg_firing_rate, sem_firing_rate, firing_rate, stim_num_trial, C_r_fphi_theta, theta_hist, phase_hist, pair_hist\n",
    "Loaded: corr_stim_unit, optimal_avg_firing_rate, stim_hist, stim_hist_caution\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40,)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spike_times_file = \"Spiketimes_PVCre_2021_0012_s06_e14.npy\"\n",
    "spike_times = np.load(data_folder+spike_times_file, allow_pickle=True)\n",
    "active = [len(spike_times[i]) > 0 for i in range(len(spike_times))]\n",
    "spike_times = spike_times[np.where(active)]\n",
    "num_unit = len(spike_times)\n",
    "spike_times.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sort spikes by firing rate**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "num_spike = list(map(len, spike_times))\n",
    "spike_times = spike_times[np.argsort(num_spike)[::-1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Load a lot of variables out of a `.pkl` file**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded: spike_count_rate, avg_firing_rate, sem_firing_rate, firing_rate, stim_num_trial, C_r_fphi_theta, theta_hist, phase_hist, pair_hist\n",
      "Loaded: corr_stim_unit, optimal_avg_firing_rate, stim_hist, stim_hist_caution\n"
     ]
    }
   ],
   "source": [
    "# yeah...\n",
    "files = ['spike_data.pkl', 'corr_data.pkl']\n",
    "for file_name in files:\n",
    "    file = open(file_name, 'rb')\n",
    "    data = pkl.load(file)\n",
    "    print('Loaded:', ', '.join(data.keys()))\n",
    "    for key, value in data.items():\n",
    "        globals()[key] = value\n",
    "\n",
    "    file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Spike and stimulus preprocessing\n",
    "\n",
    "```\n",
    "B_stim = {'pair': [mat_stim_1, mat_stim_2, ..], 'orientation': .., ...}\n",
    "```\n",
    "\n",
    "`mat_stim_i` is a matrix of shape `(1, M)` such that `mat[0][t] = 1` if there was stimuli at time $t$ and $0$ if not\n",
    "\n",
    "```\n",
    "B_spike = [unit_1_spikes, unit_2_spikes, ..]\n",
    "unit_1_spikes[t] = 1 if there was a spike, 0 if not\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reverse correlation time offset range \n",
    "max_delay = 300 # dt\n",
    "tau_id_range = np.arange(max_delay)\n",
    "\n",
    "# experiment duration\n",
    "latest_spike_time = max([np.max(s) for s in spike_times if len(s)])\n",
    "latest_stim_offtime = list(stim['stim_offtime'])[-1]\n",
    "experiment_dur = max([latest_spike_time, latest_stim_offtime])\n",
    "\n",
    "dt = 0.001 # 1 ms\n",
    "exp_time = np.arange(0, experiment_dur, dt)\n",
    "M = len(exp_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "B_stim = {}\n",
    "for key in key_list:\n",
    "    B_stim[key] = []\n",
    "    for stim_id, trials in enumerate(stim_id_trial[key]):\n",
    "        B_stim[key].append([])\n",
    "        s = []\n",
    "        for trial_id in trials:\n",
    "            t_on, t_off = stim['stim_ontime'][trial_id], stim['stim_offtime'][trial_id]\n",
    "            s += list(np.arange(int(t_on//dt), int(t_off//dt)))\n",
    "\n",
    "        B_stim[key][stim_id] = sparse.coo_matrix((np.ones(len(s)), (np.zeros(len(s), dtype=int), s)), shape=(1, M))\n",
    "s = spike_times//dt\n",
    "B_spike = []\n",
    "for unit_id in range(num_unit):\n",
    "    B_spike.append(sparse.coo_matrix((np.ones(len(s[unit_id])), (np.zeros(len(s[unit_id]), dtype=int), np.int0(s[unit_id]))), shape=(1, M)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_spike_num = np.sort(num_spike)[::-1]\n",
    "cutoff_num_spike = 1000\n",
    "num_unit = np.sum(sorted_spike_num > cutoff_num_spike)\n",
    "for key in key_list:\n",
    "    stim_hist[key] = stim_hist[key][:num_unit]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Some plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph shows for unit 0 at a particular moment:\n",
    "- One stimulus duration (blue)\n",
    "- The second stimulus (red)\n",
    "\n",
    "And spike events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stim_id = 10\n",
    "trial_id = stim_id_trial['phase'][stim_id][0]\n",
    "unit_id = 0\n",
    "t_on, t_off = stim['stim_ontime'][trial_id], stim['stim_offtime'][trial_id]\n",
    "t0, t1 = t_off-0.2, t_off+0.1\n",
    "stim_prev_id = trial_stim_id['phase'][trial_id-1]\n",
    "t_prev_on, t_prev_off = stim['stim_ontime'][trial_id-1], stim['stim_offtime'][trial_id-1]\n",
    "spikes = spike_times[unit_id][np.where((t0 < spike_times[unit_id]) & (spike_times[unit_id] < t1))]\n",
    "plt.axvspan(t_on, t_off, color=cmap_jet(1-np.abs(stim_id-10)/10), alpha=0.3)\n",
    "plt.axvspan(t_prev_on, t_prev_off, color=cmap_jet(1-np.abs(stim_prev_id-10)/10), alpha=0.3)\n",
    "plt.eventplot(spikes)\n",
    "plt.xlim([t0, t1])\n",
    "plt.title('Spikes for unit 0')\n",
    "plt.xlabel('Time')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfAAAAIUCAYAAADlkX7vAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAABGYElEQVR4nO3dd3hUZfrG8e+TDqFX6aGDgCCEqmLvvWLBgoqKrrrquuq667quvaxlV5G1V1Qsa8deUEAISJPepJdQU0h/f3/MgV+MSQhJZs6c5P5cVy5mzpzznuedM8w9p5tzDhEREQmWGL8LEBERkX2nABcREQkgBbiIiEgAKcBFREQCSAEuIiISQApwERGRAFKAS1iZmTOzu/2uQ6QsZvatmX1biekOM7M7zSymxPAU73N/SXXVKFIaBbiISOUcBvyd33+PrgeGAh9HuiCpXeL8LkCktjCzROdcrt91AJhZLGDOuQK/a6msst5Pv/vm1TTVj3lL7aI1cPkdM+tmZu+Z2SYzyzGzVWY2wczivNcv8TYRppSY7k4zK+3SfmZmt5vZGjPbZWbfm1m/EiMca2aTzWyHmWWa2SIzu6Nk22bWx8y+MbNsM1tvZncV34RpZklm9qiZzfPa2WBmH5pZj1KK6mhmr3jj5JrZcjN7vMQ4h5rZV2aWYWZZZvaZmfWuwHv4otffoV6/dgEPeq81N7OnzWytN9+FZnZFsWkHen09pZR2nzKzzWYWX2zYFWY221tW6Wb2nJk1KTGdM7N7zOxWM1sB5AF9zKyemf3bW8a53jL/svj7ZWZxZnabV2euma0zs0fMLKkC70Ocmd1iZvO9+jab2cQS7Xf3Pm/bvc/HVDM7rkQ7u5d/b28ZZAJvldc377V9Xn4V+QyZ2Z2E1r4B8r0anPdaqZvQzWxkieX0ipm1KjHOSjN71czONbMFXs1pZnbw3t5rqX20Bi6l+RjYBowB0oE2wAlU/gffRcAq4A9AInAX8JWZdXXObTWzTsAHwNvea3lAV6BTKW39D3geuA84FvgbUATc6b2eCNQH7ia0KbMJcDUwxcx6Ouc2QCi8gWlANnAHsARoDxyze0ZmdiLwvvd+jPQG3wJMMrMDnHOr99LvhsAbwMPAX4BdZtYA+AGo49W8wuvHWAutUf7bOTfdzBZ58/ygWD0JwAjgdedcvjfsfuAm4AngZkLL6m6gt5kNc84VFqvnEmA58CcgC1gHPAqc4tW3BGgKHAQ0Kjbdq8DJwAPAZKAn8E8gBThzL+/BG8BpwGPAl0ASMBxoBSw0s9be+5FB6POxA7gG+NjMTnLOfVqivfeB57xaisrrWxWWX0U+Q88CbYHLgIOBwjLaAkI/soBxwJvAbUBr4F5gsJn1d85lFhv9EKA7oc92DqH3+iMzS3HObS9vPlLLOOf0p789f0AzwAGnlDPOJd44KSWG3xn6SP1mmCP0IyC52LAUIB/4p/f8LG+8BuXM805vnFtLDH+G0Jd/ozKmiwXqeuPcUGz4y0Am0LqceS4FvioxrIHXn8f28j6+6NV7aonhu7+Uu5bSj3Qgznt+O7ALaFhsnNO8NgcVex8LgTtKtHWQN95pJZbDOqBOiXHnAf8qpx+HeNNeVGL4Bd7wfuVMe4Q3znXljPMwUAB0KbHMFgEzS1n+15fSRll9q9DyA74Fvi2nxrI+Q7triisxfoo3/JJi028Evikx3sEl3x9gJaEfz42LDUv1xju/vM+c/mrfnzahS0lbCK3J3G9mo82sazW0+YlzLmv3E+fcSkL7CId6g2YRCvQ3zOwsM2tRTltvlXj+BlAP2LNZ1MzOMbOfzGw7oXDI8sbpXmy6Y4CPnHPrSpuJ1+/OwGveZuA4C+1CyAamEFqL3Jt84KMSw44DfgJWlGj3M0Jrv/t7471KaE3w7GLTXggscs5N854fTWirSMkafyIUNiVrnOic21Vi2HTgEjP7i5mlWmj/ccl684C3S8zjc+/18t6HYwgFzzPljDMcmOqcW7p7gAttNRgP9PO2WBT3Xhnt/KZvVV1+FfwMVVR3oAXwWvGBzrkfgF+BQ0uMP8U5t63Y87nev+0rMW+pwRTg8hvOOUcoGNIIbaZebKF9w2Oq0OzGMoa18ea5lNBm5BjgFWCDtx+05BdbaW3tft4GwMxOJrSZcgFwPjAYGAhsJrT5dremwJpyat79I+I5QkFc/O8kb/q92ex+uwl7d7vDS2lzQrG6cM79CnxPKLQxs0bAiYTen5I1Li2lvfql1Li+lBqvJbRp91JCYb7J2/9bt9g8EggFWPH2NxWvtwxNga2l/GgorkkZdW0ADGhcgT6UNrzSy28fPkMVtft4hLL62aTEsK3Fn7j/P1CvMvOWGkz7wOV3nHPLgYvMzIC+hPZNPmVmK11on2SON2pCiUnL+lJsWcawtcXm+Q3wjZklEtoEfBeh/aApzrn0EtMtL6Xt3W2dCyx1zl2yewQLHfBV8kty9779smzx/r2N0L7bkvLKmXa30g7o20Io/K4vY5pFxR6/AjxjZh0I/cBJILRmXrLGYwhtdi1tXuXW40L7Xm8DbvPmcxZwP6H+3eK1kUNoU3ppSt2C4UkHmphZnXJCfCuwXynD9/PqLdmvsu5/XHJ4VZZfRT9DFbU7kMvq54xKtiu1nNbApUwuZBZwozdo92bqX0s8x9s8eQylO8HMkouNmwIMIbQps+Q8c51zXxM6YjsZ6FhilHNKPD+X0L7s3ZsZ6xLa5FnchYT2Qxb3OXBSyaOAi1lEaH9kL+dcWil/c8qYbm8mAj2AVWW0m1Fs3AlALqH9zRcCk7w1892+IHQgV/sy2lqxL4U55351zj1C6L3cvWwnElrza1jGPMoL8M8JrUVfXs443wFDrNgZDd5m/BHAz865nfvSh2Kqsvwq+hnavWZcpwK1bCT0Wd3DzIYBHQjtgxfZZ1oDl98wswOAxwltQlxK6EvrEkJfaF97o00HlgEPWegUrlxCR+kmltHsLuBzM3vIG+cfwE5CR0BjZlcR2qz8CbCa0IF0txFau5tXoq3R3jynE1orvRy40zm3w3t9InCamT1KaP9zKqHNxNtLtPN3QkfWTzaze72+tgGOc86NdM45M7sGeN87+vstQmuULYFhhAL4X2W+kWV7lFA4TfJqXEToh0oP4BDn3Km7R3TO7TSz9wkdld0KGF28IefcMjN7APiPmXUnFIY5QDtCu0Ge9bZslMnMphA60n0uoR9ChxLa6vKSN49vzWw8oX3g/yJ05H4RoQO1TgBucc4tLq1t59w3ZvYO8C8za0fo8xNPaFl/7Jz71ns/LgG+MLO/E/pcXA10I7TLoFKquPwq+hma7/17k5l9ChQ659JKqaXQQqdEjjOzVwltRWkD3EPoyP/nK9tPqeX8PopOf9H1R2jf4UvAYkIH/GwlFAzHlhivF6E1h0xCp4jdSNlHod9D6DSlNYQCZhLFjl4mdDDb+4TCO5fQvsIJQPdi49zptdUb+IbQj4INhE6xiSk2Xgyh03/WefV/BxxIaG3sxRK1dSZ0sFS6V9cyShyR7dX2EaFNuTleO28AQ/fyPr4IrCnjtcaEgmv3OcubvPfkj6WMe6LX798ckV5inAsJHRSY5S2PBcB/gLYllsPdpUz7APAzodO3sggF+XUlxokhtMl/tvce7PAeP1hWTcWmjSN0RP1ir6+bCf1QK75suxM6PXCH1/5UQj+kireze/nHlTKPUvtW0eVHiaPQK/oZIvTj9klv+RWx5xCS3x6FXmz8kd77lktoE/8rQKsS46wEXi2jj3f6/f2gv+j6M+fK2qUkEj2KXTgj3gX46mEiItVF+8BFREQCSAEuIiISQNqELiIiEkBaAxcREQkgBbiIiEgAKcBFREQCSAEuIiISQApwERGRAFKAi4iIBJACXEREJIAU4CIiIgGkABcREQkgBbiIiEgAKcBFREQCSAEuIiISQApwERGRAFKAi4iIBJACXEREJIAU4CIiIgGkABcREQkgBbiIiEgAKcBFREQCSAEuIiISQApwEREJLDNzZtalktNeYGafV3dNkWLOOb9rEBERqRQzc0BX59zSvYyXAqwA4p1zBZG
      "text/plain": [
       "<Figure size 504x504 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import gridspec\n",
    "\n",
    "fig, ax = plt.subplots(3,1,figsize=(7,7))\n",
    "gs0 = gridspec.GridSpec(3, 3, figure=fig, hspace=0, wspace=0)\n",
    "\n",
    "stim_id = 10\n",
    "trial_id = stim_id_trial['phase'][stim_id][0]\n",
    "unit_id = 0\n",
    "t_on, t_off = stim['stim_ontime'][trial_id], stim['stim_offtime'][trial_id]\n",
    "t0, t1 = t_off-0.2, t_off+0.1\n",
    "stim_prev_id = trial_stim_id['phase'][trial_id-1]\n",
    "t_prev_on, t_prev_off = stim['stim_ontime'][trial_id-1], stim['stim_offtime'][trial_id-1]\n",
    "spikes = spike_times[unit_id][np.where((t0 < spike_times[unit_id]) & (spike_times[unit_id] < t1))]\n",
    "\n",
    "ax0 = fig.add_subplot(gs0[0, :-1])\n",
    "ax1 = fig.add_subplot(gs0[1, :-1])\n",
    "ax2 = fig.add_subplot(gs0[2, :-1])\n",
    "ax3 = fig.add_subplot(gs0[:, -1])\n",
    "\n",
    "ax0.axvspan(t_on, t_off, color=cmap_jet(1-np.abs(stim_id-10)/10), alpha=0.3, label='preferred')\n",
    "ax0.axvspan(t_prev_on, t_prev_off, color=cmap_jet(1-np.abs(stim_prev_id-10)/10), alpha=0.3, label='non-preferred')\n",
    "ax0.eventplot(spikes)\n",
    "ax0.set_xlim([t0, t1])\n",
    "ax0.legend(loc='upper left')\n",
    "\n",
    "ax1.axvspan(t_on, t_off, color=cmap_jet(1-np.abs(stim_id-10)/10), alpha=0.3)\n",
    "ax1.axvspan(t_prev_on, t_prev_off, color=cmap_jet(1-np.abs(stim_prev_id-10)/10), alpha=0.3)\n",
    "ax1.eventplot(spikes-0.05)\n",
    "ax1.set_xlim([t0, t1])\n",
    "\n",
    "ax2.axvspan(t_on, t_off, color=cmap_jet(1-np.abs(stim_id-10)/10), alpha=0.3)\n",
    "ax2.axvspan(t_prev_on, t_prev_off, color=cmap_jet(1-np.abs(stim_prev_id-10)/10), alpha=0.3)\n",
    "ax2.eventplot(spikes-0.15)\n",
    "ax2.set_xlim([t0, t1])\n",
    "\n",
    "ax3.plot(np.sin(np.pi*tau_id_range/300), np.arange(300))\n",
    "ax3.set_xticks([])\n",
    "ax3.set_yticks([])\n",
    "ax3.set_title('reverse correlation')\n",
    "# ax2.legend()\n",
    "for ax in fig.get_axes():\n",
    "    ax.tick_params(bottom=False, labelbottom=False, left=False, labelleft=False)\n",
    "\n",
    "ax2.tick_params(bottom=True, labelbottom=True)\n",
    "ax2.set_xlabel('t/s')\n",
    "plt.tight_layout()\n",
    "fig.suptitle('subspace reverse correlation', size=16, y=1.05)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 504x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# subspace reverese correlation\n",
    "stim_id = 13\n",
    "ref_trial_id = stim_id_trial['phase'][stim_id][0]\n",
    "stim_prev_id = trial_stim_id['phase'][ref_trial_id-1]\n",
    "past_trials = 3\n",
    "future_trials = 10\n",
    "trial_range = np.arange(-past_trials, future_trials+1) + ref_trial_id\n",
    "\n",
    "units = np.arange(num_unit)\n",
    "orientation, phase, t_on = np.array(stim[['grat_orientation', 'grat_phase','stim_ontime']].iloc[trial_range]).T\n",
    "spikes = [\n",
    "    spike_times[unit_id][\n",
    "        # Where the spike occurs during the stimulus period in one of the right trials\n",
    "        np.where((t_on[0] < spike_times[unit_id]) & (spike_times[unit_id] < t_on[-1]))\n",
    "        ]\n",
    "    for unit_id in units]\n",
    "\n",
    "fig, ax = plt.subplots(2,1,figsize=(7,7))\n",
    "plt.subplots_adjust(hspace=0)\n",
    "s = 1\n",
    "ax[0].eventplot(spikes, linelength=s, lineoffsets=s)\n",
    "\n",
    "ref_id = list(trial_range).index(ref_trial_id)\n",
    "# ax[0].axvspan(t_on[ref_id], t_on[ref_id]+0.08, color=cmap_jet(1-np.abs(stim_id-10)/10), alpha=0.3)\n",
    "# ax[0].axvspan(t_on[ref_id-1], t_on[ref_id-1]+0.08, color=cmap_jet(1-np.abs(stim_prev_id-10)/10), alpha=0.3)\n",
    "ax[0].set(ylabel='unit')\n",
    "ax[0].invert_yaxis()\n",
    "ax[1].step(t_on, phase, 'b', where='post', alpha=0.5, label='phase')\n",
    "ax[1].set(xlabel='t / s', xlim=[t_on[0], t_on[-1]], ylim=[0, 360], ylabel='phase')\n",
    "ax[1].legend(loc='upper left')\n",
    "ax2 = ax[1].twinx()\n",
    "ax2.step(t_on, orientation, 'r', where='post', alpha=0.5, label='orientation')\n",
    "ax2.set(ylim=[0, 180], ylabel='orientation')\n",
    "ax2.legend()\n",
    "ax[0].set_title('Spikes in units')\n",
    "ax[1].set_title('Phase and orientation')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "afc70624cbfe4a5f8838bce9ae0c0ebc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=0, description='delay/ms', max=299), IntSlider(value=0, description='uni…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@widgets.interact(key=widgets.RadioButtons(options=key_list[1:],\n",
    "    description='stimulus:',\n",
    "    disabled=False), tau_id=widgets.IntSlider(0, min=0, max=max_delay-1, description='delay/ms'), unit_id=widgets.IntSlider(0, min=0, max=num_unit-1))\n",
    "\n",
    "def PSSH(tau_id, unit_id, key):\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(7,6))\n",
    "    prob = stim_hist[key][unit_id, tau_id]\n",
    "    n = sorted_spike_num[unit_id]\n",
    "    confidence_interval = np.sqrt(prob/n)\n",
    "    plt.errorbar(stim_val[key], stim_hist[key][unit_id, tau_id], yerr=confidence_interval)\n",
    "    ymin = np.min(stim_hist[key][unit_id])*0.95\n",
    "    ymax = np.max(stim_hist[key][unit_id])*1.05\n",
    "    ax.set_ylabel('$\\mathcal{P}($'+key_symbol[key]+'$|\\\\tau)$')\n",
    "    ax.set_xlabel(key_symbol[key])\n",
    "    plt.ylim([ymin, ymax])\n",
    "    plt.suptitle('Distribution of stimulus at time $\\\\tau$ before a spike of unit: %d'%unit_id)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Problem with the data\n",
    "\n",
    "The picture a couple of cells below clearly shows that there is a shift in the stimulus presentation times. Let's fix it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3.10/site-packages/pandas/core/roperator.py:13: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  return right - left\n"
     ]
    }
   ],
   "source": [
    "trial_spike_times = np.ndarray((num_unit, num_trial), dtype=object)\n",
    "delta_t_on = 0.2 # 200ms\n",
    "delta_t_off = 0.3\n",
    "for trial_id in range(num_trial):\n",
    "    t_on, t_off = stim['stim_ontime'][trial_id]-delta_t_on, stim['stim_offtime'][trial_id]+delta_t_off\n",
    "    for unit_id in range(num_unit):\n",
    "        trial_spike_times[unit_id, trial_id] = spike_times[unit_id][np.where((spike_times[unit_id] < t_off) & (spike_times[unit_id] > t_on))]\n",
    "subseq_trials = 5\n",
    "subseq_spike_times = np.ndarray(num_unit, dtype=object)\n",
    "for unit_id in range(num_unit):\n",
    "    subseq_spike_times[unit_id] = [np.concatenate(trial_spike_times[unit_id][i:i+subseq_trials]) for i in range(num_trial-subseq_trials)]\n",
    "\n",
    "subseq_spike_times_locked = {}\n",
    "subseq_spike_times_locked['ontime'] = [subseq_spike_times[unit_id] - stim['stim_ontime'][:-subseq_trials] for unit_id in range(num_unit)]\n",
    "subseq_spike_times_locked['offtime'] = [subseq_spike_times[unit_id] - stim['stim_offtime'][:-subseq_trials]  for unit_id in range(num_unit)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_range = np.arange(0, stim['stim_ontime'].max(), np.array(sorted(np.diff(stim['stim_ontime'], n=1))[10:-10]).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((28, 19995), (28, 19995), 0.7671829999999886)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subseq_spike_times_len = len(subseq_spike_times[0])\n",
    "subseq_spike_times_locked['ontime'] = np.array(subseq_spike_times_locked['ontime'])\n",
    "subseq_spike_times_locked['offtime'] = np.array(subseq_spike_times_locked['offtime'])\n",
    "from itertools import chain\n",
    "subseq_spike_times_locked['ontime'].shape, subseq_spike_times_locked['offtime'].shape, max(chain(*subseq_spike_times_locked['ontime'].flatten()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1440x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "unit_id = 1\n",
    "unit_id_2 = 3\n",
    "\n",
    "fig, ax = plt.subplots(1,3, figsize=(20,10))\n",
    "ax[0].plot(np.diff(stim['stim_ontime'], n=2), np.arange(20000-2))\n",
    "ax[0].invert_yaxis()\n",
    "ax[0].set_xlabel('$\\Delta^2[t_{on}]=\\Delta$ trial duration / s', size=20)\n",
    "ax[0].set(ylabel='trials')\n",
    "ax[1].eventplot(subseq_spike_times_locked['ontime'][unit_id], lineoffsets=1, linelengths=0.8)\n",
    "ax[1].set(title='unit %d'%unit_id, yticks=[], xlabel='t/s (locked to trial onsets)')\n",
    "ax[1].invert_yaxis()\n",
    "ax[2].eventplot(subseq_spike_times_locked['ontime'][unit_id_2], lineoffsets=1, linelengths=0.8)\n",
    "ax[2].set(title='unit %d'%(unit_id_2), yticks=[], xlabel='t/s (locked to trial ends)')\n",
    "ax[2].invert_yaxis()\n",
    "plt.subplots_adjust(wspace=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Whaaat?_\n",
    "\n",
    "Ok, we've gotta fix it.\n",
    "\n",
    "Let's take the \"bad\" times, identify the periods we want to calibrate - and align everything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2280 2286 2291 2292 2295 2298 2301 2307 2377 2378 2380 2383 2386 2464 2467 2469 2472 2475 2478 15300 15306 15312 15315 15316 15318 15327 15329 15333 15336 15338 15342 15344 15348 15349 15402 15403 15405 15408\n"
     ]
    }
   ],
   "source": [
    "stim_2diffs = np.diff(stim['stim_ontime'], n=2)\n",
    "print(*[i for i in range(len(stim_2diffs)) if stim_2diffs[i] > 0.01])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Periods:\n",
    "$[0, 2350)$, $[2350, 15350)$, $[15350, 20000]$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4135643/175591453.py:20: DeprecationWarning: Please use `gaussian_filter1d` from the `scipy.ndimage` namespace, the `scipy.ndimage.filters` namespace is deprecated.\n",
      "  period_1_dist = scipy.ndimage.filters.gaussian_filter1d(period_1_dist, sigma=10)\n",
      "/tmp/ipykernel_4135643/175591453.py:21: DeprecationWarning: Please use `gaussian_filter1d` from the `scipy.ndimage` namespace, the `scipy.ndimage.filters` namespace is deprecated.\n",
      "  period_2_dist = scipy.ndimage.filters.gaussian_filter1d(period_2_dist, sigma=10)\n",
      "/tmp/ipykernel_4135643/175591453.py:22: DeprecationWarning: Please use `gaussian_filter1d` from the `scipy.ndimage` namespace, the `scipy.ndimage.filters` namespace is deprecated.\n",
      "  period_3_dist = scipy.ndimage.filters.gaussian_filter1d(period_3_dist, sigma=10)\n"
     ]
    }
   ],
   "source": [
    "p1, p2 = 2350, 15350\n",
    "delta_t = 0.002\n",
    "time_range_smoothing = np.arange(0, 0.8, delta_t)\n",
    "period_1_dist = np.zeros(len(time_range_smoothing) - 1)\n",
    "period_2_dist = np.zeros(len(time_range_smoothing) - 1)\n",
    "period_3_dist = np.zeros(len(time_range_smoothing) - 1)\n",
    "num_units = subseq_spike_times_locked['ontime'].shape[0]\n",
    "for i in range(num_units):\n",
    "    for j, row in enumerate(subseq_spike_times_locked['ontime'][i]):\n",
    "        hist = np.histogram(row, bins=time_range_smoothing)[0]\n",
    "        if j < p1:\n",
    "            period_1_dist += hist\n",
    "        elif j < p2:\n",
    "            period_2_dist += hist\n",
    "        else:\n",
    "            period_3_dist += hist\n",
    "period_1_dist /= num_units\n",
    "period_2_dist /= num_units\n",
    "period_3_dist /= num_units\n",
    "period_1_dist = scipy.ndimage.filters.gaussian_filter1d(period_1_dist, sigma=10)\n",
    "period_2_dist = scipy.ndimage.filters.gaussian_filter1d(period_2_dist, sigma=10)\n",
    "period_3_dist = scipy.ndimage.filters.gaussian_filter1d(period_3_dist, sigma=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.collections.EventCollection at 0x7f1b53e02f80>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x1440 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3, 1, figsize=(10,20))\n",
    "ax[0].plot(time_range_smoothing[:-1], period_1_dist, label='period 1')\n",
    "ax[1].plot(time_range_smoothing[:-1], period_2_dist, label='period 2')\n",
    "ax[2].plot(time_range_smoothing[:-1], period_3_dist, label='period 3')\n",
    "ax[0].set(xlabel='t/s', ylabel='spike count', title='1st part')\n",
    "ax[1].set(xlabel='t/s', ylabel='spike count', title='2nd part')\n",
    "ax[2].set(xlabel='t/s', ylabel='spike count', title='3rd part')\n",
    "ax[0].eventplot([0.2], linelengths=0.8)\n",
    "ax[1].eventplot([0.2], linelengths=0.8)\n",
    "ax[2].eventplot([0.2], linelengths=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(time_range_smoothing).index(0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2 0.162 0.194\n"
     ]
    }
   ],
   "source": [
    "max_p_1 = time_range_smoothing[np.argmax(period_1_dist[:120])]\n",
    "max_p_2 = time_range_smoothing[np.argmax(period_2_dist[:100])]\n",
    "max_p_3 = time_range_smoothing[np.argmax(period_3_dist[:120])]\n",
    "delta_2 = max_p_1 - max_p_2\n",
    "delta_3 = max_p_1 - max_p_3\n",
    "print(max_p_1, max_p_2, max_p_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll fix the times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_p1, t_p2 = stim['stim_offtime'][p1], stim['stim_offtime'][p2]\n",
    "fix_time = lambda t: t - delta_2 * ((t < t_p2) & (t >= t_p1)) - delta_3 * ((t >= t_p2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           2.5839\n",
       "1           2.7028\n",
       "2           2.7863\n",
       "3           2.8699\n",
       "4           2.9534\n",
       "           ...    \n",
       "19990    1671.6389\n",
       "19991    1671.7224\n",
       "19992    1671.8059\n",
       "19993    1671.8894\n",
       "19994    1671.9730\n",
       "Name: stim_offtime, Length: 19995, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fix_time(stim['stim_offtime'][:-subseq_trials])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3.10/site-packages/pandas/core/roperator.py:13: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  return right - left\n"
     ]
    }
   ],
   "source": [
    "subseq_spike_times_locked_2 = {}\n",
    "subseq_spike_times_locked_2['ontime'] = [subseq_spike_times[unit_id] - fix_time(stim['stim_ontime'][:-subseq_trials]) for unit_id in range(num_unit)]\n",
    "subseq_spike_times_locked_2['offtime'] = [subseq_spike_times[unit_id] - fix_time(stim['stim_offtime'][:-subseq_trials])  for unit_id in range(num_unit)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1440x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "unit_id = 1\n",
    "\n",
    "fig, ax = plt.subplots(1,3, figsize=(20,10))\n",
    "ax[0].plot(np.diff(stim['stim_ontime'], n=2), np.arange(20000-2))\n",
    "ax[0].invert_yaxis()\n",
    "ax[0].set_xlabel('$\\Delta^2[t_{on}]=\\Delta$ trial duration / s', size=20)\n",
    "ax[0].set(ylabel='trials')\n",
    "ax[1].eventplot(subseq_spike_times_locked_2['ontime'][unit_id], lineoffsets=1, linelengths=0.8)\n",
    "ax[1].set(title='unit %d'%unit_id, yticks=[], xlabel='t/s (locked to trial onsets)')\n",
    "ax[1].invert_yaxis()\n",
    "ax[2].eventplot(subseq_spike_times_locked_2['ontime'][unit_id_2], lineoffsets=1, linelengths=0.8)\n",
    "ax[2].set(title='unit %d'%(unit_id_2), yticks=[], xlabel='t/s (locked to trial ends)')\n",
    "ax[2].invert_yaxis()\n",
    "plt.subplots_adjust(wspace=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = np.array([np.array(list(map(fix_time, spike_times[unit_id]))) for unit_id in range(len(spike_times))], dtype=object)//dt\n",
    "B_spike_fixed = []\n",
    "for unit_id in range(num_unit):\n",
    "    B_spike_fixed.append(sparse.coo_matrix((np.ones(len(s[unit_id])), (np.zeros(len(s[unit_id]), dtype=int), np.int0(s[unit_id]))), shape=(1, M)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from copy import deepcopy\n",
    "B_stim_original = deepcopy(B_stim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "B_stim = {}\n",
    "for key in key_list:\n",
    "    B_stim[key] = []\n",
    "    for stim_id, trials in enumerate(stim_id_trial[key]):\n",
    "        B_stim[key].append([])\n",
    "        s = []\n",
    "        for trial_id in trials:\n",
    "            t_on, t_off = fix_time(stim['stim_ontime'][trial_id]), fix_time(stim['stim_offtime'][trial_id])\n",
    "            s += list(np.arange(int(t_on//dt), int(t_off//dt)))\n",
    "\n",
    "        B_stim[key][stim_id] = sparse.coo_matrix((np.ones(len(s)), (np.zeros(len(s), dtype=int), s)), shape=(1, M))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate some matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_trial_index = lambda t: fix_time(stim['stim_ontime'][t])//dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "B_spike_smooth = list()\n",
    "for unit_id in range(len(B_spike)):\n",
    "    B_spike_smooth.append(scipy.ndimage.gaussian_filter(B_spike[unit_id].toarray()[0], sigma=50))\n",
    "B_spike_smooth = np.array(B_spike_smooth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1b6ae5dcc0>]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(B_spike_smooth[0][:5000])  # ~10 trials"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Reverse correlation\n",
    "\n",
    "TL;DR: We find the reverse correlation matrices. When we plot them they look meh, but if we plot them smoothed, they look awesome. However, we shouldn't use this smoothing when decoding parameters because it uses our (parameter-defined) notion of distance between points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "B_stim_total = np.sum(B_stim['phase'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "R = dict()\n",
    "for key in key_list:\n",
    "    R[key] = list()\n",
    "    for i in range(len(B_stim[key])):\n",
    "        item = []\n",
    "        for u in range(len(B_spike_smooth)):\n",
    "            item.append(B_stim[key][i].dot(np.roll(B_spike_smooth[u], 80)))\n",
    "        R[key].append(item)\n",
    "    R[key] = np.array(R[key])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "R['pair'] = R['pair'].reshape((20, 20, 40))\n",
    "Rp = R['pair']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "R_pair_smoothed = np.array([scipy.ndimage.gaussian_filter(R['pair'][:, :, i], sigma=2) for i in range(40)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 864x936 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(4, 4, figsize=(12, 13))\n",
    "fig.tight_layout(w_pad=2)\n",
    "for i, ax in enumerate(axs.flatten()):\n",
    "    ax.imshow(R['pair'][:, :, i], extent=(0, 360, 0, 180), aspect=2)\n",
    "    ax.set(xlabel=\"phase\", ylabel=\"orientation\", title=f\"Unit {i}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 864x936 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(4, 4, figsize=(12, 13))\n",
    "fig.tight_layout(w_pad=2)\n",
    "for i, ax in enumerate(axs.flatten()):\n",
    "    ax.imshow(R_pair_smoothed[i], extent=(0, 360, 0, 180), aspect=2)\n",
    "    ax.set(xlabel=\"phase\", ylabel=\"orientation\", title=f\"Unit {i}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Topological noise reduction: 100%|██████████| 100/100 [01:57<00:00,  1.18s/it]\n"
     ]
    }
   ],
   "source": [
    "res = top_noise_reduction(pd.DataFrame(Rp.reshape((400, 40)))).to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "128.81601674915015"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res[0][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Intermission: save variables to a file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('dump.pkl', 'wb') as f:\n",
    "    pickle.dump({k: globals()[k] for k in [\n",
    "        'data_folder', 'stim_file', 'spike_times_file', 'key_symbol', \n",
    "        'stim', 'num_trial', 'stim_val', 'trial_stim_id', 'key_list', 'stim_id_trial', 'num_stim', 'key', 'pair_id', 'trial_pair_id', 'pair_val', 'pair_trial_id',\n",
    "        'spike_times', 'num_unit', 'num_spike', 'spike_count_rate', 'avg_firing_rate', 'firing_rate', 'stim_num_trial', 'stim_hist', 'optimal_avg_firing_rate',\n",
    "        'latest_spike_time', 'dt', 'exp_time', 'B_stim', 'trials', 't_on', 't_off', 'B_spike', 'trial_range', 'units', 'orientation', 'phase', 'trial_spike_times',\n",
    "        'delta_t_on', 'delta_t_off', 'subseq_trials', 'subseq_spike_times', 'subseq_spike_times_locked', 'time_range', 'unit_id_2', 'p1', 'p2', 'delta_t', 'time_range_smoothing',\n",
    "        'period_1_dist', 'period_2_dist', 'period_3_dist', 'num_units', 'max_p_1', 'max_p_2', 'max_p_3', 'delta_2', 'delta_3', 't_p1', 't_p2', 'subseq_spike_times_locked_2',\n",
    "        'B_spike_fixed', 'B_stim_original', 'B_spike_smooth', 'B_stim_total', 'R', 'R_pair_smoothed', 'Rp'\n",
    "        ]}, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['data_folder', 'stim_file', 'spike_times_file', 'key_symbol', 'stim', 'num_trial', 'stim_val', 'trial_stim_id', 'key_list', 'stim_id_trial', 'num_stim', 'key', 'pair_id', 'trial_pair_id', 'pair_val', 'pair_trial_id', 'spike_times', 'num_unit', 'num_spike', 'spike_count_rate', 'avg_firing_rate', 'firing_rate', 'stim_num_trial', 'stim_hist', 'optimal_avg_firing_rate', 'latest_spike_time', 'dt', 'exp_time', 'B_stim', 'trials', 't_on', 't_off', 'B_spike', 'trial_range', 'units', 'orientation', 'phase', 'trial_spike_times', 'delta_t_on', 'delta_t_off', 'subseq_trials', 'subseq_spike_times', 'subseq_spike_times_locked', 'time_range', 'unit_id_2', 'p1', 'p2', 'delta_t', 'time_range_smoothing', 'period_1_dist', 'period_2_dist', 'period_3_dist', 'num_units', 'max_p_1', 'max_p_2', 'max_p_3', 'delta_2', 'delta_3', 't_p1', 't_p2', 'subseq_spike_times_locked_2', 'B_spike_fixed', 'B_stim_original', 'B_spike_smooth', 'B_stim_total', 'R', 'R_pair_smoothed', 'Rp', 'firing_rates_noise_red', 'firing_rates_flat', 'firing_rates_in_trials', 'avg_firing_rates'])\n"
     ]
    }
   ],
   "source": [
    "file = open(\"dump.pkl\", 'rb')\n",
    "data = pkl.load(file)\n",
    "print(data.keys())\n",
    "for key, value in data.items():\n",
    "    globals()[key] = value\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__name__ __doc__ __package__ __loader__ __spec__ __builtin__ __builtins__ _ih _oh _dh In Out get_ipython exit quit _ __ ___ os sys __vsc_ipynb_file__ _i _ii _iii _i1 np plt pd scipy sts pkl widgets mplot3d Axes3D sparse cm cmap_hot cmap_viridis cmap_jet _i2 data_folder stim_file spike_times_file key_symbol _i3 stim num_trial stim_val trial_stim_id key_list stim_id_trial num_stim key pair_id trial_pair_id pair_val pair_trial_id i _i4 _4 _i5 _i6 _i7 _7 _i8 spike_times active num_unit _8 _i9 num_spike _i10 files file_name file data value spike_count_rate avg_firing_rate sem_firing_rate firing_rate stim_num_trial C_r_fphi_theta theta_hist phase_hist pair_hist corr_stim_unit optimal_avg_firing_rate stim_hist stim_hist_caution _i11 max_delay tau_id_range latest_spike_time latest_stim_offtime experiment_dur dt exp_time M _i12 B_stim stim_id trials s trial_id t_on t_off B_spike unit_id _i13 sorted_spike_num cutoff_num_spike _i14 t0 t1 stim_prev_id t_prev_on t_prev_off spikes _i15 gridspec fig ax gs0 ax0 ax1 ax2 ax3 _i16 ref_trial_id past_trials future_trials trial_range units orientation phase ref_id _i17 PSSH _i18 trial_spike_times delta_t_on delta_t_off subseq_trials subseq_spike_times subseq_spike_times_locked _i19 time_range _i20 subseq_spike_times_len chain _20 _i21 unit_id_2 _i22 stim_2diffs _i23 p1 p2 delta_t time_range_smoothing period_1_dist period_2_dist period_3_dist num_units j row hist _i24 _24 _i25 _25 _i26 max_p_1 max_p_2 max_p_3 delta_2 delta_3 _i27 t_p1 t_p2 fix_time _i28 _28 _i29 subseq_spike_times_locked_2 _i30 _i31 B_spike_fixed _i32 deepcopy B_stim_original _i33 _i34 get_trial_index _i35 B_spike_smooth _i36 _36 _i37 _37 _i38 _38 _i39 _39 _i40 _40 _i41 _41 _i42 B_stim_total _i43 _i44 R _i45 item u _i46 _i47 _i48 _i49 _i50 _i51 _51 _i52 _52 _i53 _53 _i54 _54 _i55 _i56 _56 _i57 _57 _i58 _58 _i59 _59 _i60 _60 _i61 _61 _i62 _62 _i63 _63 _i64 _64 _i65 _65 _i66 _66 _i67 R_pair_smoothed _i68 axs _i69 _i70 _i71 _i72 _i73 _i74 _i75 _i76 _i77 _i78 _i79 _i80 _i81 _i82 _i83 _i84 _i85 _i86 _i87 _i88 _i89 _i90 _i91 _i92 _i93 _i94 _i95 _i96 _i97 _i98 _i99 _i100 R1 item1 _i101 _i102 _i103 _i104 Rp _i105 _i106 _i107 _i108 _i109 top_noise_reduction _i110 _i111 _i112 _i113 res _i114 _114 _i115 _115 _i116 _116 _i117 _117 _i118 _118 _i119 _i120 _120 _i121 _121 _i122 pickle _i123 f _i124 _i125 _i126 _i127 _i128 _128 _i129 _129 _i130 _130 _i131\n"
     ]
    }
   ],
   "source": [
    "print(*globals().keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Forward correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "firing_rates_in_trials = []   # by pair, then by trial, then by neuron\n",
    "firing_rates_flat = []\n",
    "\n",
    "for trial_ids in stim_id_trial['pair']:\n",
    "    row = []\n",
    "    for trial_id in trial_ids:\n",
    "        ind = round(get_trial_index(trial_id))\n",
    "        rr = []\n",
    "        for u in range(len(B_spike_smooth)):\n",
    "            rr.append(np.mean(B_spike_smooth[u][ind + 50:ind + 90]))\n",
    "        row.append(np.array(rr))\n",
    "        firing_rates_flat.append(np.array(rr))\n",
    "    firing_rates_in_trials.append(np.array(row))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, (50, 40))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "firing_rates_flat = np.array(firing_rates_flat)\n",
    "len(firing_rates_in_trials), firing_rates_in_trials[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_firing_rates = np.array([np.mean(fr_pair, axis=0) for fr_pair in firing_rates_in_trials]).reshape((20, 20, 40))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 864x936 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(4, 4, figsize=(12, 13))\n",
    "fig.tight_layout(w_pad=2)\n",
    "for i, ax in enumerate(axs.flatten()):\n",
    "    ax.imshow(avg_firing_rates[:, :, i], extent=(0, 360, 0, 180), aspect=2)\n",
    "    ax.set(xlabel=\"phase\", ylabel=\"orientation\", title=f\"Unit {i}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "62c650b11477402aa0a9dfe412b6209f",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2AAAAOoCAYAAAC3KZd9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAADmzUlEQVR4nOz9e5xsd1nnfX+vOvS59yk7h50TSSAEgcEIAeVWlFHnFhBG4fFhiKio3EYUbpkZxxFQxMFxZnAGnXFGwfCQCTqIqAGDI45gxEGGYzgYAkkgCQk57Oyd7GOfuw7X80fXlmaz67oqXbVWVVd/3q9Xv7p7/arW+tWqda21fnVYX3N3AQAAAACKVxl2BwAAAABgp2AABgAAAAAlYQAGAAAAACVhAAYAAAAAJWEABgAAAAAlYQAGAAAAACVhAAYAAAAAJWEABgAAAAAlYQAGAAAAACVhAAYAAAAAJWEABgAAAAAlYQAGAAAAACVhAAYAAAAAJWEABgAAAAAlYQAGAAAAACVhAAYAAAAAJWEABgAAAAAlYQA2JszsrWb2+mH3Axg31BZQHOoLKAa1NdoYgI0IM3Mze9xp037VzP5HL/d391e4+6917vdsM7s/WZ6Z2ZvM7Ejn501mZlt/BMBoGkJt/WMz+5CZnTCze7bccWAbGEJ9/YKZ3WpmC2b2FTP7ha33HhhdQ6itf2Fmd5vZSTN70Mx+y8xqW38EiDAA27mukfSDkr5Z0lMkvUDSTw+zQ8CYWJJ0nSRODIHBM0k/JmmvpOdIepWZvWS4XQLGwvskPdXdd0l6sjbOD39uuF0aXwzAtolTr16Y2c+b2WEzO2hmP7Gp/Xoz+7dmNivpLyWdb2aLnZ/zzzDLl0l6s7vf7+4PSHqzpB8v5cEAI2TQteXun3T3P5B0d4kPAxhJBdTXb7j7Z9y96e53SLpR0reX94iA0VBAbd3l7sdP3V1SW9LjTr8dBoMB2PZynqTdki6Q9HJJv2NmezffwN2XJD1X0oPuPtf5efAM83qSpL/f9P/fd6YBO9EgawvA1yukvjofm3+WpC8U021g5A20tszsh83spKRHtPEO2O8V2vsdjAHY9tKQ9EZ3b7j7+yUtSrpii/Oak3Ri0/8nJM3xPTDsUIOsLQBfr6j6+lVtnMf89wHMC9iOBlpb7v6HnY8gPl7SWyUdGkw3cToGYKOjJal+2rS6NorrlCPu3tz0/7I2BlJbsShp16b/d0ladHff4vyAUVV2bQE7yVDqy8xepY3vgn2/u6/1My9gRA3t2OXuX9bGO8u/2++8cGYMwEbHVyVdctq0SyXdu4V59TKI+oI23l4+5ZvFxzgwnsquLWAnKb2+zOwnJb1G0ve4e3hlN2AbG/axqybpsVu4H3rAAGx0vFvSL5vZhWZWMbPv1caVCf90C/M6JOksM9sd3Ob3Jf1LM7ug82XMn5d0/RaWBYy6Umurs4wpbbxSaWY2ZWYTW+o5MPrKrq+XSvp3kv6Ju3OhG4yzsmvr/zGzczp/P1HSayXdtIVloQcMwEbHGyV9VNJHJB2T9BuSXurutz7aGbn77ZLeJeluMzve5SqIvyfpzyV9XtKtkv5CfNkS46ns2vpOSSuS3i/p4s7fH9hi34FRV3Z9/VtJZ0n61KYrur11690HRlbZtfXtkj5vZkvaOH69X9Lrttp5xIyv/AAAAABAOXgHDAAAAABKwgAMAAAAAErCAAwAAAAASsIADAAAAABKwgAMAAAAAEpS6+VGZnadpOdLOuzuT+5Me7ekKzo32SPpuLtfaWaXSLpN0h2dto+7+yuyZUzUZ31qck/Qifj+bskNEjaAq0F6Je5Du5a0n553fvr8q0kHklVgraS93ef9s1XYyypObpM9T9ljULv7/VfXjmu9sdTfhvQolVJbNuVTNtv9BmNwJVTL6r8W7+p8Mm5vTsWvVbUm48VrIt4w67W4uCpJcXlW/JKarfgxtBvxDqayHs/fmnH7yiP3P+LuZ8e3Gqwy6qs+MetTM3u33sms/PrcI2XHpY3bJO3JsaedtKfHrkq240/un7Ynx41k+b08BZYtI5lJVuOVZEM5+aXDpdZXGbVV3TXr9bP3dG33ZrxSrRG3Vxrx8ivpOVN+7Mzqr5Wc97WTY0tlIu7kTD3ecU9V4h33RLJjrycnhpXkpKyZ7hykk63puH1tKp7BSryDy7aDoo5dPQ3AtBHQ+9+0Ed4rSXL3f3bqbzN7s6QTm25/l7tf+Wg6MjW5R8+48me6tns9OXmo9fdmXqWZnLlnJ/aSmjPxhrRydry6l8+NH8PanqTYk1VQS8YWEyfj+9cX4uXX1pIDUCvfWWU7vOpq/ETUluMZVFe770w+cctQomSuV9G1ZbP6tvpzurZ7K1np7aS9X32+eCJJlcn4KFXZf1bYvn7pOWH7sSfEO/gTjwubpYtWwuZz9sXFN5ccRNezM2BJDy/Mhe3LB+P2mfviZUw9HNf3Z/9/P39veINiXK+i62tmr77lWT/X/Qb9vjCVvfhYTU7wJvP6aszEB4/1XfE81pLx5/qu+EG2ZpN9TC05tiQvcFSTk9T6RHySWUteIJGkWiXuw0Qyj+l6fBY4XYvb/+rZv112fV2vgmurfvYeXfymn+7avnY0PjGfejA+55o9GG9XU8eS7So555Hy2lo6L25fvCTuw9ylJ8L2bz73gbD9CbOHwvYLJ46E7efV4uXPVtbC9odbu8J2Sfrr408K2z9w1xVhe/W2+Ng2+0By7HpbMceungZg7v7hzisY38A2Xnp+saTvHmC/gB2B2gKKQ30BxaC2gP4M4jtgz5J0yN2/vGnapWb2WTP732b2rAEsA9iJqC2gONQXUAxqC0j0+hHEyNWS3rXp/4OSLnb3I2b2NEl/ZmZPcvdv+IyNmV0j6RpJmprcPYCuAGNlMLWlmVI6C2wzA6mvyek9ZfQV2E4GUlu1/ZwXYnz19Q6YmdUkvUjSu09Nc/c1dz/S+fvTku6S9Pgz3d/dr3X3q9z9qnotuEgAsMMMtLYs+YIqsMMMtL4mOHYBpwyytqq7qC2Mr34/gvi9km539/tPTTCzs82s2vn7MkmXS7q7z+UAOw21BRSH+gKKQW0BPej1MvTvkvRsSfvN7H5Jb3D3t0t6ib7+bWZJ+k5JbzSzhjauHfgKdz/aU2+iC5EkV9CrZJcpTC+x3sNlDhOWXE4zvcx70l5pJZexzy5VnQy3s8vgp5faTi7XVU0u9Sn1HweQ3r+Hy8aWqZTacpc3g5WfrbNKsl3X491IdoVCVfMr+Cm7lHZ2mfmZ+F3AdnKVVe/zMtiValzcM8kV0OYnVsP2hfX8Xc60tLJLLift2RVMh6GM+rJ2fPXVLCLFs3iS5CqH6SXke7kMfRrzks0gXUR/+rzMfKaVRDRk7ZIUX+tNWk76uFSfCNvr1dEqsDJqy11qNYPjQxpbky2gz/ZB6Ddmos/F15PL0E8l12hP25PL2Kfn7r1Id1CJUsOHvqbXqyBe3WX6j59h2g2SbuivW8DOQG0BxaG+gGJQW0B/BnEVRAAAAABADxiAAQAAAEBJGIABAAAAQEkYgAEAAABASRiAAQAAAEBJGIABAAAAQEl6ugx9KVyqNIM8gDhKIA+5SXLEMp7kBPU0j37zHrKsliROqTWZrINkAVmeTJYj1o6jjHpSXeuvj17doa85RPWR5HxVpuOMqcqu+XjRczNx+2ScfyNJSrKQlGQttSfix9iaTLaLPmu3VoszfHZPrMTt9bh4lpv5OmxGeTqSKmvxOqisx/PPcsLGlTXbqh/t/vz5RHyYbU1lGXbJjj3LGethl9fLbQqV1Vc1PnZZ0p7Jcr7a63lWoa9nQZvxg1yrJI+hz8e4Lbmp1ei+Xq2ZnRQNuD9b0WeGZNbuyXlbs91DzmZgyuKcr1mLDwz1LOC2B41kB5WtgzQDd0gjoWHvdgEAAABgx2AABgAAAAAlYQAGAAAAACVhAAYAAAAAJWEABgAAAAAlYQAGAAAAACVhAAYAAAAAJenp6vdmdp2k50s67O5P7kz7VUk/Jenhzs1e5+7v77S9VtLLJbUk/Zy7/1W6jHZbleUgT6CdZAk04xA
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img src='data:image/png;base64,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
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_fr_blur = np.array([scipy.ndimage.gaussian_filter(avg_firing_rates[:, :, i], sigma=2) for i in range(40)])\n",
    "fig, axs = plt.subplots(4, 4, figsize=(12, 13))\n",
    "fig.tight_layout(w_pad=2)\n",
    "for i, ax in enumerate(axs.flatten()):\n",
    "    ax.imshow(avg_fr_blur[i], extent=(0, 360, 0, 180), aspect=2)\n",
    "    ax.set(xlabel=\"phase\", ylabel=\"orientation\", title=f\"Unit {i}\")\n",
    "avg_fr_blur = avg_fr_blur.swapaxes(0, 2).reshape((40, 400))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Topological noise reduction (not used)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, '../model')\n",
    "from noisereduction import top_noise_reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Topological noise reduction: 100%|██████████| 100/100 [31:33<00:00, 18.93s/it]\n"
     ]
    }
   ],
   "source": [
    "firing_rates_noise_red = top_noise_reduction(pd.DataFrame(firing_rates_flat[::5]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 40)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "firing_rates_noise_red.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Topological noise reduction: 100%|██████████| 100/100 [3:44:17<00:00, 134.57s/it] \n"
     ]
    }
   ],
   "source": [
    "firing_rates_noise_red = top_noise_reduction(pd.DataFrame(firing_rates_flat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 40)"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "firing_rates_noise_red = firing_rates_noise_red.to_numpy()\n",
    "firing_rates_noise_red.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "firing_rates_noise_red[0] in firing_rates_flat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('dump.pkl', 'wb') as f:\n",
    "    pickle.dump({k: globals()[k] for k in [\n",
    "        'data_folder', 'stim_file', 'spike_times_file', 'key_symbol', \n",
    "        'stim', 'num_trial', 'stim_val', 'trial_stim_id', 'key_list', 'stim_id_trial', 'num_stim', 'key', 'pair_id', 'trial_pair_id', 'pair_val', 'pair_trial_id',\n",
    "        'spike_times', 'num_unit', 'num_spike', 'spike_count_rate', 'avg_firing_rate', 'firing_rate', 'stim_num_trial', 'stim_hist', 'optimal_avg_firing_rate',\n",
    "        'latest_spike_time', 'dt', 'exp_time', 'B_stim', 'trials', 't_on', 't_off', 'B_spike', 'trial_range', 'units', 'orientation', 'phase', 'trial_spike_times',\n",
    "        'delta_t_on', 'delta_t_off', 'subseq_trials', 'subseq_spike_times', 'subseq_spike_times_locked', 'time_range', 'unit_id_2', 'p1', 'p2', 'delta_t', 'time_range_smoothing',\n",
    "        'period_1_dist', 'period_2_dist', 'period_3_dist', 'num_units', 'max_p_1', 'max_p_2', 'max_p_3', 'delta_2', 'delta_3', 't_p1', 't_p2', 'subseq_spike_times_locked_2',\n",
    "        'B_spike_fixed', 'B_stim_original', 'B_spike_smooth', 'B_stim_total', 'R', 'R_pair_smoothed', 'Rp', 'firing_rates_noise_red', 'firing_rates_flat', 'firing_rates_in_trials', 'avg_firing_rates'\n",
    "        ]}, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "from decoding import cohomological_parameterization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcoAAAGwCAYAAAApJSV7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAA9hAAAPYQGoP6dpAABi/0lEQVR4nO3deVxU9foH8M/MwAw7CMqmCOaeC7iB9KvcKKxuimmaWZp51W5WFpVbLtkNMLdWy5st1r2ZZpmV272IlpooCrivmVvKIiD7znx/f4wMjDODLDNzZuDzfr3mFfM9z5zzHI15POd8F5kQQoCIiIgMkkudABERkTVjoSQiIqoDCyUREVEdWCiJiIjqwEJJRERUBxZKIiKiOrBQEhER1cFO6gRslVqtxvXr1+Hq6gqZTCZ1OkRE1ABCCBQUFMDf3x9yed3XjCyUjXT9+nUEBARInQYRETXB1atX0a5duzpjWCgbydXVFYDmD9nNzU3ibIiIqD6WL1+Of/7zn9r31d/ldWGhbKTq261ubm4slERENqKiogIAsGDBAvzzn/+s16MzGed6bZz8/Hy4u7sjLy+PhZKIyEYIIfDbb7+hb9++9f4OZ69XIiJq1r799luUlJQA0NwNHDx4cIM+z0JJRETNVmxsLJ588kmMGDEClZWVjdoHCyURETVLsbGxeOONNwAAQ4YMgZ1d47rlsFASEVGzU7tIxsTEYN68eY3eFwslERE1K6YskgALJRERNSMrVqwwaZEEOI6SiIiakfvuuw/u7u6YNWuWSYokwEJJRETNSGhoKE6fPg0/Pz+T7ZO3XomIyKatWLECSUlJ2vemLJIAryiJiMiGVXfccXd3N/mVZDVeURIRkU2q3bt11qxZZimSAK8obUJBaQW2HEtDTlE5hnT1xt3+nFuWiFo2Uw8BqQsLpZW7lFWEsf9KRGZBGQBg2X/PYs5D3fDcoI4SZ0ZEJA1LFkmAhbLJioqKoFAo9NoVCgUcHBx04oyRy+VwdHQ0GLvkl6NIz86rCZbJsOJ/ZzG6bzu0cVWhuLgYxhaAkclkcHJy0r5vSGxJSQnUarXRnJ2dnRsVW1paiqqqKpPEOjk5aZfIKSsrq3Mex4bEOjo6alc8Ly8v1y7L09RYBwcH7f8rDYmtqKhAeXm50ViVSqWdmqshsZWVlSgrKzMaq1QqYW9v3+DYqqoqlJaWGo21t7eHUqlscKxardZObN3UWDs7O6hUKgCa1SSKi4tNEtuQ33tTfUfcKba5fUf8/PPP2iL55ptvYubMmUb/POr6va/rz1CPoEbJy8sTAIy+Hn74YZ14Jycno7GDBg3SiW3durXRWKVvZxE4e4tIOJ0uhBAiMDDQaOzdd9+ts9+7777baGxgYKBObP/+/Y3Gtm7dWid20KBBRmOdnJx0Yh9++OE6/9xqGzNmTJ2xhYWF2thJkybVGZuZmamNff755+uMvXjxojb2tddeqzP2xIkT2thFixbVGZuUlKSNXbp0aZ2xu3fv1sZ+9NFHdcZu2bJFG/vll1/WGfvdd99pY7/77rs6Y7/88ktt7JYtW+qM/eijj7Sxu3fvrjN26dKl2tikpKQ6YxctWqSNPXHiRJ2xr732mjb24sWLdcY+//zz2tjMzMw6YydNmqSNLSwsrDN2zJgxOv8P1xVrru+I/v3768Q2t++I4uJi8eCDD4qYmBiTfEfk5eWJO+EVpY0K8nK+cxARUTPj6OiIrVu3ws7ODo8//rhFjsmFmxupeuHm69evG1z001S3VQ5fysGUtYdRXnXrtoVMhjGhd2HluBAAze+2Cm+96sfy1itvvbb0W6/Lli1DQUEBFi9eDBcXlzpja6vr9z4/Px/+/v71WriZhbKRqgtlff6Qm+rU9Xz8+8Bl3Cwqx9Bu3hjdrx0UcplZj0lEZA1qd9zZvXt3gxddNqYh3+G89WoD7vZ3Q9xjvaROg4jIom7v3WqqItlQnHCAiIisjqWHgNSFhZKIiKyKNRVJgIWSiIisyLFjxzB//nwA1lEkAT6jJCIiK9K7d2989tlnyMjIwNy5c6VOBwALJRERWYHi4mLt8JNnn31W4mx08dYrERFJKiYmBqGhocjMzJQ6FYNYKImISDIxMTGYP38+Tp48ic2bN0udjkEslEREJInqIln987Rp0yTOyDAWSiIisrjbi6Q19G41hoWSiIgsypaKJMBCSUREFpSfn4/PPvsMgG0USYDDQ4iIyILc3Nzw66+/YsuWLZgxY4bU6dQLryiJiMjszp49q/05MDDQZookwEJJRERmFhsbi549e+KHH36QOpVG4a1XIiIym9oTnJ8/f17ibBqHV5RERGQWt68CMmfOHIkzahwWSiIiMjlrWyqrKVgoiYjIpJpTkQSspFCuWrUKQUFBcHBwQFhYGJKSkuqM37hxI7p16wYHBwf06tUL27Zt026rqKjA7Nmz0atXLzg7O8Pf3x8TJ07E9evXdfYRFBQEmUym81qyZIlZzo+IqKUQQuDSpUsAmkeRBKygUG7YsAHR0dFYtGgRUlJSEBwcjMjISKOzyO/fvx/jx4/HlClTkJqaiqioKERFReHEiRMANEu1pKSkYMGCBUhJScGmTZtw9uxZjBgxQm9fb731FtLS0rSvF1980aznSkTU3MlkMqxevRrbtm1rFkUSAGRCCCFlAmFhYRgwYAA++ugjAIBarUZAQABefPFFgw9+x40bh6KiImzZskXbNnDgQISEhGD16tUGj3Ho0CGEhobi8uXLaN++PQDNFeXLL7+Ml19+uV55lpWVoaysTPs+Pz8fAQEByMvLg5ubW31Pl4ioWdq0aRMeffRR2NvbS51KveTn58Pd3b1e3+GSXlGWl5cjOTkZERER2ja5XI6IiAgkJiYa/ExiYqJOPABERkYajQeAvLw8yGQyeHh46LQvWbIEXl5e6NOnD5YtW4bKykqj+4iLi4O7u7v2FRAQUI8zJCJq/mJjYzF69Gg8+eSTUKvVUqdjcpKOo8zKykJVVRV8fHx02n18fHDmzBmDn0lPTzcYn56ebjC+tLQUs2fPxvjx43X+1fDSSy+hb9++8PT0xP79+zF37lykpaVh5cqVBvczd+5cREdHa99XX1ESEbVktTvu9OnTB3K55E/0TK5ZTzhQUVGBsWPHQgiBTz75RGdb7aLXu3dvKJVKTJ8+HXFxcVCpVHr7UqlUBtuJiFqq5ta71RhJS3/r1q2hUCiQkZGh056RkQFfX1+Dn/H19a1XfHWRvHz5MuLj4+94DzosLAyVlZXa3lpERGRcSymSgMSFUqlUol+/fkhISNC2qdVqJCQkIDw83OBnwsPDdeIBID4+Xie+ukieP38eO3fuhJeX1x1zOXLkCORyOby9vRt5NkRELcOyZctaTJEErODWa3R0NCZNmoT+/fsjNDQU7733HoqKijB58mQAwMSJE9G2bVvExcUBAGbOnIlBgwZhxYoVeOSRR7B+/XocPnwYn376KQBNkRwzZgxSUlKwZcsWVFVVaZ9fenp6QqlUIjExEQcPHsSQIUPg6uqKxMREvPLKK3jqqafQqlUraf4giIhsRL9+/eDo6Ij58+c3+yIJABBW4MMPPxTt27cXSqVShIaGigMHDmi3DRo0SEyaNEkn/rvvvhNdunQRSqVS9OjRQ2zdulW77eLFiwKAwdfu3buFEEIkJyeLsLAw4e7uLhwcHET37t1FbGysKC0trXfOeXl5AoDIy8tr0rkTEdmiS5cuSZ1CkzTkO1zycZS2qiFjcIiIbN3777+PBx54AHfffbfUqZiEzYyjJCIi6xcbG4uXX34ZQ4cORXZ2ttTpWBwLJRG1COXl5UhLSwOKc4CSXABAWloaysvLpU3MytXu3frSSy/Vq3NkcyN5Zx4iInMrLy/H46MexYlD+7B7ghztPexxpc0QDFmegp69emPjxo1QKpVSp2l1WtIQkLqwUBJRs5ednY0Th/bizxslGPKVDF9HOWLi+z/iz5sCkMmRnZ0NPz8/qdO0KiySNXjrlYiaPT+nKuyeoMBdrWT486bAvV8W48+bAne1kmN3wk4Wydt89dVXLJK1sFASUfMnk6O9uxxfRzn
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decoding... done\n"
     ]
    }
   ],
   "source": [
    "param = cohomological_parameterization(pd.DataFrame(firing_rates_noise_red[::5, ::5]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fr_df = pd.DataFrame(firing_rates_flat)\n",
    "sns.heatmap(fr_df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.001840</td>\n",
       "      <td>0.005584</td>\n",
       "      <td>-0.001724</td>\n",
       "      <td>-0.000157</td>\n",
       "      <td>0.001694</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.001786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.005321</td>\n",
       "      <td>-0.001193</td>\n",
       "      <td>-0.002606</td>\n",
       "      <td>0.004470</td>\n",
       "      <td>-0.000058</td>\n",
       "      <td>-0.000247</td>\n",
       "      <td>0.001285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.001154</td>\n",
       "      <td>0.005865</td>\n",
       "      <td>-0.001700</td>\n",
       "      <td>-0.000399</td>\n",
       "      <td>0.001506</td>\n",
       "      <td>-0.000039</td>\n",
       "      <td>0.001431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.005685</td>\n",
       "      <td>-0.001241</td>\n",
       "      <td>-0.001995</td>\n",
       "      <td>0.004183</td>\n",
       "      <td>0.000222</td>\n",
       "      <td>-0.000510</td>\n",
       "      <td>0.000970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.006228</td>\n",
       "      <td>-0.001699</td>\n",
       "      <td>-0.000858</td>\n",
       "      <td>0.001188</td>\n",
       "      <td>-0.000236</td>\n",
       "      <td>0.000801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6\n",
       "0 -0.001840  0.005584 -0.001724 -0.000157  0.001694  0.000052  0.001786\n",
       "1  0.005321 -0.001193 -0.002606  0.004470 -0.000058 -0.000247  0.001285\n",
       "2 -0.001154  0.005865 -0.001700 -0.000399  0.001506 -0.000039  0.001431\n",
       "3  0.005685 -0.001241 -0.001995  0.004183  0.000222 -0.000510  0.000970\n",
       "4  0.000054  0.006228 -0.001699 -0.000858  0.001188 -0.000236  0.000801"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N_COMP = 7\n",
    "pca = PCA(n_components=N_COMP)\n",
    "pca.fit(avg_fr_blur)\n",
    "data_pca = pca.transform(avg_fr_blur)\n",
    "data_pca = pd.DataFrame(data_pca)\n",
    "data_pca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 50, 7)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "firing_rates_in_trials_pca = []\n",
    "for trials in firing_rates_in_trials:\n",
    "    firing_rates_in_trials_pca.append(pca.transform(pd.DataFrame(trials)))\n",
    "firing_rates_in_trials_pca = np.array(firing_rates_in_trials_pca)\n",
    "firing_rates_in_trials_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_firing_rates_pca = np.mean(firing_rates_in_trials_pca, axis=1).reshape((20, 20, N_COMP))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "ename": "InvalidIndexError",
     "evalue": "(slice(None, None, None), slice(None, None, None), 0)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInvalidIndexError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m/home/ennucore/dev/amgen/exp/ProcessExp.ipynb Cell 91\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/ennucore/dev/amgen/exp/ProcessExp.ipynb#ch0000089?line=1'>2</a>\u001b[0m fig\u001b[39m.\u001b[39mtight_layout(w_pad\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m)\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/ennucore/dev/amgen/exp/ProcessExp.ipynb#ch0000089?line=2'>3</a>\u001b[0m \u001b[39mfor\u001b[39;00m i, ax \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(axs\u001b[39m.\u001b[39mflatten()[:\u001b[39m7\u001b[39m]):\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/ennucore/dev/amgen/exp/ProcessExp.ipynb#ch0000089?line=3'>4</a>\u001b[0m     ax\u001b[39m.\u001b[39mimshow(data_pca[:, :, i], extent\u001b[39m=\u001b[39m(\u001b[39m0\u001b[39m, \u001b[39m360\u001b[39m, \u001b[39m0\u001b[39m, \u001b[39m180\u001b[39m), aspect\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m)\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/ennucore/dev/amgen/exp/ProcessExp.ipynb#ch0000089?line=4'>5</a>\u001b[0m     ax\u001b[39m.\u001b[39mset(xlabel\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mphase\u001b[39m\u001b[39m\"\u001b[39m, ylabel\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39morientation\u001b[39m\u001b[39m\"\u001b[39m, title\u001b[39m=\u001b[39m\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mUnit \u001b[39m\u001b[39m{\u001b[39;00mi\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/pandas/core/frame.py:3505\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3503\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mnlevels \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m   3504\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3505\u001b[0m indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcolumns\u001b[39m.\u001b[39;49mget_loc(key)\n\u001b[1;32m   3506\u001b[0m \u001b[39mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   3507\u001b[0m     indexer \u001b[39m=\u001b[39m [indexer]\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/pandas/core/indexes/range.py:388\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m    386\u001b[0m         \u001b[39mexcept\u001b[39;00m \u001b[39mValueError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[1;32m    387\u001b[0m             \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[0;32m--> 388\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_check_indexing_error(key)\n\u001b[1;32m    389\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key)\n\u001b[1;32m    390\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39m()\u001b[39m.\u001b[39mget_loc(key, method\u001b[39m=\u001b[39mmethod, tolerance\u001b[39m=\u001b[39mtolerance)\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/pandas/core/indexes/base.py:5637\u001b[0m, in \u001b[0;36mIndex._check_indexing_error\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   5633\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_check_indexing_error\u001b[39m(\u001b[39mself\u001b[39m, key):\n\u001b[1;32m   5634\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m is_scalar(key):\n\u001b[1;32m   5635\u001b[0m         \u001b[39m# if key is not a scalar, directly raise an error (the code below\u001b[39;00m\n\u001b[1;32m   5636\u001b[0m         \u001b[39m# would convert to numpy arrays and raise later any way) - GH29926\u001b[39;00m\n\u001b[0;32m-> 5637\u001b[0m         \u001b[39mraise\u001b[39;00m InvalidIndexError(key)\n",
      "\u001b[0;31mInvalidIndexError\u001b[0m: (slice(None, None, None), slice(None, None, None), 0)"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c09d95fca9cf47deb73bfbb32c0ea7bf",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2AAAANgCAYAAABKkgukAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAssklEQVR4nO3dX4jl93nf8c9jbZVQx3GKtYGgXUUKXdfZOgG7g+ISSFzslpUK2ou0QQLTuggv+aNQSCiouDhBuXJKUwioTRdq5ARiWfFFWcgahaYyAhM5WmNHsWQUNopbrRIqxXF8Y2xZ9NuLOU7Go9ndc8789vmdk3m9YOH8+e7MszO7D/ueM+dMjTECAADAjfeGuQcAAAA4KgQYAABAEwEGAADQRIABAAA0EWAAAABNBBgAAEATAQYAANBEgAEAADQRYAAAAE0EGAAAQJOVA6yqPlJVL1fVF65yf1XVr1XV5ap6pqreefgxgaPO7gHmYv8AU1rnEbBHkpy5xv13JTm1+HUuyX9d430A7PdI7B5gHo/E/gEmsnKAjTGeTPKX1zhyNslvjF1PJfmeqvq+dQcESOweYD72DzClG/EcsFuTvLjn+pXFbQA3kt0DzMX+AZZ2bM53XlXnsvtQfd74xjf+o7e97W1zjgMs6bOf/exfjDGOzz3Huuwe2F72DzCHKXfPjQiwl5Kc3HP9xOK21xljnE9yPkl2dnbGpUuXbsA4wNSq6n/PPcMB7B44AuwfYA5T7p4b8S2IF5L8q8UrAr0ryVfHGH9+A94PwF52DzAX+wdY2sqPgFXVx5K8O8ktVXUlyS8m+TtJMsb49SQXk9yd5HKSryX5N1MNCxxddg8wF/sHmNLKATbGuO86948kP7v2RAAHsHuAudg/wJRuxLcgAgAAcAABBgAA0ESAAQAANBFgAAAATQQYAABAEwEGAADQRIABAAA0EWAAAABNBBgAAEATAQYAANBEgAEAADQRYAAAAE0EGAAAQBMBBgAA0ESAAQAANBFgAAAATQQYAABAEwEGAADQRIABAAA0EWAAAABNBBgAAEATAQYAANBEgAEAADQRYAAAAE0EGAAAQBMBBgAA0ESAAQAANBFgAAAATQQYAABAEwEGAADQRIABAAA0EWAAAABNBBgAAEATAQYAANBEgAEAADQRYAAAAE0EGAAAQBMBBgAA0ESAAQAANBFgAAAATQQYAABAEwEGAADQRIABAAA0EWAAAABNBBgAAEATAQYAANBEgAEAADQRYAAAAE0EGAAAQBMBBgAA0ESAAQAANBFgAAAATQQYAABAEwEGAADQRIABAAA0EWAAAABNBBgAAEATAQYAANBEgAEAADQRYAAAAE0EGAAAQBMBBgAA0ESAAQAANBFgAAAATQQYAABAEwEGAADQRIABAAA0EWAAAABNBBgAAEATAQYAANBkrQCrqjNV9XxVXa6qBw+4/7aqeqKqPldVz1TV3YcfFTjq7B5gDnYPMKWVA6yqbkrycJK7kpxOcl9Vnd537D8keWyM8Y4k9yb5L4cdFDja7B5gDnYPMLV1HgG7M8nlMcYLY4xXkzya5Oy+MyPJdy8uvznJn60/IkASuweYh90DTOrYGr/n1iQv7rl+JcmP7DvzS0l+t6p+Lskbk7x3rekA/obdA8zB7gEmdaNehOO+JI+MMU4kuTvJb1bV695XVZ2rqktVdemVV165QaMAR4jdA8xhqd2T2D/AegH2UpKTe66fWNy21/1JHkuSMcbvJ/nOJLfsf0NjjPNjjJ0xxs7x48fXGAU4QuweYA6T7Z7F/fYPHHHrBNjTSU5V1R1VdXN2n2x6Yd+Z/5PkPUlSVT+Y3UXkyzzAYdg9wBzsHmBSKwfYGOO1JA8keTzJF7P7qj/PVtVDVXXP4tgvJPlAVf1hko8lef8YY0w1NHD02D3AHOweYGrrvAhHxhgXk1zcd9uH9lx+LsmPHm40gG9n9wBzsHuAKd2oF+EAAABgHwEGAADQRIABAAA0EWAAAABNBBgAAEATAQYAANBEgAEAADQRYAAAAE0EGAAAQBMBBgAA0ESAAQAANBFgAAAATQQYAABAEwEGAADQRIABAAA0EWAAAABNBBgAAEATAQYAANBEgAEAADQRYAAAAE0EGAAAQBMBBgAA0ESAAQAANBFgAAAATQQYAABAEwEGAADQRIABAAA0EWAAAABNBBgAAEATAQYAANBEgAEAADQRYAAAAE0EGAAAQBMBBgAA0ESAAQAANBFgAAAATQQYAABAEwEGAADQRIABAAA0EWAAAABNBBgAAEATAQYAANBEgAEAADQRYAAAAE0EGAAAQBMBBgAA0ESAAQAANBFgAAAATQQYAABAEwEGAADQRIABAAA0EWAAAABNBBgAAEATAQYAANBEgAEAADQRYAAAAE0EGAAAQBMBBgAA0ESAAQAANBFgAAAATQQYAABAEwEGAADQRIABAAA0EWAAAABNBBgAAEATAQYAANBEgAEAADQRYAAAAE0EGAAAQBMBBgAA0GTlAKuqM1X1fFVdrqoHr3LmJ6vquap6tqp+6/BjAtg/wDzsHmBKx1Y5XFU3JXk4yT9NciXJ01V1YYzx3J4zp5L8+yQ/Osb4SlV975QDA0eT/QPMwe4BprbqI2B3Jrk8xnhhjPFqkkeTnN135gNJHh5jfCVJxhgvH35MAPsHmIXdA0xq1QC7NcmLe65fWdy211uTvLWqPl1VT1XVmcMMCLBg/wBzsHuASa30LYgrvM1TSd6d5ESSJ6vqh8YYf7X/YFWdS3IuSW677bYbMApwxCy1f+weYGL+7wMsbdVHwF5KcnLP9ROL2/a6kuTCGOObY4w/TfLH2V1KrzPGOD/G2Blj7Bw/fnzFUYAjZrL9Y/cAK/B/H2BSqwbY00lOVdUdVXVzknuTXNh35n9k9ytAqapbsvuw/AuHGxPA/gFmYfcAk1opwMYYryV5IMnjSb6Y5LExxrNV9VBV3bM49niSL1fVc0meSPLvxhhfnnJo4Oixf4A52D3A1GqMMfcMSZKdnZ1x6dKluccAllBVnx1j7Mw9xxTsHtgu9g8whyl3z8o/iBkAAID1CDAAAIAmAgwAAKCJAAMAAGgiwAAAAJoIMAAAgCYCDAAAoIkAAwAAaCLAAAAAmggwAACAJgIMAACgiQADAABoIsAAAACaCDAAAIAmAgwAAKCJAAMAAGgiwAAAAJoIMAAAgCYCDAAAoIkAAwAAaCLAAAAAmggwAACAJgIMAACgiQADAABoIsAAAACaCDAAAIAmAgwAAKCJAAMAAGgiwAAAAJoIMAAAgCYCDAAAoIkAAwAAaCLAAAAAmggwAACAJgIMAACgiQADAABoIsAAAACaCDAAAIAmAgwAAKCJAAMAAGgiwAAAAJoIMAAAgCYCDAAAoIkAAwAAaCLAAAAAmggwAACAJgIMAACgiQADAABoIsAAAACaCDAAAIAmAgwAAKCJAAMAAGgiwAAAAJoIMAAAgCYCDAAAoIkAAwAAaCLAAAAAmggwAACAJgIMAACgiQADAABoIsAAAACaCDAAAIAmAgwAAKCJAAMAAGgiwAAAAJoIMAAAgCYCDAAAoIkAAwAAaCLAAAAAmggwAACAJmsFWFWdqarnq+pyVT14jXM/UVWjqnbWHxFgl90DzMHuAaa0coBV1U1JHk5yV5LTSe6rqtMHnHtTkn+b5DOHHRLA7gHmYPcAU1vnEbA7k1weY7wwxng1yaNJzh5w7peTfDjJ1w8xH8C32D3AHOweYFLrBNitSV7cc/3K4ra/VlXvTHJyjPE7h5gNYC+7B5iD3QNMavIX4aiqNyT51SS/sMTZc1V1qaouvfLKK1OPAhwhdg8wh1V2z+K8/QNH3DoB9lKSk3uun1jc9i1vSvL2JJ+qqi8leVeSCwc9IXWMcX6MsTPG2Dl+/PgaowBHiN0DzGGy3ZPYP8B6AfZ0klNVdUdV3Zzk3iQXvnXnGOOrY4xbxhi3jzFuT/JUknvGGJcmmRg4quweYA52DzCplQNsjPFakgeSPJ7ki0keG2M8W1UPVdU9Uw8IkNg9wDzsHmBqx9b5TWOMi0ku7rvtQ1c5++513gf
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img src='data:image/png;base64,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
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, axs = plt.subplots(2, 3, figsize=(12, 12))\n",
    "fig.tight_layout(w_pad=2)\n",
    "for i, ax in enumerate(axs.flatten()[:7]):\n",
    "    ax.imshow(avg_firing_rates_pca[:, :, i], extent=(0, 360, 0, 180), aspect=2)\n",
    "    ax.set(xlabel=\"phase\", ylabel=\"orientation\", title=f\"Unit {i}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABLMAAAQgCAYAAAAdcB6PAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAA9hAAAPYQGoP6dpAADC/ElEQVR4nOzdeZwU9Z3/8Xf1OfcMgzDDBAaJJwTxAMWJxiPOiuh64q4aEokSWQ1ggKwHUVSMBmOMogYlGoNmo2JcF7wSNi4K6AooKEaN4oVC1BlUmBnm6umjfn/4o9fRAZn+9HR3Da/n49GPB3T3t/pT1VX1rv5Md5Xjuq4rAAAAAAAAwAN82S4AAAAAAAAA2FU0swAAAAAAAOAZNLMAAAAAAADgGTSzAAAAAAAA4Bk0swAAAAAAAOAZNLMAAAAAAADgGTSzAAAAAAAA4Bk0swAAAAAAAOAZNLMAAAAAAADgGTSzgBzzwx/+UHvuuWe2ywAAeAz5AQDoLrIDXkUzC0jBNddcI8dx9Omnn3b5+PDhw3XMMcek5bVaW1t1zTXXaNmyZbs8JpFI6MYbb9SQIUOUl5enESNG6MEHH0xLPQCA1OV6flx//fU65ZRTVFFRIcdxdM0116SlFgBA6nI5O958801deumlOuigg1RcXKwBAwbopJNO0po1a9JSD7AjNLOAHHP33Xdr/fr1yf+3trZq9uzZ3fowcsUVV+iyyy7TP/3TP+n2229XdXW1vve972nhwoU9UDEAIBekIz+uvPJKvfjiizr44IN7oEIAQK6xZsfvfvc73X333Ro1apR+/etfa8aMGVq/fr0OP/xw/c///E8PVQ1IgWwXAKCzYDBoGv/hhx/q17/+tSZPnqzf/OY3kqQf/ehHOvroo3XJJZfoX/7lX+T3+9NRKgAgh1jzQ5I2bNigPffcU59++qn69euXhqoAALnMmh3nnHOOrrnmGhUVFSXvO//88zV06FBdc801qq2ttZYIdIlvZgEZsGzZMjmOoz/96U+6/vrrNXDgQOXl5em4447TO++80+m5X/zd+vvvv5/8MDF79mw5jvO1P/t49NFHFY1G9eMf/zh5n+M4uuiii/SPf/xDK1euTPv8AQB6RibzQxLnTQGAXiCT2TFy5MhOjSxJ6tu3r77zne/ojTfeSOt8AV/EN7OADLrhhhvk8/n07//+72psbNSNN96o8ePHa/Xq1V0+v1+/frrzzjt10UUX6fTTT9cZZ5whSRoxYsQOX+Pll19WYWGhhg4d2un+ww47LPn4kUcemaY5AgBkQibyAwDQu2QzO+rq6rTHHnuY6gd2hmYWkEHt7e1at26dQqGQJKlPnz76yU9+otdee03Dhw//yvMLCwt15pln6qKLLtKIESP0/e9//2tf4+OPP06euPeLBgwYIEn66KOP0jAnAIBMykR+AAB6l2xlx7PPPquVK1fqyiuvNNUP7Aw/MwQy6LzzzkuGiSR95zvfkSS99957aXuNtrY2hcPhr9yfl5eXfBwA4C2ZyA8AQO+SjezYvHmzvve972nIkCG69NJLe+x1AL6ZBfSQL38zSpKqq6s7/b9Pnz6SpK1bt6btdfPz8xWJRL5yf3t7e/JxAEDuylZ+AAC8Kxeyo6WlRf/8z/+sbdu26bnnnvvKubSAdOKbWUAKvu5bTq2trcnnfNGOriLoum7aahswYIDq6uq+Ms2PP/5YklRVVZW21wIAdE8u5wcAIDd5ITs6Ojp0xhln6G9/+5seffTRLn/GCKQTzSwgBYMHD5YkrV+//iuPtba2atOmTcnnWHX1V5adOeigg9Ta2vqVq4dsP9HjQQcdlJa6AADdl8v5AQDITbmeHYlEQueee66WLl2qBx54QEcffXRaagF2hmYWkILjjjtOoVBId955pxKJRKfH7rrrLsViMY0dOzYtr1VQUCBJamho2KXnn3rqqQoGg7rjjjuS97muq/nz5+sb3/iGvv3tb6elLgBA9+VyfgAAclOuZ8fUqVP10EMP6Y477kheARHoaZwzC0hB//79ddVVV+nKK6/UUUcdpVNOOUUFBQV6/vnn9eCDD+r444/XySefnJbXys/P17Bhw/TQQw9p3333VXl5uYYPH77Dr+4OHDhQ06ZN069+9StFo1EdeuihWrx4sZ599lndf//9O/y6MQCg5+VyfkjSf/zHf+iDDz5Qa2urJGnFihW67rrrJEk/+MEP0vaXfwDArsvl7Jg7d67uuOMO1dTUqKCgQH/84x87PX766aersLAwLbUBX0QzC0jRFVdcoT333FO/+c1vdO211yoWi2nIkCGaPXu2LrvsMvl86fvi4+9+9ztNnTpV06dPV0dHh66++uqdfhi54YYb1KdPH/32t7/Vvffeq3322Ud//OMf9b3vfS9tNQEAUpPL+XHPPfdo+fLlyf8/88wzeuaZZyRJRx55JM0sAMiSXM2OdevWSZJWrlyplStXfuXxDRs20MxCj3BczhwKAAAAAAAAj+CcWQAAAAAAAPAMmlkAAAAAAADwDJpZAAAAAAAA8IysNrNWrFihk08+WVVVVXIcR4sXL+70eHNzs6ZMmaKBAwcmr6owf/78Ts9pb2/X5MmT1bdvXxUVFWncuHGqr6/P4FwAADKJ7AAApIL8AIDeI6vNrJaWFh144IGaN29el4/PmDFDS5Ys0R//+Ee98cYbmjZtmqZMmaLHHnss+Zzp06fr8ccf18MPP6zly5fro48+0hlnnJGpWQAAZBjZAQBIBfkBAL1HzlzN0HEcLVq0SKeddlryvuHDh+uss87SrFmzkveNHDlSY8eO1XXXXafGxkb169dPDzzwgM4880xJ0ptvvqmhQ4dq5cqVOvzww7t8rUgkokgkkvx/IpHQli1b1LdvXzmO0zMzCAAe5bqutm3bpqqqqrRe9jkdyA4AyE25nB1S5vKD7ACAXdet7HBzhCR30aJFne674IIL3FGjRrn/+Mc/3EQi4T799NNuUVGRu3z5ctd1XXfp0qWuJHfr1q2dxlVXV7s333zzDl/r6quvdiVx48aNG7du3DZt2pTuXb+ZRHZw48aNWy7fcjE7XDdz+UF2cOPGjVv3b7uSHQHlsNtvv12TJk3SwIEDFQgE5PP5dPfdd+uoo46SJNXV1SkUCqmsrKzTuIqKCtXV1e1wujNnztSMGTOS/29sbFR1dbWqfn25fPl5KdXqtPlTGvdF/hbbX6187fa/7jgJ23jX75prcEO28bF840xIUmnUNLygOPL1T9qJvGDMNF6SYnHb+hSNp2Gd9tnei8KwbTlKkuvatovNn5Saayh6NWwaX/l8k2m8+/IbpvExRfWc/qzi4mLTdDIl09lx6HEzFQiklh2xfPu3FWJ5tmkkguYSPj/syDLrfMQK7BkaLbSNTxiPyvz2XabCDbY3M9xkXxn8Eds0jLt9SVIibJtIzDhesq/TiYCxBuPweEe73lxwrWeyQ+qZ/NhRdry5pkrFRantv6OKpzTui1zjj3MCjj2/inypZWc61cWaTeOfax9oruHxTw80jV/3ga2GwEb7+5C32bbDCG2zZ4f1c2zc+BlUsh9LdJTYa4iW2RZEotz2OThcaDsYSbRF9N6kW3YpO3K+mbVq1So99thjGjx4sFasWKHJkyerqqpKtbW1KU83HA4rHP7qh0tffl7qzSzZP/j7jM0Hv/WoQ7nRzEoYdyS+dDSz8m3vp7/A9vL+oH19co3rUyIHmlkp9gc6SRg/1fia7UX4u9jfdEfA+AnVdYyfiP7/Zu2Vn0NkOjsCgTwFgimuJ0H7hwE3ZJuGdfX4vIg0TMPIvJqno/lg29TN82Dfa0v+kO3N9AftK0Mgkf1mVjxom4gbSsMxmXF9cLLczEpOxiPZIfVMfuwoO4qLfCopTrWZlYbjbWMzK5iWZlb2f37aErPVUBCw73mDbbYPP74C27GqLy8dx7q27dz6RwzJ/jlWaWhmWY8l/MbjCEmK5xkXhPlzcHr2+buSHTnbzGpra9PPfvYzLVq0SCeddJIkacSIEVq3bp1uuukm1dbWqrKyUh0dHWpoaOj0F5L6+npVVlZmqXIAQLaQHQCAVJAfAOAt2W+
      "text/plain": [
       "<Figure size 1200x1300 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, axs = plt.subplots(2, 3, figsize=(12, 13))\n",
    "fig.tight_layout(w_pad=2)\n",
    "for i, ax in enumerate(axs.flatten()):\n",
    "    ax.imshow(scipy.ndimage.gaussian_filter(avg_firing_rates_pca[:, :, i], sigma=2), extent=(0, 360, 0, 180), aspect=2)\n",
    "    ax.set(xlabel=\"phase\", ylabel=\"orientation\", title=f\"Unit {i}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Point Cloud Simplification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def radial_distance(X, eps, random_state=None):\n",
    "    \"\"\"\n",
    "    point cloud simplification using radial distance (euclidean metric). \n",
    "    Start with the first point in in X and mark it as a key point. All consecutive points that have a distance less than a predetermined distance eps to the key point are removed. The first point that have a distance greater than eps to the key point is marked as the new key point. The process repeates itself from this new key point, and continues until it reaches the end of the point cloud.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    X: pandas DataFrame (n_datapoints, n_features):\n",
    "\n",
    "    eps: max radial distance - cutoff distance\n",
    "\n",
    "    random_state: seed of random generator used for choosing the inital point\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    X.iloc[ind_reduced]: dataframe with chosen datapoints \n",
    "    \"\"\"\n",
    "    if random_state is not None:\n",
    "        np.random.seed(random_state)\n",
    "        \n",
    "    ix0 = np.random.choice(X.shape[0])\n",
    "    x0 = X.iloc[ix0]\n",
    "    xt = x0\n",
    "    ixt = ix0\n",
    "    X_temp = X\n",
    "    ind_reduced = [ix0]\n",
    "\n",
    "    while True:\n",
    "        dist = np.linalg.norm(X_temp.to_numpy() - xt.to_numpy(), axis=1)\n",
    "        cond = dist < eps\n",
    "\n",
    "        X_temp = X_temp.drop(X_temp.index[np.where(cond)])\n",
    "        if len(X_temp)==0:\n",
    "            break\n",
    "\n",
    "        where_not_cond = np.where(np.logical_not(cond))\n",
    "        w = np.argmin(dist[where_not_cond])        \n",
    "        ixt = X_temp.index[w]\n",
    "        xt = X.iloc[ixt]\n",
    "        ind_reduced.append(ixt)\n",
    "\n",
    "    return X.iloc[ind_reduced], ind_reduced   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import trange\n",
    "from noisereduction import compute_gradient\n",
    "def top_noise_reduction(X, n=100, omega=0.2, fraction=0.1, plot=False):\n",
    "    \"\"\"\n",
    "    Topological denoising algorithm as in arxiv:0910.5947\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    X: dataframe(n_datapoints, n_features):\n",
    "        Dataframe containing the data\n",
    "    n: int, optional, default 100\n",
    "        Number of iterations\n",
    "    omega: float, optional, default 0.2\n",
    "        Strength of the repulsive force between datapoints\n",
    "    fraction: float between 0 and 1, optional, default 0.1\n",
    "        The fraction of datapoints from which the denoised dataset is\n",
    "        constructed\n",
    "    plot: bool, optional, default False\n",
    "        When true plot the dataset and homology each iteration\n",
    "    \"\"\"\n",
    "    N = X.shape[0]\n",
    "    inds = np.random.choice(N, round(fraction*N), replace=False)\n",
    "    S = X.iloc[inds]\n",
    "    sigma = X.stack().std()\n",
    "    c = 0.02*np.max(scipy.spatial.distance.cdist(X, X, metric='euclidean'))\n",
    "    \n",
    "    iterator = trange(0, n, position=0, leave=True)\n",
    "    iterator.set_description(\"Topological noise reduction\")\n",
    "    for i in iterator:\n",
    "        gradF = compute_gradient(S.to_numpy(), X.to_numpy(), sigma, omega)\n",
    "            \n",
    "        if i == 0:\n",
    "            maxgradF = np.max(np.sqrt(np.square(gradF).sum(axis=1)))\n",
    "        S = S + c* gradF/maxgradF\n",
    "        \n",
    "        if plot:\n",
    "            fig = pyplot.figure()\n",
    "            ax = Axes3D(fig)\n",
    "            ax.scatter(X[0],X[1],X[2],alpha=0.1)\n",
    "            ax.scatter(S[0],S[1],S[2])\n",
    "            pyplot.show()\n",
    "    return S, inds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m/home/ennucore/dev/amgen/exp/ProcessExp.ipynb Cell 96\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/ennucore/dev/amgen/exp/ProcessExp.ipynb#ch0000103?line=0'>1</a>\u001b[0m fr_pca_simpl, inds_simpl \u001b[39m=\u001b[39m top_noise_reduction(pd\u001b[39m.\u001b[39;49mDataFrame(firing_rates_in_trials_pca\u001b[39m.\u001b[39;49mreshape((\u001b[39m400\u001b[39;49m \u001b[39m*\u001b[39;49m \u001b[39m50\u001b[39;49m, \u001b[39m-\u001b[39;49m\u001b[39m1\u001b[39;49m))))\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/ennucore/dev/amgen/exp/ProcessExp.ipynb#ch0000103?line=1'>2</a>\u001b[0m fr_pca_simpl_np \u001b[39m=\u001b[39m fr_pca_simpl\u001b[39m.\u001b[39mto_numpy()\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/ennucore/dev/amgen/exp/ProcessExp.ipynb#ch0000103?line=2'>3</a>\u001b[0m fr_pca_simpl\u001b[39m.\u001b[39mshape\n",
      "\u001b[1;32m/home/ennucore/dev/amgen/exp/ProcessExp.ipynb Cell 96\u001b[0m in \u001b[0;36mtop_noise_reduction\u001b[0;34m(X, n, omega, fraction, plot)\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/ennucore/dev/amgen/exp/ProcessExp.ipynb#ch0000103?line=22'>23</a>\u001b[0m S \u001b[39m=\u001b[39m X\u001b[39m.\u001b[39miloc[inds]\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/ennucore/dev/amgen/exp/ProcessExp.ipynb#ch0000103?line=23'>24</a>\u001b[0m sigma \u001b[39m=\u001b[39m X\u001b[39m.\u001b[39mstack()\u001b[39m.\u001b[39mstd()\n\u001b[0;32m---> <a href='vscode-notebook-cell:/home/ennucore/dev/amgen/exp/ProcessExp.ipynb#ch0000103?line=24'>25</a>\u001b[0m c \u001b[39m=\u001b[39m \u001b[39m0.02\u001b[39m\u001b[39m*\u001b[39mnp\u001b[39m.\u001b[39mmax(scipy\u001b[39m.\u001b[39;49mspatial\u001b[39m.\u001b[39;49mdistance\u001b[39m.\u001b[39;49mcdist(X, X, metric\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39meuclidean\u001b[39;49m\u001b[39m'\u001b[39;49m))\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/ennucore/dev/amgen/exp/ProcessExp.ipynb#ch0000103?line=26'>27</a>\u001b[0m iterator \u001b[39m=\u001b[39m trange(\u001b[39m0\u001b[39m, n, position\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m, leave\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/ennucore/dev/amgen/exp/ProcessExp.ipynb#ch0000103?line=27'>28</a>\u001b[0m iterator\u001b[39m.\u001b[39mset_description(\u001b[39m\"\u001b[39m\u001b[39mTopological noise reduction\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/scipy/spatial/distance.py:2947\u001b[0m, in \u001b[0;36mcdist\u001b[0;34m(XA, XB, metric, out, **kwargs)\u001b[0m\n\u001b[1;32m   2945\u001b[0m \u001b[39mif\u001b[39;00m metric_info \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m   2946\u001b[0m     cdist_fn \u001b[39m=\u001b[39m metric_info\u001b[39m.\u001b[39mcdist_func\n\u001b[0;32m-> 2947\u001b[0m     \u001b[39mreturn\u001b[39;00m cdist_fn(XA, XB, out\u001b[39m=\u001b[39;49mout, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   2948\u001b[0m \u001b[39melif\u001b[39;00m mstr\u001b[39m.\u001b[39mstartswith(\u001b[39m\"\u001b[39m\u001b[39mtest_\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[1;32m   2949\u001b[0m     metric_info \u001b[39m=\u001b[39m _TEST_METRICS\u001b[39m.\u001b[39mget(mstr, \u001b[39mNone\u001b[39;00m)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "fr_pca_simpl, inds_simpl = radial_distance(pd.DataFrame(firing_rates_in_trials_pca.reshape((400 * 50, -1))), 0.025)\n",
    "fr_pca_simpl.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(926, 2)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_corr = stim_val['pair'][np.array(inds_simpl) // 50]\n",
    "param_corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_firing_rates_pca_ = avg_firing_rates_pca.reshape((-1, N_COMP))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 576x576 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "ncomp_disp = 5\n",
    "fig = plt.figure(figsize=(8,8))\n",
    "for j in range(ncomp_disp):\n",
    "  for i in range(j+1):\n",
    "    ax = fig.add_subplot(ncomp_disp,ncomp_disp, j + ncomp_disp*i + 1)\n",
    "    if i == j:\n",
    "      ax.scatter(\n",
    "        # param_corr[:, 0],\n",
    "        stim_val['pair'].reshape((-1, 2))[:, 0],\n",
    "        avg_firing_rates_pca_[:, i], s=1)\n",
    "      ax.set(xlabel='stim', ylabel='PC%d'%i)\n",
    "    else:\n",
    "      ax.scatter(avg_firing_rates_pca_[:, j], avg_firing_rates_pca_[:, i], s=1, c=stim_val['pair'].reshape((-1, 2))[:, 0], cmap='hsv')\n",
    "    \n",
    "    ax.set(xticks=[], yticks=[])\n",
    "    \n",
    "    if i==0 and j>0:\n",
    "      ax.set(xlabel='PC%d'%j)\n",
    "      ax.xaxis.set_label_position('top') \n",
    "\n",
    "    if j==ncomp_disp-1 and i<ncomp_disp-1:\n",
    "      ax.set(ylabel='PC%d'%i)\n",
    "      ax.yaxis.set_label_position('right') \n",
    "\n",
    "fig.set_facecolor('white')\n",
    "fig.suptitle('PCA')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### UMAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "from umap import UMAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3.10/site-packages/umap/umap_.py:2344: UserWarning: n_neighbors is larger than the dataset size; truncating to X.shape[0] - 1\n",
      "  warn(\n",
      "/usr/lib/python3.10/site-packages/umap/umap_.py:132: UserWarning: A large number of your vertices were disconnected from the manifold.\n",
      "Disconnection_distance = inf has removed 0 edges.\n",
      "It has fully disconnected 2 vertices.\n",
      "You might consider using find_disconnected_points() to find and remove these points from your data.\n",
      "Use umap.utils.disconnected_vertices() to identify them.\n",
      "  warn(\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "zero-size array to reduction operation maximum which has no identity",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m/home/ennucore/dev/amgen/exp/ProcessExp.ipynb Cell 103\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/ennucore/dev/amgen/exp/ProcessExp.ipynb#ch0000106?line=0'>1</a>\u001b[0m ncomp_umap \u001b[39m=\u001b[39m \u001b[39m5\u001b[39m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/ennucore/dev/amgen/exp/ProcessExp.ipynb#ch0000106?line=1'>2</a>\u001b[0m embed \u001b[39m=\u001b[39m UMAP(n_components\u001b[39m=\u001b[39;49mncomp_umap, n_neighbors\u001b[39m=\u001b[39;49m\u001b[39m5\u001b[39;49m, metric\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39meuclidean\u001b[39;49m\u001b[39m'\u001b[39;49m)\u001b[39m.\u001b[39;49mfit_transform(fr_pca_simpl)\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/umap/umap_.py:2772\u001b[0m, in \u001b[0;36mUMAP.fit_transform\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m   2742\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mfit_transform\u001b[39m(\u001b[39mself\u001b[39m, X, y\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[1;32m   2743\u001b[0m     \u001b[39m\"\"\"Fit X into an embedded space and return that transformed\u001b[39;00m\n\u001b[1;32m   2744\u001b[0m \u001b[39m    output.\u001b[39;00m\n\u001b[1;32m   2745\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2770\u001b[0m \u001b[39m        Local radii of data points in the embedding (log-transformed).\u001b[39;00m\n\u001b[1;32m   2771\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 2772\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfit(X, y)\n\u001b[1;32m   2773\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransform_mode \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39membedding\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m   2774\u001b[0m         \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moutput_dens:\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/umap/umap_.py:2684\u001b[0m, in \u001b[0;36mUMAP.fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m   2681\u001b[0m     \u001b[39mprint\u001b[39m(ts(), \u001b[39m\"\u001b[39m\u001b[39mConstruct embedding\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m   2683\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransform_mode \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39membedding\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[0;32m-> 2684\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39membedding_, aux_data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_fit_embed_data(\n\u001b[1;32m   2685\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_raw_data[index],\n\u001b[1;32m   2686\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mn_epochs,\n\u001b[1;32m   2687\u001b[0m         init,\n\u001b[1;32m   2688\u001b[0m         random_state,  \u001b[39m# JH why raw data?\u001b[39;49;00m\n\u001b[1;32m   2689\u001b[0m     )\n\u001b[1;32m   2690\u001b[0m     \u001b[39m# Assign any points that are fully disconnected from our manifold(s) to have embedding\u001b[39;00m\n\u001b[1;32m   2691\u001b[0m     \u001b[39m# coordinates of np.nan.  These will be filtered by our plotting functions automatically.\u001b[39;00m\n\u001b[1;32m   2692\u001b[0m     \u001b[39m# They also prevent users from being deceived a distance query to one of these points.\u001b[39;00m\n\u001b[1;32m   2693\u001b[0m     \u001b[39m# Might be worth moving this into simplicial_set_embedding or _fit_embed_data\u001b[39;00m\n\u001b[1;32m   2694\u001b[0m     disconnected_vertices \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mgraph_\u001b[39m.\u001b[39msum(axis\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m))\u001b[39m.\u001b[39mflatten() \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/umap/umap_.py:2717\u001b[0m, in \u001b[0;36mUMAP._fit_embed_data\u001b[0;34m(self, X, n_epochs, init, random_state)\u001b[0m\n\u001b[1;32m   2713\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_fit_embed_data\u001b[39m(\u001b[39mself\u001b[39m, X, n_epochs, init, random_state):\n\u001b[1;32m   2714\u001b[0m     \u001b[39m\"\"\"A method wrapper for simplicial_set_embedding that can be\u001b[39;00m\n\u001b[1;32m   2715\u001b[0m \u001b[39m    replaced by subclasses.\u001b[39;00m\n\u001b[1;32m   2716\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 2717\u001b[0m     \u001b[39mreturn\u001b[39;00m simplicial_set_embedding(\n\u001b[1;32m   2718\u001b[0m         X,\n\u001b[1;32m   2719\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mgraph_,\n\u001b[1;32m   2720\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mn_components,\n\u001b[1;32m   2721\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_initial_alpha,\n\u001b[1;32m   2722\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_a,\n\u001b[1;32m   2723\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_b,\n\u001b[1;32m   2724\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mrepulsion_strength,\n\u001b[1;32m   2725\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnegative_sample_rate,\n\u001b[1;32m   2726\u001b[0m         n_epochs,\n\u001b[1;32m   2727\u001b[0m         init,\n\u001b[1;32m   2728\u001b[0m         random_state,\n\u001b[1;32m   2729\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_input_distance_func,\n\u001b[1;32m   2730\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_metric_kwds,\n\u001b[1;32m   2731\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdensmap,\n\u001b[1;32m   2732\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_densmap_kwds,\n\u001b[1;32m   2733\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moutput_dens,\n\u001b[1;32m   2734\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_output_distance_func,\n\u001b[1;32m   2735\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_output_metric_kwds,\n\u001b[1;32m   2736\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moutput_metric \u001b[39min\u001b[39;49;00m (\u001b[39m\"\u001b[39;49m\u001b[39meuclidean\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39ml2\u001b[39;49m\u001b[39m\"\u001b[39;49m),\n\u001b[1;32m   2737\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mrandom_state \u001b[39mis\u001b[39;49;00m \u001b[39mNone\u001b[39;49;00m,\n\u001b[1;32m   2738\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mverbose,\n\u001b[1;32m   2739\u001b[0m         tqdm_kwds\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtqdm_kwds,\n\u001b[1;32m   2740\u001b[0m     )\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/umap/umap_.py:1066\u001b[0m, in \u001b[0;36msimplicial_set_embedding\u001b[0;34m(data, graph, n_components, initial_alpha, a, b, gamma, negative_sample_rate, n_epochs, init, random_state, metric, metric_kwds, densmap, densmap_kwds, output_dens, output_metric, output_metric_kwds, euclidean_output, parallel, verbose, tqdm_kwds)\u001b[0m\n\u001b[1;32m   1063\u001b[0m     n_epochs \u001b[39m=\u001b[39m default_epochs\n\u001b[1;32m   1065\u001b[0m \u001b[39mif\u001b[39;00m n_epochs \u001b[39m>\u001b[39m \u001b[39m10\u001b[39m:\n\u001b[0;32m-> 1066\u001b[0m     graph\u001b[39m.\u001b[39mdata[graph\u001b[39m.\u001b[39mdata \u001b[39m<\u001b[39m (graph\u001b[39m.\u001b[39;49mdata\u001b[39m.\u001b[39;49mmax() \u001b[39m/\u001b[39m \u001b[39mfloat\u001b[39m(n_epochs))] \u001b[39m=\u001b[39m \u001b[39m0.0\u001b[39m\n\u001b[1;32m   1067\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   1068\u001b[0m     graph\u001b[39m.\u001b[39mdata[graph\u001b[39m.\u001b[39mdata \u001b[39m<\u001b[39m (graph\u001b[39m.\u001b[39mdata\u001b[39m.\u001b[39mmax() \u001b[39m/\u001b[39m \u001b[39mfloat\u001b[39m(default_epochs))] \u001b[39m=\u001b[39m \u001b[39m0.0\u001b[39m\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/numpy/core/_methods.py:40\u001b[0m, in \u001b[0;36m_amax\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_amax\u001b[39m(a, axis\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, out\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, keepdims\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m,\n\u001b[1;32m     39\u001b[0m           initial\u001b[39m=\u001b[39m_NoValue, where\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m):\n\u001b[0;32m---> 40\u001b[0m     \u001b[39mreturn\u001b[39;00m umr_maximum(a, axis, \u001b[39mNone\u001b[39;49;00m, out, keepdims, initial, where)\n",
      "\u001b[0;31mValueError\u001b[0m: zero-size array to reduction operation maximum which has no identity"
     ]
    }
   ],
   "source": [
    "ncomp_umap = 5\n",
    "embed = UMAP(n_components=ncomp_umap, n_neighbors=5, metric='euclidean').fit_transform(fr_pca_simpl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "'c' argument has 926 elements, which is inconsistent with 'x' and 'y' with size 913.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/matplotlib/axes/_axes.py:4350\u001b[0m, in \u001b[0;36mAxes._parse_scatter_color_args\u001b[0;34m(c, edgecolors, kwargs, xsize, get_next_color_func)\u001b[0m\n\u001b[1;32m   4349\u001b[0m \u001b[39mtry\u001b[39;00m:  \u001b[39m# Is 'c' acceptable as PathCollection facecolors?\u001b[39;00m\n\u001b[0;32m-> 4350\u001b[0m     colors \u001b[39m=\u001b[39m mcolors\u001b[39m.\u001b[39;49mto_rgba_array(c)\n\u001b[1;32m   4351\u001b[0m \u001b[39mexcept\u001b[39;00m (\u001b[39mTypeError\u001b[39;00m, \u001b[39mValueError\u001b[39;00m) \u001b[39mas\u001b[39;00m err:\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/matplotlib/colors.py:385\u001b[0m, in \u001b[0;36mto_rgba_array\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m    384\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 385\u001b[0m     rgba \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([to_rgba(cc) \u001b[39mfor\u001b[39;00m cc \u001b[39min\u001b[39;00m c])\n\u001b[1;32m    387\u001b[0m \u001b[39mif\u001b[39;00m alpha \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/matplotlib/colors.py:385\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    384\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 385\u001b[0m     rgba \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([to_rgba(cc) \u001b[39mfor\u001b[39;00m cc \u001b[39min\u001b[39;00m c])\n\u001b[1;32m    387\u001b[0m \u001b[39mif\u001b[39;00m alpha \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/matplotlib/colors.py:206\u001b[0m, in \u001b[0;36mto_rgba\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m    205\u001b[0m \u001b[39mif\u001b[39;00m rgba \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:  \u001b[39m# Suppress exception chaining of cache lookup failure.\u001b[39;00m\n\u001b[0;32m--> 206\u001b[0m     rgba \u001b[39m=\u001b[39m _to_rgba_no_colorcycle(c, alpha)\n\u001b[1;32m    207\u001b[0m     \u001b[39mtry\u001b[39;00m:\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/matplotlib/colors.py:284\u001b[0m, in \u001b[0;36m_to_rgba_no_colorcycle\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m    283\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m np\u001b[39m.\u001b[39miterable(c):\n\u001b[0;32m--> 284\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mInvalid RGBA argument: \u001b[39m\u001b[39m{\u001b[39;00morig_c\u001b[39m!r}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    285\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(c) \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m [\u001b[39m3\u001b[39m, \u001b[39m4\u001b[39m]:\n",
      "\u001b[0;31mValueError\u001b[0m: Invalid RGBA argument: 9.0",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m/home/ennucore/dev/amgen/exp/ProcessExp.ipynb Cell 104\u001b[0m in \u001b[0;36m<cell line: 11>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/ennucore/dev/amgen/exp/ProcessExp.ipynb#ch0000107?line=8'>9</a>\u001b[0m fig, ax \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39msubplots(\u001b[39m1\u001b[39m, \u001b[39m1\u001b[39m, figsize\u001b[39m=\u001b[39m(\u001b[39m8\u001b[39m, \u001b[39m6\u001b[39m))\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/ennucore/dev/amgen/exp/ProcessExp.ipynb#ch0000107?line=9'>10</a>\u001b[0m ax \u001b[39m=\u001b[39m fig\u001b[39m.\u001b[39madd_subplot(\u001b[39m1\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m1\u001b[39m, projection\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39m3d\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m---> <a href='vscode-notebook-cell:/home/ennucore/dev/amgen/exp/ProcessExp.ipynb#ch0000107?line=10'>11</a>\u001b[0m ax\u001b[39m.\u001b[39;49mscatter3D(x, y, z, c\u001b[39m=\u001b[39;49mparam_corr[:, \u001b[39m0\u001b[39;49m], cmap\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mhsv\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/ennucore/dev/amgen/exp/ProcessExp.ipynb#ch0000107?line=11'>12</a>\u001b[0m ax\u001b[39m.\u001b[39mset(xlabel\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mU0\u001b[39m\u001b[39m\"\u001b[39m, ylabel\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mU1\u001b[39m\u001b[39m\"\u001b[39m, zlabel\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mU2\u001b[39m\u001b[39m\"\u001b[39m, xticks\u001b[39m=\u001b[39m[], yticks\u001b[39m=\u001b[39m[], zticks\u001b[39m=\u001b[39m[])\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/ennucore/dev/amgen/exp/ProcessExp.ipynb#ch0000107?line=12'>13</a>\u001b[0m fig\u001b[39m.\u001b[39msuptitle(\u001b[39m'\u001b[39m\u001b[39mUMAP 3D\u001b[39m\u001b[39m'\u001b[39m)\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/mpl_toolkits/mplot3d/axes3d.py:2416\u001b[0m, in \u001b[0;36mAxes3D.scatter\u001b[0;34m(self, xs, ys, zs, zdir, s, c, depthshade, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2413\u001b[0m \u001b[39mif\u001b[39;00m np\u001b[39m.\u001b[39mmay_share_memory(zs_orig, zs):  \u001b[39m# Avoid unnecessary copies.\u001b[39;00m\n\u001b[1;32m   2414\u001b[0m     zs \u001b[39m=\u001b[39m zs\u001b[39m.\u001b[39mcopy()\n\u001b[0;32m-> 2416\u001b[0m patches \u001b[39m=\u001b[39m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49mscatter(xs, ys, s\u001b[39m=\u001b[39;49ms, c\u001b[39m=\u001b[39;49mc, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   2417\u001b[0m art3d\u001b[39m.\u001b[39mpatch_collection_2d_to_3d(patches, zs\u001b[39m=\u001b[39mzs, zdir\u001b[39m=\u001b[39mzdir,\n\u001b[1;32m   2418\u001b[0m                                 depthshade\u001b[39m=\u001b[39mdepthshade)\n\u001b[1;32m   2420\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_zmargin \u001b[39m<\u001b[39m \u001b[39m0.05\u001b[39m \u001b[39mand\u001b[39;00m xs\u001b[39m.\u001b[39msize \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m:\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/matplotlib/__init__.py:1361\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1358\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(func)\n\u001b[1;32m   1359\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39minner\u001b[39m(ax, \u001b[39m*\u001b[39margs, data\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m   1360\u001b[0m     \u001b[39mif\u001b[39;00m data \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 1361\u001b[0m         \u001b[39mreturn\u001b[39;00m func(ax, \u001b[39m*\u001b[39;49m\u001b[39mmap\u001b[39;49m(sanitize_sequence, args), \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1363\u001b[0m     bound \u001b[39m=\u001b[39m new_sig\u001b[39m.\u001b[39mbind(ax, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m   1364\u001b[0m     auto_label \u001b[39m=\u001b[39m (bound\u001b[39m.\u001b[39marguments\u001b[39m.\u001b[39mget(label_namer)\n\u001b[1;32m   1365\u001b[0m                   \u001b[39mor\u001b[39;00m bound\u001b[39m.\u001b[39mkwargs\u001b[39m.\u001b[39mget(label_namer))\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/matplotlib/axes/_axes.py:4516\u001b[0m, in \u001b[0;36mAxes.scatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[1;32m   4513\u001b[0m \u001b[39mif\u001b[39;00m edgecolors \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m   4514\u001b[0m     orig_edgecolor \u001b[39m=\u001b[39m kwargs\u001b[39m.\u001b[39mget(\u001b[39m'\u001b[39m\u001b[39medgecolor\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mNone\u001b[39;00m)\n\u001b[1;32m   4515\u001b[0m c, colors, edgecolors \u001b[39m=\u001b[39m \\\n\u001b[0;32m-> 4516\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_parse_scatter_color_args(\n\u001b[1;32m   4517\u001b[0m         c, edgecolors, kwargs, x\u001b[39m.\u001b[39;49msize,\n\u001b[1;32m   4518\u001b[0m         get_next_color_func\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_get_patches_for_fill\u001b[39m.\u001b[39;49mget_next_color)\n\u001b[1;32m   4520\u001b[0m \u001b[39mif\u001b[39;00m plotnonfinite \u001b[39mand\u001b[39;00m colors \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m   4521\u001b[0m     c \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mma\u001b[39m.\u001b[39mmasked_invalid(c)\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/matplotlib/axes/_axes.py:4356\u001b[0m, in \u001b[0;36mAxes._parse_scatter_color_args\u001b[0;34m(c, edgecolors, kwargs, xsize, get_next_color_func)\u001b[0m\n\u001b[1;32m   4354\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   4355\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m valid_shape:\n\u001b[0;32m-> 4356\u001b[0m         \u001b[39mraise\u001b[39;00m invalid_shape_exception(c\u001b[39m.\u001b[39msize, xsize) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[1;32m   4357\u001b[0m     \u001b[39m# Both the mapping *and* the RGBA conversion failed: pretty\u001b[39;00m\n\u001b[1;32m   4358\u001b[0m     \u001b[39m# severe failure => one may appreciate a verbose feedback.\u001b[39;00m\n\u001b[1;32m   4359\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m   4360\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39mc\u001b[39m\u001b[39m'\u001b[39m\u001b[39m argument must be a color, a sequence of colors, \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m   4361\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mor a sequence of numbers, not \u001b[39m\u001b[39m{\u001b[39;00mc\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n",
      "\u001b[0;31mValueError\u001b[0m: 'c' argument has 926 elements, which is inconsistent with 'x' and 'y' with size 913."
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c30c2e1b938147c8bd27657deb0d3f42",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": "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
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img src='data:image/png;base64,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
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits import mplot3d\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "%matplotlib inline\n",
    "%matplotlib widget\n",
    "\n",
    "x, y, z = embed[:,0], embed[:,1], embed[:,2]\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n",
    "ax = fig.add_subplot(1, 1, 1, projection='3d')\n",
    "ax.scatter3D(x, y, z, c=param_corr[:, 0], cmap='hsv')\n",
    "ax.set(xlabel=\"U0\", ylabel=\"U1\", zlabel=\"U2\", xticks=[], yticks=[], zticks=[])\n",
    "fig.suptitle('UMAP 3D')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Decoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import decoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "ename": "NotImplementedError",
     "evalue": "Axes3D currently only supports the aspect argument 'auto'. You passed in 'equal'.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m/home/ennucore/dev/amgen/exp/ProcessExp.ipynb Cell 103\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/ennucore/dev/amgen/exp/ProcessExp.ipynb#ch0000093?line=0'>1</a>\u001b[0m \u001b[39m# param = decoding.cohomological_parameterization(pd.DataFrame(firing_rates_in_trials_pca.reshape((400*50, -1))[::41]))\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/ennucore/dev/amgen/exp/ProcessExp.ipynb#ch0000093?line=1'>2</a>\u001b[0m param \u001b[39m=\u001b[39m decoding\u001b[39m.\u001b[39;49mcohomological_parameterization(fr_pca_simpl)\n",
      "File \u001b[0;32m~/dev/amgen/exp/../model/decoding.py:95\u001b[0m, in \u001b[0;36mcohomological_parameterization\u001b[0;34m(X, cocycle_number, coeff, weighted)\u001b[0m\n\u001b[1;32m     93\u001b[0m idx \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39margsort(dgm1[:, \u001b[39m1\u001b[39m] \u001b[39m-\u001b[39m dgm1[:, \u001b[39m0\u001b[39m])[\u001b[39m-\u001b[39mcocycle_number]\n\u001b[1;32m     94\u001b[0m cocycle \u001b[39m=\u001b[39m cocycles[\u001b[39m1\u001b[39m][idx]\n\u001b[0;32m---> 95\u001b[0m persistence(X, homdim\u001b[39m=\u001b[39;49m\u001b[39m1\u001b[39;49m, coeff\u001b[39m=\u001b[39;49mcoeff, show_largest_homology\u001b[39m=\u001b[39;49m\u001b[39m0\u001b[39;49m,\n\u001b[1;32m     96\u001b[0m             Nsubsamples\u001b[39m=\u001b[39;49m\u001b[39m0\u001b[39;49m, save_path\u001b[39m=\u001b[39;49m\u001b[39mNone\u001b[39;49;00m, cycle\u001b[39m=\u001b[39;49midx)\n\u001b[1;32m     97\u001b[0m thresh \u001b[39m=\u001b[39m dgm1[idx, \u001b[39m1\u001b[39m] \u001b[39m-\u001b[39m EPSILON\n\u001b[1;32m     99\u001b[0m \u001b[39m# Compute connectivity\u001b[39;00m\n",
      "File \u001b[0;32m~/dev/amgen/exp/../model/decorators.py:19\u001b[0m, in \u001b[0;36mmulti_input.<locals>.wrapper\u001b[0;34m(data, *args, **kwargs)\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[39mreturn\u001b[39;00m output_data\n\u001b[1;32m     18\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m---> 19\u001b[0m     \u001b[39mreturn\u001b[39;00m f(data, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/dev/amgen/exp/../model/persistence.py:97\u001b[0m, in \u001b[0;36mpersistence\u001b[0;34m(X, homdim, coeff, threshold, show_largest_homology, distance_matrix, Nsubsamples, alpha, cycle, save_path)\u001b[0m\n\u001b[1;32m     94\u001b[0m result \u001b[39m=\u001b[39m ripser\u001b[39m.\u001b[39mripser(X, maxdim\u001b[39m=\u001b[39mhomdim, coeff\u001b[39m=\u001b[39mcoeff, do_cocycles\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,\n\u001b[1;32m     95\u001b[0m                        distance_matrix\u001b[39m=\u001b[39mdistance_matrix, thresh\u001b[39m=\u001b[39mthreshold)\n\u001b[1;32m     96\u001b[0m diagrams \u001b[39m=\u001b[39m result[\u001b[39m'\u001b[39m\u001b[39mdgms\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[0;32m---> 97\u001b[0m plot_diagrams(diagrams, show\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m)\n\u001b[1;32m     98\u001b[0m \u001b[39mif\u001b[39;00m (Nsubsamples\u001b[39m>\u001b[39m\u001b[39m0\u001b[39m):\n\u001b[1;32m     99\u001b[0m     conf \u001b[39m=\u001b[39m confidence(X, alpha, Nsubsamples, homdim, \u001b[39m2\u001b[39m)\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/persim/visuals.py:157\u001b[0m, in \u001b[0;36mplot_diagrams\u001b[0;34m(diagrams, plot_only, title, xy_range, labels, colormap, size, ax_color, diagonal, lifetime, legend, show, ax)\u001b[0m\n\u001b[1;32m    155\u001b[0m ax\u001b[39m.\u001b[39mset_xlim([x_down, x_up])\n\u001b[1;32m    156\u001b[0m ax\u001b[39m.\u001b[39mset_ylim([y_down, y_up])\n\u001b[0;32m--> 157\u001b[0m ax\u001b[39m.\u001b[39;49mset_aspect(\u001b[39m'\u001b[39;49m\u001b[39mequal\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mbox\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[1;32m    159\u001b[0m \u001b[39mif\u001b[39;00m title \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    160\u001b[0m     ax\u001b[39m.\u001b[39mset_title(title)\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/mpl_toolkits/mplot3d/axes3d.py:323\u001b[0m, in \u001b[0;36mAxes3D.set_aspect\u001b[0;34m(self, aspect, adjustable, anchor, share)\u001b[0m\n\u001b[1;32m    270\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    271\u001b[0m \u001b[39mSet the aspect ratios.\u001b[39;00m\n\u001b[1;32m    272\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    320\u001b[0m \u001b[39mmpl_toolkits.mplot3d.axes3d.Axes3D.set_box_aspect\u001b[39;00m\n\u001b[1;32m    321\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    322\u001b[0m \u001b[39mif\u001b[39;00m aspect \u001b[39m!=\u001b[39m \u001b[39m'\u001b[39m\u001b[39mauto\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[0;32m--> 323\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mNotImplementedError\u001b[39;00m(\n\u001b[1;32m    324\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mAxes3D currently only supports the aspect argument \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    325\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39mauto\u001b[39m\u001b[39m'\u001b[39m\u001b[39m. You passed in \u001b[39m\u001b[39m{\u001b[39;00maspect\u001b[39m!r}\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    326\u001b[0m     )\n\u001b[1;32m    328\u001b[0m \u001b[39mif\u001b[39;00m share:\n\u001b[1;32m    329\u001b[0m     axes \u001b[39m=\u001b[39m {\u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_shared_x_axes\u001b[39m.\u001b[39mget_siblings(\u001b[39mself\u001b[39m),\n\u001b[1;32m    330\u001b[0m             \u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_shared_y_axes\u001b[39m.\u001b[39mget_siblings(\u001b[39mself\u001b[39m),\n\u001b[1;32m    331\u001b[0m             \u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_shared_z_axes\u001b[39m.\u001b[39mget_siblings(\u001b[39mself\u001b[39m),\n\u001b[1;32m    332\u001b[0m             }\n",
      "\u001b[0;31mNotImplementedError\u001b[0m: Axes3D currently only supports the aspect argument 'auto'. You passed in 'equal'."
     ]
    }
   ],
   "source": [
    "# param = decoding.cohomological_parameterization(pd.DataFrame(firing_rates_in_trials_pca.reshape((400*50, -1))[::41]))\n",
    "param = decoding.cohomological_parameterization(fr_pca_simpl)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(788,)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_corr[:, 0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "'c' argument must be a color, a sequence of colors, or a sequence of numbers, not          decoding\n0    5.648858e-10\n1    8.865429e-01\n2    8.865876e-01\n3    8.864820e-01\n4    8.867152e-01\n..            ...\n641  8.865732e-01\n642  8.861918e-01\n643  8.865706e-01\n644  8.865345e-01\n645  8.863632e-01\n\n[646 rows x 1 columns]",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/matplotlib/axes/_axes.py:4350\u001b[0m, in \u001b[0;36mAxes._parse_scatter_color_args\u001b[0;34m(c, edgecolors, kwargs, xsize, get_next_color_func)\u001b[0m\n\u001b[1;32m   4349\u001b[0m \u001b[39mtry\u001b[39;00m:  \u001b[39m# Is 'c' acceptable as PathCollection facecolors?\u001b[39;00m\n\u001b[0;32m-> 4350\u001b[0m     colors \u001b[39m=\u001b[39m mcolors\u001b[39m.\u001b[39;49mto_rgba_array(c)\n\u001b[1;32m   4351\u001b[0m \u001b[39mexcept\u001b[39;00m (\u001b[39mTypeError\u001b[39;00m, \u001b[39mValueError\u001b[39;00m) \u001b[39mas\u001b[39;00m err:\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/matplotlib/colors.py:385\u001b[0m, in \u001b[0;36mto_rgba_array\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m    384\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 385\u001b[0m     rgba \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([to_rgba(cc) \u001b[39mfor\u001b[39;00m cc \u001b[39min\u001b[39;00m c])\n\u001b[1;32m    387\u001b[0m \u001b[39mif\u001b[39;00m alpha \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/matplotlib/colors.py:385\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    384\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 385\u001b[0m     rgba \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([to_rgba(cc) \u001b[39mfor\u001b[39;00m cc \u001b[39min\u001b[39;00m c])\n\u001b[1;32m    387\u001b[0m \u001b[39mif\u001b[39;00m alpha \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/matplotlib/colors.py:206\u001b[0m, in \u001b[0;36mto_rgba\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m    205\u001b[0m \u001b[39mif\u001b[39;00m rgba \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:  \u001b[39m# Suppress exception chaining of cache lookup failure.\u001b[39;00m\n\u001b[0;32m--> 206\u001b[0m     rgba \u001b[39m=\u001b[39m _to_rgba_no_colorcycle(c, alpha)\n\u001b[1;32m    207\u001b[0m     \u001b[39mtry\u001b[39;00m:\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/matplotlib/colors.py:277\u001b[0m, in \u001b[0;36m_to_rgba_no_colorcycle\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m    276\u001b[0m         \u001b[39mreturn\u001b[39;00m c, c, c, alpha \u001b[39mif\u001b[39;00m alpha \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m \u001b[39m1.\u001b[39m\n\u001b[0;32m--> 277\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mInvalid RGBA argument: \u001b[39m\u001b[39m{\u001b[39;00morig_c\u001b[39m!r}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    278\u001b[0m \u001b[39m# turn 2-D array into 1-D array\u001b[39;00m\n",
      "\u001b[0;31mValueError\u001b[0m: Invalid RGBA argument: 'decoding'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m/home/ennucore/dev/amgen/exp/ProcessExp.ipynb Cell 98\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/ennucore/dev/amgen/exp/ProcessExp.ipynb#ch0000097?line=0'>1</a>\u001b[0m plt\u001b[39m.\u001b[39;49mscatter(param_corr[:, \u001b[39m0\u001b[39;49m], param_corr[:, \u001b[39m1\u001b[39;49m], c\u001b[39m=\u001b[39;49mparam, cmap\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mRdBu\u001b[39;49m\u001b[39m'\u001b[39;49m)\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/matplotlib/pyplot.py:3068\u001b[0m, in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, data, **kwargs)\u001b[0m\n\u001b[1;32m   3063\u001b[0m \u001b[39m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[39m.\u001b[39mscatter)\n\u001b[1;32m   3064\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mscatter\u001b[39m(\n\u001b[1;32m   3065\u001b[0m         x, y, s\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, c\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, marker\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, cmap\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, norm\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m,\n\u001b[1;32m   3066\u001b[0m         vmin\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, vmax\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, alpha\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, linewidths\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m,\n\u001b[1;32m   3067\u001b[0m         edgecolors\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, plotnonfinite\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, data\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m-> 3068\u001b[0m     __ret \u001b[39m=\u001b[39m gca()\u001b[39m.\u001b[39;49mscatter(\n\u001b[1;32m   3069\u001b[0m         x, y, s\u001b[39m=\u001b[39;49ms, c\u001b[39m=\u001b[39;49mc, marker\u001b[39m=\u001b[39;49mmarker, cmap\u001b[39m=\u001b[39;49mcmap, norm\u001b[39m=\u001b[39;49mnorm,\n\u001b[1;32m   3070\u001b[0m         vmin\u001b[39m=\u001b[39;49mvmin, vmax\u001b[39m=\u001b[39;49mvmax, alpha\u001b[39m=\u001b[39;49malpha, linewidths\u001b[39m=\u001b[39;49mlinewidths,\n\u001b[1;32m   3071\u001b[0m         edgecolors\u001b[39m=\u001b[39;49medgecolors, plotnonfinite\u001b[39m=\u001b[39;49mplotnonfinite,\n\u001b[1;32m   3072\u001b[0m         \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49m({\u001b[39m\"\u001b[39;49m\u001b[39mdata\u001b[39;49m\u001b[39m\"\u001b[39;49m: data} \u001b[39mif\u001b[39;49;00m data \u001b[39mis\u001b[39;49;00m \u001b[39mnot\u001b[39;49;00m \u001b[39mNone\u001b[39;49;00m \u001b[39melse\u001b[39;49;00m {}), \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   3073\u001b[0m     sci(__ret)\n\u001b[1;32m   3074\u001b[0m     \u001b[39mreturn\u001b[39;00m __ret\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/matplotlib/__init__.py:1361\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1358\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(func)\n\u001b[1;32m   1359\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39minner\u001b[39m(ax, \u001b[39m*\u001b[39margs, data\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m   1360\u001b[0m     \u001b[39mif\u001b[39;00m data \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 1361\u001b[0m         \u001b[39mreturn\u001b[39;00m func(ax, \u001b[39m*\u001b[39;49m\u001b[39mmap\u001b[39;49m(sanitize_sequence, args), \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1363\u001b[0m     bound \u001b[39m=\u001b[39m new_sig\u001b[39m.\u001b[39mbind(ax, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m   1364\u001b[0m     auto_label \u001b[39m=\u001b[39m (bound\u001b[39m.\u001b[39marguments\u001b[39m.\u001b[39mget(label_namer)\n\u001b[1;32m   1365\u001b[0m                   \u001b[39mor\u001b[39;00m bound\u001b[39m.\u001b[39mkwargs\u001b[39m.\u001b[39mget(label_namer))\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/matplotlib/axes/_axes.py:4516\u001b[0m, in \u001b[0;36mAxes.scatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[1;32m   4513\u001b[0m \u001b[39mif\u001b[39;00m edgecolors \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m   4514\u001b[0m     orig_edgecolor \u001b[39m=\u001b[39m kwargs\u001b[39m.\u001b[39mget(\u001b[39m'\u001b[39m\u001b[39medgecolor\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mNone\u001b[39;00m)\n\u001b[1;32m   4515\u001b[0m c, colors, edgecolors \u001b[39m=\u001b[39m \\\n\u001b[0;32m-> 4516\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_parse_scatter_color_args(\n\u001b[1;32m   4517\u001b[0m         c, edgecolors, kwargs, x\u001b[39m.\u001b[39;49msize,\n\u001b[1;32m   4518\u001b[0m         get_next_color_func\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_get_patches_for_fill\u001b[39m.\u001b[39;49mget_next_color)\n\u001b[1;32m   4520\u001b[0m \u001b[39mif\u001b[39;00m plotnonfinite \u001b[39mand\u001b[39;00m colors \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m   4521\u001b[0m     c \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mma\u001b[39m.\u001b[39mmasked_invalid(c)\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/matplotlib/axes/_axes.py:4359\u001b[0m, in \u001b[0;36mAxes._parse_scatter_color_args\u001b[0;34m(c, edgecolors, kwargs, xsize, get_next_color_func)\u001b[0m\n\u001b[1;32m   4356\u001b[0m             \u001b[39mraise\u001b[39;00m invalid_shape_exception(c\u001b[39m.\u001b[39msize, xsize) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[1;32m   4357\u001b[0m         \u001b[39m# Both the mapping *and* the RGBA conversion failed: pretty\u001b[39;00m\n\u001b[1;32m   4358\u001b[0m         \u001b[39m# severe failure => one may appreciate a verbose feedback.\u001b[39;00m\n\u001b[0;32m-> 4359\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m   4360\u001b[0m             \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39mc\u001b[39m\u001b[39m'\u001b[39m\u001b[39m argument must be a color, a sequence of colors, \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m   4361\u001b[0m             \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mor a sequence of numbers, not \u001b[39m\u001b[39m{\u001b[39;00mc\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[1;32m   4362\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   4363\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(colors) \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m (\u001b[39m0\u001b[39m, \u001b[39m1\u001b[39m, xsize):\n\u001b[1;32m   4364\u001b[0m         \u001b[39m# NB: remember that a single color is also acceptable.\u001b[39;00m\n\u001b[1;32m   4365\u001b[0m         \u001b[39m# Besides *colors* will be an empty array if c == 'none'.\u001b[39;00m\n",
      "\u001b[0;31mValueError\u001b[0m: 'c' argument must be a color, a sequence of colors, or a sequence of numbers, not          decoding\n0    5.648858e-10\n1    8.865429e-01\n2    8.865876e-01\n3    8.864820e-01\n4    8.867152e-01\n..            ...\n641  8.865732e-01\n642  8.861918e-01\n643  8.865706e-01\n644  8.865345e-01\n645  8.863632e-01\n\n[646 rows x 1 columns]"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(param_corr[:, 0], param_corr[:, 1], c=param, cmap='RdBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fdf498f2080>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(param_corr[:, 1], param)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3.10/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(param.clip(0.885, 0.888))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.5 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}