amgen/ExpMoving.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing experimental data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Some imports*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy\n",
    "import typing\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spiketimes_Ntsr1Cre_2020_0003_s02_e05.npy  Stiminfo_PVCre_2021_0012_s06_e14.csv\n",
      "Spiketimes_Ntsr1Cre_2020_0003_s02_e10.npy  res.pkl\n",
      "Spiketimes_PVCre_2021_0012_s06_e14.npy\t   simple_cell_center_fp\n",
      "Stiminfo_Ntsr1Cre_2020_0003_s02_e05.npy    simple_cell_random_fp\n",
      "Stiminfo_Ntsr1Cre_2020_0003_s02_e10.npy\n"
     ]
    }
   ],
   "source": [
    "!ls data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### *Load squares data*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "squares_stim = np.load(open('data/Stiminfo_Ntsr1Cre_2020_0003_s02_e10.npy', 'rb'), allow_pickle=True).item()\n",
    "squares_spikes = np.load(open('data/Spiketimes_Ntsr1Cre_2020_0003_s02_e10.npy', 'rb'), allow_pickle=True).item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('AsparseNoise5_60deg',\n",
       " dict_keys(['ename', 'area', 'probename', 'electrode_spacing', 'monitor_offset', 'stimtimes', 'stimvals', 'opto', 'optotimes', 'ivs', 'run_spdthr', 'run_minon', 'runperiods']))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squares_stim['ename'], squares_stim.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['grat_x_position', 'grat_y_position', 'grat_contrast'])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squares_stim['ivs'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 10,
     "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": [
    "sns.distplot(squares_stim['ivs']['grat_x_position'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25.4911, 80.5089)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squares_stim['ivs']['grat_x_position'].min(), squares_stim['ivs']['grat_x_position'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-27.5089, 27.5089)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "squares_stim['ivs']['grat_y_position'].min(), squares_stim['ivs']['grat_y_position'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  4,   8,  11,  14,  17,  20,  26,  29,  33,  34,  35,  36,  37,\n",
       "        41,  44,  45,  46,  48,  51,  58,  59,  67,  72,  77,  82,  87,\n",
       "        93,  96, 101, 104, 112, 116, 120, 128, 129, 140, 141, 142, 144,\n",
       "       145])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squares_spikes['units']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 14,
     "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": [
    "sns.distplot(squares_spikes['spiketimes'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### *Load gratings data*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "gratings_stim = np.load(open('data/Stiminfo_Ntsr1Cre_2020_0003_s02_e05.npy', 'rb'), allow_pickle=True).item()\n",
    "gratings_spikes = np.load(open('data/Spiketimes_Ntsr1Cre_2020_0003_s02_e05.npy', 'rb'), allow_pickle=True).item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['ename', 'area', 'probename', 'electrode_spacing', 'monitor_offset', 'stimtimes', 'stimvals', 'optotimes', 'opto', 'optofreq', 'ivs', 'run_spdthr', 'run_minon', 'runperiods'])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gratings_stim.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'oriTun12_opto'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gratings_stim['ename']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['grat_height', 'opto', 'optofreq'])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gratings_stim['ivs'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9.55191999, -3.82510311,  1.18950616,  1.        ,  3.35761066,\n",
       "        4.65747346,  0.05402836])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gratings_spikes['RFpars'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-processing squares data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Turn squares data into a better format*\n",
    "\n",
    "`{'squares': (x, y, c), 'firing_rates': [fr1, fr2, ...]}`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.512 , 1.7124]),\n",
       " array([1155.5455, 1155.7459]),\n",
       " 54.692066,\n",
       " 1152.682366)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squares_stim['stimtimes'][0], squares_stim['stimtimes'][-1], squares_spikes['spiketimes'][0][0], squares_spikes['spiketimes'][0][-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_firing_rates = lambda i: ((squares_stim['stimtimes'][:, 0].reshape((-1, 1)) <= squares_spikes['spiketimes'][i]) * (squares_spikes['spiketimes'][i] < squares_stim['stimtimes'][:, 1].reshape((-1, 1)))).sum(axis=1) / (squares_stim['stimtimes'][:, 1] - squares_stim['stimtimes'][:, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 5760)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N_UNITS = squares_spikes['spiketimes'].shape[0]\n",
    "N_EXPS = squares_stim['stimtimes'].shape[0]\n",
    "N_UNITS, N_EXPS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "firing_rates = np.array([get_firing_rates(i) for i in range(N_UNITS)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "stim_data = np.array([((squares_stim['ivs']['grat_x_position'][i] - 25) * 100 / 60, 100 * (squares_stim['ivs']['grat_y_position'][i] / 55) + 50, round(squares_stim['ivs']['grat_contrast'][i])) for i in range(len(squares_stim['ivs']['grat_x_position']))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((40, 5760), (5760, 3))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squares_data = {'squares': stim_data, 'firing_rates': firing_rates}\n",
    "firing_rates.shape, stim_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "fields_plus = [np.zeros((100, 100)) for i in range(N_UNITS)]\n",
    "fields_minus = [np.zeros((100, 100)) for i in range(N_UNITS)]\n",
    "r_coord = lambda x: min(max(round(x), 0), 99)\n",
    "\n",
    "for i in range(N_EXPS):\n",
    "    for j in range(N_UNITS):\n",
    "        if squares_data['squares'][i, 2] >= 0:\n",
    "            fields_plus[j][r_coord(squares_data['squares'][i, 0]), r_coord(squares_data['squares'][i, 1])] += squares_data['firing_rates'][j, i]\n",
    "        else:\n",
    "            fields_minus[j][r_coord(squares_data['squares'][i, 0]), r_coord(squares_data['squares'][i, 1])] += squares_data['firing_rates'][j, i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(N_UNITS):\n",
    "    fields_plus[i] = scipy.ndimage.gaussian_filter(fields_plus[i], sigma=6)\n",
    "    fields_minus[i] = scipy.ndimage.gaussian_filter(fields_minus[i], sigma=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3, 5)\n",
    "ax = ax.flatten()\n",
    "# set title\n",
    "fig.suptitle('Receptive fields - positive')\n",
    "for i in range(len(ax)):\n",
    "    ax[i].imshow(fields_plus[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f394518b4f0>"
      ]
     },
     "execution_count": 37,
     "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.imshow(np.array(fields_plus).mean(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3, 5)\n",
    "ax = ax.flatten()\n",
    "# set title\n",
    "fig.suptitle('Receptive fields - negative')\n",
    "for i in range(len(ax)):\n",
    "    ax[i].imshow(fields_minus[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding centers of the receptive fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import signal\n",
    "\n",
    "def gkern(kernlen=100, std=5, x=0, y=0):\n",
    "    \"\"\"Returns a 2D Gaussian kernel array.\"\"\"\n",
    "    if not x and not y:\n",
    "        gkern1d = signal.gaussian(kernlen, std=std).reshape(kernlen, 1)\n",
    "        gkern2d = np.outer(gkern1d, gkern1d)\n",
    "        return gkern2d\n",
    "    else:\n",
    "        return np.roll(gkern(kernlen, std, 0, 0), (x - 50, y - 50))\n",
    "\n",
    "\n",
    "def find_center(field) -> typing.Tuple[typing.Tuple[int, int], float]:\n",
    "    \"\"\"\n",
    "    Find the center of the field and return it and the score\n",
    "    \"\"\"\n",
    "    center = np.unravel_index(np.argmax(scipy.ndimage.gaussian_filter(field, sigma=7)), field.shape)\n",
    "    score = np.sum(field * gkern(x=center[0], y=center[1])) / field.mean()\n",
    "    return center, score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4092185/2956312832.py:18: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  score = np.sum(field * gkern(x=center[0], y=center[1])) / field.mean()\n"
     ]
    }
   ],
   "source": [
    "centers_pos = [find_center(fields_plus[i]) for i in range(N_UNITS)]\n",
    "centers_neg = [find_center(fields_minus[i]) for i in range(N_UNITS)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": [
       "12"
      ]
     },
     "execution_count": 28,
     "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": [
    "sns.distplot([centers_pos[i][1] for i in range(N_UNITS)])\n",
    "tuning_threshold = 200\n",
    "sum(centers_pos[i][1] > tuning_threshold for i in range(N_UNITS))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tuned_units = [i for i in range(N_UNITS) if centers_pos[i][1] > tuning_threshold]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWcAAAEVCAYAAAA8UiRHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOz9e7CtW3YXhv3GmPNba+3HOefe2/fRt9WtlsCgB46BBIRIqbBsHgEMJVcqKGCFgAvchcuqQEISCb9iEz9Eykmcip0qBObhYF4pWzYOMiBcBgJ2gQSWeCh6drfUj9t9u/ve89iPtb5vzjnyxxhjzvmtvfY+59y7176HsEbVPnuftb71re+b35xjjvEbvzEGiQgOcpCDHOQgL5bwB30BBznIQQ5ykKtyUM4HOchBDvICykE5H+QgBznICygH5XyQgxzkIC+gHJTzQQ5ykIO8gHJQzgc5yEEO8gLKQTkfZCZE9C8T0R/ew3nfIKK/RkRPiOj//DzfQ0R/hYh+5zXvfQ0RCRHF57weIqI/SkTvEtHfep7PvohCRP8GEf2JPZ37jIh+zj7OfZDr5bkm9EHenxDRpwG8ASADOAPwFwB8p4icfUDX860A/oSIfNRfE5F/Z09f9wkAXwZwX14Mcv23APjVAD4qIufv92RE9DUAPgVgEJH0fs/3IomInPrfRPTHAHxWRP7VD+6K/tGQg+V89/IbbbL/IgC/GMDv+2Av587k4wB+9AVRzIBez6ffi2J+Xiv9IAd5L3JQzh+QiMgXAPxFqJIGABDRNxPRf0tED4noR8yy9fdeMTf88+aK/+fde7+BiH7YPvffEtE/0b33aSL6fUT0o/a5P0pEKyI6AfBfAfiIua1nRPSR3j0mov+KiL6zv267rv+p/f31RPQDRPQOEf04EX37rns1a+u3Afjf2/f8qm03/KZ73zpXIKJ/j4i+TESfBPDPbL3/24nokwaffIqIvmPHOX4HgD8M4Jfb9fyb9vq/QEQ/Zffz54joI91nhIj+JSL6SQA/uePS/pr9fmjn/OU77nEGwRhc838kor9h1/uXiOjVZxkTIvpaIvqr9rkfAFA/t+N+fzsR/fWt14SI/jH7+48R0X9IRH/ezvc3iejnbh9LRJ8A8B1oz/G/tPe/i4g+Z5/9cSL6ldddy0GeQ0Tk8HNHPwA+DeBX2d8fBfD3APzf7P9fBeArAH49dNP81fb/1+z9Pw/gzwB4GcAA4J+0138xgLcB/DIAAaoEPw1g2X3n3wfwMQCvAPgbAP4te+9boS5qf43/BhTqAID/JYC/0b33jQAeAlgCOAHwGQD/PBQe+8VQ2OIbr7n3P+bfu+N7nnbvfwXA77S/fxeAH+vu578BIHYNJwAeA/g6O/ZNAL/gmuv57QD+evf/f9qu/39o9/d/B/DXuvcFwA/Ydx7tON/X+HXsusddx9h9/TSAnw/gyP7/Pc84Jv8dgP+LXeuvAPCk/66b7rW7n3+sezZfAfBNNo7/CYA/fcOx/XP8OpsHH+nu8ed+0Gvt/x9+Dpbz3ct/TkRPoBP6bQD/B3v9fwHg+0Xk+0WkiMgPAPghAL+eiN4E8OsA/C4ReVdEJhH5q/a5TwD4gyLyN0Uki8gfB7AB8M3dd/4HIvIZEXkHwL8N4Lc847V+H4BfREQft/9/B4D/TEQ2AH4DFBb4oyKSROS/B/CfAvhNzz8k19/7jmO/HcC/393Pv7v1fgHwjxPRkYi8JSL/4Bmv4TsA/BER+Tt2f78Pall/TXfMvysi74jI5fPc3FPkj4rIT9g5/yyaJ3XTfPhqAL8UwL8mIhsR+WsA/sv3eR3fJyJ/SxQv/0+663iaZOgG8Y1ENIjIp0Xkp9/ntRwEB1jjg5B/VkTuQa3Wr0dzRz8O4DeZC/uQiB5Cg1ZvQq3Ed0Tk3R3n+ziA37v1uY8B+Eh3zGe6v39m671rRUSeQC3232wv/RbowvXv/WVb3/sdAD78LOfecQ/X3fu2fARX78ev9xzA/xxqXb9lbvrXP+M1fGTrXGdQa/KrumM+s/2hW5AvdH9fAPDg201j8hEA78ocL/8ZvD+57jpuFBH5KQC/B+olvE1Ef7qHgw7y3uWgnD8gMcv3jwH49+ylzwD4f4rIS93PiYh8j733ChG9tONUnwHwb2997lhE/lR3zMe6v78awOf9Mp7hUv8UgN9CRL8cwAoKI/j3/tWt7z0VkX/xGc656x6uu/dteWvH/VQRkb8oIr8aqsR+DMAfesZr+DxUIQIADJP/EIDP9ae/4fO73jsHcNz9/3k2rpvG5C0AL9s1unz17tNcvQ4iei8bqMuV+xSRPyki3wIdPwHwB97H+Q9iclDOH6z8+wB+NRH9QgB/AsBvJKL/iQW9VkT0rUT0URF5Cxq8+38Q0ctENBDRr7Bz/CEAv4uIfhmpnBDRP0NE97rv+ZeI6KNE9AqAfwWKXQPAFwF8iIge3HCN3w9ddL8fwJ8RkWKv/78B/Hwi+q12PQMR/VIi+ob3MA7X3vuOY/8sgP+V3c/LAL7b3yDlUn+bKa0NlK5Ydpxjl/wpAP88Ef0iIloC+HcA/E0R+fQzfv5L9l09H/iHAfwKIvpqG+PnYebcNB9+Bgpx/JtEtCCibwHwG284148A+AV2byuolfte5Yvo7pGIvo6I/mkbszWASzz7mB/kBjko5w9QRORLAP5jAP+6iHwGwLcB+JehC/0zAP53aM/otwKYoNbg21BXEiLyQwD+BQD/AYB3AfwUNADUy58E8JcAfBIagPq37LM/BlVKnzTX+Yo7avjrfwbgV9l5/PUnAH4NFPL4PNQt/gNQ/PF5x+Fp997LH4KyXH4EwN+xa3NhAP8bu553APyTAJ7JkheRvwzgX4Pi5m8B+LlocM6zfP4Ciuf/DRvLbzac+M8A+LsA/jZ0Q3vW8z1tTP45aBD4HWjc4j++4Vw/Ad1c/zKUafLXrzv2GeQ/guLLD0kZQ0sA3wMNpn4BwOv4R4ceulchkReFdnqQfQhp4svvNOVzkIMc5B8SOVjOBznIQQ7yAspBOR/kIAc5yAsoB1jjIAc5yEFeQDlYzgc5yEEO8gLKQTkf5CAHOcgLKAflfJCDHOQgL6AclPNBDnKQg7yAclDOBznIQQ7yAspBOR/kIAc5yAsoB+V8kIMc5CAvoByU80EOcpCDvIByUM4HOchBDvICykE5H+QgBznICygH5XyQgxzkIC+gvC/lTES/1rrt/hQRfffTP3GQ55HD+O5PDmO7PzmM7e3Iey58REQBwE9AuwJ/FsAPAvgtIvKjt3d5/+jKYXz3J4ex3Z8cxvb25P1Yzt8E4KdE5JMiMgL409DODQe5HTmM7/7kMLb7k8PY3pLE9/HZr8K8G/FnoW1zrpVFOJKjeF9bQFaL/QbLXewfAdTC12MJBDDrT2CACMLUPkeAEAGkHyERoNjv6zwFIoidgvz6ih3v322vS/9/qlcEELUf9t+s10bAk7PPf1lEXrtpjDp5rvGNqxNZnrwCKgIqdaj0ukTaMHfDNH8O/qbUMRC7N1D7nBABjDreVASUdaxI/BgfC0ACoQSCMLrnAb3GbSH9bD0W0OdW0M7N9hOAEgEMAuaC9U+/tbexBXR8V8evgJOApgzkrPODYPMwoESGRIKEdu2cbe6VbpxpPo5CW19GPobtnssAYCg4WkxYcgKToAhBQGASBBQUEC7zgPU0QCYGJRs7mw/16dr5dW5gNjeEdGwlAIiCYUiY3n6IzcP19lXe2tguaCVHfGrzjADiuq5s8c90wWwtopvbNwn1a9TPbefyv69bGyLz5UNbx9fDtnXall64Rh7nL++cu+9HOT+TENEnAHwCAFbhHv7Hr/9mSEo6uV0BAoD3DaXOmJcCyQWYJv1tx1CMoNUSdHQEOTmCrJaQZVBFSIBEhgymFAXgMYMvE3gzAVNqg+4DxgzEAAmmcFIBpgQaJ2CcICkDJQNTguSs119kds0UdIHSEIEYQcMArJaQoyXK0QAZAv7yf/evv9/29deO7eLkZfzjv+73YPGkIF5kcCpVKVAuqkChyrKOy5T1Xvvx8M2oAIisYxnZlAmhLBh5GVAGPUdYZ8T
      "text/plain": [
       "<Figure size 432x288 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3, 4)\n",
    "ax = ax.flatten()\n",
    "fig.suptitle('Receptive fields for tuned units')\n",
    "for i in range(min(len(tuned_units), len(ax))):\n",
    "    ax[i].imshow(fields_plus[tuned_units[i]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f394577b0a0>"
      ]
     },
     "execution_count": 33,
     "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": [
    "centers_pos_np = np.array([c for c, _ in centers_pos])\n",
    "plt.scatter(centers_pos_np[:, 0], centers_pos_np[:, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing grating data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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"
  },
  "vscode": {
   "interpreter": {
    "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}