amgen/AnalyzeExp.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing experimental data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Some imports*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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": 2,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spiketimes_Ntsr1Cre_2020_0003_s02_e05.npy  res.pkl\n",
      "Spiketimes_Ntsr1Cre_2020_0003_s02_e10.npy  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": 21,
   "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": 34,
   "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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squares_stim['ename'], squares_stim.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['grat_x_position', 'grat_y_position', 'grat_contrast'])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squares_stim['ivs'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": 27,
     "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": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25.4911, 80.5089)"
      ]
     },
     "execution_count": 62,
     "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": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-27.5089, 27.5089)"
      ]
     },
     "execution_count": 63,
     "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": 30,
   "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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squares_spikes['units']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 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": [
    "sns.distplot(squares_spikes['spiketimes'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### *Load gratings data*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "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": 17,
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gratings_stim.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'oriTun12_opto'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gratings_stim['ename']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['grat_height', 'opto', 'optofreq'])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gratings_stim['ivs'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9.55191999, -3.82510311,  1.18950616,  1.        ,  3.35761066,\n",
       "        4.65747346,  0.05402836])"
      ]
     },
     "execution_count": 158,
     "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": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.512 , 1.7124]),\n",
       " array([1155.5455, 1155.7459]),\n",
       " 54.692066,\n",
       " 1152.682366)"
      ]
     },
     "execution_count": 46,
     "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": 58,
   "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": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 5760)"
      ]
     },
     "execution_count": 73,
     "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": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "firing_rates = np.array([get_firing_rates(i) for i in range(N_UNITS)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((40, 5760), (5760, 3))"
      ]
     },
     "execution_count": 79,
     "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": 88,
   "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": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(N_UNITS):\n",
    "    fields_plus[i] = scipy.ndimage.gaussian_filter(fields_plus[i], sigma=4)\n",
    "    fields_minus[i] = scipy.ndimage.gaussian_filter(fields_minus[i], sigma=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAESCAYAAADkJY5uAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOz9ebBtW3bWB/7GnHOtvfc559777n0vX7YvUz3qAIGQRFMYOUD0bYWhaIwxUVhFuTBBUwWyK8qmFAaEC4ehiqgwgqAzpnGY1kYgyRRCNLJRDymhNpWdXma+9jbnnL33WmvOUX+MMeda59zm3fPey5v5UntEnDjd3nvNNdecY47xjW+MIarKQQ5ykIMc5K0n4VM9gIMc5CAHOcjrk4MCP8hBDnKQt6gcFPhBDnKQg7xF5aDAD3KQgxzkLSoHBX6QgxzkIG9ROSjwgxzkIAd5i8pBgR/kkyYi8p+JyJ//JHzu20Xk20Xknoj811e5joh8m4j8rof877NEREUkvbkj/vQREfltIvItn+pxHOTNkc/YhfqZLiLyQeDtQAZOgX8E/B5VPf0Ujeergb+qqu+pf1PVP/ZJutzXAi8B1/WQyPBQEZHPAn4C6FR1AlDV/x747z+V4zrImycHC/ytLb9GVU+ALwN+FvCffmqH88TkfcAPHpT3QX6qy0GBfwaIqn4c+GZMkQMgIj9XRP6liNwWke93C7n+75aI/EUReV5EXhWRv7v4368Wke/z9/1LEfkZi/99UET+UxH5QX/fXxSRtYgcA/8QeJeInPrXu0Tkj4jIX/X3/iMR+T3Lcfu4/vf+8xeKyLeKyCsi8sMi8psedK8i8peA3wH8Ib/OL1le57Xu/dJnRRH5kyLykoh8APhVl/7/H4rIBxyq+QkR+W0PfwoPFxH5ahH5qIj8QRF5QUQ+JiK/c/H/lY/jwyLyCRH5b0Vks/j/H/L3PC8iv8thns/z//0qEfleEbkrIh8RkT+yuPS3+/fbPlc/z+/pn/t7/1sR+ZOXxvr3ROQP+M/vEpG/JSIv+v3/3tdz/wf5JIqqHr7egl/AB4Ff4j+/B/g3wJ/2398NvAz8SuyQ/hr//W3+/38A/E3gJtABv8j//rOBF4CvAiKmKD8IrBbXfD/wHHAL+BfAf+n/+2rgo5fG+EcwWAXgPwD+xeJ/XwzcBlbAMfAR4HdisN7PxiCSL3nIvf+let0HXOe17v3bgN/lP/9u4IcW9/NPAPUxHAN3gZ/mr33nw8bzGM/qq4EJ+Hqf718JnAM3/f9/Cvj7PoZrwP8E/HH/3y8HPg58CXAE/Hc+xs9bfPZP93v9GcAngF/v//usej+LsfyHwD/3n/8dn3fx328CW+Bd/nnfDfznQA98DvAB4Jd9qtf+4Wv+Oljgb235uyJyD9uELwD/hf/93we+SVW/SVWLqn4r8F3ArxSRdwK/Avjdqvqqqo6q+k/9ff8R8GdV9X9T1ayqfxnYAz93cc0/o6ofUdVXgD8K/JbHHOvfAb5MRN7nv/824G+r6h741cAHVfUvquqkqt8D/C3g37v6lDz83h/w2t8E/KnF/fzxS/8vwJeKyEZVP6aqP/A6xlNlBL7e5/ubsLjFTxMRweb996vqK6p6D/hjwG9ejPEvquoPqOo58P9cfqiqfpuq/hu/138N/HXgFz3mmP4ZpuB/of/+7wHfoarPA1+BHXpfr6qDqn4A+HOLcR3k00AOCvytLb9eVa9hVtgXAs/4398H/EaHEG6LyG3gf4dZkc8Br6jqqw/4vPcBf/DS+57DLLIqH1n8/KFL/3uouGL6B8wK4DczB9PeB3zVpev+NuAdj/PZD7iHh937ZXkX999PHe8Z8H/ArPSPicg/EJEvfNAFF7DRqYi89yHjelk9kOhyDpwAb8Ms6+9ejPcf+d8fNMblz4jIV4nIP3GY446P9xkeQ1RVgb/BfAj/Vi4+k3ddmsf/DAucH+TTRA4slM8AUdV/6tjwnwR+PbbJ/ztV/Y8uv9Yt8Fsi8pSq3r70748Af1RV/+gjLvfc4uf3As/XYTzGUP868F+IyLcDGwyyqNf9p6r6NY/xGa8lD733B8jHuP9+mqjqNwPf7Hj0f4lZoL+QS6IWSH698hIGW3yJqv7kQ8b4nsXvz136/18D/gzwK1R1JyJ/ilmBP+4z+RYR+QYMOvsN/vePAD+hqp//WHdxkE+JHCzwzxz5U8DXiMiXAX8V+DUi8ss8ULf2QNp7VPVjWMDx/ysiN0WkE5F/xz/jzwG/2606EZFjD5JdW1zn/yIi7xGRW5hF9jf9758AnhaRG48Y4zdhlt3XA39TVYv//X8GvkBEfruPpxORrxCRL3od8/DQe3/Aa/8H4Pf6/dwEvq7+Q4xr/mvFArR7DPLIr2M8jxSfgz8H/Dci8qxf+90i8ssWY/ydIvJFInKEYdJLuYZ5VDsR+UrMiq7yIgYDfc4jrv+9/ro/D3zz4lD/V8BdEfnDIrLxufxSEfmKN3TDB3lT5aDAP0NEVV8E/grw/1DVjwC/DlOwL2LW1P+N+Xn/dgyT/SEMO/99/hnfheGxfwZ4FfgxLOi1lL8GfAsW0PoAZpmiqj+EWXMfcJf7PmjF8e6/DfwS/5z693vAL8VgleexoN2fwAKcV52H17r3pfw5jL3z/cD3+NiqBOAP+nhewXDl//iq43lM+cPYXP+vInIX+F+Anwagqv8Q+H9j3sqPAd/h79n79/8Y+HqPhfznmMLH33uOxSn+hT+TZSxjKX+d+59JBn4Nxmz6CcxT+PPAow7ogzxhqdHngxzkNUUseeh3qer/8qkey09Vca/k/RgzaHqt1x/kM1sOFvhBDvJpLiLyG0Skd5jnTwD/00F5HwQOCvwgB3kryP8Jg4N+HMPh/8+f2uEc5NNFDhDKQQ5ykIO8ReVggR/kIAc5yFtUDgr8IAc5yEHeonJQ4Ac5yEEO8haVgwI/yEEOcpC3qBwU+EEOcpCDvEXloMAPcpCDHOQtKgcFfpCDHOQgb1E5KPCDHOQgB3mLykGBH+QgBznIW1QOCvwgBznIQd6iclDgBznIQQ7yFpWDAj/IQQ5ykLeoHBT4QQ5ykIO8ReWgwA9ykIMc5C0qb0iBi8gvF5EfFpEfE5Gve+13fObLYU4eLId5uV8Oc3K/HObkavK664GLSAR+BPga4KPAdwK/RVV/8M0b3ltLDnPyYDnMy/1ymJP75TAnV5c3YoF/JfBjqvoBVR2Av4E1k/2pLIc5ebAc5uV+OczJ/XKYkyvKG1Hg78Y6flf5qP/tp7Ic5uTBcpiX++UwJ/fLYU6uKOkNvFce8Lf78BgR+VrgawHWR/Llm/c+zdn5mngOaVeQsYAqBEFjQINAABWQS5+mIiCgATTadwDJEAclDAWmCUqxkYQAMaAxotE/NwgaocTFZ4h/BUWCIqKUKSCDkPYQ9gWZMoigXWRaCWUF2qkNcgqEAVbHtyjDDhH5dar6tseZk5D6L99ce9ZfpSDS3iVFkaIXP6VCXj4X7X+qF+dLdfG1eDxa3ysQ7LuK/a3OTRtDG7C/rV6nXHzishiTCmgUm98EpYP07C2mF17ZAb/r4jsfPCeR+OVHXL88bZ9RsuaYHWePnBN4wFo5eXZeF/XVQdAAJcp9+yJkkOxro+i8TpbQqT9vDb6/RC4srbZvlsuiLtO6DHX+HcHXxfx3ASi+pvO8rjUImoTcCempW0ynd7jSnMT+y4+O3+YLdB4Xxdal5Mv7QOcX3afB/GYetSfE7rGkhe64dK/Lj5t1jEIRwgRhhDD6HFx+luLPIYjvI3u225c++tJCpzR5Iwr8o8Bzi9/fAzx/+UWq+o3ANwK8/Ytv6Xv+5Ndy+/1Pc+sH4fpP7OhePoNxghQpxyvycUdeRQDCWJDJblAFiEKJgbwOjMeBvDLN0p8V1i/sSS/chVfvoNsdlIKsV8jxMXpyRDlZkTcdeR2ZTiL765H9DSGvfZLFlE1Z2bW6e8LRJ5TrHxpYPX8XuXsGIVCevs75cyfce09k97SgCdIZrF9Sxh/7AB/9gW/h1Zd/9EOPOycnt57TL/tFv5e4K0hWO2iwBxx3E2E/IZMdcpLVDqd
      "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": 129,
   "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": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1038211/2956312832.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": 141,
   "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": 141,
     "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": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "tuned_units = [i for i in range(N_UNITS) if centers_pos[i][1] > tuning_threshold]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "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": "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
}