amgen/PopulationSampling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Decoding population activity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "**Imports**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from sym_model import Population, sigmoid\n",
    "\n",
    "import sys\n",
    "import seaborn as sns\n",
    "import umap\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "sys.path.insert(0, './model')\n",
    "\n",
    "from model import decoding\n",
    "from model import persistence\n",
    "from utils import get_orientation_phase_grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Getting the data\n",
    "\n",
    "**Sampling the activity using the model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "limit = 500\n",
    "# n_theta, n_phi = 18, 24\n",
    "n_theta, n_phi = 36, 72\n",
    "# n_theta, n_phi = 9, 12\n",
    "step_phi, step_theta = 360 // n_phi, 180 // n_theta\n",
    "N = 40 # number of cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0.] [3.05432619 6.19591884] (2592, 2)\n"
     ]
    }
   ],
   "source": [
    "grid = get_orientation_phase_grid(step_phi, step_theta)\n",
    "grid = grid.reshape((-1, 2))\n",
    "print(grid[0], grid[-1], grid.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "population = Population.random(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "phi_deg, theta_deg = grid[:, 1] * 180 / np.pi, grid[:, 0] * 180 / np.pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# res = population.sample_responses(limit, custom_grid=grid, use_sigmoid=False)\n",
    "# res, phi_deg, theta_deg = res[:, :, 0], res[:, 0, 1], res[:, 0, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = np.abs(population.response_func(grid[:, 1], grid[:, 0]).swapaxes(0, 1)) * 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "phi_reorder = sorted(list(range(len(phi_deg[:limit]))), key=lambda x: phi_deg[x])\n",
    "theta_reorder = sorted(list(range(len(theta_deg[:limit]))), key=lambda x: theta_deg[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle \\left( 2592, \\  40\\right)$"
      ],
      "text/plain": [
       "(2592, 40)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "res_reshaped = res.reshape((n_phi, n_theta, -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "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(res.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Plotting joint tunings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "phase_linspace = np.linspace(0, 360, n_phi)\n",
    "orientation_linspace = np.linspace(0, 180, n_theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3, 2)\n",
    "ax = ax.flatten()\n",
    "# set title\n",
    "fig.suptitle('Marginal Tunings - By Phase')\n",
    "for i in range(6):\n",
    "    ax[i].plot(phase_linspace, res_reshaped.mean(1)[:, i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3, 2)\n",
    "ax = ax.flatten()\n",
    "# set title\n",
    "fig.suptitle('Marginal Tunings - By Orientation')\n",
    "for i in range(6):\n",
    "    ax[i].plot(orientation_linspace, res_reshaped.mean(0)[:, i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3, 3)\n",
    "ax = ax.flatten()\n",
    "# set title\n",
    "fig.suptitle('Joint Tunings')\n",
    "for i in range(9):\n",
    "    ax[i].imshow(res_reshaped[:, :, i], cmap='viridis', extent=[0, 180, 0, 360])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fefe9397f40>"
      ]
     },
     "execution_count": 16,
     "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(res_reshaped[:, :, 0], cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "res_df = pd.DataFrame(res, columns=['x' + str(i) for i in range(N)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "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": [
    "sns.heatmap(res_df.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Let's take 6 components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.152136</td>\n",
       "      <td>6.882894</td>\n",
       "      <td>-1.545937</td>\n",
       "      <td>-0.027478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.081195</td>\n",
       "      <td>6.691970</td>\n",
       "      <td>-1.446043</td>\n",
       "      <td>-0.080447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.996579</td>\n",
       "      <td>6.447147</td>\n",
       "      <td>-1.338007</td>\n",
       "      <td>-0.133982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.937118</td>\n",
       "      <td>6.268196</td>\n",
       "      <td>-1.259540</td>\n",
       "      <td>-0.170500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.938863</td>\n",
       "      <td>6.362411</td>\n",
       "      <td>-1.261050</td>\n",
       "      <td>-0.188440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PC1       PC2       PC3       PC4\n",
       "0  1.152136  6.882894 -1.545937 -0.027478\n",
       "1  1.081195  6.691970 -1.446043 -0.080447\n",
       "2  0.996579  6.447147 -1.338007 -0.133982\n",
       "3  0.937118  6.268196 -1.259540 -0.170500\n",
       "4  0.938863  6.362411 -1.261050 -0.188440"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N_COMP = 4\n",
    "pca = PCA(n_components=N_COMP)\n",
    "pca.fit(res_df)\n",
    "data_pca = pca.transform(res_df)\n",
    "data_pca = pd.DataFrame(data_pca, columns=[f'PC{i+1}' for i in range(N_COMP)])\n",
    "data_pca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle \\left( 2592, \\  4\\right)$"
      ],
      "text/plain": [
       "(2592, 4)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pca.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## UMAP decoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# reducer = umap.UMAP()\n",
    "# embedding = reducer.fit_transform(data_pca.to_numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# %matplotlib notebook\n",
    "# plt.scatter(\n",
    "#     embedding[:, 0],\n",
    "#     embedding[:, 1])\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# embedding.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Cohomological decoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "step = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decoding... done\n"
     ]
    }
   ],
   "source": [
    "param_1 = decoding.cohomological_parameterization(data_pca[::step]).to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fefd8cc6fe0>"
      ]
     },
     "execution_count": 43,
     "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": [
    "%matplotlib inline\n",
    "plt.title(\"Parameter 1 of CP vs. phase\")\n",
    "plt.scatter(phi_deg[::step], param_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Orientation is decoded:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fefd8cc6230>"
      ]
     },
     "execution_count": 42,
     "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.title(\"Parameter 1 of CP vs. orientation\")\n",
    "plt.scatter(theta_deg[::step], param_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "data_without_features = decoding.remove_feature(data_pca[::step].to_numpy(), pd.DataFrame(param_1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "is_executing": true,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decoding... done\n"
     ]
    }
   ],
   "source": [
    "param_2 = decoding.cohomological_parameterization(\n",
    "    pd.DataFrame(data_without_features)).to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fefd8be2d40>"
      ]
     },
     "execution_count": 48,
     "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.title(\"Parameter 2 of CP vs. orientation\")\n",
    "plt.scatter(theta_deg[::step], param_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fefea7e8640>"
      ]
     },
     "execution_count": 51,
     "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.title(\"Parameter 2 of CP vs. phase\")\n",
    "plt.scatter(phi_deg[::step], param_2)"
   ]
  }
 ],
 "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
}