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": "iVBORw0KGgoAAAANSUhEUgAAATQAAAEVCAYAAABucRkMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAABAd0lEQVR4nO29e6xs2V3n9/mttR9VdV73nntvd7vbPe62pwmYyWQgxAahISgIcCJFRopgnAdxRk6cKKAZJDSizWgUEmFi0ISIKMwkHbDiScgYx5DBEZYQdoIICg87BIIfeNx+drtv9+3b955XVe3HWuuXP9auOnXuPee++tSpunXWR9qndu3atR/nV/WttX7r9/stUVUSiURiFTCLvoBEIpE4LZKgJRKJlSEJWiKRWBmSoCUSiZUhCVoikVgZkqAlEomVIQla4kRE5EBE3rzo6zgJEfnvROQfLPo6EsuDpDi084eI/B7wP6vqr5ziMRV4RlWfP+a1nwZ+unuaATkw7p5/TVW/9bSuI3G+SS20xNxR1Z9T1XVVXQf+E+APJ8+TmCVOkyRo5xwR+Y9E5HkRuSEiHxORx2deUxH5q936/ygivywivy0i+yLyxyLylu613+/e8uddN/Vv3cf5p+eYOc/PduvfKyIvishPisg1EbkqIn/7Afe9JCL/u4jsicinRORnReQPutdERP7r7n27IvL/ichfe6B/aGKhJEE7x4jIvwb8l8CPAG8AvgZ8+A5v+beB/xy4CDwPvB9AVb+ne/1f6lpdv36Kl/kYsAU8AbwH+GURufgA+/4yMOz2eXe3TPgB4HuAbwIuAH8LeO0U7yFxRiRBO9/8u8AHVfVPVbUG3gd8l4g8dcL+v6mqf6KqDvg14G+cwTW2wH+hqq2qfhw4AP6F+9lXRCzwbwH/maqOVPVzwIdued8G8M1Ev/LnVfXqvG4oMT+SoJ1vHie2ygBQ1QNiy+SJE/Z/eWZ9BKzP79KmvNYJ6L2c96R9rxAHI16YeW26rqr/B/DfEltxr4jIcyKyeRoXnzhbkqCdb14C3jR5IiJrwCXgG2d4DSNgMPP8sTmc41XAAW+c2fbk7A6q+t+o6r8MfCux6/n35nAdiTmTBO18878Af1tE/oaIlMDPAX+sql99gGO9AjxIzNqfAf+OiFgReQfwrz7AMe6IqnrgN4GfEZGBiHwz8O9PXheRf0VE3i4iOdHPVgH+tK8jMX+SoJ1fVFU/CfwD4DeAq8BbgHc94PF+BviQiOyIyI/cx/v+LvBvAjtEn94/e8Dz340fJw4YvAz8T8A/BerutU3gfwBuErvgrwH/cE7XkZgjKbD2HCIif0p0nv+zRV/LohCRnwceU9V333XnxENDaqGdM0TkW4FvAf7fRV/LWSIi3ywif72LOXsbMazjf1v0dSVOl2zRF5A4O7pWyb8H/JSqfu1u+68YG8Ru5uPANeC/An5roVeUOHVSlzORSKwMqcuZSCRWhiRoiURiZUiClkgkVoYkaIlEYmVIgpZIJFaGJGiJRGJlSIKWSCRWhiRoiURiZUiClkgkVoYkaIlEYmVIgpZIJFaGJGiJRGJlSIKWSCRWhiRoiURiZViKemiFlNpjbWHn3+fmdVW9srALWFGSXVeXE20rJz65D/TY1VlOsu1SCFqPNd4u37ew839CP3reih2eCcmuq8vUttKJlhx29sTcImRyjx1BDTOrevv2mdqNJ9l2KQQtkUg8pExERg8nyZrRpYjcQ0vtlArNJkFLJBLz5QyrYq+OoN3Lr8BJpCrkicRKsByCJoCxh8+P6TMf7nu0z/7A/fXZ89zaRE4kEg8lyyFoyFSYNGgUJQ1RvGZF7SQxm4jYreIGyEzL7fYJYSyE1DxLJFaFpRA0sQaztQneoz50j/7wURWMRaxFrAFrwZjDdRGwNoqXCJhO4I7rhk5ELQRQjee7dnb3mkgk5sdSCBrItMspRJeWhICKAdEjIygYc1TMTCdynahFQZOjYjbb0psKmoEQjrTgEkvIg9onTc94LlkKQdMyJ7zxEaT1SN1gmhataqSuCeMKrT2myJFeifR60CvRIo9LadHcEjKDZga1QsgENRLVcfb7oHGRoBiniFfEBXhpQTe+6tyrb/Q0/aKz52nv722Jh5/lEDQruAslpvLYrGuBAeo9Ym0chLQW8iKKWb8k9ApCPyOUFl9aQi6EXPC5oBbUgBpBJ6KmsbEnQREPxoNximnSL/n8ECTPop9SAxqO8Y2KgJij/lAjhy1nMyNmd2utzboTIAnaOWQpBM2Xwu5TJflIKQ4K8r2SbC/HiIBzMAIpCmRjDb+1htsqadcz2nVDOxBcX/A98AWEEkKuBAtqAaOoRDEjCOIlilkjmAZsvei7X13EGExZxsGYzidK0M4v6g/F7DjfaCdsk32OiNyssM209lR1Kp6owviMbzixcJZD0AoYvlHI94V2V+iVhp6B3AUYjkEEKQvCeo/2Yo96O6PaMjRbQrsObk3x6wHte2zPUZYtRebJM09u/dQv13pL6yx1m9HUOb6yyNje7fISD4q1yMUtpGlR5xHn0KaBBlQDiMEUOeQ5UuSQZUieQ2bRLIobNrbY1QpqDDptuTENtxHVKGAa3QiEAM7D9YXdeWJBLIWgkSvV5UDI4y+y8QZbZdiDDJt1gpNlaJnjBpZ6w9BcEOqLSrsVYMPR36i4sDbmYm/MdjnkQj5m3dbkxmNQAkIVcoauZKftc6MecGM8YG/UW+y9rzLWoIMe5BlSt2gjiHaj2K7zlVmLFDlSFFAWaJ5BkaO5jT7RzKDWRPeBnfGNTtCJoBF9oj4gLiA+BReeR5ZC0PLckT82oi57qFjEC7a25Hs5tsjjTkWOW8tpNqOYVZeU9pKjt13x2IU9ntq4wVP913i6vMYT+U0eswdsGM9ABIvQolSq7ISMl90GL7SX+HpziS8Nr/CFxd7+yuJ7luFfvYgde7Khw+7XmIMR7B8gziFFgdlYRzfW8Ot9/HqBW8twA4PrCa4UQgG+EEIeXQhqOfSLQjfIQ/SLOjAt2EajK+HTC7z5xEJYCkEzouS5p7GKZkyd+kziyiZI3D71j2VKnjv6WctGVrGdDbmUHXDJjNg2ni1TUEqGFYPXwFgbDI7WDhlpya4fsJlXi7rtlUcNuJ4g3qCtQfOuGzlrU2tjFzM3hMLgS8EXUcx8L/pXQwEhoxM17T4bzIxaC8ZBcGCyyWBQGuw5jyyFoPlgqKocqQ22js564xQJeiR+TJxiHNgGTAPUhmpccLPs81K2RS4xXq3KC/Z0j02pGZgxFqVRQ62WnbDJy26Ll9qLfL3e5uXxxuJufMVxA3jtXzQUu5byRkb/Rk7vWkauGltp/R56YYP60XWqyznjbaG+KDRbitv0yLqjv1az0a/ZKiu2yjFrWUNhHLkEAkIbLGOfc9CW7DY9dsc9DkYl7bCA/37R/4HEWbMUghZU8LXF1oK0XTiFI/pBwoygTeLHHNhGMI3B1RnDumC36PNqtk5mAh6h0pwNM6YnLVYCXg2V5uz4AdfdJlebLa5VG9ysB4u9+VVGNLau8knr6jDoeZLVoSKx1Z3Rhd5AKBSKQF46BmXLVllxqTfkQjFmI6vomZZcPF4NrUZBK80AIwFVwQWDd2mw5zyyHIJWWfpfKskPoNhVejuB3o0Gu1+jVRdXUdXY/YpeEeOSbG3Ihob2Rsl4reBLa5s8338U23cUhaPMHXnmsSZgRKNoBhNHONuMtsnw4zTKOU+ywuMer/FFgYrBOEM2ysluFjE8I8vwg5xmK6O6aKiuKPUVT7Fd8YaLezy9+RrPDK7x5vIaT+XXedSO2baWgRTkEu1Wa8t+aHjVCy+4Lb7aXuFL1SN86eAyX13s7ScWwFIImnHQu67kQygOAsWeww5bZFyjzsWcS+eQcUN2kFNYAc2wrSEbgxsKrmfxPUMocupCqTJFMz06IhZAvCCtYBqhbMBWKfVpXmj3Z+rOOsGtdfi6QOdlCCp4FVq1eAwNlhah1UCQgFchoLTqaVVpsQRii80Fgwvph+o8shSCZhvYfMFhq4AdOeyoQYYVjMYxbgnQusEcjDBA2XqycYH
      "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": "iVBORw0KGgoAAAANSUhEUgAAAWYAAAEFCAYAAADQRk3iAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAA5EklEQVR4nO2deZxU1bHHv8W+7zuoCKKIC6iAu6A+I5L4FBP3uEWjxhgTE/NMor68aDQaE5cYlYfGqIlxRRQVt6e4b6AisgjihiOIrLINwszU++PeCe146k53z53pbqa+fO7n05y6dc/pZU7fPlW/U6KqOI7jOMVDk0IPwHEcx/k6PjE7juMUGT4xO47jFBk+MTuO4xQZPjE7juMUGT4xO47jFBk+MTuO4+SJiNwmIl+IyCzDLiLyFxFZICIzRWT3bK7rE7PjOE7+3A6MSbAfBgyKjzOBm7O5aOoTs4icIiLvx8cpaV/fcRynWFDVF4AVCaccAdypEa8BnUSkd23XbZbWAAFEpAvwW2A4oMCbIjJZVVem2Y/jOE6J0Bf4NOP/ZXHb4iSnvCdmERkB/A0YCTQF3gDuAZ5W1RXxOU8T3ebfnXStTcs+NHXhG//ym2B7xytfNK/XqlkL07ahYmPSUIoWSbDd1ONA03bOF1NN24TuYb/PEz4Vlyy2r2dxcM9dTdvMNZ+YtlVfrQu2V2mV6TOl4z6m7ah1003b9h36BttfmHCk6bPk4sdN23Zz5pg2i/YtWpu2j4/uH2zvetfcnPtJIulvZ2PlJtP2+ZiBpm3Np82D7QNn2WNfesQg09b5/ueS/hxqJWm+qUmL7gPPIlqCqGaCqk7IobvQWGvtP++JWVWnichk4PdAa+CfwCbC3w6O4zjFQcIXTE3iSTiXibgmZcBWGf/vByyqzamua8yXAocQLV38kRy+HUTkTBGZLiLTb70z8YbacRwnPaqqsj/qzmTg5Dg7Yy/gS1VNXMaAuq8xdwHaAc2BVkTfDqMz7P2A50KOmd9Eufy0cBzHqQuasBSWKyJyN9Gc101EyohibM2jfnQ8MAUYCywA1gOnZXPduk7ME4BLgG2Bq4D/Bq4Qkc6x/VvAr+vYh+M4TnqkcycMgKoeX4tdgR/net26BP9OBipU9V8i0hR4BRgGXAZMi0+7tDoQmIQV4ANocd4VYcOV+5s+w7vYgYiXvkg3WNJQJP2kuGbjfNPWpkUr0/bHTWG/l4bZQahLav0R9k1uaGePfucvVpu2XbtsG2yfsfxD0+eqFl+atvUrN5i2WSvDQchXTn/d9HmgdWfTlg9rN5abtv+amm5fFvkGxxfO6GjaLjXaWzYLBwUBLprWw7TdlO2gLFK8Y64v6hL8uxO4M35cCeyZYb6tjuNyHMepH6oqCz2CWkk1j9lxHKfoqawo9AhqxSdmx3EaFWkG/+qLVCXZIjJMRF4Vkdnxhh3Hpnl9x3GcOtOw6XJ5kfYd83rgZFV9X0T6EEmyn1TVVSn34ziOkx8lcMcs+VbJNiTZx6rqrIxz3gG+p6rvJ12rWYu+OQ+ifJEtyW7dx87YsOTBDz9iZ7Qc+5/jTdtTS98Ntp/Wc89gO8DVJ9pPt/OfXg22b9+pn+nz3spPTVtDsval64Lt7fb7menTtIn9o63SuGPp2baT6bNk3SrTliR5tjIi8k2wtzTDR/UeYfo8tvQd02ZlSxzWazfT53/7hSXtAC07h1/bHV600276t+1p2pIyZdKmYuNndZJkf/Xe81m/rS0Hj6pTX/mSqiS7xqQ8EmgBfFDnUTqO46RFIwj+XUqUs7wBOK+6Md7W7h/AKWqstIvImcSbg0jTjjRp0raOQ3Ecx8mCEljKSFuSvU5EOgCPARfH+48GyZRk57OU4TiOkxcFDOplS12zMqol2XcBV4lIC2AS0cbQ99d1cI7jOGmjWpn1USjqEvw7GThSVY/KkGTfSBQQnJ1x6qmqOiPpWu3abGsOwpJXJ0mr8w0M5kOzJk2D7RV5qovaNG8ZbF+/6au8rvfZPva+tn1fCcdkx/Uebvo8t+o907ayfG2w/YvDtjN9ejy+wLStf/+RYHubQYebPmnTROzYT9LfTu92XYLti9bWukNBSZLPHugLhgwxfZL2s65r8G/DjEeznvRaDftOyQX/LEn2nSmMy3Ecp34ogaUMV/45jtO4yGGj/ELhE7PjOI2LEsjKSL1KNoCIdBCRz0Tkr/VxfcdxnLwpAUl23sG/xIuKXA90B1ao6rm1nZ9PulxSgc9nlsw0bVZgcPDg75k+7/52L9N2+Z9XBduvXvyC6bNT521M2+CW3YPt9y+eFmyvjaTIRT7v/JG99zBt3SW89/Mti17Oo6fSZni3cND1uYlnmT7tRl2Qcz9J7++0hPdq/cbwXsgHLDczXIuGOgf/Xr07++Df3scXJPiX9x2ziIyINypqJSJt442LdhaRPYCewFPpDdNxHCclSuCOOe0q2XOAZ4GTgINTGaHjOE6aNIKsjJqS7HOAKar6qSTkf4JLsh3HKQzaCLIyakqy9wb2F5Fz4vYWIrJWVX9V09El2Y7jFIQSyMpItUq2qp5YbRCRU4HhoUnZcRynYGzJSxmhKtkicpCqPpve8GyS9k9uN9KOfFvZF++994Dps9OOx5i2j74M71/7SGdb+n3Apb1NW4dz7wu2J8mkJy2ebtrS/iny0OI3TduXv/uPYPstv7Wv1zahive6jeGq1rt3syXeby2zJd4DOtqv+ydrlgTbrT2ha2P6srDcfY8jrzN9+rbvato+W7M82H5lrwNNn7mb7Hd/gIT3nx7SZWvTZ99WW5m2ksq82ZLvmGupko2q3g7cXoexOY7jpM+WfMfsOI5TkjSCjfIdx3FKixK4Y05dki0iW4vIUyIyV0TmiEj/tPtwHMfJG63K/igQqUuyReQ54HJVfVpE2gFVqro+ySefdLlvJxShfOzzt03b6j8fEWwfdukbps/sueGAHMALO/062D5m5Uumz7YJQagj24alvNcssiXeSaQtyR7Ta5hp29rIRZ9QSoGhlDixT1jGP+Gxc0yftrudnHM/Se/vNT3twOBa45bsksVTcx5DvuT72ayrJLt88p+y/ui3/s8LtghJ9q5AM1V9GkBV19Y2KTuO4zQoKd8xi8gYEZknIgtE5BvpwSLSUUQeEZF34nnytNqumbYkewCwSkQeJMpt/j/gV1rIGi2O4ziZpLjGHKcK3wgcApQB00RksqpmlmD5MTBHVQ8Xke7APBG5S1XDpV2o+xrzpfGAhgN/JJro9wcuAEYQTdSnGk/oTBGZLiLTq6rW1XEYjuM4WVJZkf1ROyOBBar6YTzR3gPUXC9VoL1E+1S0A1YAiRev68RcLcluTyTJLgPejgdZATwE7B5yVNUJqjpcVYf7PhmO4zQY6e4u1xf4NOP/ZXFbJn8FdgQWAe8CP1VNXidJtUo20YZGnePbdYCDiHaccxzHKQ5Usz4yf9nHx5k1rhYKDtYMLh4KzAD6AMOAv4pIh6QhpirJBkYRLWM8E9+2vwnckm8fSTy19F3TZlWuBntje0taDXbmBcABs/8QbNeEatxJfV32zOXB9muG55eV0aNtJ9O2ZN2qnK83vndYyguwy+x5OV8viZbNwpu5f1VR/LuDjb95VLD9+rF/T7WfpPSCa8tnm7blG1anOo582LpDT9P2yeqwRD4VclhjztxszaAMyNSq9yO6M87kNOBKjVLgFojIR8BgwEwFqy9Jtl1exHEcp5CkKzCZBgwSkW2Bz4DjgBNqnLOQaH/6F0WkJ7AD8GHSRV355zhO4yJF4YiqVojIucCTQFPgNlWdLSJnx/bxwGXA7SLyLtHSx4WquizpuqlPzCLyR+DbROvXTxMtdPt+y47jFAeV6WbvquoUYEqNtvEZjxcB38rlmqlOzCKyD7Avm5cyXiJad34uzX4cx3HypgT2yqhL8G8E8DeiPL6mRAvZZxOlzbUgumVvDtTLKv5pPfc0bUkSYKt6ddL+yWNWhitrgx3ks6pxAyz6Vs3A7mbaDv9BsP3L3xxg+vS/9i3TlhTg69QqnKa4aoOdV97/bTvAt2f3HYLtry+1fX7f25Y
      "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": 52,
   "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",
       "      <th>PC5</th>\n",
       "      <th>PC6</th>\n",
       "      <th>PC7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.152136</td>\n",
       "      <td>6.882893</td>\n",
       "      <td>-1.545938</td>\n",
       "      <td>-0.027471</td>\n",
       "      <td>-1.348491</td>\n",
       "      <td>-0.067572</td>\n",
       "      <td>3.253269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.081195</td>\n",
       "      <td>6.691970</td>\n",
       "      <td>-1.446044</td>\n",
       "      <td>-0.080450</td>\n",
       "      <td>-1.327843</td>\n",
       "      <td>-0.204249</td>\n",
       "      <td>3.526015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.996579</td>\n",
       "      <td>6.447147</td>\n",
       "      <td>-1.338009</td>\n",
       "      <td>-0.133996</td>\n",
       "      <td>-1.298363</td>\n",
       "      <td>-0.338896</td>\n",
       "      <td>3.770720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.937118</td>\n",
       "      <td>6.268197</td>\n",
       "      <td>-1.259541</td>\n",
       "      <td>-0.170523</td>\n",
       "      <td>-1.273480</td>\n",
       "      <td>-0.424933</td>\n",
       "      <td>3.869237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.938863</td>\n",
       "      <td>6.362412</td>\n",
       "      <td>-1.261052</td>\n",
       "      <td>-0.188472</td>\n",
       "      <td>-1.318064</td>\n",
       "      <td>-0.425958</td>\n",
       "      <td>3.715137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PC1       PC2       PC3       PC4       PC5       PC6       PC7\n",
       "0  1.152136  6.882893 -1.545938 -0.027471 -1.348491 -0.067572  3.253269\n",
       "1  1.081195  6.691970 -1.446044 -0.080450 -1.327843 -0.204249  3.526015\n",
       "2  0.996579  6.447147 -1.338009 -0.133996 -1.298363 -0.338896  3.770720\n",
       "3  0.937118  6.268197 -1.259541 -0.170523 -1.273480 -0.424933  3.869237\n",
       "4  0.938863  6.362412 -1.261052 -0.188472 -1.318064 -0.425958  3.715137"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N_COMP = 7\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": 53,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle \\left( 2592, \\  7\\right)$"
      ],
      "text/plain": [
       "(2592, 7)"
      ]
     },
     "execution_count": 53,
     "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": 54,
   "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": 55,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fefdadbe350>"
      ]
     },
     "execution_count": 55,
     "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": 56,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fefd8911d20>"
      ]
     },
     "execution_count": 56,
     "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": 57,
   "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": 58,
   "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": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fefd8815360>"
      ]
     },
     "execution_count": 59,
     "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": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fefd887ce50>"
      ]
     },
     "execution_count": 60,
     "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)"
   ]
  },
  {
   "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
}