amgen/PopulationSampling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Decoding population activity\n",
    "\n",
    "_In this notebook we sample population activity using the analytical model, then analyze the topology and successfully decode orientation using cohomological parametrization_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "**Imports**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\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",
    "sys.path.insert(0, './numerical')\n",
    "\n",
    "from sym_model import Population, sigmoid\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": 11,
   "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": 12,
   "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": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "population = Population.random(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "phi_deg, theta_deg = grid[:, 1] * 180 / np.pi, grid[:, 0] * 180 / np.pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = np.abs(population.response_func(grid[:, 1], grid[:, 0]).swapaxes(0, 1)) * 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 18,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAFoAAAAVCAYAAADGpvm7AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAFAUlEQVRYCe2Z4XUUNxCAz/dSwIVUwLkDCBVgdwBOBeAOyE/7Xx50AOkg0AFQAcEd4A5irgPn+2SNLO3qzB4+Hkde5j3tjEYjzWhmdqS9m11eXs7qdnJysqz7/9Otf6b4o+fD+ayC09PTZ3TvVazJJHNH8+AtaMvJi/x3BJfZl2VHe0ZIYOAR6AH498TID/o6Kni/Ql/Yh3+WRRKi/xliQQu+tHCfsdUVefWk/7Lq34F+0pGZpLdaZ6sk9pg4x+Dj4cLwnmfeP+B92nN457UcfZN2BX4lPzmajk55D74vM4C+m30JPqx4KnGRQ/jvKv4naJ3mWip9Q/sDmeJkaMc+0lzzBXgGdkPvaQYkGQuerNc1vgVgg/s5Az+u16ev/e7L/c3AsSf9MXS2sg/hr+YKAzqvzrLEzPwmokwyu3Xe6xDKWKN+pu3R9pWjFSdnmT/Bd+AnJ8uD9g34m1br156pehHdLmCTiTQC+E9hWg6TkxWAdo/2a/sdEuSl7A9HHzEhpXgavn4cQH5izKjVYCZ/Tf21PDVRz4vq7INKz7b1ZjVfRtjgG6bzhkniZLM7SqP9gA8Qtf2Jn32qbxdzHus2r7AOPUemp9TxYQDkdUFleeCiI2CtEzwDhK3pvVpuo+dv2NpLOhcxAXr2R/I4PgTHjn7iYf0ttbaWQmFTn6oxoz5jvIku/fRqMfQLbUmzliUZsAcDrFTHxTUoLzhnhtxGep2zDUCvJaNXArQpEuUmVZ5RQ9C3h3MeZpGFfxKgUCfrkLiJxDwN+YvxFzTHbB+h6yhby5IzwTWkwMFYu5kb9NbrfDXN+tplMkR2DtcKJ66GA1W/Z7++XepoB3uvQzW/IT0E32BQOdAcpe+pW4yA1mCjWWfIkyxbnI+cTo556zbptK5eB7YEXuXWlYypKuLNrOX1bXK0kYqN1gIjGkN0mjV73as9nKPjvLynLAar5y7tMfQzmieyYx4mQtfRyG2q92q1iU/Wt+TVCdGbeVMyRrbHWVPPd08La/QkyMZ4NSt36pgI7y20Y809PMbBi6CR0dnDq1u6AsEfORp5ndDVG2veBrO+gfYGNdJdr6vdNFllL9V48HprpETW0UYqBKu51yQKvJl4Ny6ZDB1Z6uLW+V7EU6SRPbterUvdg/sOOYNQgP6X9BbZWxDuwy9iS1MN2uTbKN+32DPHUpj2Da4h7RNG71Khby90tI7qTYadaq8KR5/m8HRC1LRX2RBYDRzQK8qRcY4fLXehk1PBGqJc8zbAn6KXaclGM7IJUhqY8GCe9hUbYwp8f1Iw+CW56Ov0ePtCVKztfrD1bDAI5zrabHtAGwETDYCLq3BYww7gxYHoJ7WtlATo+LqqDXW9Yea7vgdRyXroqXpnyBqoz86nNcGCfxtwXVsB1k8JBX5ES1+HYGWOaA+LYEukIOzxk56Z85oJ/jjSALx0NWmY151mY8jqnLjyGUUdOvoMRy4yIjah7iajNtGrOVlecvQDlsxNgLVMKPfiWybo0A/w47cZ7XYPK5qHn0lavhegG8i2HcePSjrUm0DJqkb6B+hgu2VpVOe/p+nYZMDegvfn2RAjVF7772ncLXR7jphluwS+4ekNTo7GQA+1ct/dJUun2IL9vs69O+yU6d9EBpvMZn2aLgyR0Srz0BoeePJ/BHjKhuJg3hV79WWpEuUfFq3LUfBE3TWjd8V5k+zAf964/JmifMD8C7QsJSruRSwGAAAAAElFTkSuQmCC",
      "text/latex": [
       "$\\displaystyle \\left( 2592, \\  40\\right)$"
      ],
      "text/plain": [
       "(2592, 40)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "res_reshaped = res.reshape((n_phi, n_theta, -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 20,
     "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": 21,
   "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": 22,
   "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": 23,
   "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": 24,
   "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",
    "    # 100 is for scaling\n",
    "    ax[i].imshow(res_reshaped[:, :, i], cmap='viridis', extent=[0, 100, 0, 100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f6a949178e0>"
      ]
     },
     "execution_count": 25,
     "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": 26,
   "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": 27,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 28,
     "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": 29,
   "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>-4.559656</td>\n",
       "      <td>6.088501e-15</td>\n",
       "      <td>5.236211</td>\n",
       "      <td>1.127316e-13</td>\n",
       "      <td>-3.530877</td>\n",
       "      <td>-4.189611e-13</td>\n",
       "      <td>2.688094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-4.542878</td>\n",
       "      <td>-5.230832e-02</td>\n",
       "      <td>5.215859</td>\n",
       "      <td>-8.860678e-02</td>\n",
       "      <td>-3.517901</td>\n",
       "      <td>1.007660e-01</td>\n",
       "      <td>2.677216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-4.492672</td>\n",
       "      <td>-1.042152e-01</td>\n",
       "      <td>5.154961</td>\n",
       "      <td>-1.765388e-01</td>\n",
       "      <td>-3.479075</td>\n",
       "      <td>2.007685e-01</td>\n",
       "      <td>2.644661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-4.409430</td>\n",
       "      <td>-1.552890e-01</td>\n",
       "      <td>5.053951</td>\n",
       "      <td>-2.631118e-01</td>\n",
       "      <td>-3.414666</td>\n",
       "      <td>2.992772e-01</td>\n",
       "      <td>2.590699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-4.293779</td>\n",
       "      <td>-2.051631e-01</td>\n",
       "      <td>4.913619</td>\n",
       "      <td>-3.476756e-01</td>\n",
       "      <td>-3.325180</td>\n",
       "      <td>3.955234e-01</td>\n",
       "      <td>2.515725</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PC1           PC2       PC3           PC4       PC5           PC6  \\\n",
       "0 -4.559656  6.088501e-15  5.236211  1.127316e-13 -3.530877 -4.189611e-13   \n",
       "1 -4.542878 -5.230832e-02  5.215859 -8.860678e-02 -3.517901  1.007660e-01   \n",
       "2 -4.492672 -1.042152e-01  5.154961 -1.765388e-01 -3.479075  2.007685e-01   \n",
       "3 -4.409430 -1.552890e-01  5.053951 -2.631118e-01 -3.414666  2.992772e-01   \n",
       "4 -4.293779 -2.051631e-01  4.913619 -3.476756e-01 -3.325180  3.955234e-01   \n",
       "\n",
       "        PC7  \n",
       "0  2.688094  \n",
       "1  2.677216  \n",
       "2  2.644661  \n",
       "3  2.590699  \n",
       "4  2.515725  "
      ]
     },
     "execution_count": 29,
     "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": 30,
   "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": 30,
     "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": 31,
   "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": 32,
   "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": 33,
   "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": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "step = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "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_1 = decoding.cohomological_parameterization(data_pca[::step]).to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f6a9170e170>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%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": 37,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f6a917a49d0>"
      ]
     },
     "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.title(\"Parameter 1 of CP vs. orientation\")\n",
    "plt.scatter(theta_deg[::step], param_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": 39,
   "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... "
     ]
    }
   ],
   "source": [
    "param_2 = decoding.cohomological_parameterization(\n",
    "    pd.DataFrame(data_without_features)).to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f9a93ba2d70>"
      ]
     },
     "execution_count": 31,
     "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": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f9a93b5f790>"
      ]
     },
     "execution_count": 32,
     "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
}