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\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 = 1500\n",
    "# n_theta, n_phi = 18, 24\n",
    "n_theta, n_phi = 9, 12\n",
    "step_phi, step_theta = 360 // n_phi, 180 // n_theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0.] [160. 330.]\n"
     ]
    }
   ],
   "source": [
    "grid = get_orientation_phase_grid(step_phi, step_theta)\n",
    "grid = grid.reshape((-1, 2)) * 180 / np.pi\n",
    "print(grid[0], grid[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "res = Population.random(10).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": 5,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('res.pkl', 'wb') as f:\n",
    "    pickle.dump(res, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle \\left( 108, \\  10\\right)$"
      ],
      "text/plain": [
       "(108, 10)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "res_reshaped = res.reshape((n_phi, n_theta, -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3.10/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot((res).ravel().clip(-0.1, 0.1))"
   ]
  },
  {
   "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('Joint 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('Joint 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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f13229a8970>"
      ]
     },
     "execution_count": 12,
     "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": [
    "## Persistence for different dimensions"
   ]
  },
  {
   "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 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "persistence.persistence(res, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# persistence.persistence(res, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# persistence.persistence(res, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "res_df = pd.DataFrame(res[:limit], columns=['x' + str(i) for i in range(20)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(res_df.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Seems like we need about 5 components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.612520</td>\n",
       "      <td>-0.547999</td>\n",
       "      <td>-0.110458</td>\n",
       "      <td>-0.530344</td>\n",
       "      <td>0.888399</td>\n",
       "      <td>-0.105346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.007680</td>\n",
       "      <td>-0.069920</td>\n",
       "      <td>-0.222961</td>\n",
       "      <td>0.443805</td>\n",
       "      <td>-0.416626</td>\n",
       "      <td>-0.541131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.228229</td>\n",
       "      <td>0.523023</td>\n",
       "      <td>0.226259</td>\n",
       "      <td>-0.233582</td>\n",
       "      <td>-0.340139</td>\n",
       "      <td>-0.273262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.178205</td>\n",
       "      <td>0.297823</td>\n",
       "      <td>0.294549</td>\n",
       "      <td>-0.225669</td>\n",
       "      <td>-0.151865</td>\n",
       "      <td>0.018889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.113908</td>\n",
       "      <td>-0.155210</td>\n",
       "      <td>0.616534</td>\n",
       "      <td>0.036769</td>\n",
       "      <td>0.003957</td>\n",
       "      <td>-0.032182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PC1       PC2       PC3       PC4       PC5       PC6\n",
       "0 -1.612520 -0.547999 -0.110458 -0.530344  0.888399 -0.105346\n",
       "1 -1.007680 -0.069920 -0.222961  0.443805 -0.416626 -0.541131\n",
       "2 -0.228229  0.523023  0.226259 -0.233582 -0.340139 -0.273262\n",
       "3 -0.178205  0.297823  0.294549 -0.225669 -0.151865  0.018889\n",
       "4  0.113908 -0.155210  0.616534  0.036769  0.003957 -0.032182"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N_COMP = 6\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": 20,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle \\left( 432, \\  6\\right)$"
      ],
      "text/plain": [
       "(432, 6)"
      ]
     },
     "execution_count": 20,
     "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": 21,
   "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": 22,
   "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": 23,
   "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": 24,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbkAAAGwCAYAAAA0WxvgAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAABJAklEQVR4nO3deXhTVd4H8G+Wtmm60tANKFAWi+xQVlkrtizKNojrKzAuI268wDACMiPqOAMuo74OCqOIKDiKWkBEFAqloILQFWQpohRaoKUUuF1p0jb3/aM2NE1SuuTmZvl+nieP5uSk99f7hHx77j33HoUoiiKIiIjckFLuAoiIiKTCkCMiIrfFkCMiIrfFkCMiIrfFkCMiIrfFkCMiIrfFkCMiIrellrsARzMajbh48SICAgKgUCjkLoeIiJpJFEWUlpaiXbt2UCobH6t5XMhdvHgRUVFRcpdBREStlJeXhw4dOjTax+NCLiAgAEDtzgkMDJS5GiIiagpBEDB9+nRkZGQgODgYgiCYvs8b43EhV3eIMjAwkCFHROQi9Ho9DAYDdDodtm3bhhEjRjTplJPHhRwREbme0NBQJCcn49KlS+jYsWOT38fZlURE5JQEQUBiYqLpeWhoKHr37t2sn8GQIyIipyMIAhISEnD33Xdj/fr1Lf45DDkiInIqdQGXmpoKnU6HgQMHtvhnMeSIiMhpNAy45ORk9O3bt8U/jyFHREROwd4BBzDkiIjICVy/ft3uAQcw5IiIyAloNBrEx8fbNeAAQCGKomiXn+QiSkpKEBQUhOLiYl4MTkTkRERRREFBASIjIxvt15zvcY7kiIhIFoIgYMGCBaioqABQe0eqmwVcc/GOJ0RE5HD1J5lcvHgRmzZtkmQ7HMkREZFDNZxFuWzZMsm2xZGcxE5fKkXSyUsI8vXCXX3bIcjXS+6SiIhkI8VlAo1hyEnoowNn8cLXx1E3teeNXb/g0z8Nwy3hN18egojI3Tg64AAPDrny8nKoVCqLdpVKBY1GY9bPFqVSCV9fX6t9iyuq8I+vMlFTZaxtUChwpRx49btsrJ09GBUVFbA1sVWhUECr1ZqeN6fv9evXYTQabdbs5+fXor6VlZWoqamxS1+tVmtaIkOv16O6utoufX19fU2rBBsMBlRVVdmlr0ajMX1WmtO3qqoKBoPBZl8fHx+o1epm962uroZer7fZ19vbG15eXs3uW1NTg8rKSpt9vby84O3t3ey+RqMR169ft0tftVoNHx8fALUz8eomLLS2b3P+3dvrO+Jmfd3xO+K+++5DamoqQkJCsH37dnTt2tXqPrnZv/vG9qMF0cMUFxeLAGw+Jk2aZNZfq9Xa7DtmzBizvm3btrXZ1zuiu9hp8XZxwEu7RFEUxU6dOtns27NnT7Of27NnT5t9O3XqZNZ30KBBNvu2bdvWrO+YMWNs9tVqtWZ9J02a1Oh+q+/uu+9utG9ZWZmp7+zZsxvtW1hYaOr75JNPNto3JyfH1HfRokWN9j127Jip7/Llyxvte/jwYVPfV199tdG+e/fuNfVdtWpVo323b99u6vvhhx822vfzzz839f38888b7fvhhx+a+m7fvr3RvqtWrTL13bt3b6N9X331VVPfw4cPN9p3+fLlpr7Hjh1rtO+iRYtMfXNychrt++STT5r6FhYWNtp39uzZpr5lZWWN9r377rvNPsON9ZXqO2LQoEFmfd3xOyItLU3s3r27GB8f32jfpn5HFBcXizfDiScOFt3W7+adiIjcUGxsLE6cOIGgoCCHbdNjLwa/ePGi1YsI7Xko4oVtx7Ap9XztE4UC3j4arJszGKNvCXXLQxFN7cvDlbV4uLL5fXm4sparfEcIgoCHHnoIzz//PMaOHdto3/pu9u++pKQE7dq1a9LF4B4bco6444koitiSeQFJJy4hUOOFB4d1RN8OwZJuk4jIGdSfZNK1a1dkZ2eb/lBrreZ8j3vsxBNHUCgU+MPADvjDwA5yl0JE5DANZ1Fu3rzZbgHXXDwnR0REdiPHZQKNYcgREZFdOFvAAQw5IiKyk5dfftmpAg7gOTkiIrKTl19+Gfn5+Vi8eLFTBBzAkCMiolaoqKiAr68vFAoFNBoNPvnkE7lLMsPDlURE1CKCIGDMmDFYvHixzev05MaQIyKiZhMEAfHx8UhLS8O6deuQn58vd0lWMeSIiKhZ6gdc3SSTdu3ayV2WVQw5IiJqMmsB5yyTTKxhyBERUZO4WsABDDkiImqiffv2IT093WUCDuAlBERE1ERTp07Fhg0b0KdPH5cIOIAhR0REjRAEAXq9HuHh4QCABx98UOaKmoeHK4mIyKq6e1HGxcWhoKBA7nJahCFHREQW6t9subCwEEVFRXKX1CIMOSIiMmNtNYHevXvLXVaLMOSIiMjEGZfLaQ2GHBERAXC/gAMYckRE9LvS0lIUFRW5TcABvISAiIh+FxUVhZSUFBQXF6NPnz5yl2MXHMkREXkwQRCwc+dO0/OOHTu6TcABDDkiIo9Vdw7uzjvvxJYtW+QuRxIMOSIiD1R/kklwcDC6du0qd0mSYMgREXkYd5xFaQtDjojIg3hSwAEMOSIij1FWVuZRAQcw5IiIPIZWq8WgQYM8JuAAQCGKoih3EY5UUlKCoKAgFBcXIzAwUO5yiIgcymg04vz58+jYsaPcpbRYc77HOZIjInJjgiDgr3/9K6qqqgAASqXSpQOuuXjHEyIiN1V/ksmlS5fw/vvvy12Sw8k6kluxYgUGDx6MgIAAhIWFYdq0aTh16tRN37dv3z7ExsZCo9GgS5cuWLNmjQOqJSJHMhgMyM/PBwwVQGntgp35+fkwGAwyV+YaGs6ifOaZZ+QuSRayhty+ffvw1FNP4aeffkJSUhKqq6uRkJCA8vJym+/JycnBpEmTMGrUKGRmZuK5557DvHnzkJiY6MDKiUhKBoMBM2fOxMjYXshdFg38Kwa5fx+AkcMGY+bMmQy6m/C0ywQa41QTTy5fvoywsDDs27cPo0ePttpn8eLF2LZtG06ePGlqmzt3Lo4cOYKDBw9a9Nfr9dDr9abnJSUliIqK4sQTIieWn5+PkYP74syFInRpo8DH03wxa+t1nLkmokt0NH748UdERkbKXaZT8oSAc9mJJ8XFxQCAkJAQm30OHjyIhIQEs7bx48cjLS3NdGK1vhUrViAoKMj0iIqKsm/RRGR3kZGR2DuvB7q0UeDMNREjP6yoDbg2Cux97znPDDhDBWA0NtpFFEVMnz7drQOuuZwm5ERRxMKFCzFy5MhGl1kvKChAeHi4WVt4eDiqq6tRVFRk0X/p0qUoLi42PfLy8uxeOxHZX0edFh9P8zVr+3iaLzq2C5OpIpnkpQL/GQP8MxJ4owdw2PbkEYVCgeXLl6Njx44MuN85zezKp59+GkePHsUPP/xw074KhcLsed0R14btAODj4wMfHx/7FElEDpMbkYBZW5PM2mZ9ZcDeZT3gMRPgrwvAxhmAvvYoF8ouATsWAYHtgR6TrL5l7NixOH36NLy9vR1XpxNzipHcM888g23btmHv3r3o0KFDo30jIiJQUFBg1lZYWAi1Wg2dTidlmUTkIPn5+Yib927tIcoQFX74oxZd2mpw5mo14hIm1s669AQnv74RcPVlbjT9ryAImDx5Mo4fP25qY8DdIOtIThRFPPPMM9iyZQtSUlIQHR190/cMHz4cX3/9tVnbrl27MGjQIHh5eUlVKhE5kE6nM5222Lt7FzpGhGDvC6WIi4tD7969PecPWmO1jfba+Qf1J5mcOXMGP//8M5RKpxi7OA1ZZ1c++eST+O9//4uvvvoKMTExpvagoCD4+tYei1+6dCkuXLiAjz/+GEDtJQS9e/fG448/jsceewwHDx7E3Llz8emnn2LGjBk33SZv60XkGgwGA65cuWI2ySQ/Px86nc5zRipll4G3+gDV183b//A+hI4Jbj+L0haXmV25evVqFBcXY+zYsYiMjDQ9Nm3aZOqTn5+P3Nxc0/Po6Gjs2LEDKSkp6N+/P/7+97/j7bffblLAEZHr8Pb2tphFGRkZ6TkBBwD+ocC9G4Cg389CevkBo5/16IB
      "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(res_df, coeff=12).to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f51aaf69b40>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAXr0lEQVR4nO3df5RcZX3H8feHjUHFAGoipeQnHFCjSMU1iVqV1h+Q2JpY+4Mo9WeLVLFy1Jagqe2RWGvrD/SAptRSVEpirRJTjSK1jZ7WJrKxQIgIhABJAMkmgiAgmOXbP+6z6WWcH3d2Z3dmnnxe5+zZmec+88x379z9zMxz79xRRGBmZnk5pNsFmJlZ5znczcwy5HA3M8uQw93MLEMOdzOzDDnczcwy5HA3a0DSKkl7Jf2427V0mqS5kkLSlG7XYhPD4Z4BSbdJekjSzyTdLemfJD2p23U1M1HhIuliSTdKelTSm8YxzizgPcD8iPiVBn0Ol3SBpJ1p3W9P16en5X33uFg+HO75+O2IeBJwMvB8YGU7N1ahb7aHJk8K1wJvB34wzruYA+yLiD0N7n8q8G3gWcBpwOHAC4F9wIJS13E9LmZj1Tf/zFZNRNwBfAN4tqQnS/qapGFJ96TLM0f7Stoo6UOS/ht4EDhW0psl3SDpfkk7JL2t1P8USbsl/bmkPZLukrRM0hJJN0n6iaT3lfofImmFpFsk7ZP0L5KekhZ/N/2+N72yfUG6zVvS/d8j6UpJc0rjhaR3SLoZuLnB339RRHwb+HmrdSXpCEmfT+vndkkrU80vB64CfjXVdmmdm78BmA28JiJ+GBGPRsSeiDg/IjY0e1zq1LFC0r/WtH1S0qfS5Telx+J+SbdKen2rvy3dbqOkD0v6vqSfSvpqaf2Pen1657FX0vtLt10g6X8k3Zse5wvTE9roC4FPpG3gp5Kuk/TstOxQSR9NY94tabWkJ1Sp1zosIvzT5z/AbcDL0+VZwDbgfOCpwGuBJwLTgC8B60q32wjspHj1OQV4HPAq4DhAwEspQv/k1P8UYD/wgdT3j4Fh4PI0/rMoQvXY1P8cYBMwEzgU+HtgTVo2FwhgSqmeZcB24JmpnpXA90rLgyJ0nwI8ocU6+S/gTS36fB74aqp9LnAT8NbS37q7yW3XAp8by+NSp9+ctJ4PT9cHgLuARcBhwH3A09Oyo4FnVdwuNgJ3UDyhHAZ8GbisZv3/A/AE4CTgYeCZafnz0v1PSX1vAM5Jy04FtgBHpu3kmcDRadkFwPr0GE0D/g34cLf/Rw7Gn64X4J8OPIhFiPwMuBe4Hfh0vfADfg24p3R9I/DBFmOvA96VLp8CPAQMpOvTUkAsLPXfAixLl28AXlZadjTwi1Jg1Ib7N0bDNV0/JIXenHQ9gN+suE6ahnsK0Icp5tRH294GbCz9rc3C/SrgbzrxuJTqfUO6/ArglnT5sHT71za6bZP731iuEZgPPJL+9tH1P7O0/PvA6Q3GOge4Il3+TYonwkXAIaU+Ah4Ajiu1vQC4tdv/Iwfjj6dl8rEsIo6MiDkR8faIeEjSEyX9fZpyuI9iKuRISQOl2+0qDyJpsaRNaYrlXmAJML3UZV9EjKTLD6Xfd5eWPwSM7jScA1yR3trfSxH2I8BRDf6GOcAnS/1/QhEYxzSqdxymA1MpQnfU7TX31cw+iierVn7pcWnQ73Jgebr8unSdiHgA+APgLOAuSV+X9IyKNcJj19ftFO+4yo9n+UigB0mPnaQT0jTej9O289ejt4uI/wAuBC4C7laxE/twYAbFu8Qtpcfwm6ndJpnDPW/vAZ5O8cr6cOAlqV2lPgdOCyrpUIq37h8FjoqII4ENNf3bsQtYnMJt9OfxUcw/1zsd6S7gbTX9nxAR36tX7zjtpXgXMafUNptiGqOKfwdOlXRYh+r5EnBK2ifyGlK4A0TElRHxCoonkx9RTKVUNat0eTbF37y3wu0+k+7r+LTtvI/SdhARn4qI51FMxZ0A/Fka9yGKaaPRx++IKHYo2yRzuOdtGsU/271pR9pftug/lWJufBjYL2kx8Mpx3P9q4EOjO0UlzZC0NC0bBh4Fjq3pf56kZ6X+R0j6vXbuUNJUSY+nCKLHSXq86hwFlN59/Euqb1qq8d3AZRXv6gsUT0ZflvSMtCP2qZLeJ2lJOzWneoYpplH+iWIa44b09xwl6dXpSeRhimmekYYD/bIzJM2X9ETgg8C/lt55NTONYq7/Z+mdwp+MLpD0fEkLJT2OYhrm58BIRDxK8cTzCUlPS32PkXRqG/Vahzjc83YBxc6yvRQ7Nr/ZrHNE3A/8KUXo3UMxPbB+HPf/yXT7b0m6P9WwMN3Xg8CHgP9Ob+EXRcQVwEeAtWkq4HpgcZv3+S2KJ7QXAhenyy9p0PedFOG0g2LO+3Lgkip3EhEPAy+neHV7FUUQfp9i6mJzmzWPujyNeXmp7RCKd2B3UkxTvZTiUE8kvVjSz1qM+QXgUorpl8dTPL5VvJfi8b+fIrC/WFp2eGq7h2KqZx/Fuz2Acyl2im9Kj+G/U7x7tEmmCH9Zh1mOJG2kODrms92uxSafX7mbmWXI4W5mliFPy5iZZciv3M3MMtS1031Onz495s6d2627NzPrS1u2bNkbES0/GNa1cJ87dy5DQ0Pdunszs74k6fbWvTwtY2aWJYe7mVmGHO5mZhlyuJuZZcjhbmaWIX/zuVkfW7luK2s272IkggGJ5QtnsWrZid0uy3qAw92sT61ct5XLNu08cH0k4sB1B7x5WsasT63ZXP9LqRq128HF4W7Wp0YanBeqUbsdXBzuZn1qQPW//bBRux1cHO5mfWr5wllttdvBxTtUzfrU6E5THy1j9XTtfO6Dg4PhE4eZmbVH0paIGGzVz9MyZmYZcribmWXI4W5mliGHu5lZhhzuZmYZcribmWXI4W5mliGHu5lZhhzuZmYZcribmWXI4W5mliGHu5lZhhzuZmYZcribmWXI4W5mliGHu5lZhhzuZmYZcribmWXI4W5mliGHu5lZhqZ0uwCzfrNy3VbWbN7FSAQDEssXzmLVshMnfQyzZhzuZm1YuW4rl23aeeD6SMSB61XDuRNjmLXiaRmzNqzZvKut9okaw6yVSuEu6TRJN0raLmlFneVHSPo3SddK2ibpzZ0v1az7RiLaap+oMcxaaRnukgaAi4DFwHxguaT5Nd3eAfwwIk4CTgE+Jmlqh2s167oBqa32iRrDrJUqr9wXANsjYkdEPAKsBZbW9AlgmiQBTwJ+AuzvaKVmPWD5wllttU/UGGatVAn3Y4DyZODu1FZ2IfBM4E5gK/CuiHi0diBJZ0oakjQ0PDw8xpLNumfVshM5Y9HsA6+yByTOWDS7rR2hnRjDrBVFi3k+Sb8HnBoRf5Su/yGwICLeWerzu8CLgHcDxwFXASdFxH2Nxh0cHIyhoaHx/wVmZgcRSVsiYrBVvyqv3HcD5feLMyleoZe9GfhKFLYDtwLPqFqsmZl1VpVwvxo4XtK8tJP0dGB9TZ+dwMsAJB0FPB3Y0clCzcysupYfYoqI/ZLOBq4EBoBLImKbpLPS8tXA+cClkrYCAs6NiL0TWLeZmTVR6ROqEbEB2FDTtrp0+U7glZ0tzczMxsqfUDUzy5DD3cwsQw53M7MMOdzNzDLkcDczy5DD3cwsQw53M7MMOdzNzDLkcDczy5DD3cwsQw53M7MMOdzNzDLkcDczy5DD3cwsQw53M7MMOdzNzDLkcDczy5DD3cwsQw53M7MMOdzNzDLkcDczy5DD3cwsQw53M7MMOdzNzDLkcDczy5DD3cwsQw53M7MMOdzNzDLkcDczy5DD3cwsQw53M7MMOdzNzDLkcDczy5DD3cwsQ5XCXdJpkm6UtF3SigZ9TpF0jaRtkr7T2TLNzKwdU1p1kDQAXAS8AtgNXC1pfUT8sNTnSODTwGkRsVPS0yaoXjMzq6DKK/cFwPaI2BERjwBrgaU1fV4HfCUidgJExJ7OlmlmZu2oEu7HALtK13entrITgCdL2ihpi6Q31BtI0pmShiQNDQ8Pj61iMzNrqUq4q05b1FyfAjwPeBVwKvAXkk74pRtFXBw
      "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, param_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f51aafa6bc0>"
      ]
     },
     "execution_count": 26,
     "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, param_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "data_without_features = decoding.remove_feature(data_pca.to_numpy(), pd.DataFrame(param_1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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[:limit])).to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "is_executing": true,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "plt.scatter([phi_deg[i] * 180 / np.pi for i in phi_reorder], [param_2[i] for i in phi_reorder])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "is_executing": true,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "plt.scatter([theta_deg[i] for i in theta_reorder], [param_2[i] for i in theta_reorder])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "is_executing": true,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "phi_reorder[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "is_executing": true,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "is_executing": true,
     "name": "#%%\n"
    }
   },
   "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"
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  }
 },
 "nbformat": 4,
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}