3_Evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%matplotlib qt\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import sys; sys.path.insert(0, r'C:\\Users\\Lukas\\Documents\\projects\\invert')\n",
    "import mne\n",
    "import pickle as pkl\n",
    "from time import time\n",
    "from scipy.spatial.distance import cdist\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "from invert.forward import get_info, create_forward_model\n",
    "from invert.simulate import generator\n",
    "from invert.util import pos_from_forward\n",
    "\n",
    "pp = dict(surface='white', hemi='both', verbose=0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Source Estimates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    No patch info available. The standard source space normals will be employed in the rotation to the local surface coordinates....\n",
      "    Changing to fixed-orientation forward solution with surface-based source orientations...\n",
      "    [done]\n"
     ]
    }
   ],
   "source": [
    "sim_type = \"single\"\n",
    "fn = f\"evaluation/sim_and_preds_{sim_type}.pkl\"\n",
    "with open(fn, 'rb') as f:\n",
    "    stc_dict, x_test, y_test, sim_info, proc_time_make, proc_time_apply = pkl.load(f)\n",
    "\n",
    "fwd = mne.read_forward_solution(\"forward_model/64ch_ico3-fwd.fif\", verbose=0)\n",
    "fwd = mne.convert_forward_solution(fwd, force_fixed=True)\n",
    "pos = pos_from_forward(fwd)\n",
    "source_model = fwd['src']\n",
    "vertices = [source_model[0]['vertno'], source_model[1]['vertno']]\n",
    "distances = cdist(pos, pos)\n",
    "argsorted_distance_matrix = np.argsort(distances, axis=1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FLEX-AP\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "FLEX-MUSIC\n"
     ]
    },
    {
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     "name": "stderr",
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     "text": [
      "c:\\Users\\Lukas\\Envs\\flap\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1217: RuntimeWarning: All-NaN slice encountered\n",
      "  return function_base._ureduce(a, func=_nanmedian, keepdims=keepdims,\n",
      "c:\\Users\\Lukas\\Envs\\flap\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1217: RuntimeWarning: All-NaN slice encountered\n",
      "  return function_base._ureduce(a, func=_nanmedian, keepdims=keepdims,\n",
      "c:\\Users\\Lukas\\Envs\\flap\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1217: RuntimeWarning: All-NaN slice encountered\n",
      "  return function_base._ureduce(a, func=_nanmedian, keepdims=keepdims,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AP\n"
     ]
    },
    {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RAP-MUSIC\n"
     ]
    },
    {
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     "text": [
      "c:\\Users\\Lukas\\Envs\\flap\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1217: RuntimeWarning: All-NaN slice encountered\n",
      "  return function_base._ureduce(a, func=_nanmedian, keepdims=keepdims,\n",
      "c:\\Users\\Lukas\\Envs\\flap\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1217: RuntimeWarning: All-NaN slice encountered\n",
      "  return function_base._ureduce(a, func=_nanmedian, keepdims=keepdims,\n",
      "c:\\Users\\Lukas\\Envs\\flap\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1217: RuntimeWarning: All-NaN slice encountered\n",
      "  return function_base._ureduce(a, func=_nanmedian, keepdims=keepdims,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eLORETA\n"
     ]
    },
    {
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Convexity Champagne\n"
     ]
    },
    {
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       "model_id": "26d6ec9d580344809ceb33cb8c834b29",
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       "version_minor": 0
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MCMV\n"
     ]
    },
    {
     "data": {
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       "model_id": "9c2455fc3068486e9dd46db9d8bf17f5",
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       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from invert.evaluate import evaluate_all\n",
    "from tqdm.notebook import tqdm\n",
    "n_samples = x_test.shape[0]\n",
    "results = []\n",
    "for solver_name in stc_dict.keys():\n",
    "    print(solver_name)\n",
    "    for i in tqdm(range(n_samples)):\n",
    "        y_pred = stc_dict[solver_name][i].data\n",
    "        y_true = y_test[i].T\n",
    "        result = evaluate_all(y_true, y_pred, pos, argsorted_distance_matrix, distances)\n",
    "        result[\"Method\"] = solver_name\n",
    "        result[\"Time Make\"] = proc_time_make[solver_name][i]\n",
    "        result[\"Time Apply\"] = proc_time_apply[solver_name][i]\n",
    "        \n",
    "        results.append(result)\n",
    "\n",
    "fn = f\"results/results_{sim_type}.pkl\"\n",
    "with open(fn, 'wb') as f:\n",
    "    pkl.dump(results, f)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_type = \"single\"\n",
    "fn = f\"results/results_{sim_type}.pkl\"\n",
    "with open(fn, 'rb') as f:\n",
    "    results = pkl.load(f)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(style=\"whitegrid\", font_scale=1.)\n",
    "\n",
    "tick_params = dict(\n",
    "    axis='y',          # changes apply to the x-axis\n",
    "    which='both',      # both major and minor ticks are affected\n",
    "    left=True,      # ticks along the bottom edge are off\n",
    "    right=False,         # ticks along the top edge are off\n",
    "    labelbottom=False\n",
    ")\n",
    "\n",
    "medianprops = {\n",
    "    \"linewidth\": 2,\n",
    "    \"linestyle\": \"dashed\"\n",
    "    }\n",
    "order = ['FLEX-MUSIC', 'TRAP-MUSIC', 'Convexity Champagne', 'eLORETA', \"MCMV\"]\n",
    "df = pd.DataFrame(results)\n",
    "\n",
    "plt.figure()\n",
    "sns.boxplot(data=df, x=\"Method\", y=\"AUC\", order=order, medianprops=medianprops)\n",
    "plt.axhline(y=0.5, color='grey', linestyle='-.')\n",
    "plt.title(\"Area under ROC curve\")\n",
    "plt.ylim(-0.05, 1.05)\n",
    "plt.tick_params(**tick_params)\n",
    "\n",
    "plt.figure()\n",
    "sns.boxplot(data=df, x=\"Method\", y=\"Mean_Squared_Error\", order=order, medianprops=medianprops)\n",
    "plt.tick_params(**tick_params)\n",
    "\n",
    "plt.figure()\n",
    "g = sns.boxplot(data=df, x=\"Method\", y=\"Normalized_Mean_Squared_Error\", order=order, medianprops=medianprops)\n",
    "g.set_yscale(\"log\")\n",
    "plt.tick_params(**tick_params)\n",
    "\n",
    "plt.figure()\n",
    "sns.boxplot(data=df, x=\"Method\", y=\"Mean_Localization_Error\", order=order, medianprops=medianprops)\n",
    "plt.tick_params(**tick_params)\n",
    "plt.ylim(-3, 43)\n",
    "\n",
    "plt.figure()\n",
    "sns.boxplot(data=df, x=\"Method\", y=\"Sparsity\", order=order, medianprops=medianprops)\n",
    "plt.tick_params(**tick_params)\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "g = sns.boxplot(data=df, x=\"Method\", y=\"Time\", order=order, medianprops=medianprops)\n",
    "g.set_yscale(\"log\")\n",
    "plt.tick_params(**tick_params)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Single and extended sources in groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(style=\"whitegrid\", font_scale=1.)\n",
    "\n",
    "tick_params = dict(\n",
    "    axis='y',          # changes apply to the x-axis\n",
    "    which='both',      # both major and minor ticks are affected\n",
    "    left=True,      # ticks along the bottom edge are off\n",
    "    right=False,         # ticks along the top edge are off\n",
    "    labelbottom=False\n",
    ")\n",
    "\n",
    "medianprops = {\n",
    "    \"linewidth\": 2,\n",
    "    \"linestyle\": \"dashed\"\n",
    "    }\n",
    "order = ['FLEX-MUSIC', 'TRAP-MUSIC', 'Convexity Champagne', 'MCMV', 'eLORETA']\n",
    "\n",
    "\n",
    "sim_type = \"single\"\n",
    "fn = f\"results/results_{sim_type}.pkl\"\n",
    "with open(fn, 'rb') as f:\n",
    "    results_single = pkl.load(f)\n",
    "\n",
    "sim_type = \"extended\"\n",
    "fn = f\"results/results_{sim_type}.pkl\"\n",
    "with open(fn, 'rb') as f:\n",
    "    results_extended = pkl.load(f)\n",
    "\n",
    "df_single = pd.DataFrame(results_single)\n",
    "df_single[\"Source Extent\"] = \"Single\"\n",
    "df_extended = pd.DataFrame(results_extended)\n",
    "df_extended[\"Source Extent\"] = \"Extended\"\n",
    "\n",
    "df = pd.concat([df_single, df_extended])\n",
    "\n",
    "plt.figure(figsize=(10,7))\n",
    "sns.boxplot(data=df, x=\"Method\", y=\"AUC\", hue=\"Source Extent\", order=order, medianprops=medianprops)\n",
    "plt.axhline(y=0.5, color='grey', linestyle='-.')\n",
    "plt.title(\"Area under ROC curve\")\n",
    "plt.ylim(-0.05, 1.05)\n",
    "plt.tick_params(**tick_params)\n",
    "\n",
    "plt.figure(figsize=(10,7))\n",
    "g = sns.boxplot(data=df, x=\"Method\", y=\"Corr\", hue=\"Source Extent\", order=order, medianprops=medianprops)\n",
    "plt.tick_params(**tick_params)\n",
    "plt.ylim(-0.05, 1.05)\n",
    "\n",
    "plt.figure(figsize=(10,7))\n",
    "g = sns.boxplot(data=df, x=\"Method\", y=\"EMD\", hue=\"Source Extent\", order=order, medianprops=medianprops)\n",
    "plt.tick_params(**tick_params)\n",
    "\n",
    "plt.figure(figsize=(10,7))\n",
    "sns.boxplot(data=df, x=\"Method\", y=\"Mean_Squared_Error\", hue=\"Source Extent\", order=order, medianprops=medianprops)\n",
    "plt.tick_params(**tick_params)\n",
    "\n",
    "plt.figure(figsize=(10,7))\n",
    "g = sns.boxplot(data=df, x=\"Method\", y=\"Normalized_Mean_Squared_Error\", hue=\"Source Extent\", order=order, medianprops=medianprops)\n",
    "g.set_yscale(\"log\")\n",
    "plt.tick_params(**tick_params)\n",
    "\n",
    "plt.figure(figsize=(10,7))\n",
    "sns.boxplot(data=df, x=\"Method\", y=\"Mean_Localization_Error\", hue=\"Source Extent\", order=order, medianprops=medianprops)\n",
    "plt.tick_params(**tick_params)\n",
    "plt.ylim(-3, 43)\n",
    "\n",
    "plt.figure(figsize=(10,7))\n",
    "sns.boxplot(data=df, x=\"Method\", y=\"Sparsity_pred\", hue=\"Source Extent\", order=order, medianprops=medianprops)\n",
    "plt.tick_params(**tick_params)\n",
    "\n",
    "plt.figure(figsize=(10,7))\n",
    "g = sns.boxplot(data=df, x=\"Method\", y=\"Time\", hue=\"Source Extent\", order=order, medianprops=medianprops)\n",
    "g.set_yscale(\"log\")\n",
    "plt.tick_params(**tick_params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FLEX-MUSIC sparsity: r = 0.80\n",
      "Convexity Champagne sparsity: r = 0.17\n",
      "FLEX-MUSIC Active Dipoles: r = 0.83\n",
      "Convexity Champagne Active Dipoles: r = 0.17\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22397b9b6a0>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import pearsonr\n",
    "method = \"FLEX-MUSIC\"\n",
    "df_temp = df[df.Method==method]\n",
    "\n",
    "a = df_temp.Sparsity_true.values\n",
    "b = df_temp.Sparsity_pred.values\n",
    "a[np.isnan(a)] = np.nanmedian(a)\n",
    "b[np.isnan(b)] = np.nanmedian(a)\n",
    "r, p = pearsonr(a, b)\n",
    "print(f\"{method} sparsity: r = {r:.2f}\")\n",
    "\n",
    "plt.figure()\n",
    "sns.scatterplot(data=df_temp, y=\"Sparsity_pred\", x=\"Sparsity_true\")\n",
    "maxval = np.max( [plt.ylim()[1], plt.xlim()[1]])\n",
    "plt.ylim(0,  maxval)\n",
    "plt.xlim(0, maxval)\n",
    "plt.plot([0, maxval], [0, maxval], 'k')\n",
    "\n",
    "\n",
    "method = \"Convexity Champagne\"\n",
    "df_temp = df[df.Method==method]\n",
    "\n",
    "a = df_temp.Sparsity_true.values\n",
    "b = df_temp.Sparsity_pred.values\n",
    "a[np.isnan(a)] = np.nanmedian(a)\n",
    "b[np.isnan(b)] = np.nanmedian(a)\n",
    "r, p = pearsonr(a, b)\n",
    "print(f\"{method} sparsity: r = {r:.2f}\")\n",
    "\n",
    "plt.figure()\n",
    "sns.scatterplot(data=df_temp, y=\"Sparsity_pred\", x=\"Sparsity_true\")\n",
    "maxval = np.max( [plt.ylim()[1], plt.xlim()[1]])\n",
    "plt.ylim(0,  maxval)\n",
    "plt.xlim(0, maxval)\n",
    "plt.plot([0, maxval], [0, maxval], 'k')\n",
    "\n",
    "method = \"FLEX-MUSIC\"\n",
    "df_temp = df[df.Method==method]\n",
    "\n",
    "r, p = pearsonr(df_temp.Active_True.values, df_temp.Active_Pred.values)\n",
    "print(f\"{method} Active Dipoles: r = {r:.2f}\")\n",
    "\n",
    "plt.figure()\n",
    "sns.scatterplot(data=df_temp, y=\"Active_Pred\", x=\"Active_True\")\n",
    "maxval = np.max( [plt.ylim()[1], plt.xlim()[1]])\n",
    "plt.ylim(0,  maxval)\n",
    "plt.xlim(0, maxval)\n",
    "plt.plot([0, maxval], [0, maxval], 'k')\n",
    "\n",
    "\n",
    "method = \"Convexity Champagne\"\n",
    "df_temp = df[df.Method==method]\n",
    "\n",
    "r, p = pearsonr(df_temp.Active_True.values, df_temp.Active_Pred.values)\n",
    "print(f\"{method} Active Dipoles: r = {r:.2f}\")\n",
    "\n",
    "plt.figure()\n",
    "sns.scatterplot(data=df_temp, y=\"Active_Pred\", x=\"Active_True\")\n",
    "maxval = np.max( [plt.ylim()[1], plt.xlim()[1]])\n",
    "plt.ylim(0,  maxval)\n",
    "plt.xlim(0, maxval)\n",
    "plt.plot([0, maxval], [0, maxval], 'k')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean_Squared_Error</th>\n",
       "      <th>Normalized_Mean_Squared_Error</th>\n",
       "      <th>Mean_Localization_Error</th>\n",
       "      <th>AUC</th>\n",
       "      <th>Corr</th>\n",
       "      <th>EMD</th>\n",
       "      <th>Sparsity_pred</th>\n",
       "      <th>Sparsity_true</th>\n",
       "      <th>Active_True</th>\n",
       "      <th>Active_Pred</th>\n",
       "      <th>Method</th>\n",
       "      <th>Time</th>\n",
       "      <th>Source Extend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.984523e-08</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000779</td>\n",
       "      <td>0.000779</td>\n",
       "      <td>FLEX-MUSIC</td>\n",
       "      <td>0.427364</td>\n",
       "      <td>Single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.161808e-08</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.920804</td>\n",
       "      <td>7268.832897</td>\n",
       "      <td>1.710411</td>\n",
       "      <td>1.722149</td>\n",
       "      <td>0.002336</td>\n",
       "      <td>0.002336</td>\n",
       "      <td>FLEX-MUSIC</td>\n",
       "      <td>0.466751</td>\n",
       "      <td>Single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.454046e-07</td>\n",
       "      <td>0.001269</td>\n",
       "      <td>1.93978</td>\n",
       "      <td>0.997959</td>\n",
       "      <td>0.692695</td>\n",
       "      <td>62176.801113</td>\n",
       "      <td>3.381233</td>\n",
       "      <td>2.512096</td>\n",
       "      <td>0.005452</td>\n",
       "      <td>0.014798</td>\n",
       "      <td>FLEX-MUSIC</td>\n",
       "      <td>0.425861</td>\n",
       "      <td>Single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.913167e-08</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000779</td>\n",
       "      <td>0.000779</td>\n",
       "      <td>FLEX-MUSIC</td>\n",
       "      <td>0.449303</td>\n",
       "      <td>Single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.874573e-07</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.921881</td>\n",
       "      <td>8764.065738</td>\n",
       "      <td>1.955567</td>\n",
       "      <td>1.912822</td>\n",
       "      <td>0.003115</td>\n",
       "      <td>0.003115</td>\n",
       "      <td>FLEX-MUSIC</td>\n",
       "      <td>0.485701</td>\n",
       "      <td>Single</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Mean_Squared_Error  Normalized_Mean_Squared_Error  Mean_Localization_Error  \\\n",
       "0        1.984523e-08                       0.000000                  0.00000   \n",
       "1        6.161808e-08                       0.000174                  0.00000   \n",
       "2        6.454046e-07                       0.001269                  1.93978   \n",
       "3        6.913167e-08                       0.000000                  0.00000   \n",
       "4        1.874573e-07                       0.000182                  0.00000   \n",
       "\n",
       "        AUC      Corr           EMD  Sparsity_pred  Sparsity_true  \\\n",
       "0  1.000000  1.000000      0.000000       1.000000       1.000000   \n",
       "1  1.000000  0.920804   7268.832897       1.710411       1.722149   \n",
       "2  0.997959  0.692695  62176.801113       3.381233       2.512096   \n",
       "3  1.000000  1.000000      0.000000       1.000000       1.000000   \n",
       "4  1.000000  0.921881   8764.065738       1.955567       1.912822   \n",
       "\n",
       "   Active_True  Active_Pred      Method      Time Source Extend  \n",
       "0     0.000779     0.000779  FLEX-MUSIC  0.427364        Single  \n",
       "1     0.002336     0.002336  FLEX-MUSIC  0.466751        Single  \n",
       "2     0.005452     0.014798  FLEX-MUSIC  0.425861        Single  \n",
       "3     0.000779     0.000779  FLEX-MUSIC  0.449303        Single  \n",
       "4     0.003115     0.003115  FLEX-MUSIC  0.485701        Single  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Paper Plots 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lukas\\AppData\\Local\\Temp\\ipykernel_14764\\471277992.py:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df.Method[df.Method==\"Convexity Champagne\"] = \"Champagne\"\n",
      "C:\\Users\\lukas\\AppData\\Local\\Temp\\ipykernel_14764\\471277992.py:34: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df.Method[df.Method==\"FLEX-MUSIC\"] = \"FLEX\\nMUSIC\"\n",
      "C:\\Users\\lukas\\AppData\\Local\\Temp\\ipykernel_14764\\471277992.py:35: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df.Method[df.Method==\"TRAP-MUSIC\"] = \"TRAP\\nMUSIC\"\n",
      "C:\\Users\\lukas\\AppData\\Local\\Temp\\ipykernel_14764\\471277992.py:36: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df.Method[df.Method==\"eLORETA\"] = \"eLOR\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FLEX\n",
      "MUSIC\n",
      "TRAP\n",
      "MUSIC\n",
      "Champagne\n",
      "MCMV\n",
      "eLOR\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'figures/accuracy.png'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\lukas\\Dokumente\\projects\\flex_paper_analyses\\3_Evaluation.ipynb Cell 15\u001b[0m in \u001b[0;36m<cell line: 140>\u001b[1;34m()\u001b[0m\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/lukas/Dokumente/projects/flex_paper_analyses/3_Evaluation.ipynb#X20sZmlsZQ%3D%3D?line=134'>135</a>\u001b[0m     plt\u001b[39m.\u001b[39mtick_params(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mtick_params)\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/lukas/Dokumente/projects/flex_paper_analyses/3_Evaluation.ipynb#X20sZmlsZQ%3D%3D?line=137'>138</a>\u001b[0m plt\u001b[39m.\u001b[39mtight_layout(pad\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m)\n\u001b[1;32m--> <a href='vscode-notebook-cell:/c%3A/Users/lukas/Dokumente/projects/flex_paper_analyses/3_Evaluation.ipynb#X20sZmlsZQ%3D%3D?line=139'>140</a>\u001b[0m fig1\u001b[39m.\u001b[39;49msavefig(\u001b[39m\"\u001b[39;49m\u001b[39mfigures/accuracy.png\u001b[39;49m\u001b[39m\"\u001b[39;49m, dpi\u001b[39m=\u001b[39;49m\u001b[39m600\u001b[39;49m)\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/lukas/Dokumente/projects/flex_paper_analyses/3_Evaluation.ipynb#X20sZmlsZQ%3D%3D?line=140'>141</a>\u001b[0m fig2\u001b[39m.\u001b[39msavefig(\u001b[39m\"\u001b[39m\u001b[39mfigures/sparsity_time.png\u001b[39m\u001b[39m\"\u001b[39m, dpi\u001b[39m=\u001b[39m\u001b[39m600\u001b[39m)\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/lukas/Dokumente/projects/flex_paper_analyses/3_Evaluation.ipynb#X20sZmlsZQ%3D%3D?line=141'>142</a>\u001b[0m fig3\u001b[39m.\u001b[39msavefig(\u001b[39m\"\u001b[39m\u001b[39mfigures/extent_estimation.png\u001b[39m\u001b[39m\"\u001b[39m, dpi\u001b[39m=\u001b[39m\u001b[39m600\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\lukas\\virtualenvs\\invertenv\\lib\\site-packages\\matplotlib\\figure.py:3046\u001b[0m, in \u001b[0;36mFigure.savefig\u001b[1;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[0;32m   3042\u001b[0m     \u001b[39mfor\u001b[39;00m ax \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxes:\n\u001b[0;32m   3043\u001b[0m         stack\u001b[39m.\u001b[39menter_context(\n\u001b[0;32m   3044\u001b[0m             ax\u001b[39m.\u001b[39mpatch\u001b[39m.\u001b[39m_cm_set(facecolor\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mnone\u001b[39m\u001b[39m'\u001b[39m, edgecolor\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mnone\u001b[39m\u001b[39m'\u001b[39m))\n\u001b[1;32m-> 3046\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcanvas\u001b[39m.\u001b[39;49mprint_figure(fname, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\lukas\\virtualenvs\\invertenv\\lib\\site-packages\\matplotlib\\backends\\backend_qtagg.py:85\u001b[0m, in \u001b[0;36mFigureCanvasQTAgg.print_figure\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     84\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mprint_figure\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m---> 85\u001b[0m     \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49mprint_figure(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[0;32m     86\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdraw()\n",
      "File \u001b[1;32mc:\\Users\\lukas\\virtualenvs\\invertenv\\lib\\site-packages\\matplotlib\\backend_bases.py:2319\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[0;32m   2315\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m   2316\u001b[0m     \u001b[39m# _get_renderer may change the figure dpi (as vector formats\u001b[39;00m\n\u001b[0;32m   2317\u001b[0m     \u001b[39m# force the figure dpi to 72), so we need to set it again here.\u001b[39;00m\n\u001b[0;32m   2318\u001b[0m     \u001b[39mwith\u001b[39;00m cbook\u001b[39m.\u001b[39m_setattr_cm(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfigure, dpi\u001b[39m=\u001b[39mdpi):\n\u001b[1;32m-> 2319\u001b[0m         result \u001b[39m=\u001b[39m print_method(\n\u001b[0;32m   2320\u001b[0m             filename,\n\u001b[0;32m   2321\u001b[0m             facecolor\u001b[39m=\u001b[39;49mfacecolor,\n\u001b[0;32m   2322\u001b[0m             edgecolor\u001b[39m=\u001b[39;49medgecolor,\n\u001b[0;32m   2323\u001b[0m             orientation\u001b[39m=\u001b[39;49morientation,\n\u001b[0;32m   2324\u001b[0m             bbox_inches_restore\u001b[39m=\u001b[39;49m_bbox_inches_restore,\n\u001b[0;32m   2325\u001b[0m             \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[0;32m   2326\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m   2327\u001b[0m     \u001b[39mif\u001b[39;00m bbox_inches \u001b[39mand\u001b[39;00m restore_bbox:\n",
      "File \u001b[1;32mc:\\Users\\lukas\\virtualenvs\\invertenv\\lib\\site-packages\\matplotlib\\backend_bases.py:1648\u001b[0m, in \u001b[0;36m_check_savefig_extra_args.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1640\u001b[0m     _api\u001b[39m.\u001b[39mwarn_deprecated(\n\u001b[0;32m   1641\u001b[0m         \u001b[39m'\u001b[39m\u001b[39m3.3\u001b[39m\u001b[39m'\u001b[39m, name\u001b[39m=\u001b[39mname, removal\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39m3.6\u001b[39m\u001b[39m'\u001b[39m,\n\u001b[0;32m   1642\u001b[0m         message\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39m%(name)s\u001b[39;00m\u001b[39m() got unexpected keyword argument \u001b[39m\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m   1643\u001b[0m                 \u001b[39m+\u001b[39m arg \u001b[39m+\u001b[39m \u001b[39m'\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m which is no longer supported as of \u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m   1644\u001b[0m                 \u001b[39m'\u001b[39m\u001b[39m%(since)s\u001b[39;00m\u001b[39m and will become an error \u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m   1645\u001b[0m                 \u001b[39m'\u001b[39m\u001b[39m%(removal)s\u001b[39;00m\u001b[39m'\u001b[39m)\n\u001b[0;32m   1646\u001b[0m     kwargs\u001b[39m.\u001b[39mpop(arg)\n\u001b[1;32m-> 1648\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\lukas\\virtualenvs\\invertenv\\lib\\site-packages\\matplotlib\\_api\\deprecation.py:415\u001b[0m, in \u001b[0;36mdelete_parameter.<locals>.wrapper\u001b[1;34m(*inner_args, **inner_kwargs)\u001b[0m\n\u001b[0;32m    405\u001b[0m     deprecation_addendum \u001b[39m=\u001b[39m (\n\u001b[0;32m    406\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mIf any parameter follows \u001b[39m\u001b[39m{\u001b[39;00mname\u001b[39m!r}\u001b[39;00m\u001b[39m, they should be passed as \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    407\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mkeyword, not positionally.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m    408\u001b[0m     warn_deprecated(\n\u001b[0;32m    409\u001b[0m         since,\n\u001b[0;32m    410\u001b[0m         name\u001b[39m=\u001b[39m\u001b[39mrepr\u001b[39m(name),\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    413\u001b[0m                  \u001b[39melse\u001b[39;00m deprecation_addendum,\n\u001b[0;32m    414\u001b[0m         \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m--> 415\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49minner_args, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49minner_kwargs)\n",
      "File \u001b[1;32mc:\\Users\\lukas\\virtualenvs\\invertenv\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:541\u001b[0m, in \u001b[0;36mFigureCanvasAgg.print_png\u001b[1;34m(self, filename_or_obj, metadata, pil_kwargs, *args)\u001b[0m\n\u001b[0;32m    494\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    495\u001b[0m \u001b[39mWrite the figure to a PNG file.\u001b[39;00m\n\u001b[0;32m    496\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    538\u001b[0m \u001b[39m    *metadata*, including the default 'Software' key.\u001b[39;00m\n\u001b[0;32m    539\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    540\u001b[0m FigureCanvasAgg\u001b[39m.\u001b[39mdraw(\u001b[39mself\u001b[39m)\n\u001b[1;32m--> 541\u001b[0m mpl\u001b[39m.\u001b[39;49mimage\u001b[39m.\u001b[39;49mimsave(\n\u001b[0;32m    542\u001b[0m     filename_or_obj, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbuffer_rgba(), \u001b[39mformat\u001b[39;49m\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mpng\u001b[39;49m\u001b[39m\"\u001b[39;49m, origin\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mupper\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m    543\u001b[0m     dpi\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfigure\u001b[39m.\u001b[39;49mdpi, metadata\u001b[39m=\u001b[39;49mmetadata, pil_kwargs\u001b[39m=\u001b[39;49mpil_kwargs)\n",
      "File \u001b[1;32mc:\\Users\\lukas\\virtualenvs\\invertenv\\lib\\site-packages\\matplotlib\\image.py:1675\u001b[0m, in \u001b[0;36mimsave\u001b[1;34m(fname, arr, vmin, vmax, cmap, format, origin, dpi, metadata, pil_kwargs)\u001b[0m\n\u001b[0;32m   1673\u001b[0m pil_kwargs\u001b[39m.\u001b[39msetdefault(\u001b[39m\"\u001b[39m\u001b[39mformat\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mformat\u001b[39m)\n\u001b[0;32m   1674\u001b[0m pil_kwargs\u001b[39m.\u001b[39msetdefault(\u001b[39m\"\u001b[39m\u001b[39mdpi\u001b[39m\u001b[39m\"\u001b[39m, (dpi, dpi))\n\u001b[1;32m-> 1675\u001b[0m image\u001b[39m.\u001b[39;49msave(fname, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mpil_kwargs)\n",
      "File \u001b[1;32mc:\\Users\\lukas\\virtualenvs\\invertenv\\lib\\site-packages\\PIL\\Image.py:2317\u001b[0m, in \u001b[0;36mImage.save\u001b[1;34m(self, fp, format, **params)\u001b[0m\n\u001b[0;32m   2315\u001b[0m         fp \u001b[39m=\u001b[39m builtins\u001b[39m.\u001b[39mopen(filename, \u001b[39m\"\u001b[39m\u001b[39mr+b\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m   2316\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m-> 2317\u001b[0m         fp \u001b[39m=\u001b[39m builtins\u001b[39m.\u001b[39;49mopen(filename, \u001b[39m\"\u001b[39;49m\u001b[39mw+b\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[0;32m   2319\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m   2320\u001b[0m     save_handler(\u001b[39mself\u001b[39m, fp, filename)\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'figures/accuracy.png'"
     ]
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\", font_scale=1.)\n",
    "\n",
    "tick_params = dict(\n",
    "    axis='y',          # changes apply to the y-axis\n",
    "    which='both',      # both major and minor ticks are affected\n",
    "    left=True,      # ticks along the bottom edge are off\n",
    "    right=False,         # ticks along the top edge are off\n",
    "    labelbottom=False\n",
    ")\n",
    "\n",
    "medianprops = {\n",
    "    \"linewidth\": 2,\n",
    "    \"linestyle\": \"dashed\"\n",
    "    }\n",
    "\n",
    "\n",
    "sim_type = \"single\"\n",
    "fn = f\"results/results_{sim_type}.pkl\"\n",
    "with open(fn, 'rb') as f:\n",
    "    results_single = pkl.load(f)\n",
    "\n",
    "sim_type = \"extended\"\n",
    "fn = f\"results/results_{sim_type}.pkl\"\n",
    "with open(fn, 'rb') as f:\n",
    "    results_extended = pkl.load(f)\n",
    "\n",
    "df_single = pd.DataFrame(results_single)\n",
    "df_single[\"Source Extent\"] = \"Single\"\n",
    "df_extended = pd.DataFrame(results_extended)\n",
    "df_extended[\"Source Extent\"] = \"Extended\"\n",
    "\n",
    "df = pd.concat([df_single, df_extended])\n",
    "df.Method[df.Method==\"Convexity Champagne\"] = \"Champagne\"\n",
    "df.Method[df.Method==\"FLEX-MUSIC\"] = \"FLEX\\nMUSIC\"\n",
    "df.Method[df.Method==\"TRAP-MUSIC\"] = \"TRAP\\nMUSIC\"\n",
    "df.Method[df.Method==\"eLORETA\"] = \"eLOR\"\n",
    "df.rename(columns = {col: col.replace(\"-\", \" \").replace(\"_\", \" \") for col in df.columns}, inplace = True)\n",
    "order = ['FLEX\\nMUSIC', 'TRAP\\nMUSIC', 'Champagne', 'MCMV', 'eLOR']\n",
    "\n",
    "fig1 = plt.figure(figsize=(16,5))\n",
    "plt.subplot(131)\n",
    "sns.boxplot(data=df, x=\"Method\", y=\"Mean Localization Error\", hue=\"Source Extent\", order=order, medianprops=medianprops)\n",
    "plt.tick_params(**tick_params)\n",
    "plt.ylim(-3, 43)\n",
    "plt.ylabel(\"Mean Localization Error [mm]\")\n",
    "plt.gca().get_legend().remove()\n",
    "\n",
    "plt.subplot(132)\n",
    "g = sns.boxplot(data=df, x=\"Method\", y=\"EMD\", hue=\"Source Extent\", order=order, medianprops=medianprops)\n",
    "plt.tick_params(**tick_params)\n",
    "plt.gca().get_legend().remove()\n",
    "\n",
    "plt.subplot(133)\n",
    "sns.boxplot(data=df, x=\"Method\", y=\"Mean Squared Error\", hue=\"Source Extent\", order=order, medianprops=medianprops)\n",
    "plt.ylim(-0.05e-5, 1e-5)\n",
    "plt.tick_params(**tick_params)\n",
    "\n",
    "\n",
    "plt.tight_layout(pad=2)\n",
    "df.rename(columns={\"Sparsity pred\": \"Sparsity\"}, inplace=True)\n",
    "\n",
    "fig2 = plt.figure(figsize=(10,5))\n",
    "plt.subplot(121)\n",
    "sns.boxplot(data=df, x=\"Method\", y=\"Sparsity\", hue=\"Source Extent\", order=order, medianprops=medianprops)\n",
    "plt.tick_params(**tick_params)\n",
    "plt.ylabel(\"L1 Norm Predicted\")\n",
    "plt.gca().get_legend().remove()\n",
    "\n",
    "plt.subplot(122)\n",
    "g = sns.boxplot(data=df, x=\"Method\", y=\"Time\", hue=\"Source Extent\", order=order, medianprops=medianprops)\n",
    "g.set_yscale(\"log\")\n",
    "plt.ylabel(\"Computation Time [s]\")\n",
    "plt.tick_params(**tick_params)\n",
    "\n",
    "plt.tight_layout(pad=2)\n",
    "\n",
    "from scipy.stats import pearsonr\n",
    "sns.set(style=\"white\", font_scale=1.)\n",
    "tick_params = dict(\n",
    "    axis='both',          # changes apply to the y-axis\n",
    "    which='both',      # both major and minor ticks are affected\n",
    "    direction=\"inout\",\n",
    "    left=True,      # ticks along the bottom edge are off\n",
    "    bottom=True,\n",
    "    right=False,         # ticks along the top edge are off\n",
    "    labelbottom=True\n",
    ")\n",
    "\n",
    "fig3 = plt.figure(figsize=(8, 6))\n",
    "for i, method in enumerate(order):\n",
    "    print(method)\n",
    "    plt.subplot(2,3,i+1)\n",
    "    df_temp = df[df.Method==method]\n",
    "\n",
    "    a = df_temp[\"Sparsity true\"].values\n",
    "    b = df_temp[\"Sparsity\"].values\n",
    "    nans = (np.isnan(a) | np.isnan(b))\n",
    "    a[nans] = np.nanmedian(a)\n",
    "    b[nans] = np.nanmedian(b)\n",
    "    r, p = pearsonr(a, b)\n",
    "    # print(f\"{method} sparsity: r = {r:.2f}\")\n",
    "\n",
    "    \n",
    "    sns.scatterplot(data=df_temp, y=\"Sparsity\", x=\"Sparsity true\")\n",
    "    plt.ylabel(\"L1 Norm Predicted\")\n",
    "    plt.xlabel(\"L1 Norm True\")\n",
    "    \n",
    "    plt.gca().set_aspect('equal', adjustable='box')\n",
    "\n",
    "    \n",
    "\n",
    "    maxval = np.max( [plt.ylim()[1], plt.xlim()[1]])\n",
    "    plt.ylim(0,  maxval)\n",
    "    plt.xlim(0, maxval)\n",
    "    \n",
    "    \n",
    "    plt.plot([0, maxval], [0, maxval], 'k')\n",
    "\n",
    "    plt.title(method)\n",
    "    r_text = f\"r = {r:.2f}\"\n",
    "    if p<0.001:\n",
    "        r_text += \" ***\"\n",
    "    elif p<0.01:\n",
    "        r_text += \" **\"\n",
    "    elif p<0.05:\n",
    "        r_text += \" *\"\n",
    "    plt.text(maxval/2.6, maxval/1.2, r_text)\n",
    "    xticks = plt.xticks()[0]\n",
    "    yticks = plt.yticks()[0]\n",
    "    longest_ticks = xticks if len(xticks)>len(yticks) else yticks\n",
    "    plt.yticks(longest_ticks)\n",
    "    plt.xticks(longest_ticks)\n",
    "    plt.ylim(0,  maxval)\n",
    "    plt.xlim(0, maxval)\n",
    "    plt.tick_params(**tick_params)\n",
    "\n",
    "    \n",
    "plt.tight_layout(pad=2)\n",
    "\n",
    "fig1.savefig(\"figures/accuracy.png\", dpi=600)\n",
    "fig2.savefig(\"figures/sparsity_time.png\", dpi=600)\n",
    "fig3.savefig(\"figures/extent_estimation.png\", dpi=600)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrrr}\n",
      "\\toprule\n",
      "{} &  Mean Localization Error &        EMD &  Mean Squared Error &  Sparsity &  Time \\\\\n",
      "Method     &                          &            &                     &           &       \\\\\n",
      "\\midrule\n",
      "Champagne  &                     7.73 &  123775.42 &        4.674609e-07 &      6.29 &  2.66 \\\\\n",
      "FLEX-MUSIC &                     2.11 &   66291.31 &        4.147606e-07 &      2.36 &  0.35 \\\\\n",
      "MCMV       &                    16.25 &  184793.86 &        9.648467e-07 &     30.04 &  1.82 \\\\\n",
      "TRAP-MUSIC &                     5.50 &  125233.79 &        5.371027e-07 &      1.73 &  0.32 \\\\\n",
      "eLOR       &                    19.05 &  185186.10 &        8.155678e-07 &     28.29 &  0.32 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lukas\\AppData\\Local\\Temp\\ipykernel_12576\\3698118562.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_table.Method[df.Method==\"FLEX\\nMUSIC\"] = \"FLEX-MUSIC\"\n",
      "C:\\Users\\Lukas\\AppData\\Local\\Temp\\ipykernel_12576\\3698118562.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_table.Method[df.Method==\"TRAP\\nMUSIC\"] = \"TRAP-MUSIC\"\n",
      "C:\\Users\\Lukas\\AppData\\Local\\Temp\\ipykernel_12576\\3698118562.py:12: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n",
      "  print(df_table.to_latex())\n"
     ]
    }
   ],
   "source": [
    "df_table = df.copy()\n",
    "\n",
    "df_table.Method[df.Method==\"FLEX\\nMUSIC\"] = \"FLEX-MUSIC\"\n",
    "df_table.Method[df.Method==\"TRAP\\nMUSIC\"] = \"TRAP-MUSIC\"\n",
    "\n",
    "df_table = df_table.groupby(\"Method\").median()\n",
    "df_table = df_table[[\"Mean Localization Error\", \"EMD\", \"Mean Squared Error\", \"L1 Norm\", \"Time\"]]\n",
    "for col in df_table.columns:\n",
    "    if not \"Squared\" in col:\n",
    "        df_table[col] = df_table[col].round(2)\n",
    "\n",
    "print(df_table.to_latex())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot brains"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    No patch info available. The standard source space normals will be employed in the rotation to the local surface coordinates....\n",
      "    Changing to fixed-orientation forward solution with surface-based source orientations...\n",
      "    [done]\n"
     ]
    }
   ],
   "source": [
    "import pickle as pkl\n",
    "import mne\n",
    "import sys; sys.path.insert(0, '../invert')\n",
    "from invert.util import pos_from_forward\n",
    "from copy import deepcopy\n",
    "import pandas as pd\n",
    "\n",
    "clim=dict(kind=\"value\", pos_lims=(0.0, 0.01, 1))\n",
    "pp = dict(surface='inflated', hemi='both', background=\"white\", verbose=0, colorbar=False, time_viewer=False)\n",
    "\n",
    "sim_type = \"extended\"\n",
    "fn = f\"evaluation/sim_and_preds_{sim_type}.pkl\"\n",
    "with open(fn, 'rb') as f:\n",
    "    stc_dict_ext, x_test_ext, y_test_ext, sim_info_ext, _, _ = pkl.load(f)\n",
    "\n",
    "sim_type = \"single\"\n",
    "fn = f\"evaluation/sim_and_preds_{sim_type}.pkl\"\n",
    "with open(fn, 'rb') as f:\n",
    "    stc_dict_sing, x_test_sing, y_test_sing, sim_info_sing, _, _ = pkl.load(f)\n",
    "\n",
    "with open(\"forward_model/64ch_info.pkl\", \"rb\") as f:\n",
    "    info = pkl.load(f)\n",
    "\n",
    "# Combine\n",
    "stc_dict = deepcopy(stc_dict_sing)\n",
    "for key, value in stc_dict_ext.items():\n",
    "    stc_dict[key].extend(value)\n",
    "x_test = np.concatenate([x_test_sing, x_test_ext], axis=0)\n",
    "y_test = np.concatenate([y_test_sing, y_test_ext], axis=0)\n",
    "sim_info = pd.concat([sim_info_sing, sim_info_ext])\n",
    "\n",
    "\n",
    "fwd = mne.read_forward_solution(\"forward_model/64ch_ico3-fwd.fif\", verbose=0)\n",
    "fwd = mne.convert_forward_solution(fwd, force_fixed=True)\n",
    "pos = pos_from_forward(fwd)\n",
    "source_model = fwd['src']\n",
    "vertices = [source_model[0]['vertno'], source_model[1]['vertno']]\n",
    "\n",
    "samples = [23, 36, 508, 514]\n",
    "imgs = []\n",
    "colorbars = []\n",
    "names = []\n",
    "for sample in samples:\n",
    "    \n",
    "    tmin = 0\n",
    "    tstep = 1/1000\n",
    "    subject = \"fsaverage\"\n",
    "\n",
    "    evoked = mne.EvokedArray(x_test[sample].T, info, tmin=0)\n",
    "    # evoked.plot_joint()\n",
    "    stc = mne.SourceEstimate(y_test[sample].T, vertices, tmin=tmin, tstep=tstep, \n",
    "                            subject=subject, verbose=0)\n",
    "    first_sample = stc.data[:, 0]\n",
    "    first_sample /= np.max(abs(first_sample))\n",
    "    stc.data = np.tile(first_sample, (20,1)).T\n",
    "    brain = stc.plot(**pp, brain_kwargs=dict(title=\"True\"), clim=clim)\n",
    "    pp[\"colorbar\"] = True\n",
    "    brain_cb = stc_list[sample].plot(**pp, brain_kwargs=dict(title=solver), clim=clim)\n",
    "    pp[\"colorbar\"] = False\n",
    "\n",
    "    img = brain.screenshot()\n",
    "    colorbar = brain_cb.screenshot()\n",
    "    brain.close()\n",
    "    brain_cb.close()\n",
    "    imgs.append( img )\n",
    "    colorbars.append( colorbar )\n",
    "    names.append(\"Ground Truth\")\n",
    "    \n",
    "    for solver, stc_list in stc_dict.items():\n",
    "        first_sample = stc_list[sample].data[:, 0]\n",
    "        first_sample /= np.max(abs(first_sample))\n",
    "        stc_list[sample].data = np.tile(first_sample, (20,1)).T\n",
    "        if solver == \"eLORETA\" or solver == \"MCMV\":\n",
    "            brain = stc_list[sample].plot(**pp, brain_kwargs=dict(title=solver), clim=dict(kind=\"value\", pos_lims=(0.2, 0.5, 1)))\n",
    "            pp[\"colorbar\"] = True\n",
    "            brain_cb = stc_list[sample].plot(**pp, brain_kwargs=dict(title=solver), clim=dict(kind=\"value\", pos_lims=(0.2, 0.5, 1)))\n",
    "            pp[\"colorbar\"] = False\n",
    "        else:\n",
    "            brain = stc_list[sample].plot(**pp, brain_kwargs=dict(title=solver), clim=clim)\n",
    "            pp[\"colorbar\"] = True\n",
    "            brain_cb = stc_list[sample].plot(**pp, brain_kwargs=dict(title=solver), clim=clim)\n",
    "            pp[\"colorbar\"] = False\n",
    "            \n",
    "        img = brain.screenshot()\n",
    "        colorbar = brain_cb.screenshot()\n",
    "        \n",
    "        brain.close()\n",
    "        brain_cb.close()\n",
    "\n",
    "        imgs.append( img )\n",
    "        colorbars.append(colorbar)\n",
    "        names.append(solver)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Plot and Save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Ground Truth'"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib qt\n",
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure(figsize=(20,8))\n",
    "for i, (name, img) in enumerate(zip(names, imgs)):\n",
    "    plt.subplot(int(len(imgs)/6)+1,6,i+1)\n",
    "    plt.imshow(img[122:691, :])\n",
    "    plt.axis('off')\n",
    "    if i<6:\n",
    "        if \"Champagne\" in name:\n",
    "            plt.title(\"Champagne\", fontsize=18)\n",
    "        else:\n",
    "            plt.title(name, fontsize=18)\n",
    "for j, colorbar in enumerate(colorbars[:6]):\n",
    "    plt.subplot(int(len(imgs)/6)+1,6,i+j+2)\n",
    "    plt.imshow(colorbar[731:761, :])\n",
    "    plt.axis('off')\n",
    "plt.tight_layout()\n",
    "fig.savefig(\"figures/brains.png\", dpi=600)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helps to decide for the right samples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>n_sources</th>\n",
       "      <th>amplitudes</th>\n",
       "      <th>snr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>[0.7807408956566793, 0.7782117061516455, 0.544...</td>\n",
       "      <td>21.766431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>[0.6414994226703741, 0.7819144808592774, 0.138...</td>\n",
       "      <td>46.935714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>[0.339622791244461, 0.9859668040487106, 0.0735...</td>\n",
       "      <td>16.049346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>3</td>\n",
       "      <td>[0.2525028471127769, 0.8796421349726212, 0.583...</td>\n",
       "      <td>94.770972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>3</td>\n",
       "      <td>[0.9514130873655432, 0.1143115201823572, 0.383...</td>\n",
       "      <td>77.249802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>3</td>\n",
       "      <td>[0.5998499648461517, 0.4349899475082236, 0.796...</td>\n",
       "      <td>77.374201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>3</td>\n",
       "      <td>[0.24392823657689203, 0.9582270089814386, 0.10...</td>\n",
       "      <td>81.931384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>3</td>\n",
       "      <td>[0.4829919480971907, 0.45328753837912167, 0.61...</td>\n",
       "      <td>84.380941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>3</td>\n",
       "      <td>[0.3695619633879521, 0.9033622108169519, 0.449...</td>\n",
       "      <td>59.652260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>3</td>\n",
       "      <td>[0.9468621389452988, 0.38910627588799757, 0.03...</td>\n",
       "      <td>51.493159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>3</td>\n",
       "      <td>[0.20753311618646172, 0.08554004453419378, 0.3...</td>\n",
       "      <td>28.520845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>3</td>\n",
       "      <td>[0.12078130299367022, 0.47515063707074096, 0.7...</td>\n",
       "      <td>38.627044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>3</td>\n",
       "      <td>[0.7542439220444541, 0.5559488572775189, 0.710...</td>\n",
       "      <td>99.464282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>3</td>\n",
       "      <td>[0.7672331929818976, 0.03204332295190008, 0.71...</td>\n",
       "      <td>61.967343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>3</td>\n",
       "      <td>[0.5903594167217446, 0.13441735889255782, 0.67...</td>\n",
       "      <td>49.739148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>3</td>\n",
       "      <td>[0.8815381589342275, 0.058146761792045705, 0.4...</td>\n",
       "      <td>37.921360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>3</td>\n",
       "      <td>[0.5168157380066214, 0.8497838548068223, 0.283...</td>\n",
       "      <td>46.905368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>3</td>\n",
       "      <td>[0.8077475049797653, 0.5802850906262039, 0.450...</td>\n",
       "      <td>70.360251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>3</td>\n",
       "      <td>[0.3515764420148217, 0.49228415226739675, 0.68...</td>\n",
       "      <td>67.390707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>3</td>\n",
       "      <td>[0.5803822658216038, 0.6490047062650679, 0.346...</td>\n",
       "      <td>3.740267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>3</td>\n",
       "      <td>[0.3111482310693354, 0.574559781794479, 0.7254...</td>\n",
       "      <td>95.402792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>3</td>\n",
       "      <td>[0.4228818439098752, 0.9029518173766801, 0.011...</td>\n",
       "      <td>39.025779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>3</td>\n",
       "      <td>[0.2061669248932313, 0.2883277167980041, 0.329...</td>\n",
       "      <td>32.499915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>3</td>\n",
       "      <td>[0.1152200120961738, 0.4002970782356161, 0.162...</td>\n",
       "      <td>53.120327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>3</td>\n",
       "      <td>[0.07732148384563792, 0.556384805871734, 0.113...</td>\n",
       "      <td>65.586683</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     n_sources                                         amplitudes        snr\n",
       "6            3  [0.7807408956566793, 0.7782117061516455, 0.544...  21.766431\n",
       "9            3  [0.6414994226703741, 0.7819144808592774, 0.138...  46.935714\n",
       "25           3  [0.339622791244461, 0.9859668040487106, 0.0735...  16.049346\n",
       "40           3  [0.2525028471127769, 0.8796421349726212, 0.583...  94.770972\n",
       "43           3  [0.9514130873655432, 0.1143115201823572, 0.383...  77.249802\n",
       "53           3  [0.5998499648461517, 0.4349899475082236, 0.796...  77.374201\n",
       "63           3  [0.24392823657689203, 0.9582270089814386, 0.10...  81.931384\n",
       "76           3  [0.4829919480971907, 0.45328753837912167, 0.61...  84.380941\n",
       "77           3  [0.3695619633879521, 0.9033622108169519, 0.449...  59.652260\n",
       "82           3  [0.9468621389452988, 0.38910627588799757, 0.03...  51.493159\n",
       "92           3  [0.20753311618646172, 0.08554004453419378, 0.3...  28.520845\n",
       "105          3  [0.12078130299367022, 0.47515063707074096, 0.7...  38.627044\n",
       "141          3  [0.7542439220444541, 0.5559488572775189, 0.710...  99.464282\n",
       "151          3  [0.7672331929818976, 0.03204332295190008, 0.71...  61.967343\n",
       "155          3  [0.5903594167217446, 0.13441735889255782, 0.67...  49.739148\n",
       "156          3  [0.8815381589342275, 0.058146761792045705, 0.4...  37.921360\n",
       "160          3  [0.5168157380066214, 0.8497838548068223, 0.283...  46.905368\n",
       "173          3  [0.8077475049797653, 0.5802850906262039, 0.450...  70.360251\n",
       "175          3  [0.3515764420148217, 0.49228415226739675, 0.68...  67.390707\n",
       "176          3  [0.5803822658216038, 0.6490047062650679, 0.346...   3.740267\n",
       "184          3  [0.3111482310693354, 0.574559781794479, 0.7254...  95.402792\n",
       "207          3  [0.4228818439098752, 0.9029518173766801, 0.011...  39.025779\n",
       "234          3  [0.2061669248932313, 0.2883277167980041, 0.329...  32.499915\n",
       "237          3  [0.1152200120961738, 0.4002970782356161, 0.162...  53.120327\n",
       "239          3  [0.07732148384563792, 0.556384805871734, 0.113...  65.586683"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_info[sim_info.n_sources==3].head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mne.viz._brain._brain.Brain at 0x1f3c63f4e80>"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clim=dict(kind=\"percent\", pos_lims=(0, 0, 100))\n",
    "pp[\"clim\"] = clim\n",
    "# for i in range(510, 520):\n",
    "stc_dict[\"FLEX-MUSIC\"][514].plot(**pp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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