diff --git "a/DeepGaze/(inverse)_inverse_effect.ipynb" "b/DeepGaze/(inverse)_inverse_effect.ipynb"
new file mode 100644--- /dev/null
+++ "b/DeepGaze/(inverse)_inverse_effect.ipynb"
@@ -0,0 +1,2909 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d8dfaa1b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 256,
+   "id": "24983a32",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/pranjul/.local/lib/python3.8/site-packages/outdated/utils.py:14: OutdatedPackageWarning: The package outdated is out of date. Your version is 0.2.1, the latest is 0.2.2.\n",
+      "Set the environment variable OUTDATED_IGNORE=1 to disable these warnings.\n",
+      "  return warn(\n",
+      "/home/pranjul/.local/lib/python3.8/site-packages/outdated/utils.py:14: OutdatedPackageWarning: The package pingouin is out of date. Your version is 0.3.12, the latest is 0.5.4.\n",
+      "Set the environment variable OUTDATED_IGNORE=1 to disable these warnings.\n",
+      "  return warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pingouin as pg\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.stats import pearsonr, spearmanr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "id": "acc101e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import base64\n",
+    "import pickle\n",
+    "import pandas as pd\n",
+    "\n",
+    "\n",
+    "# Load the DataFrame from the CSV file\n",
+    "loaded_df_csv = pd.read_csv('/raid/pranjul/agg_hg_dg2_37_subs_c.csv')\n",
+    "\n",
+    "# Define a function to deserialize the 2D arrays\n",
+    "def deserialize_array(serialized_arr):\n",
+    "    return pickle.loads(base64.b64decode(serialized_arr.encode('utf-8')))\n",
+    "\n",
+    "# Apply the deserialization function to the column\n",
+    "loaded_df_csv['hg'] = loaded_df_csv['hg'].apply(deserialize_array)\n",
+    "\n",
+    "# Apply the deserialization function to the column\n",
+    "loaded_df_csv['dg2'] = loaded_df_csv['dg2'].apply(deserialize_array)\n",
+    "\n",
+    "# Now, loaded_df contains the original DataFrame with 2D arrays in 'Array_Column'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "id": "52b0f36a",
+   "metadata": {},
+   "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>stim_folder</th>\n",
+       "      <th>stim_name</th>\n",
+       "      <th>hg</th>\n",
+       "      <th>dg2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>faces</td>\n",
+       "      <td>face01</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-19.93076364288096, -19.93076364288096, -19....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>faces</td>\n",
+       "      <td>face02</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-20.211703296648277, -20.211703296648277, -2...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>faces</td>\n",
+       "      <td>face03</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-18.991512885103706, -18.991512885103706, -1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>faces</td>\n",
+       "      <td>face04</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-19.15343600035568, -19.15343600035568, -19....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>faces</td>\n",
+       "      <td>face05</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-19.59324811656509, -19.59324811656509, -19....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>pareidolia_inv</td>\n",
+       "      <td>75_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-19.05009217447225, -19.05009217447225, -19....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>252</th>\n",
+       "      <td>pareidolia_inv</td>\n",
+       "      <td>78_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-19.205521516950164, -19.205521516950164, -1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>pareidolia_inv</td>\n",
+       "      <td>80_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-19.59326130069748, -19.59326130069748, -19....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>254</th>\n",
+       "      <td>pareidolia_inv</td>\n",
+       "      <td>81_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-18.886515709315667, -18.886515709315667, -1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>255</th>\n",
+       "      <td>pareidolia_inv</td>\n",
+       "      <td>83_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-19.379196388317176, -19.379196388317176, -1...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>256 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        stim_folder stim_name  \\\n",
+       "0             faces    face01   \n",
+       "1             faces    face02   \n",
+       "2             faces    face03   \n",
+       "3             faces    face04   \n",
+       "4             faces    face05   \n",
+       "..              ...       ...   \n",
+       "251  pareidolia_inv    75_inv   \n",
+       "252  pareidolia_inv    78_inv   \n",
+       "253  pareidolia_inv    80_inv   \n",
+       "254  pareidolia_inv    81_inv   \n",
+       "255  pareidolia_inv    83_inv   \n",
+       "\n",
+       "                                                    hg  \\\n",
+       "0    [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "1    [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "2    [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "3    [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "4    [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "..                                                 ...   \n",
+       "251  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "252  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "253  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "254  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "255  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "\n",
+       "                                                   dg2  \n",
+       "0    [[-19.93076364288096, -19.93076364288096, -19....  \n",
+       "1    [[-20.211703296648277, -20.211703296648277, -2...  \n",
+       "2    [[-18.991512885103706, -18.991512885103706, -1...  \n",
+       "3    [[-19.15343600035568, -19.15343600035568, -19....  \n",
+       "4    [[-19.59324811656509, -19.59324811656509, -19....  \n",
+       "..                                                 ...  \n",
+       "251  [[-19.05009217447225, -19.05009217447225, -19....  \n",
+       "252  [[-19.205521516950164, -19.205521516950164, -1...  \n",
+       "253  [[-19.59326130069748, -19.59326130069748, -19....  \n",
+       "254  [[-18.886515709315667, -18.886515709315667, -1...  \n",
+       "255  [[-19.379196388317176, -19.379196388317176, -1...  \n",
+       "\n",
+       "[256 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loaded_df_csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "bf64bf83",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "Image data of dtype object cannot be converted to float",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [10]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloaded_df_csv\u001b[49m\u001b[43m[\u001b[49m\u001b[43mloaded_df_csv\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mstim_name\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mface01\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mhg\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/shared/venvs/py3.8-torch1.7.1/lib/python3.8/site-packages/matplotlib/pyplot.py:2724\u001b[0m, in \u001b[0;36mimshow\u001b[0;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, filternorm, filterrad, resample, url, data, **kwargs)\u001b[0m\n\u001b[1;32m   2718\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mimshow)\n\u001b[1;32m   2719\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mimshow\u001b[39m(\n\u001b[1;32m   2720\u001b[0m         X, cmap\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, norm\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, aspect\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, interpolation\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   2721\u001b[0m         alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, vmin\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, vmax\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, origin\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, extent\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m,\n\u001b[1;32m   2722\u001b[0m         filternorm\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, filterrad\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4.0\u001b[39m, resample\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, url\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   2723\u001b[0m         data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m-> 2724\u001b[0m     __ret \u001b[38;5;241m=\u001b[39m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimshow\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2725\u001b[0m \u001b[43m        \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcmap\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnorm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnorm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maspect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maspect\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2726\u001b[0m \u001b[43m        \u001b[49m\u001b[43minterpolation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minterpolation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvmin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvmin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2727\u001b[0m \u001b[43m        \u001b[49m\u001b[43mvmax\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvmax\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morigin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morigin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2728\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfilternorm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfilternorm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilterrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfilterrad\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresample\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresample\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2729\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2730\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2731\u001b[0m     sci(__ret)\n\u001b[1;32m   2732\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m __ret\n",
+      "File \u001b[0;32m/shared/venvs/py3.8-torch1.7.1/lib/python3.8/site-packages/matplotlib/__init__.py:1447\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1444\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m   1445\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m   1446\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1447\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1449\u001b[0m     bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1450\u001b[0m     auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[1;32m   1451\u001b[0m                   \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n",
+      "File \u001b[0;32m/shared/venvs/py3.8-torch1.7.1/lib/python3.8/site-packages/matplotlib/axes/_axes.py:5523\u001b[0m, in \u001b[0;36mAxes.imshow\u001b[0;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, filternorm, filterrad, resample, url, **kwargs)\u001b[0m\n\u001b[1;32m   5518\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_aspect(aspect)\n\u001b[1;32m   5519\u001b[0m im \u001b[38;5;241m=\u001b[39m mimage\u001b[38;5;241m.\u001b[39mAxesImage(\u001b[38;5;28mself\u001b[39m, cmap, norm, interpolation, origin, extent,\n\u001b[1;32m   5520\u001b[0m                       filternorm\u001b[38;5;241m=\u001b[39mfilternorm, filterrad\u001b[38;5;241m=\u001b[39mfilterrad,\n\u001b[1;32m   5521\u001b[0m                       resample\u001b[38;5;241m=\u001b[39mresample, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m-> 5523\u001b[0m \u001b[43mim\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   5524\u001b[0m im\u001b[38;5;241m.\u001b[39mset_alpha(alpha)\n\u001b[1;32m   5525\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m im\u001b[38;5;241m.\u001b[39mget_clip_path() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   5526\u001b[0m     \u001b[38;5;66;03m# image does not already have clipping set, clip to axes patch\u001b[39;00m\n",
+      "File \u001b[0;32m/shared/venvs/py3.8-torch1.7.1/lib/python3.8/site-packages/matplotlib/image.py:702\u001b[0m, in \u001b[0;36m_ImageBase.set_data\u001b[0;34m(self, A)\u001b[0m\n\u001b[1;32m    698\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_A \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39msafe_masked_invalid(A, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m    700\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_A\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m np\u001b[38;5;241m.\u001b[39muint8 \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[1;32m    701\u001b[0m         \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39mcan_cast(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_A\u001b[38;5;241m.\u001b[39mdtype, \u001b[38;5;28mfloat\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msame_kind\u001b[39m\u001b[38;5;124m\"\u001b[39m)):\n\u001b[0;32m--> 702\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mImage data of dtype \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m cannot be converted to \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    703\u001b[0m                     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfloat\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_A\u001b[38;5;241m.\u001b[39mdtype))\n\u001b[1;32m    705\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_A\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m3\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_A\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m    706\u001b[0m     \u001b[38;5;66;03m# If just one dimension assume scalar and apply colormap\u001b[39;00m\n\u001b[1;32m    707\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_A \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_A[:, :, \u001b[38;5;241m0\u001b[39m]\n",
+      "\u001b[0;31mTypeError\u001b[0m: Image data of dtype object cannot be converted to float"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(loaded_df_csv[loaded_df_csv['stim_name'] == 'face01']['hg'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "46126a53",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7fe1532b0310>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(loaded_df_csv[loaded_df_csv['stim_name'] == 'face01']['hg'].to_numpy()[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "8ea1bc1e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7fe15323e0a0>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(loaded_df_csv[loaded_df_csv['stim_name'] == 'face01']['dg2'].to_numpy()[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "e427bf79",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7fe1531f6280>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(loaded_df_csv[loaded_df_csv['stim_name'] == 'face01_inv']['hg'].to_numpy()[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "4d9f3ef4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7fe1531b3310>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(loaded_df_csv[loaded_df_csv['stim_name'] == 'face01_inv']['dg2'].to_numpy()[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "821fee94",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7fe1517ea490>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def rotate_180(matrix):\n",
+    "    return [row[::-1] for row in matrix[::-1]]\n",
+    "\n",
+    "rotated_matrix_dg2 = rotate_180(loaded_df_csv[loaded_df_csv['stim_name'] == 'face01_inv']['dg2'].to_numpy()[0])\n",
+    "rotated_matrix_hg = rotate_180(loaded_df_csv[loaded_df_csv['stim_name'] == 'face01_inv']['hg'].to_numpy()[0])\n",
+    "\n",
+    "plt.matshow(rotated_matrix_dg2)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "9575f2b6",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7fe1517fcf10>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(rotated_matrix_hg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "264ff33c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "632c36d5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 218,
+   "id": "9db66258",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1. sp_corr_dg2_face_and_face_inv:  0.6653425815307308\n",
+      "0.9304966656993182\n",
+      "2. sp_corr_dg2_face_and_face_inv_inv:  0.9354126342890191\n",
+      "3. sp_corr_hg_face_and_face_inv:  0.4192240465094076\n",
+      "0.6081251265497933\n",
+      "4. sp_corr_hg_face_and_face_inv_inv:  0.6599420087087777\n",
+      "0.6849427707751751\n",
+      "5. sp_corr_hg_face_inv_inv_and_dg2_face_inv_inv:  0.6985421120047232\n",
+      "0.5583360122054453\n",
+      "6. sp_corr_hg_face_and_dg2_face:  0.6777837050894578\n"
+     ]
+    }
+   ],
+   "source": [
+    "####\n",
+    "#dg2_face_and_face_inv\n",
+    "\n",
+    "spearmanr(loaded_df_csv[loaded_df_csv['stim_name'] == 'face01']['dg2'].to_numpy()[0].flatten(), \n",
+    "          loaded_df_csv[loaded_df_csv['stim_name'] == 'face01_inv']['dg2'].to_numpy()[0].flatten())[0]\n",
+    "\n",
+    "\n",
+    "sp_corr_dg2_face_and_face_inv = []\n",
+    "for i in range(len(loaded_df_csv[loaded_df_csv['stim_folder'] == 'faces'])):\n",
+    "    sp_corr_dg2_face_and_face_inv.append(\n",
+    "            spearmanr(np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'faces'].iloc[i]['dg2']).flatten(), \n",
+    "                      np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'faces_inv'].iloc[i]['dg2']).flatten())[0])\n",
+    "    \n",
+    "    #break\n",
+    "\n",
+    "\n",
+    "print('1. sp_corr_dg2_face_and_face_inv: ', np.mean(sp_corr_dg2_face_and_face_inv))\n",
+    "\n",
+    "####\n",
+    "#dg2_face_and_face_inv_inv\n",
+    "\n",
+    "print(spearmanr(loaded_df_csv[loaded_df_csv['stim_name'] == 'face01']['dg2'].to_numpy()[0].flatten(), \n",
+    "          np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_name'] == 'face01_inv']['dg2'].to_numpy()[0])).flatten())[0])\n",
+    "\n",
+    "\n",
+    "\n",
+    "sp_corr_dg2_face_and_face_inv_inv = []\n",
+    "for i in range(len(loaded_df_csv[loaded_df_csv['stim_folder'] == 'faces'])):\n",
+    "    sp_corr_dg2_face_and_face_inv_inv.append(\n",
+    "            spearmanr(np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'faces'].iloc[i]['dg2']).flatten(), \n",
+    "                      np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_folder'] == 'faces_inv'].iloc[i]['dg2'])).flatten())[0])\n",
+    "    \n",
+    "    #break\n",
+    "\n",
+    "print('2. sp_corr_dg2_face_and_face_inv_inv: ', np.mean(sp_corr_dg2_face_and_face_inv_inv))\n",
+    "\n",
+    "\n",
+    "\n",
+    "####\n",
+    "#hg_face_and_face_inv\n",
+    "\n",
+    "spearmanr(loaded_df_csv[loaded_df_csv['stim_name'] == 'face01']['hg'].to_numpy()[0].flatten(), \n",
+    "          loaded_df_csv[loaded_df_csv['stim_name'] == 'face01_inv']['hg'].to_numpy()[0].flatten())[0]\n",
+    "\n",
+    "\n",
+    "sp_corr_hg_face_and_face_inv = []\n",
+    "for i in range(len(loaded_df_csv[loaded_df_csv['stim_folder'] == 'faces'])):\n",
+    "    sp_corr_hg_face_and_face_inv.append(\n",
+    "            spearmanr(np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'faces'].iloc[i]['hg']).flatten(), \n",
+    "                      np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'faces_inv'].iloc[i]['hg']).flatten())[0])\n",
+    "    \n",
+    "    #break\n",
+    "\n",
+    "\n",
+    "print('3. sp_corr_hg_face_and_face_inv: ', np.mean(sp_corr_hg_face_and_face_inv))\n",
+    "\n",
+    "\n",
+    "\n",
+    "####\n",
+    "#hg_face_and_face_inv_inv\n",
+    "\n",
+    "print(spearmanr(loaded_df_csv[loaded_df_csv['stim_name'] == 'face01']['hg'].to_numpy()[0].flatten(), \n",
+    "          np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_name'] == 'face01_inv']['hg'].to_numpy()[0])).flatten())[0])\n",
+    "\n",
+    "\n",
+    "sp_corr_hg_face_and_face_inv_inv = []\n",
+    "for i in range(len(loaded_df_csv[loaded_df_csv['stim_folder'] == 'faces'])):\n",
+    "    sp_corr_hg_face_and_face_inv_inv.append(\n",
+    "            spearmanr(np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'faces'].iloc[i]['hg']).flatten(), \n",
+    "                      np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_folder'] == 'faces_inv'].iloc[i]['hg'])).flatten())[0])\n",
+    "    \n",
+    "    #break\n",
+    "\n",
+    "\n",
+    "print('4. sp_corr_hg_face_and_face_inv_inv: ', np.mean(sp_corr_hg_face_and_face_inv_inv))\n",
+    "\n",
+    "\n",
+    "####\n",
+    "#hg_face_inv_inv_and_dg2_face_inv_inv\n",
+    "\n",
+    "\n",
+    "print(spearmanr(np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_name'] == 'face01_inv']['hg'].to_numpy()[0])).flatten(), \n",
+    "          np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_name'] == 'face01_inv']['dg2'].to_numpy()[0])).flatten())[0])\n",
+    "\n",
+    "\n",
+    "sp_corr_hg_face_inv_inv_and_dg2_face_inv_inv = []\n",
+    "for i in range(len(loaded_df_csv[loaded_df_csv['stim_folder'] == 'faces'])):\n",
+    "    sp_corr_hg_face_inv_inv_and_dg2_face_inv_inv.append(\n",
+    "            spearmanr(np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_folder'] == 'faces_inv'].iloc[i]['hg'])).flatten(), \n",
+    "                      np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_folder'] == 'faces_inv'].iloc[i]['dg2'])).flatten())[0])\n",
+    "    \n",
+    "    #break\n",
+    "\n",
+    "\n",
+    "\n",
+    "print('5. sp_corr_hg_face_inv_inv_and_dg2_face_inv_inv: ', np.mean(sp_corr_hg_face_inv_inv_and_dg2_face_inv_inv))\n",
+    "\n",
+    "\n",
+    "\n",
+    "####\n",
+    "#hg_face_and_dg2_face\n",
+    "\n",
+    "print(spearmanr(loaded_df_csv[loaded_df_csv['stim_name'] == 'face01']['hg'].to_numpy()[0].flatten(), \n",
+    "          loaded_df_csv[loaded_df_csv['stim_name'] == 'face01']['dg2'].to_numpy()[0].flatten())[0])\n",
+    "\n",
+    "\n",
+    "sp_corr_hg_face_and_dg2_face = []\n",
+    "for i in range(len(loaded_df_csv[loaded_df_csv['stim_folder'] == 'faces'])):\n",
+    "    sp_corr_hg_face_and_dg2_face.append(\n",
+    "            spearmanr(np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'faces'].iloc[i]['hg']).flatten(), \n",
+    "                      np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'faces'].iloc[i]['dg2']).flatten())[0])\n",
+    "    \n",
+    "    #break\n",
+    "\n",
+    "\n",
+    "print('6. sp_corr_hg_face_and_dg2_face: ', np.mean(sp_corr_hg_face_and_dg2_face))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 235,
+   "id": "b59c93ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1440000"
+      ]
+     },
+     "execution_count": 235,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'faces'].iloc[i]['hg']).flatten())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 238,
+   "id": "ab9637d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hg_dg2_pc_invinv = []\n",
+    "\n",
+    "for i in range(len(loaded_df_csv[loaded_df_csv['stim_folder'] == 'faces'])):\n",
+    "    hg_dg2_pc_invinv.append(np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'faces'].iloc[i]['hg']).flatten())\n",
+    "    \n",
+    "for i in range(len(loaded_df_csv[loaded_df_csv['stim_folder'] == 'faces'])):\n",
+    "    hg_dg2_pc_invinv.append(np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'faces'].iloc[i]['dg2']).flatten())\n",
+    "\n",
+    "for i in range(len(loaded_df_csv[loaded_df_csv['stim_folder'] == 'faces_inv'])):\n",
+    "    hg_dg2_pc_invinv.append(np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_folder'] == 'faces_inv'].iloc[i]['hg'])).flatten())\n",
+    "    \n",
+    "for i in range(len(loaded_df_csv[loaded_df_csv['stim_folder'] == 'faces_inv'])):\n",
+    "    hg_dg2_pc_invinv.append(np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_folder'] == 'faces_inv'].iloc[i]['dg2'])).flatten())   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 240,
+   "id": "c63718ca",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "128"
+      ]
+     },
+     "execution_count": 240,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(hg_dg2_pc_invinv)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 242,
+   "id": "7a4c6d07",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# print(np.shape(data))\n",
+    "df_hg_dg2_pc_invinv = pd.DataFrame(data=np.array(hg_dg2_pc_invinv).T)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 243,
+   "id": "1c3daf58",
+   "metadata": {},
+   "outputs": [
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+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1440000 rows × 128 columns</p>\n",
+       "</div>"
+      ],
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+       "\n",
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+       "\n",
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+       "1439999 -18.890744 -19.194291 -18.014530  \n",
+       "\n",
+       "[1440000 rows x 128 columns]"
+      ]
+     },
+     "execution_count": 243,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_hg_dg2_pc_invinv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 252,
+   "id": "d162a372",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "col_names = []\n",
+    "\n",
+    "for i in range(32):\n",
+    "    col_names.append('hg_' + str(i + 1))\n",
+    "\n",
+    "for i in range(32):\n",
+    "    col_names.append(('dg2_' + str(i + 1)))\n",
+    "\n",
+    "for i in range(32):\n",
+    "    col_names.append(('hg_invinv_' + str(i + 1)))\n",
+    "\n",
+    "for i in range(32):\n",
+    "    col_names.append(('dg2_invinv_' + str(i + 1)))\n",
+    "\n",
+    "df_hg_dg2_pc_invinv.columns = col_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 251,
+   "id": "67ee249a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "128"
+      ]
+     },
+     "execution_count": 251,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(col_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 253,
+   "id": "47cfd47d",
+   "metadata": {
+    "scrolled": true
+   },
+   "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>hg_1</th>\n",
+       "      <th>hg_2</th>\n",
+       "      <th>hg_3</th>\n",
+       "      <th>hg_4</th>\n",
+       "      <th>hg_5</th>\n",
+       "      <th>hg_6</th>\n",
+       "      <th>hg_7</th>\n",
+       "      <th>hg_8</th>\n",
+       "      <th>hg_9</th>\n",
+       "      <th>hg_10</th>\n",
+       "      <th>...</th>\n",
+       "      <th>dg2_invinv_23</th>\n",
+       "      <th>dg2_invinv_24</th>\n",
+       "      <th>dg2_invinv_25</th>\n",
+       "      <th>dg2_invinv_26</th>\n",
+       "      <th>dg2_invinv_27</th>\n",
+       "      <th>dg2_invinv_28</th>\n",
+       "      <th>dg2_invinv_29</th>\n",
+       "      <th>dg2_invinv_30</th>\n",
+       "      <th>dg2_invinv_31</th>\n",
+       "      <th>dg2_invinv_32</th>\n",
+       "    </tr>\n",
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+       "      <td>-21.697935</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
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+       "      <td>-21.356704</td>\n",
+       "      <td>-21.697935</td>\n",
+       "    </tr>\n",
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+       "      <td>-20.349102</td>\n",
+       "      <td>-21.344482</td>\n",
+       "      <td>-21.680093</td>\n",
+       "    </tr>\n",
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+       "      <td>-20.566850</td>\n",
+       "      <td>-21.613730</td>\n",
+       "      <td>-17.654493</td>\n",
+       "      <td>-20.857339</td>\n",
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+       "      <td>-20.349102</td>\n",
+       "      <td>-21.344482</td>\n",
+       "      <td>-21.680093</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
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+       "      <td>...</td>\n",
+       "      <td>-21.359445</td>\n",
+       "      <td>-19.182108</td>\n",
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+       "      <td>-21.602271</td>\n",
+       "      <td>-17.636342</td>\n",
+       "      <td>-20.859095</td>\n",
+       "      <td>-20.676476</td>\n",
+       "      <td>-20.361122</td>\n",
+       "      <td>-21.338978</td>\n",
+       "      <td>-21.668235</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>0.0</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-18.846266</td>\n",
+       "      <td>-19.219551</td>\n",
+       "      <td>-18.281835</td>\n",
+       "      <td>-19.266014</td>\n",
+       "      <td>-18.660774</td>\n",
+       "      <td>-20.000426</td>\n",
+       "      <td>-17.589690</td>\n",
+       "      <td>-18.929502</td>\n",
+       "      <td>-19.204900</td>\n",
+       "      <td>-18.027962</td>\n",
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+       "      <td>-18.908831</td>\n",
+       "      <td>-19.199340</td>\n",
+       "      <td>-18.020744</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1439997</th>\n",
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+       "      <td>...</td>\n",
+       "      <td>-18.837970</td>\n",
+       "      <td>-19.205895</td>\n",
+       "      <td>-18.254672</td>\n",
+       "      <td>-19.232398</td>\n",
+       "      <td>-18.627564</td>\n",
+       "      <td>-19.984084</td>\n",
+       "      <td>-17.582836</td>\n",
+       "      <td>-18.908831</td>\n",
+       "      <td>-19.199340</td>\n",
+       "      <td>-18.020744</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1439998</th>\n",
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+       "      <td>...</td>\n",
+       "      <td>-18.830869</td>\n",
+       "      <td>-19.193970</td>\n",
+       "      <td>-18.231130</td>\n",
+       "      <td>-19.203020</td>\n",
+       "      <td>-18.598822</td>\n",
+       "      <td>-19.969658</td>\n",
+       "      <td>-17.576807</td>\n",
+       "      <td>-18.890744</td>\n",
+       "      <td>-19.194291</td>\n",
+       "      <td>-18.014530</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1439999</th>\n",
+       "      <td>0.0</td>\n",
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+       "      <td>0.0</td>\n",
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+       "      <td>0.0</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-18.830869</td>\n",
+       "      <td>-19.193970</td>\n",
+       "      <td>-18.231130</td>\n",
+       "      <td>-19.203020</td>\n",
+       "      <td>-18.598822</td>\n",
+       "      <td>-19.969658</td>\n",
+       "      <td>-17.576807</td>\n",
+       "      <td>-18.890744</td>\n",
+       "      <td>-19.194291</td>\n",
+       "      <td>-18.014530</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1440000 rows × 128 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         hg_1  hg_2  hg_3  hg_4  hg_5  hg_6  hg_7  hg_8  hg_9  hg_10  ...  \\\n",
+       "0         0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0    0.0  ...   \n",
+       "1         0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0    0.0  ...   \n",
+       "2         0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0    0.0  ...   \n",
+       "3         0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0    0.0  ...   \n",
+       "4         0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0    0.0  ...   \n",
+       "...       ...   ...   ...   ...   ...   ...   ...   ...   ...    ...  ...   \n",
+       "1439995   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0    0.0  ...   \n",
+       "1439996   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0    0.0  ...   \n",
+       "1439997   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0    0.0  ...   \n",
+       "1439998   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0    0.0  ...   \n",
+       "1439999   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0    0.0  ...   \n",
+       "\n",
+       "         dg2_invinv_23  dg2_invinv_24  dg2_invinv_25  dg2_invinv_26  \\\n",
+       "0           -21.390130     -19.206912     -20.581087     -21.630987   \n",
+       "1           -21.390130     -19.206912     -20.581087     -21.630987   \n",
+       "2           -21.371845     -19.191411     -20.566850     -21.613730   \n",
+       "3           -21.371845     -19.191411     -20.566850     -21.613730   \n",
+       "4           -21.359445     -19.182108     -20.559160     -21.602271   \n",
+       "...                ...            ...            ...            ...   \n",
+       "1439995     -18.846266     -19.219551     -18.281835     -19.266014   \n",
+       "1439996     -18.837970     -19.205895     -18.254672     -19.232398   \n",
+       "1439997     -18.837970     -19.205895     -18.254672     -19.232398   \n",
+       "1439998     -18.830869     -19.193970     -18.231130     -19.203020   \n",
+       "1439999     -18.830869     -19.193970     -18.231130     -19.203020   \n",
+       "\n",
+       "         dg2_invinv_27  dg2_invinv_28  dg2_invinv_29  dg2_invinv_30  \\\n",
+       "0           -17.677985     -20.863334     -20.697487     -20.346460   \n",
+       "1           -17.677985     -20.863334     -20.697487     -20.346460   \n",
+       "2           -17.654493     -20.857339     -20.683699     -20.349102   \n",
+       "3           -17.654493     -20.857339     -20.683699     -20.349102   \n",
+       "4           -17.636342     -20.859095     -20.676476     -20.361122   \n",
+       "...                ...            ...            ...            ...   \n",
+       "1439995     -18.660774     -20.000426     -17.589690     -18.929502   \n",
+       "1439996     -18.627564     -19.984084     -17.582836     -18.908831   \n",
+       "1439997     -18.627564     -19.984084     -17.582836     -18.908831   \n",
+       "1439998     -18.598822     -19.969658     -17.576807     -18.890744   \n",
+       "1439999     -18.598822     -19.969658     -17.576807     -18.890744   \n",
+       "\n",
+       "         dg2_invinv_31  dg2_invinv_32  \n",
+       "0           -21.356704     -21.697935  \n",
+       "1           -21.356704     -21.697935  \n",
+       "2           -21.344482     -21.680093  \n",
+       "3           -21.344482     -21.680093  \n",
+       "4           -21.338978     -21.668235  \n",
+       "...                ...            ...  \n",
+       "1439995     -19.204900     -18.027962  \n",
+       "1439996     -19.199340     -18.020744  \n",
+       "1439997     -19.199340     -18.020744  \n",
+       "1439998     -19.194291     -18.014530  \n",
+       "1439999     -19.194291     -18.014530  \n",
+       "\n",
+       "[1440000 rows x 128 columns]"
+      ]
+     },
+     "execution_count": 253,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_hg_dg2_pc_invinv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1c71fd62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_hg_dg2_pc_invinv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 258,
+   "id": "a9376b76",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sp_pc_hginvinv, sp_pc_dg2invinv = [], []\n",
+    "\n",
+    "for i in range(32):\n",
+    "\n",
+    "    sp_pc_hginvinv.append(pg.partial_corr(data=df_hg_dg2_pc_invinv, x='hg_' + str(i + 1), y='dg2_' + str(i + 1), covar=['hg_invinv_' + str(i + 1)], method='spearman').round(3)['r'][0])\n",
+    "    sp_pc_dg2invinv.append(pg.partial_corr(data=df_hg_dg2_pc_invinv, x='hg_' + str(i + 1), y='dg2_' + str(i + 1), covar=['dg2_invinv_' + str(i + 1)], method='spearman').round(3)['r'][0])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 264,
+   "id": "0148f2c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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CxxsR8WDhLTEzs21Cs4FD0tB0+KikK4FfAu82XY+IRQW3zczMOqGWehz/WnZeW3IcZO/FMDOzHUyzgSMiPpve4nduRFzVjm0yM7NOrMUnxyPiA+C0dmqLmZltA/JMjj8u6VpgJrCuKdFzHGZmO6Y8gWNI+jmtJM1zHGZmO6g8T45/tj0aYmZm24Yi3wBoZmbboUIDh6TRkp6X1CBpcoXrIyQtkrRB0viS9CGS/kvSUklPS5pQcm26pD9IWpw+Q4q8BzMz21SeOY4tkpbyXgccDzQCdZJml7wCFuAl4Czg/LLibwNnRMQLkvYFFkqaGxFr0/ULImJWUW03M7PmtdrjkHSqpF3T8T9J+mXJU+UtGQ40RMTyiHgPmAGMLc0QESsi4mngw7L030fEC+n4FeBPQJ9cd2RmZoXKM1T1vYh4U9LRwHFkmx7+JEe5/YCXS84bU1pVJA0HugMvliRfloawrpK0czPlzpFUL6l+1apV1X6tmZk1I0/gaNoR9/PAjRHxANl/5IWTtA/wM+CrEdHUK/kucCAwDNgTuLBS2Yi4MSJqI6K2Tx93VszM2kqewLFS0g3ABGBO+gs/Vzmgb8l5TUrLRdJuwAPARRExvyk9Il6NzLvAT8mGxMzMrJ3kCQBfAuYCJ6bJ6T2BC3KUqwMGShogqTswEZidp1Ep/93A7eWT4KkXgiQBJwNL8tRpZmZto8VVVWll1KKIOLApLSJeBV5treKI2CBpElnQ6QrcGhFLJU0D6iNitqRhZAFiD+ALki6JiMFkwWoE0FvSWanKsyJiMfBzSX0AAYuBb1Rzw2ZmtnVaDBwR8UF6DmP/iHip2sojYg4wpyxtSslxHdkQVnm5fwf+vZk6vdWJmVkHyvMcxx7AUkm/Y9NNDk8qrFVmZtZp5Qkc3yu8FWZmts3IM8dxQ+kch5mZ7djyvMjpeUn7t1N7zMysk/Mch5mZVcVzHGZmVpU8L3J6TNJHybb4APhdRPyp2GaZmVlnlWd33C8BvwNOJXswb0HpuzPMzGzHkmeo6iJgWFMvIz21/WvA78MwM9sB5dmrqkvZ0NTqnOXMzGw7lKfH8ZCkucAd6XwC8GBxTTIzs84sz+T4BZJOAY5OSTdGxN3FNsvMzDqrVgOHpAHAnIj4ZTrfRVL/iFhRdOPMzKzzyTNX8R9s+k7wD1KamZntgPIEjp0i4r2mk3TcLq+ONTOzzidP4FglaeP2IpLGAv9TXJPMzKwzyxM4vgH8o6SXJL0EXAick6dySaPTi6AaJE2ucH2EpEWSNpQ/VCjpTEkvpM+ZJelHSHom1XlNeoWsmZm1kzyrql4EjpTUM52/lafitCX7dcDxQCNQJ2l2RCwryfYScBZwflnZPYGLgVoggIWp7OvAT4CzgQVkbxccjZcHm5m1m9wP8kXEW3mDRjIcaIiI5WleZAYwtqzOFRHxNJtOvgOcCDwcEWtSsHgYGC1pH2C3iJgfEQHcDpxcRZvMzGwrFfkE+H7AyyXnjSlta8rul45brVPSOZLqJdWvWrUqd6PNzKxl2+3WIRFxY0TURkRtnz59Oro5ZmbbjWbnONLT4s1qeiCwBSuBviXnNSktj5XAqLKy81J6zRbWaWZmbaClyfEvtHAtgNYCRx0wMD15vhKYCHw5Z7vmAv8saY90fgLw3YhYI+nPko4kmxw/A/hxzjrNzKwNNBs4IuKrW1NxRGyQNIksCHQFbo2IpZKmAfURMVvSMOBustfTfkHSJRExOAWI75MFH4BpEbEmHX8LmA7sQraayiuqzMzaUZ7dcZH0eWAw0KMpLSKmtVYuIuaQLZktTZtSclzHpkNPpfluBW6tkF4PHJyn3WZm1vbyvAHwerKt1P8OENmbAPsV3C4zM+uk8qyq+lREnAG8HhGXAEcBnyi2WWZm1lnlCRzr08+3Je0LvA/sU1yTzMysM8szx3G/pN2BK4FFZCuqbi6yUWZm1nnl2avq++nwLkn3Az0i4o1im2VmZp1VSw8AHhMRj1R6EFBSngcAzcxsO9RSj2Mk8AiVHwTM8wCgmZlth1p6APDidDgtIv5Qei09DW5mZjugPKuq7qqQNqutG2JmZtuGluY4DiR7WrxX2TzHbpQ8QW5mZjuWluY4/hoYA+zOpvMcb5K9gc/MzHZALc1x3JuW314YEf/cjm0yM7NOrMU5joj4AL+a1czMSuR5cvxxSdcCM4F1TYkRsaiwVpmZWaeVJ3AMST9Lt1EP4Jg2b42ZmXV6ebYc+Wx7NMTMzLYNhb7ISdJo4GqyNwDeHBGXl13fGbgdOAJYDUyIiBWSTgcuKMl6KDA0IhZLmke2O2/Trr0nRMSf8tyHmZltvcJe5CSpK3Ad8DlgEHCapEFl2b5G9p6PjwNXAVcARMTPI2JIRAwBvgL8ISIWl5Q7vem6g4aZWfsq8kVOw4GGiFgeEe8BM4CxZXnGArel41nAsZJUlue0VNbMzDqBIl/ktB/wcsl5Y0qrmCciNgBvAL3L8kwA7ihL+6mkxZK+VyHQmJlZgfIEjvIXOa0AflFgmzaS9Eng7YhYUpJ8ekQcAnwmfb7STNlzJNVLql+1alU7tNbMbMfQauCIiO9HxNqIuItsbuPAiJiSo+6VQN+S85qUVjGPpJ2AXmST5E0mUtbbiIiV6eebZAFseDPtvjEiaiOitk+fPjmaa2ZmeeSZHO8h6TxJvyT7j/pvJeXZ5LAOGChpgKTuZEFgdlme2cCZ6Xg88EhERPreLsCXKJnfkLSTpL3ScTeyvbSWYGZm7SbPctzbyTY2/HE6/zLwM7LVVc2KiA2SJgFzyZbj3hoRSyVNA+ojYjZwC/AzSQ3AGrLg0mQE8HJELC9J2xmYm4JGV+DXwE057sHMzNpInsBxcESULqN9VNKyPJVHxBxgTlnalJLjd2gmAEXEPODIsrR1ZM98mJlZB8kzOb5I0sb/wNOEdX1xTTIzs84sT4/jCOAJSS+l8/2B5yU9A0REHFpY68zMrNPJEzhGF94KMzPbZuTZ5PCP7dEQMzPbNuSZ4zAzM9vIgcPMzKriwGFmZlVx4DAzs6o4cJiZWVUcOMzMrCoOHGZmVhUHDjMzq4oDh5mZVcWBw8zMquLAYWZmVXHgMDOzqhQaOCSNlvS8pAZJkytc31nSzHR9gaT+Kb2/pPWSFqfP9SVljpD0TCpzjSQVeQ9mZrapwgKHpK7AdcDngEHAaZIGlWX7GvB6RHwcuAq4ouTaixExJH2+UZL+E+BsYGD6eNt3M7N2VGSPYzjQEBHLI+I9YAYwtizPWOC2dDwLOLalHoSkfYDdImJ+RATZ+9BPbvOWm5lZs4oMHPsBL5ecN6a0inkiYgPwBtA7XRsg6UlJj0n6TEn+xlbqNDOzAuV5A2BHeBXYPyJWSzoCuEfS4GoqkHQOcA7A/vvvX0ATzcx2TEX2OFYCfUvOa1JaxTySdgJ6Aasj4t2IWA0QEQuBF4FPpPw1rdRJKndjRNRGRG2fPn3a4HbMzAyKDRx1wEBJAyR1ByYCs8vyzAbOTMfjgUciIiT1SZPrSDqAbBJ8eUS8CvxZ0pFpLuQM4N4C78HMzMoUNlQVERskTQLmAl2BWyNiqaRpQH1EzAZuAX4mqQFYQxZcAEYA0yS9D3wIfCMi1qRr3wKmA7sAD6aPmZm1k0LnOCJiDjCnLG1KyfE7wKkVyt0F3NVMnfXAwW3bUjMzy8tPjpuZWVUcOMzMrCoOHGZmVhUHDjMzq4oDh5mZVcWBw8zMquLAYWZmVXHgMDOzqjhwmJlZVRw4zMysKg4cZmZWFQcOMzOrigOHmZlVxYHDzMyq4sBhZmZVceAwM7OqOHCYmVlVCg0ckkZLel5Sg6TJFa7vLGlmur5AUv+UfrykhZKeST+PKSkzL9W5OH32LvIezMxsU4W9OlZSV+A64HigEaiTNDsilpVk+xrwekR8XNJE4ApgAvA/wBci4hVJB5O9t3y/knKnp1fImplZOyuyxzEcaIiI5RHxHjADGFuWZyxwWzqeBRwrSRHxZES8ktKXArtI2rnAtpqZWU5FBo79gJdLzhvZtNewSZ6I2AC8AfQuy/NFYFFEvFuS9tM0TPU9Sar05ZLOkVQvqX7VqlVbcx9mZlaiU0+OSxpMNnz1v0uST4+IQ4DPpM9XKpWNiBsjojYiavv06VN8Y83MdhBFBo6VQN+S85qUVjGPpJ2AXsDqdF4D3A2cEREvNhWIiJXp55vAL8iGxMzMrJ0UGTjqgIGSBkjqDkwEZpflmQ2cmY7HA49EREjaHXgAmBwRjzdllrSTpL3ScTdgDLCkwHswM7MyhQWONGcxiWxF1LPAnRGxVNI0SSelbLcAvSU1AOcBTUt2JwEfB6aULbvdGZgr6WlgMVmP5aai7sHMzDZX2HJcgIiYA8wpS5tScvwOcGqFcpcClzZT7RFt2UYzM6tOp54cNzOzzseBw8zMquLAYWZmVXHgMDOzqjhwmJlZVRw4zMysKg4cZmZWFQcOMzOrigOHmZlVxYHDzMyq4sBhZmZVceAwM7OqOHCYmVlVHDjMzKwqDhxmZlYVBw4zM6tKoYFD0mhJz0tqkDS5wvWdJc1M1xdI6l9y7bsp/XlJJ+at08zMilVY4JDUFbgO+BwwCDhN0qCybF8DXo+IjwNXAVeksoPI3lE+GBgN/JukrjnrNDOzAhXZ4xgONETE8oh4D5gBjC3LMxa4LR3PAo6VpJQ+IyLejYg/AA2pvjx1mplZgYp85/h+wMsl543AJ5vLExEbJL0B9E7p88vK7peOW6sTAEnnAOek07ckPb8F92DWLF3R0S0wa8Ul2toa+lVKLDJwdKiIuBG4saPbYWa2vSlyqGol0LfkvCalVcwjaSegF7C6hbJ56jQzswIVGTjqgIGSBkjqTjbZPbssz2zgzHQ8HngkIiKlT0yrrgYAA4Hf5azTzMwKVNhQVZqzmATMBboCt0bEUknTgPqImA3cAvxMUgOwhiwQkPLdCSwDNgDfjogPACrVWdQ9mJnZ5pT9gW/WMSS9FRE9S87PAmojYlI6/xvgO2R/KGwg63WeHxFr27gd04H7I2JWhfSRwJ+BXcgWbfxjRDSm6z2BK4ETgDeAAK6PiJskDQF+AuwGfABcFhEzK3z3gWQrBAMYHxEvtuW9FUXSvsA1ETG+lXyb/BuXpJ8M/D4ilhXURCuInxy3TkvSaOAfgM9FxGBgKPAE8NF2bsoFEXEY8NfAk8AjaagU4GbgdWBgRAwle+5oz3TtbeCM1PbRwI8k7V6h/pOBWRFx+DYUNHaKiFdaCxqtOJnseSzbxjhwWGd2EVnvYiVARHwQEbdGxGZLqyWdLalO0lOS7pL0kZQ+XdI1kp6QtFzS+JQuSdemXQh+DezdWmMicxXw38DnJH2M7Nmif4qID1OeVRFxRTr+fUS8kI5fAf4E9Clr9/8C/g/wTUmPprR7JC2UtDQtK2/KO1rSonSPv0lpfyXpVkm/k/SkpLEpfXBKWyzpaUkDK/zO3io5Hp96V02/s+sl1Uv6vaQxKf0sSbMlPQL8RlJ/SUvStY9IulPSMkl3p50gakvqvyy1e76kj0r6FHAScGVq48da+/1b57HdLse1bcYukhaXnO/JXxY8DAYW5aznlxFxE4CkS8l2JfhxurYPcDRwYKp7FjCOrAcxiKwHswy4Ned3LUp1BfBUU9BoiaThQHdgkx5FRMyRdD3wVkT8MCX/bUSskbQLUCfpLrI/8m4CRkTEHyQ19WouIltU8repN/O7FAi/AVwdET9PvaOuOe+tSX+yoPgx4FFJH0/pQ4FDU/v6l+T/FtkuEIMkHQwsLrn2V8D8iLhI0r8AZ0fEpZJmU2F40Do/9ziso62PiCFNH2BKpUySDkl/mb4oaUKFLAdL+q2kZ4DTyYJOk3si4sM0lt40zDUCuCP1Yl4BHqmizRWfqpJ0UWrjK2Xp+wA/A76aJ8gA50p6imw+pS/ZqsIjgf9MOykQEWtS3hOAySn4zgN6APsD/wX8o6QLgX4Rsb6K+wO4M/3OXgCWkwVKgIdLvrvU0WTzNETEEuDpkmvvAfen44VkQcm2YQ4c1pktJfsLl4h4JgWWB8kmqctNByZFxCHAJWT/gTZ5t+R4qx+lBQ4HniXrpRwmqUtq42Wpjbtt/DJpN+AB4KKImF+hrk1IGgUcBxyV5lWeZNN72awI8MWS4Lt/RDwbEb8gGwpaD8yRdEyFsqUrY8q/o3zVTNP5utbuoYL34y+rcD7AIx3bPAcO68x+APxQUk1JWqWgAbAr8KqkbmQ9jtb8JzBB2eaZ+wCfba1Amhc5l2zo66GIaADqgUuVbcCJpB6k4JSGiO4Gbq9iOKYX2ZDP22m11ZEpfT4wQtlzTZQMVc0F/k5S03cenn4eACyPiGuAe4FDK3zXa5IOSoFvXNm1UyV1SXMPBwCtbdnzOPCl9N2DgENy3OubZP9uto1x4LBOKyLmANcAD6ZJ1yfI/mKdWyH794AFZP+BPZej+ruBF8h6DbeTDe0058o0dPR7YBjw2bTJJsDXyfZXa5BUDzxMtnwYsv9IRwBnpSGsxcqW6LbkIWAnSc8Cl5P2bIuIVWR7r/0ytaVpWe/3gW7A05KWpvOm716ShrAOTvdYbjLZENITwKtl114ie+j2QeAbEfFOK+3+N6CPpGXApWS9xTdaKTMDuCBN6ntyfBvi5zjMbBNq5pmWVsp0BbpFxDspCPwa+OuSAGvbEY81mllb+AjZ6qtuZEN133LQ2H65x2FmZlXxHIeZmVXFgcPMzKriwGFmZlVx4DAzs6o4cJiZWVX+P1XYuUwLRSPXAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "# Sample data\n",
+    "y1 = np.array(sp_pc_hginvinv)  # y values for first array\n",
+    "y2 = np.array(sp_pc_dg2invinv)  # y values for second array\n",
+    "\n",
+    "# Calculate mean and standard deviation for each array\n",
+    "mean_y1 = np.mean(y1)\n",
+    "std_y1 = np.std(y1)/ np.sqrt(len(y1))\n",
+    "\n",
+    "mean_y2 = np.mean(y2)\n",
+    "std_y2 = np.std(y2)/ np.sqrt(len(y2))\n",
+    "\n",
+    "# Plotting\n",
+    "plt.bar(1, mean_y1, yerr=std_y1, label='hg invinv', capsize=5)\n",
+    "plt.bar(2, mean_y2, yerr=std_y2, label='dg2 invinv', capsize=5)\n",
+    "\n",
+    "# Adding labels and title\n",
+    "plt.xlabel('HG and DG2 faces upright')\n",
+    "plt.ylabel('partial corrs rho')\n",
+    "#plt.title('Bar Plot of Two Arrays with Error Bars (Mean and Std Deviation)')\n",
+    "plt.xticks([])\n",
+    "plt.legend()\n",
+    "\n",
+    "# Display plot\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "956eb1b6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27254928",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "446f97a0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28f08fe5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53c19a3b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5b113f21",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "57441eb8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "74388aaa",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
+   "id": "cd3da464",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7636960294780757\n",
+      "1. sp_corr_dg2_par_and_par_inv:  0.6974168445822986\n",
+      "0.9455784267426408\n",
+      "2. sp_corr_dg2_par_and_par_inv_inv:  0.9295937437951678\n",
+      "0.582811116907266\n",
+      "3. sp_corr_hg_par_and_par_inv:  0.5123352250179325\n",
+      "0.8288379422623456\n",
+      "4. sp_corr_hg_par_and_par_inv_inv:  0.7420407430218114\n",
+      "0.7416952244780178\n",
+      "5. sp_corr_hg_par_inv_inv_and_dg2_par_inv_inv:  0.7047588375235664\n",
+      "0.7635020905027174\n",
+      "6. sp_corr_hg_par_and_dg2_par:  0.693216545537563\n"
+     ]
+    }
+   ],
+   "source": [
+    "#pareidolia\n",
+    "\n",
+    "loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia_inv']\n",
+    "\n",
+    "####\n",
+    "#dg2_par_and_par_inv\n",
+    "\n",
+    "print(spearmanr(loaded_df_csv[loaded_df_csv['stim_name'] == '04']['dg2'].to_numpy()[0].flatten(), \n",
+    "          loaded_df_csv[loaded_df_csv['stim_name'] == '04_inv']['dg2'].to_numpy()[0].flatten())[0])\n",
+    "\n",
+    "\n",
+    "sp_corr_dg2_par_and_par_inv = []\n",
+    "for i in range(len(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia'])):\n",
+    "    sp_corr_dg2_par_and_par_inv.append(\n",
+    "            spearmanr(np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia'].iloc[i]['dg2']).flatten(), \n",
+    "                      np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia_inv'].iloc[i]['dg2']).flatten())[0])\n",
+    "    \n",
+    "    #break\n",
+    "\n",
+    "\n",
+    "print('1. sp_corr_dg2_par_and_par_inv: ', np.mean(sp_corr_dg2_par_and_par_inv))\n",
+    "\n",
+    "\n",
+    "####\n",
+    "#dg2_par_and_par_inv_inv\n",
+    "\n",
+    "print(spearmanr(loaded_df_csv[loaded_df_csv['stim_name'] == '04']['dg2'].to_numpy()[0].flatten(), \n",
+    "          np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_name'] == '04_inv']['dg2'].to_numpy()[0])).flatten())[0])\n",
+    "\n",
+    "\n",
+    "\n",
+    "sp_corr_dg2_par_and_par_inv_inv = []\n",
+    "for i in range(len(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia'])):\n",
+    "    sp_corr_dg2_par_and_par_inv_inv.append(\n",
+    "            spearmanr(np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia'].iloc[i]['dg2']).flatten(), \n",
+    "                      np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia_inv'].iloc[i]['dg2'])).flatten())[0])\n",
+    "    \n",
+    "    #break\n",
+    "\n",
+    "\n",
+    "print('2. sp_corr_dg2_par_and_par_inv_inv: ', np.mean(sp_corr_dg2_par_and_par_inv_inv))\n",
+    "\n",
+    "\n",
+    "####\n",
+    "#hg_par_and_par_inv\n",
+    "\n",
+    "print(spearmanr(loaded_df_csv[loaded_df_csv['stim_name'] == '04']['hg'].to_numpy()[0].flatten(), \n",
+    "          loaded_df_csv[loaded_df_csv['stim_name'] == '04_inv']['hg'].to_numpy()[0].flatten())[0])\n",
+    "\n",
+    "\n",
+    "\n",
+    "sp_corr_hg_par_and_par_inv = []\n",
+    "for i in range(len(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia'])):\n",
+    "    sp_corr_hg_par_and_par_inv.append(\n",
+    "            spearmanr(np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia'].iloc[i]['hg']).flatten(), \n",
+    "                      np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia_inv'].iloc[i]['hg']).flatten())[0])\n",
+    "    \n",
+    "    #break\n",
+    "\n",
+    "\n",
+    "print('3. sp_corr_hg_par_and_par_inv: ', np.mean(sp_corr_hg_par_and_par_inv))\n",
+    "\n",
+    "\n",
+    "####\n",
+    "#hg_par_and_par_inv_inv\n",
+    "\n",
+    "print(spearmanr(loaded_df_csv[loaded_df_csv['stim_name'] == '04']['hg'].to_numpy()[0].flatten(), \n",
+    "          np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_name'] == '04_inv']['hg'].to_numpy()[0])).flatten())[0])\n",
+    "\n",
+    "\n",
+    "\n",
+    "sp_corr_hg_par_and_par_inv_inv = []\n",
+    "for i in range(len(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia'])):\n",
+    "    sp_corr_hg_par_and_par_inv_inv.append(\n",
+    "            spearmanr(np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia'].iloc[i]['hg']).flatten(), \n",
+    "                      np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia_inv'].iloc[i]['hg'])).flatten())[0])\n",
+    "    \n",
+    "    #break\n",
+    "\n",
+    "\n",
+    "print('4. sp_corr_hg_par_and_par_inv_inv: ', np.mean(sp_corr_hg_par_and_par_inv_inv))\n",
+    "\n",
+    "\n",
+    "####\n",
+    "#hg_par_inv_inv_and_dg2_par_inv_inv\n",
+    "\n",
+    "\n",
+    "print(spearmanr(np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_name'] == '04_inv']['hg'].to_numpy()[0])).flatten(), \n",
+    "          np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_name'] == '04_inv']['dg2'].to_numpy()[0])).flatten())[0])\n",
+    "\n",
+    "\n",
+    "sp_corr_hg_par_inv_inv_and_dg2_par_inv_inv = []\n",
+    "for i in range(len(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia'])):\n",
+    "    sp_corr_hg_par_inv_inv_and_dg2_par_inv_inv.append(\n",
+    "            spearmanr(np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia_inv'].iloc[i]['hg'])).flatten(), \n",
+    "                      np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia_inv'].iloc[i]['dg2'])).flatten())[0])\n",
+    "    \n",
+    "    #break\n",
+    "\n",
+    "\n",
+    "print('5. sp_corr_hg_par_inv_inv_and_dg2_par_inv_inv: ', np.mean(sp_corr_hg_par_inv_inv_and_dg2_par_inv_inv))\n",
+    "\n",
+    "\n",
+    "\n",
+    "####\n",
+    "#hg_par_and_dg2_par\n",
+    "\n",
+    "print(spearmanr(loaded_df_csv[loaded_df_csv['stim_name'] == '04']['hg'].to_numpy()[0].flatten(), \n",
+    "          loaded_df_csv[loaded_df_csv['stim_name'] == '04']['dg2'].to_numpy()[0].flatten())[0])\n",
+    "\n",
+    "\n",
+    "sp_corr_hg_par_and_dg2_par = []\n",
+    "for i in range(len(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia'])):\n",
+    "    sp_corr_hg_par_and_dg2_par.append(\n",
+    "            spearmanr(np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia'].iloc[i]['hg']).flatten(), \n",
+    "                      np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia'].iloc[i]['dg2']).flatten())[0])\n",
+    "    \n",
+    "    #break\n",
+    "\n",
+    "\n",
+    "\n",
+    "print('6. sp_corr_hg_par_and_dg2_par: ', np.mean(sp_corr_hg_par_and_dg2_par))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34067265",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "645f264d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c4f7ea52",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "id": "f499a0ea",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8412545657535644\n",
+      "1. sp_corr_dg2_obj_and_obj_inv:  0.7149331452858081\n",
+      "0.939971891385219\n",
+      "2. sp_corr_dg2_obj_and_obj_inv_inv:  0.9343630124783298\n",
+      "0.3814826472452831\n",
+      "3. sp_corr_hg_obj_and_obj_inv:  0.49015696410982557\n",
+      "0.7363035130720573\n",
+      "4. sp_corr_hg_obj_and_obj_inv_inv:  0.7165094159923406\n",
+      "0.7868139104840336\n",
+      "5. sp_corr_hg_obj_inv_inv_and_dg2_obj_inv_inv:  0.7074477491601381\n",
+      "0.699539237046813\n",
+      "6. sp_corr_hg_obj_and_dg2_obj:  0.6968771828501138\n"
+     ]
+    }
+   ],
+   "source": [
+    "loaded_df_csv[loaded_df_csv['stim_folder'] == 'objects']\n",
+    "\n",
+    "\n",
+    "####\n",
+    "#dg2_obj_and_obj_inv\n",
+    "\n",
+    "print(spearmanr(loaded_df_csv[loaded_df_csv['stim_name'] == '04_match']['dg2'].to_numpy()[0].flatten(), \n",
+    "          loaded_df_csv[loaded_df_csv['stim_name'] == '04_match_inv']['dg2'].to_numpy()[0].flatten())[0])\n",
+    "\n",
+    "\n",
+    "sp_corr_dg2_obj_and_obj_inv = []\n",
+    "for i in range(len(loaded_df_csv[loaded_df_csv['stim_folder'] == 'objects'])):\n",
+    "    sp_corr_dg2_obj_and_obj_inv.append(\n",
+    "            spearmanr(np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'objects'].iloc[i]['dg2']).flatten(), \n",
+    "                      np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'objects_inv'].iloc[i]['dg2']).flatten())[0])\n",
+    "    \n",
+    "    #break\n",
+    "\n",
+    "print('1. sp_corr_dg2_obj_and_obj_inv: ', np.mean(sp_corr_dg2_obj_and_obj_inv))\n",
+    "\n",
+    "\n",
+    "####\n",
+    "#dg2_obj_and_obj_inv_inv\n",
+    "\n",
+    "print(spearmanr(loaded_df_csv[loaded_df_csv['stim_name'] == '04_match']['dg2'].to_numpy()[0].flatten(), \n",
+    "          np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_name'] == '04_match_inv']['dg2'].to_numpy()[0])).flatten())[0])\n",
+    "\n",
+    "\n",
+    "\n",
+    "sp_corr_dg2_obj_and_obj_inv_inv = []\n",
+    "for i in range(len(loaded_df_csv[loaded_df_csv['stim_folder'] == 'objects'])):\n",
+    "    sp_corr_dg2_obj_and_obj_inv_inv.append(\n",
+    "            spearmanr(np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'objects'].iloc[i]['dg2']).flatten(), \n",
+    "                      np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_folder'] == 'objects_inv'].iloc[i]['dg2'])).flatten())[0])\n",
+    "    \n",
+    "    #break\n",
+    "\n",
+    "\n",
+    "print('2. sp_corr_dg2_obj_and_obj_inv_inv: ', np.mean(sp_corr_dg2_obj_and_obj_inv_inv))\n",
+    "\n",
+    "\n",
+    "\n",
+    "#hg_obj_and_obj_inv\n",
+    "\n",
+    "print(spearmanr(loaded_df_csv[loaded_df_csv['stim_name'] == '04_match']['hg'].to_numpy()[0].flatten(), \n",
+    "          loaded_df_csv[loaded_df_csv['stim_name'] == '04_match_inv']['hg'].to_numpy()[0].flatten())[0])\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "####\n",
+    "#hg_obj_and_obj_inv\n",
+    "\n",
+    "\n",
+    "sp_corr_hg_obj_and_obj_inv = []\n",
+    "for i in range(len(loaded_df_csv[loaded_df_csv['stim_folder'] == 'objects'])):\n",
+    "    sp_corr_hg_obj_and_obj_inv.append(\n",
+    "            spearmanr(np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'objects'].iloc[i]['hg']).flatten(), \n",
+    "                      np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'objects_inv'].iloc[i]['hg']).flatten())[0])\n",
+    "    \n",
+    "    #break\n",
+    "\n",
+    "\n",
+    "\n",
+    "print('3. sp_corr_hg_obj_and_obj_inv: ', np.mean(sp_corr_hg_obj_and_obj_inv))\n",
+    "\n",
+    "\n",
+    "####\n",
+    "#hg_obj_and_obj_inv_inv\n",
+    "\n",
+    "print(spearmanr(loaded_df_csv[loaded_df_csv['stim_name'] == '04_match']['hg'].to_numpy()[0].flatten(), \n",
+    "          np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_name'] == '04_match_inv']['hg'].to_numpy()[0])).flatten())[0])\n",
+    "\n",
+    "\n",
+    "sp_corr_hg_obj_and_obj_inv_inv = []\n",
+    "for i in range(len(loaded_df_csv[loaded_df_csv['stim_folder'] == 'objects'])):\n",
+    "    sp_corr_hg_obj_and_obj_inv_inv.append(\n",
+    "            spearmanr(np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'objects'].iloc[i]['hg']).flatten(), \n",
+    "                      np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_folder'] == 'objects_inv'].iloc[i]['hg'])).flatten())[0])\n",
+    "    \n",
+    "    #break\n",
+    "\n",
+    "\n",
+    "print('4. sp_corr_hg_obj_and_obj_inv_inv: ', np.mean(sp_corr_hg_obj_and_obj_inv_inv))\n",
+    "\n",
+    "\n",
+    "####\n",
+    "#hg_obj_inv_inv_and_dg2_obj_inv_inv\n",
+    "\n",
+    "\n",
+    "print(spearmanr(np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_name'] == '04_match_inv']['hg'].to_numpy()[0])).flatten(), \n",
+    "          np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_name'] == '04_match_inv']['dg2'].to_numpy()[0])).flatten())[0])\n",
+    "\n",
+    "\n",
+    "sp_corr_hg_obj_inv_inv_and_dg2_obj_inv_inv = []\n",
+    "for i in range(len(loaded_df_csv[loaded_df_csv['stim_folder'] == 'objects'])):\n",
+    "    sp_corr_hg_obj_inv_inv_and_dg2_obj_inv_inv.append(\n",
+    "            spearmanr(np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_folder'] == 'objects_inv'].iloc[i]['hg'])).flatten(), \n",
+    "                      np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_folder'] == 'objects_inv'].iloc[i]['dg2'])).flatten())[0])\n",
+    "    \n",
+    "    #break\n",
+    "\n",
+    "\n",
+    "print('5. sp_corr_hg_obj_inv_inv_and_dg2_obj_inv_inv: ', np.mean(sp_corr_hg_obj_inv_inv_and_dg2_obj_inv_inv))\n",
+    "\n",
+    "\n",
+    "####\n",
+    "#hg_obj_and_dg2_obj\n",
+    "\n",
+    "print(spearmanr(loaded_df_csv[loaded_df_csv['stim_name'] == '04_match']['hg'].to_numpy()[0].flatten(), \n",
+    "          loaded_df_csv[loaded_df_csv['stim_name'] == '04_match']['dg2'].to_numpy()[0].flatten())[0])\n",
+    "\n",
+    "\n",
+    "sp_corr_hg_obj_and_dg2_obj = []\n",
+    "for i in range(len(loaded_df_csv[loaded_df_csv['stim_folder'] == 'objects'])):\n",
+    "    sp_corr_hg_obj_and_dg2_obj.append(\n",
+    "            spearmanr(np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'objects'].iloc[i]['hg']).flatten(), \n",
+    "                      np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'objects'].iloc[i]['dg2']).flatten())[0])\n",
+    "    \n",
+    "    #break\n",
+    "\n",
+    "\n",
+    "print('6. sp_corr_hg_obj_and_dg2_obj: ', np.mean(sp_corr_hg_obj_and_dg2_obj))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27ca92a2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0240cb78",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 216,
+   "id": "fe03099c",
+   "metadata": {
+    "scrolled": true
+   },
+   "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>stim_folder</th>\n",
+       "      <th>stim_name</th>\n",
+       "      <th>hg</th>\n",
+       "      <th>dg2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>192</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_01_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-18.302300826718575, -18.302300826718575, -1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>193</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_02_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-18.37589334575482, -18.37589334575482, -18....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>194</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_03_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-18.496778133430396, -18.496778133430396, -1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>195</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_04_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-18.60241424151377, -18.60241424151377, -18....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>196</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_05_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-18.29694626711732, -18.29694626711732, -18....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_06_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-19.10143934365796, -19.10143934365796, -19....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>198</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_07_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-18.279331731196674, -18.279331731196674, -1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_08_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-17.89642302378649, -17.89642302378649, -17....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>200</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_09_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-18.147003897331178, -18.147003897331178, -1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>201</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_10_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-19.03680264783764, -19.03680264783764, -19....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>202</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_11_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-17.704779881026173, -17.704779881026173, -1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>203</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_12_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-17.080265010357753, -17.080265010357753, -1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>204</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_13_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-19.656219457475515, -19.656219457475515, -1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>205</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_14_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-18.62642351376405, -18.62642351376405, -18....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>206</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_15_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-18.99254452102233, -18.99254452102233, -19....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>207</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_16_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-18.644577316254583, -18.644577316254583, -1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>208</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_17_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-18.26814492069429, -18.26814492069429, -18....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>209</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_18_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-19.015339382898574, -19.015339382898574, -1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>210</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_19_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-19.286046268362163, -19.286046268362163, -1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>211</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_20_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-17.528184532009597, -17.528184532009597, -1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>212</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_21_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-18.211932701053918, -18.211932701053918, -1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>213</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_22_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-18.88242306307249, -18.88242306307249, -18....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>214</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_23_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-18.983247837426187, -18.983247837426187, -1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>215</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_24_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-18.536753992075575, -18.536753992075575, -1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>216</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_25_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-19.433619943446843, -19.433619943446843, -1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>217</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_26_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-18.782914914936814, -18.782914914936814, -1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>218</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_27_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-19.466272754087207, -19.466272754087207, -1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_28_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-19.19418612036834, -19.19418612036834, -19....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>220</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_29_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-18.158132829321154, -18.158132829321154, -1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>221</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_30_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-18.990759787360652, -18.990759787360652, -1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>222</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_31_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-18.600088883121042, -18.600088883121042, -1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>223</th>\n",
+       "      <td>pareidolia_art_inv</td>\n",
+       "      <td>par_gaze_32_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>[[-18.425567693421613, -18.425567693421613, -1...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            stim_folder        stim_name  \\\n",
+       "192  pareidolia_art_inv  par_gaze_01_inv   \n",
+       "193  pareidolia_art_inv  par_gaze_02_inv   \n",
+       "194  pareidolia_art_inv  par_gaze_03_inv   \n",
+       "195  pareidolia_art_inv  par_gaze_04_inv   \n",
+       "196  pareidolia_art_inv  par_gaze_05_inv   \n",
+       "197  pareidolia_art_inv  par_gaze_06_inv   \n",
+       "198  pareidolia_art_inv  par_gaze_07_inv   \n",
+       "199  pareidolia_art_inv  par_gaze_08_inv   \n",
+       "200  pareidolia_art_inv  par_gaze_09_inv   \n",
+       "201  pareidolia_art_inv  par_gaze_10_inv   \n",
+       "202  pareidolia_art_inv  par_gaze_11_inv   \n",
+       "203  pareidolia_art_inv  par_gaze_12_inv   \n",
+       "204  pareidolia_art_inv  par_gaze_13_inv   \n",
+       "205  pareidolia_art_inv  par_gaze_14_inv   \n",
+       "206  pareidolia_art_inv  par_gaze_15_inv   \n",
+       "207  pareidolia_art_inv  par_gaze_16_inv   \n",
+       "208  pareidolia_art_inv  par_gaze_17_inv   \n",
+       "209  pareidolia_art_inv  par_gaze_18_inv   \n",
+       "210  pareidolia_art_inv  par_gaze_19_inv   \n",
+       "211  pareidolia_art_inv  par_gaze_20_inv   \n",
+       "212  pareidolia_art_inv  par_gaze_21_inv   \n",
+       "213  pareidolia_art_inv  par_gaze_22_inv   \n",
+       "214  pareidolia_art_inv  par_gaze_23_inv   \n",
+       "215  pareidolia_art_inv  par_gaze_24_inv   \n",
+       "216  pareidolia_art_inv  par_gaze_25_inv   \n",
+       "217  pareidolia_art_inv  par_gaze_26_inv   \n",
+       "218  pareidolia_art_inv  par_gaze_27_inv   \n",
+       "219  pareidolia_art_inv  par_gaze_28_inv   \n",
+       "220  pareidolia_art_inv  par_gaze_29_inv   \n",
+       "221  pareidolia_art_inv  par_gaze_30_inv   \n",
+       "222  pareidolia_art_inv  par_gaze_31_inv   \n",
+       "223  pareidolia_art_inv  par_gaze_32_inv   \n",
+       "\n",
+       "                                                    hg  \\\n",
+       "192  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "193  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "194  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "195  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "196  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "197  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "198  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "199  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "200  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "201  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "202  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "203  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "204  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "205  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "206  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "207  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "208  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "209  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "210  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "211  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "212  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "213  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "214  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "215  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "216  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "217  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "218  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "219  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "220  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "221  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "222  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "223  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "\n",
+       "                                                   dg2  \n",
+       "192  [[-18.302300826718575, -18.302300826718575, -1...  \n",
+       "193  [[-18.37589334575482, -18.37589334575482, -18....  \n",
+       "194  [[-18.496778133430396, -18.496778133430396, -1...  \n",
+       "195  [[-18.60241424151377, -18.60241424151377, -18....  \n",
+       "196  [[-18.29694626711732, -18.29694626711732, -18....  \n",
+       "197  [[-19.10143934365796, -19.10143934365796, -19....  \n",
+       "198  [[-18.279331731196674, -18.279331731196674, -1...  \n",
+       "199  [[-17.89642302378649, -17.89642302378649, -17....  \n",
+       "200  [[-18.147003897331178, -18.147003897331178, -1...  \n",
+       "201  [[-19.03680264783764, -19.03680264783764, -19....  \n",
+       "202  [[-17.704779881026173, -17.704779881026173, -1...  \n",
+       "203  [[-17.080265010357753, -17.080265010357753, -1...  \n",
+       "204  [[-19.656219457475515, -19.656219457475515, -1...  \n",
+       "205  [[-18.62642351376405, -18.62642351376405, -18....  \n",
+       "206  [[-18.99254452102233, -18.99254452102233, -19....  \n",
+       "207  [[-18.644577316254583, -18.644577316254583, -1...  \n",
+       "208  [[-18.26814492069429, -18.26814492069429, -18....  \n",
+       "209  [[-19.015339382898574, -19.015339382898574, -1...  \n",
+       "210  [[-19.286046268362163, -19.286046268362163, -1...  \n",
+       "211  [[-17.528184532009597, -17.528184532009597, -1...  \n",
+       "212  [[-18.211932701053918, -18.211932701053918, -1...  \n",
+       "213  [[-18.88242306307249, -18.88242306307249, -18....  \n",
+       "214  [[-18.983247837426187, -18.983247837426187, -1...  \n",
+       "215  [[-18.536753992075575, -18.536753992075575, -1...  \n",
+       "216  [[-19.433619943446843, -19.433619943446843, -1...  \n",
+       "217  [[-18.782914914936814, -18.782914914936814, -1...  \n",
+       "218  [[-19.466272754087207, -19.466272754087207, -1...  \n",
+       "219  [[-19.19418612036834, -19.19418612036834, -19....  \n",
+       "220  [[-18.158132829321154, -18.158132829321154, -1...  \n",
+       "221  [[-18.990759787360652, -18.990759787360652, -1...  \n",
+       "222  [[-18.600088883121042, -18.600088883121042, -1...  \n",
+       "223  [[-18.425567693421613, -18.425567693421613, -1...  "
+      ]
+     },
+     "execution_count": 216,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia_art_inv']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 217,
+   "id": "1fd897b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.6511730129304933\n",
+      "1. sp_corr_dg2_par_art_and_par_art_inv:  0.7804680471789613\n",
+      "0.95987011323264\n",
+      "2. sp_corr_dg2_par_art_and_par_art_inv_inv:  0.9436671658032658\n",
+      "0.300804389324272\n",
+      "3. sp_corr_hg_par_art_and_par_art_inv:  0.4933945399585421\n",
+      "0.5890596716490965\n",
+      "4. sp_corr_hg_par_art_and_par_art_inv_inv:  0.6869546396747661\n",
+      "0.6504673971187132\n",
+      "5. sp_corr_hg_par_art_inv_inv_and_dg2_par_art_inv_inv:  0.7108625180661701\n",
+      "0.6598860338668044\n",
+      "6. sp_corr_hg_par_art_and_dg2_par_art:  0.6911152394142075\n"
+     ]
+    }
+   ],
+   "source": [
+    "#pareidolia_art\n",
+    "\n",
+    "loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia_art']\n",
+    "\n",
+    "####\n",
+    "#dg2_par_art_and_par_art_inv\n",
+    "\n",
+    "print(spearmanr(loaded_df_csv[loaded_df_csv['stim_name'] == 'par_gaze_01']['dg2'].to_numpy()[0].flatten(), \n",
+    "          loaded_df_csv[loaded_df_csv['stim_name'] == 'par_gaze_01_inv']['dg2'].to_numpy()[0].flatten())[0])\n",
+    "\n",
+    "\n",
+    "sp_corr_dg2_par_art_and_par_art_inv = []\n",
+    "for i in range(len(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia_art'])):\n",
+    "    sp_corr_dg2_par_art_and_par_art_inv.append(\n",
+    "            spearmanr(np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia_art'].iloc[i]['dg2']).flatten(), \n",
+    "                      np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia_art_inv'].iloc[i]['dg2']).flatten())[0])\n",
+    "    \n",
+    "    #break\n",
+    "\n",
+    "\n",
+    "print('1. sp_corr_dg2_par_art_and_par_art_inv: ', np.mean(sp_corr_dg2_par_art_and_par_art_inv))\n",
+    "\n",
+    "\n",
+    "####\n",
+    "#dg2_par_art_and_par_art_inv_inv\n",
+    "\n",
+    "print(spearmanr(loaded_df_csv[loaded_df_csv['stim_name'] == 'par_gaze_01']['dg2'].to_numpy()[0].flatten(), \n",
+    "          np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_name'] == 'par_gaze_01_inv']['dg2'].to_numpy()[0])).flatten())[0])\n",
+    "\n",
+    "\n",
+    "\n",
+    "sp_corr_dg2_par_art_and_par_art_inv_inv = []\n",
+    "for i in range(len(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia_art'])):\n",
+    "    sp_corr_dg2_par_art_and_par_art_inv_inv.append(\n",
+    "            spearmanr(np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia_art'].iloc[i]['dg2']).flatten(), \n",
+    "                      np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia_art_inv'].iloc[i]['dg2'])).flatten())[0])\n",
+    "    \n",
+    "    #break\n",
+    "\n",
+    "\n",
+    "print('2. sp_corr_dg2_par_art_and_par_art_inv_inv: ', np.mean(sp_corr_dg2_par_art_and_par_art_inv_inv))\n",
+    "\n",
+    "\n",
+    "####\n",
+    "#hg_par_art_and_par_art_inv\n",
+    "\n",
+    "print(spearmanr(loaded_df_csv[loaded_df_csv['stim_name'] == 'par_gaze_01']['hg'].to_numpy()[0].flatten(), \n",
+    "          loaded_df_csv[loaded_df_csv['stim_name'] == 'par_gaze_01_inv']['hg'].to_numpy()[0].flatten())[0])\n",
+    "\n",
+    "\n",
+    "\n",
+    "sp_corr_hg_par_art_and_par_art_inv = []\n",
+    "for i in range(len(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia_art'])):\n",
+    "    sp_corr_hg_par_art_and_par_art_inv.append(\n",
+    "            spearmanr(np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia_art'].iloc[i]['hg']).flatten(), \n",
+    "                      np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia_art_inv'].iloc[i]['hg']).flatten())[0])\n",
+    "    \n",
+    "    #break\n",
+    "\n",
+    "\n",
+    "print('3. sp_corr_hg_par_art_and_par_art_inv: ', np.mean(sp_corr_hg_par_art_and_par_art_inv))\n",
+    "\n",
+    "\n",
+    "####\n",
+    "#hg_par_art_and_par_art_inv_inv\n",
+    "\n",
+    "print(spearmanr(loaded_df_csv[loaded_df_csv['stim_name'] == 'par_gaze_01']['hg'].to_numpy()[0].flatten(), \n",
+    "          np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_name'] == 'par_gaze_01_inv']['hg'].to_numpy()[0])).flatten())[0])\n",
+    "\n",
+    "\n",
+    "\n",
+    "sp_corr_hg_par_art_and_par_art_inv_inv = []\n",
+    "for i in range(len(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia_art'])):\n",
+    "    sp_corr_hg_par_art_and_par_art_inv_inv.append(\n",
+    "            spearmanr(np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia_art'].iloc[i]['hg']).flatten(), \n",
+    "                      np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia_art_inv'].iloc[i]['hg'])).flatten())[0])\n",
+    "    \n",
+    "    #break\n",
+    "\n",
+    "\n",
+    "print('4. sp_corr_hg_par_art_and_par_art_inv_inv: ', np.mean(sp_corr_hg_par_art_and_par_art_inv_inv))\n",
+    "\n",
+    "\n",
+    "####\n",
+    "#hg_par_art_inv_inv_and_dg2_par_art_inv_inv\n",
+    "\n",
+    "\n",
+    "print(spearmanr(np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_name'] == 'par_gaze_01_inv']['hg'].to_numpy()[0])).flatten(), \n",
+    "          np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_name'] == 'par_gaze_01_inv']['dg2'].to_numpy()[0])).flatten())[0])\n",
+    "\n",
+    "\n",
+    "sp_corr_hg_par_art_inv_inv_and_dg2_par_art_inv_inv = []\n",
+    "for i in range(len(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia_art'])):\n",
+    "    sp_corr_hg_par_art_inv_inv_and_dg2_par_art_inv_inv.append(\n",
+    "            spearmanr(np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia_art_inv'].iloc[i]['hg'])).flatten(), \n",
+    "                      np.array(rotate_180(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia_art_inv'].iloc[i]['dg2'])).flatten())[0])\n",
+    "    \n",
+    "    #break\n",
+    "\n",
+    "\n",
+    "print('5. sp_corr_hg_par_art_inv_inv_and_dg2_par_art_inv_inv: ', np.mean(sp_corr_hg_par_art_inv_inv_and_dg2_par_art_inv_inv))\n",
+    "\n",
+    "\n",
+    "\n",
+    "####\n",
+    "#hg_par_art_and_dg2_par_art\n",
+    "\n",
+    "print(spearmanr(loaded_df_csv[loaded_df_csv['stim_name'] == 'par_gaze_01']['hg'].to_numpy()[0].flatten(), \n",
+    "          loaded_df_csv[loaded_df_csv['stim_name'] == 'par_gaze_01']['dg2'].to_numpy()[0].flatten())[0])\n",
+    "\n",
+    "\n",
+    "sp_corr_hg_par_art_and_dg2_par_art = []\n",
+    "for i in range(len(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia_art'])):\n",
+    "    sp_corr_hg_par_art_and_dg2_par_art.append(\n",
+    "            spearmanr(np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia_art'].iloc[i]['hg']).flatten(), \n",
+    "                      np.array(loaded_df_csv[loaded_df_csv['stim_folder'] == 'pareidolia_art'].iloc[i]['dg2']).flatten())[0])\n",
+    "    \n",
+    "    #break\n",
+    "\n",
+    "\n",
+    "\n",
+    "print('6. sp_corr_hg_par_art_and_dg2_par_art: ', np.mean(sp_corr_hg_par_art_and_dg2_par_art))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6a899d0a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ad18e039",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a0c193f6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a26cfe92",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34a02325",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d4b698b0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "19f4e4ad",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "269c73d2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11639b60",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f7efc39b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a8a06f11",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62721e14",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 232,
+   "id": "41fc51db",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 2160x1728 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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pQeBY4Pg65x8L7CKpl3QbfOOIuA+4GBhPCkAviIj7F6RyEfEsUCIF0XcBDxcOHwd8LddzPaA8/GE/0m34EZIeyK/NF+T6NZwDrCbpr6T2eqhw7cr6TyQNHXiEdLu+mVvro4CPSHoI+Czw93ZUuuA4qrfbAouIV0h/m/dHxPi+lmfN0eLU0z1kyJCYMGHCQFejsy6r+8BmcuDi8zdvi0Zt6vZsndu0vTr/330TF1g0SJoCDImIFwe6LvVIWg54NSJC0v7AARHx6X647hLAUhHxWn6y/4/AhnmM7kJvoNrN2s9DCMzMzLrPVsDZkgRMBb7UT9ddjjR8YCnSD5uvdEvwmg1Uu1mbOYA1MzPLImJQZZqkE0njT4uuioiT23VdSfcA76xIPigieqvlj4g7SdNGNVv+tcAHKpK/GRE3t1LPiJgOzDfva6v1b0UeA3xblUMfi4iXWimrWrtJGkyasaFodkRs21JFrV+1FMBKWos0F9zuwKrAs8B1wKg8BqTZcnYCvkH6Er2XNNHyJOCsiLipIu9/kMbB7EGa221NYAZwH3BuRFyDmZlZh+RAtW3Bao1rdDRYioh9GufqU/kdq38OUjfvYPm9nSzfOqPph7jyWJd7SRM0jwd+DDxBGsQ+Nv9Caqaco0hPaX4sb38MjAGGAn/Iv3SL/pM0WfSGwO3A6cDNpHn1fivp9GY/g5mZmZl1v1Z6YM8BVgeOiYiflBNzAHk86dfpkfUKyGNmfgi8BmwVEY8Wjv2A9LTiiZJ+lCcihhQsD4uIMRVlfYg0Hcbxkn4dEfe28FnMzMzMrEs11QObe193A6YAP604PJI0z9tBkpZvUNS7gZWAycXgFSAiHgYmA8uS5tIrp19TGbwW8pdXJRnWzOcwMzMzs+7XbA/sLnl7S+WSdBExXdJdpAB3O6oPtC57HngB2EDS+hHxWPmApA1I60I/0MKg7Dfy9s0m85uZ2cLoMv2oreUdGCe0tbwBImlGRKzQOGfdMt5HesZk3zZVq60kDQNOiIhPNZl/Cg2mOpM0GtiLtDzu46QldafWyDsEODgijmmp4jagmh0Du2HeTq5xvByIblCvkLy82lfzde+V9EtJP5R0CWl87UPM/6RnVXmpvM+Rlvm7pU6+wyVNkDThhRdeaKZoMzOzjlHyjlr77RYRzyyswWsH3QpsGhEfJsUu366VMSImOHjtPs3+B7NS3tZasaKcvnKjgiLiKuCjpPnXDga+RVpmbyZwEenBsLry/G0XAGuQZiJ4uFbeiDgvIoZExJDVVlutUdFmZraYkLS8pBslTZQ0SdJwSVMknSapV9J4SevVOX8NSdfm8ydK2iGnfy2XN0nScTltkKRHc4fNJGDniv2161zn5Fz+uLwkK5LWzfu9kk6SNKPO+YMkTcrvR0i6TtKt+bMenet7fy7v3Tnf1pIezCt5jS6fX6f8OyXdl1/ldhgmqUfS1ZIekfTr/O83knbPafeRZhqqSdKqkm6R9JCkCygsrCHpf3I7/lnS5ZJOAIiIWyKifHd2HLBWnfKHSfp9fl/KnWt3SnpK0mcL34eb8rM8SNoj1/9eSWeVz7f+0+9LyUr6f6SVO+4kTYu1XN7eBpwN/KaJYv6X1FN7J/C1ztTUzMwWcbsDz0TEZhGxKVCexnFaRAwm/Zt0Rp3zzwLGRMRmwJbAQ5K2Is3Wsy1pWN1hkrbI+dcHzomITYCnivsR8VSNaywPjMvXuAM4LKefCZyZ6/l0i597U1LQuDXpAexZEbEFacnag3Oei4AjImJz4K0G5T0P7BoRWwLDSe1StgVp+daNgQ8CO0paBjifdIt/K9J0mvWMBP6c2+1aYB1IQTbpTuxmwCepMj9t9iXgDw2uUbQuqaNtb+BS4Pbczq8Ce+b6/xz4ZERsBbh3bAA0G8CWe1hXqnG8nD61XiF5nOuFpKECB0XEIxHxakQ8QuqFvRf4fB4PU6uM00izHtwB7FGYrcDMzKwVvcCukk6VtHNElP+tu7yw3b7O+R8FzgWIiLfy+TsB10bEzIiYAVxDmvYR4KmIGFc4v3K/mteBcu/evcCg/H574Kr8/rIGZVS6PSKmR8QLpH/fb8jpvcAgSSsDK0bE2CbLXwo4X1JvrtPGhWPjI+Lp/PzMA7n+GwFPRsRjeWjhpQ3K/0g5T0TcCJTnnd8RuD4iXssLLNxQeaLS1JxvAr9ucI2iP0TEG6T2WIJ5P2x6C/V/IiKezOmXz1eCdVyzAWx5xoBaY1zXz9taY2TLdiN90cdUeRhsDikohfSLbD6SfkxaAOF20i+fmrdMzMzM6omIyaSe017gJEnfLR8qZmvjJWc22K/mjRzkQeoJbccKmsWOnzmF/TkLWP7xwHOkntAhwNI1rtWu+jdF0gjgU8AXCm3YjNkwNy4ptv+Cto91QLMB7O15u1vlQHNJK5J+Bc0ijTOpp7zMXK3u9nL629ZVzgPcf0q6DXErsGdEzGqu6mZmZvNTejp/VkRcCowmBbOQboOXt2OrnZvdBhyVy1pC0kqkoW2fkbSc0tSS++S0dhtHun0OsH87C85P60+XVF5dq1H5KwHP5oDvIFKvZT2PkHp61837BzTIfwdwIICkTwKr5PS7gL0kLSNpBVKwSs63O/BfwN4diBceBT4oaVDeH14nr3VIU78kIuJxSbeQelC/CvykcHgUaYzOzyNi7q9JSRvlcx8p5C3/R7yv0mIFDxbybw7sS/q1+6dCuoDzgC+TxrB8NiJea/YDmplZFxiYaa8GA6MlzSFNy3gUcDWwiqQHST1x9YKrY4HzJB1K6l08KiLGSrqYtAgPwAURcX8h2GmX44BL8y3ym6j9kPWCOpQ0LGAOabXMeuWfQ1oZ8+Bcl7o9yxHxmqTDgRslzSLFBivWOWUUcLmkh4C7gb/ncv4i6XfAg6Qe4N5CPc8mdZrdmp8bGxcRdRdbalZEvCrpK8BNkmYCf2lHudYaNdurnn8p3U1ajet64GHSIPVdSEMHdijO3yopACJCFeVcSBrg/jppMPZTpDElnyHddjgjIo4v5B8JlEiDp8+gonc2eyAirmv0GYYMGRITJkxo/GG72WVqnOfAdt4RWww0alO3Z+vcpu3V+f/um7jAokFNzDG6MJC0HPBqRISk/YEDIuLTbSx/hfIwPUnfAtaMiGPbVX67lOuZ2+MO4PCIuK8fryvSAk+PRcSPO31dm6fpsRy5F3YI8D3Sk5t7AM+SnoQcFRGv1Du/4FDSl2wE8AnSr65/A38Gzo+IylkIPpC3y1J7HrdfAtc1eX0zM7NutxVwdg6gppKetG+nPSV9mxQnPEX6N3thdJ6kjYFlgF/2R/CaHSbpi6SOt/tJsxJYP2q6B3ZR4B7YzL1brXFvYfstYm1aKpUYNWrU3P2RI0dSKpX6rwLuge2ofJu+cpGdqyLi5DZe4x7mPSdSdlBE9LZQxmDgVxXJsyNi22r5WyXpE8CpFclPRsQ+bSr/ENKwjKK7IuKrbSq/o/W3/uUAdlHjALb9FrFga6GwCLbpsGHDAOjp6en/izuANbPFTL8vZGBmZmZm1hcOYM3MzMysqziANTMzM7Ou4hUlzMwW1N/Om/f+1WfnTwNY7/D+q4+Z2WLCAayZmQ28yzTfOvZ9cmDs1dbyBoikGRGxQh/LeB9wVkTs26ZqVZY/BDg4Io5pU3kdre+CaOXvkBey+H1EXF0nzxeAb5IekJxOWgRjYp38d0fEDq3VujmdaO9O1rfMAayZmVk/ynO3Ki+9Ot9+u0XEM6SVLjsiIiYAbZvip9P1XUg8CQyNiFfy8rjnkRaHqqqTwWAn2rvTwSt4DKyZmS2mJC0v6UZJEyVNkjRc0hRJp0nqlTRe0np1zl9D0rX5/ImSdsjpX8vlTZJ0XE4bJOlRSZcAk4CdK/bXrnOdk3P54yStkdPWzfu9kk6SNKPO+YMkTcrvR0i6TtKt+bMenet7fy7v3Tnf1pIelPSApNHl82uUP0zS7/P7kqQLJfVIekLSMTn9FElfLZxTklR1+eAq9b1G0k2SHpN0Wk4/UtLowjkjJJ1dp47XSbpX0kN5Gdty+owa7fsBSWPL7Vur3JxXks7Of88/klYsLR/bQ9Ij+dpnldspIu4uLAA1DlirwTXKq6INkzRG0vW5fU+R9IX8Xe1VWjW1r9+PdrR3sb49kq7O7fDr3F67S7qqkH/ud6hZDmDNzGxxtTvwTERsFhGbAjfl9GkRMRg4m7SEeS1nAWMiYjNgS+AhSVuRlkvfFtiOtGLTFjn/+sA5EbEJaXWrufsR8VSNaywPjMvXuAM4LKefCZyZ6/l0i597U+CzwNbAycCsiNgCGAscnPNcBBwREZsDb7VY/kaklTa3AUZKWgq4AtivkGe/nNaMzYHhwGBguKS1gd8CxQUIhgOVK3kWfSkitgKGAMdIWjWn12vfc3P7PtugfvsAGwIbk9qv/ENmGdIKXZ/M116txvmHAn9ocI2izYAjgQ8BBwEbRMQ2wAXAfxbqv6Dfj83pe3sXbQEcR2qfDwI7An8EtpW0/AKUBziANTPrs9JZNzBm/GTGjJ+M1j+C0lntHc5pHdML7CrpVEk7R8S0nH55Ybt9nfM/CpwLEBFv5fN3Aq6NiJkRMQO4Btg5538qIsYVzq/cr+Z1oNwzdS8wKL/fHij3YF3WoIxKt0fE9Ih4AZgGlL+wvcAgSSsDK0bE2AUs/8aImB0RLwLPA2tExP3A6pLeJ2kz4JWI+EeT5d0WEdMi4jXgr8D7c92fkLRdDkY3Au6qU8YxkiaSejvXJv14gNrtuyPzvgeVq5tV+ghwef4OPAP8KadvBDwREU/m/csrT5S0CymA/WaDaxT9JSKejYjZwOPALTm9l/Z8P9rR3kXjI+LpPETmAWBQRLxJ+sG4l6QlgT2B61uppMfAmpn1UemYvSgds0g8M7RYiYjJkrYE9gBOknRb+VAxWxsvObPBfjVvxLwlM9+iPf9uzy68n1PYn9OB8ot1voo01vK9NN/7Wq+835B6ch8h/Wio+reSNAz4OLB9RMyS1AMskw/Xa9+OLgko6cOkXtNPRsRLLZw6UH+/ptq7xfKOBl4GJkTE9FYq6R5YMzNbLCk9fT0rIi4FRpOGAUC6nVnejq12bnYbcFQuawlJKwF3Ap+RtFy+PbpPTmu3ccDn8vv921lwREwFpksqP1TUrvKvyGXty7zewb64Fvg0cAD1bz+vROrxnSVpI9LQjkbuYt7n/kKDvHeQbrUvIWlNYJec/ijwQUmD8n75e4WkdUi98wdFxOQm6tOqTnw/mm3vZo0h/Td32IKU5x5YMzMbeAMz7dVgYLSkOcAbpGD0amAVSQ+Seo4OqHP+scB5kg4l9SwdFRFjlaZRGp/zXBAR9xeCmHY5DrhU0omkW7HT6mdv2aHA+bltxrSj/Ih4SNKKwD8jotG40mbKe0XSw8DGETG+TtabgCNz3kdJwV0jxwKXSfomjW9tX0saTvJX4O/kHz0R8aqkrwA3SZoJ/KVwzneBVYFzJAG8GRFDmqhXs46jzd+PFtq72fLeyg9ujQC+2Or5ar4HuPsNGTIkJkxo20wfC6fL1DjPgYvP37wtGrWp27OqUqnEqFGj5u6PHDmSUqmUdhaVNq1ctKCa/ljIoPP/3TdxgUWDpCnAkDx+c6ElaTng1YgISfsDB0TEp9tY/gp5DC+SvgWsGRHHtqv8xUW5HZWi1J8Cj0XEj/vhuh39fiwM3ANrZh1RKpXo6ekBmLs1s7bZCjg7B0ZTgS+1ufw9JX2bFCc8Reols9YdJumLwNLA/aRZCfpDp78fA84BrC306vbkmZm1UUQMqkzLt2E/X5F8VUSc3K7rSroHeGdF8kER0Vstf0TcSZpOqVjGYOZ/Yn52RNScIL+WiLiCigetJH0COLUi65MRsQ8LoJ31zeWtShqXXOljLT4kVav8luube1ub6nFtZ/07/f3I5XW0vRtxAGsLPffkmdlAyoFq24LVGtdYoCCiooxe0hyeHRERNwM3t7G8ttY3B01tK69K+Z1u366qf6fr24gDWDNrrxs2nPf+pX/Mn2ZmZtZHnkbLzMzMzLqKA1gzMzMz6yoOYM2sI0qXvciYSa8yZtKraO/JlC5bqGclMjOzLuIxsGaZZztor9KB76F04HsGuhrWLS7To20t78BYJAZeS5oRESv0sYz3AWdFxL5tqlZb5aVeT4iIT9U4vjdp8vxTOnT9I0krsl3SpvI6Wl9L3ANrlpVKJYYOHcrQoUOJiEU6eC2VSkia+1qUP6vZwkbJO2rtt1tEPLOwBq/NiIjfdTIYjIiftSt4zeV1tL6WOIA1WwwtTsG6WS2Slpd0o6SJkiZJGi5piqTTJPVKGi9pvTrnryHp2nz+REk75PSv5fImSToupw2S9KikS4BJwM4V+2vXuc7JufxxktbIaevm/V5JJ0maUef8QZIm5fcjJF0n6db8WY/O9b0/l/funG9rSQ9KekDS6PL5dcq/U9J9+VVuh2GSeiRdLekRSb/OE+sjafecdh/w2VplF+p8dn5/saRzc12fyNe4UNLDSkv4ls85VNLk/Dc8v3x+jfJLkk7I73sknZrPmyxp55w+TtImhXN6JFVd+rVKfc+SdHeu7745/TeS9iycc3H5mDXHAayZ2ULAveIDYnfgmYjYLCI2Ja0ZDzAtIgYDZwNn1Dn/LGBMRGwGbAk8JGkr4BBgW2A70kpMW+T86wPnRMQmpNWt5u5HxFM1rrE8MC5f4w7gsJx+JnBmrufTLX7uTUlB49ak+W1nRcQWwFjg4JznIuCIiNgceKtBec8Du0bElsBwUruUbQEcB2wMfBDYUdIywPnAXqQVo97bYv1XAbYHjgd+R1ooYBNgsKTNlYZM/A+p/XcENmqx/CUjYptc75E57QpgPwBJa5KW1m12bfo1gZ2ATwHlntlieUsDHwNubLGeizUHsLZYqBsc3LDhvNdL49OrmGZVOeBqL/eKD4heYNfc47ZzREzL6ZcXttvXOf+jwLkAEfFWPn8n4NqImBkRM4BrgJ1z/qciYlzh/Mr9al4Hfp/f3wsMyu+3B67K7y9rUEal2yNiekS8AEwDbsjpvcAgSSsDK0bE2CbLXwo4X1JvrtPGhWPjI+LpiJgDPJDrvxFpBa/HIiKAS1us/w35vF7guYjozeU/lMvfhvTD4uWIeIN57dSsa/K22N5XAuUe0v2Aq1so77qImBMRfwXWyGl/AHaR9E7gk8AdEfFqi/VcrPkhLlt4/e28ee9ffXb+NID1Dm+qKK/m1X5uU+t2ETFZ0pbAHsBJksrLYkYxWxsvObPBfjVv5GANUk9oO/7dnl14P6ewP2cByz8eeI60dOk7gNdqXKvd9S/Wvby/JPBmm8qfW9+I+KeklyR9mNTLfOQClAegXN5rknqAT+TyftPHOi923ANrZjaQevaa95ram17FtJ69BrqGi6x8q3lWRFwKjCYNA4AUUJS3Y6udm90GHJXLWkLSSsCdwGckLSdpeWCfnNZu44DP5ff7t7PgiJgKTJdUXt62UfkrAc/mXtCDgCUa5H+E1NO7bt4/YEHrWsNfgKGSVpG0JPPaqa+uAP4LWCkiHmxTeYeQeuhvapDXKrT0S0jSWsD3SOOGVgWeBa4DRkXEKy2WtSVwAvARYDVgKulL/YvKpwHz+JDjgC+Qxgy9CTxImhbkylau2y08pVNrarZX8R//qb1p64DAbOEzMNNeDQZGS5oDvEEKRq8GVpH0IKnnrF5wdSxwnqRDSb11R0XE2Pww0fic54KIuF/SoDbX/TjgUkknkoKfafWzt+xQ0rCAOcCYBuWfA/xW0sG5LnV7lnPv4+HAjZJmkQL8FdtT7bm9pT8g/Q1eJsUW7Wifq0ljj7/fhrIAbgF+BVwfEa+3qczFRtMBbP6ldDewOnA96QuxDek/4N0l7RgRLzVZ1tGkL8ErpEHL/wTeTRpYvgdwSSHv0sDNwDBgCmlg+TtyviskbRoR3232c3QL355tTTvaqzzxPoD2nszI/d+92M5jWvcHlH8U2CIiIm4m/fsyV35IfnREfLOJ858DPl0l/XTg9Iq0KaR/46ru17nGCoX3VzNv7OU/ge0iIiTtD9T8AVC8VkRcDFxcODao8L547KGI+DCApG8BNR9YiojHgA8Xkr6Z03uAnkK+owvvb6LJh6uK9YqIEdU+V+Ux4LKIOC/3wF5L6myrVX6p8H5Y4f2LzBsDW/57N4ybatU37xf/nm+QYh9bAK30wJ5DCl6PiYiflBMlnU4a/3IyTYwJkbQb6QnFW4F9I2J6xfGlKk75Kil4HUt6ynFmzrcC6T+M/5b0uxaeBjSryhPvz+MfUO3X6K5K6eJHGTPxZQC0y+8Z+cX1KY3wQ4RW01bA2XlaqqnAl9pc/p6Svk2KE54CRrS5/E4rSfo4sAypp/O6ga2OtVtTAWzufd2N1AP604rDI4HDgYMkfb0cYNYxGngVOLAyeIW5v0iK9snbk4tlR8QMSSeRfll9hfb/xzswGvVuDbsBa52Dg6zRg3FNPhRnrWv0o6A0YsOG30kPLeq8Yo9kWb5N//mK5Ksi4uR2XVfSPcA7K5IPiojeavkj4k7SQ1PFMgaTbkkXzY6IbWlRRFxBGqNZLP8TwKkVWZ+MiH1oA0mHkO7qFt0VEV9ttayIOKFK+W39O7azvta6Zntgd8nbW/Ig7bkiYrqku0gB7nakQe1VSdqUdJvhOuBlSbuQfkUGaXqN2yvLZ978cE9UKbKc9rEmP4ctau4r/D9qxuPzp2XNBAdmCzv3jA+MHOC0LVitcY2Wg8wqZfQCm/e9NjXLn2/IRZvLv4g0TLBT5bf179jp+lp9zQaw5X/5J9c4/hgpgN2AOgEsadJkSJMe95Ae4CrqlfTZiPhbIe1F0oNbHwAersj/wbxdR9KynkPNrI+a/EEA7tU2M7OB0+w0Wivlba2n+MrpKzcoZ/W8PZQ0MHrPXPYGpImMB5OeSly6cE55ZYoTJS1bTszTk3ynkK/qtSUdLmmCpAkvvPBCg+q1R6MJ3puZAL4cHIyZ+DLa5feULn60X+pu1qzSiA2J2z819+XgtYb7Tpj3mvF4ehXTzMysZf29kEE5YF4C2L+wyse/8/QbGwFDSHO2lVdCOZM0ZmUH0jJ9/0eaCHhP0tCDaaQguHLoAQARcR5wHsCQIUPaOSF1TQ3HujVxG9C3vFtT+vndjLk3raaorf6XkYdvT+mIHQa4VmZtUlwR7qV/zJ9mZraYaTaALfewrlTjeDl9aoNyysf/VQheAchTgVxPCmC3IQew+WGtnUi9rfuS1oGeDvwf8G3SdF5vkuZ6s8VU6YgdHLCadbPL1N4OhgNDbS3PzBYqzQ4hKN+/3qDG8fXzttYY2cpyptY4Xl4MYdliYkTMiIjvRMQGEfHOiHhPRBxMemJzBWBildkLbBFROusGxoyfzJjxk9H6R1A6yzMxdFq5R3vMvU+jrf6X0s/vHugqdT23qfWVpBGSzm6QZ3NJYyU9JOlBScMb5L9A0sbtrWn7SJoiqeb8hpI69h+SpPdJurpxzpbK9H/4bdJsD+ztebubpHcUZwqQtCKwIzCLtLRdPeNIK3QMkrR8lSm3yhMSP9lkvQ7O28uazD+wWnhAxuYpHbMXpWM8UX5/co92+7lNrSzP3aryv6WV+300Czg4Ih7LS+XeK+nmvDzsfCLiy2245oCJiI79RxURz5Du/LazTP9PoE2a6oGNiMdJEwEPIi0sUDQKWB74VTEglbSRpLetshERs4BfkCYWPin/R1vOP5g0UfKbzFtppHzsXZV1krQrabWPx4GfN/M5zMy6WXm1uDGTXkV7T6Z02YsDXaWuJml5STdKmihpkqThucfvNEm9ksZLWq/O+WtIujafP1HSDjn9a7m8SZKOy2mDJD0q6RJgErBzxf7aNa5xiKTJksaTOovK6etKGpfreZKkGQARMTmvjFUOwJ4nLdde6zP0SBqS38+QNDr33v5R0jb5+BOS9s55lpN0paS/5s9+T/n8GuWfmx+kfkjSqEL6FEmjJN2XP8NGOX1VSbfk/BeQnnmpqfy5JQ2TNEbS9bm+p0j6Qv4b9irNZ1+z3WqUPUjSpPx+hKRrJN0k6TFJp+X0IyWNLpxTt5e8or49kq6W9IikXyvZXdJVhfzDJP2+XhssrpodQgBpsYDngbMkXSfph5L+RFqFazJwYkX+h5l/2iuA/yHN+XocMFbS/0q6FLiHFNiekAPmokfyl+aM/KW8hTQX3SvAp5tYPGGR08xMBma2aCkd+B7idxvMfXnluD7bHXgmIjaLiE2Bm3L6tIgYDJwNnFHn/LOAMRGxGbAl6UHjrYBDgG1Jc6MfJmmLnH994JyI2IS0utXc/Yh4qrJwSWuSOol2BHYCirf6zwTOzPV8ulrlJG0DLE3q6GnG8sCfcv2mAycBu5IWFPpezvMV4JWI2Jj07/lWDco8MSKGkOaAHyqpuOTsixGxJXAuUL4dORL4c67DtcA6TdYd0sIORwIfAg4CNoiIbYALgP/MeRq2Wx2bA8NJMyYNl7Q28FvmLbhEPv6bJsvbghQLbUyaFnRH4I/AtkozLbVa3mKl6QA2B5VDSOv7bgt8HViX9GXYLiJearKcfwM7Az8grQF8NPAp4M/AJyLizCqn/Rr4D9JqW8eSvtCnAZtGxEPNfoZFSalUYujQoQwdOpSIcABrLfG4YjMAeoFdJZ0qaeeIKD+wfHlhu32d8z9KCr6IiLfy+TsB10bEzIiYAVxD+jcP4KmIKA61q9yvtC3QExEvRMTrvH1lrO2Bck/dfMPocvD7K+CQFoYmvM68IL6XFJy/kd8Pyuk7kQOqiJgEPNigzP0k3QfcD2zC24Pwa/L23kL5HyFNq0lE3Mi8Z2Oa8ZeIeDYiZpOC9lsKn6Vcft12a+C2iJgWEa8BfwXeHxEvAE9I2k7SqqTZlO5qsrzxEfF0/vs8AAyKiDdJf4O9JC1JmnHp+hbruVhoaRqtiPgH6ZdlM3lrdvvn/6hPZP5e21r5vwF8o5m8A6rRMp2Zp3yygeZxxWbpdrukLYE9SMPaygvxFGdEaOfsCJV3Czty91Bp2N2NpN7PRs+mFL0REeXPOweYDRARc3Iw1Wo9PkDqWd06Il6RdDHpTmvZ7Lx9i/ZM6zm78H5OYX9OB8ov1vk3wH6kWZGuLbRhX8o7mjS70oSImL7ANV6E9fc8sIYf5jAzm88ATHul9JDTyxFxqaSpQPmBpuHAKXk7tsbpkFaePAo4Q9ISpFlx7gQulnQKafzmPqTb2QviHuDM3LP3b9Kc6BPzsXGkOdOvAPYvfKalSbfeL4mItj5Bn91FCtZuV5q9YHCdvO8iBenTJK0BfJK0Cmc9dwAHkn5QfBJYpc81fruq7dZH15I65LYgPZvTV2OAC0nThnr4QA0OYLuNJzQ3M2uXwcBoSXOAN0jB6NXAKpIeJPWQHVDn/GOB8yQdSupBOyoixuaexvE5zwURcb+kQa1WLiKelVQiBdFTSbeZy44DLpV0IumWc3n4w36k2/CrShqR00ZERPHcvjgH+KWkv5J6HB+ixiqdETFR0v053z9o7tb6KOBySQ8BdwN/b0ut5zmO6u22wHLv8sPAxhExvuEJjct7Kz+4NQL4Yl/LW1Q5gDUzs8VSRNxMeiB4LqXJcUZHRMOetIh4Dvh0lfTTgdMr0qYwb6rI+fbrXOMi4KIqh/5Jev4kJO0PbJjzX0oeQ9qMiBhWeL9C4X2pIl/52GvA/4uI1/KT/X8kPZBWq/wRNdIHFd5PAIbl9y8Bu7VQ/xXytodC727F5yoeq9puNcqeQv4bRcTFpGeAysc+VZH3bfsLUN+jK/IdTRpGYDU4gDUzM+s+WwFnK0XcU0kPOfeH5UjDB5YiDZH4Sn7ArFsMVLtZmzmANTMzy4o9g2X5dvPnK5KvioiT23VdSfeQVpcsOigieqvlj4g7SdNGNVv+tcAHKpK/mXuhm5YfKJpv3tdW69+KPAb4tiqHPtbsDEhl1dpNaR76X1VknR0R27ZU0Xnlta2+VpsD2C5VntAcQHtPZuT+7/ackGZmHZAD1bYFqzWusUDBUgvl79M4V5/K71j9c9C3eQfL721n+Z2uryUOYLtU6cD3OGA1MzOzxVIrK3FZkzxJvJmZmVnnuAe2AzxJvJmZmVnnuAfWzMzMzLqKA1gzMzMz6yoOYM3MzMysqziANTMzM7Ou4gDWzMzMzLqKA1gzMzMz6yoOYM3MzMysqziANTMzM7Ou4gDWzMzMzLqKA1gzMzMz6yoOYM3MzMysqziANTMzM7Ou4gDWzMzMzLqKA1gzMzMz6yoOYM3MzMysqziANTMzM7Ou4gDWzMzMzLqKA1gzMzMz6yoOYM3MzMysqziANTMzM7Ou0lIAK2ktSRdKekbSbElTJJ0haZUWyuiRFHVey9Q5d19JN0t6UdJrkv4u6XpJ27XyOczMzMysey3ZbEZJ6wJ3A6sD1wOPANsAxwK7S9oxIl5q4dqjaqS/WeXaSwK/BA4EHgOuAKYB7wW2B7YCxrVwbTMzMzPrUk0HsMA5pOD1mIj4STlR0unA8cDJwJHNFhYRpRauPYoUvJ4MfDci5hQPSlqqhbLMzMzMrIs1NYQg977uBkwBflpxeCQwEzhI0vJtrV269nuBE4BxEfHflcErQES80e7rmpmZmdnCqdke2F3y9pbKADIipku6ixTgbgfc1kyBkoYDHwBeBx4G/hQRs6tk3RdYGviNpGWBPYH1gOnAnyNiYpOfwczMzMwWAc0GsBvm7eQaxx8jBbAb0GQAC/ymYv95SV+NiKsr0rfO2+VI427XKR6U9Fvg4IiYVe0ikg4HDgdYZ511qmUxMzMzsy7S7CwEK+XttBrHy+krN1HW9cBewFrAssBGwA/zuVdI2r0i/+p5+33SEIYtgRVIvb0TgM+RxudWFRHnRcSQiBiy2mqrNVE9MzMzM1uY9fs8sBHx44j4fUT8MyJei4hHI+I7wNdzfX5Yo44vA3tFxP0RMTMi7gH2BmaQxt/+R799CDMzMzMbMM0GsOUe1pVqHC+nT+1DXS4gTaG1uaQVC+nlMm+LiH8XT4iIZ4F7SJ9jSB+ubWZmZmZdotkA9tG83aDG8fXzttYY2YYi4jXSg1kAxdkMyteeWuPUV/J22QW9tpmZmZl1j2YD2NvzdjdJbzsn95buCMyiD4sJSNoQWIUUxL5YOPTHvN20xqmb5O2TC3ptMzMzM+seTQWwEfE4cAswCPhqxeFRpB7TX0XEzHKipI0kbVTMKOkDkt5dWb6k1YCL8u5vIqK4GtedwAPATpL2qTjvMOBDwN9ID3SZmZmZ2SKulYe4vgI8D5wl6TpJP5T0J9IqXJOBEyvyP5xfRUOBZyT9UdJ5kk6RdBlpGq7tSUHofxVPiIgAvkgaQvBbSddLGi3p/4DzSIsofDEi3mrhs5gtskqlEpLmvkql0kBXyczMrK2aXko2Ih6XNAT4HrA7sAfwLHAmMCoiXql3fnYvaf7XrYAtgHeRhgz0AlcCP4+I16tc+0FJW5JW/dotX/tF4NfA9yPi0cpzzBZXpVKJnp4egLlbMzOzRUnTASxARPwDOKTJvKqS1guMaOWahXOfXNBzzczMzGzR0e/zwJqZmZmZ9YUDWDMzMzPrKg5gzczMzKyrOIA1MzMzs67iANbMrAFPTWZmtnBpaRYCM7PFkacmMzNbuLgH1gace7fa5Lmeea/Xp6ZXMe25ngGrmpmZWTu5B9YGnHu3zMzMrBXugTUzMzOzruIA1szMzMy6igNYMzMzM+sqDmDNzMzMrKs4gDUzMzOzruJZCMzMailOPfb61PnTzMxsQDiAtYHj4MDMzMwWgIcQmJmZmVlXcQBrtogpjb6YMWMnMmbsRPTeXSiNvnigq2RmZtZWHkJgtogpfWMEpW+MGOhqmJmZdYx7YM3MzMysqziANTMzM7Ou4gDWzMzMzLqKA1gzswb8YJyZ2cLFD3HZgCsHBwB67y6M/PoX/RCSLVT8YJyZ2cLFAawNOAcHZmZm1goPITAzMzOzruIA1szMzMy6igNYMzMzM+sqDmDNzMzMrKu0FMBKWkvShZKekTRb0hRJZ0haZUErIOkjkt6SFJJOqnJ8bUnnSLpH0r/ydZ+RdKekQyQttaDXXhClUglJc1+lUqk/L29mZma22Gt6FgJJ6wJ3A6sD1wOPANsAxwK7S9oxIl5q5eKSVgR+CcwCVqiRbV3gC8A9wHXAy8CqwCeBC4GDJO0WEW+2cu0FVSqV6OnpAZi7NTMzM7P+08o0WueQgtdjIuIn5URJpwPHAycDR7Z4/TOBlYAf5vOruRtYJSLmFBNzz+stwC7AZ4ErW7y2mZmZmXWhpoYQ5N7X3YApwE8rDo8EZpJ6Qpdv9sKSPg0cAhwDPFMrX0S8Xhm85vQ3SD2yAOs3e10zMzMz627NjoHdJW9vqQwmI2I6cBewHLBdM4VJWh04H7guIi5tsg6VZSwB7JF3H1yQMszMzMys+zQ7hGDDvJ1c4/hjpB7aDYDbmijvfFLw3PSQA0nvAY4GBKwG7AqsB1wWETc0W46ZmZmZdbdmA9iV8nZajePl9JUbFSTpS8DewPCIeK7J6wO8hzRcoSyAHwHfaXC9w4HDAdZZZ50WLmdmZmZmC6N+nQdW0iDgDOCqiGjpoauIeCQiRAq63096cOxw4A5J765z3nkRMSQihqy22moLXHczMzMzWzg0G8CWe1hXqnG8nD61QTkXAq8CX2nyuvOJiLci4u8RcSZwBGnc7fcWtDwzMzMz6y7NBrCP5u0GNY6XZwGoNUa2bEvSVFwv5IULQlIAF+XjJ+a065qs1x/ydliT+c3MzMysyzU7Bvb2vN1N0juKMxHkxQh2JC1GMK5BOZeQZiuotD7wEeAB4F7g/ibr9R952/lFDJ7rmff+9anzp5mZmZlZv2gqgI2IxyXdQppp4KvATwqHRwHLAz+PiJnlREkb5XMfKZRzTLXyJY0gBbA3RsR/VxzbEpgYEW9VpK9AWggB4MZmPoeZmZmZdb9WVuL6CmlVrLMkfQx4GNiWNEfsZODEivwP5636WMfvAjtKuhv4O6mnd23SUrIr5zr9sI/XMDMzM7Mu0XQAm3thh5AemNqdtIjAs6Re0FER8Upnqsj5wAxgG9JY1+WAV0hDDa4ELoyIzg8hMDMzM7OFQis9sETEP0jLvzaTt+me14i4GLi4xrEb8RABMzMzM8v6dR5YMzMzM7O+cgBrZmZmZl3FAayZmZmZdRUHsGZmZmbWVRzAtqg0+mLGjJ3ImLET0Xt3oTT64oGukpmZmdlipaVZCAxK3xhB6RsjBroaZmZmZost98CamZmZWVdxAGtmZmZmXcUBrJmZmZl1FQewZmZmZtZVHMCamZmZWVdxAGtmZmZmXcUBrJmZmZl1FQewZmZmZtZVHMCamZmZWVdxAGtmZmZmXcUBrJmZmZl1FQewZmZmZtZVHMCamZmZWVdxAGtmZmZmXcUBrJmZmZl1FQewZmZmZtZVHMCamZmZWVdxAGtmZmZmXcUBrJmZmZl1FQewZmZmZtZVHMCamZmZWVdxAGtmZmZmXcUBrJmZmZl1lZYCWElrSbpQ0jOSZkuaIukMSau0UMY3JP1fPneGpH9L6pV0uqS16py3saQrJT0v6TVJj0oaJWnZVj6DmZmZmXW3JZvNKGld4G5gdeB64BFgG+BYYHdJO0bES00UdQQwAxgDPAcsBWwBHA8cKmlYRNxfce1tgT/lvFcD/wA+CnwX+Jikj0XE7GY/i5mZmZl1r6YDWOAcUvB6TET8pJwo6XRS8HkycGQT5WwaEa9VJko6DDgvl7NHIX0J4CJgOeDTEfG7nP4O4Ergc/n6p7TwWczMzMysSzU1hCD3vu4GTAF+WnF4JDATOEjS8o3Kqha8Zlfm7foV6UOBDwF3lIPXXM4c4L/y7pGS1OjaZmZmZtb9mh0Du0ve3pIDx7kiYjpwF6mHdLs+1GWvvH2wIv2jeXtT5QkR8QQwGXg/8ME+XNvMzMzMukSzQwg2zNvJNY4/Ruqh3QC4rZkCJX0ZWAtYARgMfBx4CvjWAlx7g/x6vMp1DgcOz7szJD3aTP0G0HuAFzt6hS8sVp3Vbs/2c5u2V+fbE/rapjdFxO7tqoqZWV81G8CulLfTahwvp6/cwrW/DGxb2P8LcGBE/K2d146I80hja7uCpAkRMWSg67GocHu2n9u0vdyeZmatG7B5YCNiu4gQqfdht5x8r6RPDFSdzMzMzGzh12wAW+7lXKnG8XL61FYrEBEvRcStpCD2VeBXFXO7duzaZmZmZtZ9mg1gy+NGN6hxvDxzQK1xqg1FxFRgLLAasEl/Xnsh0zXDHbqE27P93Kbt5fY0M2uRIqJxpjSN1t9I02itW5yJQNKKwLOAgNUjYuYCV0YaD2wNbBERD+S0j5IeDLsjIoZW5P8g6cGtp4APRDMfxszMzMy6WlM9sBHxOHALMAj4asXhUcDywK+KwaukjSRtVMwoaR1Ja1S7hqQjSMHrP4DewqExwMPARyTtXcj/DuDUvPszB69mZmZmi4ememCh6lKyD5NmEdiFdPt+h+JSspICID+oVU77DHAVaajA30hLya5Kmj92MGmJ2U9FxJiKa1cuJft34GPAENIctF5K1szMzGwx0fQsBLkXdghwMSlw/TqwLnAmsF0xeK3jvpz/ncCewAnAAUAA/wtsXBm85mvfQ+qdvZ70sNfxpIe3vgfsOpDBq6SSpKjy+uNA1akbKBkh6R5JMyT9W9KYYi97ztcj6eoGZQ3Kbf6pNtdxG0mldpa5gPWo/I49I+m3+Udlf1x/iqQfNcizaa7bsBbLftvfN3/WjsyJOtDtmOuwSLSlmdlAa3YeWAAi4h/AIU3mnW/W7Ij4OylobVlE/BX4/IKc2w+mAZWTfNeat9aSc4DD8va/Sd/F/YHrJX0rIk6td3KFZ4HtgUfaXMdtSEsll9pc7oIofsc+CHwfuE3SJn0Zd96kfYBmfqC2wwXADR0sfyDbERattjQzGzAtBbBW05sRMW6gK9Et8lCSI4GjIuJnhUN/kPQv4AeSbo2I+5opL/fAL+rtX/yOjZP0d+BOYA/SsJyWSFo2Il5tJm9E3N9q+QsqIp4Gnu7gJdrajrBYt6WZ2YAZsIUMFnWS1pR0oaQnJL0qabKkkyQtXZFvWUmnSXpK0mxJT0r6YUWeL0t6KB9/StJ/VRzfRNJNkl6WNFPSw5IqH7ZbmBxLGgN9fpVjPwCmA0cXEyUdnm+/virpRkn/UThWdQhBo3bLeT4i6fY8jGFavg27haQRwE9ynvIt5568v5akKyU9n+vzuKTv961JWnZv3m4s6WxJj0qalb8/P5X0rmLmXP+vSTpD0gvkByUlLZO/f//I7TRR0h4V585321vSV/I5MyXdAKxZWUFJy0k6S9K/JL0m6S+SdqvMV3HO2257S1q+mc/XB+V2HNTstdyWZmYDzz2wbSKpsi3fA7wMfA14hTSPbYk0z+0R+RyRxvVuT7qVeS/wH8DOhXK/QQrqTgN6gK2A70uaFRFn52w3kB6q+3/AbGBDYKH8Rym30/bAORHxVuXxiJgm6XbgI4Xk7Umf6WvAMqTZJ64jjYuudZ2G7aY0xvBW4Hbgi8BMYEfS3+BG0rjsr+frA/w7by8BlgUOJy2g8UHgbTNu9INBefsE8F7gROAFYO38/iqgclW7bwB3AAcx78fr1cwbKvE4sB/wO0lDylPZVZL0aeCnwM9If4ehwIVVsp4P7A18h/SD5TDgRkm7RMSfm/ycywFLNPn5FsSgvP1Xi9dyW5qZDaSI8KsPL1JQGlVeH6/ItyRwIPAasHRO+0TOu3eNst9FmplhZEX690j/4C5BCpQDGDzQbdFke7031/fYOnnOAF7N73uAN4B1Csd3zGXsnvcH5f1PNdtueX8sMIE8G0eVehyd/hOZL30GsFc/f8dezN+hJUk/hm4nBdRrVvmeldun2GYB3FeR92M5fWhF+h3AVYX9KcCPCvvjgT9UnHN+LmtY3v8QMAf4YiHPO4BJwM2FtB7g6srPWqctqn6+drej29Ivv/zya+F+eQhBe0wj9QYWX/dIOk7SXyW9SgrCfk2agWGdfN5HgZcj4nc1yt2eNMfuVZKWLL9IU4qtAaxF6uX9B/AzScMlrd6Zjzig7ov0ACAAEXEX8Dypt6uahu0maXnSbBq/jIhW5xB+APih0iwK6zTK3Carkr5Db5BWp/sgMDwinpV0kKT7Jc3Ix8s9cpWr1/1fxf7HSQH9XRXtdBtpxpH55ONbku4cFF1Tsb81aXGTueNKIy2AchWwU6MPW3HNZj9fM2q2Y4vXcluamQ0gDyFojzcjYkIxQdLxwGjS7e4xpGEEW5NuFy6Ts61KeoK+lvfk7UM1jq8dEU/lsXAnk249LivpLuCY6McHRlrwImmYw/vr5Hk/8M/C/vNV8jxPlbGCWcN2A94iBQX12r+W4aT2/jGwsqSJwNcj4rYFKKtZ00hBUpACpWciIiTtQxrScC7p9vLLpHa5lnnfs7LnKvbfQ+oRf6PK9eYb3lE4Zwnm/5tU7q8JzIiIWVXqsJykd0YT09+1+PmaUbUdF+BabkszswHkALZzPk+6nXdiOUHSxhV5XqJ2EAbpHxiATzH/P5iQepCIiEeAz0laijR+9lTS+Li1orDs78IgIt6UNBbYU9IJlfXLD5QMI/2jWlatV3l1agefzbTbnPyq1/5VRcQ/gRFKq8FtQ7pV+ztJ60Rz8yEviPl+JGWfB+6JiK+UEyQNrZIPUtBW9DLph8JnWqjHi6SArPJvUrn/LLCCpOUqAq81gFnNBFxZK5+vGbXasdVruS3NzAaQhxB0zrKknsaiL1Ts3wa8W7Un4B8LvAq8LyImVHlNL2aOiDci4k/A6aTAbOU+f4rOOJN0y/LLVY59izSG9exC2pbFW/WSdiT9Iz++RvkN2y3SnJ/3AAfnh+mqeT1fr2rvVETMiTQl0yjSAzL1epU7pZnvWS23kXoNZ1Rrp2onRMSbwP3ApysOfbZi/y+kIG/fckJu532Zd9u6GX35fK1yW5qZdQn3wHbOrcAxku4hPZH8BWC9KnluBi6T9D3SSmVrAh+JiCMiYqrSSlBnSno/6YGQd5CCv10iYh9JHwZ+BFxBeiJ9FeCbwMSIeJmFUERcJ+lnwE9zr/TvSd/F4cAI4Nvx9jlgXyD1KI9k3iwE90XETTXKb9huOeu3gD+S5p89jzQLwfbAhIj4PfMWRjhW0p9ID/v8i/Q3u4S0hPI7STMV/Is0E0R/u5XUjieSAvI9SA8UNXvuzcCtkk4lDbl4F7A5sExEfLvGeT8ArpF0LqmnfCgVC3lExMOSLgfOlrQi6b+Bw0izNRzV/Mfr0+drldvSzKxbDPRTZN3+osaTvsAKwEWkW4svk1bF+RSpJ2XTQr5lSQHo06TekSeBkyvK+n+kKbZeJY2lvQf4Wj62OvArUvD6GimQupyF/Kli0vjTEfmzzCTN/TqGihkZyE9WkxY++Htugz+Qxv+W8wzK7bpns+1WyDOUFODOIk2JdTuweaGOpwHPkIYb9JAC1vNJwxBmkW4D/54OzgJR6zuWjy2Rvz/PkwLs35IeTps7K0POF8DRVc5/J6kH+W+kHud/ATcV25KKJ+dz2tH5OzuL9EDTbhSenM95liPNpftc/m5PAD5R7e9b67M2+/n62o5uy9ba0i+//PJroF+KaPUBbLOFi6TBwIPADhExdqDrY2ZmZp3lIQTW1SRtQerBmsXA3MI3MzOzfuYA1rrdL0jjfg+LiKkDXBczMzPrBx5CYGZmZmZdxdNomZmZmVlXcQBrZmZmZl3FAayZWT+R9AFJIenvdRbQqHbeNnluYzMzwwGsmVl/2j9v1wZ2auG8bYCR7a+OmVl3cgBrZtZ/DgDGkRbvOKBRZklLSFq647UyM+syDmDNzPqBpE2AwaQV+n4HfF7SkhV5LpY0QdJnJD1EWl3vm6RVuMjDD0JST//W3sxs4eJ5YM3M+scBwBukJVyfzfsfJy03WzSItITx90jL0T4KrAR8Hdg+5/l356trZrbwcgBrZtY/9gf+GBEvSboZeIUUxFYGsKsCH4+IB8oJkqYARMS4/qmqmdnCzUMIzMw6TNI2wLrAbwAi4nXgGuAzkpapyP7PYvBqZmbzcwBrZtZ55eEDPZJWlrQycCPwLmDPirzP9XPdzMy6jocQmJl1kKR3APsBSwFPVclyAGlcbJnX9zYza8ABrJlZZw0F3keaTWB8xbFDgP0krRgR0+uU8TqApGUi4rXOVNPMrHs4gDUz66wDgKnAmRExu3hA0r+Bg4F9gEvqlPFI3h4r6U/AvyPi0Q7U1cysK3gMrJlZh0haCvgccFVl8AoQEfcBD9N4UYM7gdHAscA9wM/bXFUzs66iCA+3MjMzM7Pu4R5YMzMzM+sqDmDNzMzMrKs4gDUzMzOzruIA1szMzMy6igNYMzMzM+sqDmDNzMzMrKs4gDUzMzOzruIA1szMzMy6yv8HbXBUGo+qrywAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.figure(figsize=(30, 24))\n",
+    "\n",
+    "# Example data (replace these arrays with your own)\n",
+    "data_1 = [\n",
+    "    np.array(sp_corr_hg_face_and_face_inv),  # Group 1 data\n",
+    "    np.array(sp_corr_hg_obj_and_obj_inv),  # Group 2 data\n",
+    "    np.array(sp_corr_hg_par_and_par_inv),  # Group 3 data\n",
+    "    np.array(sp_corr_hg_par_art_and_par_art_inv),  # Group 4 data\n",
+    "]\n",
+    "\n",
+    "\n",
+    "# Example additional data (replace these arrays with your own)\n",
+    "data_2 = [\n",
+    "    np.array(sp_corr_dg2_face_and_face_inv),  # Group 1 data\n",
+    "    np.array(sp_corr_dg2_obj_and_obj_inv),  # Group 2 data\n",
+    "    np.array(sp_corr_dg2_par_and_par_inv),  # Group 3 data\n",
+    "    np.array(sp_corr_dg2_par_art_and_par_art_inv)   # Group 4 data\n",
+    "]\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Example additional data (replace these arrays with your own)\n",
+    "data_3 = [\n",
+    "    np.array(sp_corr_hg_face_and_dg2_face),  # Group 1 data\n",
+    "    np.array(sp_corr_hg_obj_and_dg2_obj),  # Group 2 data\n",
+    "    np.array(sp_corr_hg_par_and_dg2_par),  # Group 3 data\n",
+    "    np.array(sp_corr_hg_par_art_and_dg2_par_art),   # Group 4 data\n",
+    "]\n",
+    "\n",
+    "# Example additional data (replace these arrays with your own)\n",
+    "\n",
+    "data_4 = [\n",
+    "    np.array(sp_corr_hg_face_inv_inv_and_dg2_face_inv_inv),  # Group 1 data\n",
+    "    np.array(sp_corr_hg_obj_inv_inv_and_dg2_obj_inv_inv),  # Group 2 data\n",
+    "    np.array(sp_corr_hg_par_inv_inv_and_dg2_par_inv_inv),  # Group 3 data\n",
+    "    np.array(sp_corr_hg_par_art_inv_inv_and_dg2_par_art_inv_inv),   # Group 4 data\n",
+    "]\n",
+    "\n",
+    "\n",
+    "# Example additional data (replace these arrays with your own)\n",
+    "data_5 = [\n",
+    "    np.array(sp_corr_hg_face_and_face_inv_inv),  # Group 1 data\n",
+    "    np.array(sp_corr_hg_obj_and_obj_inv_inv),  # Group 2 data\n",
+    "    np.array(sp_corr_hg_par_and_par_inv_inv),  # Group 3 data\n",
+    "    np.array(sp_corr_hg_par_art_and_par_art_inv_inv),   # Group 4 data\n",
+    "]\n",
+    "\n",
+    "\n",
+    "# Example additional data (replace these arrays with your own)\n",
+    "data_6 = [\n",
+    "    np.array(sp_corr_dg2_face_and_face_inv_inv),  # Group 1 data\n",
+    "    np.array(sp_corr_dg2_obj_and_obj_inv_inv),  # Group 2 data\n",
+    "    np.array(sp_corr_dg2_par_and_par_inv_inv),  # Group 3 data\n",
+    "    np.array(sp_corr_dg2_par_art_and_par_art_inv_inv)   # Group 4 data\n",
+    "]\n",
+    "\n",
+    "\n",
+    "# Combine the data arrays\n",
+    "all_data = [data_1, data_2, data_3, data_4, data_5, data_6]\n",
+    "\n",
+    "# Calculate means and standard deviations for each set of data\n",
+    "means = [[np.mean(group) for group in dataset] for dataset in all_data]\n",
+    "std_devs = [[np.std(group) / np.sqrt(len(group)) for group in dataset] for dataset in all_data]\n",
+    "\n",
+    "# Bar chart with error bars using one standard deviation\n",
+    "fig, ax = plt.subplots()\n",
+    "bar_width = 0.8  # Width of the bars\n",
+    "bar_positions = np.arange(len(means[0]))*8  # Positions for the bars\n",
+    "\n",
+    "# Plot the first set of data\n",
+    "bars1 = ax.bar(bar_positions - bar_width*2.5, means[0], yerr=std_devs[0], capsize=2, label='sp_corr_hg_img_and_img_inv', width=bar_width, color=['orange', 'orange', 'orange', 'orange'], alpha=0.25)\n",
+    "\n",
+    "# Plot the second set of data\n",
+    "bars2 = ax.bar(bar_positions - bar_width*1.5, means[1], yerr=std_devs[1], capsize=2, label='sp_corr_dg2_img_and_img_inv', width=bar_width, color=['orange', 'orange', 'orange', 'orange'], alpha=0.4)\n",
+    "\n",
+    "\n",
+    "# Plot the second set of data\n",
+    "bars3 = ax.bar(bar_positions - bar_width/2, means[2], yerr=std_devs[2], capsize=2, label='sp_corr_hg_img_and_dg2_img', width=bar_width, color=['orange', 'orange', 'orange', 'orange'], alpha=0.55)\n",
+    "\n",
+    "# Plot the second set of data\n",
+    "bars4 = ax.bar(bar_positions + bar_width/2, means[3], yerr=std_devs[3], capsize=2, label='sp_corr_hg_img_inv_inv_and_dg2_img_inv_inv', width=bar_width, color=['orange', 'orange', 'orange', 'orange'], alpha=0.7)\n",
+    "\n",
+    "\n",
+    "# Plot the second set of data\n",
+    "bars5 = ax.bar(bar_positions + bar_width*1.5, means[4], yerr=std_devs[4], capsize=2, label='sp_corr_hg_img_and_img_inv_inv', width=bar_width, color=['orange', 'orange', 'orange', 'orange'], alpha=0.85)\n",
+    "\n",
+    "# Plot the second set of data\n",
+    "bars6 = ax.bar(bar_positions + bar_width*2.5, means[5], yerr=std_devs[5], capsize=2, label='sp_corr_dg2_img_and_img_inv_inv', width=bar_width, color=['orange', 'orange', 'orange', 'orange'], alpha=1.0)\n",
+    "\n",
+    "\n",
+    "# Adding x-axis labels\n",
+    "ax.set_xticks(bar_positions)\n",
+    "ax.set_xticklabels(['Faces', 'Objects', 'Pareidolia', 'Pareidolia \\n Art'], fontsize=15)\n",
+    "\n",
+    "# Increase y-axis label font size\n",
+    "ax.yaxis.label.set_size(20)\n",
+    "\n",
+    "# Increase y-axis tick label font size\n",
+    "ax.yaxis.set_tick_params(labelsize=20)\n",
+    "\n",
+    "# Adding a legend\n",
+    "#ax.legend(fontsize=20)\n",
+    "\n",
+    "# Set y-axis limits from 0 to 1\n",
+    "ax.set_ylim(0.3, .95)\n",
+    "ax.set_yticks(np.linspace(0.3, .95, num=6))\n",
+    "\n",
+    "# Show the tick marks on the left side of the y-axis\n",
+    "#ax.tick_params(axis='y', direction='out', length=5)  # Adjust 'length' as needed\n",
+    "ax.tick_params(tick1On=True)\n",
+    "\n",
+    "# Set font size for axis labels and title\n",
+    "ax.set_ylabel('')\n",
+    "ax.set_xlabel('')\n",
+    "\n",
+    "# Get the current axes\n",
+    "ax = plt.gca()\n",
+    "\n",
+    "# Remove the top and right spines\n",
+    "ax.spines['top'].set_visible(False)\n",
+    "ax.spines['right'].set_visible(False)\n",
+    "\n",
+    "# Set font size for tick labels\n",
+    "#ax.tick_params(axis='both', labelsize=12)\n",
+    "\n",
+    "# Move the legend outside of the plot frame\n",
+    "plt.legend(loc='upper left', bbox_to_anchor=(1.05, 1), frameon=False)\n",
+    "\n",
+    "\n",
+    "#plt.savefig('inv_inv_effect.png', dpi= 600, bbox_inches='tight')\n",
+    "\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "172d9f84",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c4bfe0e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31a7dad3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "94e90d9c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c093607e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0b83a1d4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25ba2bbc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a7656ce8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "565de330",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0c0a9a67",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "85126341",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
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+ "nbformat_minor": 5
+}