cebra.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e348ff60-2fd7-4941-b0d5-77071ca1f4e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cebra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "694337dd-5566-415e-a5a8-136262863988",
   "metadata": {},
   "outputs": [],
   "source": [
    "from cebra import CEBRA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5f1cfd57-1cc0-498e-a005-bcc3495911b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ffd812f3-16fd-4bda-9b6f-aa66c9ffecfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xgboost in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (2.1.1)\n",
      "Requirement already satisfied: numpy in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from xgboost) (1.26.4)\n",
      "Requirement already satisfied: scipy in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from xgboost) (1.14.1)\n",
      "Requirement already satisfied: mne in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (1.8.0)\n",
      "Requirement already satisfied: decorator in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from mne) (5.1.1)\n",
      "Requirement already satisfied: jinja2 in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from mne) (3.1.4)\n",
      "Requirement already satisfied: lazy-loader>=0.3 in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from mne) (0.4)\n",
      "Requirement already satisfied: matplotlib>=3.6 in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from mne) (3.9.2)\n",
      "Requirement already satisfied: numpy<3,>=1.23 in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from mne) (1.26.4)\n",
      "Requirement already satisfied: packaging in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from mne) (24.1)\n",
      "Requirement already satisfied: pooch>=1.5 in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from mne) (1.8.2)\n",
      "Requirement already satisfied: scipy>=1.9 in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from mne) (1.14.1)\n",
      "Requirement already satisfied: tqdm in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from mne) (4.66.5)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from matplotlib>=3.6->mne) (1.3.0)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from matplotlib>=3.6->mne) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from matplotlib>=3.6->mne) (4.54.1)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from matplotlib>=3.6->mne) (1.4.7)\n",
      "Requirement already satisfied: pillow>=8 in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from matplotlib>=3.6->mne) (10.4.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from matplotlib>=3.6->mne) (3.1.4)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from matplotlib>=3.6->mne) (2.9.0.post0)\n",
      "Requirement already satisfied: platformdirs>=2.5.0 in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from pooch>=1.5->mne) (3.10.0)\n",
      "Requirement already satisfied: requests>=2.19.0 in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from pooch>=1.5->mne) (2.32.3)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from jinja2->mne) (2.1.3)\n",
      "Requirement already satisfied: colorama in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from tqdm->mne) (0.4.6)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from python-dateutil>=2.7->matplotlib>=3.6->mne) (1.16.0)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from requests>=2.19.0->pooch>=1.5->mne) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from requests>=2.19.0->pooch>=1.5->mne) (3.10)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from requests>=2.19.0->pooch>=1.5->mne) (2.2.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from requests>=2.19.0->pooch>=1.5->mne) (2024.8.30)\n"
     ]
    }
   ],
   "source": [
    "!pip install xgboost\n",
    "!pip install mne\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "80269c4b-f2ba-4d5d-b8a4-eb480a463ec5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (2.2.3)\n",
      "Requirement already satisfied: numpy>=1.26.0 in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from pandas) (1.26.4)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from pandas) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from pandas) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from pandas) (2024.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "423bf8c1-6c67-47bc-aed9-137b439695dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import mne\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import accuracy_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "8de46b28-8b24-4785-a306-9421f6cce644",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\khani\\anaconda3\\envs\\cebra\\Lib\\site-packages\\cebra\\datasets\\__init__.py:103: UserWarning:\n",
      "\n",
      "Could not initialize one or more datasets: No module named 'h5py'. For using the datasets, consider installing the [datasets] extension via pip.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import cebra.datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5d9f4b0e-3563-463f-aa40-b30deb40c453",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading C:\\Users\\khani\\skolteh\\WEEK1\\WEEK1\\S104\\5_Files_for_model\\epochs_eeg_2-epo.fif ...\n",
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =   -2000.00 ...    7000.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "200 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n"
     ]
    }
   ],
   "source": [
    "subject = 'S104'\n",
    "eeg = mne.read_epochs(subject+'/5_Files_for_model/epochs_eeg_2-epo.fif', preload=True)\n",
    "digits_df = pd.read_csv(subject+'/5_Files_for_model/Digits_trials_final.xlsx')\n",
    "labels = digits_df['Digit_text'].values  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "da9c35f0-2b66-4372-85dc-9b4fda4c3cc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 25, 18433)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eeg.get_data().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "a0bb26ab-e85a-4055-b9cf-f0e204604923",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = digits_df['Digit_text'].values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "3f769883-a437-471f-9422-c5d5505faf93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 2, 6, 7, 9, 2, 6, 3, 5, 7, 8, 7, 0, 5, 7, 0, 0, 4, 1, 5, 3, 6,\n",
       "       7, 1, 2, 4, 6, 9, 2, 8, 8, 6, 3, 4, 5, 9, 8, 1, 3, 1, 7, 3, 5, 1,\n",
       "       3, 4, 7, 8, 9, 1, 8, 3, 2, 4, 0, 5, 0, 7, 9, 1, 3, 8, 2, 5, 4, 5,\n",
       "       6, 5, 0, 6, 6, 6, 7, 6, 0, 3, 9, 1, 4, 8, 1, 1, 6, 7, 9, 7, 5, 3,\n",
       "       9, 3, 4, 9, 2, 8, 8, 1, 1, 6, 9, 0, 4, 9, 1, 7, 8, 5, 0, 8, 0, 3,\n",
       "       2, 7, 2, 0, 6, 6, 4, 7, 2, 1, 5, 6, 2, 1, 5, 5, 9, 7, 6, 3, 1, 9,\n",
       "       9, 5, 9, 0, 8, 5, 6, 8, 3, 4, 7, 8, 3, 9, 0, 4, 2, 6, 4, 4, 8, 2,\n",
       "       0, 6, 4, 3, 3, 5, 3, 5, 6, 2, 9, 7, 8, 2, 8, 3, 0, 1, 7, 0, 7, 7,\n",
       "       0, 6, 6, 8, 5, 9, 0, 8, 1, 4, 4, 1, 4, 4, 8, 0, 5, 2, 9, 5, 3, 2,\n",
       "       6, 3], dtype=int64)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "1fe3ba9b-45bb-4ab7-b737-06d64e0603a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = digits_df['Digit_text']\n",
    "labels.to_hdf('labels.h5', key='labels', mode='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "10969b6b-5978-41ae-8c85-a9d856113a65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      5\n",
       "1      2\n",
       "2      6\n",
       "3      7\n",
       "4      9\n",
       "      ..\n",
       "195    5\n",
       "196    3\n",
       "197    2\n",
       "198    6\n",
       "199    3\n",
       "Name: Digit_text, Length: 200, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9019f2f3-5ba4-4aba-b8be-6b0fc3cc5bca",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(expanded_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1596913e-b31d-4437-9de7-7083a12fda71",
   "metadata": {},
   "outputs": [],
   "source": [
    "expanded_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7901c454-0739-4228-b677-a6283c4a6b6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "eeg.get_data().shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f906ee9d-c198-47c2-a50c-93bf52b156c8",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Вариант с объединением каналов и временных точек"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f02a6650-83e3-4654-93e6-296e18677934",
   "metadata": {},
   "source": [
    "cebra воспринимает 2d numpy array, поэтому объединим каналы и временные точки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "278233d4-b8ef-46c5-8cf2-d1fa25240b43",
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs_data = eeg.get_data()  \n",
    "reshaped_data = epochs_data.reshape(epochs_data.shape[0], -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fdbfa2d4-eef5-4a78-a576-71b4ffd181c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(reshaped_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d6965546-9070-4023-a495-60e42137babc",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_df = pd.DataFrame(labels, columns=['Digit'])\n",
    "labels_df.to_csv(subject + '/5_Files_for_model/Labels.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "13494775-b9e3-46e3-b7d4-2b0a24ca464b",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = cebra.load_data(file=subject + '/5_Files_for_model/Labels.csv', columns=['Digit']).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "96730edf-e7ae-4071-8a6f-a72554e1b494",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "201"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cb551d97-f774-4cec-8a48-7da787864363",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels=labels[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "491aae83-504d-4423-bf42-c330b93da75c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#labels = labels.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "26eba5de-37af-4f67-9572-5581cf10294c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['5', '2', '6', '7', '9', '2', '6', '3', '5', '7', '8', '7', '0',\n",
       "       '5', '7', '0', '0', '4', '1', '5', '3', '6', '7', '1', '2', '4',\n",
       "       '6', '9', '2', '8', '8', '6', '3', '4', '5', '9', '8', '1', '3',\n",
       "       '1', '7', '3', '5', '1', '3', '4', '7', '8', '9', '1', '8', '3',\n",
       "       '2', '4', '0', '5', '0', '7', '9', '1', '3', '8', '2', '5', '4',\n",
       "       '5', '6', '5', '0', '6', '6', '6', '7', '6', '0', '3', '9', '1',\n",
       "       '4', '8', '1', '1', '6', '7', '9', '7', '5', '3', '9', '3', '4',\n",
       "       '9', '2', '8', '8', '1', '1', '6', '9', '0', '4', '9', '1', '7',\n",
       "       '8', '5', '0', '8', '0', '3', '2', '7', '2', '0', '6', '6', '4',\n",
       "       '7', '2', '1', '5', '6', '2', '1', '5', '5', '9', '7', '6', '3',\n",
       "       '1', '9', '9', '5', '9', '0', '8', '5', '6', '8', '3', '4', '7',\n",
       "       '8', '3', '9', '0', '4', '2', '6', '4', '4', '8', '2', '0', '6',\n",
       "       '4', '3', '3', '5', '3', '5', '6', '2', '9', '7', '8', '2', '8',\n",
       "       '3', '0', '1', '7', '0', '7', '7', '0', '6', '6', '8', '5', '9',\n",
       "       '0', '8', '1', '4', '4', '1', '4', '4', '8', '0', '5', '2', '9',\n",
       "       '5', '3', '2', '6', '3'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "3f0588b1-73ac-4d14-89d7-1a188087cfdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "cebra_model = CEBRA(\n",
    "    model_architecture = \"offset10-model\",\n",
    "    batch_size = None,\n",
    "    temperature_mode=\"auto\",\n",
    "    learning_rate = 0.001,\n",
    "    max_iterations = 1000,\n",
    "    output_dimension = 8,\n",
    "    device = \"cpu\",\n",
    "    verbose = True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f82dee64-6f71-412c-9713-3ba6705f756b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['offset10-model',\n",
       " 'offset10-model-mse',\n",
       " 'offset5-model',\n",
       " 'offset1-model-mse',\n",
       " 'offset1-model',\n",
       " 'offset1-model-v2',\n",
       " 'offset1-model-v3',\n",
       " 'offset1-model-v4',\n",
       " 'offset1-model-v5',\n",
       " 'offset40-model-4x-subsample',\n",
       " 'resample-model',\n",
       " 'offset20-model-4x-subsample',\n",
       " 'resample5-model',\n",
       " 'offset4-model-2x-subsample',\n",
       " 'resample1-model',\n",
       " 'supervised10-model',\n",
       " 'supervised1-model',\n",
       " 'offset36-model',\n",
       " 'offset36-model-dropout',\n",
       " 'offset36-model-more-dropout']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cebra.models.get_options()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "112b3cf2-299c-4c5d-86fc-78b9b7cee08c",
   "metadata": {},
   "outputs": [],
   "source": [
    "params_grid = dict(\n",
    "    model_architecture = \"offset10-model\",\n",
    "    output_dimension = [3, 8, 16],\n",
    "    learning_rate = [0.001, 0.0005],\n",
    "    max_iterations = [100, 1000, 5000],\n",
    "    temperature_mode = \"auto\",\n",
    "    verbose = False,\n",
    "    device= \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a5dc18bc-d579-4a69-8cfd-7f2173e2b641",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cebra.grid_search.GridSearch at 0x2252bfdf080>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search = cebra.grid_search.GridSearch()\n",
    "grid_search.fit_models(datasets={\"reshaped_data\": (reshaped_data, labels)}, params=params_grid, models_dir=\"saved_models\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "5ae69877-7ca8-485e-8de1-85f1caff9d31",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "pos: -2.0749 neg:  6.7236 total:  4.6487 temperature:  0.3088: 100%|███████████████████████████████████████████████████| 1000/1000 [11:31<00:00,  1.45it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>CEBRA(device=&#x27;cpu&#x27;, learning_rate=0.001, max_iterations=1000,\n",
       "      model_architecture=&#x27;offset10-model&#x27;, temperature_mode=&#x27;auto&#x27;,\n",
       "      verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;CEBRA<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>CEBRA(device=&#x27;cpu&#x27;, learning_rate=0.001, max_iterations=1000,\n",
       "      model_architecture=&#x27;offset10-model&#x27;, temperature_mode=&#x27;auto&#x27;,\n",
       "      verbose=True)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "CEBRA(device='cpu', learning_rate=0.001, max_iterations=1000,\n",
       "      model_architecture='offset10-model', temperature_mode='auto',\n",
       "      verbose=True)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cebra_model.fit(reshaped_data, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "2d8fbc74-849d-4cb7-b73d-0c909a09d706",
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding = cebra_model.transform(reshaped_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "83074bfc-b8e5-4001-8163-81111611046b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(embedding, labels, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "a22e8ddd-0c01-4276-9b76-4d94e8fee1df",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "e8109d56-8613-4efd-a34b-972eb5f536b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = LogisticRegression()\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "cac576f7-b218-4498-9c8e-d3e71e7c396c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.35\n"
     ]
    }
   ],
   "source": [
    "y_pred = clf.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d85d5b15-709f-4615-8b37-92138522e3ec",
   "metadata": {},
   "outputs": [
    {
     "ename": "NotImplementedError",
     "evalue": "Invalid type: targets must be either floats or integers, got y:object.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[53], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m decoder \u001b[38;5;241m=\u001b[39m cebra\u001b[38;5;241m.\u001b[39mKNNDecoder()\n\u001b[1;32m----> 2\u001b[0m decoder\u001b[38;5;241m.\u001b[39mfit(X_train, y_train)\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\cebra\\Lib\\site-packages\\cebra\\integrations\\sklearn\\decoder.py:140\u001b[0m, in \u001b[0;36mKNNDecoder.fit\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    137\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mknn \u001b[38;5;241m=\u001b[39m sklearn\u001b[38;5;241m.\u001b[39mneighbors\u001b[38;5;241m.\u001b[39mKNeighborsClassifier(\n\u001b[0;32m    138\u001b[0m         n_neighbors\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_neighbors, metric\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmetric)\n\u001b[0;32m    139\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 140\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[0;32m    141\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid type: targets must be either floats or integers, got y:\u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    142\u001b[0m     )\n\u001b[0;32m    144\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mknn\u001b[38;5;241m.\u001b[39mfit(X, y)\n\u001b[0;32m    145\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
      "\u001b[1;31mNotImplementedError\u001b[0m: Invalid type: targets must be either floats or integers, got y:object."
     ]
    }
   ],
   "source": [
    "decoder = cebra.KNNDecoder()\n",
    "decoder.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fb9f9fc-64df-41be-aa76-163df4c445b6",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Multisession mode"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "792e9bc0-7042-4ada-a90f-907d27ff56d9",
   "metadata": {},
   "source": [
    "каждая эпоха - сессия\n",
    "каждая сессия будет содержать данные об 1 эпохе в формате 2д нумпай массива, название - номер эпохи"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5feee8e0-71f3-47ba-9785-fa4cb669419b",
   "metadata": {},
   "outputs": [],
   "source": [
    "eeg_data=eeg.get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fde0c8e9-d8a6-4e3d-a5a7-1c4418718ece",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        [-1.15401836e-05, -1.63872410e-05, -1.60365125e-05, ...,\n",
       "          5.64087059e-05,  5.57129897e-05,  5.15267930e-05],\n",
       "        [-1.19907099e-05, -1.27509747e-05, -1.26216064e-05, ...,\n",
       "          5.78654981e-05,  5.54478866e-05,  5.33254424e-05],\n",
       "        ...,\n",
       "        [-7.44896170e-05, -7.34170244e-05, -6.98475851e-05, ...,\n",
       "          1.65785950e-05,  2.14112260e-05,  2.27082946e-05],\n",
       "        [-9.64801584e-05, -9.47620429e-05, -9.06627465e-05, ...,\n",
       "          2.77398613e-05,  3.17286758e-05,  3.45721564e-05],\n",
       "        [-7.61413685e-05, -7.54789726e-05, -7.24686397e-05, ...,\n",
       "          1.74524739e-05,  2.09144855e-05,  2.23993266e-05]],\n",
       "\n",
       "       [[-4.44137549e-05, -4.70899686e-05, -5.05319695e-05, ...,\n",
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       "        ...,\n",
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       "\n",
       "       [[ 1.04983541e-04,  1.11842244e-04,  1.16244701e-04, ...,\n",
       "         -9.95283790e-06, -8.08455115e-06, -6.68892062e-06],\n",
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       "        ...,\n",
       "        [ 3.06855400e-05,  2.57118027e-05,  2.20708444e-05, ...,\n",
       "         -1.52043958e-05, -1.01448222e-05, -7.52079904e-06],\n",
       "        [ 3.06803813e-05,  2.49077093e-05,  2.26582797e-05, ...,\n",
       "         -2.32796083e-05, -1.87804071e-05, -1.66047503e-05],\n",
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       "\n",
       "       ...,\n",
       "\n",
       "       [[ 1.46575785e-05,  1.56517308e-05,  1.33044005e-05, ...,\n",
       "         -1.30513254e-05, -5.70572911e-06, -7.66453923e-06],\n",
       "        [ 2.13096337e-05,  2.25302738e-05,  2.14560605e-05, ...,\n",
       "         -2.71498011e-05, -2.10052549e-05, -2.08079746e-05],\n",
       "        [ 2.88554547e-05,  3.03153065e-05,  2.94377824e-05, ...,\n",
       "         -3.79374396e-05, -3.17666600e-05, -3.14979116e-05],\n",
       "        ...,\n",
       "        [ 2.73863134e-05,  3.42044477e-05,  3.64291336e-05, ...,\n",
       "         -3.01699129e-05, -2.98817122e-05, -3.43471220e-05],\n",
       "        [ 3.25579058e-05,  3.96736395e-05,  4.27592568e-05, ...,\n",
       "         -2.34617673e-05, -2.21313567e-05, -2.67947362e-05],\n",
       "        [ 2.25541280e-05,  2.93866051e-05,  3.21460648e-05, ...,\n",
       "         -1.57680188e-05, -1.58110415e-05, -2.03513810e-05]],\n",
       "\n",
       "       [[ 3.86848515e-05,  3.86769279e-05,  3.09184579e-05, ...,\n",
       "         -4.42588171e-05, -5.00002971e-05, -5.12587831e-05],\n",
       "        [ 3.37421516e-05,  3.60971862e-05,  2.85261431e-05, ...,\n",
       "         -2.86302802e-05, -3.27345806e-05, -3.10150863e-05],\n",
       "        [ 3.31464616e-05,  3.36256890e-05,  2.51430301e-05, ...,\n",
       "         -2.95946629e-05, -3.56863638e-05, -3.57072968e-05],\n",
       "        ...,\n",
       "        [ 6.05650166e-05,  5.31324877e-05,  4.12126938e-05, ...,\n",
       "          2.07229587e-05,  1.40026614e-05,  1.16726687e-05],\n",
       "        [ 6.70827649e-05,  5.86033566e-05,  4.63244214e-05, ...,\n",
       "          1.53875553e-05,  9.05387333e-06,  8.03541388e-06],\n",
       "        [ 5.96986029e-05,  5.05934804e-05,  3.69370391e-05, ...,\n",
       "          1.67946855e-05,  1.03009988e-05,  7.94785046e-06]],\n",
       "\n",
       "       [[-1.93557589e-05, -1.54981681e-05, -1.02991762e-05, ...,\n",
       "          4.04940038e-05,  4.17977135e-05,  3.78237455e-05],\n",
       "        [-6.44149804e-06, -4.30428554e-06,  1.25266342e-06, ...,\n",
       "          3.27278976e-05,  3.39051039e-05,  2.91279102e-05],\n",
       "        [ 7.14369980e-06,  9.10357085e-06,  1.39102076e-05, ...,\n",
       "          3.76677272e-05,  3.74054507e-05,  3.39478429e-05],\n",
       "        ...,\n",
       "        [ 4.71535077e-06,  1.00515672e-05,  1.87324895e-05, ...,\n",
       "          1.74337256e-05,  1.75251644e-05,  1.59134197e-05],\n",
       "        [ 2.04889693e-06,  7.58963597e-06,  1.67753478e-05, ...,\n",
       "          2.84417983e-05,  2.94100882e-05,  2.79099404e-05],\n",
       "        [ 7.56516511e-06,  1.24847838e-05,  2.20768488e-05, ...,\n",
       "          2.21365844e-05,  2.30033347e-05,  2.20066977e-05]]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eeg_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bbe309b0-50e6-49a9-8b23-34d2d8885710",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_ep, n_ch, n_ts = eeg_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6dc070da-e2b1-4393-83f5-a570a0fc0283",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 25, 18433)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_ep, n_ch, n_ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1adc454e-fc2c-49dd-9a20-71fad2fbf700",
   "metadata": {},
   "outputs": [],
   "source": [
    "expanded_labels = np.array([np.repeat(label, n_ts) for label in labels])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "44b175f8-d3e7-4fe9-b257-2de3cdc13b95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 5, 5, ..., 5, 5, 5],\n",
       "       [2, 2, 2, ..., 2, 2, 2],\n",
       "       [6, 6, 6, ..., 6, 6, 6],\n",
       "       ...,\n",
       "       [2, 2, 2, ..., 2, 2, 2],\n",
       "       [6, 6, 6, ..., 6, 6, 6],\n",
       "       [3, 3, 3, ..., 3, 3, 3]], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expanded_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7eb006da-3abe-4214-8065-f40c0c9a839a",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'expanded_labels' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[16], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m X_train, X_test, y_train, y_test \u001b[38;5;241m=\u001b[39m train_test_split(eeg_data, expanded_labels, test_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'expanded_labels' is not defined"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(eeg_data, expanded_labels, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "808c643e-7b2a-4393-a458-63d65de52bd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8, 8, 8, ..., 8, 8, 8],\n",
       "       [2, 2, 2, ..., 2, 2, 2],\n",
       "       [3, 3, 3, ..., 3, 3, 3],\n",
       "       ...,\n",
       "       [2, 2, 2, ..., 2, 2, 2],\n",
       "       [8, 8, 8, ..., 8, 8, 8],\n",
       "       [1, 1, 1, ..., 1, 1, 1]], dtype=int64)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "16d155d8-ca18-46eb-b9ba-af26e61e09f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_ep, n_ch, n_ts = X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "7c193015-54f7-4544-b3ff-1750c2cd664d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(160, 25, 18433)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_ep, n_ch, n_ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "854fd03a-be05-4cd4-9da8-5d340f13e4f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "epoch_data_dict = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96105f4f-9aae-487c-8b0c-bc15e27d4c09",
   "metadata": {},
   "source": [
    "Возникала ошибка с типом и получался слишком тяжелый файл, поэтому дальше проверки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ea741760-3e00-4a5e-8fe6-2621ca09c60f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Key: 151, Type: <class 'str'>\n",
      "Key: 152, Type: <class 'str'>\n",
      "Key: 153, Type: <class 'str'>\n",
      "Key: 154, Type: <class 'str'>\n",
      "Key: 155, Type: <class 'str'>\n",
      "Key: 156, Type: <class 'str'>\n",
      "Key: 157, Type: <class 'str'>\n",
      "Key: 158, Type: <class 'str'>\n",
      "Key: 159, Type: <class 'str'>\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(n_ep):\n",
    "    key = str(epoch)\n",
    "    print(f\"Key: {key}, Type: {type(key)}\") \n",
    "    epoch_data = X_train[epoch].reshape(n_ts, n_ch)\n",
    "    epoch_data_dict[key] = epoch_data  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ee23ad1c-88a2-4d3b-a913-ef986bbe99a4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0': array([[-4.06555264e-05, -4.09400600e-05, -4.38263705e-05, ...,\n",
       "         -2.06949444e-05, -2.23047555e-05, -2.62320300e-05],\n",
       "        [-2.85480764e-05, -3.34544893e-05, -3.88663066e-05, ...,\n",
       "         -3.41572886e-05, -3.88310800e-05, -3.80514721e-05],\n",
       "        [-3.53740797e-05, -3.13618912e-05, -3.29461873e-05, ...,\n",
       "         -5.62945252e-06, -7.22565437e-06, -7.20258049e-06],\n",
       "        ...,\n",
       "        [-9.13592521e-06, -2.83694681e-05, -2.47836306e-05, ...,\n",
       "         -3.92945258e-05, -3.65496526e-05, -2.12959894e-05],\n",
       "        [ 6.64688969e-06,  3.19394494e-05,  4.72948268e-05, ...,\n",
       "         -4.97719084e-05, -4.92024337e-05, -2.47416247e-05],\n",
       "        [ 2.32900202e-05,  6.51892988e-05,  7.62981144e-05, ...,\n",
       "         -3.09119923e-06,  1.02088725e-05,  3.17511331e-05]]),\n",
       " '1': array([[ 1.46575785e-05,  1.56517308e-05,  1.33044005e-05, ...,\n",
       "          3.27265457e-06,  8.13554436e-07,  2.71934141e-06],\n",
       "        [ 3.66400423e-06,  4.14612168e-06,  9.83462633e-06, ...,\n",
       "          3.85860985e-05,  3.59643163e-05,  3.53311952e-05],\n",
       "        [ 3.19338469e-05,  2.84257840e-05,  3.07363152e-05, ...,\n",
       "          1.81298328e-05,  1.51499971e-05,  1.07975939e-05],\n",
       "        ...,\n",
       "        [-5.19113273e-05, -3.79737066e-05, -1.72868149e-05, ...,\n",
       "         -3.07154114e-05, -2.41400176e-05, -2.83547251e-05],\n",
       "        [-3.03064844e-05, -2.51635047e-05, -2.41260695e-05, ...,\n",
       "         -3.27828384e-05, -3.03605229e-05, -2.94797464e-05],\n",
       "        [-2.62631984e-05, -1.05032123e-05,  5.95022766e-06, ...,\n",
       "         -1.57680188e-05, -1.58110415e-05, -2.03513810e-05]]),\n",
       " '2': array([[-7.35473513e-05, -8.35613100e-05, -8.61345688e-05, ...,\n",
       "          5.67578081e-06,  6.48202331e-06,  1.22817289e-06],\n",
       "        [ 8.88811371e-07, -2.81896541e-06, -5.01476585e-08, ...,\n",
       "          1.54097634e-05,  8.82610402e-06,  1.12489552e-05],\n",
       "        [ 2.32437669e-05,  3.30091571e-05,  3.80607926e-05, ...,\n",
       "          2.11562856e-05,  3.31971023e-05,  2.90430253e-05],\n",
       "        ...,\n",
       "        [ 6.36781770e-05,  7.17178118e-05,  7.09951491e-05, ...,\n",
       "          4.55873778e-05,  4.32655652e-05,  4.19461503e-05],\n",
       "        [ 4.40631629e-05,  4.80829804e-05,  5.14070380e-05, ...,\n",
       "          6.37799749e-05,  6.24981985e-05,  6.01003048e-05],\n",
       "        [ 5.81992099e-05,  5.56022569e-05,  4.97360088e-05, ...,\n",
       "          5.63924914e-05,  6.89556327e-05,  7.13000263e-05]]),\n",
       " '3': array([[ 5.00623792e-05,  3.67399225e-05,  2.40043883e-05, ...,\n",
       "          8.52644152e-05,  6.97031574e-05,  6.16760590e-05],\n",
       "        [ 5.65333066e-05,  5.50219411e-05,  6.48393470e-05, ...,\n",
       "          7.48136590e-05,  6.84024344e-05,  6.21820727e-05],\n",
       "        [ 6.16234320e-05,  5.83005385e-05,  4.84870579e-05, ...,\n",
       "          1.32276822e-04,  1.20077515e-04,  9.98375181e-05],\n",
       "        ...,\n",
       "        [-1.68700408e-05, -2.70899473e-05, -4.03342237e-05, ...,\n",
       "          2.44120685e-07, -1.48295358e-05, -2.60237230e-05],\n",
       "        [-2.78484094e-05, -1.90738865e-05, -8.01057467e-06, ...,\n",
       "          7.39626512e-06,  7.34924924e-06,  3.36418748e-06],\n",
       "        [-2.90443677e-06, -4.51677624e-06, -3.60619447e-06, ...,\n",
       "         -2.38544617e-05, -1.83127613e-05, -2.18096720e-05]]),\n",
       " '4': array([[ 3.47923037e-06,  3.19555761e-06,  3.63426693e-06, ...,\n",
       "         -7.55179008e-06, -6.75321917e-06, -4.27206396e-06],\n",
       "        [-2.41076509e-06, -3.00115471e-06, -3.73295279e-06, ...,\n",
       "         -1.54939862e-05, -1.69387404e-05, -2.06582554e-05],\n",
       "        [-2.39110195e-05, -2.37343411e-05, -2.26479606e-05, ...,\n",
       "         -4.01605030e-05, -4.18788368e-05, -4.05613646e-05],\n",
       "        ...,\n",
       "        [ 3.72834938e-06,  5.59632736e-06,  8.17019009e-06, ...,\n",
       "          1.05236268e-05,  9.72093221e-06,  7.77033802e-06],\n",
       "        [ 6.60791238e-06,  3.87694672e-06,  5.65314258e-06, ...,\n",
       "          1.58846560e-05,  1.91371364e-05,  2.03272284e-05],\n",
       "        [ 1.98378184e-05,  1.82509393e-05,  1.64566391e-05, ...,\n",
       "          4.30460659e-06,  7.32159151e-06,  7.30008014e-06]]),\n",
       " '5': array([[-1.01309652e-05, -1.32020486e-05, -2.82342371e-05, ...,\n",
       "         -3.67843168e-05, -2.22115796e-05, -1.69094710e-05],\n",
       "        [-1.37501002e-05, -5.01751356e-06,  9.33670208e-08, ...,\n",
       "          4.31267763e-05,  3.01007221e-05,  7.04189915e-06],\n",
       "        [-6.22255811e-06, -1.33230524e-05, -2.90118096e-05, ...,\n",
       "         -1.26705490e-04, -1.54630674e-04, -1.40776203e-04],\n",
       "        ...,\n",
       "        [ 2.46105265e-06,  4.06544950e-06, -2.56173365e-07, ...,\n",
       "         -7.40190580e-06, -5.69329995e-06, -4.33081868e-06],\n",
       "        [-2.70540249e-06,  5.68453657e-08,  6.90557101e-07, ...,\n",
       "         -1.34680586e-05, -5.70651991e-06, -2.28152089e-06],\n",
       "        [-3.05475965e-06, -1.64327935e-06,  7.36906713e-07, ...,\n",
       "          5.45622788e-06,  6.97862242e-06,  1.01090909e-05]]),\n",
       " '6': array([[ 2.38818466e-05,  2.93344638e-05,  2.81380944e-05, ...,\n",
       "          4.16464391e-05,  2.10238868e-05,  7.70314728e-06],\n",
       "        [ 1.04863902e-05,  1.85674799e-05,  2.11729766e-05, ...,\n",
       "         -7.86764649e-05, -7.39364041e-05, -5.48873832e-05],\n",
       "        [-2.52559475e-05, -6.40386190e-07,  1.98193375e-05, ...,\n",
       "         -7.81875315e-06, -1.06491716e-05, -1.31660026e-05],\n",
       "        ...,\n",
       "        [-2.74003214e-05, -3.65770829e-05, -7.98860783e-05, ...,\n",
       "         -7.09128699e-06, -1.02208896e-05, -7.56587224e-06],\n",
       "        [-2.33431347e-05, -5.51594057e-05, -7.94162042e-05, ...,\n",
       "          3.71208398e-06, -6.03862736e-06, -1.49296893e-05],\n",
       "        [-1.49107882e-05, -1.03925522e-05, -1.77857855e-05, ...,\n",
       "          9.56728968e-07, -1.25331817e-05, -2.73474016e-05]]),\n",
       " '7': array([[ 4.52167151e-05,  4.88303194e-05,  6.44416650e-05, ...,\n",
       "          4.55176123e-05,  4.43013632e-05,  4.41739212e-05],\n",
       "        [ 4.72390420e-05,  4.95192035e-05,  5.06791293e-05, ...,\n",
       "          7.62724521e-05,  6.46235276e-05,  5.94029771e-05],\n",
       "        [ 5.92840770e-05,  5.07259974e-05,  3.23861059e-05, ...,\n",
       "          6.50938237e-05,  6.22625303e-05,  5.61889974e-05],\n",
       "        ...,\n",
       "        [-1.93543747e-06, -1.33465583e-05, -2.99305502e-05, ...,\n",
       "          1.47876835e-05,  7.93845811e-06,  1.64982384e-06],\n",
       "        [ 4.60347746e-06,  1.50418346e-05,  2.48095948e-05, ...,\n",
       "          1.38356245e-05,  5.94898165e-06,  2.23612756e-06],\n",
       "        [ 7.83417818e-06,  5.92254355e-06, -7.54555049e-06, ...,\n",
       "          1.18193921e-05,  2.33391665e-06,  2.92229765e-06]]),\n",
       " '8': array([[-9.23113566e-06, -2.09467489e-05, -1.53592227e-05, ...,\n",
       "          6.72580200e-07, -1.62811921e-05, -2.17688903e-05],\n",
       "        [-2.67844298e-05, -3.87311702e-05, -4.53123685e-05, ...,\n",
       "         -6.96855295e-06, -1.33296744e-05, -1.93215274e-05],\n",
       "        [-1.51160020e-05, -2.91769766e-06,  1.16542424e-06, ...,\n",
       "          7.29599435e-07,  4.27776240e-06, -4.99538100e-06],\n",
       "        ...,\n",
       "        [-2.84367397e-05, -2.38435059e-05, -3.20172003e-05, ...,\n",
       "         -3.82025019e-06, -6.27847703e-06, -1.63096392e-05],\n",
       "        [-2.50006142e-05, -2.63123366e-05, -3.58383550e-05, ...,\n",
       "         -4.41654229e-05, -5.78084037e-05, -6.67100030e-05],\n",
       "        [-6.96902644e-05, -6.08145347e-05, -4.37407725e-05, ...,\n",
       "         -6.15412591e-06, -8.94317691e-06, -6.38277606e-06]]),\n",
       " '9': array([[ 3.90421519e-05,  5.53267928e-05,  6.26042820e-05, ...,\n",
       "          4.40207841e-05,  3.14136487e-05,  2.87810017e-05],\n",
       "        [ 4.28870953e-05,  5.53269492e-05,  5.57728163e-05, ...,\n",
       "          2.97476781e-05,  2.13236599e-05,  1.66575210e-05],\n",
       "        [ 1.71190622e-05,  1.58776347e-05,  1.27845833e-05, ...,\n",
       "          2.96817907e-05,  1.98777798e-05,  1.49526022e-05],\n",
       "        ...,\n",
       "        [-3.89191300e-05, -5.46432238e-05, -6.63218016e-05, ...,\n",
       "         -5.72901899e-06, -2.54645365e-05, -4.53994871e-05],\n",
       "        [-5.94405610e-05, -5.96594073e-05, -5.51740814e-05, ...,\n",
       "          2.96751077e-05,  1.97814134e-05,  1.17903610e-05],\n",
       "        [ 9.61407204e-06,  1.02951844e-06, -1.93772175e-05, ...,\n",
       "          1.31799789e-05, -1.11203763e-05, -2.92879449e-05]]),\n",
       " '10': array([[-2.59926983e-05, -2.79773740e-05, -3.54033618e-05, ...,\n",
       "         -3.60058584e-05, -3.79674530e-05, -4.47929851e-05],\n",
       "        [-4.91760766e-05, -4.68216822e-05, -3.82643011e-05, ...,\n",
       "         -5.15379870e-05, -4.74777480e-05, -4.55027148e-05],\n",
       "        [-4.75139968e-05, -4.86478384e-05, -4.86341887e-05, ...,\n",
       "         -5.21376096e-05, -4.93272455e-05, -4.80723065e-05],\n",
       "        ...,\n",
       "        [ 1.71593656e-05,  1.51467675e-05,  9.28691043e-06, ...,\n",
       "         -3.71907890e-06,  1.47245310e-05,  2.31230097e-05],\n",
       "        [ 3.51205599e-05,  5.29839926e-05,  5.37933956e-05, ...,\n",
       "          3.17940467e-05,  1.75260684e-05,  5.66574090e-06],\n",
       "        [ 4.46230979e-06, -1.97731742e-06, -1.46973634e-05, ...,\n",
       "          4.26744373e-05,  3.10565047e-05,  1.73617300e-05]]),\n",
       " '11': array([[ 5.00774513e-05,  6.81022575e-05,  6.22520529e-05, ...,\n",
       "          5.55012184e-06, -6.82017435e-06, -2.36666165e-05],\n",
       "        [-5.19359528e-05, -7.11009779e-05, -7.19772797e-05, ...,\n",
       "          2.20099664e-05,  1.49933667e-05, -3.66939844e-06],\n",
       "        [-1.65698293e-05, -1.59373776e-05, -2.46720701e-05, ...,\n",
       "          6.81319170e-06,  1.91841500e-05,  1.93926317e-05],\n",
       "        ...,\n",
       "        [ 5.40529036e-05,  5.44686955e-05,  5.25087453e-05, ...,\n",
       "          5.13637970e-05,  5.62255664e-05,  5.99064515e-05],\n",
       "        [ 5.87944305e-05,  5.69090953e-05,  5.85583766e-05, ...,\n",
       "          5.41380396e-05,  5.57218955e-05,  5.55996849e-05],\n",
       "        [ 5.75248414e-05,  6.33067684e-05,  6.46101689e-05, ...,\n",
       "          5.73117504e-05,  5.21695001e-05,  4.69512961e-05]]),\n",
       " '12': array([[-1.13653959e-05, -1.42764475e-05, -1.10404671e-05, ...,\n",
       "         -5.55306542e-06, -1.36079188e-05, -2.03430773e-05],\n",
       "        [-2.03206437e-05, -1.90103256e-05, -1.57225095e-05, ...,\n",
       "          8.83376561e-06,  1.19204321e-06, -1.12876696e-05],\n",
       "        [-1.59546016e-05, -1.79440667e-05, -2.53052367e-05, ...,\n",
       "         -7.68878090e-05, -8.09378180e-05, -7.51263142e-05],\n",
       "        ...,\n",
       "        [ 3.70814596e-05,  2.52941463e-05,  1.08402428e-05, ...,\n",
       "          3.93500050e-05,  4.22562371e-05,  4.32295346e-05],\n",
       "        [ 4.35454567e-05,  4.11355359e-05,  3.60840495e-05, ...,\n",
       "          3.74997326e-05,  3.55870106e-05,  3.02081608e-05],\n",
       "        [ 3.37730708e-05,  3.70294656e-05,  3.52555253e-05, ...,\n",
       "          2.17967354e-05,  2.03105938e-05,  1.94874683e-05]]),\n",
       " '13': array([[-2.35371663e-05, -2.89212912e-05, -3.39613180e-05, ...,\n",
       "         -6.25395915e-05, -7.20128010e-05, -7.85197117e-05],\n",
       "        [-8.18093395e-05, -8.10600031e-05, -7.52096530e-05, ...,\n",
       "         -4.61752497e-05, -3.98991178e-05, -4.80336530e-05],\n",
       "        [-6.23870583e-05, -6.69711662e-05, -6.00579806e-05, ...,\n",
       "         -5.88270013e-05, -6.23726737e-05, -6.25139146e-05],\n",
       "        ...,\n",
       "        [-2.12464474e-05, -2.05028118e-05, -1.05386171e-05, ...,\n",
       "         -2.85932074e-05, -3.07673399e-05, -3.08441849e-05],\n",
       "        [-2.94620968e-05, -2.56287076e-05, -2.01544954e-05, ...,\n",
       "         -2.35245770e-05, -2.26064567e-05, -2.06376826e-05],\n",
       "        [-1.95944231e-05, -2.29451907e-05, -2.59886092e-05, ...,\n",
       "         -2.13232761e-05, -2.10256312e-05, -1.96354940e-05]]),\n",
       " '14': array([[-2.73396745e-05,  7.74716136e-06,  2.15700020e-05, ...,\n",
       "          2.45442479e-05,  4.02979203e-05,  4.52236454e-05],\n",
       "        [ 2.09522168e-05, -1.26916048e-05, -1.99851711e-05, ...,\n",
       "         -8.52165103e-05, -9.65865111e-05, -8.46951079e-05],\n",
       "        [-4.97950678e-05, -2.97705210e-05, -4.10722387e-05, ...,\n",
       "         -7.17995645e-05, -6.59833822e-05, -5.97173712e-05],\n",
       "        ...,\n",
       "        [-7.29600215e-05, -4.01915277e-05, -5.49931246e-06, ...,\n",
       "         -1.01822652e-05, -1.25959114e-05, -2.30071691e-05],\n",
       "        [-3.76042117e-05, -3.90406749e-05, -1.98755806e-05, ...,\n",
       "         -4.90785787e-05, -5.41628833e-05, -6.45010077e-05],\n",
       "        [-6.79790101e-05, -4.08831111e-05, -6.58721729e-06, ...,\n",
       "          6.62276489e-06,  3.28281094e-05,  3.67045359e-05]]),\n",
       " '15': array([[ 4.77976391e-06,  1.17830277e-05,  1.49460620e-05, ...,\n",
       "          2.62857229e-05,  2.68321928e-05,  2.43468385e-05],\n",
       "        [ 2.44613821e-05,  2.73125333e-05,  2.65011167e-05, ...,\n",
       "          4.90565981e-05,  4.74035332e-05,  4.73341133e-05],\n",
       "        [ 4.86874305e-05,  4.98292065e-05,  4.74775406e-05, ...,\n",
       "          5.80591914e-05,  5.79047046e-05,  5.69595068e-05],\n",
       "        ...,\n",
       "        [-4.39872883e-06, -1.94965487e-05, -2.19364210e-05, ...,\n",
       "          4.92999197e-06,  8.88790419e-06,  4.71081512e-06],\n",
       "        [-1.15306075e-05, -2.23408788e-05, -3.36998273e-05, ...,\n",
       "         -6.96470233e-05, -7.46476799e-05, -7.04914710e-05],\n",
       "        [-5.76116072e-05, -5.23267336e-05, -6.56953853e-05, ...,\n",
       "         -5.50112454e-05, -1.71986103e-05,  5.45157718e-06]]),\n",
       " '16': array([[ 3.05860267e-05,  3.54414660e-05,  2.31435533e-05, ...,\n",
       "          2.86582281e-06, -5.37593041e-06, -5.17959506e-06],\n",
       "        [-4.30064574e-06, -7.86461533e-06, -1.62909691e-05, ...,\n",
       "          3.14941244e-05,  2.23615898e-05,  1.82665190e-05],\n",
       "        [ 1.70883632e-05,  5.47680202e-06, -9.00326722e-06, ...,\n",
       "         -2.25049916e-05, -3.38805876e-05, -6.45943364e-05],\n",
       "        ...,\n",
       "        [-2.17552097e-05, -2.14571646e-05, -2.70830697e-05, ...,\n",
       "          5.99133382e-05,  5.74713777e-05,  4.30493346e-05],\n",
       "        [ 2.62654303e-05,  1.45394406e-05,  1.31516108e-05, ...,\n",
       "          2.19486592e-05,  1.62403212e-05,  1.60900781e-05],\n",
       "        [ 5.79849620e-06, -6.32764704e-06, -1.45995673e-05, ...,\n",
       "          6.73261093e-05,  6.67365166e-05,  7.55396177e-05]]),\n",
       " '17': array([[-7.72383464e-06, -1.69737832e-05, -2.18848218e-05, ...,\n",
       "         -4.03858976e-05, -4.62737989e-05, -5.04049567e-05],\n",
       "        [-4.29344036e-05, -3.22877422e-05, -3.06916372e-05, ...,\n",
       "         -4.75348497e-05, -4.49424369e-05, -3.32370873e-05],\n",
       "        [-2.36590022e-05, -2.38043758e-05, -2.80315107e-05, ...,\n",
       "         -7.42672346e-05, -5.95792153e-05, -4.52313179e-05],\n",
       "        ...,\n",
       "        [ 3.42546809e-05,  2.79436572e-05,  2.43883951e-05, ...,\n",
       "         -9.01166359e-08,  9.66790594e-06,  2.30048063e-05],\n",
       "        [ 3.06386100e-05,  2.94423462e-05,  2.45474021e-05, ...,\n",
       "          3.49091861e-05,  3.55796838e-05,  3.61119673e-05],\n",
       "        [ 3.53255491e-05,  2.97741281e-05,  2.16974477e-05, ...,\n",
       "          3.20819272e-05,  3.61124912e-05,  3.50253285e-05]]),\n",
       " '18': array([[-1.09058110e-05, -1.17274176e-05, -1.09117555e-05, ...,\n",
       "          6.60521937e-06,  6.19130242e-06,  6.28674661e-06],\n",
       "        [ 6.88972204e-06,  5.71236342e-06,  2.92535333e-06, ...,\n",
       "          7.49580931e-06,  1.06061480e-05,  1.13532124e-05],\n",
       "        [ 1.11891341e-05,  1.28183920e-05,  1.31485340e-05, ...,\n",
       "         -2.85674491e-06, -3.74364026e-06, -4.56423959e-06],\n",
       "        ...,\n",
       "        [-3.60107115e-05, -3.23131208e-05, -3.50330738e-05, ...,\n",
       "         -4.92227009e-05, -4.34532631e-05, -3.31581650e-05],\n",
       "        [-3.35375589e-05, -4.12708578e-05, -4.75966699e-05, ...,\n",
       "         -4.73961554e-05, -4.23840684e-05, -3.72007016e-05],\n",
       "        [-3.01530999e-05, -2.02301690e-05, -1.95522261e-05, ...,\n",
       "         -4.15647482e-05, -4.62877615e-05, -5.14719686e-05]]),\n",
       " '19': array([[ 2.07093290e-05,  2.45308584e-05,  2.85110000e-05, ...,\n",
       "          4.47291277e-05,  4.69424122e-05,  4.58043651e-05],\n",
       "        [ 4.64257791e-05,  4.80002163e-05,  4.83540498e-05, ...,\n",
       "          4.23758029e-05,  3.24106622e-05,  1.42498357e-05],\n",
       "        [ 4.62892876e-06,  1.25322686e-05,  2.84497874e-05, ...,\n",
       "          1.97786630e-05,  2.16948556e-05,  1.69132036e-05],\n",
       "        ...,\n",
       "        [-3.34440956e-05, -7.11177854e-05, -9.19640661e-05, ...,\n",
       "         -2.57544871e-05,  5.64026004e-06,  3.19204810e-05],\n",
       "        [ 1.25003016e-05, -1.86863126e-05, -4.06887484e-05, ...,\n",
       "         -1.41262781e-05,  1.34783159e-05,  3.01820146e-05],\n",
       "        [ 1.59968513e-05, -1.66189166e-05, -3.83773695e-05, ...,\n",
       "         -1.04806873e-04, -1.03961233e-04, -8.27203185e-05]]),\n",
       " '20': array([[-4.09570312e-05, -5.44022841e-05, -6.35734687e-05, ...,\n",
       "         -6.09785675e-05, -7.61206247e-05, -6.73711620e-05],\n",
       "        [-5.16281871e-05, -3.57965582e-05, -1.62025281e-05, ...,\n",
       "         -2.50294524e-05, -3.05120593e-05, -4.45016958e-05],\n",
       "        [-6.18812337e-05, -7.26058570e-05, -6.89578810e-05, ...,\n",
       "         -1.32593777e-05, -1.13393235e-05, -2.24726518e-05],\n",
       "        ...,\n",
       "        [ 2.04541266e-05,  2.09277296e-05,  1.41271839e-05, ...,\n",
       "          7.54904568e-06,  6.83306496e-07, -1.11967975e-05],\n",
       "        [-1.69028190e-05, -3.80662664e-06,  2.05803626e-05, ...,\n",
       "         -1.55951966e-05,  1.95417397e-05,  4.35267648e-05],\n",
       "        [ 4.43626741e-05,  3.69979862e-05,  2.62831636e-05, ...,\n",
       "          1.46447992e-05, -2.32017464e-06,  1.34480688e-05]]),\n",
       " '21': array([[-1.07731821e-05,  2.28696422e-06,  2.68332205e-05, ...,\n",
       "          7.02967591e-05,  5.84465670e-05,  3.99101191e-05],\n",
       "        [ 2.99559579e-05,  3.22138403e-05,  3.95766547e-05, ...,\n",
       "          1.02352205e-05,  1.59909996e-06, -1.02553292e-06],\n",
       "        [-4.30877253e-06, -2.98774830e-06, -5.57292333e-06, ...,\n",
       "         -9.93649792e-06, -2.30236856e-05, -3.35543591e-05],\n",
       "        ...,\n",
       "        [ 2.40790723e-05,  1.20789218e-05,  6.91462583e-06, ...,\n",
       "          1.00418083e-05,  8.86070575e-06,  1.17092241e-05],\n",
       "        [ 2.15787986e-05,  3.88541557e-05,  4.50390871e-05, ...,\n",
       "         -1.35982709e-05, -2.55422547e-05, -2.64035443e-05],\n",
       "        [-8.85571353e-06,  1.74123288e-05,  4.14345159e-05, ...,\n",
       "          1.93630895e-05,  1.75580426e-05,  1.22499951e-05]]),\n",
       " '22': array([[-1.34924967e-05, -1.45095764e-05, -1.45054291e-05, ...,\n",
       "         -4.38309107e-05, -8.47670890e-06,  5.89167075e-06],\n",
       "        [ 7.52962751e-06, -5.81635322e-06, -2.92129043e-05, ...,\n",
       "          5.41428562e-05,  4.61516101e-05,  3.80251731e-05],\n",
       "        [ 3.52370771e-05,  3.75946001e-05,  3.56171586e-05, ...,\n",
       "          5.08342964e-05,  4.12490554e-05,  4.03874583e-05],\n",
       "        ...,\n",
       "        [-1.69579253e-05, -2.38990269e-05, -2.61355308e-05, ...,\n",
       "          9.59532917e-06,  8.01822262e-06,  4.35619586e-06],\n",
       "        [-2.93427229e-06, -7.06638593e-06, -5.29526460e-06, ...,\n",
       "          2.76370065e-05,  2.15030122e-05,  1.22753099e-05],\n",
       "        [ 7.64089600e-06,  8.13589213e-06,  6.28245107e-06, ...,\n",
       "          2.59091630e-05,  1.45108961e-05,  7.49013361e-06]]),\n",
       " '23': array([[-7.41404219e-05, -7.27761289e-05, -6.71452508e-05, ...,\n",
       "         -4.63976939e-05, -4.35518123e-05, -5.12197403e-05],\n",
       "        [-6.14501405e-05, -6.05433233e-05, -5.69997246e-05, ...,\n",
       "         -5.01025133e-05, -5.77243300e-05, -5.92663273e-05],\n",
       "        [-5.21217735e-05, -3.94066446e-05, -3.43299835e-05, ...,\n",
       "         -3.43876127e-05, -4.06520085e-05, -4.79319642e-05],\n",
       "        ...,\n",
       "        [ 3.51983581e-05,  3.05219182e-05,  2.82279252e-05, ...,\n",
       "          1.84564378e-05,  1.60793570e-05,  1.26596578e-05],\n",
       "        [ 1.30434810e-05,  1.43013358e-05,  1.53954752e-05, ...,\n",
       "          4.26105398e-05,  4.08062879e-05,  3.99347591e-05],\n",
       "        [ 3.57096360e-05,  2.80267668e-05,  2.35306925e-05, ...,\n",
       "          3.08115741e-05,  2.78181724e-05,  2.22746767e-05]]),\n",
       " '24': array([[ 8.36198251e-06,  8.33811919e-06,  1.14196773e-05, ...,\n",
       "          1.32237055e-05,  2.17600100e-05,  2.10271537e-05],\n",
       "        [ 1.27527292e-05,  4.41114253e-06,  2.52399400e-06, ...,\n",
       "          2.33337705e-05,  1.93438191e-05,  1.47038991e-05],\n",
       "        [ 6.28050293e-06,  6.81198344e-07, -2.59916919e-06, ...,\n",
       "         -1.36302970e-05, -9.12870837e-06, -2.95687960e-06],\n",
       "        ...,\n",
       "        [-6.42776067e-05, -6.14319360e-05, -5.68181749e-05, ...,\n",
       "         -7.94553707e-05, -7.85070661e-05, -7.60374169e-05],\n",
       "        [-7.53142303e-05, -7.42194898e-05, -7.09481901e-05, ...,\n",
       "         -6.65877160e-05, -6.71259040e-05, -7.06042847e-05],\n",
       "        [-7.41295953e-05, -7.44201243e-05, -7.30216925e-05, ...,\n",
       "         -6.40908620e-05, -6.22705775e-05, -5.88149014e-05]]),\n",
       " '25': array([[-3.94369345e-05, -3.29843097e-05, -1.25510287e-05, ...,\n",
       "         -2.57628526e-05, -1.97400750e-05, -1.67380567e-05],\n",
       "        [-1.04940918e-05, -2.15069649e-05, -5.47295022e-05, ...,\n",
       "         -3.99717064e-05, -5.24723328e-05, -6.71878588e-05],\n",
       "        [-6.44532702e-05, -3.74398878e-05, -6.78910965e-06, ...,\n",
       "         -1.67292346e-05, -2.95525024e-05, -3.09531206e-05],\n",
       "        ...,\n",
       "        [ 1.58457351e-05,  5.27047814e-06, -7.60406351e-07, ...,\n",
       "          3.42939384e-05,  2.95452392e-05,  2.86935137e-05],\n",
       "        [ 3.11274125e-05,  3.56145938e-05,  4.98971713e-05, ...,\n",
       "         -2.55278737e-05, -1.83257853e-05, -4.36696610e-06],\n",
       "        [ 1.75346431e-05,  3.27997077e-05,  4.57738497e-05, ...,\n",
       "         -1.60548680e-05, -2.60716115e-05, -2.06952191e-05]]),\n",
       " '26': array([[-2.79102114e-05, -3.01300443e-05, -3.03123779e-05, ...,\n",
       "         -3.93470618e-05, -3.94106501e-05, -3.73221083e-05],\n",
       "        [-3.73303410e-05, -3.88264380e-05, -3.72480317e-05, ...,\n",
       "         -3.46231827e-05, -3.77733159e-05, -4.09161839e-05],\n",
       "        [-4.22830963e-05, -4.18146046e-05, -4.18799063e-05, ...,\n",
       "         -5.27836746e-05, -5.50799159e-05, -5.32477316e-05],\n",
       "        ...,\n",
       "        [ 3.50054984e-06,  4.25252620e-06,  3.85311478e-06, ...,\n",
       "         -1.94469721e-05, -2.31500690e-05, -2.55263876e-05],\n",
       "        [-1.96060828e-05, -9.03316959e-06, -7.04671811e-06, ...,\n",
       "         -7.97043776e-06, -8.43685666e-06, -4.88382875e-06],\n",
       "        [-1.67018072e-06, -5.95040865e-06, -9.94988022e-06, ...,\n",
       "          1.99518786e-06, -4.59239629e-07, -6.68160453e-07]]),\n",
       " '27': array([[-2.22161398e-05, -2.15115106e-05, -1.87646347e-05, ...,\n",
       "         -1.85853912e-06, -8.24418476e-06, -1.76091853e-05],\n",
       "        [-2.47691787e-05, -2.64158025e-05, -2.13463918e-05, ...,\n",
       "         -3.48480785e-06, -3.71559190e-06, -5.07097548e-06],\n",
       "        [-5.61294200e-06, -6.97862924e-06, -8.12264079e-06, ...,\n",
       "          2.80490281e-06,  9.82146867e-06,  9.30672468e-06],\n",
       "        ...,\n",
       "        [-5.61687775e-05, -5.92215511e-05, -7.10565291e-05, ...,\n",
       "          7.40009341e-07, -7.98613564e-06, -2.37930508e-05],\n",
       "        [-4.38077841e-05, -5.90742820e-05, -5.65689443e-05, ...,\n",
       "         -4.65653975e-05, -4.18272211e-05, -2.03950349e-05],\n",
       "        [ 6.17861451e-06,  3.73954731e-06, -5.81266522e-06, ...,\n",
       "         -4.07206935e-05, -2.18266250e-05, -4.47153752e-06]]),\n",
       " '28': array([[-6.20219580e-05, -6.61628146e-05, -6.75930933e-05, ...,\n",
       "         -6.44391912e-05, -6.34186508e-05, -6.35169708e-05],\n",
       "        [-6.47064298e-05, -6.71199523e-05, -6.87171196e-05, ...,\n",
       "         -6.11197829e-05, -5.97533181e-05, -5.82727553e-05],\n",
       "        [-5.79163461e-05, -5.59037799e-05, -5.44353279e-05, ...,\n",
       "         -5.53537575e-05, -5.97396065e-05, -6.26094043e-05],\n",
       "        ...,\n",
       "        [ 3.41693999e-06,  5.69532540e-06,  9.48320485e-06, ...,\n",
       "          6.44526881e-06,  2.70938699e-06, -2.79003439e-06],\n",
       "        [-7.79615402e-06, -1.16796882e-05, -1.83596294e-05, ...,\n",
       "         -4.98424697e-06, -4.18667878e-06, -1.03618402e-06],\n",
       "        [-1.72519788e-06, -2.64891173e-06,  2.36382220e-06, ...,\n",
       "         -3.65961241e-05, -4.32973029e-05, -5.19817695e-05]]),\n",
       " '29': array([[ 3.73587827e-05,  5.04029776e-05,  3.97709155e-05, ...,\n",
       "          6.68907596e-05,  7.16365321e-05,  7.16390205e-05],\n",
       "        [ 7.62762138e-05,  7.28576633e-05,  5.45821458e-05, ...,\n",
       "          2.53049548e-05,  3.30866642e-05,  4.37958333e-05],\n",
       "        [ 5.65406408e-05,  6.99873199e-05,  7.38300223e-05, ...,\n",
       "         -2.60254019e-05, -7.87072258e-06,  2.43401155e-05],\n",
       "        ...,\n",
       "        [-2.97818933e-05, -3.45070912e-05, -5.37222695e-05, ...,\n",
       "         -7.66422090e-06, -1.16757838e-05, -1.29233167e-05],\n",
       "        [-1.10054780e-05, -1.34417251e-05, -1.70798994e-05, ...,\n",
       "         -3.57935460e-05, -2.97720926e-05, -3.07701994e-05],\n",
       "        [-3.99687815e-05, -5.29060562e-05, -6.41822626e-05, ...,\n",
       "         -9.53740091e-05, -6.99284501e-05, -3.16566329e-05]]),\n",
       " '30': array([[-8.57421037e-05, -8.71232987e-05, -8.44852038e-05, ...,\n",
       "         -1.27768202e-04, -1.25039223e-04, -1.12606765e-04],\n",
       "        [-9.55917494e-05, -7.84501535e-05, -6.58293575e-05, ...,\n",
       "         -1.10722423e-04, -1.27859661e-04, -1.29910695e-04],\n",
       "        [-1.17173193e-04, -1.02858146e-04, -9.66859952e-05, ...,\n",
       "         -1.15516115e-04, -1.10945730e-04, -9.87490857e-05],\n",
       "        ...,\n",
       "        [ 6.87611318e-05,  1.59122901e-05, -1.32169025e-05, ...,\n",
       "          7.33795750e-05,  6.76876662e-05,  4.90608436e-05],\n",
       "        [ 3.46228226e-05,  2.87128569e-05,  2.61258083e-05, ...,\n",
       "          9.05370471e-05,  7.55363362e-05,  4.72088686e-05],\n",
       "        [ 2.92956793e-05,  1.33462672e-05, -1.76975152e-06, ...,\n",
       "          8.18492190e-05,  9.31183167e-05,  8.97007485e-05]]),\n",
       " '31': array([[-2.19028352e-05, -2.35123734e-05, -2.68237181e-05, ...,\n",
       "         -1.97095687e-05, -1.93806409e-05, -1.80390816e-05],\n",
       "        [-1.94870208e-05, -2.10098133e-05, -2.14344691e-05, ...,\n",
       "         -2.93971370e-05, -2.92991990e-05, -2.93987450e-05],\n",
       "        [-2.66863935e-05, -2.50917255e-05, -2.36766136e-05, ...,\n",
       "         -1.75586283e-05, -2.09676418e-05, -1.98060443e-05],\n",
       "        ...,\n",
       "        [-1.11062982e-05, -1.31775168e-05, -1.87001224e-05, ...,\n",
       "         -2.47043972e-05, -7.24701204e-06, -2.44860371e-06],\n",
       "        [-1.29738100e-06,  5.50332697e-06,  8.90819592e-06, ...,\n",
       "         -6.39787731e-06, -1.03059647e-05, -1.93938886e-05],\n",
       "        [-2.17260858e-05, -2.38553275e-05, -3.68900255e-05, ...,\n",
       "         -6.54312453e-06, -7.51827110e-06, -9.32845705e-06]]),\n",
       " '32': array([[-1.61492808e-05, -1.42679046e-05, -1.25985880e-05, ...,\n",
       "         -1.69164869e-05, -1.29782593e-05, -1.53378969e-05],\n",
       "        [-1.68739571e-05, -1.45333306e-05, -1.41280370e-05, ...,\n",
       "         -2.35980019e-06, -7.61087767e-06, -1.01298365e-05],\n",
       "        [-1.11960280e-05, -1.38359546e-05, -1.41503288e-05, ...,\n",
       "         -6.66856295e-06, -4.65921858e-06, -4.53776920e-06],\n",
       "        ...,\n",
       "        [ 5.11772269e-05,  5.21254224e-05,  5.22046321e-05, ...,\n",
       "          1.94704335e-05,  1.69030900e-05,  1.23467071e-05],\n",
       "        [ 5.12241513e-06,  5.63538208e-07,  1.89343814e-06, ...,\n",
       "          8.57057785e-06, -4.20928200e-06, -7.24384381e-06],\n",
       "        [-3.48251501e-06, -4.97364908e-06, -6.08180790e-06, ...,\n",
       "         -1.22769579e-05, -2.52840637e-05, -3.23545210e-05]]),\n",
       " '33': array([[ 3.47237292e-05,  3.23986787e-05,  2.80241329e-05, ...,\n",
       "          3.90440109e-05,  3.81167811e-05,  3.70414127e-05],\n",
       "        [ 3.76037642e-05,  3.92788097e-05,  4.10313187e-05, ...,\n",
       "          2.64262799e-05,  2.62656667e-05,  2.46000636e-05],\n",
       "        [ 2.51159472e-05,  2.52030932e-05,  2.25017484e-05, ...,\n",
       "          1.87993592e-05,  1.88334761e-05,  1.96330675e-05],\n",
       "        ...,\n",
       "        [-8.59717784e-06, -8.39956556e-06, -8.33931153e-06, ...,\n",
       "          7.41337180e-06,  1.03123630e-05,  1.74677316e-05],\n",
       "        [ 2.60828929e-05,  2.87271341e-05,  2.51998918e-05, ...,\n",
       "         -1.63742970e-05, -1.60293348e-05, -1.59030005e-05],\n",
       "        [-1.82197673e-05, -2.26099237e-05, -2.33076426e-05, ...,\n",
       "          1.04290657e-05,  1.84468754e-05,  3.40466031e-05]]),\n",
       " '34': array([[-1.11479085e-05,  1.67636954e-05,  2.12628947e-05, ...,\n",
       "         -1.00359648e-05, -6.94192568e-06,  2.40592017e-05],\n",
       "        [ 4.69885999e-05,  3.70135895e-05,  1.60787895e-05, ...,\n",
       "          1.70651692e-05, -1.04311339e-05, -4.03428276e-05],\n",
       "        [-4.44969992e-05, -4.37612252e-05, -5.29733479e-05, ...,\n",
       "          7.21038159e-05,  7.88511607e-05,  7.43589262e-05],\n",
       "        ...,\n",
       "        [ 1.52293187e-05,  1.25319293e-05,  1.18164435e-05, ...,\n",
       "         -5.05748585e-06, -9.60463058e-06, -7.95632604e-06],\n",
       "        [-1.27325948e-05, -1.18091721e-05, -5.02538705e-06, ...,\n",
       "         -6.08953633e-05, -1.72787950e-05,  6.71930820e-06],\n",
       "        [-1.47237542e-05, -5.90263808e-05, -7.58758688e-05, ...,\n",
       "         -2.39080346e-05, -4.14124370e-05, -5.84028676e-05]]),\n",
       " '35': array([[-2.66091070e-06, -2.42627743e-06, -2.48166339e-06, ...,\n",
       "          1.03698630e-05,  7.49297169e-06,  6.72380111e-06],\n",
       "        [ 8.09863013e-06,  1.18061716e-05,  1.59178508e-05, ...,\n",
       "          2.46669642e-05,  2.33058308e-05,  2.12798623e-05],\n",
       "        [ 2.00848317e-05,  2.06904097e-05,  2.27873188e-05, ...,\n",
       "          2.64382415e-05,  2.48271263e-05,  2.39293622e-05],\n",
       "        ...,\n",
       "        [ 3.01556138e-05,  3.07450864e-05,  2.69713219e-05, ...,\n",
       "          2.09628088e-05,  2.17330235e-05,  2.31053964e-05],\n",
       "        [ 2.32371440e-05,  1.99828301e-05,  1.18212574e-05, ...,\n",
       "          3.20886247e-05,  3.00305037e-05,  3.21176813e-05],\n",
       "        [ 3.71007554e-05,  4.03251761e-05,  4.04677485e-05, ...,\n",
       "          3.12123630e-05,  2.69581033e-05,  2.18052119e-05]]),\n",
       " '36': array([[-5.69694712e-05, -5.29883000e-05, -3.92122165e-05, ...,\n",
       "         -4.87301395e-06, -5.25946689e-06, -3.69355166e-06],\n",
       "        [-2.06485893e-05, -4.60577976e-05, -4.72496249e-05, ...,\n",
       "         -4.83603344e-07,  9.39310121e-06,  1.72945292e-05],\n",
       "        [ 2.96542839e-05,  2.77558629e-05,  6.41690747e-07, ...,\n",
       "         -3.09286916e-05,  7.36742732e-06,  5.17128065e-05],\n",
       "        ...,\n",
       "        [ 2.73073420e-05,  1.71654756e-05,  8.99152928e-06, ...,\n",
       "          4.82891628e-05,  4.86263634e-05,  5.10538521e-05],\n",
       "        [ 4.64289806e-05,  3.95863099e-05,  3.74614210e-05, ...,\n",
       "          2.75301845e-05,  3.19161409e-05,  3.10482537e-05],\n",
       "        [ 2.39422297e-05,  1.83678803e-05,  2.05389424e-05, ...,\n",
       "          1.15279727e-05,  8.93877950e-06,  1.15562589e-05]]),\n",
       " '37': array([[-7.16596769e-05, -6.83725302e-05, -6.71569433e-05, ...,\n",
       "         -6.13780794e-05, -6.02578184e-05, -6.10778006e-05],\n",
       "        [-6.07946859e-05, -6.04533125e-05, -6.15325916e-05, ...,\n",
       "         -3.89802590e-05, -3.73058720e-05, -3.63317849e-05],\n",
       "        [-3.22788947e-05, -2.91948909e-05, -3.08087256e-05, ...,\n",
       "         -4.37732297e-06, -3.47337323e-06, -1.28988461e-06],\n",
       "        ...,\n",
       "        [-1.03559569e-05, -6.69638530e-06, -1.41309665e-05, ...,\n",
       "         -8.35871560e-06, -2.45191241e-05, -3.35541372e-05],\n",
       "        [-1.46036082e-05, -2.97042038e-06, -2.15315631e-05, ...,\n",
       "         -5.72722129e-05, -7.63126154e-05, -6.97701398e-05],\n",
       "        [-4.12685913e-05, -1.03237971e-05,  1.97227819e-05, ...,\n",
       "         -1.18736949e-04, -1.19192948e-04, -9.99814438e-05]]),\n",
       " '38': array([[-6.43732492e-05, -3.77161596e-05, -1.35605860e-05, ...,\n",
       "         -6.28036942e-05, -6.79076766e-05, -8.73228055e-05],\n",
       "        [-1.07030050e-04, -1.25595034e-04, -1.57823772e-04, ...,\n",
       "         -1.94017630e-05, -3.05825379e-05, -5.46668816e-05],\n",
       "        [-8.43259040e-05, -9.37183140e-05, -9.24664200e-05, ...,\n",
       "         -1.96363653e-05, -5.42818589e-06, -6.37108724e-06],\n",
       "        ...,\n",
       "        [ 3.02141234e-05,  2.95204900e-05,  2.67576052e-05, ...,\n",
       "          4.98522277e-05,  4.59245675e-05,  4.59455296e-05],\n",
       "        [ 4.56557354e-05,  4.12355075e-05,  3.56344899e-05, ...,\n",
       "          1.84291530e-05,  1.47746541e-05,  1.71578358e-05],\n",
       "        [ 2.18684090e-05,  2.43158302e-05,  2.70116670e-05, ...,\n",
       "          1.71269476e-05,  1.55516973e-05,  1.20848663e-05]]),\n",
       " '39': array([[-2.49811383e-05, -3.09572533e-05, -3.40685037e-05, ...,\n",
       "         -9.42785027e-06, -1.53602887e-05, -1.97885420e-05],\n",
       "        [-2.24293362e-05, -2.24681644e-05, -2.25429976e-05, ...,\n",
       "         -3.98947086e-05, -4.02403639e-05, -3.86168176e-05],\n",
       "        [-4.17041301e-05, -4.73783948e-05, -5.04005402e-05, ...,\n",
       "         -5.72886347e-05, -5.56184532e-05, -4.60599658e-05],\n",
       "        ...,\n",
       "        [-1.41601895e-05, -1.87705627e-05, -2.30894093e-05, ...,\n",
       "         -8.48617674e-06, -6.41772840e-06, -5.62409150e-06],\n",
       "        [-4.80985682e-06, -7.97537814e-06, -1.71107386e-05, ...,\n",
       "          4.21124980e-07,  6.18868989e-06,  5.88615194e-06],\n",
       "        [ 4.14163787e-06,  3.56330793e-06,  4.18330410e-06, ...,\n",
       "          9.52011192e-07, -2.62116737e-06, -6.14925602e-06]]),\n",
       " '40': array([[ 1.04983541e-04,  1.11842244e-04,  1.16244701e-04, ...,\n",
       "          1.43939949e-04,  1.42874298e-04,  1.40003569e-04],\n",
       "        [ 1.54588284e-04,  1.59884643e-04,  1.47410334e-04, ...,\n",
       "          1.47430357e-04,  1.41256154e-04,  1.33735564e-04],\n",
       "        [ 1.24513099e-04,  1.17249205e-04,  1.17121024e-04, ...,\n",
       "          1.66853832e-04,  1.62541692e-04,  1.43046185e-04],\n",
       "        ...,\n",
       "        [ 6.92317280e-06,  1.89594084e-05,  2.59736516e-05, ...,\n",
       "         -1.30517892e-05, -1.12373564e-05, -1.01643100e-05],\n",
       "        [-1.32673704e-05, -1.64854300e-05, -1.66546306e-05, ...,\n",
       "         -3.27828566e-05, -2.79551077e-05, -1.44125670e-05],\n",
       "        [ 1.44563080e-06,  1.86634202e-06, -1.08968461e-05, ...,\n",
       "         -2.23164334e-05, -1.90677765e-05, -1.65383299e-05]]),\n",
       " '41': array([[-3.51132917e-06,  9.23839798e-06,  1.36155477e-05, ...,\n",
       "         -1.94299573e-05, -1.19685683e-05, -7.80385199e-06],\n",
       "        [-5.97950793e-06, -1.79285726e-05, -3.27677808e-05, ...,\n",
       "          1.46456978e-06, -2.22066792e-08,  6.43735257e-06],\n",
       "        [ 7.30019474e-06, -4.34298590e-06, -1.40741422e-05, ...,\n",
       "         -1.43539955e-05, -5.56455689e-06,  8.53048641e-06],\n",
       "        ...,\n",
       "        [ 1.79833205e-05,  1.43056068e-05,  6.58972658e-06, ...,\n",
       "          2.15040727e-05,  9.16490899e-06, -1.62226606e-05],\n",
       "        [-4.41572192e-05, -4.72857319e-05, -2.55221094e-05, ...,\n",
       "          5.28361625e-06, -2.20074435e-06, -2.53056660e-05],\n",
       "        [-4.86446770e-05, -5.25082542e-05, -3.58870057e-05, ...,\n",
       "         -1.55831513e-05,  1.79186936e-05,  2.91374436e-05]]),\n",
       " '42': array([[-5.97462940e-06, -7.58816122e-06, -7.54819939e-06, ...,\n",
       "          8.08634868e-05,  7.49308383e-05,  7.34024361e-05],\n",
       "        [ 7.24865604e-05,  7.23266130e-05,  7.37811570e-05, ...,\n",
       "          5.76441853e-05,  4.32139714e-05,  2.82975543e-05],\n",
       "        [ 3.05597168e-05,  4.16214716e-05,  3.37586644e-05, ...,\n",
       "         -2.15422115e-05, -1.91357740e-05,  7.99311783e-06],\n",
       "        ...,\n",
       "        [-6.66805772e-06,  3.28528949e-06,  6.05336345e-06, ...,\n",
       "          7.94015705e-06,  8.01563692e-06,  8.24157996e-06],\n",
       "        [ 4.43314957e-06,  3.31281899e-06,  6.41656652e-06, ...,\n",
       "          1.32158175e-05,  1.64910452e-05,  1.67199269e-05],\n",
       "        [ 1.43524712e-05,  1.07151873e-05,  6.51274513e-06, ...,\n",
       "         -2.91346332e-05, -2.44360599e-05, -6.02785713e-06]]),\n",
       " '43': array([[ 1.81573469e-07,  6.21615754e-06,  8.23445134e-06, ...,\n",
       "          3.31533447e-05,  3.69827430e-05,  3.96434589e-05],\n",
       "        [ 4.15789218e-05,  4.62491371e-05,  5.25419127e-05, ...,\n",
       "          5.71589444e-05,  5.49631986e-05,  5.35403778e-05],\n",
       "        [ 5.22832088e-05,  5.09639867e-05,  5.00574606e-05, ...,\n",
       "          4.74774970e-05,  4.62302123e-05,  4.59292751e-05],\n",
       "        ...,\n",
       "        [-5.48370845e-05, -5.23620256e-05, -5.61826746e-05, ...,\n",
       "          1.68404113e-05,  1.74713500e-06, -1.09679822e-05],\n",
       "        [-1.82973836e-05, -1.98066246e-05, -1.54786885e-05, ...,\n",
       "         -4.05803257e-06, -3.63938670e-06, -5.19480091e-06],\n",
       "        [ 1.34133404e-06, -1.73727415e-06, -1.50367096e-05, ...,\n",
       "          1.71565553e-05,  1.88386684e-06, -4.32419665e-06]]),\n",
       " '44': array([[-1.04049905e-05,  4.82857740e-06,  1.78331193e-05, ...,\n",
       "         -3.74252268e-05, -3.58065736e-05, -3.95466341e-05],\n",
       "        [-5.04545133e-05, -6.49689246e-05, -7.12038163e-05, ...,\n",
       "         -3.62292812e-05, -4.20559700e-05, -5.14435269e-05],\n",
       "        [-7.47766098e-05, -9.96509625e-05, -1.14949951e-04, ...,\n",
       "         -4.42463825e-05, -3.51164599e-05, -3.07171977e-05],\n",
       "        ...,\n",
       "        [ 3.18511484e-05,  2.87388284e-05,  2.72559082e-05, ...,\n",
       "          3.65142769e-05,  3.62234132e-05,  3.66357272e-05],\n",
       "        [ 3.74623014e-05,  3.92588736e-05,  4.17876799e-05, ...,\n",
       "          3.56903111e-05,  3.61483653e-05,  3.54414042e-05],\n",
       "        [ 3.40474398e-05,  2.89252985e-05,  2.27013661e-05, ...,\n",
       "          2.04305215e-05,  2.22311737e-05,  2.28458484e-05]]),\n",
       " '45': array([[-2.85784336e-06, -1.53351573e-06, -7.13205679e-07, ...,\n",
       "          2.39363544e-05,  2.83651934e-05,  3.31425690e-05],\n",
       "        [ 3.09198185e-05,  2.02921419e-05,  1.12100142e-05, ...,\n",
       "          2.35359694e-05,  2.15006112e-05,  2.00361519e-05],\n",
       "        [ 2.26593493e-05,  2.84948765e-05,  2.84625803e-05, ...,\n",
       "          1.35122000e-05,  1.07951455e-05,  9.12892210e-06],\n",
       "        ...,\n",
       "        [ 9.06564765e-06,  1.98036378e-05,  2.82144483e-05, ...,\n",
       "          2.48644774e-05,  1.96184865e-05,  1.24812668e-05],\n",
       "        [ 9.75981038e-06,  1.06268917e-05,  1.54299414e-05, ...,\n",
       "          1.08793383e-05,  1.22379561e-05,  7.77080277e-06],\n",
       "        [ 5.62727928e-06,  5.59790942e-06,  8.79988147e-06, ...,\n",
       "          3.27728667e-05,  3.56349192e-05,  3.13283417e-05]]),\n",
       " '46': array([[-5.78912550e-05, -6.33537638e-05, -6.74541297e-05, ...,\n",
       "         -1.13815877e-04, -9.71593836e-05, -6.07568763e-05],\n",
       "        [-2.83820300e-05, -1.49265870e-05, -1.70885778e-05, ...,\n",
       "         -1.14547065e-05, -3.51304698e-06,  4.29648071e-06],\n",
       "        [ 1.02702306e-05,  5.21434549e-06, -8.00881844e-06, ...,\n",
       "         -1.90229766e-05,  7.41771510e-06,  2.38840094e-05],\n",
       "        ...,\n",
       "        [ 5.21218317e-06,  3.83066163e-06,  2.03532932e-06, ...,\n",
       "         -9.72308862e-06, -3.15075376e-05, -4.23148776e-05],\n",
       "        [-3.26020199e-05, -2.79365104e-05, -1.40328511e-05, ...,\n",
       "         -4.91537648e-05, -5.14519415e-05, -4.78008260e-05],\n",
       "        [-4.89085069e-05, -5.12954030e-05, -4.45762453e-05, ...,\n",
       "         -2.81023913e-05, -1.96811015e-05, -1.69229133e-05]]),\n",
       " '47': array([[ 2.03341260e-05,  2.26707853e-05,  2.53633170e-05, ...,\n",
       "          3.30644834e-05,  3.32359486e-05,  3.39259022e-05],\n",
       "        [ 3.16924088e-05,  2.99517214e-05,  2.99872445e-05, ...,\n",
       "          2.65251547e-05,  2.37784880e-05,  2.16129793e-05],\n",
       "        [ 2.18586119e-05,  2.47226890e-05,  2.59045792e-05, ...,\n",
       "          1.96340570e-05,  1.80295538e-05,  1.58107923e-05],\n",
       "        ...,\n",
       "        [-5.83329675e-05, -7.14016642e-05, -6.41909064e-05, ...,\n",
       "          2.14849078e-05,  1.90951669e-05, -2.48500528e-05],\n",
       "        [-4.65968988e-05, -3.71520837e-05, -4.04945713e-05, ...,\n",
       "         -6.86311614e-05, -7.49077954e-05, -6.74601688e-05],\n",
       "        [-5.39450739e-05, -5.39444482e-05, -6.83534454e-05, ...,\n",
       "         -1.16050789e-04, -1.14486866e-04, -8.60605214e-05]]),\n",
       " '48': array([[ 4.54397377e-05,  4.36847149e-05,  3.72202412e-05, ...,\n",
       "         -2.10806902e-05, -2.90640673e-05, -5.85494745e-05],\n",
       "        [-9.65386425e-05, -1.07677508e-04, -8.23228766e-05, ...,\n",
       "         -1.67027156e-05, -2.04768749e-05, -2.77610852e-05],\n",
       "        [-3.99233722e-05, -4.54027722e-05, -2.77768195e-05, ...,\n",
       "         -1.87342412e-05, -7.55339988e-06,  5.46435922e-06],\n",
       "        ...,\n",
       "        [ 5.59525106e-05,  5.81888016e-05,  5.93349068e-05, ...,\n",
       "          4.24747232e-05,  3.33170283e-05,  2.79155975e-05],\n",
       "        [ 2.55717168e-05,  2.53779472e-05,  2.77702729e-05, ...,\n",
       "          6.21258587e-05,  5.86974456e-05,  6.14233286e-05],\n",
       "        [ 6.76683194e-05,  6.74017283e-05,  6.56386328e-05, ...,\n",
       "          1.48451645e-05,  2.17203469e-05,  3.16412734e-05]]),\n",
       " '49': array([[ 3.56212900e-06, -1.75511946e-06, -1.04448272e-05, ...,\n",
       "         -4.34467038e-05, -1.91507825e-05, -2.22661274e-05],\n",
       "        [-5.54650478e-06,  3.04967016e-05,  4.23963720e-05, ...,\n",
       "          6.90657680e-06,  3.87346574e-07, -1.05118988e-05],\n",
       "        [-1.20559898e-05, -6.89898479e-06, -5.21653010e-06, ...,\n",
       "         -1.64264111e-05,  2.02709489e-05,  5.44638824e-05],\n",
       "        ...,\n",
       "        [ 1.74008364e-05,  1.45665026e-05,  1.42095778e-05, ...,\n",
       "          2.07704470e-05,  2.34057898e-05,  2.23514762e-05],\n",
       "        [ 1.50569876e-05,  9.78304251e-06,  1.34093034e-05, ...,\n",
       "          2.62315752e-05,  2.34801646e-05,  2.46162290e-05],\n",
       "        [ 2.62191461e-05,  2.75678976e-05,  2.74601152e-05, ...,\n",
       "          3.47424793e-05,  3.27252710e-05,  3.07498049e-05]]),\n",
       " '50': array([[ 6.54648611e-05,  6.60238147e-05,  6.69932851e-05, ...,\n",
       "          6.68612774e-05,  6.26718029e-05,  5.79651933e-05],\n",
       "        [ 4.99192538e-05,  4.77357789e-05,  5.06002434e-05, ...,\n",
       "          5.88423791e-05,  6.13507000e-05,  6.33163581e-05],\n",
       "        [ 6.42571613e-05,  6.32047449e-05,  5.95427009e-05, ...,\n",
       "          5.95729653e-05,  6.01109423e-05,  6.41157676e-05],\n",
       "        ...,\n",
       "        [-1.88526483e-05, -1.58392595e-05, -1.63763289e-05, ...,\n",
       "         -6.69985775e-06, -8.13726638e-06, -8.73484805e-06],\n",
       "        [-8.62648449e-06, -7.36227457e-06, -8.19751585e-06, ...,\n",
       "          1.80424413e-05,  1.41095834e-05,  5.97947155e-06],\n",
       "        [-1.17974048e-06, -4.75747720e-06, -1.74612933e-06, ...,\n",
       "          7.63103344e-06,  1.00171419e-05,  6.31195417e-06]]),\n",
       " '51': array([[-3.84671148e-05, -2.28951540e-05, -2.77096860e-05, ...,\n",
       "         -3.72224531e-05, -6.78318975e-05, -8.47058909e-05],\n",
       "        [-8.30710414e-05, -7.89676851e-05, -7.64205688e-05, ...,\n",
       "         -4.29095417e-05, -5.00838541e-05, -6.21062427e-05],\n",
       "        [-7.79000620e-05, -8.25715542e-05, -7.69839899e-05, ...,\n",
       "         -9.67271335e-05, -9.84299841e-05, -9.44891071e-05],\n",
       "        ...,\n",
       "        [ 2.64405262e-05,  2.62023568e-05,  2.63954444e-05, ...,\n",
       "          1.90206865e-05,  1.89218954e-05,  1.99215174e-05],\n",
       "        [ 2.32841030e-05,  2.51831061e-05,  2.49642981e-05, ...,\n",
       "          3.56139826e-05,  3.51862873e-05,  3.34765573e-05],\n",
       "        [ 3.26920854e-05,  3.36671110e-05,  3.78576333e-05, ...,\n",
       "          2.87861494e-05,  2.70880482e-05,  2.50681969e-05]]),\n",
       " '52': array([[-2.79314299e-05, -1.60828240e-05, -3.91706436e-07, ...,\n",
       "          7.61595018e-08, -5.01996283e-07,  1.40629959e-06],\n",
       "        [ 6.36945288e-06,  1.13761771e-05,  1.31167089e-05, ...,\n",
       "          1.92360039e-05,  1.67305498e-05,  1.37559691e-05],\n",
       "        [ 9.61008118e-06,  8.04538649e-06,  8.00490579e-06, ...,\n",
       "          1.22834872e-05,  1.25396564e-05,  1.06812086e-05],\n",
       "        ...,\n",
       "        [ 2.33349365e-05,  2.40860154e-05,  2.57606662e-05, ...,\n",
       "          4.41096126e-05,  3.83186416e-05,  3.50178598e-05],\n",
       "        [ 3.05193062e-05,  2.64719620e-05,  2.66340321e-05, ...,\n",
       "          2.06281093e-05,  1.53337478e-05,  1.54175705e-05],\n",
       "        [ 2.03332747e-05,  1.96285309e-05,  1.71631073e-05, ...,\n",
       "          2.72460056e-05,  3.11589647e-05,  3.24690445e-05]]),\n",
       " '53': array([[-1.28153815e-05, -8.15592739e-06, -1.90064329e-05, ...,\n",
       "         -5.68925134e-05, -4.82784199e-05, -4.14115893e-05],\n",
       "        [-1.62486340e-05,  8.49287153e-06, -3.42222461e-06, ...,\n",
       "         -4.28869498e-05, -5.00552342e-05, -2.68313615e-05],\n",
       "        [-6.18846616e-06,  1.32836283e-06,  1.51648192e-05, ...,\n",
       "          2.95580849e-05,  1.44751994e-05, -4.73491491e-06],\n",
       "        ...,\n",
       "        [-1.00555235e-05, -2.28052559e-05, -2.04659900e-05, ...,\n",
       "          3.43192114e-05,  3.38317222e-05,  3.19328392e-05],\n",
       "        [ 3.09994502e-05,  1.86510770e-05, -1.35780681e-06, ...,\n",
       "         -3.50015102e-06, -2.19670051e-06,  1.39037650e-06],\n",
       "        [ 9.25900258e-06,  1.32707364e-05,  1.19113038e-05, ...,\n",
       "          1.32375253e-05,  1.44189698e-05,  2.06340683e-05]]),\n",
       " '54': array([[-7.91700586e-06, -9.51053062e-06, -1.00879561e-05, ...,\n",
       "          4.82527903e-05,  4.75127999e-05,  4.68753933e-05],\n",
       "        [ 5.16063665e-05,  4.92341605e-05,  4.54538022e-05, ...,\n",
       "          5.10802711e-05,  8.24066519e-05,  7.27614242e-05],\n",
       "        [ 5.87193354e-05,  4.59100193e-05,  2.51702913e-05, ...,\n",
       "          4.72911452e-05,  4.17024094e-05,  3.34119213e-05],\n",
       "        ...,\n",
       "        [ 2.23346469e-05,  7.27463976e-06, -8.15786916e-06, ...,\n",
       "         -4.52679124e-05, -4.10533939e-05, -3.41896157e-05],\n",
       "        [-3.33120188e-05, -3.70174748e-05, -3.97127696e-05, ...,\n",
       "          1.50851465e-05,  2.30774622e-05,  2.75193852e-05],\n",
       "        [ 2.48672823e-05,  5.74562478e-07, -2.88462907e-05, ...,\n",
       "         -1.95944158e-05, -3.45992407e-06,  4.09285849e-06]]),\n",
       " '55': array([[-5.40508481e-05, -5.70199700e-05, -4.90465318e-05, ...,\n",
       "         -1.85738918e-05, -1.63461536e-05, -3.38889113e-05],\n",
       "        [-4.77134417e-05, -5.07624463e-05, -5.25041687e-05, ...,\n",
       "          1.05360532e-05, -3.98235397e-06, -1.39352951e-05],\n",
       "        [-5.56634086e-06, -2.37148356e-06, -5.44221575e-06, ...,\n",
       "         -1.35006057e-05, -1.18057478e-05, -1.86361285e-05],\n",
       "        ...,\n",
       "        [ 2.76462451e-05,  2.71638655e-05,  2.18648802e-05, ...,\n",
       "          4.74117915e-05,  6.12449585e-05,  4.99910748e-05],\n",
       "        [ 3.23724998e-05,  1.80498228e-05,  7.77794958e-06, ...,\n",
       "          3.86811953e-05,  3.89375855e-05,  5.81864015e-06],\n",
       "        [-2.11294646e-05, -2.47825938e-05, -1.40988595e-05, ...,\n",
       "         -8.55806866e-05, -4.28776402e-05,  1.72321306e-05]]),\n",
       " '56': array([[-6.01297179e-05, -6.02779073e-05, -5.02691764e-05, ...,\n",
       "         -6.11890209e-05, -4.67530299e-05, -1.91736799e-05],\n",
       "        [-1.50423639e-05, -3.14385870e-05, -4.36859700e-05, ...,\n",
       "         -1.84778892e-05, -1.54368900e-05, -1.96705405e-05],\n",
       "        [-2.34192903e-05, -2.89532381e-05, -3.05022659e-05, ...,\n",
       "         -3.27847329e-06, -1.49016096e-05, -3.24377543e-05],\n",
       "        ...,\n",
       "        [ 7.36401580e-06,  1.57386705e-06,  6.08199343e-06, ...,\n",
       "          8.18535045e-06,  1.01316436e-05,  1.00179568e-05],\n",
       "        [ 1.29467562e-05,  1.88679041e-05,  2.43600334e-05, ...,\n",
       "          3.52515963e-05,  2.52176906e-05,  1.95965076e-05],\n",
       "        [ 1.66359932e-05,  8.17730233e-06,  7.80295863e-07, ...,\n",
       "          3.16511105e-05,  2.91342913e-05,  2.65817071e-05]]),\n",
       " '57': array([[ 1.64305093e-05,  2.82319138e-06,  7.63114429e-07, ...,\n",
       "          6.20183482e-06,  4.65545600e-06, -1.94575750e-06],\n",
       "        [-1.65570236e-05, -3.01973250e-05, -2.96669514e-05, ...,\n",
       "         -1.39844678e-05, -1.06535908e-05, -5.63421600e-06],\n",
       "        [ 2.79527808e-06,  1.89182956e-05,  2.66602528e-05, ...,\n",
       "          1.50363621e-05, -6.06505591e-06, -2.09107002e-05],\n",
       "        ...,\n",
       "        [ 5.22823684e-05,  3.74654774e-05,  2.63835300e-05, ...,\n",
       "         -1.07134229e-05, -1.09376451e-05, -1.04845640e-05],\n",
       "        [-1.48827269e-06,  8.45240811e-06,  9.26355096e-06, ...,\n",
       "         -1.61393345e-05, -5.17759918e-06, -3.51990207e-06],\n",
       "        [-8.42893314e-06, -1.37960353e-06,  2.51879137e-05, ...,\n",
       "          3.02763965e-05,  4.09593267e-05,  3.99427918e-05]]),\n",
       " '58': array([[-1.58837720e-05, -1.34784532e-05, -9.62538070e-06, ...,\n",
       "         -2.52769514e-05, -3.16736805e-05, -3.83044026e-05],\n",
       "        [-4.27622690e-05, -4.05407482e-05, -3.11144104e-05, ...,\n",
       "         -2.12814284e-05, -2.01102794e-05, -2.03151958e-05],\n",
       "        [-2.08835881e-05, -1.98162561e-05, -1.88788745e-05, ...,\n",
       "         -3.16199585e-05, -3.26022528e-05, -2.58890977e-05],\n",
       "        ...,\n",
       "        [-2.63498941e-06, -7.39707639e-06, -6.92661024e-06, ...,\n",
       "         -1.18922399e-05, -1.29889431e-05, -1.43095676e-05],\n",
       "        [-2.07526755e-05, -2.00805371e-05, -8.69377891e-06, ...,\n",
       "         -5.62099649e-06, -1.61729349e-06,  7.66973926e-07],\n",
       "        [-2.22872677e-06, -6.83657800e-06, -9.84699363e-06, ...,\n",
       "          8.40614484e-06,  1.35144801e-05,  1.22654710e-05]]),\n",
       " '59': array([[ 4.77155590e-05,  4.66924939e-05,  4.64666045e-05, ...,\n",
       "          5.37815395e-05,  5.26329313e-05,  5.73585858e-05],\n",
       "        [ 6.28060152e-05,  6.40085200e-05,  6.38587589e-05, ...,\n",
       "          6.60314909e-05,  6.75390111e-05,  6.71898597e-05],\n",
       "        [ 6.68329230e-05,  6.56593620e-05,  6.38581114e-05, ...,\n",
       "          3.89233137e-05,  3.51519302e-05,  3.19507453e-05],\n",
       "        ...,\n",
       "        [-5.98152983e-05, -3.69856352e-05, -4.77323156e-05, ...,\n",
       "         -2.71426015e-05, -2.57760330e-05, -3.80205493e-05],\n",
       "        [-3.75417439e-05,  3.62973509e-07,  4.15159520e-05, ...,\n",
       "         -2.17005163e-05, -2.74500799e-05, -5.88785369e-05],\n",
       "        [-7.58551832e-05, -4.50228799e-05, -1.48224271e-05, ...,\n",
       "         -3.96916148e-05, -3.26502231e-05, -2.49339664e-05]]),\n",
       " '60': array([[ 1.47815299e-05,  1.72965501e-05,  1.70303956e-05, ...,\n",
       "          2.06151090e-05,  2.26631655e-05,  1.79509025e-05],\n",
       "        [ 1.57097165e-05,  1.23592627e-05,  1.17421268e-05, ...,\n",
       "          5.24456209e-06,  2.81139910e-06, -7.64181536e-08],\n",
       "        [ 1.26931207e-06,  3.25974474e-06,  3.97416579e-06, ...,\n",
       "         -1.75139814e-06, -4.89014155e-07, -3.18362322e-06],\n",
       "        ...,\n",
       "        [ 8.36041872e-05,  8.45444738e-05,  8.50513243e-05, ...,\n",
       "          9.16477147e-05,  8.36226609e-05,  7.79671755e-05],\n",
       "        [ 8.38518172e-05,  9.24209380e-05,  9.31224786e-05, ...,\n",
       "          9.51794573e-05,  9.97929164e-05,  9.83605205e-05],\n",
       "        [ 9.36172000e-05,  9.18406295e-05,  8.67794733e-05, ...,\n",
       "          7.61541014e-05,  7.75139051e-05,  7.99958580e-05]]),\n",
       " '61': array([[ 3.73004368e-05,  3.82776525e-05,  3.73347466e-05, ...,\n",
       "          4.40350195e-05,  5.18072120e-05,  5.50001314e-05],\n",
       "        [ 5.24393981e-05,  5.07398836e-05,  5.17312910e-05, ...,\n",
       "          6.70785084e-05,  7.00093951e-05,  7.15497226e-05],\n",
       "        [ 7.04436025e-05,  7.09960514e-05,  7.28712330e-05, ...,\n",
       "          5.76512575e-05,  5.87425275e-05,  6.03163244e-05],\n",
       "        ...,\n",
       "        [-1.93489432e-05, -2.30389578e-05, -2.96137459e-05, ...,\n",
       "         -5.79288599e-06, -6.80390804e-06, -7.84858730e-06],\n",
       "        [-2.02824922e-05, -3.37097517e-05, -3.77235956e-05, ...,\n",
       "         -1.28310503e-05, -1.47639857e-05, -1.23665195e-05],\n",
       "        [-8.28618158e-06, -4.19229309e-06, -5.12520455e-06, ...,\n",
       "         -2.44753173e-05, -1.76830254e-05, -2.14089905e-05]]),\n",
       " '62': array([[-2.27725614e-05, -2.40472073e-05, -2.00105678e-05, ...,\n",
       "         -1.90670980e-05, -1.70024468e-05, -1.55462985e-05],\n",
       "        [-1.43918323e-05, -1.10895462e-05, -6.46777744e-06, ...,\n",
       "          1.77856327e-06,  4.16908233e-06,  2.50215476e-06],\n",
       "        [-4.17568259e-07, -8.87440081e-07,  4.17633217e-08, ...,\n",
       "          8.06204753e-06,  8.61548779e-06,  6.98188478e-06],\n",
       "        ...,\n",
       "        [-1.20775449e-05,  1.29109549e-06, -8.35336050e-06, ...,\n",
       "         -7.09613450e-05, -7.69958497e-05, -9.15712662e-05],\n",
       "        [-1.06495849e-04, -1.20845907e-04, -1.10163273e-04, ...,\n",
       "         -3.12769153e-05, -2.36404703e-05, -5.27441625e-05],\n",
       "        [-7.86297314e-05, -8.14504747e-05, -7.84349177e-05, ...,\n",
       "         -7.33641573e-05, -7.66286466e-05, -7.46215737e-05]]),\n",
       " '63': array([[ 1.22796464e-05,  7.02177113e-06,  4.91396486e-06, ...,\n",
       "          1.48985546e-05,  1.33474023e-05,  1.19358929e-05],\n",
       "        [ 7.74344153e-06,  2.98235204e-06,  2.07086555e-06, ...,\n",
       "          1.76601170e-05,  1.91988565e-05,  2.00972245e-05],\n",
       "        [ 1.91074123e-05,  1.58513249e-05,  1.27311050e-05, ...,\n",
       "          8.31655325e-06,  8.25670031e-06,  5.08496578e-06],\n",
       "        ...,\n",
       "        [-5.94684279e-05, -6.14327728e-05, -6.08831360e-05, ...,\n",
       "         -5.59330474e-05, -5.87681825e-05, -5.61274392e-05],\n",
       "        [-4.99602575e-05, -4.75419038e-05, -4.84479278e-05, ...,\n",
       "         -5.82692956e-05, -6.16176840e-05, -6.06413196e-05],\n",
       "        [-5.74183614e-05, -5.37406195e-05, -5.09072488e-05, ...,\n",
       "         -6.92106842e-05, -6.71772868e-05, -6.43147068e-05]]),\n",
       " '64': array([[ 1.99523529e-05,  1.62192027e-05,  1.15454031e-05, ...,\n",
       "          2.35167317e-05,  1.04580122e-05,  2.26748170e-06],\n",
       "        [ 4.53242876e-07,  4.90712409e-06,  1.34485454e-05, ...,\n",
       "          1.43435491e-05,  9.16960016e-06,  1.26665655e-05],\n",
       "        [ 1.67116468e-05,  1.33425547e-05,  2.56226372e-06, ...,\n",
       "          2.22201761e-05,  2.07767735e-05,  2.02666888e-05],\n",
       "        ...,\n",
       "        [ 2.64130795e-05,  2.72995590e-06, -1.46426646e-05, ...,\n",
       "         -7.29079545e-07, -7.12441033e-06, -2.24453943e-05],\n",
       "        [-2.70366399e-05, -2.51158544e-05, -2.35223652e-05, ...,\n",
       "         -4.35794891e-06, -1.72834152e-05, -2.05391989e-05],\n",
       "        [-6.30067507e-06,  8.79455092e-06,  1.48261715e-05, ...,\n",
       "          3.19329433e-06, -3.64095195e-06, -4.25848611e-07]]),\n",
       " '65': array([[-4.02956211e-05, -4.15274008e-05, -4.25132712e-05, ...,\n",
       "         -4.31352601e-05, -4.04309540e-05, -4.14243732e-05],\n",
       "        [-3.52689458e-05, -1.80817187e-05, -8.63909554e-06, ...,\n",
       "          7.28459827e-06,  1.13301285e-05,  1.42034423e-05],\n",
       "        [ 7.74108958e-06, -7.72731482e-07, -1.19815684e-06, ...,\n",
       "         -1.05362051e-05, -6.31535795e-06, -2.28221870e-06],\n",
       "        ...,\n",
       "        [ 3.10688083e-05,  3.29575378e-05,  3.61058301e-05, ...,\n",
       "          3.98061165e-05,  4.00839963e-05,  3.67383072e-05],\n",
       "        [ 3.35761324e-05,  3.03348861e-05,  2.87714993e-05, ...,\n",
       "          5.64285801e-05,  5.67075331e-05,  4.98043410e-05],\n",
       "        [ 3.83375009e-05,  2.53936032e-05,  1.59899864e-05, ...,\n",
       "          4.02830337e-05,  4.19684438e-05,  4.16205621e-05]]),\n",
       " '66': array([[-4.83956967e-07,  3.49374227e-06,  4.12259533e-06, ...,\n",
       "          2.24432861e-05,  1.81246705e-05,  9.53241033e-06],\n",
       "        [-5.10580833e-07, -6.38461961e-06, -1.00624811e-05, ...,\n",
       "         -3.09629249e-05, -3.52571660e-05, -3.28053720e-05],\n",
       "        [-2.14119409e-05, -6.28179487e-06, -1.70478984e-06, ...,\n",
       "          4.67762220e-06, -1.69775785e-06,  6.16102898e-06],\n",
       "        ...,\n",
       "        [-1.54130339e-06, -2.98949385e-06, -7.11832899e-06, ...,\n",
       "          8.51315326e-06,  1.08568320e-05,  1.39626145e-05],\n",
       "        [ 1.15514358e-05,  4.55135068e-06, -2.30759710e-07, ...,\n",
       "         -7.86633427e-06, -2.00258928e-05, -2.32525290e-05],\n",
       "        [-1.22491820e-05, -8.44550050e-06, -1.50600454e-05, ...,\n",
       "         -4.24646350e-05, -1.33351614e-05,  1.45806580e-05]]),\n",
       " '67': array([[ 4.00525778e-06,  6.29167425e-06,  1.12570879e-05, ...,\n",
       "          9.38579831e-07, -6.32051024e-06, -9.16670706e-06],\n",
       "        [-1.01030673e-05, -5.61249180e-06,  4.71282783e-06, ...,\n",
       "          1.87122478e-05,  1.57213144e-05,  1.36298049e-05],\n",
       "        [ 1.38742043e-05,  1.52428083e-05,  1.59731117e-05, ...,\n",
       "          2.88848842e-05,  2.51218062e-05,  2.23532479e-05],\n",
       "        ...,\n",
       "        [-2.49972400e-05, -4.43656791e-05, -5.95511556e-05, ...,\n",
       "         -1.28425791e-05, -2.55349314e-05, -1.80009574e-05],\n",
       "        [-3.12284683e-05, -6.63476894e-05, -7.14748094e-05, ...,\n",
       "         -7.51236221e-05, -4.96006141e-05, -1.48156050e-05],\n",
       "        [-6.98360009e-06, -1.44736223e-05, -2.79650740e-05, ...,\n",
       "         -5.37630622e-05, -6.95452909e-05, -6.75334013e-05]]),\n",
       " '68': array([[-2.90198441e-05, -3.23148306e-05, -3.42530766e-05, ...,\n",
       "         -3.86015854e-05, -3.68548099e-05, -3.61483108e-05],\n",
       "        [-3.41008854e-05, -3.26050322e-05, -3.80819365e-05, ...,\n",
       "         -3.48489266e-05, -3.05553840e-05, -2.78817042e-05],\n",
       "        [-2.81124321e-05, -2.91180313e-05, -2.99343665e-05, ...,\n",
       "         -1.60048094e-05, -1.21386165e-05, -1.14427839e-05],\n",
       "        ...,\n",
       "        [-4.05552746e-05, -4.02461301e-05, -3.84696650e-05, ...,\n",
       "         -4.71132007e-05, -4.39276591e-05, -4.08227643e-05],\n",
       "        [-3.60395643e-05, -3.61868442e-05, -4.03208396e-05, ...,\n",
       "         -4.91377250e-05, -5.14390631e-05, -5.59594737e-05],\n",
       "        [-5.49274591e-05, -4.23199926e-05, -3.18806196e-05, ...,\n",
       "         -3.58322322e-05, -3.08884555e-05, -2.67435717e-05]]),\n",
       " '69': array([[-3.85901221e-05, -3.79302401e-05, -3.97593503e-05, ...,\n",
       "         -4.45849982e-05, -4.46778577e-05, -4.34673166e-05],\n",
       "        [-4.01477882e-05, -3.91691392e-05, -3.96962932e-05, ...,\n",
       "         -5.21545116e-05, -6.09598283e-05, -6.54283358e-05],\n",
       "        [-6.32773226e-05, -5.24442075e-05, -3.70573325e-05, ...,\n",
       "         -2.64402734e-05, -2.80043932e-05, -3.03257129e-05],\n",
       "        ...,\n",
       "        [-1.46132170e-05, -1.54889258e-05, -1.52867779e-05, ...,\n",
       "         -6.39493828e-06, -1.13154010e-05, -1.66295140e-05],\n",
       "        [-1.68832630e-05, -1.25444521e-05, -9.59669069e-06, ...,\n",
       "         -1.57244085e-05, -1.52870089e-05, -1.29996033e-05],\n",
       "        [-1.05418940e-05, -1.02139084e-05, -9.61326077e-06, ...,\n",
       "          6.73394379e-06,  3.17066861e-06, -4.52599409e-07]]),\n",
       " '70': array([[-3.55239899e-05, -3.89051092e-05, -3.74908195e-05, ...,\n",
       "         -4.74460940e-05, -4.92996805e-05, -4.91359460e-05],\n",
       "        [-4.79361952e-05, -4.93451334e-05, -5.55314073e-05, ...,\n",
       "         -6.62628881e-05, -6.36061959e-05, -6.14305754e-05],\n",
       "        [-6.19028870e-05, -6.18522390e-05, -6.14127421e-05, ...,\n",
       "         -5.04147247e-05, -5.21577676e-05, -5.41518239e-05],\n",
       "        ...,\n",
       "        [-7.11075245e-06,  1.12451753e-05,  1.63489058e-05, ...,\n",
       "         -4.87545822e-06, -2.94554939e-05, -3.76488715e-05],\n",
       "        [-2.64908958e-05, -2.75403272e-05, -3.38490754e-05, ...,\n",
       "         -2.90945827e-05, -1.36969693e-05,  7.82834206e-07],\n",
       "        [-5.52032452e-06, -3.39735052e-05, -5.22995942e-05, ...,\n",
       "          3.25129549e-05,  1.20022087e-05,  3.10294818e-05]]),\n",
       " '71': array([[ 3.70633029e-06,  2.49324944e-06,  2.58589262e-06, ...,\n",
       "          1.94448808e-06,  3.59884871e-06,  1.14411193e-06],\n",
       "        [ 3.12905229e-07,  4.95246184e-07, -6.52798747e-07, ...,\n",
       "         -9.05791967e-06, -1.22039955e-05, -1.50295436e-05],\n",
       "        [-1.49246007e-05, -1.46636658e-05, -1.30227245e-05, ...,\n",
       "         -2.08849688e-05, -2.23418847e-05, -2.39674118e-05],\n",
       "        ...,\n",
       "        [ 4.33629139e-05,  4.55203881e-05,  4.40663134e-05, ...,\n",
       "          2.87292387e-05,  3.15318503e-05,  3.02448498e-05],\n",
       "        [ 2.33957890e-05,  1.96096880e-05,  1.91932668e-05, ...,\n",
       "          2.60690013e-05,  2.84992966e-05,  3.21193475e-05],\n",
       "        [ 4.15512368e-05,  5.02669936e-05,  5.06601391e-05, ...,\n",
       "          1.74524739e-05,  2.09144855e-05,  2.23993266e-05]]),\n",
       " '72': array([[3.86848515e-05, 3.86769279e-05, 3.09184579e-05, ...,\n",
       "         1.12138914e-05, 9.18910791e-06, 1.16581341e-05],\n",
       "        [1.50989054e-05, 2.17989200e-05, 2.60725647e-05, ...,\n",
       "         2.96254329e-05, 3.12293596e-05, 3.66175154e-05],\n",
       "        [4.03224949e-05, 3.97944823e-05, 3.85213716e-05, ...,\n",
       "         4.69132465e-05, 5.20649046e-05, 5.36460648e-05],\n",
       "        ...,\n",
       "        [2.91652532e-06, 3.98089060e-06, 6.07594529e-06, ...,\n",
       "         1.21841204e-05, 1.65935780e-05, 1.93940850e-05],\n",
       "        [1.77031397e-05, 1.51697550e-05, 1.82986150e-05, ...,\n",
       "         2.85802016e-05, 2.20007805e-05, 2.31623908e-05],\n",
       "        [2.62941558e-05, 2.07182147e-05, 1.06689777e-05, ...,\n",
       "         1.67946855e-05, 1.03009988e-05, 7.94785046e-06]]),\n",
       " '73': array([[-1.33023950e-05, -1.36970684e-05, -1.73392109e-05, ...,\n",
       "         -9.78099069e-06, -9.91364323e-06, -1.25104625e-05],\n",
       "        [-1.30684884e-05, -1.37669085e-05, -1.99069509e-05, ...,\n",
       "         -3.62260835e-05, -3.23000604e-05, -2.14063639e-05],\n",
       "        [-1.85035024e-05, -2.54376137e-05, -3.48589892e-05, ...,\n",
       "          2.58638488e-06,  1.76929257e-06,  1.81580197e-06],\n",
       "        ...,\n",
       "        [ 4.83857275e-06,  8.54471000e-06,  1.48908030e-05, ...,\n",
       "          9.92859441e-06,  1.37375646e-05,  1.68773277e-05],\n",
       "        [ 1.66251248e-05,  1.41161554e-05,  1.29207892e-05, ...,\n",
       "          1.28167894e-05,  1.13039005e-05,  8.08135610e-06],\n",
       "        [ 7.76658908e-06,  9.78874778e-06,  1.30903800e-05, ...,\n",
       "          1.24290837e-05,  9.72818907e-06,  8.86618091e-06]]),\n",
       " '74': array([[ 1.81446376e-05,  1.66384616e-05,  1.40459624e-05, ...,\n",
       "          1.77940865e-06,  2.53054377e-08,  2.11671681e-06],\n",
       "        [ 2.32503589e-06,  4.29647662e-06,  9.72116777e-06, ...,\n",
       "          2.25040276e-05,  2.30095284e-05,  2.35870393e-05],\n",
       "        [ 2.56287767e-05,  2.38629655e-05,  2.19394606e-05, ...,\n",
       "          5.61450943e-05,  5.08570665e-05,  5.10441241e-05],\n",
       "        ...,\n",
       "        [-4.56435955e-05, -4.91872270e-05, -6.98332442e-05, ...,\n",
       "         -8.07047491e-06, -5.94160520e-06, -3.64193693e-05],\n",
       "        [-5.50238838e-05, -3.87978907e-05, -3.10232790e-05, ...,\n",
       "         -5.98613915e-05, -6.24688255e-05, -7.05902348e-05],\n",
       "        [-8.18861517e-05, -9.18493970e-05, -1.01016827e-04, ...,\n",
       "         -1.40264456e-04, -1.23619073e-04, -9.48158267e-05]]),\n",
       " '75': array([[ 4.92598738e-05,  4.47526327e-05,  4.38046009e-05, ...,\n",
       "          5.82846551e-05,  5.23589006e-05,  5.06775104e-05],\n",
       "        [ 4.77247595e-05,  4.51974593e-05,  4.76782770e-05, ...,\n",
       "          2.91692359e-05,  2.76437568e-05,  2.70775508e-05],\n",
       "        [ 3.51898016e-05,  4.46997983e-05,  4.59110779e-05, ...,\n",
       "          5.65900882e-05,  5.61299967e-05,  5.57448548e-05],\n",
       "        ...,\n",
       "        [-3.84981795e-05, -3.85449275e-05, -3.28145943e-05, ...,\n",
       "         -2.94566253e-05, -3.14552344e-05, -3.31358169e-05],\n",
       "        [-4.02572878e-05, -4.91749488e-05, -5.72146055e-05, ...,\n",
       "         -2.16669705e-05, -2.91421584e-05, -2.85910228e-05],\n",
       "        [-2.73766054e-05, -2.87788989e-05, -2.25564636e-05, ...,\n",
       "         -2.94848523e-05, -3.00157881e-05, -3.34664692e-05]]),\n",
       " '76': array([[-3.26131121e-05, -2.99665735e-05, -3.16246078e-05, ...,\n",
       "         -2.67020205e-05, -2.31164649e-05, -2.13204476e-05],\n",
       "        [-2.10921553e-05, -2.09111386e-05, -1.88574031e-05, ...,\n",
       "         -1.29034588e-05, -1.22923639e-05, -9.37686400e-06],\n",
       "        [-9.24128744e-06, -1.10856117e-05, -1.20023615e-05, ...,\n",
       "          5.09783968e-06,  6.49261210e-06,  6.75139563e-06],\n",
       "        ...,\n",
       "        [ 3.58338984e-05,  3.29876930e-05,  2.69566735e-05, ...,\n",
       "          1.20463110e-05,  6.52140943e-06,  1.96015276e-06],\n",
       "        [ 2.23398320e-06,  1.32182740e-05,  2.67346750e-05, ...,\n",
       "          1.84430846e-05,  9.41353755e-06,  5.92723791e-06],\n",
       "        [ 1.49452421e-06, -3.85026823e-07, -4.68469040e-07, ...,\n",
       "          3.23200184e-05,  3.20045729e-05,  3.40331171e-05]]),\n",
       " '77': array([[ 4.58348513e-05,  3.90602800e-05,  3.25163310e-05, ...,\n",
       "          6.57696364e-05,  6.77195276e-05,  5.29913596e-05],\n",
       "        [ 5.05699463e-05,  5.28213241e-05,  5.71797318e-05, ...,\n",
       "          6.13579687e-05,  7.38243180e-05,  8.56504412e-05],\n",
       "        [ 8.67709969e-05,  7.19376039e-05,  5.29885037e-05, ...,\n",
       "          9.54888528e-05,  1.01890379e-04,  8.55801263e-05],\n",
       "        ...,\n",
       "        [-8.77661751e-06, -1.37667039e-05, -1.62263659e-05, ...,\n",
       "         -1.14446439e-05,  5.52688743e-06,  1.74784727e-05],\n",
       "        [ 1.67991075e-05,  7.93773324e-06,  2.79033020e-06, ...,\n",
       "         -4.83006224e-05, -3.44309992e-05, -1.50106316e-05],\n",
       "        [ 1.21563007e-05,  2.65524359e-05,  1.85581448e-05, ...,\n",
       "          1.94396398e-06,  2.04305070e-06,  1.85580618e-07]]),\n",
       " '78': array([[-8.61234366e-06, -1.14736931e-05, -1.73543922e-05, ...,\n",
       "         -3.35525983e-05, -2.57484971e-05, -2.80796448e-05],\n",
       "        [-2.65544168e-05, -1.50121514e-05, -1.01438172e-05, ...,\n",
       "         -1.55689177e-05, -1.59848969e-05, -1.75504156e-05],\n",
       "        [-1.75633704e-05, -1.74963552e-05, -2.21963637e-05, ...,\n",
       "         -2.37548302e-05, -1.47829778e-05, -7.65703226e-06],\n",
       "        ...,\n",
       "        [-1.89071652e-05, -2.63424863e-05, -3.05414396e-05, ...,\n",
       "         -4.67876380e-05, -5.32495760e-05, -5.61437082e-05],\n",
       "        [-5.64794536e-05, -5.16057335e-05, -4.07059124e-05, ...,\n",
       "         -4.45050027e-05, -4.45524056e-05, -4.21829500e-05],\n",
       "        [-3.70899943e-05, -3.38973259e-05, -3.71845817e-05, ...,\n",
       "         -3.00293959e-05, -3.31676893e-05, -3.49704205e-05]]),\n",
       " '79': array([[ 3.45625704e-05,  3.73560397e-05,  3.92477996e-05, ...,\n",
       "          4.37035087e-05,  4.27558407e-05,  4.27166815e-05],\n",
       "        [ 4.58115865e-05,  5.33261082e-05,  5.78203762e-05, ...,\n",
       "          5.02872535e-05,  4.49466497e-05,  3.67504872e-05],\n",
       "        [ 2.90191583e-05,  2.74276845e-05,  2.92664990e-05, ...,\n",
       "          3.07792943e-05,  2.77171912e-05,  2.86128379e-05],\n",
       "        ...,\n",
       "        [-5.79434600e-05, -5.32514678e-05, -4.84587581e-05, ...,\n",
       "         -3.72312170e-05, -3.26691297e-05, -2.77918534e-05],\n",
       "        [-2.71090485e-05, -2.95531863e-05, -3.58790312e-05, ...,\n",
       "         -4.10931934e-05, -5.58984539e-05, -5.58826359e-05],\n",
       "        [-5.26637377e-05, -4.76254609e-05, -3.74434894e-05, ...,\n",
       "         -6.34614189e-05, -5.56024352e-05, -4.86343670e-05]]),\n",
       " '80': array([[-2.70933342e-05, -2.89908839e-05, -3.11890435e-05, ...,\n",
       "         -4.35860820e-05, -4.19223652e-05, -3.15738471e-05],\n",
       "        [-2.38138327e-05, -1.41328128e-05, -1.33224139e-05, ...,\n",
       "          3.84961413e-06,  5.83867086e-06,  6.59069474e-06],\n",
       "        [ 8.75992009e-06,  1.17954141e-05,  1.15027833e-05, ...,\n",
       "         -5.45942203e-06, -9.68491349e-06, -7.73514603e-06],\n",
       "        ...,\n",
       "        [ 3.51764720e-05,  2.98567484e-05,  2.67629530e-05, ...,\n",
       "          1.89660423e-05,  1.50699743e-05,  1.19223878e-05],\n",
       "        [ 1.05518193e-05,  1.44614805e-05,  1.91977342e-05, ...,\n",
       "          2.53247363e-05,  2.02709925e-05,  1.42749168e-05],\n",
       "        [ 1.19638962e-05,  1.36277959e-05,  1.52469756e-05, ...,\n",
       "          3.94794006e-05,  3.82076578e-05,  3.65609449e-05]]),\n",
       " '81': array([[-1.09139131e-04, -9.15306518e-05, -6.07552392e-05, ...,\n",
       "         -1.41406181e-05, -1.91631752e-05, -3.77368888e-05],\n",
       "        [-7.03142578e-05, -9.22664913e-05, -7.94491716e-05, ...,\n",
       "          2.86675095e-06,  3.87254113e-06, -7.14196312e-06],\n",
       "        [-1.92044590e-05, -2.11122860e-05, -1.62738561e-05, ...,\n",
       "         -2.01321709e-05, -1.41391465e-05, -2.84099442e-05],\n",
       "        ...,\n",
       "        [ 4.36110568e-05,  4.04137463e-05,  4.21831355e-05, ...,\n",
       "          2.39831952e-05,  3.04527985e-05,  3.71315400e-05],\n",
       "        [ 3.78146688e-05,  3.37800047e-05,  3.19380597e-05, ...,\n",
       "          3.52996576e-05,  4.37133676e-05,  4.23091915e-05],\n",
       "        [ 3.92587790e-05,  3.91059948e-05,  3.44688888e-05, ...,\n",
       "          1.61007665e-05,  1.80792540e-05,  2.54945899e-05]]),\n",
       " '82': array([[ 4.57944298e-05,  4.64063851e-05,  4.82336254e-05, ...,\n",
       "          4.19563075e-05,  3.67853427e-05,  3.24791581e-05],\n",
       "        [ 2.59180324e-05,  2.22761828e-05,  2.43330924e-05, ...,\n",
       "         -3.28402784e-05, -3.86434440e-05, -3.51891686e-05],\n",
       "        [-3.01374002e-05, -5.11887583e-06,  3.78421573e-05, ...,\n",
       "          2.84341950e-05,  2.44943130e-05,  2.26523334e-05],\n",
       "        ...,\n",
       "        [-1.53523288e-05, -1.18405660e-05,  2.15269893e-06, ...,\n",
       "         -9.46930504e-06, -2.47135686e-05, -3.24617577e-05],\n",
       "        [-2.51602560e-05, -1.46149250e-05, -1.46479451e-05, ...,\n",
       "         -3.84530758e-05, -3.19288629e-05, -2.45052415e-05],\n",
       "        [-1.34337215e-05, -8.00214536e-07,  6.74355260e-06, ...,\n",
       "         -2.87456714e-05, -3.61973107e-05, -4.76747919e-05]]),\n",
       " '83': array([[-7.44506542e-05, -7.34503119e-05, -7.14855196e-05, ...,\n",
       "         -3.77854194e-05, -5.12911829e-05, -7.54473804e-05],\n",
       "        [-9.65951840e-05, -1.04779603e-04, -1.05542553e-04, ...,\n",
       "         -3.66202403e-05, -3.94597664e-05, -3.31499396e-05],\n",
       "        [-3.81879763e-05, -5.81345848e-05, -6.61905433e-05, ...,\n",
       "         -4.69005354e-05, -4.12709851e-05, -3.34268698e-05],\n",
       "        ...,\n",
       "        [ 5.85367379e-05,  5.71037199e-05,  5.54312537e-05, ...,\n",
       "          6.03166300e-05,  5.75946033e-05,  5.64496440e-05],\n",
       "        [ 5.48668504e-05,  5.35336840e-05,  5.47597010e-05, ...,\n",
       "          4.33842470e-05,  4.74960834e-05,  4.71977437e-05],\n",
       "        [ 4.55911322e-05,  4.56262133e-05,  4.46874255e-05, ...,\n",
       "          3.91870199e-05,  3.73886724e-05,  3.82104918e-05]]),\n",
       " '84': array([[ 1.76793867e-06,  3.32040213e-06,  4.61877107e-06, ...,\n",
       "          1.54660684e-05,  8.04396223e-06,  3.18119532e-06],\n",
       "        [ 5.93102186e-06,  1.02557624e-05,  9.33291903e-06, ...,\n",
       "          3.08853269e-05,  2.60607940e-05,  2.76088231e-05],\n",
       "        [ 2.98013329e-05,  2.42008573e-05,  1.23428918e-05, ...,\n",
       "          1.86988455e-05,  1.18875141e-05,  1.51792972e-06],\n",
       "        ...,\n",
       "        [ 5.56411323e-05,  5.82593239e-05,  5.43936003e-05, ...,\n",
       "          4.13904272e-05,  4.48355531e-05,  3.89487359e-05],\n",
       "        [ 2.78955686e-05,  2.90284861e-05,  3.93243972e-05, ...,\n",
       "          4.17452902e-05,  1.63870045e-05,  1.01957912e-05],\n",
       "        [ 1.35245464e-05,  2.70521377e-05,  4.55980262e-05, ...,\n",
       "         -1.20893201e-05, -2.86239665e-05, -2.39304809e-05]]),\n",
       " '85': array([[-1.82448312e-05, -1.00734096e-05, -5.66302560e-06, ...,\n",
       "         -1.37817706e-05, -1.31118250e-05, -1.03515422e-05],\n",
       "        [-6.78920696e-06, -3.94096151e-06, -4.00489216e-06, ...,\n",
       "          2.89464606e-06, -8.33430022e-06, -1.32918949e-05],\n",
       "        [-1.45347885e-05, -1.04292867e-05, -7.36440825e-06, ...,\n",
       "         -2.38534121e-05, -3.02120370e-05, -3.52991883e-05],\n",
       "        ...,\n",
       "        [-1.25151628e-05, -1.36094668e-05, -1.40277843e-05, ...,\n",
       "         -1.24470380e-05, -1.30166045e-05, -1.29078990e-05],\n",
       "        [-1.93132055e-05, -2.26559314e-05, -2.16942572e-05, ...,\n",
       "         -5.12010629e-05, -4.99857451e-05, -3.92515285e-05],\n",
       "        [-3.64577681e-05, -3.94400886e-05, -4.49659819e-05, ...,\n",
       "         -7.10046697e-06, -1.89726525e-05, -2.57483243e-05]]),\n",
       " '86': array([[-5.43558854e-05, -5.96850514e-05, -7.20801909e-05, ...,\n",
       "         -4.59996700e-05, -2.22433464e-05, -1.35495729e-05],\n",
       "        [-1.88564591e-05, -2.32813090e-05, -1.96647452e-05, ...,\n",
       "         -3.39741600e-05, -5.99610285e-05, -7.14178386e-05],\n",
       "        [-5.41532536e-05, -5.00735914e-05, -7.81938797e-05, ...,\n",
       "         -3.68392539e-05, -3.53409669e-05, -4.03517224e-05],\n",
       "        ...,\n",
       "        [ 9.03744785e-06,  6.18180547e-06,  2.14097854e-05, ...,\n",
       "          3.60813533e-06,  5.33746288e-06,  4.09095301e-05],\n",
       "        [ 9.54396819e-05,  1.34989488e-04,  1.28768370e-04, ...,\n",
       "          2.30091246e-05,  1.24183571e-05,  1.90454175e-05],\n",
       "        [ 1.70368057e-05,  1.00400994e-05,  1.50833730e-05, ...,\n",
       "         -4.87838679e-06,  5.49575198e-05,  9.75171060e-05]]),\n",
       " '87': array([[-8.08860568e-05, -8.09148405e-05, -8.11681602e-05, ...,\n",
       "         -9.36887809e-05, -8.99458973e-05, -9.02212123e-05],\n",
       "        [-8.77231214e-05, -8.51923833e-05, -8.48169875e-05, ...,\n",
       "         -6.25783141e-05, -6.40944781e-05, -6.46798726e-05],\n",
       "        [-6.17790283e-05, -5.97288818e-05, -5.75213999e-05, ...,\n",
       "         -3.11297372e-05, -3.52841525e-05, -3.71638162e-05],\n",
       "        ...,\n",
       "        [ 3.73768635e-05,  3.47790919e-05,  3.50711052e-05, ...,\n",
       "          3.07605151e-05,  2.85978585e-05,  2.75146122e-05],\n",
       "        [ 2.88789925e-05,  3.10495270e-05,  3.09350580e-05, ...,\n",
       "          3.53332825e-06,  4.48857827e-06,  3.89069783e-06],\n",
       "        [ 2.25674853e-06,  1.06766799e-06, -1.86907414e-07, ...,\n",
       "          2.20253496e-05,  1.26468985e-05,  2.84976227e-06]]),\n",
       " '88': array([[6.59686630e-05, 6.25978937e-05, 5.55261213e-05, ...,\n",
       "         1.51888789e-05, 1.50691039e-05, 1.45146505e-05],\n",
       "        [1.89129532e-05, 3.01638647e-05, 4.19001881e-05, ...,\n",
       "         8.90502633e-06, 5.10190912e-05, 8.04316223e-05],\n",
       "        [9.63109051e-05, 9.81131379e-05, 8.51909499e-05, ...,\n",
       "         2.89969521e-05, 2.58698656e-05, 2.16443350e-05],\n",
       "        ...,\n",
       "        [1.65231322e-05, 2.19134126e-05, 2.26125994e-05, ...,\n",
       "         2.35356074e-05, 1.92731350e-05, 1.75293353e-05],\n",
       "        [1.95153480e-05, 2.19655012e-05, 2.37054082e-05, ...,\n",
       "         2.40841036e-05, 2.58303116e-05, 2.52899736e-05],\n",
       "        [2.34892796e-05, 2.35654443e-05, 2.40157915e-05, ...,\n",
       "         2.10656417e-05, 2.06093173e-05, 2.45167812e-05]]),\n",
       " '89': array([[ 4.76930436e-05,  5.13096966e-05,  5.89047268e-05, ...,\n",
       "          2.79639207e-05,  2.37081385e-05,  2.36797805e-05],\n",
       "        [ 3.66121276e-05,  5.45874209e-05,  6.10067727e-05, ...,\n",
       "          4.81212046e-05,  4.95164131e-05,  5.19013010e-05],\n",
       "        [ 5.56946252e-05,  5.68213763e-05,  5.54655417e-05, ...,\n",
       "          4.63743854e-05,  4.24994469e-05,  3.96313990e-05],\n",
       "        ...,\n",
       "        [-4.54309338e-05, -6.80452504e-05, -7.02273464e-05, ...,\n",
       "         -1.60526361e-05,  9.72305224e-06,  2.33610881e-05],\n",
       "        [ 2.67492283e-06, -2.93097346e-05, -3.17715239e-05, ...,\n",
       "         -2.74422769e-06, -2.70008599e-07,  3.91708568e-07],\n",
       "        [ 9.24470078e-06,  8.16885949e-06, -3.79019821e-06, ...,\n",
       "         -3.07124064e-05, -1.96496821e-05, -9.46660293e-06]]),\n",
       " '90': array([[ 3.00313441e-05,  2.91015876e-05,  2.39397723e-05, ...,\n",
       "          2.16913177e-05,  1.75282003e-05,  1.45627573e-05],\n",
       "        [ 1.86177713e-05,  3.31789342e-05,  3.40282677e-05, ...,\n",
       "          1.62684191e-05,  4.80266899e-05,  5.96776699e-05],\n",
       "        [ 2.92356999e-05, -6.74601552e-06, -1.00285206e-05, ...,\n",
       "          1.60179188e-05,  1.42041299e-05,  1.71593074e-05],\n",
       "        ...,\n",
       "        [-6.74026378e-05, -1.91565832e-05,  1.70976709e-05, ...,\n",
       "          1.76483973e-05,  1.51808063e-05,  3.30693183e-06],\n",
       "        [-6.85132090e-06, -1.49476036e-05, -3.02750250e-05, ...,\n",
       "         -6.19384737e-05, -7.74466243e-05, -8.61338267e-05],\n",
       "        [-7.89389160e-05, -5.71713244e-05, -3.14968347e-05, ...,\n",
       "         -1.28487400e-05, -1.84549917e-05, -2.30915375e-05]]),\n",
       " '91': array([[ 1.81363030e-05,  2.19622580e-05,  2.39217406e-05, ...,\n",
       "          4.25823273e-05,  3.98040684e-05,  3.85750791e-05],\n",
       "        [ 3.88112567e-05,  3.93404371e-05,  3.93366317e-05, ...,\n",
       "          5.11070539e-05,  4.35686743e-05,  4.06798899e-05],\n",
       "        [ 3.98880002e-05,  3.98911834e-05,  4.44871184e-05, ...,\n",
       "          2.17626712e-05,  2.23799416e-05,  2.53531125e-05],\n",
       "        ...,\n",
       "        [-5.06296601e-05, -5.19794012e-05, -4.34992471e-05, ...,\n",
       "         -6.64492763e-05, -7.05151178e-05, -8.13948354e-05],\n",
       "        [-1.11820642e-04, -1.10649715e-04, -7.88581310e-05, ...,\n",
       "         -8.50989672e-05, -8.75769038e-05, -9.28019435e-05],\n",
       "        [-7.46309961e-05, -4.76116475e-05, -4.41253687e-05, ...,\n",
       "         -4.34862304e-05, -4.30733235e-05, -1.78803093e-05]]),\n",
       " '92': array([[ 1.04093771e-04,  1.11017449e-04,  1.10472407e-04, ...,\n",
       "          1.04058898e-04,  1.01179743e-04,  9.43942287e-05],\n",
       "        [ 8.57094346e-05,  8.54208338e-05,  8.32489459e-05, ...,\n",
       "          8.41714646e-05,  8.14709856e-05,  8.74116813e-05],\n",
       "        [ 9.30978931e-05,  8.27011681e-05,  7.48434686e-05, ...,\n",
       "          8.25377283e-05,  8.52647645e-05,  8.73135941e-05],\n",
       "        ...,\n",
       "        [-5.19419300e-05, -4.01117541e-05, -3.18982966e-05, ...,\n",
       "         -2.56159638e-06,  3.87920954e-06, -1.55011389e-06],\n",
       "        [-9.48809702e-06, -9.27449128e-06, -5.94257426e-06, ...,\n",
       "         -1.46754146e-05, -1.60444542e-05, -1.83042466e-05],\n",
       "        [-2.25747899e-05, -2.50091180e-05, -2.43786253e-05, ...,\n",
       "         -1.25543356e-05, -1.48063609e-05, -1.81076266e-05]]),\n",
       " '93': array([[-8.01206043e-05, -7.59882460e-05, -7.04975973e-05, ...,\n",
       "         -7.79897891e-05, -7.91508573e-05, -8.07019242e-05],\n",
       "        [-8.36205072e-05, -8.30447316e-05, -8.28425400e-05, ...,\n",
       "         -7.81223789e-05, -7.83373252e-05, -7.94852240e-05],\n",
       "        [-7.75198132e-05, -7.70041079e-05, -7.62498530e-05, ...,\n",
       "         -7.62520576e-05, -7.38142262e-05, -6.98928634e-05],\n",
       "        ...,\n",
       "        [ 7.51185416e-06, -1.80071165e-05, -2.59853241e-05, ...,\n",
       "          1.40792808e-05,  6.54996211e-06, -5.46541423e-06],\n",
       "        [-1.03123748e-05, -1.53183119e-05, -2.59962926e-05, ...,\n",
       "         -6.76510263e-06, -1.83239135e-05, -4.96413622e-06],\n",
       "        [ 1.03281245e-05,  2.26195261e-05,  2.74908180e-05, ...,\n",
       "          7.37413757e-06,  2.38887005e-05,  3.19439459e-05]]),\n",
       " '94': array([[-1.36847248e-05, -1.23646087e-05, -1.15756193e-05, ...,\n",
       "         -1.21919793e-05, -1.26212490e-05, -1.23572481e-05],\n",
       "        [-1.02222766e-05, -1.05048839e-05, -1.28123993e-05, ...,\n",
       "         -2.44712373e-05, -2.39441379e-05, -2.53053640e-05],\n",
       "        [-2.60169327e-05, -2.32285038e-05, -2.31294598e-05, ...,\n",
       "         -2.28526587e-05, -2.15872897e-05, -2.08424408e-05],\n",
       "        ...,\n",
       "        [ 1.41977080e-05,  2.65549297e-05,  2.66290772e-05, ...,\n",
       "          1.47884984e-05,  1.25384358e-05, -1.28752015e-06],\n",
       "        [-1.39769681e-05, -2.55107225e-05, -4.53091379e-05, ...,\n",
       "         -2.83364989e-05, -2.62906160e-05, -3.39856524e-05],\n",
       "        [-2.76796527e-05, -1.82250606e-05, -2.00515769e-05, ...,\n",
       "         -7.16444265e-05, -6.84282131e-05, -3.54249387e-05]]),\n",
       " '95': array([[-6.14713281e-05, -6.11801224e-05, -6.02022737e-05, ...,\n",
       "         -6.63862011e-05, -6.73438699e-05, -6.68862340e-05],\n",
       "        [-6.79298973e-05, -6.76255877e-05, -6.87326319e-05, ...,\n",
       "         -6.18071717e-05, -6.46625922e-05, -6.44240936e-05],\n",
       "        [-6.36033074e-05, -6.40439612e-05, -6.54984760e-05, ...,\n",
       "         -5.37058841e-05, -5.28831406e-05, -5.43834467e-05],\n",
       "        ...,\n",
       "        [ 8.86015641e-06,  1.22735264e-05,  1.76177400e-05, ...,\n",
       "         -3.02929243e-06,  3.32003401e-06,  1.16995725e-05],\n",
       "        [ 1.67583548e-05,  1.80502157e-05,  1.38627947e-05, ...,\n",
       "          1.75000241e-05,  2.06340501e-05,  2.34652089e-05],\n",
       "        [ 3.07703704e-05,  3.59859332e-05,  2.95781210e-05, ...,\n",
       "          8.75863861e-06,  1.35559039e-05,  1.66235604e-05]]),\n",
       " '96': array([[-5.20186659e-05, -5.10201135e-05, -5.54210746e-05, ...,\n",
       "         -7.38792151e-05, -6.72256938e-05, -6.41685619e-05],\n",
       "        [-6.28080015e-05, -6.16693724e-05, -6.36658369e-05, ...,\n",
       "         -6.30573413e-05, -6.50006405e-05, -6.70316440e-05],\n",
       "        [-6.65080152e-05, -7.19167729e-05, -7.43231722e-05, ...,\n",
       "         -9.27748479e-05, -8.41333531e-05, -6.75314514e-05],\n",
       "        ...,\n",
       "        [ 1.52069988e-05,  1.30864983e-05,  1.19572896e-05, ...,\n",
       "          6.94731079e-06,  1.61532057e-06, -3.52986513e-06],\n",
       "        [-7.89270530e-07,  3.51245080e-06,  7.39843654e-06, ...,\n",
       "          1.99050519e-05,  1.98891630e-05,  1.93548676e-05],\n",
       "        [ 2.13787753e-05,  2.29909947e-05,  2.30338246e-05, ...,\n",
       "          3.20879408e-05,  2.87713465e-05,  2.21746959e-05]]),\n",
       " '97': array([[-1.90136049e-07, -2.20217989e-05, -3.97049262e-05, ...,\n",
       "          3.06896145e-05,  4.16958319e-05,  3.50803275e-05],\n",
       "        [ 1.89710117e-05,  1.06431817e-05,  9.22809522e-06, ...,\n",
       "         -4.43701829e-05, -2.60555407e-05, -1.57296217e-05],\n",
       "        [-3.02599183e-05, -5.55109582e-05, -7.69769977e-05, ...,\n",
       "         -3.21151092e-05, -4.27092964e-05, -6.36523400e-05],\n",
       "        ...,\n",
       "        [ 1.34336578e-05,  3.45308799e-05,  5.52116435e-05, ...,\n",
       "         -7.38084873e-06,  1.23568225e-05,  2.85021706e-05],\n",
       "        [ 3.05234062e-05,  1.54417776e-05,  4.07119785e-07, ...,\n",
       "          2.18613627e-06, -6.62992670e-06, -3.28844217e-05],\n",
       "        [-5.20583599e-05, -3.93191913e-05, -8.59841930e-06, ...,\n",
       "          1.43311026e-05,  1.87822334e-05,  2.61210098e-05]]),\n",
       " '98': array([[-4.78231914e-06, -3.77181209e-06, -1.07859305e-05, ...,\n",
       "         -3.73814510e-05, -3.53429314e-05, -2.55731302e-05],\n",
       "        [-1.88565518e-05, -1.64102967e-05, -5.18895331e-06, ...,\n",
       "          1.17267373e-05,  9.14392695e-06, -1.46751336e-06],\n",
       "        [-7.20325488e-06, -6.75365891e-06, -6.17587239e-06, ...,\n",
       "         -7.70106271e-06, -9.28693953e-06, -2.15462660e-05],\n",
       "        ...,\n",
       "        [ 1.91215781e-06, -8.86044018e-06, -4.64900404e-06, ...,\n",
       "          8.94096229e-05,  8.86149282e-05,  6.60549777e-05],\n",
       "        [ 4.28554340e-05,  2.85996466e-05,  1.73736626e-05, ...,\n",
       "          4.35097354e-05,  3.42033090e-05,  2.65961498e-05],\n",
       "        [ 2.60135002e-05,  3.36322664e-05,  1.36700683e-05, ...,\n",
       "          2.67083451e-05,  3.00962620e-05,  4.56726484e-05]]),\n",
       " '99': array([[ 3.08430936e-05,  3.41092709e-05,  7.72553358e-06, ...,\n",
       "          5.22249820e-06, -3.26606619e-06, -8.64179947e-06],\n",
       "        [-1.19459301e-05, -1.39401445e-05, -5.96639029e-06, ...,\n",
       "         -7.82300958e-06,  9.73577130e-07, -4.68962935e-06],\n",
       "        [-1.61508106e-05, -2.24790329e-05, -2.86006762e-05, ...,\n",
       "          9.71625377e-06,  2.48507240e-05,  2.73903988e-05],\n",
       "        ...,\n",
       "        [-1.84755136e-05, -2.46928612e-05, -3.34833931e-05, ...,\n",
       "         -3.25477013e-05, -3.37189449e-05, -3.37289857e-05],\n",
       "        [-3.24696521e-05, -3.05297835e-05, -2.62892445e-05, ...,\n",
       "         -1.79153940e-05, -2.35131774e-05, -3.19857136e-05],\n",
       "        [-3.57997087e-05, -3.48009198e-05, -3.43085885e-05, ...,\n",
       "         -2.00954037e-05, -2.30029655e-05, -3.00198062e-05]]),\n",
       " '100': array([[ 2.12102532e-05,  2.06469431e-05,  2.21145528e-05, ...,\n",
       "          4.15408904e-05,  3.72592076e-05,  3.13430355e-05],\n",
       "        [ 2.96984690e-05,  2.57106967e-05,  2.25015046e-05, ...,\n",
       "          2.25477743e-05,  2.02649262e-05,  1.87975093e-05],\n",
       "        [ 1.82891890e-05,  2.04311636e-05,  2.08555739e-05, ...,\n",
       "         -2.18459991e-06, -1.18399817e-06, -3.02079889e-06],\n",
       "        ...,\n",
       "        [-6.49056310e-05, -7.36255097e-05, -8.14604864e-05, ...,\n",
       "         -3.85735730e-05, -4.41472403e-05, -4.40135664e-05],\n",
       "        [-3.85990243e-05, -3.44874315e-05, -2.89161399e-05, ...,\n",
       "         -9.64362607e-06, -2.16344733e-06, -5.51493304e-06],\n",
       "        [-1.13672277e-05, -1.12464404e-05, -8.22664424e-06, ...,\n",
       "         -4.42106648e-05, -5.32141712e-05, -6.11247087e-05]]),\n",
       " '101': array([[-1.50746018e-05, -1.36323297e-05, -1.42420195e-05, ...,\n",
       "         -2.56321473e-05, -3.39847502e-05, -4.08152118e-05],\n",
       "        [-4.43631179e-05, -4.27452469e-05, -4.04212151e-05, ...,\n",
       "         -1.77266029e-05, -2.28957852e-05, -2.43054892e-05],\n",
       "        [-2.05863962e-05, -1.93628348e-05, -1.74740871e-05, ...,\n",
       "         -3.02864646e-05, -2.53023572e-05, -2.08932070e-05],\n",
       "        ...,\n",
       "        [-2.06981986e-05, -2.11993392e-05, -1.85658773e-05, ...,\n",
       "         -2.78529369e-05, -2.51178171e-05, -1.76567974e-05],\n",
       "        [-1.55876078e-05, -1.98765993e-05, -1.86851976e-05, ...,\n",
       "         -3.39518483e-05, -2.84809830e-05, -2.33431711e-05],\n",
       "        [-9.93419053e-06,  4.62519438e-06,  6.10526013e-06, ...,\n",
       "         -2.80678905e-05, -2.89067539e-05, -2.33518731e-05]]),\n",
       " '102': array([[-6.60605147e-05, -6.38706551e-05, -6.45581385e-05, ...,\n",
       "          1.65690290e-05,  1.93812848e-05,  2.31991671e-05],\n",
       "        [ 1.90536266e-05,  3.29037061e-06, -1.55568105e-05, ...,\n",
       "          8.66569917e-06,  3.89545448e-06,  6.57848841e-06],\n",
       "        [ 7.65404639e-06, -6.74359353e-06, -1.47088695e-05, ...,\n",
       "         -2.96943199e-06,  2.80434488e-05,  5.77807732e-05],\n",
       "        ...,\n",
       "        [-6.34179014e-05, -6.18274571e-05, -5.95675810e-05, ...,\n",
       "         -6.51387527e-05, -5.59768559e-05, -4.66214769e-05],\n",
       "        [-4.70594605e-05, -5.10153368e-05, -5.26024305e-05, ...,\n",
       "         -5.15250431e-05, -5.58583961e-05, -4.83359981e-05],\n",
       "        [-3.28681599e-05, -2.17545221e-05, -2.05706710e-05, ...,\n",
       "         -4.95861714e-05, -5.34351275e-05, -5.44269642e-05]]),\n",
       " '103': array([[ 3.16810219e-05,  2.46984073e-05,  1.82741842e-05, ...,\n",
       "          3.27456473e-05,  3.28796050e-05,  3.19498467e-05],\n",
       "        [ 3.04716341e-05,  2.76605406e-05,  2.38251487e-05, ...,\n",
       "          2.02040846e-05,  1.56927235e-05,  1.24487815e-05],\n",
       "        [ 1.27023259e-05,  1.31426586e-05,  1.29337195e-05, ...,\n",
       "          1.85280751e-05,  2.06845016e-05,  2.25802178e-05],\n",
       "        ...,\n",
       "        [-2.40739628e-05, -1.01341102e-05,  1.69225507e-06, ...,\n",
       "         -5.30714024e-05, -7.02311663e-05, -5.49094730e-05],\n",
       "        [-5.18121924e-05, -5.61362576e-05, -5.27118282e-05, ...,\n",
       "         -4.94073402e-06,  6.86964495e-06,  2.31975246e-05],\n",
       "        [ 2.55195973e-05,  2.36163542e-05,  2.50293160e-05, ...,\n",
       "          1.68872302e-05,  3.13120909e-05,  1.27375115e-05]]),\n",
       " '104': array([[-5.86548304e-05, -4.74405897e-05, -3.33604548e-05, ...,\n",
       "         -1.17409560e-04, -1.06576124e-04, -1.08710941e-04],\n",
       "        [-1.19130593e-04, -1.24035781e-04, -1.32569068e-04, ...,\n",
       "         -4.39612559e-05, -3.72177819e-05, -2.15817909e-05],\n",
       "        [-9.03240471e-06,  3.09451889e-06, -2.23237635e-06, ...,\n",
       "          1.72188738e-05,  1.36450044e-05,  1.19033202e-05],\n",
       "        ...,\n",
       "        [ 1.56845665e-04,  1.83966418e-04,  1.65536039e-04, ...,\n",
       "          6.95003546e-05,  7.76059678e-05,  5.49514989e-05],\n",
       "        [ 2.39755918e-05,  3.49687093e-06, -1.80042389e-05, ...,\n",
       "          4.37093740e-06, -3.65140440e-05, -5.26163021e-05],\n",
       "        [-4.34863396e-05, -1.96795918e-05,  2.04627781e-06, ...,\n",
       "          2.75153379e-05,  2.01571456e-05, -1.12461285e-05]]),\n",
       " '105': array([[ 9.56453914e-06,  1.41517239e-05,  2.05508040e-05, ...,\n",
       "          3.78051518e-05,  4.13820126e-05,  3.48780413e-05],\n",
       "        [ 3.19595019e-05,  3.40103725e-05,  3.70878843e-05, ...,\n",
       "          2.91556826e-05,  3.82692888e-05,  4.32166926e-05],\n",
       "        [ 5.03424089e-05,  5.31488404e-05,  4.73477630e-05, ...,\n",
       "          4.23741149e-05,  4.15149661e-05,  4.22248886e-05],\n",
       "        ...,\n",
       "        [-2.41429807e-05, -2.02603987e-05, -1.25288589e-05, ...,\n",
       "         -9.58246892e-06, -5.55454881e-06, -4.11771407e-06],\n",
       "        [-6.36414234e-06, -5.91666640e-06, -5.07313780e-06, ...,\n",
       "          5.22662049e-06,  3.79169728e-06, -2.26758880e-06],\n",
       "        [-5.66889503e-06, -1.90007074e-06, -5.39137091e-06, ...,\n",
       "         -3.03096640e-05, -3.57556528e-05, -3.88160151e-05]]),\n",
       " '106': array([[ 5.65442642e-05,  6.23828091e-05,  5.26614822e-05, ...,\n",
       "          6.81338934e-05,  6.74012190e-05,  5.43683200e-05],\n",
       "        [ 3.02519838e-05,  1.19546876e-05,  1.95247430e-05, ...,\n",
       "          4.35105467e-05,  4.13430826e-05,  4.44523830e-05],\n",
       "        [ 4.47905695e-05,  4.26336483e-05,  5.07241748e-05, ...,\n",
       "          1.96685360e-05,  2.02984884e-05,  1.77163056e-05],\n",
       "        ...,\n",
       "        [-6.12302829e-05, -6.90043817e-05, -7.25452628e-05, ...,\n",
       "         -3.37229030e-05, -3.14688732e-05, -4.67372738e-05],\n",
       "        [-6.18971389e-05, -6.30345094e-05, -5.71153359e-05, ...,\n",
       "         -2.67634441e-05, -3.08435920e-05, -3.89611305e-05],\n",
       "        [-4.63157448e-05, -4.77161884e-05, -3.93619521e-05, ...,\n",
       "         -4.88807527e-05, -5.55527440e-05, -5.69537624e-05]]),\n",
       " '107': array([[-8.83271714e-05, -1.04747349e-04, -1.24465325e-04, ...,\n",
       "         -1.27659645e-04, -1.18915596e-04, -9.46567889e-05],\n",
       "        [-5.38875065e-05, -2.11724855e-05, -4.31963144e-06, ...,\n",
       "         -1.05725907e-04, -7.69264880e-05, -3.60610684e-05],\n",
       "        [-1.25096194e-05, -8.97580139e-06, -1.30565513e-05, ...,\n",
       "         -9.58131786e-05, -9.67949018e-05, -1.06136409e-04],\n",
       "        ...,\n",
       "        [-5.46213005e-06, -7.03902879e-06, -8.15111798e-06, ...,\n",
       "          2.25206168e-05,  2.02262963e-05,  1.23824993e-05],\n",
       "        [ 1.04353567e-05,  1.17458667e-05,  9.02549073e-06, ...,\n",
       "          2.33160445e-05,  2.28252266e-05,  2.53375838e-05],\n",
       "        [ 2.49738951e-05,  2.34635281e-05,  2.20533911e-05, ...,\n",
       "          3.79259509e-05,  3.48854810e-05,  3.02075314e-05]]),\n",
       " '108': array([[ 5.44935065e-05,  4.76225505e-05,  3.81191821e-05, ...,\n",
       "          9.48538800e-05,  5.44418981e-05,  1.88741469e-05],\n",
       "        [ 7.02341936e-07, -7.22090817e-06, -2.33447781e-06, ...,\n",
       "          5.24447933e-05,  4.10034445e-05,  4.11091351e-05],\n",
       "        [ 4.96931134e-05,  5.80438063e-05,  4.82769792e-05, ...,\n",
       "          3.67386165e-05,  2.51161055e-05,  3.39502403e-05],\n",
       "        ...,\n",
       "        [-3.45811313e-05, -3.01196942e-05, -3.39982143e-05, ...,\n",
       "         -2.27319997e-05, -2.72252564e-05, -2.88681276e-05],\n",
       "        [-3.04076966e-05, -3.41092673e-05, -3.54634612e-05, ...,\n",
       "         -4.75473425e-05, -4.70296764e-05, -4.76415225e-05],\n",
       "        [-4.45664155e-05, -4.00702120e-05, -4.11834189e-05, ...,\n",
       "         -4.22704725e-05, -4.27099039e-05, -4.21035293e-05]]),\n",
       " '109': array([[ 1.14307142e-04,  8.50556899e-05,  4.95264940e-05, ...,\n",
       "          1.77282036e-05,  2.20314578e-05,  2.53812923e-05],\n",
       "        [ 2.66617099e-05,  3.12225056e-05,  3.69314039e-05, ...,\n",
       "          9.38060930e-06,  1.42325944e-05,  1.11538257e-05],\n",
       "        [ 4.06076288e-06, -5.53729251e-07,  1.01153692e-05, ...,\n",
       "          7.91282764e-06,  2.32304192e-05,  2.26043485e-05],\n",
       "        ...,\n",
       "        [-8.59525971e-05, -8.32350997e-05, -8.15226376e-05, ...,\n",
       "         -8.24007875e-05, -8.48914933e-05, -8.83454413e-05],\n",
       "        [-9.29232701e-05, -9.98365213e-05, -1.04665778e-04, ...,\n",
       "         -6.99362354e-05, -6.73471150e-05, -7.10680833e-05],\n",
       "        [-7.96924360e-05, -8.13696897e-05, -7.72407293e-05, ...,\n",
       "         -9.03147084e-05, -8.91452873e-05, -8.95218764e-05]]),\n",
       " '110': array([[ 7.68983409e-06,  8.76092417e-06,  8.68190909e-06, ...,\n",
       "          3.06128059e-05,  3.02893040e-05,  3.16987680e-05],\n",
       "        [ 2.95395967e-05,  2.64904666e-05,  2.57627562e-05, ...,\n",
       "          7.29271005e-06,  9.73910483e-06,  2.23921575e-06],\n",
       "        [-5.52191341e-06, -8.88189425e-06, -4.17738465e-06, ...,\n",
       "          2.38784269e-05,  2.13632320e-05,  1.86564812e-05],\n",
       "        ...,\n",
       "        [-3.50636510e-05, -4.89793892e-05, -5.89925003e-05, ...,\n",
       "         -2.40226036e-05, -1.87452843e-05, -1.79327035e-05],\n",
       "        [-1.84168130e-05, -2.12249215e-05, -2.79242577e-05, ...,\n",
       "         -1.60131167e-05, -1.28721422e-05, -1.44401511e-05],\n",
       "        [-1.46688462e-05, -1.33930271e-05, -1.70615458e-05, ...,\n",
       "         -2.12481464e-05, -2.49853747e-05, -3.19982319e-05]]),\n",
       " '111': array([[-2.77728413e-05, -1.82097810e-05, -1.00832376e-05, ...,\n",
       "         -2.72993202e-05, -2.43922987e-05, -1.31659463e-05],\n",
       "        [-3.67504367e-06, -3.10259202e-06, -7.70576298e-06, ...,\n",
       "         -2.03568125e-05, -2.40115405e-05, -2.92345194e-05],\n",
       "        [-3.23769455e-05, -3.59671321e-05, -4.39924152e-05, ...,\n",
       "         -2.77657055e-05, -3.87643813e-05, -4.49587715e-05],\n",
       "        ...,\n",
       "        [ 2.38679222e-05,  2.47553708e-05,  2.65512754e-05, ...,\n",
       "          3.78820914e-05,  3.63510808e-05,  3.85810927e-05],\n",
       "        [ 4.92894942e-05,  5.37033011e-05,  4.72898064e-05, ...,\n",
       "          3.46981651e-05,  3.28602146e-05,  2.33094142e-05],\n",
       "        [ 4.76553896e-06, -7.47828153e-06, -1.54689087e-06, ...,\n",
       "          3.62395876e-05,  4.14702336e-05,  4.80652016e-05]]),\n",
       " '112': array([[-1.41897963e-05, -8.46419789e-06,  1.36639683e-05, ...,\n",
       "         -1.85073841e-05, -1.45860240e-05, -1.46579350e-05],\n",
       "        [-1.40273505e-05, -1.43408579e-05, -3.47037967e-05, ...,\n",
       "         -2.09787577e-05, -1.37297966e-05, -5.90338368e-06],\n",
       "        [ 3.20712388e-06,  9.56372969e-06,  7.12009341e-06, ...,\n",
       "         -1.84255914e-05, -2.64972846e-06, -4.52252016e-06],\n",
       "        ...,\n",
       "        [-1.04877063e-05, -1.30494609e-05, -2.02639239e-05, ...,\n",
       "         -1.80094412e-05, -2.49375080e-05, -2.86129252e-05],\n",
       "        [-2.61694495e-05, -1.43558582e-05, -1.59510546e-06, ...,\n",
       "         -2.36197775e-05, -2.67310170e-05, -1.53945894e-05],\n",
       "        [-1.96461451e-06,  2.18768560e-06, -3.16454316e-06, ...,\n",
       "         -2.12124651e-05, -2.29860962e-05, -1.89790790e-05]]),\n",
       " '113': array([[-2.32007242e-05, -2.26553020e-05, -2.09311165e-05, ...,\n",
       "         -3.45371495e-06, -2.27103874e-06, -5.61693469e-06],\n",
       "        [-1.07711685e-05, -1.40737739e-05, -1.49297675e-05, ...,\n",
       "         -9.94668767e-07,  2.13976907e-08,  1.65486881e-06],\n",
       "        [ 2.26349857e-06,  4.92319123e-06,  6.37721269e-06, ...,\n",
       "         -2.54138213e-05, -2.93252251e-05, -2.67292980e-05],\n",
       "        ...,\n",
       "        [ 1.86467605e-05,  1.84152414e-05,  1.08159074e-05, ...,\n",
       "          1.39737576e-05,  1.58301918e-05,  1.75998612e-05],\n",
       "        [ 1.74982597e-05,  1.72028867e-05,  2.10633461e-05, ...,\n",
       "          3.05365211e-05,  2.40689260e-05,  2.20833408e-05],\n",
       "        [ 2.03893869e-05,  1.53226829e-05,  9.08344828e-06, ...,\n",
       "          2.87881339e-05,  3.25067049e-05,  3.63359541e-05]]),\n",
       " '114': array([[ 2.32843249e-05,  2.22113540e-05,  2.01200455e-05, ...,\n",
       "          2.13875192e-05,  2.24943105e-05,  2.51232996e-05],\n",
       "        [ 2.46944110e-05,  2.49714431e-05,  2.68753647e-05, ...,\n",
       "          4.09147797e-05,  4.03707309e-05,  3.95300158e-05],\n",
       "        [ 3.91161157e-05,  3.70022499e-05,  3.57827412e-05, ...,\n",
       "          4.51112574e-05,  4.57547576e-05,  4.76454879e-05],\n",
       "        ...,\n",
       "        [-3.60977720e-05, -3.90498353e-05, -3.94916860e-05, ...,\n",
       "         -3.34781835e-05, -3.54420990e-05, -3.54302319e-05],\n",
       "        [-3.30155344e-05, -3.25509573e-05, -3.41937921e-05, ...,\n",
       "         -3.97040421e-05, -4.52464774e-05, -5.37066335e-05],\n",
       "        [-5.84096560e-05, -5.26571566e-05, -4.36701375e-05, ...,\n",
       "         -2.24652158e-05, -2.03025920e-05, -2.02810643e-05]]),\n",
       " '115': array([[ 9.80462646e-05,  9.46664004e-05,  9.33389238e-05, ...,\n",
       "          7.55131477e-05,  7.80797855e-05,  7.30789907e-05],\n",
       "        [ 7.49299434e-05,  8.24538438e-05,  9.42403276e-05, ...,\n",
       "          8.20749992e-05,  7.76422239e-05,  8.60568252e-05],\n",
       "        [ 1.02310412e-04,  1.01551093e-04,  8.08307668e-05, ...,\n",
       "          8.19096458e-05,  7.53098939e-05,  8.19857669e-05],\n",
       "        ...,\n",
       "        [-2.45040064e-06,  5.69842678e-06,  1.15151688e-05, ...,\n",
       "          1.21896460e-06,  1.02092793e-06,  1.94095264e-06],\n",
       "        [ 4.83108215e-06,  7.64703418e-06,  9.95522532e-06, ...,\n",
       "         -1.37964844e-05, -1.32891555e-05, -1.20857540e-05],\n",
       "        [-3.26718737e-06,  5.45222156e-06,  4.34336471e-06, ...,\n",
       "          4.08348797e-06,  2.80354720e-06, -5.74026399e-06]]),\n",
       " '116': array([[ 8.95118046e-06,  9.69352186e-06,  9.15065812e-06, ...,\n",
       "          2.05578253e-05,  1.90549890e-05,  1.79940525e-05],\n",
       "        [ 1.92737934e-05,  2.05410088e-05,  2.06881632e-05, ...,\n",
       "          1.14153099e-05,  1.22565434e-05,  8.77033381e-06],\n",
       "        [ 8.42803183e-06,  9.24018786e-06,  5.70040993e-06, ...,\n",
       "         -4.33378403e-07,  6.58449210e-07,  1.13180943e-06],\n",
       "        ...,\n",
       "        [ 5.45597104e-05,  5.27738230e-05,  5.10256614e-05, ...,\n",
       "          5.23046292e-05,  5.29882527e-05,  5.58856846e-05],\n",
       "        [ 5.46526062e-05,  5.28647979e-05,  5.26017393e-05, ...,\n",
       "          5.98426632e-05,  5.65165137e-05,  5.15956490e-05],\n",
       "        [ 4.54203182e-05,  4.07816624e-05,  3.92874681e-05, ...,\n",
       "          4.60971205e-05,  4.72632892e-05,  4.69385959e-05]]),\n",
       " '117': array([[ 4.83028889e-05,  5.54490216e-05,  4.97818619e-05, ...,\n",
       "          4.54042056e-05,  4.59665425e-05,  5.52849961e-05],\n",
       "        [ 6.56950142e-05,  6.00862004e-05,  5.05875614e-05, ...,\n",
       "          8.75830883e-05,  8.22315269e-05,  7.71003688e-05],\n",
       "        [ 6.90421439e-05,  6.45188775e-05,  6.01663705e-05, ...,\n",
       "          5.67418210e-05,  5.39658758e-05,  5.43211609e-05],\n",
       "        ...,\n",
       "        [-6.06478170e-05, -6.29789865e-05, -4.77549111e-05, ...,\n",
       "          1.73239278e-05,  1.71719639e-05,  7.62139553e-06],\n",
       "        [ 1.96783162e-06,  6.86980093e-06,  1.49529587e-05, ...,\n",
       "         -6.93149050e-05, -7.48123275e-05, -6.41533261e-05],\n",
       "        [-4.88571532e-05, -3.83082042e-05, -2.35478128e-05, ...,\n",
       "         -2.47715652e-05, -2.58597302e-05, -2.79559863e-05]]),\n",
       " '118': array([[-6.09009148e-05, -6.11333307e-05, -6.28935595e-05, ...,\n",
       "         -7.20538592e-05, -7.26580765e-05, -6.89513909e-05],\n",
       "        [-6.13953962e-05, -5.35110303e-05, -5.33730199e-05, ...,\n",
       "         -6.35428660e-05, -7.01772587e-05, -7.00606906e-05],\n",
       "        [-6.47776469e-05, -5.36757434e-05, -4.55186091e-05, ...,\n",
       "         -4.05398096e-05, -4.73939253e-05, -5.24479256e-05],\n",
       "        ...,\n",
       "        [-2.90242606e-05, -7.20699381e-06,  1.91897307e-05, ...,\n",
       "         -3.61043094e-05, -5.58571519e-05, -5.91490789e-05],\n",
       "        [-4.83570584e-05, -3.88225853e-05, -2.89118034e-05, ...,\n",
       "          5.93435034e-05,  5.83423498e-05,  4.88265359e-05],\n",
       "        [ 1.30652998e-05, -2.89083182e-05, -1.86906145e-05, ...,\n",
       "          3.97150507e-05,  2.08137499e-05,  1.67359030e-05]]),\n",
       " '119': array([[-7.99250483e-06, -1.08537679e-05, -1.17645004e-05, ...,\n",
       "         -3.24476750e-05, -3.41402592e-05, -3.52400639e-05],\n",
       "        [-3.70793496e-05, -3.83304905e-05, -3.89320667e-05, ...,\n",
       "         -4.70927043e-05, -4.68822836e-05, -4.92887448e-05],\n",
       "        [-5.05872958e-05, -4.72704451e-05, -4.45171863e-05, ...,\n",
       "         -3.57700810e-05, -3.57930039e-05, -3.75505770e-05],\n",
       "        ...,\n",
       "        [-2.86669797e-06, -3.02027388e-06, -2.66183110e-06, ...,\n",
       "          1.93898504e-05,  1.61660446e-05,  1.02178910e-05],\n",
       "        [ 5.99346095e-06,  7.14657153e-06,  1.07044152e-05, ...,\n",
       "          8.78100946e-06,  8.30237059e-06,  1.12628359e-05],\n",
       "        [ 1.48425524e-05,  1.51515842e-05,  1.73572607e-05, ...,\n",
       "          1.80789630e-05,  1.33876911e-05,  7.24585061e-06]]),\n",
       " '120': array([[-1.00063899e-05, -3.76319463e-06,  1.23744940e-05, ...,\n",
       "          1.30163144e-05,  1.38252326e-05,  3.40072693e-05],\n",
       "        [ 4.81543320e-05,  4.86593271e-05,  4.09856257e-05, ...,\n",
       "          2.77082181e-05,  4.41879820e-05,  7.60748517e-05],\n",
       "        [ 8.22581933e-05,  6.10858697e-05,  3.97951480e-05, ...,\n",
       "          3.28954593e-05,  3.29665054e-05,  2.55027280e-05],\n",
       "        ...,\n",
       "        [ 7.74340224e-05,  6.97622017e-05,  4.43651297e-05, ...,\n",
       "          1.57575087e-05,  9.03809359e-06,  1.89381990e-05],\n",
       "        [ 4.10592002e-05,  5.27048942e-05,  3.82996295e-05, ...,\n",
       "          5.69498152e-05,  8.31069265e-05,  1.01258040e-04],\n",
       "        [ 1.18712691e-04,  1.25061313e-04,  9.12549513e-05, ...,\n",
       "          4.94896594e-05,  4.42001074e-05,  5.67277420e-05]]),\n",
       " '121': array([[-1.56016667e-05, -1.51451968e-05, -1.75244950e-05, ...,\n",
       "         -5.23221843e-06, -2.36787878e-06,  5.57736826e-07],\n",
       "        [-2.56013607e-07, -2.33371816e-06, -6.79661662e-06, ...,\n",
       "         -7.78574849e-06, -9.33514275e-06, -1.09711782e-05],\n",
       "        [-1.13116967e-05, -1.13903634e-05, -1.11486042e-05, ...,\n",
       "         -9.05674915e-06, -1.15467574e-05, -1.34039065e-05],\n",
       "        ...,\n",
       "        [ 2.55529358e-05,  2.46397431e-05,  2.64250066e-05, ...,\n",
       "          2.71934005e-05,  2.55586510e-05,  2.44798230e-05],\n",
       "        [ 2.14526135e-05,  1.92388416e-05,  1.86383131e-05, ...,\n",
       "          3.16682999e-05,  2.92715649e-05,  2.37060667e-05],\n",
       "        [ 1.99437400e-05,  2.05731030e-05,  2.09720911e-05, ...,\n",
       "          3.73292241e-05,  3.57818171e-05,  3.03611050e-05]]),\n",
       " '122': array([[-5.80803135e-05, -6.05240712e-05, -6.70629888e-05, ...,\n",
       "          3.36714402e-06,  1.68448660e-05, -3.67453572e-06],\n",
       "        [-1.83362808e-05, -2.04087519e-05, -2.51651127e-05, ...,\n",
       "         -3.69143927e-05, -2.76913634e-05, -1.27995927e-05],\n",
       "        [-5.95138863e-06, -2.13173171e-05, -3.81502578e-05, ...,\n",
       "         -1.49287280e-05, -3.45637054e-05, -4.57413153e-05],\n",
       "        ...,\n",
       "        [-5.76180937e-05, -3.00001684e-05, -3.16669066e-05, ...,\n",
       "         -1.37526049e-05, -2.08433703e-05, -2.80775894e-05],\n",
       "        [-5.05144708e-05, -6.18306003e-05, -4.99988564e-05, ...,\n",
       "         -7.15622264e-06, -2.38216144e-05, -3.56762248e-05],\n",
       "        [-4.82023715e-05, -6.98270596e-05, -8.65773982e-05, ...,\n",
       "         -9.18443402e-05, -9.56900767e-05, -1.03316801e-04]]),\n",
       " '123': array([[-2.83920326e-05, -2.90324369e-05, -2.82189303e-05, ...,\n",
       "         -4.37439885e-05, -4.08956112e-05, -3.83139995e-05],\n",
       "        [-3.92541187e-05, -3.83725055e-05, -3.60643098e-05, ...,\n",
       "         -2.36203923e-05, -2.17572087e-05, -1.85273184e-05],\n",
       "        [-1.66848695e-05, -1.57639570e-05, -1.56701080e-05, ...,\n",
       "         -3.55752563e-05, -3.67882676e-05, -3.85959211e-05],\n",
       "        ...,\n",
       "        [ 2.12317318e-05,  4.98635745e-06,  4.53414287e-07, ...,\n",
       "         -3.27612179e-06, -1.58788516e-05, -1.77675411e-05],\n",
       "        [-1.85391946e-05, -1.75244786e-05, -1.32849927e-05, ...,\n",
       "          1.32334380e-05, -2.34283721e-06,  4.94733877e-06],\n",
       "        [ 7.09208098e-06,  2.25730764e-06,  2.96267967e-06, ...,\n",
       "         -7.55577275e-06,  3.99157827e-08, -3.84977483e-07]]),\n",
       " '124': array([[-1.09901721e-05, -2.23591396e-05, -2.06918885e-05, ...,\n",
       "          5.75720114e-05,  4.29488355e-05,  2.65480194e-05],\n",
       "        [ 1.13309725e-05, -4.52771189e-07, -2.10701346e-05, ...,\n",
       "         -4.31663320e-05, -2.33572282e-05, -1.93901287e-05],\n",
       "        [-3.27035777e-05, -1.86438065e-05,  2.05052165e-05, ...,\n",
       "          1.83141638e-05,  3.30742324e-07, -1.17739673e-05],\n",
       "        ...,\n",
       "        [ 6.06945287e-05,  4.75649285e-05,  4.30638102e-05, ...,\n",
       "          5.69767653e-05,  7.05111961e-05,  7.49161991e-05],\n",
       "        [ 7.04686609e-05,  5.73603393e-05,  3.42285712e-05, ...,\n",
       "          6.28143389e-05,  5.78375475e-05,  4.87706457e-05],\n",
       "        [ 5.11187609e-05,  5.65025148e-05,  5.16164109e-05, ...,\n",
       "          4.38493735e-05,  4.03146369e-05,  4.92466024e-05]]),\n",
       " '125': array([[-2.24145424e-06, -1.16060637e-05, -3.51131939e-06, ...,\n",
       "          7.09923643e-06,  9.09304654e-05,  1.46010963e-04],\n",
       "        [ 1.38075033e-04,  1.07868676e-04,  9.41963808e-05, ...,\n",
       "          3.35718255e-06, -9.32748480e-06, -1.68878996e-05],\n",
       "        [-1.74975667e-05, -1.97749814e-05, -1.35907885e-05, ...,\n",
       "          9.24047490e-05,  1.01433769e-04,  9.17247744e-05],\n",
       "        ...,\n",
       "        [ 7.37206665e-06,  2.39104120e-05,  2.76254996e-05, ...,\n",
       "         -1.77442325e-05, -2.43579398e-05, -2.83312256e-05],\n",
       "        [-2.93694993e-05, -2.58857326e-05, -1.94844779e-05, ...,\n",
       "          6.27821009e-07, -5.13505165e-06, -1.11433246e-05],\n",
       "        [-1.34692300e-05, -9.47570061e-06,  3.27830179e-07, ...,\n",
       "         -2.31972062e-05, -2.47210119e-05, -2.38056346e-05]]),\n",
       " '126': array([[ 2.31109334e-05,  2.22301387e-05,  1.87785754e-05, ...,\n",
       "          3.53076284e-05,  3.49003494e-05,  2.96873550e-05],\n",
       "        [ 2.49812438e-05,  2.18498753e-05,  2.25002168e-05, ...,\n",
       "          4.98436784e-05,  5.45433249e-05,  5.56458399e-05],\n",
       "        [ 5.62335554e-05,  5.70156553e-05,  5.64707007e-05, ...,\n",
       "          4.72726533e-05,  4.62612261e-05,  4.41991397e-05],\n",
       "        ...,\n",
       "        [ 3.27166090e-05,  5.28384371e-05,  6.20999053e-05, ...,\n",
       "         -4.14798160e-06, -5.93602044e-06, -8.54719656e-06],\n",
       "        [-9.39031088e-06, -1.25959978e-05, -1.70655658e-05, ...,\n",
       "         -2.45161427e-05, -1.59892570e-05,  8.91046875e-06],\n",
       "        [ 2.56972235e-05,  1.27708836e-05, -1.44464457e-05, ...,\n",
       "          1.07161986e-05, -8.64548838e-06, -1.28878910e-05]]),\n",
       " '127': array([[-9.11085237e-07, -1.29396731e-05, -1.60585150e-05, ...,\n",
       "         -2.45448173e-05, -4.25451217e-05, -4.43786048e-05],\n",
       "        [-3.91639478e-05, -3.81667414e-05, -3.53476244e-05, ...,\n",
       "          3.24168759e-05,  3.24521279e-05,  2.94232268e-05],\n",
       "        [ 1.92105290e-05,  1.21976736e-05,  1.18133657e-05, ...,\n",
       "         -3.53796640e-05, -2.53446469e-05,  1.15087205e-05],\n",
       "        ...,\n",
       "        [ 1.20137129e-05,  2.87597450e-05,  6.69372366e-06, ...,\n",
       "          2.75510793e-05,  3.86388820e-05,  2.62136891e-05],\n",
       "        [-5.59177181e-07, -2.34276631e-05, -4.10129906e-05, ...,\n",
       "         -8.00455964e-05, -7.12256660e-05, -6.39676291e-05],\n",
       "        [-5.09847268e-05, -2.44006987e-05, -5.03469255e-06, ...,\n",
       "         -7.00813371e-06, -8.14637133e-07, -1.16174033e-05]]),\n",
       " '128': array([[ 2.23901861e-05,  2.84353082e-05,  2.88807951e-05, ...,\n",
       "          1.54200698e-05,  1.42417948e-05,  1.10813971e-05],\n",
       "        [ 1.03636521e-05,  9.65859908e-06,  5.93939649e-06, ...,\n",
       "          2.08866568e-05,  1.90190494e-05,  1.71906358e-05],\n",
       "        [ 1.36403341e-05,  1.17173804e-05,  1.09957846e-05, ...,\n",
       "          6.02501586e-06,  4.96058692e-06,  3.94771405e-06],\n",
       "        ...,\n",
       "        [-8.54817554e-05, -8.46352632e-05, -8.56067927e-05, ...,\n",
       "         -7.68920072e-05, -7.82885472e-05, -7.88116813e-05],\n",
       "        [-7.62436612e-05, -7.64793367e-05, -7.92409337e-05, ...,\n",
       "         -7.12167675e-05, -6.33790987e-05, -6.09069248e-05],\n",
       "        [-6.86447383e-05, -7.67008460e-05, -7.40349060e-05, ...,\n",
       "         -7.26814978e-05, -7.61077827e-05, -8.37349871e-05]]),\n",
       " '129': array([[-7.18637093e-05, -1.32490713e-05,  7.12656129e-06, ...,\n",
       "         -7.29542371e-05, -7.66886951e-05, -7.47374579e-05],\n",
       "        [-7.45403522e-05, -8.14701925e-05, -8.98164071e-05, ...,\n",
       "         -5.41336922e-05, -5.67335810e-05, -6.87023130e-05],\n",
       "        [-7.57708985e-05, -8.24581730e-05, -9.63679049e-05, ...,\n",
       "         -4.61296077e-05, -1.62606721e-05, -1.51312142e-05],\n",
       "        ...,\n",
       "        [ 4.10557914e-05,  4.13254675e-05,  5.10411846e-05, ...,\n",
       "          4.26518345e-05,  4.45028127e-05,  4.24299724e-05],\n",
       "        [ 3.71500282e-05,  3.64500593e-05,  3.89381821e-05, ...,\n",
       "          4.43431527e-05,  5.09058409e-05,  5.46541050e-05],\n",
       "        [ 5.32753947e-05,  4.26348342e-05,  3.54825315e-05, ...,\n",
       "          2.38894445e-05,  3.62754145e-05,  4.21241093e-05]]),\n",
       " '130': array([[ 3.73775947e-05,  3.70705711e-05,  3.85138264e-05, ...,\n",
       "         -8.99855650e-07, -1.11500449e-05, -1.05438239e-05],\n",
       "        [-3.54118515e-06,  4.65756602e-06,  1.29978562e-05, ...,\n",
       "         -7.50351910e-06, -1.00368907e-05, -1.35072914e-05],\n",
       "        [-1.58118837e-05, -1.51939494e-05, -1.19048755e-05, ...,\n",
       "          1.56796468e-05,  1.88582035e-05,  2.01252042e-05],\n",
       "        ...,\n",
       "        [-4.20366086e-05, -4.35887596e-05, -4.52564891e-05, ...,\n",
       "         -4.52497989e-05, -4.81477291e-05, -5.12665611e-05],\n",
       "        [-5.45779912e-05, -5.71375276e-05, -6.10253592e-05, ...,\n",
       "         -3.68733381e-05, -3.41971645e-05, -3.62147694e-05],\n",
       "        [-3.80165584e-05, -3.62751016e-05, -3.63722938e-05, ...,\n",
       "         -2.85134756e-05, -2.61123259e-05, -2.50568555e-05]]),\n",
       " '131': array([[-4.89259173e-06, -1.49141952e-05, -2.03338914e-05, ...,\n",
       "         -1.70905751e-05, -1.89295224e-05, -8.40673783e-06],\n",
       "        [-1.97204281e-05, -2.61505884e-05,  5.43040301e-07, ...,\n",
       "          2.89821273e-05,  3.33613971e-05,  3.71451570e-05],\n",
       "        [ 3.38481368e-05,  3.20306390e-05,  3.21252919e-05, ...,\n",
       "          4.70244959e-05,  5.00161877e-05,  4.89893682e-05],\n",
       "        ...,\n",
       "        [-5.12296683e-05, -5.20195244e-05, -5.76811290e-05, ...,\n",
       "         -2.80848981e-05, -1.50722435e-05, -1.44623182e-05],\n",
       "        [-1.45669637e-05, -1.06025964e-05, -1.43315265e-05, ...,\n",
       "         -1.46297716e-05, -1.25047409e-05, -9.60510260e-06],\n",
       "        [-1.77388283e-05, -3.21016050e-05, -3.95178322e-05, ...,\n",
       "         -2.47243679e-05, -3.38629070e-05, -4.24948594e-05]]),\n",
       " '132': array([[9.15129112e-06, 1.41529890e-05, 1.73134376e-05, ...,\n",
       "         2.00225732e-05, 2.34037961e-05, 2.70589826e-05],\n",
       "        [2.57310203e-05, 2.42704828e-05, 2.54929691e-05, ...,\n",
       "         4.20379947e-05, 4.08025480e-05, 4.05350766e-05],\n",
       "        [4.46843442e-05, 4.43514036e-05, 4.12729605e-05, ...,\n",
       "         5.22037190e-05, 5.17220360e-05, 4.91793216e-05],\n",
       "        ...,\n",
       "        [4.77690155e-05, 4.41305347e-05, 4.27157538e-05, ...,\n",
       "         3.06094007e-05, 3.16646328e-05, 3.11535296e-05],\n",
       "        [2.89679610e-05, 2.98106788e-05, 3.37909514e-05, ...,\n",
       "         4.26932711e-05, 4.19635035e-05, 4.23625825e-05],\n",
       "        [4.14513925e-05, 4.04400707e-05, 3.88664048e-05, ...,\n",
       "         4.25075086e-05, 3.98720331e-05, 3.54612384e-05]]),\n",
       " '133': array([[-1.01118603e-04, -6.12665535e-05, -4.58056493e-05, ...,\n",
       "         -2.86910199e-05, -5.09136225e-05, -6.71732414e-05],\n",
       "        [-8.54362224e-05, -9.64069695e-05, -7.39756724e-05, ...,\n",
       "          3.17130252e-05,  1.68683036e-05,  6.43740759e-06],\n",
       "        [ 4.57688520e-06,  1.42551517e-06,  3.54694316e-06, ...,\n",
       "         -7.10202366e-05, -4.20409415e-05, -1.01554951e-05],\n",
       "        ...,\n",
       "        [-5.44133109e-05, -5.75591948e-05, -7.80686751e-05, ...,\n",
       "         -1.37151746e-05, -1.06549205e-05, -6.91220703e-06],\n",
       "        [-4.44295529e-06, -1.13345759e-05, -3.26158988e-05, ...,\n",
       "          3.37389829e-05,  1.96076144e-05, -4.43692488e-06],\n",
       "        [-3.17826853e-05, -4.17346673e-05, -2.93744361e-05, ...,\n",
       "          1.59348510e-05,  2.35035145e-06, -2.46016316e-05]]),\n",
       " '134': array([[ 2.55184423e-05,  1.74817615e-05,  1.28345810e-05, ...,\n",
       "          1.27086550e-05,  6.80242374e-06,  4.73683895e-06],\n",
       "        [ 5.90555373e-06,  8.04256524e-06,  7.17992452e-06, ...,\n",
       "          1.87058558e-05,  2.44786825e-05,  2.65360777e-05],\n",
       "        [ 2.80472432e-05,  2.64841547e-05,  2.28819936e-05, ...,\n",
       "          3.68737128e-05,  3.66339482e-05,  3.41481973e-05],\n",
       "        ...,\n",
       "        [-5.54795988e-05, -4.14825736e-05, -2.44182975e-05, ...,\n",
       "         -4.40790791e-05, -3.15450234e-05, -1.43472298e-05],\n",
       "        [-5.81501627e-06, -9.19127433e-06, -9.33630054e-06, ...,\n",
       "          1.96195797e-05,  3.44593309e-06, -5.88523790e-06],\n",
       "        [-1.44300820e-05, -2.29661837e-05, -2.09577502e-05, ...,\n",
       "         -5.25190558e-07,  6.24059157e-06,  1.06697053e-05]]),\n",
       " '135': array([[ 4.25215367e-05,  5.09041092e-05,  6.09590024e-05, ...,\n",
       "          7.68449609e-05,  8.53113015e-05,  9.71746558e-05],\n",
       "        [ 9.96848539e-05,  9.23650150e-05,  7.79417896e-05, ...,\n",
       "          7.78050817e-05,  7.67182210e-05,  8.14278610e-05],\n",
       "        [ 8.48939962e-05,  8.55338149e-05,  9.22810650e-05, ...,\n",
       "          6.73508112e-05,  6.85908381e-05,  5.95742567e-05],\n",
       "        ...,\n",
       "        [ 2.71823101e-05,  6.33174022e-06, -2.78926382e-06, ...,\n",
       "         -1.41954006e-05, -2.98126124e-05, -4.26715524e-05],\n",
       "        [-3.83458937e-05, -2.27633409e-05,  3.87643558e-06, ...,\n",
       "          1.10891096e-05, -1.76005306e-05, -4.10369539e-05],\n",
       "        [-2.82401434e-05, -1.06756706e-05, -6.56510019e-06, ...,\n",
       "         -5.48447715e-05, -4.58747272e-05, -2.68172844e-05]]),\n",
       " '136': array([[-2.04696153e-05, -2.14376978e-05, -2.45954725e-05, ...,\n",
       "         -3.09639581e-05, -2.81269404e-05, -2.67600481e-05],\n",
       "        [-2.80724198e-05, -2.86250906e-05, -2.98579125e-05, ...,\n",
       "         -1.33348258e-05, -1.42947920e-05, -1.12490297e-05],\n",
       "        [-9.94267521e-06, -1.37894585e-05, -1.23168629e-05, ...,\n",
       "          3.43227202e-06,  1.23033385e-06, -4.56306395e-07],\n",
       "        ...,\n",
       "        [ 2.12000168e-06, -9.71890472e-07, -5.81443101e-06, ...,\n",
       "         -2.76470018e-05, -2.93980011e-05, -2.80405729e-05],\n",
       "        [-2.55592659e-05, -2.45911997e-05, -2.21054615e-05, ...,\n",
       "         -9.85676070e-06, -9.34975742e-06, -1.08611539e-05],\n",
       "        [-1.34291558e-05, -1.49032985e-05, -1.74860998e-05, ...,\n",
       "         -2.69629550e-06, -1.40134262e-06, -3.52417510e-06]]),\n",
       " '137': array([[-6.38013953e-05, -9.33822230e-05, -1.08100583e-04, ...,\n",
       "         -4.14950955e-05, -4.18253148e-05, -3.27315465e-05],\n",
       "        [-3.18123311e-05, -4.73743130e-05, -6.01148859e-05, ...,\n",
       "         -6.36519690e-05, -5.77954306e-05, -5.47299969e-05],\n",
       "        [-4.96073699e-05, -4.17715273e-05, -2.98553077e-05, ...,\n",
       "         -5.81827298e-05, -2.73051592e-05,  1.17497511e-05],\n",
       "        ...,\n",
       "        [ 6.48832138e-05,  8.28000047e-05,  8.98901271e-05, ...,\n",
       "          2.47942862e-05,  2.48298256e-05,  2.37701661e-05],\n",
       "        [ 2.73743790e-05,  4.07340776e-05,  4.80258059e-05, ...,\n",
       "          2.61077384e-05,  2.91671731e-05,  3.51810850e-05],\n",
       "        [ 3.65819105e-05,  3.72773247e-05,  4.63906945e-05, ...,\n",
       "          4.54479377e-05,  4.61827512e-05,  4.84729426e-05]]),\n",
       " '138': array([[-4.72613756e-05, -4.63109100e-05, -4.53473040e-05, ...,\n",
       "         -3.49399124e-05, -3.00063894e-05, -2.50378816e-05],\n",
       "        [-2.31476261e-05, -2.19082558e-05, -2.02806877e-05, ...,\n",
       "         -1.69196592e-05, -1.67176004e-05, -1.74886372e-05],\n",
       "        [-1.48817608e-05, -1.11347445e-05, -1.20920959e-05, ...,\n",
       "         -8.67756080e-06, -7.58486476e-06, -4.62266507e-06],\n",
       "        ...,\n",
       "        [-1.94874665e-05, -2.63034617e-05, -3.92858419e-05, ...,\n",
       "          3.93816117e-06,  2.33929245e-06, -2.04684102e-06],\n",
       "        [-1.38318921e-06,  2.18794071e-06, -2.72685929e-06, ...,\n",
       "         -2.28819954e-05, -4.04220555e-05, -3.59651749e-05],\n",
       "        [-1.74191337e-05, -1.38335408e-05, -1.82036729e-05, ...,\n",
       "         -1.30218750e-05, -2.12265604e-05, -2.30513524e-05]]),\n",
       " '139': array([[-2.49227178e-05, -2.99845487e-05, -1.55302223e-05, ...,\n",
       "          2.28231602e-05,  1.90935916e-05,  1.98445438e-08],\n",
       "        [-1.22706351e-05, -3.35934857e-07,  1.20797822e-05, ...,\n",
       "          4.68164799e-05,  3.91011126e-05,  2.36465967e-05],\n",
       "        [-1.84953581e-06, -2.71771059e-05, -3.85837920e-05, ...,\n",
       "          6.09965718e-05,  6.01007596e-05,  5.04471936e-05],\n",
       "        ...,\n",
       "        [ 1.41298624e-05,  2.03554791e-05,  2.69255288e-05, ...,\n",
       "          4.04132770e-05,  1.18913276e-05, -1.55431480e-05],\n",
       "        [-2.09116279e-05, -3.02716926e-05, -5.22753180e-05, ...,\n",
       "          5.08821540e-05,  4.19834796e-05,  4.22378980e-05],\n",
       "        [ 3.40451770e-05,  8.44865099e-06, -2.05504830e-06, ...,\n",
       "         -2.21909340e-05, -2.08335841e-05, -4.50150328e-05]]),\n",
       " '140': array([[-4.44137549e-05, -4.70899686e-05, -5.05319695e-05, ...,\n",
       "         -4.11460969e-05, -4.33063760e-05, -4.11910842e-05],\n",
       "        [-3.92300244e-05, -3.89839879e-05, -3.99958481e-05, ...,\n",
       "         -2.77998042e-05, -2.58259315e-05, -3.00417687e-05],\n",
       "        [-3.61059574e-05, -3.84749255e-05, -3.89928755e-05, ...,\n",
       "         -6.23752130e-05, -5.38204367e-05, -4.59070725e-05],\n",
       "        ...,\n",
       "        [ 3.96880605e-05,  2.30798305e-05,  1.24669814e-05, ...,\n",
       "          7.91767616e-06,  1.49174484e-05,  6.44767943e-06],\n",
       "        [-1.74224022e-06,  5.33258481e-06,  2.08082092e-05, ...,\n",
       "          1.94181721e-05,  1.96064830e-05,  2.31464419e-05],\n",
       "        [ 2.08652000e-05,  1.63622262e-05,  1.65252968e-05, ...,\n",
       "         -1.48119534e-05, -4.32159277e-06,  2.52918871e-05]]),\n",
       " '141': array([[-8.91263262e-05, -7.87463578e-05, -6.79215664e-05, ...,\n",
       "         -7.58844326e-05, -8.49031931e-05, -8.15481908e-05],\n",
       "        [-8.14496088e-05, -8.04395168e-05, -7.89559563e-05, ...,\n",
       "         -9.39336387e-05, -9.01760650e-05, -6.65109110e-05],\n",
       "        [-3.23344939e-05, -1.74650286e-05, -2.43738486e-05, ...,\n",
       "         -3.87252439e-05, -4.39844152e-05, -5.29623067e-05],\n",
       "        ...,\n",
       "        [-9.13882832e-06, -2.57612032e-06,  7.20872004e-06, ...,\n",
       "          2.36901815e-05,  2.33592073e-05,  2.49342938e-05],\n",
       "        [ 1.91098261e-05,  1.34037400e-05,  2.07771591e-05, ...,\n",
       "          2.98190062e-05,  3.18567800e-05,  3.56409691e-05],\n",
       "        [ 3.43570609e-05,  2.39862547e-05,  1.26110181e-05, ...,\n",
       "          3.29069844e-05,  3.05189205e-05,  2.05423839e-05]]),\n",
       " '142': array([[ 4.06883419e-06,  3.81565405e-06,  1.41910641e-06, ...,\n",
       "         -2.03935579e-05, -1.73231329e-05, -1.58918720e-05],\n",
       "        [-1.59470110e-05, -2.01341099e-05, -2.19489339e-05, ...,\n",
       "         -1.20325594e-05, -1.43375582e-05, -1.79540293e-05],\n",
       "        [-2.20154270e-05, -2.70398250e-05, -2.89437139e-05, ...,\n",
       "         -1.39067079e-05, -1.36210538e-05, -1.30466260e-05],\n",
       "        ...,\n",
       "        [ 5.74089318e-05,  4.71541753e-05,  2.54929491e-05, ...,\n",
       "          7.09562955e-05,  6.60858277e-05,  6.69038054e-05],\n",
       "        [ 4.97352157e-05,  3.12280026e-05,  3.72013128e-05, ...,\n",
       "          6.34306634e-05,  5.82590510e-05,  5.63865979e-05],\n",
       "        [ 5.17589069e-05,  4.07974949e-05,  2.43726427e-05, ...,\n",
       "          4.24943501e-05,  3.61515340e-05,  2.87953208e-05]]),\n",
       " '143': array([[ 8.28785687e-06,  7.86453165e-06,  5.99072519e-06, ...,\n",
       "          1.46729471e-05,  1.61426779e-05,  1.74951674e-05],\n",
       "        [ 1.85282533e-05,  1.88709619e-05,  1.81768173e-05, ...,\n",
       "          1.61080534e-05,  1.45175900e-05,  1.10115989e-05],\n",
       "        [ 9.25891982e-06,  7.72906969e-06,  3.89493289e-06, ...,\n",
       "          1.36017684e-06, -2.27556015e-07,  1.71807926e-07],\n",
       "        ...,\n",
       "        [-4.77687499e-05, -4.91183418e-05, -4.38068128e-05, ...,\n",
       "         -2.42531241e-05, -2.64557038e-05, -2.84892485e-05],\n",
       "        [-2.95895879e-05, -3.35050718e-05, -3.97194344e-05, ...,\n",
       "         -3.61509410e-05, -3.38907921e-05, -2.82204037e-05],\n",
       "        [-2.23909465e-05, -2.13114108e-05, -2.63525708e-05, ...,\n",
       "         -2.62052199e-05, -3.00366846e-05, -3.69494010e-05]]),\n",
       " '144': array([[ 6.46038679e-05,  6.55147524e-05,  6.63108658e-05, ...,\n",
       "          5.39902940e-05,  5.17293010e-05,  5.07853692e-05],\n",
       "        [ 5.02886796e-05,  4.79461123e-05,  4.71835119e-05, ...,\n",
       "          3.24561915e-05,  3.06114634e-05,  3.06524234e-05],\n",
       "        [ 2.96725866e-05,  2.79618398e-05,  2.72613324e-05, ...,\n",
       "          1.83110642e-05,  1.73961489e-05,  1.39283029e-05],\n",
       "        ...,\n",
       "        [-1.93771302e-05,  1.33005506e-06,  1.45906461e-05, ...,\n",
       "         -2.75350831e-05, -2.99111543e-05, -3.41625564e-05],\n",
       "        [-4.25826765e-05, -4.51332671e-05, -4.26019833e-05, ...,\n",
       "          6.20502715e-06,  1.15418034e-05,  5.15731199e-06],\n",
       "        [ 4.74708668e-06,  1.24313892e-05,  1.63477143e-05, ...,\n",
       "         -1.77454149e-05, -2.07910634e-05, -3.15342259e-05]]),\n",
       " '145': array([[ 4.28841275e-07,  2.16316471e-06,  1.84394207e-06, ...,\n",
       "          8.12753478e-06,  4.70502391e-06,  1.33178969e-06],\n",
       "        [-1.72795728e-06, -1.16909256e-07,  1.84254429e-06, ...,\n",
       "         -2.77595373e-05, -2.86108880e-05, -2.47817097e-05],\n",
       "        [-2.56394142e-05, -2.82423171e-05, -2.24646101e-05, ...,\n",
       "         -3.16801561e-05, -2.75751318e-05, -2.87945641e-05],\n",
       "        ...,\n",
       "        [-2.28989538e-05, -2.26207594e-05, -2.34909967e-05, ...,\n",
       "         -9.76303363e-06, -1.01624273e-05, -1.45927688e-05],\n",
       "        [-1.66962964e-05, -1.48802274e-05, -1.39692665e-05, ...,\n",
       "         -2.01013972e-05, -1.54187510e-05, -1.04552291e-05],\n",
       "        [-4.33312562e-06, -4.75017174e-07, -2.79557958e-06, ...,\n",
       "          9.82290840e-06,  3.85956037e-06,  1.29792807e-06]]),\n",
       " '146': array([[ 2.85751685e-05,  2.74874455e-05,  2.81841676e-05, ...,\n",
       "          3.78113837e-05,  3.18797975e-05,  2.26447573e-05],\n",
       "        [ 1.13155138e-05, -1.10024291e-06, -1.14285394e-05, ...,\n",
       "          2.00141767e-05,  1.90051778e-05,  1.75547357e-05],\n",
       "        [ 1.47362380e-05,  1.27291787e-05,  1.01663354e-05, ...,\n",
       "          3.12993416e-06, -2.47644653e-06, -1.74351462e-05],\n",
       "        ...,\n",
       "        [-7.82312418e-05, -8.84316541e-05, -7.25687350e-05, ...,\n",
       "         -5.59540968e-05, -6.43768071e-05, -5.83703004e-05],\n",
       "        [-4.64423938e-05, -3.97967779e-05, -4.10194116e-05, ...,\n",
       "         -5.57383137e-05, -3.57918580e-05, -3.07407645e-05],\n",
       "        [-3.31291158e-05, -3.32790696e-05, -3.29218092e-05, ...,\n",
       "         -7.48870225e-05, -6.44866159e-05, -5.65312002e-05]]),\n",
       " '147': array([[-5.12838160e-05, -5.39926514e-05, -5.40772780e-05, ...,\n",
       "         -4.00387871e-05, -3.99629134e-05, -4.22041885e-05],\n",
       "        [-4.28633830e-05, -4.35945694e-05, -4.44291873e-05, ...,\n",
       "         -2.11779661e-05, -2.17895304e-05, -1.98169328e-05],\n",
       "        [-1.90078954e-05, -2.19529484e-05, -2.16677327e-05, ...,\n",
       "         -3.51428316e-05, -4.10569264e-05, -4.26093320e-05],\n",
       "        ...,\n",
       "        [ 4.25593898e-05,  7.06339488e-05,  6.48018613e-05, ...,\n",
       "         -1.38956584e-05,  9.91246543e-06,  4.16984512e-05],\n",
       "        [ 9.49278692e-05,  1.20363642e-04,  8.46721523e-05, ...,\n",
       "          7.07165891e-05,  4.54665787e-05,  4.45926453e-05],\n",
       "        [ 3.33268363e-05,  4.88056594e-06,  1.24683775e-05, ...,\n",
       "         -2.81136599e-05, -2.08918336e-05,  7.30763077e-07]]),\n",
       " '148': array([[ 1.38900796e-05,  9.98541964e-06, -3.81442146e-06, ...,\n",
       "         -2.03963828e-05,  1.93152955e-05,  4.84827506e-05],\n",
       "        [ 4.70890009e-05,  3.18990751e-05,  2.08512301e-05, ...,\n",
       "         -1.66991849e-05, -1.53034453e-05, -1.76299091e-05],\n",
       "        [-1.71827451e-05, -1.44046126e-05, -8.55559210e-06, ...,\n",
       "         -5.39917055e-05, -4.87849939e-05, -7.85934117e-06],\n",
       "        ...,\n",
       "        [ 1.12624721e-05, -6.80886660e-06, -1.45000404e-05, ...,\n",
       "          2.74640170e-05,  2.52969112e-05,  2.13346339e-05],\n",
       "        [ 1.57986833e-05,  9.95105620e-06,  5.98329916e-06, ...,\n",
       "          1.02916047e-05, -3.25493147e-06, -1.05165745e-05],\n",
       "        [ 1.38983512e-06,  1.89920138e-05,  3.25949550e-05, ...,\n",
       "          1.66009886e-05,  2.14347237e-05,  2.56509957e-05]]),\n",
       " '149': array([[-3.47537866e-06,  2.54095494e-06,  6.42117857e-06, ...,\n",
       "          6.94417395e-06, -1.78702535e-07, -7.30853117e-06],\n",
       "        [-2.01295152e-05, -4.07372172e-05, -5.61016423e-05, ...,\n",
       "         -1.14565883e-05, -2.23038660e-06, -2.42316150e-06],\n",
       "        [-5.12037013e-06,  8.63350670e-06,  3.00147294e-05, ...,\n",
       "          2.96995113e-05,  4.56898233e-05,  5.88274052e-05],\n",
       "        ...,\n",
       "        [ 9.29107409e-06,  1.38221112e-05, -2.09139162e-05, ...,\n",
       "          6.10801499e-06,  3.28105270e-05,  5.84097143e-05],\n",
       "        [ 5.99975392e-05,  3.99344972e-05,  1.53059300e-05, ...,\n",
       "          9.10861636e-05,  9.50766553e-05,  9.48053494e-05],\n",
       "        [ 8.41781803e-05,  5.77145765e-05,  3.49201000e-05, ...,\n",
       "          2.87773546e-05,  4.65875819e-05,  7.02277612e-05]]),\n",
       " '150': array([[-4.46163358e-05, -4.82384603e-05, -7.67671445e-05, ...,\n",
       "         -4.81635689e-05, -4.94925807e-05, -1.83603479e-05],\n",
       "        [ 1.70193052e-05,  2.85285896e-05,  1.79471081e-05, ...,\n",
       "         -3.13786541e-06,  9.77039690e-06,  6.20053197e-06],\n",
       "        [-1.77129266e-06, -1.09739476e-05, -2.96139806e-05, ...,\n",
       "          9.39764413e-06,  1.43153784e-05,  7.82688687e-07],\n",
       "        ...,\n",
       "        [-4.24212931e-06, -9.50474168e-06, -1.34332922e-05, ...,\n",
       "          1.01621060e-06,  1.71421220e-06,  3.36587368e-06],\n",
       "        [ 2.76548394e-06,  1.32161108e-06,  6.14672615e-07, ...,\n",
       "         -2.37382736e-07, -4.40113990e-06, -4.91296214e-06],\n",
       "        [ 3.73619713e-07,  2.58634373e-06,  8.28444939e-08, ...,\n",
       "          3.71120427e-06,  1.18557873e-05,  1.64275953e-05]]),\n",
       " '151': array([[-1.93557589e-05, -1.54981681e-05, -1.02991762e-05, ...,\n",
       "         -2.33006740e-05, -2.45692609e-05, -2.40626632e-05],\n",
       "        [-2.21342325e-05, -1.93308679e-05, -2.12528757e-05, ...,\n",
       "         -1.45540862e-05, -8.76865033e-06, -9.75723106e-06],\n",
       "        [-1.21619087e-05, -1.09799530e-05, -1.13630749e-05, ...,\n",
       "          2.40453483e-06,  3.47244907e-07,  1.62918627e-06],\n",
       "        ...,\n",
       "        [ 4.86803146e-05,  4.77707035e-05,  4.86161880e-05, ...,\n",
       "          3.61265629e-05,  3.48598405e-05,  3.36445373e-05],\n",
       "        [ 2.64384817e-05,  2.49261684e-05,  3.53046889e-05, ...,\n",
       "          3.90621899e-05,  3.14345089e-05,  2.79946980e-05],\n",
       "        [ 2.36055675e-05,  1.93322849e-05,  1.82696840e-05, ...,\n",
       "          2.21365844e-05,  2.30033347e-05,  2.20066977e-05]]),\n",
       " '152': array([[ 1.51891581e-05,  1.25122351e-05,  5.98668794e-06, ...,\n",
       "          2.74136582e-05,  2.35178195e-05,  1.81644991e-05],\n",
       "        [ 1.72576802e-05,  1.44695277e-05,  1.14613231e-05, ...,\n",
       "         -1.42667204e-05, -9.58854980e-06, -1.66700318e-06],\n",
       "        [ 3.14864633e-06,  6.35580091e-06,  4.88328760e-06, ...,\n",
       "          4.90890852e-06,  6.89108538e-06,  9.01807198e-06],\n",
       "        ...,\n",
       "        [ 4.26589431e-05,  4.30087130e-05,  4.74217741e-05, ...,\n",
       "          5.13165833e-05,  5.33698658e-05,  5.41021218e-05],\n",
       "        [ 4.99509042e-05,  4.18170057e-05,  3.89633133e-05, ...,\n",
       "          4.43789249e-05,  4.84146185e-05,  5.49516808e-05],\n",
       "        [ 5.91495700e-05,  6.34749595e-05,  6.46845874e-05, ...,\n",
       "          5.19330933e-05,  5.07111472e-05,  4.77887479e-05]]),\n",
       " '153': array([[-5.25217147e-05, -5.50536606e-05, -5.56521045e-05, ...,\n",
       "         -7.54999273e-05, -7.90726044e-05, -8.50658180e-05],\n",
       "        [-8.58461353e-05, -7.50385661e-05, -6.17210972e-05, ...,\n",
       "         -4.13737907e-05, -5.25261203e-05, -5.86645983e-05],\n",
       "        [-6.16251200e-05, -7.11667672e-05, -7.98758192e-05, ...,\n",
       "         -4.39498290e-05, -5.43998467e-05, -6.83877006e-05],\n",
       "        ...,\n",
       "        [-1.80085954e-05, -2.41090420e-05, -2.36373799e-05, ...,\n",
       "         -6.50567972e-06, -6.08012260e-06, -6.34416165e-06],\n",
       "        [-3.74894421e-06, -4.43157433e-06, -3.65045776e-06, ...,\n",
       "          2.96540065e-07, -6.27135933e-06, -1.02706481e-05],\n",
       "        [-1.23625477e-05, -1.16166375e-05, -9.17933994e-06, ...,\n",
       "         -5.28528381e-06, -4.77948606e-06, -5.36385596e-06]]),\n",
       " '154': array([[-2.50917583e-05, -2.49292261e-05, -1.70433650e-05, ...,\n",
       "          5.72606768e-05,  5.09825732e-05,  3.69826448e-05],\n",
       "        [ 2.51415368e-05,  1.47791052e-05,  2.61745345e-06, ...,\n",
       "         -4.60953815e-05, -3.47069108e-05, -3.51178969e-05],\n",
       "        [-4.33495225e-05, -4.32787929e-05, -3.86745924e-05, ...,\n",
       "         -4.27960331e-05, -4.09902823e-05, -3.25393448e-05],\n",
       "        ...,\n",
       "        [-2.04337357e-05, -2.24214473e-05, -1.93310425e-05, ...,\n",
       "         -1.84036307e-05, -1.06788648e-05, -6.63950505e-06],\n",
       "        [-1.36791562e-06,  4.18427180e-06,  3.19881087e-06, ...,\n",
       "         -2.09561676e-05, -1.50351007e-05, -1.10202091e-05],\n",
       "        [-1.06637590e-05, -9.97341158e-06, -7.43857072e-06, ...,\n",
       "         -6.11655287e-06, -7.71335908e-06, -1.29218151e-05]]),\n",
       " '155': array([[-8.74622401e-06, -1.36281287e-05, -1.54294376e-05, ...,\n",
       "         -1.64926951e-05, -8.64089998e-06,  2.02060733e-06],\n",
       "        [ 9.90901412e-07, -3.75741229e-06, -3.53215069e-06, ...,\n",
       "         -1.89614439e-05, -2.74256290e-05, -4.38288916e-05],\n",
       "        [-4.98056506e-05, -4.42893725e-05, -3.53628639e-05, ...,\n",
       "         -5.28318424e-06, -9.42320275e-06, -1.56234091e-05],\n",
       "        ...,\n",
       "        [-5.12375009e-05, -5.15637803e-05, -4.10381581e-05, ...,\n",
       "         -3.34350771e-05, -3.07754381e-05, -2.95755162e-05],\n",
       "        [-3.32255622e-05, -3.81443788e-05, -4.49894360e-05, ...,\n",
       "         -2.68897129e-05, -3.42371568e-05, -4.57217720e-05],\n",
       "        [-5.37001833e-05, -5.48671305e-05, -5.08206613e-05, ...,\n",
       "         -4.10626417e-05, -4.29714237e-05, -4.66380880e-05]]),\n",
       " '156': array([[ 2.37598942e-05,  2.35296866e-05,  1.91501949e-05, ...,\n",
       "         -1.76166632e-05, -1.68696297e-05, -1.56387832e-05],\n",
       "        [-1.49785546e-05, -1.49938651e-05, -1.29880818e-05, ...,\n",
       "         -3.42366820e-06, -5.49296146e-06, -6.30584327e-06],\n",
       "        [-6.64826985e-06, -8.82382028e-06, -7.40451742e-06, ...,\n",
       "          7.05499451e-06,  9.02731608e-06,  6.75518640e-06],\n",
       "        ...,\n",
       "        [ 5.62914065e-05,  5.09026540e-05,  4.47220846e-05, ...,\n",
       "          6.62961756e-05,  6.68753200e-05,  7.04006088e-05],\n",
       "        [ 6.89102235e-05,  6.21149738e-05,  5.73422876e-05, ...,\n",
       "          5.82258217e-05,  6.11111464e-05,  6.01372194e-05],\n",
       "        [ 5.68936048e-05,  5.52932688e-05,  5.48597454e-05, ...,\n",
       "          4.90131715e-05,  5.22128721e-05,  5.36417210e-05]]),\n",
       " '157': array([[-4.49566414e-06, -5.98414681e-06, -3.00209172e-06, ...,\n",
       "         -1.71684405e-05, -1.23315285e-05, -1.27633884e-05],\n",
       "        [-1.43288498e-05, -1.69716186e-05, -2.49226923e-05, ...,\n",
       "         -2.85278165e-05, -3.22658452e-05, -3.48352733e-05],\n",
       "        [-3.84217201e-05, -4.10000175e-05, -4.16941330e-05, ...,\n",
       "         -4.05487481e-05, -3.19401443e-05, -2.72518555e-05],\n",
       "        ...,\n",
       "        [ 3.36882877e-05,  3.49706825e-05,  3.49020665e-05, ...,\n",
       "          2.23112220e-05,  2.83066529e-05,  3.23252716e-05],\n",
       "        [ 2.98799678e-05,  2.57946340e-05,  2.37819877e-05, ...,\n",
       "          2.70589971e-05,  2.50369521e-05,  2.59651733e-05],\n",
       "        [ 2.64935807e-05,  2.87679486e-05,  3.00798110e-05, ...,\n",
       "          3.39074068e-05,  3.57103054e-05,  4.22236189e-05]]),\n",
       " '158': array([[-8.16927277e-05, -7.47003214e-05, -6.32795345e-05, ...,\n",
       "         -6.89316366e-05, -5.57242711e-05, -4.25403341e-05],\n",
       "        [-4.51667438e-05, -6.28178313e-05, -7.46248625e-05, ...,\n",
       "         -1.03904524e-04, -9.61280530e-05, -9.45685315e-05],\n",
       "        [-9.97586685e-05, -9.73103452e-05, -7.87149620e-05, ...,\n",
       "         -6.61714585e-05, -5.81915447e-05, -5.90486707e-05],\n",
       "        ...,\n",
       "        [ 1.92364041e-05,  2.13927106e-05,  2.11901261e-05, ...,\n",
       "          2.26664852e-05,  2.34203726e-05,  1.95115972e-05],\n",
       "        [ 1.76254762e-05,  1.97186018e-05,  1.84520231e-05, ...,\n",
       "          2.69126049e-05,  2.16343487e-05,  1.90825504e-05],\n",
       "        [ 2.12812029e-05,  2.34841591e-05,  2.51809488e-05, ...,\n",
       "          2.86535833e-05,  2.84140751e-05,  3.10662872e-05]]),\n",
       " '159': array([[ 2.78109364e-05,  2.66041898e-05,  2.66970183e-05, ...,\n",
       "          3.89291599e-05,  2.66988682e-05,  1.86705183e-05],\n",
       "        [ 1.48779909e-05,  1.44418191e-05,  2.13941057e-05, ...,\n",
       "          6.90777961e-06,  3.68806991e-06, -1.08449512e-06],\n",
       "        [-2.57407646e-06,  9.82183906e-07,  8.29442342e-06, ...,\n",
       "         -7.71510713e-06, -1.02953236e-05, -1.15305293e-05],\n",
       "        ...,\n",
       "        [-4.13523230e-05, -3.28771748e-05, -2.44914627e-05, ...,\n",
       "         -2.48884571e-05, -1.93075302e-05, -2.07475932e-05],\n",
       "        [-2.80659515e-05, -3.33368043e-05, -3.46649940e-05, ...,\n",
       "         -2.25120239e-05, -2.02806350e-05, -2.08738238e-05],\n",
       "        [-2.22739181e-05, -1.97465106e-05, -1.74344568e-05, ...,\n",
       "         -2.60566831e-05, -2.10280959e-05, -1.92720108e-05]])}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epoch_data_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "51666c3d-11fe-4982-a2f9-9ee357ade1e5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер массива 0: 3686600 байт\n",
      "Размер массива 1: 3686600 байт\n",
      "Размер массива 2: 3686600 байт\n",
      "Размер массива 3: 3686600 байт\n",
      "Размер массива 4: 3686600 байт\n",
      "Размер массива 5: 3686600 байт\n",
      "Размер массива 6: 3686600 байт\n",
      "Размер массива 7: 3686600 байт\n",
      "Размер массива 8: 3686600 байт\n",
      "Размер массива 9: 3686600 байт\n",
      "Размер массива 10: 3686600 байт\n",
      "Размер массива 11: 3686600 байт\n",
      "Размер массива 12: 3686600 байт\n",
      "Размер массива 13: 3686600 байт\n",
      "Размер массива 14: 3686600 байт\n",
      "Размер массива 15: 3686600 байт\n",
      "Размер массива 16: 3686600 байт\n",
      "Размер массива 17: 3686600 байт\n",
      "Размер массива 18: 3686600 байт\n",
      "Размер массива 19: 3686600 байт\n",
      "Размер массива 20: 3686600 байт\n",
      "Размер массива 21: 3686600 байт\n",
      "Размер массива 22: 3686600 байт\n",
      "Размер массива 23: 3686600 байт\n",
      "Размер массива 24: 3686600 байт\n",
      "Размер массива 25: 3686600 байт\n",
      "Размер массива 26: 3686600 байт\n",
      "Размер массива 27: 3686600 байт\n",
      "Размер массива 28: 3686600 байт\n",
      "Размер массива 29: 3686600 байт\n",
      "Размер массива 30: 3686600 байт\n",
      "Размер массива 31: 3686600 байт\n",
      "Размер массива 32: 3686600 байт\n",
      "Размер массива 33: 3686600 байт\n",
      "Размер массива 34: 3686600 байт\n",
      "Размер массива 35: 3686600 байт\n",
      "Размер массива 36: 3686600 байт\n",
      "Размер массива 37: 3686600 байт\n",
      "Размер массива 38: 3686600 байт\n",
      "Размер массива 39: 3686600 байт\n",
      "Размер массива 40: 3686600 байт\n",
      "Размер массива 41: 3686600 байт\n",
      "Размер массива 42: 3686600 байт\n",
      "Размер массива 43: 3686600 байт\n",
      "Размер массива 44: 3686600 байт\n",
      "Размер массива 45: 3686600 байт\n",
      "Размер массива 46: 3686600 байт\n",
      "Размер массива 47: 3686600 байт\n",
      "Размер массива 48: 3686600 байт\n",
      "Размер массива 49: 3686600 байт\n",
      "Размер массива 50: 3686600 байт\n",
      "Размер массива 51: 3686600 байт\n",
      "Размер массива 52: 3686600 байт\n",
      "Размер массива 53: 3686600 байт\n",
      "Размер массива 54: 3686600 байт\n",
      "Размер массива 55: 3686600 байт\n",
      "Размер массива 56: 3686600 байт\n",
      "Размер массива 57: 3686600 байт\n",
      "Размер массива 58: 3686600 байт\n",
      "Размер массива 59: 3686600 байт\n",
      "Размер массива 60: 3686600 байт\n",
      "Размер массива 61: 3686600 байт\n",
      "Размер массива 62: 3686600 байт\n",
      "Размер массива 63: 3686600 байт\n",
      "Размер массива 64: 3686600 байт\n",
      "Размер массива 65: 3686600 байт\n",
      "Размер массива 66: 3686600 байт\n",
      "Размер массива 67: 3686600 байт\n",
      "Размер массива 68: 3686600 байт\n",
      "Размер массива 69: 3686600 байт\n",
      "Размер массива 70: 3686600 байт\n",
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      "Размер массива 74: 3686600 байт\n",
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      "Размер массива 78: 3686600 байт\n",
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      "Размер массива 80: 3686600 байт\n",
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      "Размер массива 85: 3686600 байт\n",
      "Размер массива 86: 3686600 байт\n",
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      "Размер массива 90: 3686600 байт\n",
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      "Размер массива 99: 3686600 байт\n",
      "Размер массива 100: 3686600 байт\n",
      "Размер массива 101: 3686600 байт\n",
      "Размер массива 102: 3686600 байт\n",
      "Размер массива 103: 3686600 байт\n",
      "Размер массива 104: 3686600 байт\n",
      "Размер массива 105: 3686600 байт\n",
      "Размер массива 106: 3686600 байт\n",
      "Размер массива 107: 3686600 байт\n",
      "Размер массива 108: 3686600 байт\n",
      "Размер массива 109: 3686600 байт\n",
      "Размер массива 110: 3686600 байт\n",
      "Размер массива 111: 3686600 байт\n",
      "Размер массива 112: 3686600 байт\n",
      "Размер массива 113: 3686600 байт\n",
      "Размер массива 114: 3686600 байт\n",
      "Размер массива 115: 3686600 байт\n",
      "Размер массива 116: 3686600 байт\n",
      "Размер массива 117: 3686600 байт\n",
      "Размер массива 118: 3686600 байт\n",
      "Размер массива 119: 3686600 байт\n",
      "Размер массива 120: 3686600 байт\n",
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      "Размер массива 122: 3686600 байт\n",
      "Размер массива 123: 3686600 байт\n",
      "Размер массива 124: 3686600 байт\n",
      "Размер массива 125: 3686600 байт\n",
      "Размер массива 126: 3686600 байт\n",
      "Размер массива 127: 3686600 байт\n",
      "Размер массива 128: 3686600 байт\n",
      "Размер массива 129: 3686600 байт\n",
      "Размер массива 130: 3686600 байт\n",
      "Размер массива 131: 3686600 байт\n",
      "Размер массива 132: 3686600 байт\n",
      "Размер массива 133: 3686600 байт\n",
      "Размер массива 134: 3686600 байт\n",
      "Размер массива 135: 3686600 байт\n",
      "Размер массива 136: 3686600 байт\n",
      "Размер массива 137: 3686600 байт\n",
      "Размер массива 138: 3686600 байт\n",
      "Размер массива 139: 3686600 байт\n",
      "Размер массива 140: 3686600 байт\n",
      "Размер массива 141: 3686600 байт\n",
      "Размер массива 142: 3686600 байт\n",
      "Размер массива 143: 3686600 байт\n",
      "Размер массива 144: 3686600 байт\n",
      "Размер массива 145: 3686600 байт\n",
      "Размер массива 146: 3686600 байт\n",
      "Размер массива 147: 3686600 байт\n",
      "Размер массива 148: 3686600 байт\n",
      "Размер массива 149: 3686600 байт\n",
      "Размер массива 150: 3686600 байт\n",
      "Размер массива 151: 3686600 байт\n",
      "Размер массива 152: 3686600 байт\n",
      "Размер массива 153: 3686600 байт\n",
      "Размер массива 154: 3686600 байт\n",
      "Размер массива 155: 3686600 байт\n",
      "Размер массива 156: 3686600 байт\n",
      "Размер массива 157: 3686600 байт\n",
      "Размер массива 158: 3686600 байт\n",
      "Размер массива 159: 3686600 байт\n",
      "Общий размер всех массивов: 589856000 байт\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "total_size = 0\n",
    "for key, value in epoch_data_dict.items():\n",
    "    total_size += value.nbytes \n",
    "    print(f\"Размер массива {key}: {value.nbytes} байт\")\n",
    "\n",
    "print(f\"Общий размер всех массивов: {total_size} байт\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "65d96f3e-a0a5-4695-99ff-aabca9921e03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159'])\n"
     ]
    }
   ],
   "source": [
    "print(epoch_data_dict.keys())  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0152f1b4-ab1d-46af-8e4e-d695a0e1050d",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savez('epochs.npz', **epoch_data_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36575a40-79fa-4342-913b-d40f59742b34",
   "metadata": {},
   "source": [
    "Загружаем numpy arrays "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "5b368881-aa30-4d11-86d4-1d24e8e4c239",
   "metadata": {},
   "outputs": [],
   "source": [
    "epoch_data = np.load('epochs.npz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e7a42572-aa6e-4ba3-a12c-35260e855c64",
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = np.array([i for i in range(n_ep)])\n",
    "datas = [epoch_data[str(i)] for i in range(n_ep)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "d2036981-3ff5-46b9-9490-3b4960d91098",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "160"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(datas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "61980eae-4abd-4e6f-b3ac-cf1b1b6b34d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = np.tile(np.arange(n_ep), (n_ts, 1)).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "410485b0-12c5-4ee6-861f-f7884f419fe2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   0,   0, ...,   0,   0,   0],\n",
       "       [  1,   1,   1, ...,   1,   1,   1],\n",
       "       [  2,   2,   2, ...,   2,   2,   2],\n",
       "       ...,\n",
       "       [157, 157, 157, ..., 157, 157, 157],\n",
       "       [158, 158, 158, ..., 158, 158, 158],\n",
       "       [159, 159, 159, ..., 159, 159, 159]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3b7e779e-ba37-443e-a308-defee9207996",
   "metadata": {},
   "outputs": [],
   "source": [
    "multi_embeddings = dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ad6f4f90-7984-482c-85c3-c3ce51acd256",
   "metadata": {},
   "outputs": [],
   "source": [
    "multi_cebra_model = CEBRA(\n",
    "    model_architecture = \"offset10-model\",\n",
    "    batch_size = None,\n",
    "    temperature_mode=\"auto\",\n",
    "    learning_rate = 0.001,\n",
    "    max_iterations = 1000,\n",
    "    output_dimension = 3,\n",
    "    device = \"cpu\",\n",
    "    verbose = True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "5895388e-a97f-43b2-8f9c-d6df8bc4e97e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "157eb8ac-8b66-4146-bfa8-11bedc07c8dd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NotImplementedError",
     "evalue": "Multisession implementation does not support discrete index yet.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[91], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m multi_cebra_model\u001b[38;5;241m.\u001b[39mfit(datas, epochs)\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\cebra\\Lib\\site-packages\\cebra\\integrations\\sklearn\\cebra.py:1193\u001b[0m, in \u001b[0;36mCEBRA.fit\u001b[1;34m(self, X, adapt, callback, callback_frequency, *y)\u001b[0m\n\u001b[0;32m   1188\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_adapt_fit(X,\n\u001b[0;32m   1189\u001b[0m                     \u001b[38;5;241m*\u001b[39my,\n\u001b[0;32m   1190\u001b[0m                     callback\u001b[38;5;241m=\u001b[39mcallback,\n\u001b[0;32m   1191\u001b[0m                     callback_frequency\u001b[38;5;241m=\u001b[39mcallback_frequency)\n\u001b[0;32m   1192\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1193\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpartial_fit(X,\n\u001b[0;32m   1194\u001b[0m                      \u001b[38;5;241m*\u001b[39my,\n\u001b[0;32m   1195\u001b[0m                      callback\u001b[38;5;241m=\u001b[39mcallback,\n\u001b[0;32m   1196\u001b[0m                      callback_frequency\u001b[38;5;241m=\u001b[39mcallback_frequency)\n\u001b[0;32m   1197\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate_\n\u001b[0;32m   1199\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\cebra\\Lib\\site-packages\\cebra\\integrations\\sklearn\\cebra.py:1098\u001b[0m, in \u001b[0;36mCEBRA.partial_fit\u001b[1;34m(self, X, callback, callback_frequency, *y)\u001b[0m\n\u001b[0;32m   1066\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Partially fit the estimator to the given dataset.\u001b[39;00m\n\u001b[0;32m   1067\u001b[0m \n\u001b[0;32m   1068\u001b[0m \u001b[38;5;124;03mIt is useful when the whole dataset is too big to fit in memory at once.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1095\u001b[0m \n\u001b[0;32m   1096\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1097\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstate_\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate_ \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1098\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_fit(X, \u001b[38;5;241m*\u001b[39my)\n\u001b[0;32m   1099\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_partial_fit(\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate_,\n\u001b[0;32m   1100\u001b[0m                   callback\u001b[38;5;241m=\u001b[39mcallback,\n\u001b[0;32m   1101\u001b[0m                   callback_frequency\u001b[38;5;241m=\u001b[39mcallback_frequency)\n\u001b[0;32m   1102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\cebra\\Lib\\site-packages\\cebra\\integrations\\sklearn\\cebra.py:886\u001b[0m, in \u001b[0;36mCEBRA._prepare_fit\u001b[1;34m(self, X, *y)\u001b[0m\n\u001b[0;32m    884\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice_ \u001b[38;5;241m=\u001b[39m sklearn_utils\u001b[38;5;241m.\u001b[39mcheck_device(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[0;32m    885\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moffset_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compute_offset()\n\u001b[1;32m--> 886\u001b[0m dataset, is_multisession \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_data(X, y)\n\u001b[0;32m    888\u001b[0m loader, solver_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_loader(\n\u001b[0;32m    889\u001b[0m     dataset,\n\u001b[0;32m    890\u001b[0m     max_iterations\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_iterations,\n\u001b[0;32m    891\u001b[0m     is_multisession\u001b[38;5;241m=\u001b[39mis_multisession)\n\u001b[0;32m    892\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_model(dataset, is_multisession)\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\cebra\\Lib\\site-packages\\cebra\\integrations\\sklearn\\cebra.py:647\u001b[0m, in \u001b[0;36mCEBRA._prepare_data\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    644\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(X, \u001b[38;5;28mlist\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(X[\u001b[38;5;241m0\u001b[39m], Iterable) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\n\u001b[0;32m    645\u001b[0m         X[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[0;32m    646\u001b[0m     is_multisession \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m--> 647\u001b[0m     dataset \u001b[38;5;241m=\u001b[39m _get_dataset_multi(X, y)\n\u001b[0;32m    648\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    649\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _are_sessions_equal(X, y):\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\cebra\\Lib\\site-packages\\cebra\\integrations\\sklearn\\cebra.py:638\u001b[0m, in \u001b[0;36mCEBRA._prepare_data.<locals>._get_dataset_multi\u001b[1;34m(X, y)\u001b[0m\n\u001b[0;32m    632\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(X[session]) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(y[labels][session]):\n\u001b[0;32m    633\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    634\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid number of samples in session \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msession\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m for set of label \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlabels\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    635\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX has \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(X[session])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, while y has \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(y[labels][session])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    636\u001b[0m             )\n\u001b[1;32m--> 638\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cebra\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mDatasetCollection(\u001b[38;5;241m*\u001b[39m[\n\u001b[0;32m    639\u001b[0m     _get_dataset(X_session, (y_session,))\n\u001b[0;32m    640\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m X_session, y_session \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(X, \u001b[38;5;241m*\u001b[39my)\n\u001b[0;32m    641\u001b[0m ])\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\cebra\\Lib\\site-packages\\cebra\\data\\datasets.py:225\u001b[0m, in \u001b[0;36mDatasetCollection.__init__\u001b[1;34m(self, *datasets)\u001b[0m\n\u001b[0;32m    223\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cindex \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    224\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m discrete:\n\u001b[1;32m--> 225\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[0;32m    226\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMultisession implementation does not support discrete index yet.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    227\u001b[0m     )\n\u001b[0;32m    228\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    229\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dindex \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "\u001b[1;31mNotImplementedError\u001b[0m: Multisession implementation does not support discrete index yet."
     ]
    }
   ],
   "source": [
    "multi_cebra_model.fit(datas, epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36ebdde3-2ded-4962-b6b3-f7a9a74ac8e5",
   "metadata": {},
   "source": [
    "NotImplementedError: Multisession implementation does not support discrete index yet. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b02733dd-cda3-4663-8b6b-347f8dd4ffdc",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Попробуем в режиме single session, но через цикл"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b0a27c75-e7e8-459b-987f-a51b29664dc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "7de80319-1fc1-459f-a3d4-eb7800c279c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting CEBRA for [8 8 8 ... 8 8 8]\n"
     ]
    },
    {
     "ename": "NotImplementedError",
     "evalue": "Invalid index combination.\nContinuous: False,\nDiscrete: True,\nHybrid training: False,\nFull dataset: True. This index combination might still be implemented in the future. Until then, please train using the PyTorch API.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[97], line 16\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFitting CEBRA for \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      5\u001b[0m cebra_model \u001b[38;5;241m=\u001b[39m CEBRA(\n\u001b[0;32m      6\u001b[0m     model_architecture \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moffset10-model\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m      7\u001b[0m     batch_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     14\u001b[0m     time_offsets\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m\n\u001b[0;32m     15\u001b[0m     )\n\u001b[1;32m---> 16\u001b[0m cebra_model\u001b[38;5;241m.\u001b[39mfit(X, y)\n\u001b[0;32m     17\u001b[0m embeddings[y] \u001b[38;5;241m=\u001b[39m cebra_model\u001b[38;5;241m.\u001b[39mtransform(X)\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\cebra\\Lib\\site-packages\\cebra\\integrations\\sklearn\\cebra.py:1193\u001b[0m, in \u001b[0;36mCEBRA.fit\u001b[1;34m(self, X, adapt, callback, callback_frequency, *y)\u001b[0m\n\u001b[0;32m   1188\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_adapt_fit(X,\n\u001b[0;32m   1189\u001b[0m                     \u001b[38;5;241m*\u001b[39my,\n\u001b[0;32m   1190\u001b[0m                     callback\u001b[38;5;241m=\u001b[39mcallback,\n\u001b[0;32m   1191\u001b[0m                     callback_frequency\u001b[38;5;241m=\u001b[39mcallback_frequency)\n\u001b[0;32m   1192\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1193\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpartial_fit(X,\n\u001b[0;32m   1194\u001b[0m                      \u001b[38;5;241m*\u001b[39my,\n\u001b[0;32m   1195\u001b[0m                      callback\u001b[38;5;241m=\u001b[39mcallback,\n\u001b[0;32m   1196\u001b[0m                      callback_frequency\u001b[38;5;241m=\u001b[39mcallback_frequency)\n\u001b[0;32m   1197\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate_\n\u001b[0;32m   1199\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\cebra\\Lib\\site-packages\\cebra\\integrations\\sklearn\\cebra.py:1098\u001b[0m, in \u001b[0;36mCEBRA.partial_fit\u001b[1;34m(self, X, callback, callback_frequency, *y)\u001b[0m\n\u001b[0;32m   1066\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Partially fit the estimator to the given dataset.\u001b[39;00m\n\u001b[0;32m   1067\u001b[0m \n\u001b[0;32m   1068\u001b[0m \u001b[38;5;124;03mIt is useful when the whole dataset is too big to fit in memory at once.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1095\u001b[0m \n\u001b[0;32m   1096\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1097\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstate_\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate_ \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1098\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_fit(X, \u001b[38;5;241m*\u001b[39my)\n\u001b[0;32m   1099\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_partial_fit(\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate_,\n\u001b[0;32m   1100\u001b[0m                   callback\u001b[38;5;241m=\u001b[39mcallback,\n\u001b[0;32m   1101\u001b[0m                   callback_frequency\u001b[38;5;241m=\u001b[39mcallback_frequency)\n\u001b[0;32m   1102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\cebra\\Lib\\site-packages\\cebra\\integrations\\sklearn\\cebra.py:888\u001b[0m, in \u001b[0;36mCEBRA._prepare_fit\u001b[1;34m(self, X, *y)\u001b[0m\n\u001b[0;32m    885\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moffset_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compute_offset()\n\u001b[0;32m    886\u001b[0m dataset, is_multisession \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_data(X, y)\n\u001b[1;32m--> 888\u001b[0m loader, solver_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_loader(\n\u001b[0;32m    889\u001b[0m     dataset,\n\u001b[0;32m    890\u001b[0m     max_iterations\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_iterations,\n\u001b[0;32m    891\u001b[0m     is_multisession\u001b[38;5;241m=\u001b[39mis_multisession)\n\u001b[0;32m    892\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_model(dataset, is_multisession)\n\u001b[0;32m    894\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_configure_for_all(dataset, model, is_multisession)\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\cebra\\Lib\\site-packages\\cebra\\integrations\\sklearn\\cebra.py:678\u001b[0m, in \u001b[0;36mCEBRA._prepare_loader\u001b[1;34m(self, dataset, max_iterations, is_multisession)\u001b[0m\n\u001b[0;32m    667\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_prepare_loader\u001b[39m(\u001b[38;5;28mself\u001b[39m, dataset: cebra\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mDataset, max_iterations: \u001b[38;5;28mint\u001b[39m,\n\u001b[0;32m    668\u001b[0m                     is_multisession: \u001b[38;5;28mbool\u001b[39m):\n\u001b[0;32m    669\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Prepare the data loader based on the dataset properties.\u001b[39;00m\n\u001b[0;32m    670\u001b[0m \n\u001b[0;32m    671\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    676\u001b[0m \u001b[38;5;124;03m        A data loader.\u001b[39;00m\n\u001b[0;32m    677\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 678\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _init_loader(\n\u001b[0;32m    679\u001b[0m         is_cont\u001b[38;5;241m=\u001b[39mdataset\u001b[38;5;241m.\u001b[39mcontinuous_index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m    680\u001b[0m         is_disc\u001b[38;5;241m=\u001b[39mdataset\u001b[38;5;241m.\u001b[39mdiscrete_index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m    681\u001b[0m         is_hybrid\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhybrid,\n\u001b[0;32m    682\u001b[0m         is_full\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbatch_size \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m    683\u001b[0m         is_multi\u001b[38;5;241m=\u001b[39mis_multisession,\n\u001b[0;32m    684\u001b[0m         shared_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mdict\u001b[39m(\n\u001b[0;32m    685\u001b[0m             dataset\u001b[38;5;241m=\u001b[39mdataset,\n\u001b[0;32m    686\u001b[0m             batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbatch_size,\n\u001b[0;32m    687\u001b[0m             num_steps\u001b[38;5;241m=\u001b[39mmax_iterations,\n\u001b[0;32m    688\u001b[0m         ),\n\u001b[0;32m    689\u001b[0m         extra_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mdict\u001b[39m(\n\u001b[0;32m    690\u001b[0m             time_offsets\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtime_offsets,\n\u001b[0;32m    691\u001b[0m             conditional\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconditional,\n\u001b[0;32m    692\u001b[0m             delta\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdelta,\n\u001b[0;32m    693\u001b[0m         ),\n\u001b[0;32m    694\u001b[0m     )\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\cebra\\Lib\\site-packages\\cebra\\integrations\\sklearn\\cebra.py:267\u001b[0m, in \u001b[0;36m_init_loader\u001b[1;34m(is_cont, is_disc, is_full, is_multi, is_hybrid, shared_kwargs, extra_kwargs)\u001b[0m\n\u001b[0;32m    260\u001b[0m error_message \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid index combination.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    261\u001b[0m                  \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mContinuous: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mis_cont\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m,\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    262\u001b[0m                  \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDiscrete: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mis_disc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m,\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    263\u001b[0m                  \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHybrid training: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mis_hybrid\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m,\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    264\u001b[0m                  \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFull dataset: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mis_full\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    266\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m raise_not_implemented_error:\n\u001b[1;32m--> 267\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(error_message \u001b[38;5;241m+\u001b[39m (\n\u001b[0;32m    268\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    269\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThis index combination might still be implemented in the future. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    270\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUntil then, please train using the PyTorch API.\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[0;32m    271\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    272\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m    273\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIndex combination not covered. Please report this issue and add the following \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    274\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minformation to your bug report: \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m error_message)\n",
      "\u001b[1;31mNotImplementedError\u001b[0m: Invalid index combination.\nContinuous: False,\nDiscrete: True,\nHybrid training: False,\nFull dataset: True. This index combination might still be implemented in the future. Until then, please train using the PyTorch API."
     ]
    }
   ],
   "source": [
    "for X, y in zip(datas, y_train):\n",
    "    print(f\"Fitting CEBRA for {y}\")\n",
    "    cebra_model = CEBRA(\n",
    "        model_architecture = \"offset10-model\",\n",
    "        batch_size = None,\n",
    "        temperature_mode=\"auto\",\n",
    "        learning_rate = 0.001,\n",
    "        max_iterations = 1000,\n",
    "        output_dimension = 3,\n",
    "        device = \"cpu\",\n",
    "        verbose = True,\n",
    "        time_offsets=10\n",
    "        )\n",
    "    cebra_model.fit(X, y)\n",
    "    embeddings[y] = cebra_model.transform(X)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb3c38b8-36bf-4eee-9f3f-50da1de164e1",
   "metadata": {},
   "source": [
    "## попроубем взять один электрод"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "ada2f44f-6395-4c79-bf92-ba07d750f7c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "eeg_data=eeg.get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "6e01e474-cc7f-4f65-abad-8694dd989823",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_ep, n_ch, n_ts = eeg_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "0dc2e69f-8caf-492c-bf1b-4bad77239566",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      5\n",
       "1      2\n",
       "2      6\n",
       "3      7\n",
       "4      9\n",
       "      ..\n",
       "195    5\n",
       "196    3\n",
       "197    2\n",
       "198    6\n",
       "199    3\n",
       "Name: Digit_text, Length: 200, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "8e5c50bd-0f61-4f31-8653-d797e3c04689",
   "metadata": {},
   "outputs": [],
   "source": [
    "electrode = eeg_data[:, 0, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "ddc77489-c746-4a69-8ea6-1e9b495d7696",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.70633029e-06,  2.49324944e-06,  2.58589262e-06, ...,\n",
       "         4.06469517e-05,  3.88408153e-05,  3.64769985e-05],\n",
       "       [-4.44137549e-05, -4.70899686e-05, -5.05319695e-05, ...,\n",
       "         4.73775908e-05,  6.89116496e-05,  8.55451726e-05],\n",
       "       [ 1.04983541e-04,  1.11842244e-04,  1.16244701e-04, ...,\n",
       "        -9.95283790e-06, -8.08455115e-06, -6.68892062e-06],\n",
       "       ...,\n",
       "       [ 1.46575785e-05,  1.56517308e-05,  1.33044005e-05, ...,\n",
       "        -1.30513254e-05, -5.70572911e-06, -7.66453923e-06],\n",
       "       [ 3.86848515e-05,  3.86769279e-05,  3.09184579e-05, ...,\n",
       "        -4.42588171e-05, -5.00002971e-05, -5.12587831e-05],\n",
       "       [-1.93557589e-05, -1.54981681e-05, -1.02991762e-05, ...,\n",
       "         4.04940038e-05,  4.17977135e-05,  3.78237455e-05]])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "electrode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "40041026-a99e-4590-b1ed-70210c999649",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      5\n",
       "1      2\n",
       "2      6\n",
       "3      7\n",
       "4      9\n",
       "      ..\n",
       "195    5\n",
       "196    3\n",
       "197    2\n",
       "198    6\n",
       "199    3\n",
       "Name: Digit_text, Length: 200, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "352281f5-f284-4f88-a084-4fc64968cf35",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(electrode, labels, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c21e9200-f4ca-4f9d-8011-bfe9affeeca4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.array(y_train), columns=[\"discrete\"])\n",
    "df.to_hdf(\"labels.h5\", key=\"auxiliary_variables\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "68e3ca02-2ccc-46ad-a0b5-d0991e1ed829",
   "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>discrete</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>160 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     discrete\n",
       "0           8\n",
       "1           2\n",
       "2           3\n",
       "3           2\n",
       "4           2\n",
       "..        ...\n",
       "155         0\n",
       "156         7\n",
       "157         2\n",
       "158         8\n",
       "159         1\n",
       "\n",
       "[160 rows x 1 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "3a1290f7-57ab-4aba-9cd1-6f97952518fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(160, 18433)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "3299a5dd-fe39-42a7-9172-cc11e4da0ac9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(160,)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "cb182fde-4961-4a0b-a00e-77f003481863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-4.06555264e-05, -4.09400600e-05, -4.38263705e-05, ...,\n",
       "        -8.92812895e-05, -7.80913615e-05, -6.16725374e-05],\n",
       "       [ 1.46575785e-05,  1.56517308e-05,  1.33044005e-05, ...,\n",
       "        -1.30513254e-05, -5.70572911e-06, -7.66453923e-06],\n",
       "       [-7.35473513e-05, -8.35613100e-05, -8.61345688e-05, ...,\n",
       "         5.89047268e-05,  6.46356129e-05,  6.40744474e-05],\n",
       "       ...,\n",
       "       [-4.49566414e-06, -5.98414681e-06, -3.00209172e-06, ...,\n",
       "         5.88866715e-05,  5.85688176e-05,  6.54270189e-05],\n",
       "       [-8.16927277e-05, -7.47003214e-05, -6.32795345e-05, ...,\n",
       "        -1.55098423e-05, -1.38074111e-05, -1.08755376e-05],\n",
       "       [ 2.78109364e-05,  2.66041898e-05,  2.66970183e-05, ...,\n",
       "        -4.87716388e-05, -4.49112267e-05, -4.40310832e-05]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "8035d032-4f86-488d-92b1-c76e1f82bf04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79     8\n",
       "197    2\n",
       "38     3\n",
       "24     2\n",
       "122    2\n",
       "      ..\n",
       "106    0\n",
       "14     7\n",
       "92     2\n",
       "179    8\n",
       "102    1\n",
       "Name: Digit_text, Length: 160, dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "58fda323-05d0-4438-8207-865b405a31b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "cebra_model = CEBRA(\n",
    "    model_architecture = \"offset10-model\",\n",
    "    batch_size = None,\n",
    "    temperature_mode=\"auto\",\n",
    "    learning_rate = 0.001,\n",
    "    max_iterations = 1000,\n",
    "    output_dimension = 8,\n",
    "    device = \"cpu\",\n",
    "    verbose = True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "9f05ff69-8f0d-4c9e-ac77-162419a70ace",
   "metadata": {},
   "outputs": [
    {
     "ename": "NotImplementedError",
     "evalue": "Invalid index combination.\nContinuous: False,\nDiscrete: True,\nHybrid training: False,\nFull dataset: True. This index combination might still be implemented in the future. Until then, please train using the PyTorch API.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[95], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m cebra_model\u001b[38;5;241m.\u001b[39mfit(X_train, y_train)\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\cebra\\Lib\\site-packages\\cebra\\integrations\\sklearn\\cebra.py:1193\u001b[0m, in \u001b[0;36mCEBRA.fit\u001b[1;34m(self, X, adapt, callback, callback_frequency, *y)\u001b[0m\n\u001b[0;32m   1188\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_adapt_fit(X,\n\u001b[0;32m   1189\u001b[0m                     \u001b[38;5;241m*\u001b[39my,\n\u001b[0;32m   1190\u001b[0m                     callback\u001b[38;5;241m=\u001b[39mcallback,\n\u001b[0;32m   1191\u001b[0m                     callback_frequency\u001b[38;5;241m=\u001b[39mcallback_frequency)\n\u001b[0;32m   1192\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1193\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpartial_fit(X,\n\u001b[0;32m   1194\u001b[0m                      \u001b[38;5;241m*\u001b[39my,\n\u001b[0;32m   1195\u001b[0m                      callback\u001b[38;5;241m=\u001b[39mcallback,\n\u001b[0;32m   1196\u001b[0m                      callback_frequency\u001b[38;5;241m=\u001b[39mcallback_frequency)\n\u001b[0;32m   1197\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate_\n\u001b[0;32m   1199\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\cebra\\Lib\\site-packages\\cebra\\integrations\\sklearn\\cebra.py:1098\u001b[0m, in \u001b[0;36mCEBRA.partial_fit\u001b[1;34m(self, X, callback, callback_frequency, *y)\u001b[0m\n\u001b[0;32m   1066\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Partially fit the estimator to the given dataset.\u001b[39;00m\n\u001b[0;32m   1067\u001b[0m \n\u001b[0;32m   1068\u001b[0m \u001b[38;5;124;03mIt is useful when the whole dataset is too big to fit in memory at once.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1095\u001b[0m \n\u001b[0;32m   1096\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1097\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstate_\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate_ \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1098\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_fit(X, \u001b[38;5;241m*\u001b[39my)\n\u001b[0;32m   1099\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_partial_fit(\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate_,\n\u001b[0;32m   1100\u001b[0m                   callback\u001b[38;5;241m=\u001b[39mcallback,\n\u001b[0;32m   1101\u001b[0m                   callback_frequency\u001b[38;5;241m=\u001b[39mcallback_frequency)\n\u001b[0;32m   1102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\cebra\\Lib\\site-packages\\cebra\\integrations\\sklearn\\cebra.py:888\u001b[0m, in \u001b[0;36mCEBRA._prepare_fit\u001b[1;34m(self, X, *y)\u001b[0m\n\u001b[0;32m    885\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moffset_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compute_offset()\n\u001b[0;32m    886\u001b[0m dataset, is_multisession \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_data(X, y)\n\u001b[1;32m--> 888\u001b[0m loader, solver_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_loader(\n\u001b[0;32m    889\u001b[0m     dataset,\n\u001b[0;32m    890\u001b[0m     max_iterations\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_iterations,\n\u001b[0;32m    891\u001b[0m     is_multisession\u001b[38;5;241m=\u001b[39mis_multisession)\n\u001b[0;32m    892\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_model(dataset, is_multisession)\n\u001b[0;32m    894\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_configure_for_all(dataset, model, is_multisession)\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\cebra\\Lib\\site-packages\\cebra\\integrations\\sklearn\\cebra.py:678\u001b[0m, in \u001b[0;36mCEBRA._prepare_loader\u001b[1;34m(self, dataset, max_iterations, is_multisession)\u001b[0m\n\u001b[0;32m    667\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_prepare_loader\u001b[39m(\u001b[38;5;28mself\u001b[39m, dataset: cebra\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mDataset, max_iterations: \u001b[38;5;28mint\u001b[39m,\n\u001b[0;32m    668\u001b[0m                     is_multisession: \u001b[38;5;28mbool\u001b[39m):\n\u001b[0;32m    669\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Prepare the data loader based on the dataset properties.\u001b[39;00m\n\u001b[0;32m    670\u001b[0m \n\u001b[0;32m    671\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    676\u001b[0m \u001b[38;5;124;03m        A data loader.\u001b[39;00m\n\u001b[0;32m    677\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 678\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _init_loader(\n\u001b[0;32m    679\u001b[0m         is_cont\u001b[38;5;241m=\u001b[39mdataset\u001b[38;5;241m.\u001b[39mcontinuous_index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m    680\u001b[0m         is_disc\u001b[38;5;241m=\u001b[39mdataset\u001b[38;5;241m.\u001b[39mdiscrete_index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m    681\u001b[0m         is_hybrid\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhybrid,\n\u001b[0;32m    682\u001b[0m         is_full\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbatch_size \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m    683\u001b[0m         is_multi\u001b[38;5;241m=\u001b[39mis_multisession,\n\u001b[0;32m    684\u001b[0m         shared_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mdict\u001b[39m(\n\u001b[0;32m    685\u001b[0m             dataset\u001b[38;5;241m=\u001b[39mdataset,\n\u001b[0;32m    686\u001b[0m             batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbatch_size,\n\u001b[0;32m    687\u001b[0m             num_steps\u001b[38;5;241m=\u001b[39mmax_iterations,\n\u001b[0;32m    688\u001b[0m         ),\n\u001b[0;32m    689\u001b[0m         extra_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mdict\u001b[39m(\n\u001b[0;32m    690\u001b[0m             time_offsets\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtime_offsets,\n\u001b[0;32m    691\u001b[0m             conditional\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconditional,\n\u001b[0;32m    692\u001b[0m             delta\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdelta,\n\u001b[0;32m    693\u001b[0m         ),\n\u001b[0;32m    694\u001b[0m     )\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\cebra\\Lib\\site-packages\\cebra\\integrations\\sklearn\\cebra.py:267\u001b[0m, in \u001b[0;36m_init_loader\u001b[1;34m(is_cont, is_disc, is_full, is_multi, is_hybrid, shared_kwargs, extra_kwargs)\u001b[0m\n\u001b[0;32m    260\u001b[0m error_message \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid index combination.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    261\u001b[0m                  \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mContinuous: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mis_cont\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m,\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    262\u001b[0m                  \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDiscrete: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mis_disc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m,\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    263\u001b[0m                  \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHybrid training: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mis_hybrid\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m,\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    264\u001b[0m                  \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFull dataset: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mis_full\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    266\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m raise_not_implemented_error:\n\u001b[1;32m--> 267\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(error_message \u001b[38;5;241m+\u001b[39m (\n\u001b[0;32m    268\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    269\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThis index combination might still be implemented in the future. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    270\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUntil then, please train using the PyTorch API.\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[0;32m    271\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    272\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m    273\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIndex combination not covered. Please report this issue and add the following \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    274\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minformation to your bug report: \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m error_message)\n",
      "\u001b[1;31mNotImplementedError\u001b[0m: Invalid index combination.\nContinuous: False,\nDiscrete: True,\nHybrid training: False,\nFull dataset: True. This index combination might still be implemented in the future. Until then, please train using the PyTorch API."
     ]
    }
   ],
   "source": [
    "cebra_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "5ce25e4f-8ccb-47cf-b16d-f4e84e60f16d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "pos: -2.5903 neg:  4.1794 total:  1.5891 temperature:  0.3421: 100%|███████████████| 1000/1000 [00:28<00:00, 35.65it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>CEBRA(device=&#x27;cpu&#x27;, learning_rate=0.001, max_iterations=1000,\n",
       "      model_architecture=&#x27;offset10-model&#x27;, temperature_mode=&#x27;auto&#x27;,\n",
       "      verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;CEBRA<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>CEBRA(device=&#x27;cpu&#x27;, learning_rate=0.001, max_iterations=1000,\n",
       "      model_architecture=&#x27;offset10-model&#x27;, temperature_mode=&#x27;auto&#x27;,\n",
       "      verbose=True)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "CEBRA(device='cpu', learning_rate=0.001, max_iterations=1000,\n",
       "      model_architecture='offset10-model', temperature_mode='auto',\n",
       "      verbose=True)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cebra_model.fit(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "282b01d7-0538-4bed-9b9d-b4c060ae6549",
   "metadata": {},
   "outputs": [],
   "source": [
    "emb_test = cebra_model.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "2eee2739-dbbb-4ab7-91e8-9c482b6b689f",
   "metadata": {},
   "outputs": [],
   "source": [
    "emb = cebra_model.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "5810d184-8477-4995-87c1-20d524b60a76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.22666234,  0.08511046,  0.24147899, ...,  0.0172937 ,\n",
       "        -0.65924025, -0.26834229],\n",
       "       [ 0.52024376, -0.21834224,  0.22291146, ...,  0.16682354,\n",
       "        -0.61130458, -0.15557119],\n",
       "       [ 0.43894243, -0.43459886,  0.20035638, ..., -0.02292271,\n",
       "        -0.57830995, -0.18411285],\n",
       "       ...,\n",
       "       [ 0.42145878,  0.58107758,  0.19330342, ...,  0.13897166,\n",
       "         0.31313553, -0.3078568 ],\n",
       "       [ 0.72798061,  0.21288837,  0.13821149, ...,  0.12361278,\n",
       "        -0.30943346, -0.05376828],\n",
       "       [ 0.38150069, -0.05597585,  0.20044899, ...,  0.05469256,\n",
       "        -0.44078547, -0.1229077 ]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "997078de-21ba-4ae3-a734-3f81e47f257b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: plotly in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (5.24.1)\n",
      "Requirement already satisfied: tenacity>=6.2.0 in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from plotly) (9.0.0)\n",
      "Requirement already satisfied: packaging in c:\\users\\khani\\anaconda3\\envs\\cebra\\lib\\site-packages (from plotly) (24.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "52a068a9-95f6-4741-865b-4dab0dbe7ccf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting umap-learn\n",
      "  Downloading umap_learn-0.5.6-py3-none-any.whl.metadata (21 kB)\n",
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      "Installing collected packages: llvmlite, numba, pynndescent, umap-learn\n",
      "Successfully installed llvmlite-0.43.0 numba-0.60.0 pynndescent-0.5.13 umap-learn-0.5.6\n"
     ]
    }
   ],
   "source": [
    "!pip install umap-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "267208ce-291d-4104-a132-58db1269c094",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\khani\\anaconda3\\envs\\cebra\\Lib\\site-packages\\sklearn\\manifold\\_t_sne.py:1162: FutureWarning:\n",
      "\n",
      "'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import umap\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "\n",
    "tsne = TSNE(n_components=2, perplexity=30, n_iter=1000, random_state=42)\n",
    "embeddings_2d = tsne.fit_transform(emb)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "scatter = plt.scatter(embeddings_2d[:, 0], embeddings_2d[:, 1], s=50)\n",
    "\n",
    "plt.title(\"t-SNE Visualization of Embeddings\")\n",
    "plt.xlabel(\"Component 1\")\n",
    "plt.ylabel(\"Component 2\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "48b97896-cba9-4153-a12e-bbb6831644fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c044f2f2-4c5c-4108-b2d2-0cab6e33ffcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\khani\\AppData\\Local\\Temp\\ipykernel_23376\\430317278.py:11: UserWarning:\n",
      "\n",
      "No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "x = emb[:, 0]\n",
    "y = emb[:, 1]\n",
    "z = emb[:, 2]\n",
    "\n",
    "sc = ax.scatter(x, y, z, cmap='viridis', s=50) \n",
    "plt.colorbar(sc)\n",
    "ax.set_xlabel('Embedding Dimension 1')\n",
    "ax.set_ylabel('Embedding Dimension 2')\n",
    "ax.set_zlabel('Embedding Dimension 3')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "94dff314-a9c8-4c50-b6b0-05505497ba16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.1\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "clf = LogisticRegression()\n",
    "clf.fit(emb, y_train)\n",
    "y_pred = clf.predict(emb_test)\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79e6967c-24bc-4bd7-82b6-9ed4ce3e31f4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}