data_analysis_per_class.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizer\n",
    "### Use this script to visualize the neuron traces according to the paper \"A Generalized Linear Integrate-and-Fire Neural Model Produces Diverse Spiking Behaviors\" by Stefan Mihalas and Ernst Niebur. Further, data was created with a fix length of 1sec (1ms time steps), with noise on the input current, and/or temporal jitter on the time point of the step for dynamic inputs. \n",
    "\n",
    "### The script will also calculate the inter-spike intervalls (ISIs) for a single trial and for all repeating trials, whenever possible. For repeating trials, all ISIs are grouped and further statics represent the outcome of all repetitions per class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import torch\n",
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import numpy.polynomial.polynomial as poly\n",
    "\n",
    "from tactile_encoding.utils.utils import value2key, create_directory\n",
    "from utils.functions import return_isi_fix_len\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "data_path_braille = './data/braille_mn_output'  # path to output from Braille data\n",
    "data_path_original = './data/original_mn_output'  # path to output from MN paper\n",
    "plot_out = './plots/braille'  # path to save plots\n",
    "create_directory(plot_out)\n",
    "data_types = ['', '_noisy', '_temp_jitter', '_offset', '_noisy_temp_jitter',\n",
    "              '_noisy_offset', '_temp_jitter_offset', '_noisy_temp_jitter_offset']\n",
    "\n",
    "braille_letters = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L',\n",
    "                   'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', 'Space']\n",
    "\n",
    "max_trials = 100\n",
    "\n",
    "classes_list = {\n",
    "    'A': \"Tonic spiking\",\n",
    "    'B': \"Class 1\",\n",
    "    'C': \"Spike frequency adaptation\",\n",
    "    'D': \"Phasic spiking\",\n",
    "    'E': \"Accommodation\",\n",
    "    'F': \"Threshold variability\",\n",
    "    'G': \"Rebound spike\",\n",
    "    'H': \"Class 2\",\n",
    "    'I': \"Integrator\",\n",
    "    'J': \"Input bistability\",\n",
    "    'K': \"Hyperpolarizing spiking\",\n",
    "    'L': \"Hyperpolarizing bursting\",\n",
    "    'M': \"Tonic bursting\",\n",
    "    'N': \"Phasic bursting\",\n",
    "    'O': \"Rebound burst\",\n",
    "    'P': \"Mixed mode\",\n",
    "    'Q': \"Afterpotentials\",\n",
    "    'R': \"Basal bistability\",\n",
    "    'S': \"Preferred frequency\",\n",
    "    'T': \"Spike latency\",\n",
    "}\n",
    "\n",
    "'''\n",
    "neuron parameters:\n",
    "a: 2.743\n",
    "A1: 0.03712\n",
    "A2: -0.5089\n",
    "b: 11.4\n",
    "G: 47.02\n",
    "k1: 200\n",
    "k2: 20\n",
    "R1: 0\n",
    "R2: 1\n",
    "'''\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre-processing\n",
    "### extract and concatenate all ISIs for each label and sensor over all data after training and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load ISIs from paper\n",
    "norm_count = True\n",
    "norm_time = True\n",
    "# for possible datatypes look at the top\n",
    "filename = 'data_encoding_fix_len_noisy_temp_jitter_offset'\n",
    "infile = open(f\"{data_path_original}/{filename}.pkl\", 'rb')\n",
    "data_original = pickle.load(infile)\n",
    "infile.close()\n",
    "isi_original = return_isi_fix_len(\n",
    "    data_original, max_trials, norm_count=norm_count, norm_time=norm_time)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare to paper output"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Per class and channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_names = os.listdir(data_path_braille)\n",
    "file_names = np.sort(file_names)\n",
    "isi_list = []\n",
    "isi_dict = {}\n",
    "for entry in range(27):\n",
    "    isi_dict[entry] = []\n",
    "for _, file_name in enumerate(file_names):\n",
    "    [mn_spk, input_current, trial_label] = torch.load(\n",
    "        data_path_braille + '/' + file_name, map_location=torch.device('cpu'))\n",
    "    # convert to numpy\n",
    "    mn_spk = mn_spk.numpy()\n",
    "    input_current = input_current.numpy()\n",
    "    trial_label = trial_label.numpy()\n",
    "\n",
    "    # extract traces from single batch\n",
    "    for batch in range(mn_spk.shape[0]):\n",
    "        isi_list = []\n",
    "\n",
    "        # loop over all channels\n",
    "        for channel in range(mn_spk[batch].shape[-1]):\n",
    "            # calc ISI per channel and append to list\n",
    "            isi_list.append(np.diff(np.where(mn_spk[batch][:, channel] == 1.0))*1E-2)\n",
    "\n",
    "        # init dict per label\n",
    "        if len(isi_dict[trial_label[batch]]) < 1:\n",
    "            isi_dict[trial_label[batch]] = isi_list\n",
    "        # extend dict if contains already values\n",
    "        else:\n",
    "            # extend the dict for each channel\n",
    "            for entry in range(len(isi_dict[trial_label[batch]])):\n",
    "                if len(isi_list[entry]) > 0:\n",
    "                    isi_dict[trial_label[batch]][entry] = np.append(isi_dict[trial_label[batch]][entry], isi_list[entry], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[7], line 29\u001b[0m\n\u001b[1;32m     26\u001b[0m     plt\u001b[39m.\u001b[39mscatter(isi, count)\n\u001b[1;32m     27\u001b[0m     plt\u001b[39m.\u001b[39mxlim((\u001b[39m0\u001b[39m, \u001b[39m1.1\u001b[39m))\n\u001b[0;32m---> 29\u001b[0m plt\u001b[39m.\u001b[39;49mtight_layout()\n\u001b[1;32m     30\u001b[0m plt\u001b[39m.\u001b[39msavefig(\n\u001b[1;32m     31\u001b[0m     \u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mplot_out\u001b[39m}\u001b[39;00m\u001b[39m/comparison_class_\u001b[39m\u001b[39m{\u001b[39;00mbraille_letters[entry]\u001b[39m}\u001b[39;00m\u001b[39m_channel_\u001b[39m\u001b[39m{\u001b[39;00mchannel\u001b[39m}\u001b[39;00m\u001b[39m_scatter.png\u001b[39m\u001b[39m'\u001b[39m, dpi\u001b[39m=\u001b[39m\u001b[39m300\u001b[39m)\n\u001b[1;32m     32\u001b[0m plt\u001b[39m.\u001b[39mclose()\n",
      "File \u001b[0;32m~/.virtualenvs/learning/lib/python3.8/site-packages/matplotlib/pyplot.py:2349\u001b[0m, in \u001b[0;36mtight_layout\u001b[0;34m(pad, h_pad, w_pad, rect)\u001b[0m\n\u001b[1;32m   2347\u001b[0m \u001b[39m@_copy_docstring_and_deprecators\u001b[39m(Figure\u001b[39m.\u001b[39mtight_layout)\n\u001b[1;32m   2348\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mtight_layout\u001b[39m(\u001b[39m*\u001b[39m, pad\u001b[39m=\u001b[39m\u001b[39m1.08\u001b[39m, h_pad\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, w_pad\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, rect\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[0;32m-> 2349\u001b[0m     \u001b[39mreturn\u001b[39;00m gcf()\u001b[39m.\u001b[39;49mtight_layout(pad\u001b[39m=\u001b[39;49mpad, h_pad\u001b[39m=\u001b[39;49mh_pad, w_pad\u001b[39m=\u001b[39;49mw_pad, rect\u001b[39m=\u001b[39;49mrect)\n",
      "File \u001b[0;32m~/.virtualenvs/learning/lib/python3.8/site-packages/matplotlib/figure.py:3509\u001b[0m, in \u001b[0;36mFigure.tight_layout\u001b[0;34m(self, pad, h_pad, w_pad, rect)\u001b[0m\n\u001b[1;32m   3507\u001b[0m previous_engine \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_layout_engine()\n\u001b[1;32m   3508\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mset_layout_engine(engine)\n\u001b[0;32m-> 3509\u001b[0m engine\u001b[39m.\u001b[39;49mexecute(\u001b[39mself\u001b[39;49m)\n\u001b[1;32m   3510\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(previous_engine, TightLayoutEngine) \\\n\u001b[1;32m   3511\u001b[0m         \u001b[39mand\u001b[39;00m previous_engine \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m   3512\u001b[0m     _api\u001b[39m.\u001b[39mwarn_external(\u001b[39m'\u001b[39m\u001b[39mThe figure layout has changed to tight\u001b[39m\u001b[39m'\u001b[39m)\n",
      "File \u001b[0;32m~/.virtualenvs/learning/lib/python3.8/site-packages/matplotlib/layout_engine.py:178\u001b[0m, in \u001b[0;36mTightLayoutEngine.execute\u001b[0;34m(self, fig)\u001b[0m\n\u001b[1;32m    176\u001b[0m renderer \u001b[39m=\u001b[39m fig\u001b[39m.\u001b[39m_get_renderer()\n\u001b[1;32m    177\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mgetattr\u001b[39m(renderer, \u001b[39m\"\u001b[39m\u001b[39m_draw_disabled\u001b[39m\u001b[39m\"\u001b[39m, nullcontext)():\n\u001b[0;32m--> 178\u001b[0m     kwargs \u001b[39m=\u001b[39m get_tight_layout_figure(\n\u001b[1;32m    179\u001b[0m         fig, fig\u001b[39m.\u001b[39;49maxes, get_subplotspec_list(fig\u001b[39m.\u001b[39;49maxes), renderer,\n\u001b[1;32m    180\u001b[0m         pad\u001b[39m=\u001b[39;49minfo[\u001b[39m'\u001b[39;49m\u001b[39mpad\u001b[39;49m\u001b[39m'\u001b[39;49m], h_pad\u001b[39m=\u001b[39;49minfo[\u001b[39m'\u001b[39;49m\u001b[39mh_pad\u001b[39;49m\u001b[39m'\u001b[39;49m], w_pad\u001b[39m=\u001b[39;49minfo[\u001b[39m'\u001b[39;49m\u001b[39mw_pad\u001b[39;49m\u001b[39m'\u001b[39;49m],\n\u001b[1;32m    181\u001b[0m         rect\u001b[39m=\u001b[39;49minfo[\u001b[39m'\u001b[39;49m\u001b[39mrect\u001b[39;49m\u001b[39m'\u001b[39;49m])\n\u001b[1;32m    182\u001b[0m \u001b[39mif\u001b[39;00m kwargs:\n\u001b[1;32m    183\u001b[0m     fig\u001b[39m.\u001b[39msubplots_adjust(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/.virtualenvs/learning/lib/python3.8/site-packages/matplotlib/_tight_layout.py:266\u001b[0m, in \u001b[0;36mget_tight_layout_figure\u001b[0;34m(fig, axes_list, subplotspec_list, renderer, pad, h_pad, w_pad, rect)\u001b[0m\n\u001b[1;32m    261\u001b[0m         \u001b[39mreturn\u001b[39;00m {}\n\u001b[1;32m    262\u001b[0m     span_pairs\u001b[39m.\u001b[39mappend((\n\u001b[1;32m    263\u001b[0m         \u001b[39mslice\u001b[39m(ss\u001b[39m.\u001b[39mrowspan\u001b[39m.\u001b[39mstart \u001b[39m*\u001b[39m div_row, ss\u001b[39m.\u001b[39mrowspan\u001b[39m.\u001b[39mstop \u001b[39m*\u001b[39m div_row),\n\u001b[1;32m    264\u001b[0m         \u001b[39mslice\u001b[39m(ss\u001b[39m.\u001b[39mcolspan\u001b[39m.\u001b[39mstart \u001b[39m*\u001b[39m div_col, ss\u001b[39m.\u001b[39mcolspan\u001b[39m.\u001b[39mstop \u001b[39m*\u001b[39m div_col)))\n\u001b[0;32m--> 266\u001b[0m kwargs \u001b[39m=\u001b[39m _auto_adjust_subplotpars(fig, renderer,\n\u001b[1;32m    267\u001b[0m                                   shape\u001b[39m=\u001b[39;49m(max_nrows, max_ncols),\n\u001b[1;32m    268\u001b[0m                                   span_pairs\u001b[39m=\u001b[39;49mspan_pairs,\n\u001b[1;32m    269\u001b[0m                                   subplot_list\u001b[39m=\u001b[39;49msubplot_list,\n\u001b[1;32m    270\u001b[0m                                   ax_bbox_list\u001b[39m=\u001b[39;49max_bbox_list,\n\u001b[1;32m    271\u001b[0m                                   pad\u001b[39m=\u001b[39;49mpad, h_pad\u001b[39m=\u001b[39;49mh_pad, w_pad\u001b[39m=\u001b[39;49mw_pad)\n\u001b[1;32m    273\u001b[0m \u001b[39m# kwargs can be none if tight_layout fails...\u001b[39;00m\n\u001b[1;32m    274\u001b[0m \u001b[39mif\u001b[39;00m rect \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m kwargs \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    275\u001b[0m     \u001b[39m# if rect is given, the whole subplots area (including\u001b[39;00m\n\u001b[1;32m    276\u001b[0m     \u001b[39m# labels) will fit into the rect instead of the\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    280\u001b[0m     \u001b[39m# auto_adjust_subplotpars twice, where the second run\u001b[39;00m\n\u001b[1;32m    281\u001b[0m     \u001b[39m# with adjusted rect parameters.\u001b[39;00m\n",
      "File \u001b[0;32m~/.virtualenvs/learning/lib/python3.8/site-packages/matplotlib/_tight_layout.py:82\u001b[0m, in \u001b[0;36m_auto_adjust_subplotpars\u001b[0;34m(fig, renderer, shape, span_pairs, subplot_list, ax_bbox_list, pad, h_pad, w_pad, rect)\u001b[0m\n\u001b[1;32m     80\u001b[0m \u001b[39mfor\u001b[39;00m ax \u001b[39min\u001b[39;00m subplots:\n\u001b[1;32m     81\u001b[0m     \u001b[39mif\u001b[39;00m ax\u001b[39m.\u001b[39mget_visible():\n\u001b[0;32m---> 82\u001b[0m         bb \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m [martist\u001b[39m.\u001b[39;49m_get_tightbbox_for_layout_only(ax, renderer)]\n\u001b[1;32m     84\u001b[0m tight_bbox_raw \u001b[39m=\u001b[39m Bbox\u001b[39m.\u001b[39munion(bb)\n\u001b[1;32m     85\u001b[0m tight_bbox \u001b[39m=\u001b[39m fig\u001b[39m.\u001b[39mtransFigure\u001b[39m.\u001b[39minverted()\u001b[39m.\u001b[39mtransform_bbox(tight_bbox_raw)\n",
      "File \u001b[0;32m~/.virtualenvs/learning/lib/python3.8/site-packages/matplotlib/artist.py:1415\u001b[0m, in \u001b[0;36m_get_tightbbox_for_layout_only\u001b[0;34m(obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1409\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   1410\u001b[0m \u001b[39mMatplotlib's `.Axes.get_tightbbox` and `.Axis.get_tightbbox` support a\u001b[39;00m\n\u001b[1;32m   1411\u001b[0m \u001b[39m*for_layout_only* kwarg; this helper tries to use the kwarg but skips it\u001b[39;00m\n\u001b[1;32m   1412\u001b[0m \u001b[39mwhen encountering third-party subclasses that do not support it.\u001b[39;00m\n\u001b[1;32m   1413\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   1414\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 1415\u001b[0m     \u001b[39mreturn\u001b[39;00m obj\u001b[39m.\u001b[39;49mget_tightbbox(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49m{\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs, \u001b[39m\"\u001b[39;49m\u001b[39mfor_layout_only\u001b[39;49m\u001b[39m\"\u001b[39;49m: \u001b[39mTrue\u001b[39;49;00m})\n\u001b[1;32m   1416\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[1;32m   1417\u001b[0m     \u001b[39mreturn\u001b[39;00m obj\u001b[39m.\u001b[39mget_tightbbox(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/.virtualenvs/learning/lib/python3.8/site-packages/matplotlib/axes/_base.py:4385\u001b[0m, in \u001b[0;36m_AxesBase.get_tightbbox\u001b[0;34m(self, renderer, call_axes_locator, bbox_extra_artists, for_layout_only)\u001b[0m\n\u001b[1;32m   4383\u001b[0m \u001b[39mfor\u001b[39;00m axis \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_axis_map\u001b[39m.\u001b[39mvalues():\n\u001b[1;32m   4384\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxison \u001b[39mand\u001b[39;00m axis\u001b[39m.\u001b[39mget_visible():\n\u001b[0;32m-> 4385\u001b[0m         ba \u001b[39m=\u001b[39m martist\u001b[39m.\u001b[39;49m_get_tightbbox_for_layout_only(axis, renderer)\n\u001b[1;32m   4386\u001b[0m         \u001b[39mif\u001b[39;00m ba:\n\u001b[1;32m   4387\u001b[0m             bb\u001b[39m.\u001b[39mappend(ba)\n",
      "File \u001b[0;32m~/.virtualenvs/learning/lib/python3.8/site-packages/matplotlib/artist.py:1415\u001b[0m, in \u001b[0;36m_get_tightbbox_for_layout_only\u001b[0;34m(obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1409\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   1410\u001b[0m \u001b[39mMatplotlib's `.Axes.get_tightbbox` and `.Axis.get_tightbbox` support a\u001b[39;00m\n\u001b[1;32m   1411\u001b[0m \u001b[39m*for_layout_only* kwarg; this helper tries to use the kwarg but skips it\u001b[39;00m\n\u001b[1;32m   1412\u001b[0m \u001b[39mwhen encountering third-party subclasses that do not support it.\u001b[39;00m\n\u001b[1;32m   1413\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   1414\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 1415\u001b[0m     \u001b[39mreturn\u001b[39;00m obj\u001b[39m.\u001b[39;49mget_tightbbox(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49m{\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs, \u001b[39m\"\u001b[39;49m\u001b[39mfor_layout_only\u001b[39;49m\u001b[39m\"\u001b[39;49m: \u001b[39mTrue\u001b[39;49;00m})\n\u001b[1;32m   1416\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[1;32m   1417\u001b[0m     \u001b[39mreturn\u001b[39;00m obj\u001b[39m.\u001b[39mget_tightbbox(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/.virtualenvs/learning/lib/python3.8/site-packages/matplotlib/axis.py:1325\u001b[0m, in \u001b[0;36mAxis.get_tightbbox\u001b[0;34m(self, renderer, for_layout_only)\u001b[0m\n\u001b[1;32m   1322\u001b[0m     renderer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfigure\u001b[39m.\u001b[39m_get_renderer()\n\u001b[1;32m   1323\u001b[0m ticks_to_draw \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_update_ticks()\n\u001b[0;32m-> 1325\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_update_label_position(renderer)\n\u001b[1;32m   1327\u001b[0m \u001b[39m# go back to just this axis's tick labels\u001b[39;00m\n\u001b[1;32m   1328\u001b[0m tlb1, tlb2 \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_ticklabel_bboxes(ticks_to_draw, renderer)\n",
      "File \u001b[0;32m~/.virtualenvs/learning/lib/python3.8/site-packages/matplotlib/axis.py:2304\u001b[0m, in \u001b[0;36mXAxis._update_label_position\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   2300\u001b[0m     \u001b[39mreturn\u001b[39;00m\n\u001b[1;32m   2302\u001b[0m \u001b[39m# get bounding boxes for this axis and any siblings\u001b[39;00m\n\u001b[1;32m   2303\u001b[0m \u001b[39m# that have been set by `fig.align_xlabels()`\u001b[39;00m\n\u001b[0;32m-> 2304\u001b[0m bboxes, bboxes2 \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_get_tick_boxes_siblings(renderer\u001b[39m=\u001b[39;49mrenderer)\n\u001b[1;32m   2306\u001b[0m x, y \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlabel\u001b[39m.\u001b[39mget_position()\n\u001b[1;32m   2307\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlabel_position \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mbottom\u001b[39m\u001b[39m'\u001b[39m:\n",
      "File \u001b[0;32m~/.virtualenvs/learning/lib/python3.8/site-packages/matplotlib/axis.py:2099\u001b[0m, in \u001b[0;36mAxis._get_tick_boxes_siblings\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   2097\u001b[0m \u001b[39mfor\u001b[39;00m ax \u001b[39min\u001b[39;00m grouper\u001b[39m.\u001b[39mget_siblings(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxes):\n\u001b[1;32m   2098\u001b[0m     axis \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(ax, \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00maxis_name\u001b[39m}\u001b[39;00m\u001b[39maxis\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m-> 2099\u001b[0m     ticks_to_draw \u001b[39m=\u001b[39m axis\u001b[39m.\u001b[39;49m_update_ticks()\n\u001b[1;32m   2100\u001b[0m     tlb, tlb2 \u001b[39m=\u001b[39m axis\u001b[39m.\u001b[39m_get_ticklabel_bboxes(ticks_to_draw, renderer)\n\u001b[1;32m   2101\u001b[0m     bboxes\u001b[39m.\u001b[39mextend(tlb)\n",
      "File \u001b[0;32m~/.virtualenvs/learning/lib/python3.8/site-packages/matplotlib/axis.py:1265\u001b[0m, in \u001b[0;36mAxis._update_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1263\u001b[0m major_labels \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmajor\u001b[39m.\u001b[39mformatter\u001b[39m.\u001b[39mformat_ticks(major_locs)\n\u001b[1;32m   1264\u001b[0m major_ticks \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_major_ticks(\u001b[39mlen\u001b[39m(major_locs))\n\u001b[0;32m-> 1265\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmajor\u001b[39m.\u001b[39;49mformatter\u001b[39m.\u001b[39;49mset_locs(major_locs)\n\u001b[1;32m   1266\u001b[0m \u001b[39mfor\u001b[39;00m tick, loc, label \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(major_ticks, major_locs, major_labels):\n\u001b[1;32m   1267\u001b[0m     tick\u001b[39m.\u001b[39mupdate_position(loc)\n",
      "File \u001b[0;32m~/.virtualenvs/learning/lib/python3.8/site-packages/matplotlib/ticker.py:699\u001b[0m, in \u001b[0;36mScalarFormatter.set_locs\u001b[0;34m(self, locs)\u001b[0m\n\u001b[1;32m    696\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mset_locs\u001b[39m(\u001b[39mself\u001b[39m, locs):\n\u001b[1;32m    697\u001b[0m     \u001b[39m# docstring inherited\u001b[39;00m\n\u001b[1;32m    698\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlocs \u001b[39m=\u001b[39m locs\n\u001b[0;32m--> 699\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39;49m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mlocs) \u001b[39m>\u001b[39;49m \u001b[39m0\u001b[39;49m:\n\u001b[1;32m    700\u001b[0m         \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_useOffset:\n\u001b[1;32m    701\u001b[0m             \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_compute_offset()\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for _, entry in enumerate(isi_dict):\n",
    "    # iterate over channel\n",
    "    for channel in range(len(isi_dict[entry])):\n",
    "        # print('\\nWorking on channel: ', channel)\n",
    "        # extract single ISIs and their count\n",
    "        isi, count = np.unique(isi_dict[entry][channel][0], return_counts=True)\n",
    "\n",
    "        # only plot if ISIs found in channel\n",
    "        if len(isi) > 0:\n",
    "            if norm_time:\n",
    "                isi = isi/max(isi)\n",
    "            if norm_count:\n",
    "                count = count/max(count)\n",
    "\n",
    "            # create box plots with all classes from paper and single sensor and class for Braille data\n",
    "            figname = f'comparison ISI\\nLetter: {braille_letters[entry]} Sensor: {channel}'\n",
    "            plt.figure(figsize=(12, 12))\n",
    "            plt.suptitle(figname)\n",
    "\n",
    "            # compare to original traces\n",
    "            for num, isi_original_sel in enumerate(isi_original):             \n",
    "                # create scatter plot for Braille and original ISIs\n",
    "                plt.subplot(5, 4, num+1)\n",
    "                plt.title(f'{classes_list[braille_letters[num]]}')\n",
    "                plt.scatter(isi_original_sel[0], isi_original_sel[1])\n",
    "                plt.scatter(isi, count)\n",
    "                plt.xlim((0, 1.1))\n",
    "\n",
    "            plt.tight_layout()\n",
    "            plt.savefig(\n",
    "                f'{plot_out}/comparison_class_{braille_letters[entry]}_channel_{channel}_scatter.png', dpi=300)\n",
    "            plt.close()\n",
    "            # plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[9], line 99\u001b[0m\n\u001b[0;32m     96\u001b[0m     plt\u001b[39m.\u001b[39mxlim((\u001b[39m0\u001b[39m, \u001b[39m1.1\u001b[39m))\n\u001b[0;32m     98\u001b[0m plt\u001b[39m.\u001b[39mtight_layout()\n\u001b[1;32m---> 99\u001b[0m plt\u001b[39m.\u001b[39;49msavefig(\n\u001b[0;32m    100\u001b[0m     \u001b[39mf\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39m{\u001b[39;49;00mplot_out\u001b[39m}\u001b[39;49;00m\u001b[39m/comparison_class_\u001b[39;49m\u001b[39m{\u001b[39;49;00mbraille_letters[entry]\u001b[39m}\u001b[39;49;00m\u001b[39m_channel_\u001b[39;49m\u001b[39m{\u001b[39;49;00mchannel\u001b[39m}\u001b[39;49;00m\u001b[39m_bar.png\u001b[39;49m\u001b[39m'\u001b[39;49m, dpi\u001b[39m=\u001b[39;49m\u001b[39m300\u001b[39;49m)\n\u001b[0;32m    101\u001b[0m plt\u001b[39m.\u001b[39mclose()\n\u001b[0;32m    102\u001b[0m \u001b[39m# plt.show()\u001b[39;00m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\matplotlib\\pyplot.py:942\u001b[0m, in \u001b[0;36msavefig\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    939\u001b[0m \u001b[39m@_copy_docstring_and_deprecators\u001b[39m(Figure\u001b[39m.\u001b[39msavefig)\n\u001b[0;32m    940\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39msavefig\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m    941\u001b[0m     fig \u001b[39m=\u001b[39m gcf()\n\u001b[1;32m--> 942\u001b[0m     res \u001b[39m=\u001b[39m fig\u001b[39m.\u001b[39msavefig(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m    943\u001b[0m     fig\u001b[39m.\u001b[39mcanvas\u001b[39m.\u001b[39mdraw_idle()  \u001b[39m# Need this if 'transparent=True', to reset colors.\u001b[39;00m\n\u001b[0;32m    944\u001b[0m     \u001b[39mreturn\u001b[39;00m res\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\matplotlib\\figure.py:3272\u001b[0m, in \u001b[0;36mFigure.savefig\u001b[1;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[0;32m   3268\u001b[0m     \u001b[39mfor\u001b[39;00m ax \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxes:\n\u001b[0;32m   3269\u001b[0m         stack\u001b[39m.\u001b[39menter_context(\n\u001b[0;32m   3270\u001b[0m             ax\u001b[39m.\u001b[39mpatch\u001b[39m.\u001b[39m_cm_set(facecolor\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mnone\u001b[39m\u001b[39m'\u001b[39m, edgecolor\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mnone\u001b[39m\u001b[39m'\u001b[39m))\n\u001b[1;32m-> 3272\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcanvas\u001b[39m.\u001b[39mprint_figure(fname, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\matplotlib\\backend_bases.py:2338\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[0;32m   2334\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m   2335\u001b[0m     \u001b[39m# _get_renderer may change the figure dpi (as vector formats\u001b[39;00m\n\u001b[0;32m   2336\u001b[0m     \u001b[39m# force the figure dpi to 72), so we need to set it again here.\u001b[39;00m\n\u001b[0;32m   2337\u001b[0m     \u001b[39mwith\u001b[39;00m cbook\u001b[39m.\u001b[39m_setattr_cm(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfigure, dpi\u001b[39m=\u001b[39mdpi):\n\u001b[1;32m-> 2338\u001b[0m         result \u001b[39m=\u001b[39m print_method(\n\u001b[0;32m   2339\u001b[0m             filename,\n\u001b[0;32m   2340\u001b[0m             facecolor\u001b[39m=\u001b[39mfacecolor,\n\u001b[0;32m   2341\u001b[0m             edgecolor\u001b[39m=\u001b[39medgecolor,\n\u001b[0;32m   2342\u001b[0m             orientation\u001b[39m=\u001b[39morientation,\n\u001b[0;32m   2343\u001b[0m             bbox_inches_restore\u001b[39m=\u001b[39m_bbox_inches_restore,\n\u001b[0;32m   2344\u001b[0m             \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m   2345\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m   2346\u001b[0m     \u001b[39mif\u001b[39;00m bbox_inches \u001b[39mand\u001b[39;00m restore_bbox:\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\matplotlib\\backend_bases.py:2204\u001b[0m, in \u001b[0;36mFigureCanvasBase._switch_canvas_and_return_print_method.<locals>.<lambda>\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   2200\u001b[0m     optional_kws \u001b[39m=\u001b[39m {  \u001b[39m# Passed by print_figure for other renderers.\u001b[39;00m\n\u001b[0;32m   2201\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mdpi\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mfacecolor\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39medgecolor\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39morientation\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m   2202\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mbbox_inches_restore\u001b[39m\u001b[39m\"\u001b[39m}\n\u001b[0;32m   2203\u001b[0m     skip \u001b[39m=\u001b[39m optional_kws \u001b[39m-\u001b[39m {\u001b[39m*\u001b[39minspect\u001b[39m.\u001b[39msignature(meth)\u001b[39m.\u001b[39mparameters}\n\u001b[1;32m-> 2204\u001b[0m     print_method \u001b[39m=\u001b[39m functools\u001b[39m.\u001b[39mwraps(meth)(\u001b[39mlambda\u001b[39;00m \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs: meth(\n\u001b[0;32m   2205\u001b[0m         \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39m{k: v \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m kwargs\u001b[39m.\u001b[39mitems() \u001b[39mif\u001b[39;00m k \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m skip}))\n\u001b[0;32m   2206\u001b[0m \u001b[39melse\u001b[39;00m:  \u001b[39m# Let third-parties do as they see fit.\u001b[39;00m\n\u001b[0;32m   2207\u001b[0m     print_method \u001b[39m=\u001b[39m meth\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\matplotlib\\_api\\deprecation.py:410\u001b[0m, in \u001b[0;36mdelete_parameter.<locals>.wrapper\u001b[1;34m(*inner_args, **inner_kwargs)\u001b[0m\n\u001b[0;32m    400\u001b[0m     deprecation_addendum \u001b[39m=\u001b[39m (\n\u001b[0;32m    401\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mIf any parameter follows \u001b[39m\u001b[39m{\u001b[39;00mname\u001b[39m!r}\u001b[39;00m\u001b[39m, they should be passed as \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    402\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mkeyword, not positionally.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m    403\u001b[0m     warn_deprecated(\n\u001b[0;32m    404\u001b[0m         since,\n\u001b[0;32m    405\u001b[0m         name\u001b[39m=\u001b[39m\u001b[39mrepr\u001b[39m(name),\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    408\u001b[0m                  \u001b[39melse\u001b[39;00m deprecation_addendum,\n\u001b[0;32m    409\u001b[0m         \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m--> 410\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39minner_args, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39minner_kwargs)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\matplotlib\\backends\\backend_agg.py:520\u001b[0m, in \u001b[0;36mFigureCanvasAgg.print_png\u001b[1;34m(self, filename_or_obj, metadata, pil_kwargs, *args)\u001b[0m\n\u001b[0;32m    471\u001b[0m \u001b[39m@_api\u001b[39m\u001b[39m.\u001b[39mdelete_parameter(\u001b[39m\"\u001b[39m\u001b[39m3.5\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39margs\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m    472\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mprint_png\u001b[39m(\u001b[39mself\u001b[39m, filename_or_obj, \u001b[39m*\u001b[39margs,\n\u001b[0;32m    473\u001b[0m               metadata\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, pil_kwargs\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[0;32m    474\u001b[0m     \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    475\u001b[0m \u001b[39m    Write the figure to a PNG file.\u001b[39;00m\n\u001b[0;32m    476\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    518\u001b[0m \u001b[39m        *metadata*, including the default 'Software' key.\u001b[39;00m\n\u001b[0;32m    519\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 520\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_print_pil(filename_or_obj, \u001b[39m\"\u001b[39;49m\u001b[39mpng\u001b[39;49m\u001b[39m\"\u001b[39;49m, pil_kwargs, metadata)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\matplotlib\\backends\\backend_agg.py:467\u001b[0m, in \u001b[0;36mFigureCanvasAgg._print_pil\u001b[1;34m(self, filename_or_obj, fmt, pil_kwargs, metadata)\u001b[0m\n\u001b[0;32m    462\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    463\u001b[0m \u001b[39mDraw the canvas, then save it using `.image.imsave` (to which\u001b[39;00m\n\u001b[0;32m    464\u001b[0m \u001b[39m*pil_kwargs* and *metadata* are forwarded).\u001b[39;00m\n\u001b[0;32m    465\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    466\u001b[0m FigureCanvasAgg\u001b[39m.\u001b[39mdraw(\u001b[39mself\u001b[39m)\n\u001b[1;32m--> 467\u001b[0m mpl\u001b[39m.\u001b[39;49mimage\u001b[39m.\u001b[39;49mimsave(\n\u001b[0;32m    468\u001b[0m     filename_or_obj, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbuffer_rgba(), \u001b[39mformat\u001b[39;49m\u001b[39m=\u001b[39;49mfmt, origin\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mupper\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m    469\u001b[0m     dpi\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfigure\u001b[39m.\u001b[39;49mdpi, metadata\u001b[39m=\u001b[39;49mmetadata, pil_kwargs\u001b[39m=\u001b[39;49mpil_kwargs)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\matplotlib\\image.py:1656\u001b[0m, in \u001b[0;36mimsave\u001b[1;34m(fname, arr, vmin, vmax, cmap, format, origin, dpi, metadata, pil_kwargs)\u001b[0m\n\u001b[0;32m   1654\u001b[0m pil_kwargs\u001b[39m.\u001b[39msetdefault(\u001b[39m\"\u001b[39m\u001b[39mformat\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mformat\u001b[39m)\n\u001b[0;32m   1655\u001b[0m pil_kwargs\u001b[39m.\u001b[39msetdefault(\u001b[39m\"\u001b[39m\u001b[39mdpi\u001b[39m\u001b[39m\"\u001b[39m, (dpi, dpi))\n\u001b[1;32m-> 1656\u001b[0m image\u001b[39m.\u001b[39msave(fname, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mpil_kwargs)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\PIL\\Image.py:2320\u001b[0m, in \u001b[0;36mImage.save\u001b[1;34m(self, fp, format, **params)\u001b[0m\n\u001b[0;32m   2317\u001b[0m         fp \u001b[39m=\u001b[39m builtins\u001b[39m.\u001b[39mopen(filename, \u001b[39m\"\u001b[39m\u001b[39mw+b\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m   2319\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m-> 2320\u001b[0m     save_handler(\u001b[39mself\u001b[39;49m, fp, filename)\n\u001b[0;32m   2321\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m:\n\u001b[0;32m   2322\u001b[0m     \u001b[39mif\u001b[39;00m open_fp:\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\PIL\\PngImagePlugin.py:1374\u001b[0m, in \u001b[0;36m_save\u001b[1;34m(im, fp, filename, chunk, save_all)\u001b[0m\n\u001b[0;32m   1372\u001b[0m     _write_multiple_frames(im, fp, chunk, rawmode)\n\u001b[0;32m   1373\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m-> 1374\u001b[0m     ImageFile\u001b[39m.\u001b[39;49m_save(im, _idat(fp, chunk), [(\u001b[39m\"\u001b[39;49m\u001b[39mzip\u001b[39;49m\u001b[39m\"\u001b[39;49m, (\u001b[39m0\u001b[39;49m, \u001b[39m0\u001b[39;49m) \u001b[39m+\u001b[39;49m im\u001b[39m.\u001b[39;49msize, \u001b[39m0\u001b[39;49m, rawmode)])\n\u001b[0;32m   1376\u001b[0m \u001b[39mif\u001b[39;00m info:\n\u001b[0;32m   1377\u001b[0m     \u001b[39mfor\u001b[39;00m info_chunk \u001b[39min\u001b[39;00m info\u001b[39m.\u001b[39mchunks:\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\PIL\\ImageFile.py:518\u001b[0m, in \u001b[0;36m_save\u001b[1;34m(im, fp, tile, bufsize)\u001b[0m\n\u001b[0;32m    515\u001b[0m \u001b[39mif\u001b[39;00m exc:\n\u001b[0;32m    516\u001b[0m     \u001b[39m# compress to Python file-compatible object\u001b[39;00m\n\u001b[0;32m    517\u001b[0m     \u001b[39mwhile\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n\u001b[1;32m--> 518\u001b[0m         l, s, d \u001b[39m=\u001b[39m encoder\u001b[39m.\u001b[39;49mencode(bufsize)\n\u001b[0;32m    519\u001b[0m         fp\u001b[39m.\u001b[39mwrite(d)\n\u001b[0;32m    520\u001b[0m         \u001b[39mif\u001b[39;00m s:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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5KTQ0VDt27Mi0//Lly1WnTh15eXmpYcOGWrt2rc1yY4zGjh2rSpUqydvbW2FhYTp48GBOhgagiCOfALgyMgqAqyKfAOQHh4tSS5cu1YgRIzRu3Djt2rVLjRs3Vnh4uE6fPp1u/23btumxxx7TgAED9MMPP6hbt27q1q2b9u3bZ+3z9ttv67333tPs2bO1fft2lShRQuHh4bp27VrOZwagyCGfALgyMgqAqyKfAOQXh68pNWXKFA0cOFD9+/eXJM2ePVtr1qzRvHnzNHr0aLv+7777rjp06KCRI0dKkt544w1t3LhRM2bM0OzZs2WM0bRp0/TKK6+oa9eukqSFCxfK399fq1evVq9evey2mZSUpKSkJOvXCQkJkqTExERHp+NcSeav/2c01rQ+uZlLXmwjt7Iz17zcTwHdR2rSFev/M/r5TeuT2c+30/rcpucibX/GmCx6Zs4V8knK24y6+Wfkdku0mEz3md7yxMREm5+bW3+Gbt1WVvvIaDuZ7TejbWQkNemKdRzKYDtZvR7T63Pr83DzPm6bdF6TWeVKdrKgwCKjCv97qKx+P2XWJ7f7cJZMXteOvqYzXJb0Vxbn2TZvXXbL9yS320xveU6XOTSPPFpGPhXhfMpunwzYvKdQJu/T0nlPk9H7tMzWyWz5re/jbss+s5hrXu3z5rnk9H1lZu9XM3oPm9GY01s/q/ds6c0zR/I7n4wDkpKSjLu7u1m1apVNe58+fUyXLl3SXScoKMhMnTrVpm3s2LGmUaNGxhhjDh8+bCSZH374wabPfffdZ4YNG5buNseNG2ck8eDBo5A9jh075kgk2XCVfDKGjOLBo7A+CkNGkU88eBTOB/nEgwcPV31klU8OHSl19uxZpaSkyN/f36bd399fBw4cSHeduLi4dPvHxcVZl6e1ZdTnVmPGjNGIESOsX1+4cEHVqlXT0aNH5evr68iUXF5iYqKCgoJ07NgxlS5dOr+Hk6eYW8Fzu+ZljNHFixcVGBiY4224Sj5JRSejCuvPucTcCioyyr7PrYpKPkmF92e9sM5LYm45QT4VTPysFzyFdV5S/ueTw6fvuQJPT095enratfv6+ha6H5A0pUuXZm4FUGGd2+2YV2F6s1HUMqqw/pxLzK2gIqMyVtTySSq8P+uFdV4Sc3MU+VRw8bNe8BTWeUn5l08OXei8QoUKcnd3V3x8vE17fHy8AgIC0l0nICAg0/5p/zqyTQC4FfkEwJWRUQBcFfkEID85VJTy8PBQs2bNFBkZaW1LTU1VZGSkWrZsme46LVu2tOkvSRs3brT2r169ugICAmz6JCYmavv27RluEwBuRT4BcGVkFABXRT4ByFeZXnEqHUuWLDGenp5m/vz55ueffzaDBg0yZcqUMXFxccYYY5544gkzevRoa/9vvvnGFCtWzLzzzjtm//79Zty4caZ48eLmxx9/tPaZOHGiKVOmjPn888/N3r17TdeuXU316tXN1atXszWma9eumXHjxplr1645Oh2Xx9wKpsI6N1eflyvmkzGu/7zlVGGdlzHMraBy9bm5Yka5+nOWG4V1boV1XsYwt/xEPjkXcyt4Cuu8jMn/uTlclDLGmOnTp5uqVasaDw8P06JFC/Ptt99al7Vp08b07dvXpv+yZcvMHXfcYTw8PEz9+vXNmjVrbJanpqaaV1991fj7+xtPT0/Ttm1b88svv+RkaACKOPIJgCsjowC4KvIJQH6wGGNMfh+tBQAAAAAAgKLFoWtKAQAAAAAAAHmBohQAAAAAAACcjqIUAAAAAAAAnI6iFAAAAAAAAJzOZYtSM2fOVHBwsLy8vBQaGqodO3Zk2n/58uWqU6eOvLy81LBhQ61du9ZmuTFGY8eOVaVKleTt7a2wsDAdPHjwdk4hQ47Mbc6cObr33ntVtmxZlS1bVmFhYXb9+/XrJ4vFYvPo0KHD7Z6GHUfmNX/+fLsxe3l52fQpqN+z+++/325uFotFnTp1svZxle/Zf//7X3Xu3FmBgYGyWCxavXp1lutER0eradOm8vT0VK1atTR//ny7Po6+fgsa8umGgpRPUuHNKPLpL+TTDYU1o8inGwpSPkmFM6PIp5wrrPkkFd6MIp9uIJ9uYz7l123/MrNkyRLj4eFh5s2bZ3766SczcOBAU6ZMGRMfH59u/2+++ca4u7ubt99+2/z888/mlVdeMcWLFzc//vijtc/EiRONr6+vWb16tdmzZ4/p0qWLqV69url69aqzpmWMcXxujz/+uJk5c6b54YcfzP79+02/fv2Mr6+vOX78uLVP3759TYcOHcypU6esjz/++MNZUzLGOD6viIgIU7p0aZsxx8XF2fQpqN+zc+fO2cxr3759xt3d3URERFj7uML3zBhj1q5da/71r3+ZlStXGklm1apVmfb/7bffjI+PjxkxYoT5+eefzfTp0427u7tZt26dtY+jz1dBQz79paDkkzGFN6PIp7+QTzcU1owin/5SUPLJmMKbUeRTzhTWfDKm8GYU+fQX8un25ZNLFqVatGhhnn32WevXKSkpJjAw0EyYMCHd/o8++qjp1KmTTVtoaKh5+umnjTHGpKammoCAADN58mTr8gsXLhhPT0/z6aef3oYZZMzRud3qzz//NKVKlTILFiywtvXt29d07do1r4fqEEfnFRERYXx9fTPcXmH6nk2dOtWUKlXKXLp0ydrmCt+zW2UntEaNGmXq169v09azZ08THh5u/Tq3z5erI58y5qr5ZEzhzSjy6S/k0w2FNaPIp78UlHwypmhkFPmUfYU1n4wpvBlFPmWMfMq7fHK50/eSk5O1c+dOhYWFWdvc3NwUFhammJiYdNeJiYmx6S9J4eHh1v6///674uLibPr4+voqNDQ0w23eDjmZ262uXLmi69evq1y5cjbt0dHR8vPzU0hIiAYPHqxz587l6dgzk9N5Xbp0SdWqVVNQUJC6du2qn376ybqsMH3P5s6dq169eqlEiRI27fn5PcuprF5refF8uTLyKXOumE9S4c0o8slWUc8nqfBmFPlkz9XzSSKjbkY+Fd58kgpvRpFPmSOf8i6fXK4odfbsWaWkpMjf39+m3d/fX3FxcemuExcXl2n/tH8d2ebtkJO53eqll15SYGCgzQ9Fhw4dtHDhQkVGRmrSpEnavHmzOnbsqJSUlDwdf0ZyMq+QkBDNmzdPn3/+uT7++GOlpqaqVatWOn78uKTC8z3bsWOH9u3bp6eeesqmPb+/ZzmV0WstMTFRV69ezZOfcVdGPmXOFfNJKrwZRT7ZKur5JBXejCKfbBWEfJLIqJuRT4U3n6TCm1HkU8bIp7z9nhXLk63AKSZOnKglS5YoOjra5oJxvXr1sv6/YcOGatSokWrWrKno6Gi1bds2P4aapZYtW6ply5bWr1u1aqW6devqgw8+0BtvvJGPI8tbc+fOVcOGDdWiRQub9oL4PQMyU5jySSoaGUU+oaggnwomMgpFRWHKKPKp4H3PXIHLHSlVoUIFubu7Kz4+3qY9Pj5eAQEB6a4TEBCQaf+0fx3Z5u2Qk7mleeeddzRx4kRt2LBBjRo1yrRvjRo1VKFCBR06dCjXY86O3MwrTfHixXXnnXdax1wYvmeXL1/WkiVLNGDAgCz34+zvWU5l9ForXbq0vL298+RnwZWRT+lz5XySCm9GkU+2ino+SYU3o8inzLliPklk1M3Ip8KbT1LhzSjyKX3k01998up75nJFKQ8PDzVr1kyRkZHWttTUVEVGRtpUXW/WsmVLm/6StHHjRmv/6tWrKyAgwKZPYmKitm/fnuE2b4eczE2S3n77bb3xxhtat26dmjdvnuV+jh8/rnPnzqlSpUp5Mu6s5HReN0tJSdGPP/5oHXNB/55JN25hm5SUpN69e2e5H2d/z3Iqq9daXvwsuDLyyZ6r55NUeDOKfLJV1PNJKrwZRT5lzhXzSSKjbkY+Fd58kgpvRpFP6SOfbkM+5cnl0vPYkiVLjKenp5k/f775+eefzaBBg0yZMmWst5N84oknzOjRo639v/nmG1OsWDHzzjvvmP3795tx48ale7vQMmXKmM8//9zs3bvXdO3aNd9uPenI3CZOnGg8PDzMihUrbG4tefHiRWOMMRcvXjT//Oc/TUxMjPn999/Npk2bTNOmTU3t2rXNtWvXXHZer732mlm/fr05fPiw2blzp+nVq5fx8vIyP/30k83cC+L3LE3r1q1Nz5497dpd5XuWNpYffvjB/PDDD0aSmTJlivnhhx/MkSNHjDHGjB492jzxxBPW/mm3DB05cqTZv3+/mTlzZrq3DM3s+SroyKeCl085mVtBySjyiXy6VWHNKPKp4OVTTuaWxtUzinzKmcKaTzmZW0HJKPKJfDLm9ueTSxaljDFm+vTppmrVqsbDw8O0aNHCfPvtt9Zlbdq0MX379rXpv2zZMnPHHXcYDw8PU79+fbNmzRqb5ampqebVV181/v7+xtPT07Rt29b88ssvzpiKHUfmVq1aNSPJ7jFu3DhjjDFXrlwx7du3NxUrVjTFixc31apVMwMHDsyXX2COzGv48OHWvv7+/uahhx4yu3btstleQf2eGWPMgQMHjCSzYcMGu2250vcsKioq3Z+vtPn07dvXtGnTxm6dJk2aGA8PD1OjRg0TERFht93Mnq/CgHy6oSDlkzGFN6PIJ9t1ino+GVN4M4p8uqEg5ZMxhTOjyKecK6z5ZEzhzSjy6S/k0+3JJ4sxxuTNMVcAAAAAAABA9rjcNaUAAAAAAABQ+FGUAgAAAAAAgNNRlAIAAAAAAIDTUZQCAAAAAACA01GUAgAAAAAAgNNRlAIAAAAAAIDTUZQCAAAAAACA01GUAgAAAAAAgNNRlAIAAAAAAIDTUZQCAAAAAACA01GUAgAAAAAAgNNRlAIAAAAAAIDTUZQCAAAAAACA01GUAgAAAAAAgNNRlAIAAAAAAIDTUZQCAAAAAACA01GUAgAAAAAAgNNRlIJTREdHy2KxKDo62trWr18/BQcH59uYACANGQXAVZFPAFwV+YS8QFEKuElSUpJeeuklBQYGytvbW6Ghodq4cWO21l21apXCw8MVGBgoT09PValSRd27d9e+ffts+qWFd0aPN998M8fj37Ztm1q3bi0fHx8FBARo2LBhunTpUpbrzZ8/P9MxLV68OMN127VrJ4vFoqFDh+Z43ACyJzcZdavMXrsZZcHEiRNzNf4vvvhCTZs2lZeXl6pWrapx48bpzz//zPb6hw8f1uOPPy4/Pz95e3urdu3a+te//mXXb9myZbr77rtVpkwZlS9fXm3atNGaNWtyNXYAmSOfyCfAVTnjM16aixcvatSoUapevbo8PT1VuXJlde/eXVeuXMnx+HP6GU/KfmauXLlSPXv2VI0aNeTj46OQkBC9+OKLunDhQo7HnV3FbvsegAzMmTNHqamp+T0MG/369dOKFSs0fPhw1a5dW/Pnz9dDDz2kqKgotW7dOtN1f/zxR5UtW1bPP/+8KlSooLi4OM2bN08tWrRQTEyMGjduLEmqW7euFi1aZLf+okWLtGHDBrVv3z5HY9+9e7fatm2runXrasqUKTp+/LjeeecdHTx4UF9//XWm6953333pjmnq1Knas2eP2rZtm+56K1euVExMTI7GC7i6wpZRN8vOa7ddu3bq06ePTdudd96Zo3FL0tdff61u3brp/vvv1/Tp0/Xjjz/q3//+t06fPq33338/y/V3796t+++/X5UrV9aLL76o8uXL6+jRozp27JhNv+nTp2vYsGHq1KmTJk6cqGvXrmn+/Pn629/+ps8++0wPP/xwjucAuAryiXwCXFVhy6fsfsaTpISEBLVp00bHjx/XoEGDVKtWLZ05c0ZbtmxRUlKSfHx8HB57bj7jpclOZg4aNEiBgYHq3bu3qlatqh9//FEzZszQ2rVrtWvXLnl7ezs89mwzgBNERUUZSSYqKiq/h5Kh7du3G0lm8uTJ1rarV6+amjVrmpYtW+Zom3FxcaZYsWLm6aefzrJvrVq1TO3atXO0H2OM6dixo6lUqZJJSEiwts2ZM8dIMuvXr3d4e1euXDGlSpUy7dq1S3f51atXTXBwsHn99deNJPPss8/meOxAfitKGZWd1+7teE3Xq1fPNG7c2Fy/ft3a9q9//ctYLBazf//+TNdNSUkxDRo0MKGhoebKlSuZ9q1du7a56667TGpqqrUtISHBlCxZ0nTp0iV3kwDyAflki3wCXEdRyqebZfQZb/DgwaZMmTLmt99+y9WYb5bbz3jZzcz0vocLFiwwksycOXMcGrOjivzpe+PHj5fFYtGvv/6q3r17y9fXVxUrVtSrr74qY4yOHTumrl27qnTp0goICNB//vMfu20kJSVp3LhxqlWrljw9PRUUFKRRo0YpKSnJpl9ERIQefPBB+fn5ydPTU/Xq1Uv3ry/BwcH629/+pq1bt6pFixby8vJSjRo1tHDhwhzP8/7771eDBg20d+9etWnTRj4+PqpVq5ZWrFghSdq8ebNCQ0Pl7e2tkJAQbdq0yW4bJ06c0JNPPil/f395enqqfv36mjdvnl2/48ePq1u3bipRooT8/Pz0wgsv2D0XUvrnG7/zzjtq1aqVypcvL29vbzVr1sw6xpulHdK9evVqNWjQwDqedevW2fU9cOCAjh49muVztGLFCrm7u2vQoEHWNi8vLw0YMEAxMTF2f+3KDj8/P/n4+GR52OOOHTt06NAh/eMf/3B4H5KUmJiojRs3qnfv3ipdurS1vU+fPipZsqSWLVvm8Da//PJLXbx4McMxvf3220pNTdU///nPHI0Z2UNGkVFp8iqjHHntXr16VdeuXcvWdjPz888/6+eff9agQYNUrNhfB2kPGTJExph0n8ObbdiwQfv27dO4cePk7e2tK1euKCUlJd2+iYmJ8vPzk8VisbaVLl1aJUuWvL1/5SuCyCfyKQ35RD65GvKJfErjrM94Fy5cUEREhAYNGqTq1asrOTk53efHEXn5GS+rzLz//vvt2v7+979Lkvbv35/9QefEbS15FQDjxo0zkkyTJk3MY489ZmbNmmU6depkJJkpU6aYkJAQM3jwYDNr1ixzzz33GElm8+bN1vVTUlJM+/btjY+Pjxk+fLj54IMPzNChQ02xYsVM165dbfZ11113mX79+pmpU6ea6dOnm/bt2xtJZsaMGTb9qlWrZkJCQoy/v795+eWXzYwZM0zTpk2NxWIx+/bty9E827RpYwIDA01QUJAZOXKkmT59uqlXr55xd3c3S5YsMQEBAWb8+PFm2rRppnLlysbX19ckJiZa14+LizNVqlQxQUFB5vXXXzfvv/++6dKli5Fkpk6dau135coVc8cddxgvLy8zatQoM23aNNOsWTPTqFEjuyp63759TbVq1WzGWaVKFTNkyBAzY8YMM2XKFNOiRQsjyXz11Vc2/SSZxo0bm0qVKpk33njDTJs2zdSoUcP4+PiYs2fP2vVt06ZNls9RWFiYqVu3rl37pk2bjCTzxRdfZLkNY4w5f/68OX36tNm7d6958sknjSTz4YcfZrrOsGHDjCRz8ODBbO3jVlu3bjWSzNKlS+2WtW7d2jRt2tThbXbp0sV4e3vb/BykOXLkiPH29jaffvqpMeb2/NUSN5BRZFSavMio7L52JZkSJUoYi8ViJJm6deuaxYsXZ7n9jHz88cdGktm+fbvdsipVqpiHH3440/VffPFFI8lERkaaZs2aGUnGw8PD9OzZ05w7d86mb8+ePY27u7t57733zO+//272799vhgwZYry9vc22bdtyPAfYI5/IpzTkE/nkasgn8imNsz7jffnll9a2Rx55xLi7uxuLxWJatWplfvjhh2zt41Z58RkvN5n566+/GknmrbfecnjsjqAo9b/AGjRokLXtzz//NFWqVDEWi8VMnDjR2n7+/Hnj7e1t+vbta21btGiRcXNzM1u2bLHZ7uzZs40k880331jb0jukNzw83NSoUcOmrVq1akaS+e9//2ttO336tPH09DQvvvhijubZpk0bI8l88skn1rYDBw4YScbNzc18++231vb169cbSSYiIsLaNmDAAFOpUiW7MOjVq5fx9fW1zm3atGlGklm2bJm1z+XLl02tWrWyFVi3PkfJycmmQYMG5sEHH7RpT/uFf+jQIWvbnj17jCQzffp0u77ZCaz69evb7ccYY3766ScjycyePTvLbRhjTEhIiJFkJJmSJUuaV155xaSkpGTY/88//zT+/v6mRYsW2dp+epYvX273M5OmR48eJiAgwKHtnTt3znh4eJhHH3003eXdu3c3rVq1sn5NUer2IaPIqDR5kVHZfe22atXKTJs2zXz++efm/fffNw0aNDCSzKxZs7LcR3omT55sJJmjR4/aLbvrrrvM3Xffnen6aW+Qy5cvb/7xj3+YFStWmFdffdUUK1bMtGrVyuZUmPj4eNO2bVtrDksyFSpU4APfbUA+kU9pyCfyydWQT+RTGmd9xpsyZYo1C1q0aGEWL15sZs2aZfz9/U3ZsmXNyZMns7Wfm+XFZ7zcZOaAAQOMu7u7+fXXXx0euyOK/Ol7aZ566inr/93d3dW8eXMZYzRgwABre5kyZRQSEqLffvvN2rZ8+XLVrVtXderU0dmzZ62PBx98UJIUFRVl7XvzYbkJCQk6e/as2rRpo99++00JCQk246lXr57uvfde69cVK1a027ejSpYsqV69elm/DgkJUZkyZVS3bl2FhoZa29P+n7YvY4w+++wzde7cWcYYm3mGh4crISFBu3btkiStXbtWlSpVUvfu3a3b8/HxsTlcMjM3P0fnz59XQkKC7r33Xuv2bxYWFqaaNWtav27UqJFKly5t9xwZY2xuU5qRq1evytPT067dy8vLujw7IiIitG7dOs2aNUt169bV1atXMzyMW5IiIyMVHx+f41P3bh5bRuPP7tjTrFixQsnJyemOKSoqSp999pmmTZuWo7EiZ8goMiq3GeXIa/ebb77R888/ry5duuiZZ57Rzp071aBBA7388ssO58nNY8tpRqXdYeauu+7Sxx9/rEceeUSvv/663njjDW3btk2RkZHWvml3jOnbt6+WL1+uefPmqVKlSnr44Yd16NAhh8eOrJFP5BP5RD65KvKJfHLWZ7y0LLBYLIqMjNTjjz+uwYMHa/Xq1Tp//rxmzpyZrf3cOnYpd5/xcpqZn3zyiebOnasXX3xRtWvXdnjsjuDue/9TtWpVm699fX3l5eWlChUq2LWfO3fO+vXBgwe1f/9+VaxYMd3tnj592vr/b775RuPGjVNMTIzdLSETEhLk6+ub4XgkqWzZsjp//nz2J3WLKlWq2JzDLt2YT1BQkF2bJOu+zpw5owsXLujDDz/Uhx9+mO620+Z55MgR1apVy24/ISEh2RrjV199pX//+9/avXu3zTm4t25PyvvnyNvbO93zftPOvc3uuf4tW7a0/r9Xr16qW7eupBvnUqdn8eLFcnd3V8+ePR0dslXa2DIav6PXKVi8eLHKlSunjh072rT/+eefGjZsmJ544gndddddOR4vHEdG2bZJZFSa7GRUbl+7Hh4eGjp0qPXNjCN30rp5bDnNqLTljz32mE37448/rjFjxmjbtm0KCwuTJPXo0UPFihXTl19+ae3XtWtX6+3Zly5d6tDYkTXyybZNIp/SkE/kU34jn2zbJPIpTV5/xkvbTufOnVWyZElr37vvvlvVq1fXtm3bcjR2Ke8+40nZy8wtW7ZowIABCg8P15tvvunwPhxFUep/3N3ds9Um3ajKpklNTVXDhg01ZcqUdPumhcHhw4fVtm1b1alTR1OmTFFQUJA8PDy0du1aTZ061e62mdnZt6My2mZW+0obW+/evdW3b990+zZq1CjH40qzZcsWdenSRffdd59mzZqlSpUqqXjx4oqIiNAnn3zi8LgdValSJZ04ccKu/dSpU5KkwMBAh7dZtmxZPfjgg1q8eHG6RamrV69q1apVCgsLk7+/v+OD/p9KlSrZjPVmp06dcmjsR48e1ZYtWzRo0CAVL17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      "text/plain": [
       "<Figure size 1200x1200 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for _, entry in enumerate(isi_dict):\n",
    "    # iterate over channel\n",
    "    for channel in range(len(isi_dict[entry])):\n",
    "        # print('\\nWorking on channel: ', channel)\n",
    "        # extract single ISIs and their count\n",
    "        isi, count = np.unique(isi_dict[entry][channel][0], return_counts=True)\n",
    "\n",
    "        # only plot if ISIs found in channel\n",
    "        if len(isi) > 0:\n",
    "            if norm_time:\n",
    "                isi = isi/max(isi)\n",
    "            if norm_count:\n",
    "                count = count/max(count)\n",
    "\n",
    "            # create box plots with all classes from paper and single sensor and class for Braille data\n",
    "            figname = f'comparison ISI\\nLetter: {braille_letters[entry]} Sensor: {channel}'\n",
    "            plt.figure(figsize=(12, 12))\n",
    "            plt.suptitle(figname)\n",
    "\n",
    "            # mean and std for channel\n",
    "            mean_isi_braille = [np.mean(isi), np.std(isi)]\n",
    "            mean_count_braille = [np.mean(count), np.std(count)]\n",
    "            # print('Mean Braille: ', [mean_isi_braille[0], mean_count_braille[0]])\n",
    "            \n",
    "            # median for channel\n",
    "            median_isi_braille, median_count_braille = np.median(isi), np.median(count)\n",
    "            # print('Median Braille: ', [median_isi_braille, median_count_braille])\n",
    "            \n",
    "        \n",
    "            # compare to original traces\n",
    "            for num, isi_original_sel in enumerate(isi_original):\n",
    "                # TODO check repetitions of plots!!!\n",
    "                # # problem: differnet length of input\n",
    "\n",
    "                # # https://stats.stackexchange.com/questions/194023/how-to-measure-similarity-of-two-lists-of-continuous-data-with-different-length\n",
    "                # # I: try a curve fit on both distributions and from fitted curve the distance can be calculated\n",
    "\n",
    "                # II: calc some statistics and use corralation\n",
    "                import math\n",
    "                # calc mean and std\n",
    "                mean_isi_original_sel = [np.mean(isi_original_sel[0]), np.std(isi_original_sel[0])]\n",
    "                mean_count_original_sel = [np.mean(isi_original_sel[1]), np.std(isi_original_sel[1])]\n",
    "                \n",
    "                # print('Mean original: ', [mean_isi_original_sel[0], mean_count_original_sel[0]])\n",
    "                distance_mean = math.dist([mean_isi_original_sel[0], mean_count_original_sel[0]], [mean_isi_braille[0], mean_count_braille[0]])\n",
    "                # print('Mean dist.: ', distance_mean)\n",
    "\n",
    "                # calc meadian\n",
    "                median_isi_original_sel, median_count_original_sel = np.median(isi_original_sel[0]), np.median(isi_original_sel[1])\n",
    "                # print('Median original: ', [median_isi_original_sel, median_count_original_sel])\n",
    "                \n",
    "                distance_median = math.dist([median_isi_original_sel, median_count_original_sel], [median_isi_braille, median_count_braille])\n",
    "                # print('Median dist.: ', distance_median)\n",
    "                # calc Eukledean distance\n",
    "                # print('Input: ', [isi_original[0], count_original[0]], [isi_braille[0], count_braille[0]])\n",
    "                \n",
    "                # print('Distance: ', distance)\n",
    "\n",
    "                # # calc covariance between mean isi and count\n",
    "                # covariance = np.cov([isi_original[0], count_original[0]], [isi_braille[0], count_braille[0]])\n",
    "                # print('covariance: ', covariance)\n",
    "\n",
    "                # calculate Pearson's correlation\n",
    "                # https://machinelearningmastery.com/how-to-use-correlation-to-understand-the-relationship-between-variables/\n",
    "                # from scipy.stats import pearsonr\n",
    "                # print('Input for Pearson\\'s correlation:')\n",
    "                # print([isi_original[0], count_original[0]], [isi_braille[0], count_braille[0]])\n",
    "                # # calc Pearson's correlation isi_braille[0]\n",
    "                # corr, _ = pearsonr([isi_original[0], count_original[0]], [isi_braille[0], count_braille[0]])\n",
    "                # print('Pearson\\'s correlation: ', corr)\n",
    "                \n",
    "\n",
    "                # create bar plot for Braille and original ISIs\n",
    "                plt.subplot(5, 4, num+1)\n",
    "                # plt.title(f'{isi_original_sel} spikes in {classes_list[braille_letters[num]]}')\n",
    "                plt.title('mean, median: {:.2f}, {:.2f}' .format(distance_mean, distance_median))\n",
    "                if norm_time:\n",
    "                    plt.bar(isi_original_sel[0],\n",
    "                            isi_original_sel[1], width=0.01)\n",
    "                    plt.bar(isi, count, width=0.01)\n",
    "                else:\n",
    "                    plt.bar(isi_original_sel[0], isi_original_sel[1])\n",
    "                    plt.bar(isi, count)\n",
    "                plt.xlim((0, 1.1))\n",
    "\n",
    "            plt.tight_layout()\n",
    "            plt.savefig(\n",
    "                f'{plot_out}/comparison_class_{braille_letters[entry]}_channel_{channel}_bar.png', dpi=300)\n",
    "            plt.close()\n",
    "            # plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\"><img alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by/4.0/88x31.png\" /></a><br />This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\">Creative Commons Attribution 4.0 International License</a>."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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