{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compute FWHM of PSTHs for 11 and 30Hz\n",
    "\n",
    "Only PSTHs with a significance level below 0.05 are used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:21: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.*` instead of `tqdm._tqdm_notebook.*`\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import spatial_maps as sp\n",
    "import septum_mec.analysis.data_processing as dp\n",
    "import septum_mec.analysis.registration\n",
    "import expipe\n",
    "import os\n",
    "import pathlib\n",
    "import numpy as np\n",
    "import exdir\n",
    "import pandas as pd\n",
    "import optogenetics as og\n",
    "import quantities as pq\n",
    "import shutil\n",
    "from distutils.dir_util import copy_tree\n",
    "import seaborn as sns\n",
    "from functools import reduce\n",
    "from septum_mec.analysis.stimulus_response import stimulus_response_latency, compute_response\n",
    "import scipy.stats\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "from tqdm._tqdm_notebook import tqdm_notebook\n",
    "from septum_mec.analysis.statistics import load_data_frames, make_paired_tables, make_statistics_table \n",
    "tqdm_notebook.pandas()\n",
    "\n",
    "%matplotlib widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "std_gaussian_kde = 0.04\n",
    "window_size = 0.03\n",
    "tmin = 0. # delay to compute FWHM after stimulus onset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_loader = dp.Data()\n",
    "actions = data_loader.actions\n",
    "project = data_loader.project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = pathlib.Path('output/stimulus-response-fwhm')\n",
    "(output / 'data').mkdir(parents=True, exist_ok=True)\n",
    "(output / 'figures').mkdir(parents=True, exist_ok=True)\n",
    "(output / 'statistics').mkdir(parents=True, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = ['#d95f02','#e7298a']\n",
    "labels = [\n",
    "    '11 Hz', \n",
    "    '30 Hz'\n",
    "]\n",
    "queries = [\n",
    "    'frequency==11 and stim_location==\"ms\"',\n",
    "    'frequency==30 and stim_location==\"ms\"']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of sessions above threshold 194\n",
      "Number of animals 4\n",
      "Number of individual gridcells 139\n",
      "Number of gridcell recordings 230\n"
     ]
    }
   ],
   "source": [
    "data, labels, colors, queries = load_data_frames(queries, labels, colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use baseline==False and p_e_peak < 0.05\n",
    "session_units_sig = data.query('p_e_peak == p_e_peak')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>action</th>\n",
       "      <th>baseline</th>\n",
       "      <th>entity</th>\n",
       "      <th>frequency</th>\n",
       "      <th>i</th>\n",
       "      <th>ii</th>\n",
       "      <th>session</th>\n",
       "      <th>stim_location</th>\n",
       "      <th>stimulated</th>\n",
       "      <th>tag</th>\n",
       "      <th>...</th>\n",
       "      <th>bs_ctrl</th>\n",
       "      <th>ns_inhibited</th>\n",
       "      <th>ns_not_inhibited</th>\n",
       "      <th>gridcell</th>\n",
       "      <th>bs_not_gridcell</th>\n",
       "      <th>label</th>\n",
       "      <th>label_num</th>\n",
       "      <th>query</th>\n",
       "      <th>color</th>\n",
       "      <th>cell_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1839-120619-4</td>\n",
       "      <td>False</td>\n",
       "      <td>1839</td>\n",
       "      <td>30.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>ms</td>\n",
       "      <td>True</td>\n",
       "      <td>stim ii</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>30 Hz</td>\n",
       "      <td>1.0</td>\n",
       "      <td>frequency==30 and stim_location==\"ms\"</td>\n",
       "      <td>#e7298a</td>\n",
       "      <td>bs_not_gridcell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1839-120619-4</td>\n",
       "      <td>False</td>\n",
       "      <td>1839</td>\n",
       "      <td>30.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>ms</td>\n",
       "      <td>True</td>\n",
       "      <td>stim ii</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>30 Hz</td>\n",
       "      <td>1.0</td>\n",
       "      <td>frequency==30 and stim_location==\"ms\"</td>\n",
       "      <td>#e7298a</td>\n",
       "      <td>gridcell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1839-120619-4</td>\n",
       "      <td>False</td>\n",
       "      <td>1839</td>\n",
       "      <td>30.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>ms</td>\n",
       "      <td>True</td>\n",
       "      <td>stim ii</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>30 Hz</td>\n",
       "      <td>1.0</td>\n",
       "      <td>frequency==30 and stim_location==\"ms\"</td>\n",
       "      <td>#e7298a</td>\n",
       "      <td>ns_inhibited</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1839-120619-4</td>\n",
       "      <td>False</td>\n",
       "      <td>1839</td>\n",
       "      <td>30.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>ms</td>\n",
       "      <td>True</td>\n",
       "      <td>stim ii</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>30 Hz</td>\n",
       "      <td>1.0</td>\n",
       "      <td>frequency==30 and stim_location==\"ms\"</td>\n",
       "      <td>#e7298a</td>\n",
       "      <td>bs_not_gridcell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1839-120619-4</td>\n",
       "      <td>False</td>\n",
       "      <td>1839</td>\n",
       "      <td>30.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>ms</td>\n",
       "      <td>True</td>\n",
       "      <td>stim ii</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>30 Hz</td>\n",
       "      <td>1.0</td>\n",
       "      <td>frequency==30 and stim_location==\"ms\"</td>\n",
       "      <td>#e7298a</td>\n",
       "      <td>bs_not_gridcell</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1279</th>\n",
       "      <td>1833-010719-2</td>\n",
       "      <td>False</td>\n",
       "      <td>1833</td>\n",
       "      <td>11.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>ms</td>\n",
       "      <td>True</td>\n",
       "      <td>stim i</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>11 Hz</td>\n",
       "      <td>0.0</td>\n",
       "      <td>frequency==11 and stim_location==\"ms\"</td>\n",
       "      <td>#d95f02</td>\n",
       "      <td>bs_not_gridcell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>1833-010719-2</td>\n",
       "      <td>False</td>\n",
       "      <td>1833</td>\n",
       "      <td>11.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>ms</td>\n",
       "      <td>True</td>\n",
       "      <td>stim i</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>11 Hz</td>\n",
       "      <td>0.0</td>\n",
       "      <td>frequency==11 and stim_location==\"ms\"</td>\n",
       "      <td>#d95f02</td>\n",
       "      <td>bs_not_gridcell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>1833-010719-2</td>\n",
       "      <td>False</td>\n",
       "      <td>1833</td>\n",
       "      <td>11.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>ms</td>\n",
       "      <td>True</td>\n",
       "      <td>stim i</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>11 Hz</td>\n",
       "      <td>0.0</td>\n",
       "      <td>frequency==11 and stim_location==\"ms\"</td>\n",
       "      <td>#d95f02</td>\n",
       "      <td>bs_not_gridcell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>1833-010719-2</td>\n",
       "      <td>False</td>\n",
       "      <td>1833</td>\n",
       "      <td>11.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>ms</td>\n",
       "      <td>True</td>\n",
       "      <td>stim i</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>11 Hz</td>\n",
       "      <td>0.0</td>\n",
       "      <td>frequency==11 and stim_location==\"ms\"</td>\n",
       "      <td>#d95f02</td>\n",
       "      <td>bs_not_gridcell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>1833-010719-2</td>\n",
       "      <td>False</td>\n",
       "      <td>1833</td>\n",
       "      <td>11.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>ms</td>\n",
       "      <td>True</td>\n",
       "      <td>stim i</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>11 Hz</td>\n",
       "      <td>0.0</td>\n",
       "      <td>frequency==11 and stim_location==\"ms\"</td>\n",
       "      <td>#d95f02</td>\n",
       "      <td>bs_not_gridcell</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>647 rows × 68 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             action  baseline  entity  frequency      i     ii  session  \\\n",
       "13    1839-120619-4     False    1839       30.0  False   True        4   \n",
       "14    1839-120619-4     False    1839       30.0  False   True        4   \n",
       "15    1839-120619-4     False    1839       30.0  False   True        4   \n",
       "16    1839-120619-4     False    1839       30.0  False   True        4   \n",
       "17    1839-120619-4     False    1839       30.0  False   True        4   \n",
       "...             ...       ...     ...        ...    ...    ...      ...   \n",
       "1279  1833-010719-2     False    1833       11.0   True  False        2   \n",
       "1280  1833-010719-2     False    1833       11.0   True  False        2   \n",
       "1281  1833-010719-2     False    1833       11.0   True  False        2   \n",
       "1282  1833-010719-2     False    1833       11.0   True  False        2   \n",
       "1283  1833-010719-2     False    1833       11.0   True  False        2   \n",
       "\n",
       "     stim_location  stimulated      tag  ...  bs_ctrl ns_inhibited  \\\n",
       "13              ms        True  stim ii  ...      NaN        False   \n",
       "14              ms        True  stim ii  ...      NaN         True   \n",
       "15              ms        True  stim ii  ...      NaN         True   \n",
       "16              ms        True  stim ii  ...      NaN        False   \n",
       "17              ms        True  stim ii  ...      NaN        False   \n",
       "...            ...         ...      ...  ...      ...          ...   \n",
       "1279            ms        True   stim i  ...      NaN        False   \n",
       "1280            ms        True   stim i  ...      NaN        False   \n",
       "1281            ms        True   stim i  ...      NaN        False   \n",
       "1282            ms        True   stim i  ...      NaN        False   \n",
       "1283            ms        True   stim i  ...      NaN        False   \n",
       "\n",
       "      ns_not_inhibited  gridcell  bs_not_gridcell  label  label_num  \\\n",
       "13               False     False             True  30 Hz        1.0   \n",
       "14               False      True            False  30 Hz        1.0   \n",
       "15               False     False            False  30 Hz        1.0   \n",
       "16               False     False             True  30 Hz        1.0   \n",
       "17               False     False             True  30 Hz        1.0   \n",
       "...                ...       ...              ...    ...        ...   \n",
       "1279             False     False             True  11 Hz        0.0   \n",
       "1280             False     False             True  11 Hz        0.0   \n",
       "1281             False     False             True  11 Hz        0.0   \n",
       "1282             False     False             True  11 Hz        0.0   \n",
       "1283             False     False             True  11 Hz        0.0   \n",
       "\n",
       "                                      query    color        cell_type  \n",
       "13    frequency==30 and stim_location==\"ms\"  #e7298a  bs_not_gridcell  \n",
       "14    frequency==30 and stim_location==\"ms\"  #e7298a         gridcell  \n",
       "15    frequency==30 and stim_location==\"ms\"  #e7298a     ns_inhibited  \n",
       "16    frequency==30 and stim_location==\"ms\"  #e7298a  bs_not_gridcell  \n",
       "17    frequency==30 and stim_location==\"ms\"  #e7298a  bs_not_gridcell  \n",
       "...                                     ...      ...              ...  \n",
       "1279  frequency==11 and stim_location==\"ms\"  #d95f02  bs_not_gridcell  \n",
       "1280  frequency==11 and stim_location==\"ms\"  #d95f02  bs_not_gridcell  \n",
       "1281  frequency==11 and stim_location==\"ms\"  #d95f02  bs_not_gridcell  \n",
       "1282  frequency==11 and stim_location==\"ms\"  #d95f02  bs_not_gridcell  \n",
       "1283  frequency==11 and stim_location==\"ms\"  #d95f02  bs_not_gridcell  \n",
       "\n",
       "[647 rows x 68 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session_units_sig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_fwhm(psth, times, t_min=None):\n",
    "    \"\"\"\n",
    "    Compute PSTH. The function handles a single peak or multiple peaks. In the latter case, the peak containing the max peak is used\n",
    "    \"\"\"\n",
    "    times_accepted = times > tmin\n",
    "    max_idx = np.argmax(psth[times_accepted])\n",
    "    idxs_greater = np.where(psth[times_accepted] > 0.5 * np.ptp(psth[times_accepted]))[0]\n",
    "    \n",
    "    if np.all(np.diff(idxs_greater) == 1):\n",
    "        fwhm = times[times_accepted][idxs_greater[-1]] - times[times_accepted][idxs_greater[0]]\n",
    "    else:\n",
    "        # deal with multiple peaks. when multiple peaks are found, the one containing max_idx is used\n",
    "        diff_splits = np.where((np.diff(idxs_greater) == 1) == False)[0]\n",
    "        idxs_list = []\n",
    "        for i, ds in enumerate(diff_splits):\n",
    "            if i == 0:\n",
    "                idxs_list.append(idxs_greater[:ds+1])\n",
    "            elif i < len(diff_splits):\n",
    "                idxs_list.append(idxs_greater[diff_splits[i-1]+1:ds+1])\n",
    "        idxs_list.append(idxs_greater[ds+1:])\n",
    "        \n",
    "        for idxs in idxs_list:\n",
    "            if max_idx in idxs:\n",
    "                fwhm = times[times_accepted][idxs[-1]] - times[times_accepted][idxs[0]]\n",
    "        \n",
    "    return fwhm, psth[times_accepted][max_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "times = np.arange(-0.005, window_size, 1e-4)\n",
    "tmin = 0.005\n",
    "psths = []\n",
    "def process(row):\n",
    "    action_id = row['action']\n",
    "    channel_group = row['channel_group']\n",
    "    unit_name = row['unit_name']\n",
    "    name = f'{action_id}_{channel_group}_{unit_name}'\n",
    "    spike_times = data_loader.spike_train(action_id, channel_group, unit_name)\n",
    "    \n",
    "    spike_times = np.array(spike_times)\n",
    "    \n",
    "    stim_times = data_loader.stim_times(action_id) \n",
    "    \n",
    "    stim_times = np.array(stim_times)\n",
    "    \n",
    "    _, spikes, kernel, p_e, p_i = stimulus_response_latency(\n",
    "        spike_times, stim_times, window_size, std_gaussian_kde)\n",
    "    \n",
    "    psths.append(kernel(times))\n",
    "    fwhm, peak = compute_fwhm(kernel(times), times, tmin)\n",
    "    return pd.Series({\n",
    "        'fwhm': fwhm,\n",
    "        'peak': peak\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f8d93b5929ac48ca88f0b9acd49b97db",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=647), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "session_units_sig = session_units_sig.merge(\n",
    "    session_units_sig.progress_apply(process, axis=1), \n",
    "    left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Quality control\n",
    "fig = plt.figure()\n",
    "for psth in psths:\n",
    "    plt.plot(times, psth, color='C0', alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "keys = [\n",
    "    'fwhm',\n",
    "    'peak'\n",
    "]\n",
    "\n",
    "results, labels = make_paired_tables(session_units_sig, keys, queries=queries, labels=labels, cell_types=['gridcell', 'ns_inhibited', 'ns_not_inhibited'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot FWHM grouped by stimulation frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rc('axes', titlesize=12)\n",
    "plt.rcParams.update({\n",
    "    'font.size': 12,\n",
    "    'figure.figsize': (1.7, 3),\n",
    "    'figure.dpi': 150\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def violinplot2(data, xticks, colors):\n",
    "    pos = [i * 0.6 for i in range(len(data))]\n",
    "    print(pos)\n",
    "    violins = plt.violinplot(data, pos, showmeans=True, showextrema=False)\n",
    "    \n",
    "    for i, b in enumerate(violins['bodies']):\n",
    "        b.set_color(colors[i])\n",
    "        b.set_alpha (0.8)\n",
    "\n",
    " \n",
    "\n",
    "    # for i, body in enumerate(violins['cbars']):\n",
    "    #     body.set_color('C{}'.format(i))\n",
    "\n",
    " \n",
    "\n",
    "    for category in ['cbars', 'cmins', 'cmaxes', 'cmedians', 'cmeans']:\n",
    "        if category in violins:\n",
    "            violins[category].set_color(['k', 'k'])\n",
    "            violins[category].set_linewidth(2.0)\n",
    "    plt.xticks(pos, xticks, rotation=45)\n",
    "    plt.gca().spines['top'].set_visible(False)\n",
    "    plt.gca().spines['right'].set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0, 0.6]\n",
      "[0.0, 0.6]\n",
      "[0.0, 0.6]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 255x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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KhZATlWTsdREiYILQZYQTFZcD85LzwAhCwgnxwARBSBFbDbDlGoQ2sSS+yihsYLGT4oEJgpAitliJlhFZ8BIKIT0PwlAETBCEdAmLFZf/8hJYRhQT58CKImCCIKSILVYhDJPzvsDZCi224mNrzXZs725EwAShi7BTVWyYYPgIbj2ktU7EpqpLn99DiIAJQhcRTlWjItbkBEwpNeuFiYAJgpAWdrrqZiCT9MAAFQlYON1fHdFFwAShi7AlV0KhEhYwPIUNrRPIPkIETBC6CDtdczmwpGYgY+IQUjwwQRDSwpZqYJMPIWcErCwCJghCStiKn0oODBULmJ+s3Q4jAiYIXYQt+84DSziEVJ4CKwImCEKapOmBWQtVETBBEFLCVmIPLGHD8SxkRQRMEISUsLXA6VfiHhhgkaVEgiCkSDVwIWTSZRQzIaQImCAIKWFrgdtGOekQMvbA+kzAktqZW1gB2DDEH9vlPskjVLZA9rAXdnBUfUYtjH6+KXlgfpis3Q4jAiY0zOiPL2Lq4Z+6ZnsRSnkc8bsfY9Vp7+7gyPoHG0QCk5YHJgImrFRqezdRG9uFrVWiT/SQzNBhVPdu7vTQ+gc/TKUOzNkTD0xYyURvquzhx+INjBBMHoCwv3IqncYGIXMc3ISN45ol9hGSxBcSIOl4ZwUTRuqVRggJbqF4HyECJjSO8njuO8sVXKqktv8SZgUsDWzK9juA/OUJjaO8SL6iN4EFUOBlOjakvsOm5IHN2E/JbocQARMaRnmZKHkffyd+s8mfUWJY0imjmJmG7C8Fk788oXG8eM7Hzv6vFMqTuaCeob/0SwRMaByVyc5Ox8Psp7mEkL1Dn823iIAJDeM8rWiLLsAl8RUqIwKWGKkJTBSWJl1f1mFEwITGyeaemwOzFvBQmXwnR9VfKDW77CcV++mY7RQiYELDqEwO9w6IiiGtde+3bK6Tw+ovkm6jMxeVsv0OIAImNIzK5Od5B9bNQEoSPznSCvHiX5mEkMJKRWXzbpdnOzeJr1C5gY6Oq59QWW+m4iEV+5n+esv316sRUkXlCs7jmhGw0CXxs5IDS4xMSjmweIF4VjwwYYWisgVQChvlwKzrfey+LyRDWh5SPAkpHpiwUlHZeTkwG6KUcp6ZkAhph5CpCWSH6K9XI6SKyg1EIeTsLCRKcmCJkvVSDiH76y3fX69GSBWVG0A9Lwfm4YmAJYbKZdLxwOIQMtdfRcciYELDeLmBmU6sMCcHJgKWHGl7YLn+esv316sRUiUOIe1MCBlKCJkwKp9FqbnLtRIiDvfz/VWzJwImNIzKD7ocWGjdG8yGKOWhcoOdHlrfoHIpJfElhBRWOio/GBWyhsztBeYVhjo6rr4in00nhAyt2+27IB6YsELxcoOzs5BhOLPwWELI5FD5jFuvmLCA2ZkQUjwwYYWiCkOzObCZIta869QqJIIqZJ+zXj4x4hBSBExYqXi5waiMIpwpYvXyEj4miROwdEJIPOXs9xEiYELDqPwgeK4OzNoQVEbCx6QZyLoQMundg+IyioH+ErDEXo3Wegj4GPAe4CRgEngQ+LIx5roWbZ4A/B3wNuBoYD9wE/A5Y8xTi1z3FuAvgNOAtUAReBT4NnC5MWbBvw6t9SuBTwG/BhwG7AauBT5jjHmmldfQT6j80OwGHqHvQsi8zEAmiRqIyiiSFjDxwOqjtR4GbgYuAE4G1gNTwFuAa7XWF7RgUwMPAX8CjOAEaAA4F3hIa/3WOtd9EfgZ8N+B4WgsPnAWcBnwY6318zrwaa3fBNwPvBv3c3kcJ35/Djyutf7lZl9Dv+HWQnouDxb4KE9CyKRRAznwSKUOTHkKNdhfzSeTCiG/jvN2HgFOMca8xhhzIvA+nHhcqLX+zUaNaa2zwNU4AbkcOMYY83rgGOBrOCH7gdZ67bzr3gv8FRBEx8OMMb9ijDka+AOcV/gO4O/nXXcE8JPI7sXR/V4HvAi4CjgcuEprvaL7xricV5QHEw8sFdRA1oXpSSfxQ9wsZJ+FkG0LmNb6FOAc3I/ovcaYnfFzxpjLgc9HDy9swuw5wDpgB/BBY0wpslcFzgdux4V4H5l33d9Ex38yxnzJGDPzZ2CM+Q+cqAGcp7We20LhfJxI3WOM+YQxxo+umQT+CNiC8yzf18Rr6EviPJgNnICJB5YsaiCdJL6NQ0jxwJ7HuUAGuNsY8+QCz38jOp4R5bQa4f3R8fJItGaI8lf/Ej08O/5+5EWdGj38tzp2fxwdh4FXLHC/b82/ILr/pfPvt1LxZqrxxQNLAzWYSzyJ71ZNiIDV4/ToeMdCT0bJ7+3RwzcvZUxr7QFvWMwmcGd0PFlrfXz0dRl4Oy5n9USd6+a2o8xE9zsGOLHB+52xUP5sJREn8m0YoDzPPRYSQw3kXMV8kkn80LoVFCrKsfURSQTE66Lj04ucsw0nEi9twN6xQPyxXs/mTlyeKxPZ3GmMmcblzRbjD6JjDdgUfR2P3wJb61y3LToWgBMWGVff4+WH3CyZX5MQMgXUUPIe2MwMZC7jGib2EUm8mqOj4/5FzhmNjkc2Ya+uTWNMAIw3YTP2tC6KHv7EGBNfH99vwhhTqXP56JyvG7pfvzLTkSL0o6UpUgeWJGooD17CZRSxgA31l/cFyQhY/BFcXuSc0rxzG7GXmE2t9Rqcd3Ykribsbxe4XyP3auh+/cxMRwprUcpzOTEhMWY8MGuTK6Xo0wQ+JCNgQRPnNvIbacbekja11kcCNwKvic79gDFmc1r363dUbhDlRX820koncdRAzuWqVIJhZJ8m8CEZAStGx8ViifivfLoJe23b1FqvwyXgX4cr8/jTqJxiofs1cq9F77cS8ApzqvFlFjJxVNab7UiRkIDZqJWOGuq/MsYkBOxAdFy7yDlx3mhfE/bq2owKXdcsZlNrfSZwDy7JXwXONsY8r0xizv1WLTLDODfv1chr6FtU3FYaoiS+CFjSxHmwxDwwyYEtSrwm8cWLnBM/t3EpY8aYZ5lN0NezeTxRGcRCNrXW78aFjWuBg8BvGWOurGMrHr+Hm2FciHgcZdwM6IplZnNbkG6sKaGG0xIw8cAW4t7oePpCT2qtj2NWGO5q0OZ9i9kE3hgdt0eCN/d+7wGuwJU8bAXeaIy5rd6NjDFjzJZULHW/e6MZ0BXLTEsdkGaGKZF4KUXgBMwTD2xBfhgdz4oWYM/nz6LjrcaYbQ3ajL2lD9RZfxjb/M7cb2qtTwO+y+xi7DcaY0wT9/vQ/Cei+//xQvdbicwPIUXAkscbyqOSLKWQHFh9jDGbcB5PBvhRlDgHQGt9DvDx6OGn51+rtT5Fa/2yqEZrLt/DFYueDFyhtV4VnZ/XWl8CnIkLM786x1YGJ145XJ7qbcaYPQ2+jEuAQ8CbtNaXxKIZ3ff70Ti2RF+vaGY2twXxwFIi6RzYzDrIkf4TsKSWpp8PvCr6t0Fr/ThucXS8ROeTxpgbF7jupuicy5hdj4gxpqy1Phv4OfAu4K1a6w04ITkCl5R/pzFmboHpO5mt9K8BVy7sEM5wnjHm4eh++7TW5+I6T5wHvFdrvQXQwCqcuL3DGFNr4GfR18wVMCWb2qbCTA4sSKglRRiC54kHVo9ISE7HVbpvBF6OS6DfCrzLGPPZFmzej1uc/S2cgJyKK4W4CjjNGHPLvEvmrrM8FjhjiX9r5l5sjLkaV27x7zgB/GVcicVlwGuNMeubfQ39iMoVXI0SRCFkYfELhKZRQznIeDIL2QCJNQcyxkzhWuZc2MQ1L17i+e3ABxu0dR7Oe2oZY8zjuI6yQh2emwNTqKwIWNKoOAdWS1bAvGHxwIQVjpctSA4sZbzhhNdDShJfECKy+Wg/SNdaGtlSLXGSrgOzoYVMf4aQImBCU7iQUaGUQmVys/kwITHUcC5qK92+gFlroxDSQ430X7gvAiY0hcrmXSGrUpLATwk1nE+uqeHcZobigQkrHTUTQjoPTEgeNZSHjHLlD+0yZzs1lem/t3v/vSIhVVQmEjA8VKa/drjpFuYm8dvuCRYLWB/OQIIImNAkKpsnaljFc7cYEJJCDUcfEpb2u88FLoHfjyUUIAImNInK5GZnIYV0yGdQGS+RPFg/9wIDETChWaKw0c0+rujmtKmhlJrTkaLNPNjMMqL+zFeKgAlNMXcZkZAerhYsgVIKyYEJwgJ48qeTJm49ZAKlFH3czBBEwIQWUeKBpYpKqidYHy/kBhEwoVWUh8xCpkdSXVkliS8ICyFJ/FRJrKlh3ImiD7dUAxEwoVXEA0sVbzAqV5EQclFEwITWEA8sVdyCbhGwpRABE1pCifeVKmowl1gSX3JggjAfCSFTRQ0m5IHZyAOTHJggzEH6gKWKGkwmiT+zI9FAfy68FwETWkNyYKmiBrNtC9hMM0MlHpggzEO6UaSJGohmIdtppxNfKiGkIMxDQshUUQPZ9rtRxAl8T0G+P/cuEAETWsLNQkoImRoDUQjZjgcW5b/IZ/t27wIRMKE11Mx/QgrEIaRtp5uOjfNf/ZnABxEwoWVEvNJEzfHAWm4rPacffr8iAia0hsxCporKZ2c7d7eaB7PW2RjozwQ+iIAJbSFeWGrkopbScW/8FrAWF0L2aQIfRMCEVhEPLFWUUpDLtFdKEc1CIiGkIMxH6sDSRuUz7c1Exkl88cAEQVhu4jwYrc5EWlwOLCcCJgjPRULI9Mm3GUJGHli/FrGCCJjQFhJCponKerRVLywemCAIHSNO4rczDakU5Pr3bd6/r0xIFVlKlD4q67W3njv2wDL9+zbv31cmLAMSQqZKNmoa2W4OLCshpCA8F9Gu9Mm0mQOLUNn+/WWJgAltICFkmrQd+kUOWD/vot6/r0xIGSlkTZ2ZpURtJsEy/ft7EgEThG6l3XdnpF/93HxSBExoAwkhU0WpRJxc5YmACcIC9O8boyto03NaCR8vImBCG6yEt0jnmNGvdn/Mffw5IwImCF3KTO6+RQGauayPP2dEwAShWwltIuLTzr4g3Y4ImCB0Le1WsEYm2tzdu5sRARPaoI+TK92Ab5mtRm2FSMHCdrY26m5EwAShWwnD9pywuAY2EA9MEBagf98Y3YD1w9li1HbsBOKBCYKw3Pih2xOy1RAyXoZUC5IdVxchAiYIXYqNhKfdFJj1xQMThHlI+Jg6tdDNILbrgVX71wNLbMM4rfUQ8DHgPcBJwCTwIPBlY8x1Ldo8Afg74G3A0cB+4Cbgc8aYpxq04QG3AWcAOWOMX+e8s4BbljD3E2PM7zU2+j7HgsxCpout+rNNCVvBA3yLrSz4J98XJOKBaa2HgZuBC4CTgfXAFPAW4Fqt9QUt2NTAQ8CfACPAo8AAcC7wkNb6rQ2a+ixOvJbi1Oi4G7izzr8nGx2/ILSLrfjtJfGVgjASwj4lKQ/s68BpwCPAO4wxOwG01ucClwIXaq3vNMbc2IgxrXUWuBpYC1wO/A9jTElrnQe+CHwY+IHWep0xZrSOjQzweeCjDb6GWMC+Yoy5uMFrViyWqEZJSA1b9l0I2Wo3iWhTXFvuXwFr2wPTWp8CnIPbfvO9sXgBGGMux4kIwIVNmD0HWAfsAD5ojClF9qrA+cDtwGHAR+qM6SW4ULNR8YJZAXu8iWtWOBJCpoUNo9CvDQFTSjk7pVrCo+sekgghzwUywN3GmIVCrG9ExzOinFYjvD86Xh6J1gzGGAv8S/Tw7PkXaq3/EhfCvhnYicvLLUrk8f1S9PCJBscoCKlhSzU3g9hWDkxBaLHTImCLcXp0vGOhJ40xzwDbo4dvXspYlHR/w2I2cfkogJO11sfPe+710fGrwCuB+5e6J6CBAjBujNnRwPmClRAyTex0dWYGsuWGhDMCVl363B4liRzYuuj49CLnbANOBF7agL1jgcElbO4EApzn99LoccxVwEXGmK0Abi5gSeLwcb3W+jW4EPaV0T3WA5cZYyS0fB4SQqaFnapCGLbXTTUWsKkq1lpUH7aWTsIDOzo67l/knDjRfmQT9uraNMYEwPhCNo0xP43FqwliAXs1rvTjI8Bv4co3/hp4RGv96SZt9jfigaWKnaxgA9vehhwZDxuE2FoIfVpKkYSADUXH8iLnlOad24i9JG0uRSxgA8BncKUgBeAlwJdxrsYntdZL5tNWDieA4y8AACAASURBVAks0hPqEk5W2puBhNkNPULr7PUhSYSQAY0LYSMf2c2WDSfhBlwNPAtcY4y5as73NwMf0VofAD4NXKC1vtQYcyCBe/Y44n2liZ2oQBC6zW1bRCmFyigIQmfvqJEER9gdJCFgReBwnPdSjzinNd2gvZgB6nthzdhcFGPM15Y45QvAJ3AFtW8Brmj3nj2PnflPSIFwogxB2P7mthkPG1pnrw9JIoSMvZG1i5wT56n2NWGvrs2o7GFNEzbbwhhTYbYK/6S079cT9HOf4i4gPFRqPwcG7no/JDxUWvrcHiQJAYvXJL54kXPi5zYuZcwY8yyzCfp6No/HzUA2ZLMRtNaLeZAw+7Pq36KaJrCSA0uVcKwEfgjZ9j0wgtDZ60OSELB7o+PpCz2ptT4OiAtY72rQ5n2L2QTeGB23R4LXMlrrV2utx4FSVEKx0DkDwCuih7IeEmQWMmWcB9Z+CKkyHtYPCcfazrR0JUkI2A+j41l64aKrP4uOtxpjtjVo88ro+IFo/WM9m99p0N5iGNwyKJhdATCfD+NmOw/gligJNkQ8sPQIR6eS8cCykQc2KgK2IMaYTbikdgb4kdY6LmxFa30O8PHo4fPqqLTWp2itX6a1PmbeU9/DFbGeDFyhtV4VnZ/XWl8CnIkLM7+awPgrwD9GDz+stT4/Wg2A1trTWv8F8Lno+U/E6zIF8b7SwtaC2RCyXQ8sG3lgImCLcj5uEfQrgA1a64e11ttwnSSywCfrdKK4CZdD+9zcbxpjyrh1juPAu4Bntdb341rdnAdUgXfW60TRAp8FfoBzKb4C7I/utxfXaSMDXGiM+VZC9+t9JIRMjfDgNAQuy9h2Ej/ruST+gakkhtZ1JCJgkZCcDlyES6q/HDeDeCvwLmPMZ1uweT+uwPRbwKHo6xC3VOg0Y8xSzQebuVdgjDkb+EPghujbp+IS9lcCZxpjLkrqfn2BhJCpEewtYmsBKuu1v/wnFrDxcl92pUisI6sxZgrXMufCJq558RLPbwc+2Oa4fkGD7zRjzA+ZzekJi2CljCI1wn1FFz7m2vcvVMaLOrOGBPuLZE84vP0BdhHSE19oDRsiIWQ6BHsnIw8ss/TJjZDNYP2AYM9kMva6CBEwoTVCCSHTItw94bZCyyf09sxloBY4u32GCJjQElY8sNQInp1wHlguGQ9M5TxsLSTYLR6YIDhs/+412ElsaF2oVwud55QEuQxUA4Jnxpc+t8cQAROawoaRcIX9u9dgJwn3TGLLNWxo2y9ijVD5DLYaEOwa77vJFxEwoSms79oTWxGwVPB3HcJWA1Q+k1wH1VwGGwSEE2XseH91pRABE5rC+hVXxNpnn+TdQrB9zO2knU8ofASUp1BZ54X528cSs9sNiIAJTeEELAQbYoP+K4zsNMH2MWzFRyUoYAAUMlD1CbYdTNZuhxEBE5rCVktuBtKG2Gp/hSPdgL91LAohE6sxB0Dls9hKgL9VPDBhBWOrJbfbs7WEtXLfJYU7SVisEOyddCFkIVkPTBUy2IqPvzWp5cPdgQiY0BRhrTQTQmJDkDAyMfwtB6HqQ9Zrv5X0fArZqJRigrCP9okUAROawlZLURU+YEPCSn92OegE/uYD2IqfuPcF0ZrIjHJ5sKf7xwsTAROaIqwUsTYqoQhDwmp/9pnqBP6m/diyjyokm/+aYSCLLfvUNvbPploiYEJT2Mr0TBW+tSFWPLBEsNbim0jABtIRMFXIujzYpsX2oO4tRMCEpggrxdkQMgwIy8XFLxAaItw9SXiojPVDSHgGMkYN5LBl3wll2B+TLyJgQlOE5cmZENLa0Ama0Da1p/ZiyzVUIYNqZzfuxShksIHbYq1f1kWKgAlNEZbneWCl/mvR0glqT+7FlmqowVxq91BKuTCy7OOv35vafZYTETChKcLp8dl1kCJgiWCtxX9yb6r5rxg1mMWWatSe3JPqfZYLETChKYLSuOtEoTysDQin+yMU6SThnknXB78awEB6HhiAGsw5AXt8T1/kwUTAhKYIp52AqWwewoBg+lCnh9Tz1B7f7cLHgWx6+a+YQnY2D7aj95cViYAJTRFOj2FDH5UtuBYtImBtU3tsVsDSRinlZiOna9QebWtT+65ABExoGGstwdQYNgxQuQKEPsFUf3U3WG6sH1KNBWxooU3ok0cNuTCy+ogImLCCsJUpbK0ShZAFbBgQioC1hb9xv2syGNpUlhAtRJwH85/a1/N7RYqACQ0TTB7Ahj4oz+XAAp9gsn/W1XWC6kPPYKdrMJhLrgPrEqh8BjxFOFWl+tjuZblnWoiACQ0TTO6HwEdlsuBlsaFPWJkirMh6yFapPbiLcLqKGk539nE+aiiHnapSe3DXst43aUTAhIbxJ/c7D8zLojzPlVKEPsHEvk4PrScJ9ky6Fs9lP9UC1oVQQ3nsdJXqg7t6upxCBExomGBiLwQ1VMa92VQmB4GPPykC1grV+3Zgp6swkE2+/9dSDGaxfkg4OoVvevf3JwImNEwwvg87R8DIZLFBjWC8P6q6l5vqfTuxU1XU8PLMPs5FKYUazmOnqlTv27ns908KETChYfzx3djAh4yrV1KZHDao4R/q7URwJwjHpqltcLOAnRAwADWcd4n8+3b0bGtwETChYfyxZ7B+1c1AEoeQNfyx3q8nWm4q9+zATlUhn0EltIFts6jBnNvw9tmJnu3SKgImNIQNQ+dpzc2BZfNYv4Z/SASsWap3b+9Y+BijPOVmI4tVKndv79g42kEETGiIoHjAbakWBjCTA8thgyr+wd7NoXSCYHTKtc+ZrqJGOidgAGq4QDhVoXrXtp6cjRQBExrCH90xEz7GBZfOA6sSTI5KLVgTVO/cNid8XJ7q+3qooSiM3D2Jv7H3Wk2LgAkNURvdgQ2qqMysx6C8DHgZl8gf3dHB0fUWldu3YosV1Eih00NxYeRwnrBYoXL71k4Pp2lEwISG8A9sx/pVyD435HFeWIXagW2dGViP4e86hP/0KHa61vHwMcYbyWOLURhZCzo9nKYQARMaorp/C7ZWQWWf6zWobAHrV6kd6L1P705QuXULtlhxax+Xu3i1HoM5CC3h6BTVh5/p9Giaokt+gkK3U9u/xeXAcvM9sILzwPaJgC2FDS2V27YQTlZQq7rD+4KoqHWkQDhZoXLrlk4PpylEwIQlCUoTrgrfX8ADyxWwtQrVfZs7NLreofb4bsI9k+AHy9b7q1HUSB5brFJ7YCfhRLnTw2kYETBhSWp7NzvxyuRc4n4OsQfmH9wlM5FLULnlaed9DRfSbx3dJKqQhaznvLAeSuaLgAlLUt2z0eW/cs+fNVOZLKhMFEaKF1aPsFiheu927GQFtbrzs48LoVYVsJMVKjdv7pmlRSJgwpJUdxtsrYzKDSz4vJcrYGtlqrs3LPPIeofK7VsJx8uQUZDvbO1XPdRI3nVq3TLaM0uLRMCEJanufoqwVqorYCo3GAmYWeaR9QbWWio3bYq8r4Fl67zaLCrjwVCecLJC+cZNnR5OQ4iACYsSVqap7d8ahZD1BGwAWy1TfebJZR5db+BvHnW1X6Xuqf2qh7c6CiPv3NYT/fJFwIRFqe7egK2WUJ432wdsHio/SFgrUd2/RRL5C1C5cSPhZAWG891T+1WPaGu38OA0lTu7P5nf5T9NodNUn3kCWyuhcoN1z1GZLMrLYKslqrufWsbRdT/hVJXKHduwE2W8Lk3ez0UphVpdwE6UKf9sY9cn80XAhEWp7HiMsFpC5esLGIDKD2GrJSo7Hl2mkfUGldu2EI6VwFNQSH/j2iRQqwoumb/5QNcn80XAhLpYaynveNSFkPmhRc9VuUHC6rQI2BystVRu2IidKHd18n4+z0nm37Cx08NZFBEwoS7+gW2ExVHXB79OAj/GeWDTVHY+hg3DZRphd+M/tQ9/60HCcvcn7+fjrXFhZOX2LYTFSqeHUxcRMKEu5W0PElanUbnBJb0HlStgw5BgakzqwSLK1xvCiTLeSKH7k/fzKWQh4xGOl6nc8nSnR1OXHvupCstJedtD2Oo0XmHx8BFc8tcrRF7YtgeXYXTdTTg2TeWe7q68X4w4mR+Olyn/zHRtt9bEsopa6yHgY8B7gJOASeBB4MvGmOtatHkC8HfA24Cjgf3ATcDnjDENTXdprT3gNuAMIGeM8Rc595XAp4BfAw4DdgPXAp8xxvRWn5E2sWFIZdsDhJUpMmuOaegalR8irExT3voAq884N+URdjfln2/CTpQh57l1hsCBykSHR7U0RxZWz3ytRgqEo9MEOw9Re/RZ8r9ybAdHtjCJCJjWehgnLKcBNeAJYC3wFuAtWusLjTEXNWlTA3dGdsaBR4GTgXOBP9Ba/54x5mcNmPosTryWut+bgBuAAeAA8DiggT8H3qO1/nVjzCPNvIZeprZvM/7EfmzgLzkDGaMKw4TFUcrbH3rO7kUrDeuHlG/cSDheRh02mzt8+U/P6+CoGmP/718287XyFN6qAuFEmfL1pisFLKkQ8us48XoEOMUY8xpjzInA+wAfuFBr/ZuNGtNaZ4GrceJ1OXCMMeb1wDHA13Ai8wOt9dpFbGS01v8AfLyB+x0B/CSye3F0v9cBLwKuAg4HrtJar5h3ZPnpe7CVKbzCUMOzZypbwALh9DiVnY+lO8AupnrvdoLdk+CHHd11KAnU6gHsZIXqg7sI9kx2ejjPo20B01qfApwDhMB7jTEzW9QYYy4HPh89vLAJs+cA64AdwAeNMaXIXhU4H7gdF+J9pM6YXoLzCD/a4P3Ox4nUPcaYT8RhpjFmEvgjYAvO+3tfE6+hpyltupuwMoUqDDd8jcuDjRBWpihtvjvF0XU35etNVDpR6JnSiXqofAYGsq6w9YbuW+uahAd2LpAB7jbGLLQY7hvR8Ywop9UI74+Ol0eiNYMxxgL/Ej08e/6FWuu/BNYDbwZ24vJyjd7vW/OfiO5/ab379SNhuUh5+8OElSJeYaSpa72BYWy5SGnjHSmNrrvxtx2ktn6P2/Nx9eKlJ72Ct3rAhZE3bcaWu2t9ZBI5sNOj44J/scaYZ7TW24ETcaJy+WLGoqT7GxazicuNAZystT5+rtcHvD46fhWXkH/NEvc7JhpbI/c7Q2udM8Z0128xYcpb7sWWiygv23QeSxVGCA/tprZ3M/7Ys2QPf1FKo+xOytdtIJyowFDueTtuP/X2r3ZoVG0ylINRCEenqNyxlYHffGmnRzRDEgK2LjouViyyDScSjbzyY4E4a1zP5k4gwHl+L40ex1wFXGSM2Qrg5gIWJR6/BeqtXt0WHQvACYuMqy+Y3nAbYWUS1aT3BW6rNZUbdGHkxttZddq7UxhhdxIWK1Ru2+LWPb7g+T+7uTN8vcTM+shxl8wv/MZLuiY0TiKEPDo6LrYrZryg6sgm7NW1aYwJcDOTz7NpjPlpLF4NEt9vwhhTr+R47oKwRl5Dz2IDn9LGOwhLk3gDq1qy4Q2MEJYnmd5wa8Kj624qN20iPFSGjNcz6x4bZXZ95Cj+U/s6PZwZkhCwuMpxsZ0ASvPObcRekjYbuV8j90rifl1NeftDBJMHsDZsuHxiPt7AKpdH2/ogwfT40hf0ATa0lG/YSNgnyfv5qIzn6sImypSv756VFkkIWDM7YTZSztvszprtlggv9/26mtKTNxOWJ/AGVrX8JlTZPCqbIyxPUjIrwwurPbSLYNc4VP2u2HE7DVTc7PDu7QSjU50eDpCMgBWj42JTLvFHeSPd7opzvk7KZiP3a+ReSdyva7FhwPRTN7cVPsZ4A6sJyxNMP3FjQqPrbsrXG1e4umqg63YcSgpVyEIug52oUPl5d7ScTkLADkTHukWlzOaNGgmeD8z5ekGbUaHrmiZsNnK/VVrrhVuOPjfv1T0JgIQpb3sQ/9BebOg3Vf+1EN7gasLSJKWn7yWYOpTQCLuT4NkJqg8/gy325rrHZlBrojDyxo3YWrPBS/IkIWDxmsQXL3JO/NySzYWMMc8ym6CvZ/N43AxkQzaXIB6/h5thXIh4HGWeO+PZV0w/dj1hebyt8DHGhZF554U92d9eWPkGg52swEAOlevOHYeSQg3nwQ8J9hap3rej08NJRMDujY6nL/Sk1vo4ZoXhrgZt3reYTeCN0XF7JHgtY4wZA2J/eKn73RvNgPYdYa3C1JM3EU5P4A2uWfqCBvAGVxNOjzP1WEtr+XsCW/Ep3/I04XgZb01/e18wr+V0FzQ7TELAfhgdz9ILF139WXS81RizrUGbV0bHD9RZfxjb/E6D9hq934fmPxHd/48Tvl/XUTK3EhZdtUirs4/z8QZXE1aKVLY/Qu3grkRsdhuVO7cRjk4BFgbrZSD6C7V6ADtVpfb4HvydnU0PtC1gxphNwBW4kO5HWuu4MBSt9TnMLqb+9PxrtdanaK1fFlXDz+V7uGLRk4ErtNarovPzWutLgDNxYWZSpc2XAIeAN2mtL4lFM7rv96NxbIm+7kuKD/+UYHocb3B1YiUAKpNzLXbKE0w98tNEbHYb5RtMz7WMbheV9WAwh53s/PrIpLpRnI9rP/MKYIPW+mGt9TbcsqEs8EljzEKJkJtwOajPzf2mMaaMW3c4DrwLeFZrfT+uP9d5QBV4pzEmkR0HjDH7cGs6q5H93dH9ngF+Hydu7+jXJUT++F7Km+8hLE3gDR2WqO3M0GEE0+MUH7m671pN+1tG8Tfux07VUKv6P3yci1sfGa08qNRtsZf+OJIwEgnJ6cBFuKT6y3EziLcC7zLGfLYFm/cDp+IWWB+Kvg5xS4VOM8bcksTY59zvauB1wL/jepr9Mq7E4jLgtcaY9Uner5soPvxfhKVxVG4g8R5eamAE61fxR3dRfrq/OlSUb9zk1j0O53qvZXS7DEb7R45OU7lrW8eGobp937d+Qmu9ft26da+45pprOj2UGWwY8MyX30F5y/1khg9PLIE/F398LwrLyBv+gKPP/mLi9juBLdU4+KH/wN+4H++oYdQKyX/NJRwrQTWgcOZJrPnsb7drrqX4e4V9bAjzKW26E390p+ug2mbxaj0ywy6MLJnb8cf3pnKP5aZy93bCsaimeaC/1j02ilpVcMn8Dfs6lswXAVvhTN57JcHUGN7QYSiVzp+DyhZQuQHC6UMUH/hRKvdYbiq3bMZOVFCr+m/dY6OorAdDOWyxQuWWzR0ZgwjYCqa6bwvlzXcTlibIJJy8n09m+HCCqYNMPnAVYa179xlshODZCWrr92KnqysueT8f1zO/QuXWLVh/+SdpRMBWMMX7riSYPoRXGE59Aw41sArCkODQHqYfvz7Ve6VN5danscUKDGSf17RwxTGUgyAk3D9F7ZHl37hrhf/0Vy7B1BjFh39KOHUQb/iI1O+nlMIbPpxgapSJu6/o2ZIKG1oqt7ndqle69wVRZf5InnCyTOUXy9/nUwRshTJ5/w8JigfAyyZWeb8U3tBhhJVpqrs3UN7c6Kqy7sJ/ai/B7glsNUAN9faOQ0mhVhWwxSrVB3YRFpc3PSACtgIJqyWXvC+OkhlZu2xJaOVl8IYPIygeZPyO7y7LPZOmcvtWwskq3nC+b9vmNIsqZCGjCCcrVO9d3gXeImArkOKD/4k/9iyEYWqlE/XIDB9BWJqgvPV+ytsfXtZ7t4utBVTu2e7a5kj4+BzUSMHNRt65bVnvKwK2wrB+lYk7LycoHsBbdeSylwCoTM4t8i4eZOK2by/rvdul9uhu7MFpsHbF1n7VQ43kXU3YY7sJD5WWviAhRMBWGMWHfow/ugOCWipV942QGVlLMD1GaeMdVHY90ZExtELlnu2ExSpqOL9ia7/qoXIZ1611urqsfcJEwFYQYa3C+G3fxp/cjzey/N5XjMrm8QZXExRHGb/lXzsyhmaxfkj1gZ1uw9phSd4vROyFVe4RARNSoPjAVdRGd0Dg4w11xvuKyYwcSTB9iOmNt1PZ8WhHx9IItSf3YsdKEEr4WA81HIWRj+9ettlIEbAVQliZZvz27xBMxN5XZ3/1sRcWFkcZu/HrdHtTgdoDOwmnqqihnISPdVC5DGQ9bKlG7eG2GiU3jAjYCmHi7u/hH9wJNui49xWTWXUkwfQ45S33Ud50Z6eHUxdrLdUHd2GnqyDh46KoIeeFVR9Ynq0jRMBWAEHxIBN3fo9gYh+ZVUd1jQehMjlXnT+5n7Ebv9a11fnBsxMEuyexlWBFts1pBjWcw07XqD22Gxum71WLgK0Axn/xrwSHdoPylr3uaykyI2sJK0WqzzzJ1KNXd3o4C1J7bDe2VEMNZKV4dSkKWWwQEo6V8J9OpGHyooiA9TnVfVuYfOBHBJP7yax+Qdd4XzHKy7iE/vheDt30z4SV7ts3uPbos67zxJB4X0uhlEIN5rClKrXH0s+DiYD1OYdu+ArBxD5UfgivMNTp4SyIN3w4NqhRO7CNiTu7a4mRDa2bgSzVJHxsECdgNWpP7En9XiJgfUxp051Mm9sIpsbIrD6608Opi1IemdVHE0zsZeLO7+If2t3pIc0QbB/DTlRcPiff35vWJoUazGJLPv7GA6n3CBMB61OsX2Ps+n8kGN+LN3xY6v2+2kUNrAIvhz++l7EbLun0cGaoPbkXW47yX10WfnctuQxgsVMV/K3p5sFEwPqUyXv/jeqzGwir02RGjuz0cJZEKUVmzQsIJg8w/fjPKG99oNNDAnDbppV91ICEj42ilIKBHLbsvLA0EQHrQ/yJfRz6xf/CH99LZvXRKK83Qh8vN4A3tAZ/Yh8Hr/0HbNC5/QZj/KdHsRUfVeiNn2G3oAoZbMXH3ywCJjTJ2M++jD/2DHhexxZst0pm1VGurGLXeibv/UFHxxIWKwR7JqEaQEGWDzWDKmSdB5ZyKYUIWJ9R2nIfU49dRzB5gMyaF/Zc3kZ5GTKrj8Yf38OhX3wTf2Jfx8YS7DjkxCurVt7Gte1SyEI1cN1ry+ltaC+/lT7C+lXGrrmYYGIf3tAavNxAp4fUEt7gGvA8/IO7GLv+Sx0bh799DFv1IS/eV7OojAeeglpAsGs8tfuIgPURE3d+l8qzGwgr02RWHdXp4bSMS+i/kGDyAFOPX09p890dGUewa9z1vs9J/qsl8hlsNUh101sRsD6hNrqT8dsuJRjfTXbNC3omcV8Pl9A/jGB8Lwevubgje0kGuyegFkj9V4uoXAZqAeGeydTuIQLWB1hrOXjNxa4ANJPvuvWOrZJZdRS2VqK6eyMTt1267PcP9xaxfih7P7ZKzsPWQoK9xdRuIb+ZPmB6/c8pbbyDoHiQbA8m7uuhPI/M6hcSjO9h/I7LqO7bsmz3tqElPFB0HpiEkK2RzWBrAcE+8cCEOoSlScau+0eC8T1kRo7o+or7ZlEDI6hcgWB8D2PXXLxsjQ/tRBnrh+5+mf74QFhuVNaDIHSdbFNCBKzHOXTzP1M7sBUb1PBG1nZ6OImjlHJe2NQYpc33MPXI8rTcCcdKEIQoz+sbj3bZySgILOGhUmofPCJgPUzlmfVM3nclwfjeqOarP3+dKpsjs+pI/PHdjN3wFYLp9KblY8JDJaxvQfJfrZPxsEGIrYXYqWoqt5DfTo9iw5CDV3+eYGIvqjCCVxju9JBSxRs+AsIQf3QHh278eur3s9NVt4GHNDBsGeUpUApCiy2lU8wqAtajFB/4DyrbHyEoTXR1q5ykiGvD/Il9FB+4isrOx1O9ny3VRMASQHmRgE2LgAkRQfEgh276Z/zxPa7HfWZlVIp7hSG8wohb7H3Nxan20LdlH2zoPAihdWIBS2k5kQhYD3Lopq/jH9wFgDd0eIdHs7xkVh9NWJ6ksuNRig/+Z3o3CkKwgOhXAlgIJIkv4BL3xQd/jD+5n8ya7utxnzYqk40S+ns5dNM/pZbQt9EbbmX9dFNAgbW4cDwFRMB6CGstY9d+gWByP15hBC/fnT3u08YbOhxsiH9wJ+O/+Nd0btLd++z2HGltsSYC1kNMr/855W0PEUyPr4jEfT1murdO7Gfyvh9SO7A9+XvkPOc9JG55hWGjnYpSWk8qAtYjWL/KoZ9/zW1OO7J2xSTu6+EVhlH5Abcp7s9T6KGfyzAb/wgtYy0oUltPKgLWI0ze90Oq+57G+mVXEyWQWXU0QfEg00/eTHn7w4naVoXszAya0AYWN5ObUkdbEbAeIKxMM37Hd1yjwpGjUJ782gBUroA3uJqwOMr4zd9IdLmKGsm7pTAiYC1jrXWlLhmFt6qQyj1WdhzSI0ze+wNXNmFDvKHme9yPTqXX0jcp1g63tutPZtWR1PZtofT0vZSfvpfBdb+ayHi8kYIrwkxp+n9FEFo3i+sp1FA6TQZEwLqcsFpi4q7vu5nHkSNbKpt47RceSWFkybLtgte3dJ3K5Fzjw+Io47d9KzEBU6sLM2v5hBYJLGQ81GBOkvgrleJDP3GNCsMQb3B1p4fTlWRGjiAsTVDe+gCVnY8lY3PtsHvzWZtaCUDf44eQ8dzPMiVEwLoYGwZM3v19wuIo3sjaFVe02igqk8MbXEU4NcbEHd9NxuZgDjWch4wHfpCIzZWG9QNU1sNbm169oghYF1Myt1E7sJ3Qr7SU+1pJZIbXEkyNMb3hVuexJmHz6BFXTlGTMLIl/BByGbyjR1K7heTAupjigz8mmBojM3RYW72+HvzoLyc4qu5E5Qqo/ABhaZziQz/hsF//s7ZtZl60GpVzbZHF920eWw3whvJkXpRe6kMErEvxD+2mtOkuwtIEuaNOastWqzN8vUZm6HCC4ijFh/+LNWf9ads7MzkB87BVCSFbItrRKXNsetGDhJBdyvT6nxOWJlC5gb7rc58WamAVNqjhjz1DJYHC1szxh0E+KwLWAja0znPNZ9zPMSVEwLqUqfU3EZYnZOaxCZRSeAMjhKVJpp+8uW172ZPXogpOwJZrM5G+oRqgshm81QOpJvETCyG11kPAx4D3ACcBk8CDwJeNMde1aPME4O+AtwFHA/uBm4DPZE7RDgAAG2RJREFUGWOeWuS6VwKfAn4NOAzYDVwLfMYY80yda94PfHuJIX3FGPM/m3wZTeOP76W6az1hZYrcmmPSvl1f4Q2sJpjcx/RTN3P47/xNWzO33gtGUCN5VEZBNUhtOUw/Yqs+qpAhc9IRqc6eJ+KBaa2HgZuBC4CTgfXAFPAW4Fqt9QUt2NTAQ8CfACPAo8AAcC7wkNb6rXWuexNwP/Bu3Ot7HFgL/DnwuNa6Xkb71Oi4Fbizzr9l2ZiwvPUBwuoUKjuw4hdtN4sqDGH9KsH4PvwD29qzpRTZdUeiBnKuQ6vQMLbsowo5ci85MtX7JBVCfh04DXgEOMUY8xpjzInA+wAfuFBr/ZuNGtNaZ4GrccJzOXCMMeb1wDHA13BC9gOt9dp51x0B/CR6/uLoutcBLwKuAg4HrtJaL5RUigXsb40xZ9b5l0Lbg+dT2f4QtjqNV1iZ/b7aQSkPlRskrE5T3vZQ2/ZyLzsaNZAVAWuWsg8DWbIvS7ftU9sCprU+BTgHCIH3GmN2xs8ZYy4HPh89vLAJs+cA64AdwAeNMaXIXhU4H7gdFxp+ZN515+NE6h5jzCeMMX503STwRzgP6mScsM4nFrB0d4togMqORwkrJdQKbVjYLl5hCFudprLj0bZtZfVRqIEslGuSB2sQ64fgh6iBLNmXHpXqvZLwwM4FMsDdxpgnF3j+G9HxjCin1Qjvj46XR6I1gzHGAv8SPTy7znXfmm8wsnPpQtdprY8DjgCqwMYGx5gK1q9SO7gT65dRuYFODqVnUbkBbK1CbX/7EX9OH+UWIltcYaawJLZUQw1myZ68Fm843Rn0JATs9Oh4x0JPRknzuGXmm5cyprX2gDcsZhOXjwI4WWt9fHTdMcCJDV53htZ6bnFU7H1tiL22TlE7uAvrVwEFbdYxrVRUtoD1K9RGd7S9c5EayDkvbDCb2tZg/YYTsBy5V6U/AZVEhnhddHx6kXO24cTlpQ3YOxYYXMLmTiDAeX4vjR7H47C4RHy9cQAUgBPm2I8F7Amt9VnAH0Z2y8DDwKXGmHo2E8Uf2wV+FZXNy9rHVsnksGGArUwTFEfJrm4vjMm9+kVU79lOOF2DNeIVL4a1Fko11JpBcq9OX8CS8MDiLN3+Rc4ZjY6NTEnMzfotaNMYEwDxdjSxzfi6CWNMZYlxzB9LLGBvB27BzVj+BvC7uHKMDVrr9temNEBYmsCGgXhfbaCU816tDQjLk23by7/2WNRQHluqSWeKpai4ol+1ZoBcygl8SEbA4kxzeZFzSvPObcReszabGcf8+8QC5gEfBY7DeWmvAr4H5IF/1lr/4SK2EyEsT4IN214Gs9JRyoMwwFaKbdvKnHg4mResQuUykNIGrf2Cna6ihvPkTz0mtR5gc0kihAxoXAgb+fhqdt1GbLPV6wCuwIWg3zTG3D7n+08A52qty8AHgS9prX+UZp7M1iqyI3QSKA+sJawt9nnWoCmlyL3uOGqbD2CL1dS6i/YDdqqKt3aY3OuOX5b7JSFgRVzpwmLJgTinNd2gvZgB6ntU823G1zUyjueMxRjz90uM6UKcgL0IN8Fw1xLnt4zKFWbefEIbRB8CXkIzuYXTTqD80ycJnh3HWiv5yQWw1QACizdSIP+645blnkmEkAei49pFzonzTfuasFfXZlToGi9xj23G162aN8O40DgaHQswM5Man99ea4glULkB531ZmbJvB2stKC+xUpTsK16Ad6Tr0kpJwsiFsMUKajhP7tRj8EbS2cRjPkkIWLwm8cWLnBM/t2SNlTHmWWYT9PVsHo+bgZxrMx6Hh5thXGwcZdzM5Qxa68Hnnf1c4p9Vqn+9mcE1LgEdSAeEVrHWQhigvAzeQDKL4VXGI3/aCaiRAmGxuvQFKwxrrQuvR/Lk3/jiZbtvEgJ2b3Q8faEnoyLRWFAaDb3uW8wm8MbouD0SPIwxY8CmBq+7N5rJRGv9u1rrIlDUWi84SxrVmsXPLVSsmxjZI45DZfLYQN4kLRPUwHPeV5I7mBf+28l4IwXsVFVmI+dTCcBavNUDFE5rtF69fZIQsB9Gx7OiBdjzicsPbjXGbGvQ5pXR8QN11i3GNr9T57oPzb8gsvPHC1z3MC5v5rHwEiOAj0fHJ4wxT9QddQJkDz8Olcm5GbRQvLBWsEENlcmRPfxFie6hmdVHkTl2jWuxMyUfMHOxxQpqpED+DSegBpevgWbbv11jzCbcLF4G+JHWOi4oRWt9DrNv/k/Pv1ZrfYrW+mVRFf1cvocrMj0ZuEJrvSo6P6+1vgQ4ExdmfnXedZcAh4A3aa0vicUvuv77kb0t0dfx+J8FLosefkZr/e454ytorf8e+EvcrOVfNfZTaR2vMOTeeLkBbAIzaCsRWy2hcgPkjl639MlNoJSi8ObIC5usV2q48rChxRYreKsKFN588rLeO6leLefjaqZehSv6fBw3Mxkv7fmkMebGBa67KTrnMmbXMWKMKWutzwZ+DrwLeKvWegNOgOI1i+80xswtTMUYs09rfS6u88R5wHu11lv+d3t3HiVnVeZx/NtdvaSX9JIN0iGJmYCPBCHBAZRVAgrKNoyy6GFwQ8URjIiiCAkIRlFBRQTFM4sLjjrMuBuPR0cHRBRXlBlkHs3ooCZACNk63Uk6XVXzx71v95tOdyfd6e6qt+r3OSenO1XvW9xKU7++977PvS9gwHRCuJ3n7kPnsVbE1z6VsMvFxwhzZIcRLhb0A5e7+/fG+O8yLo0LlrHr8Ycp7uqFxsm7JVWlKvT1kmtuZ9rCib8XQOPyxfTe+xt4podib1+Y1K9yxR27oT5Hbm4b9Uu7pvS/PSEB5u7PmNnxwDWEZTiHEya77wfucPevjOM1f25mS4FVwJmEYtOthHBa7e7D3q3V3b9lZscA1xMCaRmhov8rwM3uvtcKX3fvidv9vI6wOH0pcBTwJPBV4DZ3f3Ss72G8GhcspaahmXzPJnJM7mr+SlMsFkMPrGMujQsmPsBys1tpWNbFzk29FDbuT1VQdajtbKJx+eJwN/MpVKMtQqaOmT166KGHLlmzZs2ox/VveZJ1HzmHvqd+R/2cQ7Wp4RgUdm4n372BaYuOZd7VayZ0Diyx2zew/a4fU9ylPcIStZ1NtF17GrUd+7qYP6JxJZ8+GWWoruNgGucfSf+WdRR2dpNr6Sx1kzKjsGMbtdPaaF5y2qSEF0C9zaHzjvMn5bVlbDSAL1PNR5xObVM7hd6t+z5YACgWChR2dlPb1EbzktNL3RyZAgqwMtVy1FnkWjog30ehb8e+TxAKO7ZS29BEw0GLJ2X+S8qPAqxM5Vpn0HzEGdS2dFDo2Vzq5pS9YrFIoWcztS2dtB57waQNH6W86KdcxqYfdyG1zZ0UdnZT7Nf6u9GEbXMK5KbPpnXZuaVujkwRBVgZa5x/JE2LX0CuuZ389o37PqFKFYtF8t0bybXOZvpxF1E7rbXUTZIpogArc+3L30ht68ywU2u/lq8Mp7hrOxQL5DoOpu2Evyt1c2QKKcDK3LSFR9N02AnkWjrId4+2a3d1KhaL5LdtIDd9Nm3HXRwufEjVUIBlQOcZbyU3fQ7FXT0Udqn6O63Qsxlq66iftZC2k19d6ubIFFOAZUDDwc+m9ZiXkWubTX7bk7rBalTM95Pf/jR17QfRcfqbqdW60aqjAMuIjtPeRN2MBVBTS6FnU6mbUxby254i19RO44JltOjKY1VSgGVErqWTzjPfSl37XPLdG6t+Qr+ws5tiXy+59oOYed71qvuqUvqpZ0jLsnPDhH5rJ/1bn6jaoWSxkCe/9Uly7QfTduKraJg73D6aUg0UYBlSU1PDjHOvo67zECjkKfRWZ4V+futT1DS00NB1OO0vfEOpmyMlpADLmPqZ8+k8YwV1HV3ktz1Nsb+6dgYt7Oim2NdDXcdcZv3te6htmJi7Dkk2KcAyqPWYC2iyU8hNn0n/5vVVM5Qs5nfTv/UJch1dtJ/8WhrnH1XqJkmJKcAyqKa2lpnn30D9rEXU1NRWRYFrsVikf8sTcavoo2k/9fWlbpKUAQVYRtW1zWHm36wk19lFoXcLhV09pW7SpCr0bIJCnroZ85l1wfuoqRvuZlVSbRRgGdZ8+HKmH3cRdR1z6d+8jmK+Mrc4LvT1ku/eSF3nPGacdQ31sxbu+ySpCgqwjOt8ydU0LlhKrqmd/i2VNx9WLOTJb15HXcfBtC47m5ajzyt1k6SMKMAyrra+kdkX3kLdzPmhtGL7M/s+KSOKxSL5LeupaWylYd4RzDj3OmpqpvauN1LeFGAVoH72Imaccy11nfPI92yqmPmwQs8miv27qZtxCLMveL/WOspedFeiCtG67Bx2Pf4w237yBfo3r6N+1iLI5UrdrHEr9u0k372R+tnPYsZL30FD13NK3SQpQwqwCtL50mvYte5Rin297N6wttTNOWB1nfNoXXYurce8rNRNkTKlAKsgtQ3TmH3RB9nw+RX0b15f6uYcsMaFyzTvJaNSgFWY+lkL6VrxVYq7d5a6KQespqFJ4SWjUoBVoJraWmoam0vdDJFJp6uQIpJZCjARySwFmIhklgJMRDJLASYimaUAE5HMUoCJSGYpwEQksxRgIpJZCjARySwFmIhkVk2lbUFczsxsW0NDw/QFCxaUuikiZWXt2rXfdPcx7xeuxdxTq7evr4+1a9f+udQNEakE6oGJSGZpDkxEMksBJiKZpQATkcxSgIlIZinARCSzFGAiklkKMBHJLAWYiGSWAkxEMksBJiKZpQATkcxSgIlIZinARCSzFGAiklkKMBHJLAWYiGSWAkxEADCzmlK3YawUYCKCmdW4ezF+/ywzay91m/aHAkykyg0Jr4uBHwGnJ8+Vsm37ogATqWJDwusi4ItAF3AmQPJcuVKAiVSpYcLrS8DO+PT0kjVsDBRgIlVohPB6BvjneMhiM8uZWX2p2rg/FGAiVSA9lzVCeG0C3grcBOwG2oCCu+8uQXP3mwJMpIKZ2SEQ5rKSEEuF1wUMhtcKd/8CsAPYDtSTgXwo+waKyPiY2euBH5vZObBniJnZK4F7gQ0MhhcMzn0tAOYN6bmVXV6UXYNE5MCZ2RHAe4FDgPeZ2VkwEGLNwPOBXuCaJLzMrAHYRpgLqwNaUr21nLsX4veXmtnSqX5Pw1GAyaQp9xqiCvdb4Fbgf4AjgVtTIdYLfBA42d3vgYF5sT537wGejK/REp+rc/d8/H4V8GlCKDZO5RsaTk2xWNZlHpJRQyaKTwRw9wdL26rqYGa1qd7SCuBK4FDgMeCd7r5muOPjELEG+C6wHLjY3f8tddxK4GagGzje3X87JW9oFOqByYQbEl4XAg8AbzOzpnKcR6k0qTAC+DhwJ7AWOBz4UNITg4GfVSF1bh54Iv61M3VcEl5bgBPKIbxAASYTbJhlKf8an3oOkE9/WGTyJCEWfxZJiP0vIcRuNbOz43Hpq5PJz2Zz/NoGYGY3EsJrK2HY+ejUvZPRKcBkwoywLGUT4bL8dCBXwuZVnSEh9kngfcCfAAM+MFyIRUk1/nwzuwK4kRBeJ5VTeIECTCbICMWRWwiFkU8QroYdoiHk1Ioh1hALUh8CZhA+90ewd08s+QXzp/j1VOB6yjS8QAEm42RmdanvR6rsvsrdP064NF8DNGoIOXHM7Eozuy0VPMMdk3P3PjM7HPgh0Ap8B/g9YVifDrF8PK07fj0SmAacUo7hBQowGSMzOx7A3fuTD84Ild1vcffPxdOSOZWWKW5uxTKz5wN3AFcDNw4XYjG88mb2bEJ4zQJucPezgI8QJvb3CLFoS+rrSe7+X5P4Vg6IAkz2m5ldBjxoZndD+I2dfHDM7OWEyu6nCJXdX0yduiN+XTTk9TQnNn7rgLvi9yuBm9L/nkntVgyvB4GZwHvdfXU85J8Ik/vD9cS+Rhg6vqBcrjaORAEm+8XMOoAXxL++0czugD2GHevjn3cPqeyG8GEDGNjlM04uJ8WRN5vZWyb5LVQUd/8LcAtwe3zoOmKIxX/bfjMz9gyvGyH8XNy9Pw7v72QwxN4fy15w91vc/XdT/LbGTAEm+8XdtwAfi3+KwJVJiMXnfwIsdffPwGBld3z6mfh1bnxuYC7MzK4j9CBuN7MZU/FeKoW7rwduY88QWx0n7g8j7Kw6NLySObHa+BpJiD1GmPN6u5m1ZmUVhQJM9pu7/zfwD4ShSxJiH08dsg0GelfpJR7b49dnxdfZFY9bCawmBNxSd980qW+gAg0TYu8ys08CP2b48MrH8wpDQuyzwK+Ay9x9e7nvxJpQgMmYxDmRuxkMsStSw8nd6UW/Kcll+ebkgVRl91bg1BiOMg6pEPtofOhyQhX96uHCK3VeOsQ+BJxRrlcbR6IAkzGLIfYpQnFk0hO7Kz43MLGfkkziJ5XdqxgMr7KsL8qaGGIfJkzMQ/hs18Ke843DnJcOscz1gLWYW8bNzGYTLsdfEh/6hLtfGZ8b+I0fr259k7BDwj2EYeN2FF4Tzsy6gHcCK+JDHwBWjRRgWacemOzBzObva5sUG9wnvQ04m9ALKwJvTl+dTBW7/oXQC5sDvAvooczW1FWK2BP7EIPDyWsZUmJRSRRgMsDMrgK+DpyZKoEYekwuznU9B/gJ0EEYTqYn9pMQ64+n7Yh/ZgEFQnhpzmsMxnJVMDWc3KvEYjLaVkoaQkry4ZjD4DYq9xMmhb+XKoUYWtmd1Bfd5O43mdki4O3A3xOWDd3p7ivieQsIVy+PBpar5zUyCzup/hWwhBD6DwB/cvdnRj1x+NfqAt4BXBUfWk34eVXMcFIBJgPM7FRgDdBECKgPEEPMzOpjz2toZfeNqfOXAG8CrmDvEDsBeNzd1yHDsrBP/QcJC98Tm4D/I9wx6Ndxx9SxvObQELshVY2feQowAfbYlfNk4PuEPdF/RPhA3efuPXHY+AB71xfVJcPFYULsHnd/9dS/o2wxs0sIFziKhDKV9YS9u5YSdo7YQhgW3uvuvx/ja3cRtsS5DHieuz8ygU0vKQWYDBghxB4kXNXaHh+bxQjFkanXWQK8gdBrAOhy9yeRYZnZUcC3CCsVXuHuX46P1wMHERZtn08YUn4OuNvdfzPG/8Zcwn0en5rItpeaAkz2MEKI/RI4jHDV8T3ufnM8dq/wSr3OUYTyis+W+4LgUjOzM4GvAZ929zfHx+oIgZMsubqd0KvtJ+z48ZFy3iViqugqpOwhtYvnA8DphA/MXxPmxb5EWEC8x51qRnidRwj1RwqvfTsKaCQuuUotti6ktiy6ijAnWQBeAVweL5xUNQWY7GVIiL2YEGL1hMnlU8ysOVUiMdrr9O3rGAEGb2P2bAj/bja4T30+FWKrCPVdjcBrgPPj7hOZWHg9GRRgMqxUiN0PvIgQYicB7wGWj1QnJuPyC8KyqhfHfdUGNomM36dDbCXwCcK60lWERfBD97SvGgowGVEqxH7I4HDyREI1/Yv3VbEve0qHTPJ9XIf4R+AbhGH6y4YbGg4JsSsJk/4dhNukTc/K7hETTQFWBcxs2njPHWFO7CTCEpUXqSe2/4b0qorxa8HddxIm8XcDrwRea2ZNw5yfXp51LfAHwg1r24ceWy10FbLCmdnrgMWEhdbjLiId4erkfYTdD9Zovmt0ZnYMcCxwGuGXwC+A77v7r1PHvIOwjhFCL/ejI801xoD7BuGXyvnu/o1JbH7ZUg+sgpnZdEIF9tuBS2JB47gM0xPbQbjt1hsA9cJGEYtU1xDWi74cuBi4FfiymZ2YHOfutxGuNEIoIL7azFpSr5MMOxvcfQehBwbhl0lVUoBVMHfvJux33kBY0PuaCQyxc4GNwHXuvn0fp1atuDzoHkIN3Q2Ef7ebCAvhFwHvjls4J5/FlYR1qBDC7N1mthgG7t3YmOrtHgb0An+ekjdThhRgFSo1YZxsUtdGqKifqBD7AbAwPQSSPZnZc4l1c8Cr3X21u68h3CH7o4RfAMcCbUnNV5wTe2c8BsIvnlvM7GLYYzvumwk94EcY7IlVHQVY5UuGF78hhNg1TECIxW93jnqgLCQsD/qcu98LYXlQnNf6D8L6xs1Ac7zCeHByYqz5uhrYQBh2ftHM/tPMvm1mDxF6ak8Drx3PThWVQgFWoeJwo5VwpxmAtxGGMu1MQIgl/40Da2XFO4ZQALwRBtY2JqsX6ggbO84B/hF4CHjMzD5lg7c2u51QsHp7fI1TgJcA84EfEPZV86l6M+Woaif/qkQtMJ2wjc19wH1xruUSQohhZp+JG+DJxEt6qM9LNoJMPXcJYacJCPfb3A60Ei6KnGFmXe7+MXf/jpl9l7CgezFhcfevgKezuIf9RFOAVbYXEiZ6f2ZmTe6+w90vNTNQiE2F7xOGebOA5wE/BzCzoxmc41pF2PHjYcIE/yuBMwg3D344FhHXuPvjwONT2/zypwCrABZuIjvccO6nhO1XfuruO+Ll9z6F2JRZT5isX+/uP089nicMGf/d3e9OPf55M/sjoSd2MvB84IeVtIPqRFMha4aZWbu7b93HMQuAHe7+dPx7+m5B9xBCbCuhLkkhNsHMrCXZRTUpBo7fL0l26ohXjGtSz11BKBD+JXACYVsdhdgwFGAZZWaXEXaKWO1jvEGGQqx0Ruktp1c7LCMMN3/m7icOd6wEugqZQXH94XnARYS5kueO5fwhC4MvBf6FwauTl5rZ/AluskT7uHKb1O61AjmquEB1fynAMihWYv8u/vVK4AoLd7MZy2uMFGK3ABdaBd6Cqxyllgc1poaJr49f708fI3tTgGVM6n/mdBHp5cBbJiDEkgXB39acy+RJhdYJhLqudIX9LcCrCBX2X4/PaZ5nBJoDy5hkDsXM3gtcT7hL0EmE4cfdwF0+xvsuDpkTm3cgu1bI/onD/ocInYhHCOUWXYR7Zz4NnOruj5WuhdmgHlj21MQ5sGPj328iLCuBeDuzA+yJKbymRgdhK+ltwHHA2YSlRN8FTlJ47R/1wDIoVtP/grBHvbn7ZjNbRQgzGGdPTKaWhVudzSL0uorAz4AN7r65pA3LEAVYBpnZ8YTq7T8Ay5LtbMxsJXBzPEwhVqZGK6WQsdEQMpseJvS27nT37cm2zh5uGX9DPGZcw0mZfAqviaMeWEaZ2UxC9Xay00F6Il49MakKCrAKohCTaqMhZAUZcjVx6HDyTWa2dMSTRTJIAVZhRgmxKwjLhHQDDqkYGkJWqCHDyfcDK4Djkh0QRCqBAqyCDQmxWcmEv0ilUIBVuPQeVCKVRgEmIpmlSXwRySwFmIhklgJMRDJLASYimaUAE5HMUoCJSGYpwEQksxRgIpJZCjARySwFmIhklgJMRDJLASYimaUAE5HMUoCJSGYpwEQksxRgIpJZCjARySwFmIhklgJMRDJLASYimfX/F4jRUXKIQEgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 255x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 255x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for cell_type, result, in results.items():\n",
    "    fig = plt.figure()\n",
    "    violinplot2(\n",
    "        [result['fwhm']['11 Hz'].dropna().values, result['fwhm']['30 Hz'].dropna().values], \n",
    "        colors=colors,\n",
    "        xticks=[\"11 Hz  \", \"  30 Hz\"],\n",
    "    )\n",
    "    plt.title(cell_type)\n",
    "    figname = f'{cell_type}-stim-response-fwhm-11-30Hz'\n",
    "    fig.savefig(\n",
    "        output / 'figures' / f'{figname}.png', \n",
    "        bbox_inches='tight', transparent=True)\n",
    "    fig.savefig(\n",
    "        output / 'figures' / f'{figname}.svg', \n",
    "        bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0, 0.6]\n",
      "[0.0, 0.6]\n",
      "[0.0, 0.6]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 255x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 255x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 255x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for cell_type, result, in results.items():\n",
    "    fig = plt.figure()\n",
    "    violinplot2(\n",
    "        [result['peak']['11 Hz'].dropna().values, result['peak']['30 Hz'].dropna().values], \n",
    "        colors=colors,\n",
    "        xticks=[\"11 Hz  \", \"  30 Hz\"],\n",
    "    )\n",
    "    plt.title(cell_type)\n",
    "    figname = f'{cell_type}-stim-response-peak-11-30Hz'\n",
    "    fig.savefig(\n",
    "        output / 'figures' / f'{figname}.png', \n",
    "        bbox_inches='tight', transparent=True)\n",
    "    fig.savefig(\n",
    "        output / 'figures' / f'{figname}.svg', \n",
    "        bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats = {}\n",
    "for cell_type, result in results.items():\n",
    "    stats[cell_type], _ = make_statistics_table(result, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats['gridcell']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for cell_type, stat in stats.items():\n",
    "    stat.to_latex(output / \"statistics\" / f\"statistics_{cell_type}.tex\")\n",
    "    stat.to_csv(output / \"statistics\" / f\"statistics_{cell_type}.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save to expipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "action = project.require_action(\"stimulus-response-fwhm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "action.modules['parameters'] = {\n",
    "    'window_size': window_size,\n",
    "    'std_gaussian_kde': std_gaussian_kde,\n",
    "    'tmin': tmin\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "action.data['results'] = 'results.csv'\n",
    "session_units_sig.to_csv(action.data_path('results'), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "copy_tree(output, str(action.data_path()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "septum_mec.analysis.registration.store_notebook(action, \"10-calculate-stimulus-response-fwhm.ipynb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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