{
 "cells": [
  {
   "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": [
      "08:31:25 [I] klustakwik KlustaKwik2 version 0.2.6\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import expipe\n",
    "import pathlib\n",
    "import numpy as np\n",
    "import spatial_maps.stats as stats\n",
    "import septum_mec\n",
    "import septum_mec.analysis.data_processing as dp\n",
    "import septum_mec.analysis.registration\n",
    "import head_direction.head as head\n",
    "import spatial_maps as sp\n",
    "import speed_cells.speed as spd\n",
    "import re\n",
    "import joblib\n",
    "import multiprocessing\n",
    "import shutil\n",
    "import psutil\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from distutils.dir_util import copy_tree\n",
    "from neo import SpikeTrain\n",
    "import scipy\n",
    "\n",
    "from tqdm.notebook import tqdm_notebook as tqdm\n",
    "tqdm.pandas()\n",
    "\n",
    "from spike_statistics.core import permutation_resampling\n",
    "\n",
    "from spikewaveform.core import calculate_waveform_features_from_template, cluster_waveform_features\n",
    "\n",
    "from septum_mec.analysis.plotting import violinplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "color_control = '#4393c3'\n",
    "color_stimulated = '#d6604d'\n",
    "\n",
    "color_bs = '#5aae61'\n",
    "color_ns = '#9970ab'\n",
    "\n",
    "figsize_violin = (1.7, 3)\n",
    "figsize_gen = (4, 3)\n",
    "\n",
    "output_path = pathlib.Path(\"output\") / \"waveform-analysis\"\n",
    "(output_path / \"statistics\").mkdir(exist_ok=True, parents=True)\n",
    "(output_path / \"figures\").mkdir(exist_ok=True, parents=True)\n",
    "output_path.mkdir(exist_ok=True)"
   ]
  },
  {
   "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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N cells: 1284\n"
     ]
    }
   ],
   "source": [
    "identify_neurons = actions['identify-neurons']\n",
    "sessions = pd.read_csv(identify_neurons.data_path('sessions'))\n",
    "units = pd.read_csv(identify_neurons.data_path('units'))\n",
    "session_units = pd.merge(sessions, units, on='action')\n",
    "#########################3\n",
    "# session_units = session_units.drop_duplicates('unit_id')\n",
    "#################################\n",
    "print('N cells:',session_units.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>date</th>\n",
       "      <th>entity_date</th>\n",
       "      <th>Hz11</th>\n",
       "      <th>Hz30</th>\n",
       "      <th>channel_group</th>\n",
       "      <th>max_depth_delta</th>\n",
       "      <th>max_dissimilarity</th>\n",
       "      <th>unit_id</th>\n",
       "      <th>unit_idnum</th>\n",
       "      <th>unit_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1849-060319-3</td>\n",
       "      <td>True</td>\n",
       "      <td>1849</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>baseline ii</td>\n",
       "      <td>60319</td>\n",
       "      <td>1849-060319</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>0.05</td>\n",
       "      <td>f129d848-ebee-4555-965f-420e402eb820</td>\n",
       "      <td>703</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1849-060319-3</td>\n",
       "      <td>True</td>\n",
       "      <td>1849</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>baseline ii</td>\n",
       "      <td>60319</td>\n",
       "      <td>1849-060319</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>0.05</td>\n",
       "      <td>b50f3878-32aa-40e9-8753-9f401ef32a23</td>\n",
       "      <td>704</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1849-060319-3</td>\n",
       "      <td>True</td>\n",
       "      <td>1849</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>baseline ii</td>\n",
       "      <td>60319</td>\n",
       "      <td>1849-060319</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>0.05</td>\n",
       "      <td>b1626e1a-0469-4ee0-a812-88ee18accd39</td>\n",
       "      <td>705</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1849-060319-3</td>\n",
       "      <td>True</td>\n",
       "      <td>1849</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>baseline ii</td>\n",
       "      <td>60319</td>\n",
       "      <td>1849-060319</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>0.05</td>\n",
       "      <td>feb62d54-f173-4a3c-939c-8c1465e71b0f</td>\n",
       "      <td>706</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1849-060319-3</td>\n",
       "      <td>True</td>\n",
       "      <td>1849</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>baseline ii</td>\n",
       "      <td>60319</td>\n",
       "      <td>1849-060319</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>0.05</td>\n",
       "      <td>3498a09b-b943-4917-866a-8910dbcfd0cd</td>\n",
       "      <td>707</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          action  baseline  entity  frequency      i    ii  session  \\\n",
       "0  1849-060319-3      True    1849        NaN  False  True        3   \n",
       "1  1849-060319-3      True    1849        NaN  False  True        3   \n",
       "2  1849-060319-3      True    1849        NaN  False  True        3   \n",
       "3  1849-060319-3      True    1849        NaN  False  True        3   \n",
       "4  1849-060319-3      True    1849        NaN  False  True        3   \n",
       "\n",
       "  stim_location  stimulated          tag   date  entity_date   Hz11  Hz30  \\\n",
       "0           NaN       False  baseline ii  60319  1849-060319  False  True   \n",
       "1           NaN       False  baseline ii  60319  1849-060319  False  True   \n",
       "2           NaN       False  baseline ii  60319  1849-060319  False  True   \n",
       "3           NaN       False  baseline ii  60319  1849-060319  False  True   \n",
       "4           NaN       False  baseline ii  60319  1849-060319  False  True   \n",
       "\n",
       "   channel_group  max_depth_delta  max_dissimilarity  \\\n",
       "0              1              100               0.05   \n",
       "1              1              100               0.05   \n",
       "2              1              100               0.05   \n",
       "3              1              100               0.05   \n",
       "4              1              100               0.05   \n",
       "\n",
       "                                unit_id  unit_idnum  unit_name  \n",
       "0  f129d848-ebee-4555-965f-420e402eb820         703        104  \n",
       "1  b50f3878-32aa-40e9-8753-9f401ef32a23         704        108  \n",
       "2  b1626e1a-0469-4ee0-a812-88ee18accd39         705         85  \n",
       "3  feb62d54-f173-4a3c-939c-8c1465e71b0f         706         94  \n",
       "4  3498a09b-b943-4917-866a-8910dbcfd0cd         707         98  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session_units.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "action\n",
       "1833-010719-2    33\n",
       "1833-010719-1    29\n",
       "1833-290519-1    28\n",
       "1833-020719-3    27\n",
       "1849-280219-1    26\n",
       "                 ..\n",
       "1849-110319-3     4\n",
       "1839-060619-4     4\n",
       "1839-060619-3     4\n",
       "1839-120619-1     3\n",
       "1839-060619-1     2\n",
       "Name: unit_name, Length: 87, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# session_units.groupby('action').count().unit_name.hist()\n",
    "session_units.groupby('action').count().sort_values('unit_name', ascending=False).unit_name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Process all data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def features(row):\n",
    "    action_id = row['action']\n",
    "    channel_id = row['channel_group']\n",
    "    unit = row['unit_name']\n",
    "    template = data_loader.template(action_id, channel_id, unit)\n",
    "    spike_times = data_loader.spike_train(action_id, channel_id, unit)\n",
    "    half_widths, peak_to_troughs = calculate_waveform_features_from_template(\n",
    "        template.data, template.sampling_rate)\n",
    "    peak_amps = template.data.min(axis=1)\n",
    "    half_widths = half_widths * 1000 # to ms\n",
    "    peak_to_troughs = peak_to_troughs * 1000 # to ms\n",
    "    idxs = np.argsort(peak_amps)\n",
    "    peak_to_trough = np.nan\n",
    "    for p2t in peak_to_troughs[idxs]:\n",
    "        if np.isfinite(p2t) and p2t > .1:\n",
    "            peak_to_trough = p2t\n",
    "            break\n",
    "    half_width = np.nan\n",
    "    for hw in half_widths[idxs]:\n",
    "        if np.isfinite(hw):\n",
    "            half_width = hw\n",
    "            break\n",
    "    \n",
    "    return pd.Series({\n",
    "        'half_width': half_width,\n",
    "        'peak_to_trough': peak_to_trough,\n",
    "        'average_firing_rate': float(len(spike_times) / spike_times.t_stop),\n",
    "        'template': template.data[idxs[0]]\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "39294999a86e4f8ea477c5180a80e8c6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1284), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "results = session_units.merge(\n",
    "    session_units.progress_apply(features, axis=1), \n",
    "    left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> \u001b[0;32m/home/mikkel/apps/expipe-project/septum-mec/septum_mec/analysis/data_processing.py\u001b[0m(619)\u001b[0;36mtemplate\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m    617 \u001b[0;31m                    lim=lim)\n",
      "\u001b[0m\u001b[0;32m    618 \u001b[0;31m            }\n",
      "\u001b[0m\u001b[0;32m--> 619 \u001b[0;31m        \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_templates\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maction_id\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mchannel_group\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0munit_id\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m    620 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m    621 \u001b[0;31m    \u001b[0;32mdef\u001b[0m \u001b[0mspike_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchannel_group\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "ipdb> action_id\n",
      "'1834-010319-3'\n",
      "ipdb> channel_group\n",
      "0\n",
      "ipdb> unit_id\n",
      "72\n"
     ]
    }
   ],
   "source": [
    "%debug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = results.loc[:, ['half_width', 'peak_to_trough']].dropna()\n",
    "\n",
    "idxs_df = cluster_waveform_features(df.half_width, df.peak_to_trough)\n",
    "\n",
    "results.loc[df.index, 'bs'] = idxs_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=figsize_gen)\n",
    "size = 5\n",
    "mew = .5\n",
    "marker_bs = '.'\n",
    "marker_ns = '+'\n",
    "\n",
    "plt.scatter(\n",
    "    results.query('bs==0')['half_width'], \n",
    "    results.query('bs==0')['peak_to_trough'], \n",
    "    c=color_ns, s=size, marker=marker_ns, linewidth=mew, label='NS')\n",
    "\n",
    "plt.scatter(\n",
    "    results.query('bs==1')['half_width'], \n",
    "    results.query('bs==1')['peak_to_trough'], \n",
    "    c=color_bs, s=size, marker=marker_bs, linewidth=mew, label='BS')\n",
    "\n",
    "plt.xlabel('half width')\n",
    "plt.ylabel('peak to through')\n",
    "\n",
    "plt.legend(bbox_to_anchor=(0,1.02,1,0.2), loc=\"lower left\",\n",
    "                mode=\"expand\", borderaxespad=0, ncol=2)\n",
    "\n",
    "plt.savefig(output_path / \"figures\" / \"clusters.svg\", bbox_inches=\"tight\")\n",
    "plt.savefig(output_path / \"figures\" / \"clusters.png\", dpi=600, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=figsize_gen)\n",
    "\n",
    "size = 5\n",
    "mew = .5\n",
    "marker_bs = '.'\n",
    "marker_ns = '+'\n",
    "\n",
    "plt.scatter(\n",
    "    results.query('bs==0')['half_width'], \n",
    "    results.query('bs==0')['peak_to_trough'], \n",
    "    c=color_ns, s=size, marker=marker_ns, linewidth=mew, label='NS')\n",
    "\n",
    "plt.scatter(\n",
    "    results.query('bs==1')['half_width'], \n",
    "    results.query('bs==1')['peak_to_trough'], \n",
    "    c=color_bs, s=size, marker=marker_bs, linewidth=mew, label='BS')\n",
    "\n",
    "plt.scatter(\n",
    "    results.query('bs==1 and average_firing_rate > 10')['half_width'], \n",
    "    results.query('bs==1 and average_firing_rate > 10')['peak_to_trough'], \n",
    "    c='red', s=size, marker=marker_bs, linewidth=mew, label='BS rate > 10 Hz')\n",
    "\n",
    "plt.xlabel('half width')\n",
    "plt.ylabel('peak to through')\n",
    "\n",
    "plt.legend(bbox_to_anchor=(0,1.02,1,0.2), loc=\"lower left\",\n",
    "                mode=\"expand\", borderaxespad=0, ncol=2)\n",
    "\n",
    "plt.savefig(output_path / \"figures\" / \"clusters_and_rate.svg\", bbox_inches=\"tight\")\n",
    "plt.savefig(output_path / \"figures\" / \"clusters_and_rate.png\", dpi=600, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "stim = results.query('stimulated').loc[:, ['half_width', 'peak_to_trough']].dropna()\n",
    "\n",
    "idxs_stim = cluster_waveform_features(stim.half_width, stim.peak_to_trough)\n",
    "\n",
    "results.loc[stim.index, 'bs_stim'] = idxs_stim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "control = results.query('not stimulated').loc[:, ['half_width', 'peak_to_trough']].dropna()\n",
    "\n",
    "idxs_control = cluster_waveform_features(control.half_width, control.peak_to_trough)\n",
    "\n",
    "results.loc[control.index, 'bs_ctrl'] = idxs_control"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9ordFURT/o67iiqJ4RSUPRVG80uVtHkIIA/AykAEEAf8tpXzHbfnNwOOADXhZSvl/F1vnhAkTtNTUNofwK4riI0eOHCmXUrb6oG5fNJguASqklN8XQsQA3wDvQEtiWQWMB84Bnwoh3pFSlrS3wtTUVDZt2uSDUBVFaY8Qos0p/76otvwD+G3z7zqcJQyX4UC2lLJKSmkBPgGu8UEMiqL4WJeXPKSU9QBCiHDgLeA3bosjAPebjtQBff6eEYrSF/mkwVQIkQ58BLwhpVzntqgWCHd7HQ5U+yIGRVF8yxcNponAB8D9UsodFyw+Bgxtbgupx1ll+UNXx6Aoiu/5osH0MSAa+K0QwtX28X9AqJTyRSHEw8A2nKWel6WUBT6IQVEUH/NFm8fPgZ+3s/yfwD+7eruK/3NoGkU1TSRHmtC3c6c1pXdQg8SUblNU08Rre89QVOPxTdcUP6aSh9JtkiNN/GDCQJIjTT0ditIFesWsWqVv0Ot0pEYF93QYShdRJQ9FUbyikoeiKF5RyUNRFK+o5KEoildU8lAUxSsqeSiK4hWVPBRF8YpKHoqieEUlD0VRvKKSh6IoXlHJQ1EUr6jkoSiKV1TyUBTFKyp5KIriFZU8FEXxikoeiqJ4RSUPRVG8opKHoiheUclDURSvqOShKIpXVPJQFMUrKnkoiuKVfpU8HJpGQXUjDk3r6VAUpdfrV8lDPbFMUbpOv0oe6olliuZwYC4pRHM4ejqUXq9fJQ/XE8vUQ5b7L0tZMeVvr8NSVtzTofR6/Sp5KIoxPom4eYswxif1dCi9nnpWrdKv6PR6ghJTejqMPkGVPBRF8YrPSh5CiAnA76WUUy54/yFgKVDW/NaPpZTSV3EoiuIbPkkeQohHgO8D51pZPBa4U0r5lS+2rShK9/BVteUUcGsby8YCvxJCfCKE+JWPtq8oio/5JHlIKTcC1jYWbwB+AlwHTBJCzPZFDIqi+Fa3NpgKIXTAs1LKcimlBXgPuKw7Y1AUpWt0d1dtBHBYCDEcZ3vIdcDL3RyDoihdoFuShxBiERAmpXxRCPEY8BFgBnZIKd/vjhgURelaPkseUspcYGLz7+vc3n8DeMNX21UUpXt4lDyEEHFAiOu1lPKszyJSFKVXuGjyEEK8CFwPlAA6QAOu8nFciqL4OU9KHqOBIVJKdQcdRVFaeNJVWwiE+zoQRVF6lzZLHkKIz3FWURKAk0KI082LNCmlqrYoSj/XXrXl9m6LQlGUXqfN5CGlPAMghLhwEJdVCJEH/EVKWeXL4BRF8V+etHkE42z3eBM4A6QCQcBrPoxL8ZC6I7zSUzxJHvFSyt9IKbdJKZcBRinlb4EoH8emeEDdEV7pKZ4kjwghxDCA5n/DhRCxQJhPI1M8ou4Ir/QUT8Z53A+sFUKkAGeB+4CFwP/4MjDFM647witKd7to8pBSfoHzBj7u9vkmHEVRegtPhqfn4Bzv4VIrpbzUdyEpitIbeFJtGdb8rw5nCeQ234XTOQ5No6imieRIk3qwk9IjNIcDS1kxxvgkdPq+/XCCi+6dlNLc/NMkpfwUuLwb4vKK6nlQelp/eiKdJ9WW3/FttSUF8NuHfKqeB6Wn9acn0nlSbTnu9vsBYKuPYuk01fPQt/TGamh/eiKdJ5WytTjHdFwBJAGNPo1IUZqpaqh/8yR5vABkAh8CGcBqXwbUW6hh4b6nqqFOmsOBuaQQzeFfLQaeJI+hUsr/lFJullI+BAzxdVC9gboq+p6rGtoTVRZ/OmH9tRHWk+RhEkKEAAghgoEA34bk31wljsSIIHVV9LGeLN1Zyoope3st9ccO9ngC8ddGWE+Sx7PAASHE28A3wCrfhuTfXCWOklpzj10V29KZk82frrQuvi7dtbfPxvgkIidPp2bXh5hLCnv0u3E1wvrbuBFPoikCJuCcy3KVlHKDb0Pyb/5cD+/MyeaPRWNff9ft7bNOryds+Gjib10M4HffjT/wpKt2mZTyGqDS18H0Bv7cHdyZk80fi8a+/q4vts+uK77mcPjdd+MPPEkeWnOVRdI8QExK+ZhPo1K80pmTrT+NT3DxdJ/743fjCU+Sx4W3IVR9k4qieNTmsRnnQ6k1VOJQfESNm+l9PEkebwOzgOHNP8Pa/7jizh97MfxRbx830x//zp5UW3RSyh/6PJI+ytWiHzdvUb+sN3s6P8Wfe7E80R//zm2WPIQQRiGEETgthLhSCBHk9l6/cuFVxf31xa44nvZi9NViu6cliu4cTeqLUoI/9lb5WnvVFolzRu11wLrm313v9RsOTSMn5yylm77t53cfH3Cx8RHuA3zaOmg1h4Pc02d4dW9ury22t8ahaTg0je9fMcCvShS+GNPirwO5fKnNPZVSDpJSZgLfa/59kJRyENCvqjBFNU2sz27EOmNBy1XF/SrjyRVHczhoLC4g51TueUnIxVJWjOGDt7hjSLBPT7LurpcX1TTxxhdn0et0HSpR+LoU1h9LCb7QXrVlkhDiXuANIcS9zT8/Af7cfeH1vORIE3dNyCAjcyAA5pJCwHkAupKAISGZwlpzmwe7payYY5s2sfZI9XlJyMUYn0TCrYsYNGiAT4vtvrjitpeQvG3H8HXjaX8sJfhCe99eNZCM8+lwyc0/8cAjnqxYCDFBCPFxK+/fLIT4UgjxuRDiRx0PuXu518XNJYWUrH8Jc3E+9ccOUrpxDfXHDlJY1dBysLd21TTGJzH81lu5+xpBRubA7xy03XUw++KK215C8rYdo7c3nvYX7T2r9jBwWAjxf1LKwo6sVAjxCPB9nOND3N834JxYN7552adCiHeklCUdjrwHWSrLqf1kB6Gjx1Kz+0Ni58a3HOyuq+ad49OJt9a03Ag3OCmVNC+21dG7abX3eV+MlPRFQvLnKQDKtzy5AXKHEkezU8Ctrbw/HMiWUlZJKS3AJ8A1Xqy/W1xYJA9KTCHxjnsIGTKCkFGXEzHuauLnLcaUkExqVDA6TSOmqZI7x6cTa6nukipCR4vw3hT5O9MWoqoA/ZdP/uJSyo2AtZVFEUCN2+s6INIXMXQF9yK5+y31G08dp2LzOhpPHW85cVy9MmVvryOy6CSG2ATM0xcQGJfYqRg6WoT3psjvjzNqFf/nUfIQQswSQvyXEOKWTm6vFgh3ex2Os23FLxliE4iYPA1DbMJ5J1ioGEXCXfdTnTgYu92OuaSQgop61h6qpG7oZVTv+oCzZwvYkN1EcZ3lvHW6rvIOm82jq31H2w28aWdQvQ+KNy6aPJofvXAPzpLED4QQKzuxvWPAUCFETPNgs2uAzzuxPp+yVpRSu3s71orS8xKJPjCQ+rRhvPFVAWdy8yjbtJbgY19wQ852Qg59RsTk6QRGxbF4fHrLWAcXVxI6Jw/7zdW+O6sefXUwXH/kydFyjZRyvpTyWWA+MKmjGxFCLBJC3CultAIPA9twJo2XpZQFHV1fV3EdyHa7naaifJqK8nHYbC2/G2ITiJl7B2WGSBrLijm5czfmcmfbrqt6MDAjnchrptN0eD/Dpk0j+fYfUpeSxZp9+VTUW3jji7MUVjW0lDJcV/lQMarNq703J1hvOSnba5PpLfugOHkyt8UghNBLKR04Hznp0V9WSpkLTGz+fZ3b+/8E/tnxULue60C+PdOIftNL6IKCiJk+h6oPneEl3nEPlaYYXtuTy7j0SPamXE2CMYpUhwNzaRGaMarljlPGuISWnpUUTeMHEwaSGBFEYoSJmKZKyjevb5n34Boj4qoOuT+aUHM4yM05y/rsRu6akOFxr4NrX34wYWCHeyq8eT6Kt89Uaa9NpjP7oHQ/T0oeb+LsUl2Fs3fkTd+G1H1cB3JSqAGdMYjoabOd7RkL7yZh4d3oYxMoqmlk+rAE9uXXMPvyQSRFBZNzKpdDG9bxyi5JUU3TeV2g5pJCsNuJM1cRAKRGBWNKSD6vlOEaL1J/7OB3qi7ejjbtzNgIb3povB3I1V6bjBrf0bt4UvJ4Dmc1YxjwEpDv04i6ketA1iJSSVq0tKUEYEp2jsjYf7aKVR9l89DUIdw1IaNlHMfaI9Vcj8aUGCsJYQYA7HY7J7/6mrovPiNrymTqdm8n8prphA0f3eb2DTFx36m6uEabGuOT0HXgit6ZsRHenLS+ONHV+I7epb3h6UlCiCycpQ0zcBCwAR90U2zdxr3B0L3ePTotkv+8biiXpke1XC2TI03cfY0g6sqpbDlYzNncPADO5Oax+osC3oq+nJMh6YRPnkbN7g9bnTznGi9iSk77TkNlT4yb8KaHpiefqdIZ/fG+G77S3hE6EefT4kTzvy8Af8FZCumz3IvjgXo9l6aE03DkGxoLzqI5HOh1OtJiQgkbMhxDUiqG6HgABmak89OZo1lw1TC2nSynLiWL+HmLMcYnYYhNIHzSdWh2G5rD4VGCuFjjoTcnwcX+T28/sTyJX41p6UKaprX7k5WVNetin/H1z7x587TuYnc4tPyqBs3ucGiapmm1h7/W5IM/0E4/9ZjWVFzQ5ufae7+puEA7s2q5dmbVco/WoWmall/VoP3P1mNaflVDq3E2FRdo+X995rz1XUxTcYF29vlntFPZOa1us711therr3R0m558Jw67XWsqLtAcdntXhdmnZWVl7dPaOC97NCl4+tOdyeNCdqtVqzqwT8s+nq3ZbLbvLG/vYHQd/DabTWsszNMaC/PO+1x7CeJiJ443J4HDbtdOZedo/731aKvbbG+dF0tmvtDRbarE0PXaSx5qQkIr3Iu/+sBAzg0YwZu51u+MFoXvFoPdqxuuKlBxnQVTchqm5DQ0na5leVuNjp50g3rTNqLT68nIHNjS+NuRdfZET0hHt6nm2XQvT0aYRlzw+irfheMfLkwIrR3ErgTjPpDMPWG4Tv47x6cTXl/Knm8kVpvtvOUXNjq6Ek9BdWO73aCdGUzlbUNnTzSQ9tZG2f7Co7unCyFMQojA5qHqff5mQIFxiedNamvtIDaXFFK0/iVyc85SHhTN61/mUVTTRFK4ke8NDsJutwMQb63h07e2sGrXGb46mtPu1bSwqoGXdx5HczjaveL6+53G1UjR/sGT5LEK57NbPsU5v2WCTyPyA8V1llYntV2oDBNrjzjn9blOdlt5CTUf/JNXdhyisKoBY3wSk2+by0PXDGTsiEHtXk1jLdXcVPAJ8daadq+4/j6Yyp+Tm0psXae9cR5ZzeM8TgA7cc6IXQMM6qbYeownJ6chIZnIm+Yzc4zz5r6u+3loDgeDJ1/N7LIvibVUo+l01ITGc8WYLAyB347Jc2gaeVUN5FU1tBzIQXGJDL12MkHNJZ62DvSeLs5frEvUH5Obp1VCxXPtlTxecPuZgXM06gvA37ohrh7lyclZXGfh9eMNbDlSSmFVA6dP5dJYlEfpm69gio1n1IIFBMUlkpudw8sfH6Ogov68qfiFVQ288Mlp/vbJaXKzc2gqysdSVtwyixfOv4L70xiMi42V6Onk1hrXdwn4XWLrrdq7DeHU1t7vj89taU1iRBBzR6cQHx6EtbKUV7YdYPG4FOoxEYeOkMQU5zyX99YyjkjCBkPpJ/+mYdxUQvZ9RNy8O1h65UDOHjlKybvvo6OJ2GmziZ6zkDJDJCnNvTF3jk8npqkSc6NGxZYNHj1UyP3GRb7oeeiN9/9wLw35U1LrzTzpbfmxEEIKIU4LIXKAw90Ql98rqTWz9VgJsqSOtIFp/HDmpYRkDmProGlUmmIA50nGTYvZlzGJ+uShWKbP5/WiICzT52NKSMZRU8GWQ8VsiriMxok3cGLXbs7kl/Dal2dbemPirTVUbl4PQMzN38NcWozDZmu3JHJhyaCrSy2+6hL1ZXuEP5aGejtP/vr3AVOAfwF3A0d9GZA/cT/pLjywkyNNXDEwmlf3nuVIUT2ZgzNIjwvn7slZRDVWcPrkaaw2GzZwtovEhGKMTQSdhobzRAmMiuMHIow5J94FvY734q+g7ovPuD45kMSIIODbq3xQYgrWynKKX/4j9ccOtlt1uLBk0NVDsn11knekoVU1fPY8T5JHoZSyCAiXUn6MH99ztKu5n3QXHtg6TWNiuJnFIpSRyWEtYzyiG8o59MpqVr+/n717v+GFbYfYuOcEhVUNANwpQji5dRty337e2Mg+XCAAABNYSURBVJdH1KXjGT57DiGDBHdffwkxM29mR5GNklqzcztuV3lDTJxzPk1MXLtVhwtLBl1dzfBVb0pHGlr9uUenv/BkSn6NEGIuoAkhfgzE+TimHudqMzDEJhB7y+04rFZCS4+y+PKhODQNu91Ow/FDHP/gAz4NyCTGFECEpY41RUbGB9Wh1ZqZJ3REZmYyoayO4MKvqfqqgbfrIokPDWCXcRRzvzzI7TOvI85aw5Gv9rO1IprFl8QwaNAA7oqztHoCmZLTSLnrvpa2DE8fo9DVj1zwVW9KR6bk+2OPTn/jSfJYCgwGfgX8J/CATyPyA5ayYsreXkvk5OkYYuIoWfsi5qJ8WPwfbKwM5XuDjFR98AGDp05hDhG8d6CIQXIXRWmTWO0IQp86gxm5Jzhad5ziBojWZbHkgy3ExQl2hAzj2jQTB+yjuSoyDlvxSeK0BuY4zhC4bQeWWxdBc5vJhXzx3BVv+MN9N/whhv7Ok2pLAzAO+AnO2wf2+QZTQ2wCIaMup3qX89YlSd//MfE/fICa2AEsHp8OaGwOyKQqJJb4sCAmVBxgT/RwGhvqcODAHmDggDGNJjuMjQ2iyg6VV87lZPpYwvUO0nO+QLOYKSgooHr3DoKHjqRmz0fUDr2cMkMkr+090zImQdXpFX/lSfJ4ARgATMf5qITXfRpRD3FvHLVWlFJ/YB9BAzIJiImnIiSe05GZPLsrh6Kz+SQE6ZgxOpWiOovzIdhXXIc1MpFwrFxVfYjpdQcYGw5VZjP59XammLOp0gXRUHsOY20p1FRzSdkhPsw5R+P46zCPn8Z7ly7kpbJQimoaWTw+nZLaJl7dm8s3edUqgSh+yZPkMVhK+TjQ2Hzz4j7ZYOpqHHUN5DKkZ3Ji125OfnOAl3ceJyYkkMXDw4n7+O8cfnMtG/YX8con2aRYKtlxspbyJhuJlio+DxnKtqDhfFFl42pjEzV19ezTJfH3PAtN5+oZYS3lo8wZHIi9hDGhZox7dxB64itmZ0VRX17G2/tyKMovZOuxYsYPiOZfR4tbGgU1h6Plzu6d6Xb1pwFnSu/lSfIIFELEAQghwoE+ecS5JsM5NI2yf7zG2VM5vDNiHlaLlczTn1P55V5259ZSfM0CGsddx7kAIxUB4XyZU8Gw6uMkGyEvKA57QCB6HdRj4Is6O+WBoQytOEFCYwVp1bkcDUvnnB0Glh/ns8Nn2Rs8kMJNb9Bw+ABhmo1ZCQ6SP9nC7UOCGZoQxvRhCdiau4otZcWUvvkKpW++0qluV3U3LaUreNJg+huck+KSgT3Agz6NqIe4JsMtGZdG042LKClvQJffyL/2HWOvbjDXfPoltbHD+fu5JmzBIRhN4Uwp2MMwkcV22zCG5+2j0G6gMDaOlPozlEamozc3YNRs5KeOo9ZhZ19IPBHWehob4XDUMEaVH2LTuUwYdSPfFDUxd3QiV109FptIo7DewurPcmmyOgg26Pnp5MGkxCeRsPBugPO6XTs6orQ3jhBV/M9Fk4eUcqcQYiSQAuRJKftkBdzV9We32/nb1xXkn3NwTYqBbWWDCQ6EY7HDmBNUTtPxvXwwbDa19gAOhA0m5awkLyKSY6EjsRgCwQH5oamADoLCCDfXkBMQCXoD2KyMqpLkZUzAaNAzZMhgUo7uQzd6JktG2hCXjcFeWQaAYfsmfjh9PoExCS03XtbpdC13dnfnKkl4MnQd/KfXRundPBmefitwEue0/JNCiOk+j6oHuLr+4q01fK98L3cMgNHy38Q4arAAuvBYQk9+RWBoMLX1dVTZbOQHRLHPEkGlPQCbwwbW5hqd3Qp2BzigLjAcHBBgPocOyE0ajlGzMa70G7blVHO2UceLRxrIj0jHXllGyfqXQHOQcOsiMjMHMjAmlPTokHaHVauShNITPGnz+C0wQUp5OXA18D++DalnmRKSSbjxZo7aI0m68VZ+NHkwD0/J4JrASkqCo3kr4nIM52owWeohMJASfQhodhwBQRBgADQwBIMuAJ3NDDgwmGu4mhKCNRtjznxJUNlZkpNTMKVlMOa6axgcG4JIdHv+t65jc0fU7feUnuBJm0eFlLIUQEpZIoSo9XFM3cbV6wC0FOMtZcUMzEjnrlgLdrudjz45SVJjPu9WmjBFjqciIJxJFHHCYaRRs1MYmelcmQ6wWiEw0Pk7Gho6Amxm4q31lAcYmFb3NTNHpZIflUxw3lHumTmGjMyBDK1uJNZSjTE+icQ77unTJQhvH1Op+B9PkkedEGIbzhsCjQVChBArAKSUj/kyOF9z9V6gaUTPmIMhJo7yLesJu+o6zMHRxAUHMCTva3YZBhFtrSChsYyK4BgOEU2NsbkdA0BzgM0KhiBAa04iOkzYaDKEkl4ludRewhfiBqonjeDD/WfQJU/ip1Fx2MuKiXM4PJ5u39up59H2HZ4kj81uv/fYE+19wdjce2EpL6Vq54dYpt2K7fKpnN2xg40BQ5l79TD2RgoCNRiuC+FAVTLgoCY0EaxNzuSh2QhvqKAuNBGsDRAYDAYDJms9txds51jcMBqDwogZKTAaYol21DO/dA/RN8wmzlZL+eb1xN5ye79ps+jMHeMV/+JJb8tr3RFIT3A9lzYoMYXykBheOVoHmLhpyhwMxypAc6AvL2Koo4IPAwbREBhEmKUWi86I3m6hyWACXYAzcQCiLofRQU3kNhmYUrYP3TW3kG1IY2acnUszkxCh8c77nC6Y35Io4uYtwhCbgLWiFIemUVzd2KdPoLbmpKgSSe/jScmjz3M9z+QnMY0AxDRVYvr8QwYGTsRoOUbm9Bmk1Abyzv48HNZG5hRuJ1Jn4V/pkzgQMhAszraOmsg0RPbbTAkzYp23mC32NOaMSGK4sYHKzeuJn7eIgIBQAtyqJkHNdxwrf3sd5ukL2JDd1C9PoL4yS9bXd3HzJyp5NNPrdKRHhwCgOUyMmDeP+uICSnUmQs0a04fEErh3B9sNA4gfkAKnjvPTJAuHB0WRs+dT/m0cQWh0PNrQxaSOziQ4dQDRdRaSwo1YS88Re8vtbVZLXF2tgXGJ/CC29en4fV1fmSXb0TE3vZlPkocQQg88D4wBzMBSKWW22/LngElAXfNbt0gpa3wRizd0ej2aTse2j/ezMWgMsV+WsajJzuC6PAbdNJlK/Qg26Qbz07FjuHnYUE4lhDG+wYohMpbtJyvIikgiLCCA1KhgzCWFLY2h7lei8+r4boO2+sIJ1J/1pzE3vipXzQVMUsorgV8CKy9YPha4QUo5pfnHbxKHS4UxiuxBE5g7MgFTZDSbTjZQWlUPdXVsLQH9gEyCk9IorrPw5vFaQj7/kNEhFu66oOjd1sGk7oTVN/WnMTe+qrZMArYCSCn3CCHGuRY0l0qGAi8KIRKBl6SUL/soDq+lRIewdMpwohoriD20k3cdqejjkhg0fCg/CYnHWlXmfFaLXs/dk7OItSRgSkgm9YKDpq2h4H2ljq/0X75KjxGAe2nCLoRwJapQ4E/AEmAm8DMhxGgfxeE1Vx08MCCAQVdeSXBgAEy9BVPKABJttZi2b8RWXoJepyMtJpTgpNQOXW3U3byV3s5XyaMW542DWrYjpbQ1/94APCelbJBS1gH/xtk24ncs5SXUfvYRgwal8b2po9jdFElxnaVf1WsVpS2+Sh6fArMAhBATgUNuy7KAT4UQAUIIA84qzn4fxdEpxrhEIiddT3BiCgMGpnHXxAx0uvPrtZbykp4OU1F6hK+Sx9tAkxDiM5wPyn5ICPGwEGKOlPIY8AbOe4PsBF6XUh7xURydYikvwRiXSHFtE1v351BaWMyHx0sprm1qWV7zyQ6VQJR+yScNplJKB84bJrs77rb8GeAZX2y7q7gSQ+Sk64kBxufvJb7IRtxVN5IU4WzkdJVMjM0PplaU/kQNEmuDe2Iorm1C3Dir5f0LP6co/VHf74zuBFfi2HashEpjpEoUiuJGJY+LSIowcXl6VEtVpTWuNpCO8vb/KYo/UMnjIoprm9ifV/2dE93VSOoqmRwq7NggWdf/UwlE6a1U8riIpAgTNwxPbCl5WMpLzutlcZVMXAnmwp6XtnpiLlyvovQ2Knl4ICnChKW8hLzcfGo+2QFAyIgxLW0gl6REcsPwRGIsNed13V6sK1clDqU3U8nDA5byErI//pgPc2ppuvQaABqOHvhOUriw61Z15Sp9mUoeF+EaKDZkyhRmjc0kMczwnaTg3n6hunIVb/W29i+VPNpx7sSR86od4cWnyP74Y86dOHJeUlDtF0pn9cYGdDVIrBWW8hJK6q0Y9n1K+LiraTx9ksbso5TZjXweOgQOHmZITNx3EoiieKs3XoBU8miWl5uPISaOGEsNn2x6h90kc2uwkeDqKk5+9G+GTr0OkTmUeGMkMZahqjqidLnelDignycPV3vGxk+P8+XXx0hMiGdYOLxfGUKZwUEpduKHjeObchOJCUlExiXinITfu/7IiuIL/S55nD5wkLTURPILSqj94hP2hA5ha2UgWEycO5XDmeBQioNjMQQEYq+uILz4FDeMSML0zS4sYarnRFFc+k3yKK5t4sA3R9n92QFirXWcCUul1JJEU62NQHsT9YGhHA8bSKi1gXR7NbMnZHF10k2EZo0kFFTiUJQL9MnkkZebT3pGGl8fzSGzvoB/FcMHRWaa7Dqqg9JxBOnQdGAPMYDmIJo6bqg9iCljKMJSTPrwYQydfPl561SJQ1HO1+eSR15uPi+9u4fwyEhkjRVRcoSPYi+jSW8g1NqAxRCKQWfn2pRg8vJLGB0fzKWJ0VySPoPQrJEt7SCKorSvzyWPglO5HDebaKzUMSAkiOsSA8lKtlFgdjBjZCofHClm+PjLuHZoPMW1Td9p4VaJQ1E80+eSx8TrJ/Egn1BjCGHMpSOIsYziMreE8JMx396ovbd1jSmKP+lzyQOcCeRbKkEoii+o4emKonhFJQ9FUbyikoeiKF5RyUNRFK+o5KEoildU8lAUxSsqeShKP9NVj0dVyUNR+pGufL6ySh6K0o905U25VfJQlH6mq+ZvqeShKIpXVPJQFMUrKnkoiuIVlTwURfGKSh6KonilV9zP48iRI+VCiDM9HYei9EMD21qg0zStOwNRFKWPUNUWRVG8opKHoiheUclDURSvqOShKIpXVPJQFMUrKnkoiuKVXjHOw1tCCD3wPDAGMANLpZTZbssfAm5vfvm+lHJZ90fZORfbR7fPvAdskVL+rfuj7DwP/pY3Ak8AOuAr4D4pZa8bh+DBfv4nsAhwACuklG/3SKD0/ZLHXMAkpbwS+CWw0rVACJEJLAauAiYCM4QQo1tdi39rcx/d/DcQ3a1Rdb32/pbhwDPAbCnlBCAXiOuJILtAe/sZBfwcuBKYATzbIxE26+vJYxKwFUBKuQcY57YsD5gppbQ3X6EMQFP3h9hp7e0jQogFOK9SW7s/tC7V3n5eBRwCVgohdgMlUsqy7g+xS7S3n+eAM0Bo84+j26Nz09eTRwRQ4/baLoQIBJBSWqWU5UIInRDiD8DXUsoTPRJl57S5j0KIUTiLuI/3RGBdrM39xFnKmAo8CtwIPCiEyOrm+LpKe/sJzoveUWA/8MfuDOxCfT151ALhbq/1Ukqb64UQwgSsbf7Mz7o5tq7S3j7eCaQC/wbuAh4WQszs3vC6THv7WQF8KaUsllLWA7uAS7s7wC7S3n7eCCQDg4ABwFwhxBXdHN+3gfXUhrvJp8AsACHERJxFW5pf64AtwAEp5Y+llPaeCbHT2txHKeUjUsoJUsopwKvA/0ope2v1pc39xHkVHiWEiGu+Sk/EeXXujdrbzyqgETBLKZuAaiCq2yNs1qcnxrm1XI/G2Qp/N84/TDYQAKwH9rj9l19JKT/v7jg7o719lFK+4/a5/wcU94Hellb3UwhxO/BfzR//u5Ty9z0Taed4sJ/LgJk42zs+AR7pqV6lPp08FEXxnb5ebVEUxUdU8lAUxSsqeSiK4hWVPBRF8YpKHoqieEUlD6WFEOIuIcRTHn52ihBiQ/Pv9wshjgkhFnr4f5OEEM+38v5TQoi7XOvsaExK9+rTs2qVbnMr8D0p5aGLfhKQUhZz8RG9vwH+3NnAFN9R4zyUFs1X/bsACxAP/FVK+WLz5Lr7cE4e1IB5wCjgJziHvj8NnAAWSilzmtf1Fc7h1FU4h49PkVLuF0LsB+4AXpNSThRCzMeZKMoAI86RsKk4p9evBr5oLSZffg+KZ1S1RbmQFbgBZ4J4sPm9LOAmKeUknMO+b3B9uPlE/ga405U4mm1p/twkIAeYJoQYgTPJmAGEEAbgf4FpzZ9taF7n/wCVUkpX6aS1mJQeppKHcqH9zcOdi4GQ5vdKgdeEEK/gHDZt8GA9m3AOq54J/BpngpgDbHT7TDzOJFHRvM3POhCT0sNU8lAudF49VggRCSzDece1pTgnZukuthIp5WEgE7gCeB8IA25p/t2lFIgSQsQ3vx7vtsx9G6pu7YdU8lAuphbnTM/Pgd04k0eKh//3Y6BMSukAdgKlUspzroXNU83vB7YJIbbjbPNwOSqEWNP58BVfUQ2miqJ4RZU8FEXxikoeiqJ4RSUPRVG8opKHoiheUclDURSvqOShKIpXVPJQFMUr/x9BW5yP175jJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=figsize_gen)\n",
    "size = 5\n",
    "mew = .5\n",
    "marker_bs = '.'\n",
    "marker_ns = '+'\n",
    "plt.scatter(\n",
    "    results.query('bs_stim==0')['half_width'], \n",
    "    results.query('bs_stim==0')['peak_to_trough'], \n",
    "    c=color_stimulated, s=size, marker=marker_ns, \n",
    "    linewidth=mew, label='Stimulated NS', alpha=.5)\n",
    "\n",
    "plt.scatter(\n",
    "    results.query('bs_stim==1')['half_width'], \n",
    "    results.query('bs_stim==1')['peak_to_trough'], \n",
    "    c=color_stimulated, s=size, marker=marker_bs, \n",
    "    linewidth=mew, label='Stimulated BS', alpha=.5)\n",
    "\n",
    "\n",
    "plt.scatter(\n",
    "    results.query('bs_ctrl==0')['half_width'], \n",
    "    results.query('bs_ctrl==0')['peak_to_trough'], \n",
    "    c=color_control, s=size, marker=marker_ns, \n",
    "    linewidth=mew, label='Control NS', alpha=.5)\n",
    "\n",
    "plt.scatter(\n",
    "    results.query('bs_ctrl==1')['half_width'], \n",
    "    results.query('bs_ctrl==1')['peak_to_trough'], \n",
    "    c=color_control, s=size, marker=marker_bs, \n",
    "    linewidth=mew, label='Control BS', alpha=.5)\n",
    "\n",
    "plt.xlabel('half width')\n",
    "plt.ylabel('peak to through')\n",
    "plt.legend(bbox_to_anchor=(0,1.02,1,0.2), loc=\"lower left\",\n",
    "                mode=\"expand\", borderaxespad=0, ncol=2)\n",
    "\n",
    "plt.savefig(output_path / \"figures\" / \"compare-clusters.svg\", bbox_inches=\"tight\")\n",
    "plt.savefig(output_path / \"figures\" / \"compare-clusters.png\", dpi=600, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "results.average_firing_rate = results.apply(lambda x: float(x.average_firing_rate), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "results.frequency = results.apply(\n",
    "    lambda x: \n",
    "        float(x.frequency.replace('Hz', '')) if isinstance(x.frequency, str) \n",
    "        else float(x.frequency), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=100\n",
    "density=True\n",
    "cumulative=True\n",
    "histtype='step'\n",
    "lw = 2\n",
    "\n",
    "plt.figure(figsize=figsize_gen)\n",
    "plt.title('Narrow spiking')\n",
    "_, bins, _ = plt.hist(\n",
    "    results.query('bs_ctrl==0')['average_firing_rate'], \n",
    "    bins=bins, density=density, cumulative=cumulative, \n",
    "    histtype=histtype, color=color_control, lw=lw, label='Control');\n",
    "\n",
    "_, bins, _ = plt.hist(\n",
    "    results.query('bs_stim==0')['average_firing_rate'], \n",
    "    bins=bins, density=density, cumulative=cumulative, \n",
    "    histtype=histtype, color=color_stimulated, lw=lw, label='Stimulated');\n",
    "\n",
    "plt.xlim(-.5, 56)\n",
    "# plt.legend(bbox_to_anchor=(0,1.02,1,0.2), loc=\"lower left\",\n",
    "#                 mode=\"expand\", borderaxespad=0, ncol=2)\n",
    "\n",
    "plt.savefig(output_path / \"figures\" / \"cumulative_ns.svg\", bbox_inches=\"tight\")\n",
    "plt.savefig(output_path / \"figures\" / \"cumulative_ns.png\", dpi=600, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TA+k/BGRYLVmkiHgxIl6QNA24nuTKW6X4Nkq6kuQBUz+oUmySjgGeioildacrEx/wMkmiOoSkW3V5eq6mY/GVkQhGW6lYFfV9xjFXSxZN0vuAu4CrIuJqKhZfRBwN7E4yXvCWuqayYzsWmCVpObA38D3gXXXtZcf3CPD9iBiJiEdILpA71LV3LL4yEsFoKxWrYqWkmenrOcAvygpE0ruBZcDpEbE4PV2J+CT9laQz08OXSRLU/VWIDSAiDoyIgyJiJvAQ8DngtqrER5KoFgBI2gnYFnhJ0u9LGiCpFDoSXxkl+RKSLH03b6xUrJrTgMskbQOsISnJy/JF4B3A2ZJqYwUnAxdXIL4bgcsl/RyYDJySxlOVv7tmqvRv+6/AFZJWkMxiHEuSTH8ATCKZNfhVJwLxykIz84IiM3MiMDOcCMwMJwIzw4nAzHAiMDOcCMwMJwIzA/4fBOBBRAn5aukAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=100\n",
    "density=True\n",
    "cumulative=True\n",
    "histtype='step'\n",
    "lw = 2\n",
    "\n",
    "plt.figure(figsize=figsize_gen)\n",
    "plt.title('Narrow spiking')\n",
    "_, bins, _ = plt.hist(\n",
    "    results.query('bs_ctrl==0')['average_firing_rate'], \n",
    "    bins=bins, density=density, cumulative=cumulative, \n",
    "    histtype=histtype, color=color_control, lw=lw, label='Control');\n",
    "\n",
    "_, bins, _ = plt.hist(\n",
    "    results.query('bs_stim==0 and frequency==30')['average_firing_rate'], \n",
    "    bins=bins, density=density, cumulative=cumulative, \n",
    "    histtype=histtype, color=color_stimulated, lw=lw, label='Stimulated');\n",
    "\n",
    "plt.xlim(-.5, 56)\n",
    "# plt.legend(bbox_to_anchor=(0,1.02,1,0.2), loc=\"lower left\",\n",
    "#                 mode=\"expand\", borderaxespad=0, ncol=2)\n",
    "\n",
    "plt.savefig(output_path / \"figures\" / \"cumulative_ns_30.svg\", bbox_inches=\"tight\")\n",
    "plt.savefig(output_path / \"figures\" / \"cumulative_ns_30.png\", dpi=600, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1kv5DQIbZkkWKiF9HxIuSpgM3klx5qxTfRklXAxcD11YpNklHA89ExKKm3ZWJD3iZJFEdStKsujLd19C3+MpIBJ1mKlZFc5ux62zJokl6D3AvcE1EXEfF4ouIo4DdSPoL3tx0qOzYjgFmS1oC7A18C3hH0/Gy41sNfDsixiJiNckFcoem432Lr4xE0GmmYlUslzQrfT0XuK+sQCS9E1gMnB4RV6S7KxGfpL+QdGa6+TJJgnq4CrEBRMSBEXFQRMwCHgM+C9xRlfhIEtUCAEk7AdsCL0n6HUkjJJVCX+IroyRfSJKl7+eNmYpVcypwmaRtgFUkJXlZvgC8DThbUqOv4CTgogrEdzNwpaQfAVOAk9N4qvJ3106V/m3/DbhK0lKSUYxjSJLptcDWJKMGP+lHIJ5ZaGaeUGRmTgRmhhOBmeFEYGY4EZgZTgRmhhOBmeFEYGbA/wMJm2EFt/gbAgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=100\n",
    "density=True\n",
    "cumulative=True\n",
    "histtype='step'\n",
    "lw = 2\n",
    "\n",
    "plt.figure(figsize=figsize_gen)\n",
    "plt.title('Narrow spiking')\n",
    "_, bins, _ = plt.hist(\n",
    "    results.query('bs_ctrl==0')['average_firing_rate'], \n",
    "    bins=bins, density=density, cumulative=cumulative, \n",
    "    histtype=histtype, color=color_control, lw=lw, label='Control');\n",
    "\n",
    "_, bins, _ = plt.hist(\n",
    "    results.query('bs_stim==0 and frequency==11')['average_firing_rate'], \n",
    "    bins=bins, density=density, cumulative=cumulative, \n",
    "    histtype=histtype, color=color_stimulated, lw=lw, label='Stimulated');\n",
    "\n",
    "plt.xlim(-.5, 56)\n",
    "# plt.legend(bbox_to_anchor=(0,1.02,1,0.2), loc=\"lower left\",\n",
    "#                 mode=\"expand\", borderaxespad=0, ncol=2)\n",
    "\n",
    "plt.savefig(output_path / \"figures\" / \"cumulative_ns_11.svg\", bbox_inches=\"tight\")\n",
    "plt.savefig(output_path / \"figures\" / \"cumulative_ns_11.png\", dpi=600, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fVQTBXDMV66i5zzbvzMpxEhGvA/4T+HpmnkuN9yUz3w/sTDFe8PKmTXXZjw8AB0TE94E9gH8BXtO0faj7UUUQzDVTsY5ui4gV5c8HAddU2JauRcRrgauAT2bm2vLp2u1LRPxpRBxXPnySIsxurtt+ZOa+mblfZq4A1gOHA98Z1X5UUZJfSpF81/PCTMU6+wRwZkS8DLibosyug+OBVwEnRERjrOCjwN/XbF8uAb4WET8AtgA+RtH2Ov6btBrZ/1vOLJTkhCJJBoEkDAJJGASSMAgkYRBIwiCQhEEgCfh/Pn1sEtaAsqwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = 100\n",
    "\n",
    "plt.figure(figsize=figsize_gen)\n",
    "plt.title('Broad spiking')\n",
    "_, bins, _ = plt.hist(\n",
    "    results.query('bs_ctrl==1')['average_firing_rate'], \n",
    "    bins=bins, density=density, cumulative=cumulative, \n",
    "    histtype=histtype, color=color_control, lw=lw);\n",
    "\n",
    "_, bins, _ = plt.hist(\n",
    "    results.query('bs_stim==1')['average_firing_rate'], \n",
    "    bins=bins, density=density, cumulative=cumulative, \n",
    "    histtype=histtype, color=color_stimulated, lw=lw);\n",
    "\n",
    "plt.xlim(-.5, 44)\n",
    "\n",
    "plt.savefig(output_path / \"figures\" / \"cumulative_bs.svg\", bbox_inches=\"tight\")\n",
    "plt.savefig(output_path / \"figures\" / \"cumulative_bs.png\", dpi=600, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qsh/vAw6JiO8BewP/Crym6fWh7kcVQdBtpmId3RkRy8qfDwNuqLAtPYuI1wLXAZ/IzNXl07Xbl4j4s4g4qXz4NEWY3Va3/cjMAzLzwMxcBqwDjgW+Par9qKIkv5Ii+W7ilzMV6+zjwDkR8TJgA0WZXQcnA68CTomIxljBR4B/qNm+XAF8LSK+D2wDfJSi7XX8N2k1sv+3nFkoyQlFkgwCSRgEkjAIJGEQSMIgkIRBIAmDQBLw/9i/XumDqC+5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = 100\n",
    "\n",
    "plt.figure(figsize=figsize_gen)\n",
    "plt.title('Broad spiking')\n",
    "_, bins, _ = plt.hist(\n",
    "    results.query('bs_ctrl==1')['average_firing_rate'], \n",
    "    bins=bins, density=density, cumulative=cumulative, \n",
    "    histtype=histtype, color=color_control, lw=lw);\n",
    "\n",
    "_, bins, _ = plt.hist(\n",
    "    results.query('bs_stim==1 and frequency==11')['average_firing_rate'], \n",
    "    bins=bins, density=density, cumulative=cumulative, \n",
    "    histtype=histtype, color=color_stimulated, lw=lw);\n",
    "\n",
    "plt.xlim(-.5, 44)\n",
    "\n",
    "plt.savefig(output_path / \"figures\" / \"cumulative_bs_11.svg\", bbox_inches=\"tight\")\n",
    "plt.savefig(output_path / \"figures\" / \"cumulative_bs_11.png\", dpi=600, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zZ0+9zDmzclxk5tOZ+VRELAIuo/g2reu+rI2ICyhugflNargfEbEUeCQzr214emj7MYog6DRTsY4a+2xzzqwcJxHxJuA/gW9k5oXUeF8y84PA9hTjBa9u2FSX/fgQsH9EfB/YBfgX4A0N2yvdj1EEQaeZinV0V0TsU/58IHDjCNvStYh4I3Ad8KnMXF4+Xbt9iYg/jYgTyofPUITZ7XXbj8zcKzP3zsx9gJXAkcB3h7UfoyjJr6RIvlt4eaZinX0SOCciXgWspiiz6+BE4HXASRExO1bwMeDva7YvVwDnRcQPgE2Aj1O0vY7/Js2G9v+WMwslOaFIkkEgCYNAEgaBJAwCSRgEkjAIJGEQSAL+HxutVYjgU4b6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = 100\n",
    "\n",
    "plt.figure(figsize=figsize_gen)\n",
    "plt.title('Broad spiking')\n",
    "_, bins, _ = plt.hist(\n",
    "    results.query('bs_ctrl==1')['average_firing_rate'], \n",
    "    bins=bins, density=density, cumulative=cumulative, \n",
    "    histtype=histtype, color=color_control, lw=lw);\n",
    "\n",
    "_, bins, _ = plt.hist(\n",
    "    results.query('bs_stim==1 and frequency==30')['average_firing_rate'], \n",
    "    bins=bins, density=density, cumulative=cumulative, \n",
    "    histtype=histtype, color=color_stimulated, lw=lw);\n",
    "\n",
    "plt.xlim(-.5, 44)\n",
    "\n",
    "plt.savefig(output_path / \"figures\" / \"cumulative_bs_30.svg\", bbox_inches=\"tight\")\n",
    "plt.savefig(output_path / \"figures\" / \"cumulative_bs_30.png\", dpi=600, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U-test: U value 135025.0 p value 0.11036062942886643\n",
      "U-test: U value 6176.0 p value 0.12484872783599776\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 122.4x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 122.4x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=figsize_violin)\n",
    "# test = 'permutation_resampling'\n",
    "test = 'mann_whitney'\n",
    "\n",
    "plt.title('Broad spiking')\n",
    "violinplot(\n",
    "    results.query('bs_ctrl==1')['average_firing_rate'].to_numpy(), \n",
    "    results.query('bs_stim==1')['average_firing_rate'].to_numpy(), \n",
    "    test=test)\n",
    "\n",
    "plt.savefig(output_path / \"figures\" / \"rates_bs.svg\", bbox_inches=\"tight\")\n",
    "plt.savefig(output_path / \"figures\" / \"rates_bs.png\", dpi=600, bbox_inches=\"tight\")\n",
    "\n",
    "plt.figure(figsize=figsize_violin)\n",
    "plt.title('Narrow spiking')\n",
    "violinplot(\n",
    "    results.query('bs_ctrl==0')['average_firing_rate'].to_numpy(), \n",
    "    results.query('bs_stim==0')['average_firing_rate'].to_numpy(), \n",
    "    test=test)\n",
    "\n",
    "plt.savefig(output_path / \"figures\" / \"rates_ns.svg\", bbox_inches=\"tight\")\n",
    "plt.savefig(output_path / \"figures\" / \"rates_ns.png\", dpi=600, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U-test: U value 85419.0 p value 0.4550292852318226\n",
      "U-test: U value 3843.0 p value 0.06623560612855536\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 122.4x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 122.4x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=figsize_violin)\n",
    "# test = 'permutation_resampling'\n",
    "test = 'mann_whitney'\n",
    "\n",
    "plt.title('Broad spiking')\n",
    "violinplot(\n",
    "    results.query('bs_ctrl==1')['average_firing_rate'].to_numpy(), \n",
    "    results.query('bs_stim==1 and frequency==11')['average_firing_rate'].to_numpy(), \n",
    "    test=test)\n",
    "\n",
    "plt.savefig(output_path / \"figures\" / \"rates_bs_11.svg\", bbox_inches=\"tight\")\n",
    "plt.savefig(output_path / \"figures\" / \"rates_bs_11.png\", dpi=600, bbox_inches=\"tight\")\n",
    "\n",
    "plt.figure(figsize=figsize_violin)\n",
    "plt.title('Narrow spiking')\n",
    "violinplot(\n",
    "    results.query('bs_ctrl==0')['average_firing_rate'].to_numpy(), \n",
    "    results.query('bs_stim==0 and frequency==11')['average_firing_rate'].to_numpy(), \n",
    "    test=test)\n",
    "\n",
    "plt.savefig(output_path / \"figures\" / \"rates_ns_11.svg\", bbox_inches=\"tight\")\n",
    "plt.savefig(output_path / \"figures\" / \"rates_ns_11.png\", dpi=600, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U-test: U value 49606.0 p value 0.03757091416858637\n",
      "U-test: U value 2333.0 p value 0.5891054506000802\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 122.4x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 122.4x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=figsize_violin)\n",
    "# test = 'permutation_resampling'\n",
    "test = 'mann_whitney'\n",
    "\n",
    "plt.title('Broad spiking')\n",
    "violinplot(\n",
    "    results.query('bs_ctrl==1')['average_firing_rate'].to_numpy(), \n",
    "    results.query('bs_stim==1 and frequency==30')['average_firing_rate'].to_numpy(), \n",
    "    test=test)\n",
    "\n",
    "plt.savefig(output_path / \"figures\" / \"rates_bs.svg\", bbox_inches=\"tight\")\n",
    "plt.savefig(output_path / \"figures\" / \"rates_bs.png\", dpi=600, bbox_inches=\"tight\")\n",
    "\n",
    "plt.figure(figsize=figsize_violin)\n",
    "plt.title('Narrow spiking')\n",
    "violinplot(\n",
    "    results.query('bs_ctrl==0')['average_firing_rate'].to_numpy(), \n",
    "    results.query('bs_stim==0 and frequency==30')['average_firing_rate'].to_numpy(), \n",
    "    test=test)\n",
    "\n",
    "plt.savefig(output_path / \"figures\" / \"rates_ns_30.svg\", bbox_inches=\"tight\")\n",
    "plt.savefig(output_path / \"figures\" / \"rates_ns_30.png\", dpi=600, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\n",
    "    'average_firing_rate',\n",
    "    'half_width',\n",
    "    'peak_to_trough'\n",
    "]\n",
    "\n",
    "\n",
    "def summarize(data):\n",
    "    return \"{:.2f} ± {:.2f} ({})\".format(data.mean(), data.sem(), sum(~np.isnan(data)))\n",
    "\n",
    "\n",
    "bs = pd.DataFrame()\n",
    "\n",
    "bs['Control'] = results.query('bs_ctrl==1')[columns].agg(summarize)\n",
    "bs['Stimulated'] = results.query('bs_stim==1')[columns].agg(summarize)\n",
    "\n",
    "ns = pd.DataFrame()\n",
    "\n",
    "ns['Control'] = results.query('bs_ctrl==0')[columns].agg(summarize)\n",
    "ns['Stimulated'] = results.query('bs_stim==0')[columns].agg(summarize)\n",
    "\n",
    "\n",
    "def MWU(column, df, cluster, extra):\n",
    "    '''\n",
    "    Mann Whitney U\n",
    "    '''\n",
    "    Uvalue, pvalue = scipy.stats.mannwhitneyu(\n",
    "        df.query('bs_ctrl=={} {}'.format(cluster, extra))[column].dropna(), \n",
    "        df.query('bs_stim=={} {}'.format(cluster, extra))[column].dropna(),\n",
    "        alternative='two-sided')\n",
    "\n",
    "    return \"{:.2f}, {:.3f}\".format(Uvalue, pvalue)\n",
    "\n",
    "\n",
    "def PRS(column, df, cluster, extra):\n",
    "    '''\n",
    "    Permutation ReSampling\n",
    "    '''\n",
    "    pvalue, observed_diff, diffs = permutation_resampling(\n",
    "        df.query('bs_ctrl=={} {}'.format(cluster, extra))[column].dropna(), \n",
    "        df.query('bs_stim=={} {}'.format(cluster, extra))[column].dropna())\n",
    "\n",
    "    return \"{:.2f}, {:.3f}\".format(observed_diff, pvalue)\n",
    "\n",
    "\n",
    "bs['MWU'] = list(map(lambda x: MWU(x, results, 1, ''), columns))\n",
    "bs['PRS'] = list(map(lambda x: PRS(x, results, 1, ''), columns))\n",
    "\n",
    "ns['MWU'] = list(map(lambda x: MWU(x, results, 0, ''), columns))\n",
    "ns['PRS'] = list(map(lambda x: PRS(x, results, 0, ''), columns))\n",
    "\n",
    "bs.to_latex(output_path / \"statistics\" / \"broad_spiking.tex\")\n",
    "bs.to_csv(output_path / \"statistics\" / \"broad_spiking.csv\")\n",
    "\n",
    "ns.to_latex(output_path / \"statistics\" / \"narrow_spiking.tex\")\n",
    "ns.to_csv(output_path / \"statistics\" / \"narrow_spiking.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\n",
    "    'average_firing_rate',\n",
    "    'half_width',\n",
    "    'peak_to_trough'\n",
    "]\n",
    "\n",
    "\n",
    "def summarize(data):\n",
    "    return \"{:.2f} ± {:.2f} ({})\".format(data.mean(), data.sem(), sum(~np.isnan(data)))\n",
    "\n",
    "\n",
    "bs = pd.DataFrame()\n",
    "\n",
    "bs['Control'] = results.query('bs_ctrl==1')[columns].agg(summarize)\n",
    "bs['Stimulated'] = results.query('bs_stim==1 and frequency==11')[columns].agg(summarize)\n",
    "\n",
    "ns = pd.DataFrame()\n",
    "\n",
    "ns['Control'] = results.query('bs_ctrl==0')[columns].agg(summarize)\n",
    "ns['Stimulated'] = results.query('bs_stim==0 and frequency==11')[columns].agg(summarize)\n",
    "\n",
    "\n",
    "def MWU(column, df, cluster, extra):\n",
    "    '''\n",
    "    Mann Whitney U\n",
    "    '''\n",
    "    Uvalue, pvalue = scipy.stats.mannwhitneyu(\n",
    "        df.query('bs_ctrl=={} {}'.format(cluster, extra))[column].dropna(), \n",
    "        df.query('bs_stim=={} {} and frequency==11'.format(cluster, extra))[column].dropna(),\n",
    "        alternative='two-sided')\n",
    "\n",
    "    return \"{:.2f}, {:.3f}\".format(Uvalue, pvalue)\n",
    "\n",
    "\n",
    "def PRS(column, df, cluster, extra):\n",
    "    '''\n",
    "    Permutation ReSampling\n",
    "    '''\n",
    "    pvalue, observed_diff, diffs = permutation_resampling(\n",
    "        df.query('bs_ctrl=={} {}'.format(cluster, extra))[column].dropna(), \n",
    "        df.query('bs_stim=={} {} and frequency==11'.format(cluster, extra))[column].dropna())\n",
    "\n",
    "    return \"{:.2f}, {:.3f}\".format(observed_diff, pvalue)\n",
    "\n",
    "\n",
    "bs['MWU'] = list(map(lambda x: MWU(x, results, 1, ''), columns))\n",
    "bs['PRS'] = list(map(lambda x: PRS(x, results, 1, ''), columns))\n",
    "\n",
    "ns['MWU'] = list(map(lambda x: MWU(x, results, 0, ''), columns))\n",
    "ns['PRS'] = list(map(lambda x: PRS(x, results, 0, ''), columns))\n",
    "\n",
    "bs.to_latex(output_path / \"statistics\" / \"broad_spiking_11.tex\")\n",
    "bs.to_csv(output_path / \"statistics\" / \"broad_spiking_11.csv\")\n",
    "\n",
    "ns.to_latex(output_path / \"statistics\" / \"narrow_spiking_11.tex\")\n",
    "ns.to_csv(output_path / \"statistics\" / \"narrow_spiking_11.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\n",
    "    'average_firing_rate',\n",
    "    'half_width',\n",
    "    'peak_to_trough'\n",
    "]\n",
    "\n",
    "\n",
    "def summarize(data):\n",
    "    return \"{:.2f} ± {:.2f} ({})\".format(data.mean(), data.sem(), sum(~np.isnan(data)))\n",
    "\n",
    "\n",
    "bs = pd.DataFrame()\n",
    "\n",
    "bs['Control'] = results.query('bs_ctrl==1')[columns].agg(summarize)\n",
    "bs['Stimulated'] = results.query('bs_stim==1 and frequency==30')[columns].agg(summarize)\n",
    "\n",
    "ns = pd.DataFrame()\n",
    "\n",
    "ns['Control'] = results.query('bs_ctrl==0')[columns].agg(summarize)\n",
    "ns['Stimulated'] = results.query('bs_stim==0 and frequency==30')[columns].agg(summarize)\n",
    "\n",
    "\n",
    "def MWU(column, df, cluster, extra):\n",
    "    '''\n",
    "    Mann Whitney U\n",
    "    '''\n",
    "    Uvalue, pvalue = scipy.stats.mannwhitneyu(\n",
    "        df.query('bs_ctrl=={} {}'.format(cluster, extra))[column].dropna(), \n",
    "        df.query('bs_stim=={} {} and frequency==30'.format(cluster, extra))[column].dropna(),\n",
    "        alternative='two-sided')\n",
    "\n",
    "    return \"{:.2f}, {:.3f}\".format(Uvalue, pvalue)\n",
    "\n",
    "\n",
    "def PRS(column, df, cluster, extra):\n",
    "    '''\n",
    "    Permutation ReSampling\n",
    "    '''\n",
    "    pvalue, observed_diff, diffs = permutation_resampling(\n",
    "        df.query('bs_ctrl=={} {}'.format(cluster, extra))[column].dropna(), \n",
    "        df.query('bs_stim=={} {} and frequency==30'.format(cluster, extra))[column].dropna())\n",
    "\n",
    "    return \"{:.2f}, {:.3f}\".format(observed_diff, pvalue)\n",
    "\n",
    "\n",
    "bs['MWU'] = list(map(lambda x: MWU(x, results, 1, ''), columns))\n",
    "bs['PRS'] = list(map(lambda x: PRS(x, results, 1, ''), columns))\n",
    "\n",
    "ns['MWU'] = list(map(lambda x: MWU(x, results, 0, ''), columns))\n",
    "ns['PRS'] = list(map(lambda x: PRS(x, results, 0, ''), columns))\n",
    "\n",
    "bs.to_latex(output_path / \"statistics\" / \"broad_spiking_30.tex\")\n",
    "bs.to_csv(output_path / \"statistics\" / \"broad_spiking_30.csv\")\n",
    "\n",
    "ns.to_latex(output_path / \"statistics\" / \"narrow_spiking_30.tex\")\n",
    "ns.to_csv(output_path / \"statistics\" / \"narrow_spiking_30.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# example waveforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize(a):\n",
    "    t = a - a.min()\n",
    "    return t / t.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'half_width','peak_to_trough'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=figsize_gen)\n",
    "\n",
    "\n",
    "lw = 3\n",
    "\n",
    "row = results.query('bs==1').sort_values('half_width', ascending=False).iloc[50]\n",
    "template = data_loader.template(\n",
    "    row.action, row.channel_group, row.unit_name)\n",
    "\n",
    "mean_wf = template.data\n",
    "peak_wf = mean_wf[np.argmin(mean_wf.min(1))]\n",
    "plt.plot(normalize(peak_wf.T), color=color_bs, lw=lw)\n",
    "\n",
    "\n",
    "row = results.query('bs==0').sort_values('half_width').iloc[10]\n",
    "template = data_loader.template(\n",
    "    row.action, row.channel_group, row.unit_name)\n",
    "\n",
    "mean_wf = template.data\n",
    "peak_wf = mean_wf[np.argmin(mean_wf.min(1))]\n",
    "plt.plot(normalize(peak_wf.T), color=color_ns, lw=lw)\n",
    "\n",
    "plt.savefig(output_path / \"figures\" / \"example_waveforms.svg\", bbox_inches=\"tight\")\n",
    "plt.savefig(output_path / \"figures\" / \"example_waveforms.png\", dpi=600, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Store results in Expipe action"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "action = project.require_action(\"waveform-analysis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "action.data['results'] = 'results.csv'\n",
    "results.to_csv(action.data_path('results'), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "stuff = {\n",
    "    \"figures\": \"figures\",\n",
    "    \"statistics\": \"statistics\"\n",
    "}\n",
    "\n",
    "for key, value in stuff.items():\n",
    "    action.data[key] = value\n",
    "    data_path = action.data_path(key)\n",
    "    data_path.parent.mkdir(exist_ok=True, parents=True)\n",
    "    source = output_path / value\n",
    "    if source.is_file():\n",
    "        shutil.copy(source, data_path)\n",
    "    else:\n",
    "        copy_tree(str(source), str(data_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "septum_mec.analysis.registration.store_notebook(action, \"10_waveform_analysis.ipynb\")"
   ]
  }
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