septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/20_stimulus-lfp-response-mec.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "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",
    "import matplotlib\n",
    "from distutils.dir_util import copy_tree\n",
    "from neo import SpikeTrain\n",
    "import scipy\n",
    "import seaborn as sns\n",
    "from tqdm.notebook import tqdm_notebook as tqdm\n",
    "tqdm.pandas()\n",
    "\n",
    "from spike_statistics.core import permutation_resampling_test\n",
    "\n",
    "from spikewaveform.core import calculate_waveform_features_from_template, cluster_waveform_features\n",
    "\n",
    "from septum_mec.analysis.plotting import violinplot, despine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#############################\n",
    "\n",
    "perform_zscore = False\n",
    "\n",
    "if not perform_zscore:\n",
    "    zscore_str = \"-no-zscore\"\n",
    "else:\n",
    "    zscore_str = \"\"\n",
    "\n",
    "#################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "plt.rc('axes', titlesize=12)\n",
    "plt.rcParams.update({\n",
    "    'font.size': 12, \n",
    "    'figure.figsize': (6, 4), \n",
    "    'figure.dpi': 150\n",
    "})\n",
    "\n",
    "output_path = pathlib.Path(\"output\") / (\"stimulus-lfp-response-mec\" + zscore_str)\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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_loader = dp.Data()\n",
    "actions = data_loader.actions\n",
    "project = data_loader.project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "identify_neurons = actions['identify-neurons']\n",
    "sessions = pd.read_csv(identify_neurons.data_path('sessions'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "lfp_action = actions['stimulus-lfp-response' + zscore_str]\n",
    "lfp_results = pd.read_csv(lfp_action.data_path('results'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "lfp_results = pd.merge(sessions, lfp_results, how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "lfp_results = lfp_results.query('stim_location!=\"ms\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def action_group(row):\n",
    "    a = int(row.channel_group in [0,1,2,3])\n",
    "    return f'{row.action}-{a}'\n",
    "lfp_results['action_side_a'] = lfp_results.apply(action_group, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "lfp_results['stim_strength'] = lfp_results['stim_p_max'] / lfp_results['theta_energy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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_side_a</th>\n",
       "      <th>channel_group</th>\n",
       "      <th>signal_to_noise</th>\n",
       "      <th>stim_strength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>1833-010719-1-0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.001902</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>1833-010719-1-1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.003522</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>1833-020719-1-0</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.002942</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>1833-020719-1-1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.012323</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>1833-020719-3-0</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.002042</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       action_side_a  channel_group  signal_to_noise  stim_strength\n",
       "71   1833-010719-1-0              7         0.001902            NaN\n",
       "67   1833-010719-1-1              3         0.003522            NaN\n",
       "583  1833-020719-1-0              7        -0.002942            NaN\n",
       "579  1833-020719-1-1              3         0.012323            NaN\n",
       "375  1833-020719-3-0              7        -0.002042            NaN"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lfp_results_hemisphere = lfp_results.sort_values(\n",
    "#     by=['action_side_a', 'stim_strength', 'signal_to_noise'], ascending=[True, False, False]\n",
    "lfp_results_hemisphere = lfp_results.sort_values(\n",
    "    by=['action_side_a', 'channel_group'], ascending=[True, False]\n",
    ").drop_duplicates(subset='action_side_a', keep='first')\n",
    "lfp_results_hemisphere.loc[:,['action_side_a','channel_group', 'signal_to_noise', 'stim_strength']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = ['#1b9e77','#d95f02','#7570b3','#e7298a']\n",
    "labels = ['Baseline I', '11 Hz', 'Baseline II', '30 Hz']\n",
    "# Hz11 means that the baseline session was indeed before an 11 Hz session\n",
    "queries = ['baseline and i and Hz11', 'frequency==11', 'baseline and ii and Hz30', 'frequency==30']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare pairwise comparison: same animal same side same date different sessions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_entity_date_side(row):\n",
    "    s = row.action_side_a.split('-')\n",
    "    del s[2]\n",
    "    return '-'.join(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "lfp_results_hemisphere['entity_date_side'] = lfp_results_hemisphere.apply(make_entity_date_side, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/lib/histograms.py:898: RuntimeWarning: invalid value encountered in true_divide\n",
      "  return n/db/n.sum(), bin_edges\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 525x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pEH3j9ovcE2bWB9gP2Bf4GuF2Yj1hWdR/AJe4+1KtkWa2E3AMsAXhfvkcwr30O4A/uPtSzV1mtizwI8K65GMIE3C9TVjv/VJ3/6ycF5Ov01mqE97HwErAocARNPc1mkRYK32Cuy/V4bhSZeuqqhqwzWxVwj2MupzmkUcJy2huBfzFzPZx94/jpCo/Bg4AFgMXdnaZpWPNWajfX93JyBtO7w0Mq3Y5KmTW+4f8qrWjWCrCzEYTataN5NSuzWwg8ACwbTw0FXiFsMrhmvFvnJl9w91fTKU7Hrgs7n4IvEToG7RN/NvHzLZ394ZUmrUJPdZHEUb0vEUYYrsecBZwsJnt7O4t78O0Xg3hvv2BwGeEW59rEH5YbAEYYd2K9PvQWWWrmg5bXrNMfwRei9sl4i+ngwizp30DmGZm/yXcw7mQMNzhEHd/rXOLKyLlGnnD6fsQAsEn3eTvw/iaOoWZ9Taz4Wa2O/Ag4fv6AneflnPpaYRgPQPYzN1Xd/dN3X1Vwu3DjwjTOP80lfcQmis8+7n7yjHN6sBOhEA3FtgnlWYQcB8hIN4DrOru5u5fJUxL/QBhMqt744+I9lgB2J9QWx7h7hsTatw3x/Mnm9mSzjGdXLaq6apN4rj722a2EWFFrj2ArxCGcP0NuMjdH6tm+aTz3L/W8yzss7jg+X/udRJDe2insraore3XWU91LbBcZz1ZJ1iB8Jru7KD8HzOzYucvBH6W5/j2hJr3Oe7+XPqEuz9nZr8DziV8hyaMMGx2NmEp43Sah83sAkI/onST+GGEZuYXgL3TNW93n25m+xAqYGsSFnD6XbEXU4bfuvvlqedYYGYnEFpY+xB+jDxQpbJVRYcGbHcfVeL82BLnZwAnxj/poRb2WczCPvUFz9cO7sfgAer1LJmXOw67N7AMIRD1B04CBpnZCTkBaes4cqbQJFLJXBXpsY3vEO5xDwUmmNkl7v5yKs9f5Mlnr7i9Lf38qTTzzewu4GRgd9ofFO/L8xwzzexTwo+nIVUsW1V02Rq2dD+NTY3MXlh8npvZee5h373L0aw8vHBFraeOsc6Aw4ErCV+u3cEnwLEdmH+hcdj9CLXCK+Pz9wZ+mL4mLog01My2IAz9WiNuNyLcy4bULVB3/8TMLiS0YB4EHGRm0wn9hx4GHsyztPH6cXu4me1Z4DUkiwmsXfLVlvZBgePJl0Q6fnV22apCAVs6zeyFdWz4p18WvaZ/fZ+lhm0N7T+Q4QMGd2TRpAO8f8iv7hx5w+kTUaezdok9ta8xs5WAs4EjzOwCd38PwMyWIXQeG0dzT3IIzdkvEIa+7pwn3zPN7HnCj4D/IwS0A+JfvZndBhzr7kmtP/nVnHRkK2ZIifPlKDwhQ1CTetzZZasKBWwR6TAxwGVu/usu6h5CwO5NqDm/lzq+LaHmeQXwDPAq8GaseR9OnoAN4O53A3fH4VDbEDqa7UKohY4jBMI94uXz4v7u7n5/hV9be3XlslWMAraISDakJymoAYhN4Mlwrl0LdMYdmXsg9pReE6hx95fd/XPCPeP7CD2wTwcuAHY3s+ViLdsJHb3WA/IGRTNbkxA4p3XyQiVduWwVU+1hXSIiUp5d47YJeD4+Xj11/nlymFktYUIVaFlBOwJ4GbjZzGpy0wHp6QZ7x23SCeywfEOj4uQt9wDPAb8u/DI6RFcuW8UoYEunaWpson99nxZ/D+54HE/vfuqSvwd3Om6pdMv1z+ywSZF2M7MaM/sOoYMYwF3u/n58nJ4E5KzYWzxJty5hIpHknm66d+YdhHvE6wOXxnHMSboRwHlx99nUTJNXEcZ0jyGMZ141lWb5mOc6Md/ODopduWwVoyZx6TTz6xaz9+SWHcpunlx6KvheNfpdKT3CFWY2J+dYX8JkIElP++dpnpoZd3/RzO4gTEl6MjDezN4hTE2a1L4fAXYAljGzZd39c3f/yMwOJUxE8iPgB2b2FiEmjCaM0Z4B/CD1XLPNbA9CbXZ74B0zm0xoqjfC0LN6wkQsnboEaFcuWyXpm1BEpGtYnzAdc/pvA8Jyw/cDhwBbuPvMnHT7E5q4nyN8p29ICFD3Abu5+45AMvXz7kkid7+F0MnsLsL0n+sSZgN7k3D/eh13fzX9RO7+31jOc4H/EX5MrEOY+/uPwMbuPrFd70IbdeWyVUpNU1Ohsfbdn5m9OmbMmHUfeOCB0hdLSfWff8rbx6/Y4tgal09fsrzmezNmccV5rV8566xf7KDlIKsj371NEakS1bBFREQyQPewpap+cOKmrDys+DwGnTj3tYhIl6WA3Q00NTbSMDf3tlbna5ibO5NhabW1fdXcLSJSBgXsbqBh7syl7h2LiEj3ooAtHWr2wnn0WjAwPl56YQ8RESmPArZ0qLETL2FOvzBfQ76FPUREpDzqJS4iIpIBqmF3U6udP4neg0dULL/ZC+cxduIlrU73eV9NKyoiUgkK2N1U78EjlkxYUgm9Fgxc0rQtIiKdTwFb2uyxvU5i2IDmIN7U2MT8usUFr6+rW8x1k59rcUwLe4iIlEcBW9ps2IBahg8YvGR/7tyFXHTe463KQwt7iIiUR9+WIiIiGaCALSIikgEK2CIiIhmge9jSoU4+fRsGDSq8eIcW9hARKY8CtnSoQYP6aXEPEZEKUJO4iIhIBihgi4h0MWZ2pJk1mdlhrUx3TEw3vg3P2SembTKzcWVcf1i8tr61zyVto4AtItKFmNmmwMVtSLc5cGHlSyRdhQK2iEgXYWZjgYeAZVqZ7psx3aAOKJZ0Eep0JiJSZWY2ADgdOBPo3Yp0A4Gfxj9VwLo5/QOLiFSRmY0B3gB+Hg+dCUwrI53FdGcCjcBPgA86qJjSBaiGLSId5o3xvXsDw6pdjgqZtdaEhoYOyHcksArwDHCsuz9vZoeXkW6VmPapmO5FMzu2A8pXNjPrAxReAail69y9VZ3qejoFbBHpEG+M770PcCWwQrXLUiGfvDG+97FrTWi4s8L5vg/s6u4PtjLdu8C33P1vFS5PezQBTxY5PwKw+LhkK4K0pIAtIh3lWmC5aheiglYgvKaKBmx3nwJMaUO6NwhN4l2GuzcAW+c7Z2bLAE/E3YeB8zurXN2FAraIiOS6ycxuqlRmsan8TmAD4DVg3xjcpRUUsEWkoxxON2sSB6p6j7gTvQF8WuKaLwFjyszvKmAnYCawm7vPaUfZeiwFbClLU2MT/etbflzq5i6if/3CJfvz5i3q7GJJF7bWhIY73xjfeyLqdJZFv3D3m4tdEGdhu7ZURmZ2GnAEsAj4jru/XZki9jwK2FKW+XWL2XvyFi2OXTH5qSqVRrIiBrhSNTXppsxsH+CCuHuku/+rmuXJOo3DFhGRijOzLYE/AjXAhe4+obolyj4FbBERqSgzGw3cCwwA/kKY1EXaSU3iAkBjUyOzF9YVPD974fxOLI2IZJWZDQMeJIy5fgkY5+5N1S1V96CALQDMXljHhn/6ZcHz/ev7sDct72H/4MRNWXnYkKL51tb2q0j5RKTrM7P+hBr1WsBUYBd3n1fVQnUjCtgC5O8Fnta/vu9Sx2pr+zJ4cP+OLJaIZMuPgG/ExzOAa81sELD0FwjUu/vYzipYd6CALUD+XuAiIq20bOrxJiWu7SlD5CpGAVtEpItx91FtTDeyHc9ZT+jRXe71fwD+kHPsTMLqYdIB1Etc2my5/gOrXQQRkR5DAVvarFeNPj4iIp1FTeJSUKle4OoBLiLSeRSwe4i2jLNWL3ARka5DAbuHaMs4axER6Tp0E1JERCQDFLBFREQyQAFbREQkA3QPuwd7bK+TGDagFoC6uYuWWt9a46xFRLoOBewebNiAWoYPGAxA//qFS53XOGsRka5DAbuHyLe4R93cRUsC9bx5i6pRLBERKZMCdg+Rb3GP3CZwERHputTmKSIikgEK2CIiIhmggC0iIpIBuofdg2lxDxGR7FDAzoCmxkYa5s4seL5h7ow25avFPUSqy8xGAe8UON0EzAbeBf4KXOrun3ZS0SrGzMYDNwAfuPvI1PF/AtsA57n7mdUpXXFmNgE4GHjc3ceWe66jKGBnQMPcmbx9/IqtSjN74Tx6LRiY2l96NS4R6VImAXNS+32AocD6wFeBw81sO3d/pRqFk+pTwO6mxk68hDn9apfsazUukS7vOHf/Z+5BMxsO3AjsCtxlZuu4e2NnF64DHATUAm1rIuyBFLBFRLowd59pZgcDHwBrATsCf6tuqdrP3d+tdhmyRr3ERUS6OHefSWgyh9BELj2QatgZtdr5k+g9eAQQ7lePnXhJi/Of99XCHSLdTN+4/SL3hJn1AfYD9gW+BgwH6oEPgX8Al7j7G3nS7QQcA2xBuF8+h/DD4A7gD+6+1JzFZrYs8CPgO8AYQsXvbWAioWPcZ+W8mHydzlKd8D4GVgIOBY4A1o3JJgHXABPcvamjytZVKWBnVO/BI+iz7PIA1NQNYEGvZVucf2CnoxmaWm2rrm4x101+rsU1Wo1LOtqpJ97fGxhW7XJUyKyLLt2toRpPbGajCTXrRnKaw81sIPAAsG08NBV4BfgSsGb8G2dm33D3F1Ppjgcui7sfAi8BIwhBdBtgHzPb3t0bUmnWJvRYHwU0AG8B84H1gLOAg81sZ3d/vZ0vuYZw3/5A4DPgDWANwg+LLQADTs95HzqrbFWjgN0N5Jsn/ObJLxa4uplW45KOdOqJ9+8DXAmsUO2yVMgnp554/7EXXbrbnZ3xZGbWGxgCfB34NaG2eJ67T8u59DRCsJ4B7OLuz6Xy2BS4h1Bb/SmwTzw+BLgwXrafu9+WSrMj8BdgbLz+tnh8EHAfISDeA/zQ3T+M51YE/kDoGHevmW3o7u0ZmrICsD+htnyVuzeY2QDgWmAccLKZ/b9kmFsnl61q9I0tIh3lWrpPsIbwWq7twPwfM7Om5I/QpD0DuJfQ2exC4Gd50m1PqHmfkw7WAHH/d3H3K6lTBgwgjPO+PSfNw8AFwF1Aukn8MEIz8wvA3klAjGmmE4L7NEKNfnzZr7qw37r75UkN390XACcQxqf3ATarYtmqQgFbRKRrmAQ8mfp7BngVSBarPwm4PNa8l3D3rQnB9+oC+dbFbW3q2DuEHwRDgQlmtmFOnr9w933cfWLq8F5xe1u6mTyVZj4hyAPsXuhFtsJ9eZ5jJpBMHpOeprGzy1YVahIXkY5yON2sSRw4tgPzLzQOux+hVnhlfP7ewA/T17j7YjMbamZbEGrja8TtRoR72ZCqoLn7J2Z2IXAGYTz0QWY2HXgUeBh40N1zx0cnvdMPN7M9C7yGZIantUu+2tI+KHA8ac5Ox6/OLltVKGB3U6XmCQfNFS4d66JLd7vz1BPvn4g6nbVL7Kl9jZmtBJwNHGFmF7j7ewBmtgyh89g4mnuSQ2jOfgF4Edg5T75nmtnzhB8B/0cIaAfEv3ozuw041t2T2deWi9ukI1sxxb98yrNUD/UcNanHnV22qlDA7qY0T7h0BTHAZW7+6y7qHkLA7k2oOb+XOr4toeZ5Bc1N6W/Gmvfh5AnYAO5+N3B3HA61DaGj2S6EWug4QiDcI14+L+7v7u73V/i1tVdXLlvFKGCLiGRDejrSGoDYBJ4M59rV3R/Lk25k7oE4FGxNoMbdX3b3zwn3jO8j9MA+ndDxbHczWy7Wsp3Q0Ws9IG9QNLM1CYFzWicvVNKVy1Yx6nQmIpINu8ZtE/B8fLx66vzz5DCzWsKEKtCygnYE8DJws5nV5KYDHkk9Tjq5JZ3ADosBP/e5+hBq+88RhqF1pq5ctopRwBYR6cLMrMbMvkPoIAZwl7u/Hx+nJwE5y8z6ptKtS5hIJLmnm+4lfgfhHvH6wKVxHHOSbgRwXtx91t1nxcdXAR8Rhk/da2arptIsH/NcJ+bb2UGxK5etYtQkLiLSNVxhZnNyjvUlTAaS9LR/Hjg6OenuL5rZHYQpSU8GxpvZO4SpSZPa9yPADsAyZrasu3/u7h+Z2aHAzYTJSX5gZm8RYsJowjCxGcAPUs8128z2INRmtwfeMbPJhKZ6A/oThort19lLgHblslWSatgiIl3D+sBWOX8bEMZh3w8cAmwRxyKn7U9o4n6O8J2+ISFA3Qfs5u47AsnKWEvGILv7LYROZncRpv9cF1gNeJNw/z1POVsAACAASURBVHodd381/UTu/t9YznOB/xF+TKxDmPv7j8DGOWO3O01XLlul1DQ1LTV/eo9hZq+OGTNm3QceeKDaRSmq/vNPefv4FVscW+Py6UvmEn9vxiyuOO+pFuePO+PrrDKiu4ymkSrJd29TRKqkYk3isXPDqcD3CU0xXxCab37j7n9tQ36jCLPxFPOyu3+1tXlnTWNjE/NrWi7uMXfeYvr0ChMg1dUtrkaxRESkE1UkYMcOC48CmwOLCVPsDScstL6jmZ3t7ue0MttkqrxZwGsFrnmzDcXNnLr59dwy5KaWBy96oTqFERGRqqhUDfsqQrB+CdgjNQPPgcD1wNlm9qS7/70VeSYB+w53P7rolSIiIt1cuzudxXVaxxF64x2QBGsAd78J+FXcPbuVWScBO7M9+kRERCqlEr3EDyQMrH/a3SfnOZ+sILNVemxcGZKAPak9hesWmhpLXyMiIt1aJZrEt4zbJ/KddPcPzGwaYbjANsBN+a5LM7PBhNVmQDVs5ixasNSxx1d/khkDe+e5Oji1dpuOLJKIiHSySgTsMXH7VpFrphIC9lpl5rkBYUjJh8DyZvZjwmT3fYA3gD+5+5NtKm0X09TYSMPc3GGVLTXOm7XUsUW9F7GwT988Vwc1vTQiR0SkO6lEwE5m4Ck2mXoSkUaUmWfSHD4UmEzzXLYQZuw5xsyuB45y90yPaWqYO3OpMda55tcsC7m9xEVEpEepRMBO5qddut22WbLgeG2Ra9KSgD0A+D1hybgphPVaDwR+DhxKmAHoh/kySDOzVwucGl1meURERKqqEgG7gfI7r5U7rdq/Y54vuvvvUsffBc4zs6mEOXCPMrOrcqfP6wkm7DCekauuXvD80P7l/jYSEZEsqETAnktouh5Q5JpkubO6cjKMc9zeUuy8mf2csArNnoTF2ovlt16+47HmvW45ZepqhvQbyPABg6tdDBER6SSVCNgzCAF7eJFrknvXn1Tg+RIvEgJ24WpmRq12/iR6D26+3f/BrDlw2estrqkZNKSziyUiIlVUiYD9GiFwjipyTXLujXIzjeu6Nrp7Q4FLkmb4THc6a8ozxvrzvgPp1a95DfY5fRctdU1NjRZaExHpSSoRsJ8F9qB5PHYLZjYSSCZMeSrfNTnXDyUMERsK7A0UWg5to7jNN1lLZny2aP5Sx8ZOvIQ5/ZrvQfev78PebNGZxRIRkS6mEtW0O+N2rJlZnvNHxe3j7j61VGbuPhuYHnfH57vGzL5L6OG9iMIBXUREpNtod8B29zeBWwljpSeaWTKRCmY2Djgt7v4yN62ZjTaztc1spZxTF8Tt7mZ2gZn1T6X5LnBD3L3I3T9s72sQERHp6iq1WtfxwFfi3+tm9gqhSXu1eP6MAit1PRqvuZFUbdrdbzKzDYAfA6cTJkp5E/gSsHK87A+E8dgiIiLdXkUCtrvPNLMtgVOAfYF1CJ3BHgcud/dWN1u7+ylm9hBwLOH++AaEtbHvB6529wcqUfau6O5dj2L4iOZ1UurmLuKKyS1v/y/Xf2BuMhHJKDNbk1A52YEwQdQs4BngKnd/pEi64cCZhOGtI4HZwJOE1sdnWlmGscBjcXf1UrcwzWwCcDDhdufY1jyXtE2lati4+zzCEppntyLNqBLn/w60Zg3tzGlqagpTj6YMqe9H//rmecLrG5aeb6aXeomLdAtmthNwN2G+ijpCR9rlCUF4TzP7tbufkifdlwjBeXRM9z9C0N4L2MPMjnD36zvnVUhnqFjAlraZX9fALbnzhF/2OvB63utFpPswsxHAnwjB+jbC+ghz4rn9gT8CPzazZ9z9zznJbycE60eA77n7bDPrRWjp/BVwtZk97e6vddLLkQ6mapqISPUcRujvMxUYnwRrAHe/Fbg27h6VThSbr7chzDS5fxxdg7s3uvuFhKmb+wJndHD5pRMpYIuIVM87hBr2b919YZ7z/4vb1XKOj4/be9x9Rp50V8ftt81MHV66CTWJi0iHmb7uxb2BYdUuR4XMWnHyKYVmXmwTd7+d0LRdyCZx+2bO8WSiqicKpPsPUA8Minn8u61lbI2cjmulHOLuEzquNN2PAnYXdOgRYxi5SvEp0mtr+3VSaUTaZvq6F+8DXAmsUO2yVMgn09e9+NgVJ59yZ+lL28fMhgA/Ag4hBN4LU+d6AWvE3bfypXf3xWb2AaFmvhadFLCBOYSOcIWsASTzbrzb8cXpXhSwu6CBA/sweHD/0heKdG3XAstVuxAVtALhNXVYwDazvYFzgDFAf+A94Gh3/1fqsqE0f3d/WiS7mYSAPaLINRXl7i8CW+c7Z2br0jw99bnu/o/OKld3oYAtItJ1bAaklwMeCuxmZv9y9y/isfRi9wuK5JUsVFBb5JpC3sk/03TbxCFoDxJ+wN1BK4b/SjN1OhORjnI4lV1St9o+IbymjnQFMBj4MqFj2XxCD/F/mFlSwWrtffSlJ3Io7b+Epu1if2X925pZLXAfobb/H+Bgd29LmXo81bBFpEOsOPmUO6eve/FE1OmsbO7+fnw4D7jRzJ4BXiJ0HBsHTCAM5UoMKJJd0ju8rg1F2acVM50Vu6YXYa2JTQnN+3u6e7FWASlCAVtEOkwMcMXus0oR7u5mNhHYHxhLc8BeSLjHPbxI8uTedTVbOS4hzNg2D9jD3aeXuF6KUJO4iEiVmNkwM9s4znhWyLS4XRHC5CiAx2OjCuTbl9CsDvBGBYraamZ2HKGneyNwgLu/VI1ydCcK2CIi1fMc4X7xoUWuSSZN+SB17Nm43ZL8NiO0oC4AXmxPAdvCzHYHfhN3T3f3ezq7DN2RAnYHa2pspP7zTwv+Nc6bVe0iikj1PBy3h8VacQtmNoqwmAeEjluJO+L2u2aWr4/A0XF7u7vPz3O+w5jZxoTZ23oBN7j7xZ35/N2Z7mF3sIa5M3n7+BULnp9fsyzkLv4hIj3FxYSOW2sCt5rZ0clUo2a2EWFBkIHAv4B0LfVRQk/trYC/mNk+7v5x7OT1Y+AAwhLHF9KJzGxVwg+LQcBDwJGd+fzdnQK2iEiVuPvbZrYvYXrS7wK7m5kTen+vFS97Btg7PRTK3ZvM7CDgceAbwDQzmwSsTLjX3USY+rOzV+q6kuaZzGqAe+Owrt55rn3R3Y/rtJJ1AwrYIiJV5O73m9mGhGUxdwTWIQzFeoKw6tb17r44T7q3Yy38DGAP4Csx3d+Ai9y93Dm9K2nZ1OMdS1xb35EF6Y4UsEVEqszdp9CG5uPYfH5i/GtvGf5JqBWXe/14mlcNS46NbW85pDAF7CpY7fxJ9B4cRnF8MGsOXPZ6i/M1g4ZUo1giItKFKWBXQe/BI+iz7PIA9Fq09K2dmhp13hcRkZYUGURERDJAAVtERCQDFLBFREQyQAFbREQkAxSwRUREMkABW0REJAMUsEVERDJAAVtERCQDFLBFREQyQAFbREQkAxSwRUREMkABW0REJAMUsEVERDJAAVtERCQDtLxmB2tqalzq2OyF8+i1YGB8PL+ziyQiIhmkgN3BPlu0dEAeO/ES5vSrBaB/fR/2ZovOLpaIiGSMmsRFREQyQDXsDtbU1MT8mmVbHOtX35f+vcJb37++bzWKJSIiGaOA3cHm1zVwy5CbWhzbYUqVCiMiIpmlJvEuaLn+A6tdBBER6WIUsLugXjX6ZxERkZYUGURERDJA97Cr4NAjxjByldULnq+t7deJpRERkSxQwK6CgQP7MHhw/2oXQ0REMkRN4iIiIhmggC0iIpIBCtgiIiIZoIAtIiKSAQrYIiIiGaCALSIikgEK2CIiIhmggC0iIpIBCtgiIiIZoIAtIiKSAQrYIiIiGaCALSIikgEK2CIiIhmggC0iIpIBCtgiIiIZoIAtIiKSAQrYIiIiGdCn2gXIssamRmYvrCt6zWeL5ndSaUREpDtTwG6H2Qvr2PBPvyx6zfJ1i9mBbTupRCIi0l2pSVxERCQDFLBFREQyQAFbREQkA3QPu8Ie2+skhg2oXbI/d/p0rrrs9RbXLNdvQGcXS0REMk4Bu8KGDahl+IDBS/Z7969d+qIaNWyIiEjrKGC3Q1NjE/3rW76FdXMX0b9+YfN+XX1nF0tERLohBex2mF+3mL0nb9Hi2BWTn6pSaUREpDtT26yIiEgGKGCLiIhkgAK2iIhIBugedoX94MRNWXnYkCX79V/MZNpP12txTe1A7+xiiYhIxilgV1htbV8GD+6/ZL++sS8Dmz5vcU2vXjWdXSwREck4NYmLiIhkgAK2iIhIBihgi4iIZIACtoiISAYoYIuIiGSAAraIiEgGVGxYl5nVAqcC3wdWB74Angd+4+5/bWOeqwJnATsDKwCfAo8CF7j7a5Uot4iISBZUpIZtZoOAfwA/B9YAXgXmATsCD5rZz9uQpwEvAD8ABgMvAwOAA4EXzGynSpRdREQkCyrVJH4VsDnwEjDa3b/m7qsBBwH1wNlmtn25mZlZH+B+YDhwE7CSu28KrARcSQjct5nZ8AqVX0REpEtrd8A2s9HAOKAROMDd30vOuftNwK/i7tmtyHYcMAZ4FzjM3efH/BYBxwP/BoYAJ7a3/CIiIllQiRr2gUBv4Gl3n5zn/NVxu1W8J12O8XF7UwzSS7h7E/D7uLtfK8sqIiKSSZUI2FvG7RP5Trr7B8C0uLtNqczMrBewWbE8gSfjdg0zW6XMcoqIiGRWJXqJj4nbt4pcMxVYDVirjPxWBgaWyPM9oIFQs18r7neoaVOnLHVs+py5Sx1rnDuL+n4NS/Yb5s7o0HKJiEjPUImAvULcflrkmplxO6IV+RXM090bzGwOMKzMPNvtqsteL+u6z87ZlIU5q3OJiIi0VyUCdm3cLihyzfyca8vJr2J5mtmrBU6t/e6777LrrruWLNTH078oeQ3A8w3DqGG5otf023ccNb21sql0bVOmTLnX3feodjlEJKhE1Gig/HvhTWXm1xrl5FlI46JFi+ZNmTKlVJP66Lgt1uwPwBf0oeTb+s7Ucsom2VX250VEpFyVCNhzgaGEsdGFJPek68rMLzGAwrXssvN09/XKeN6Ckhp6e/ORnkGfFxHpCJXoJZ70qio2iUlyn/mTVuRXMM84sUrS7lxOniIiIplWiYCdzOk9qsg1ybk3SmXm7h8Cc0rkuQqhh3hZeYqIiGRdJQL2s3G7Zb6TZjYSSCZMearMPP9TLE/g63E7LQZ4ERGRbq0SAfvOuB0bF+zIdVTcPu7uU8vM8464PcTM+hXJc0KZ+YmIiGRauwO2u78J3Epoop5oZslEKpjZOOC0uPvL3LRmNtrM1jazlXJO3UzoYbsGcKuZLROv72dmlwNbE5rNr2hv+UVERLKgpqmpPaOigrhq1mPAVwjDsl4h9BxfLV5yhrufnyfd1HjNje4+PufcpsAjhM5lc4HXCQF8GLAI2NndH2t34UVERDKgIstruvtMwv3mcwidwNYh9PB+HNg7X7AuI8/ngA2B64DP4uNG4M/A5grWIiLSk1Skhi0iIiIdqyI1bBEREelYCtgiIiIZoIAtIiKSAQrYIiIiGaCALSIikgGZXZTZzGqBU4HvA6sDXwDPA79x97+2Mc9VgbOAnYEVgE+BR4EL3P21IunWB84EtgWGAB8BDwLnufsHRdJtBZxOmGp1EPAeMDE+32dteQ2SX9Y/L2Y2HrihRJEuc/cTWvkyRCQjMjmsy8wGEb4YNwcWA5MI476TOcvPdvdzWpmnAU/GfOYAb9I8UcsC4Nvu/lCedN8AHiYsBToDmAYYMBiYDWzn7i/lSbcv8CdCK8cHwMfAekB/4F1ga3cvtU63lKGbfF4uBU4A3gEKzZ9/h7tf3prXISLZkdUm8asIX74vAaPd/WvuvhpwEFAPnG1m25ebWVyu837Cl+9NwEruvimwEnAl4cv1tjijWzrdMOCeeP7CmG4T4MuECV6GAn/OnQ89ftnfRHj/jwNWcfeNCbO+/ZsQSG4t/+2QEjL9eYk2jNufuPvWBf4UrEW6scwFbDMbDYwjzHp2QLoW6u43Ab+Ku2e3IttxwBhCzfYwd58f81sEHE8IokOAE3PSHU/4kn3G3U939/qY7gtgf+BtQq3roJx0PwH6Abe5+5Xu3hTTfQx8m1Bj27o1QUTy6yafF2gO2K+0opwi0o1kLmADBxIWGnna3SfnOX913G4V7zGWY3zc3hS/dJeIwfT3cXe/Aumuy80w5nN9bjozGwB8r0i6WTSvgJb7fNJ6mf68wJIlapM59LX+u0gPlcWAnayR/US+k7HTzrS4u02pzMysF7BZsTwJ9yoB1jCzVWK6lWhe3KRUuq3MrG98vBGhSbQpdb5QurFFCy/lyPrnBZpr168ntXIR6Xmy2Es8Wb7zrSLXTCV8Oa5VRn4rAwNL5PkeYRWy3jHP91LlaCJ0BCpUDggdyVaN+SfpPkqaUoukG2Vmfd19cfGXIEVk/fMCzQF7kpmNBfaN+S4AXgSud/dCeYpIN5HFGvYKcftpkWtmxu2IVuRXME93byDcV07nmaT73N0XlihHvnTllL8XoSlU2i7rnxdoDti7E5axPRr4JrArYXjY62Z2VBllF5EMy2LAro3bBUWuSWqutUWuyc2vtXm2phyVSCdtk/XPCzQH7F7Aj4GRhFr4V4CbCR0YfxeHCopIN5XFJvEGyv+hUc4g84ZWPn+SZ2enk7bJ+ucFwhC/McC17v7v1PFJwIFmtgA4DLjUzCbqPrdI95TFgD2XMDRmQJFrknuMdWXmlxhA4RpQbp5JunLKUYl00jZZ/7zg7ueWKNPZhID9ZUKHuKdKXC8iGZTFJvEZcTu8yDXJ/b9PWpFfwTzjRBnL5eSZpFsmp0dvvnLkS1dO+RtpeV9TWi/rn5eSYk/35PrVy00nItmSxYCdzNE8qsg1ybmSY1bd/UOaOwgVynMVQo/fdJ5JOXrRPMVloXIsIPQUTqdbqcCMVul0b8UOTNJ2Wf+8AGBmA5e6uqXk/7JGFIh0U1kM2M/G7Zb5TsZJJpIvxHKbBv9TLE/C4hwA0+IXNu4+mzB/dDnpnk0F3smE5tHeNI/nLZROTZvtl+nPi5ntamZzgblmlrcXexzrnZzLNzmMiHQDWQzYySxgY+Oc3LmS4S2Pu/vUMvO8I24PKVDrTfKcUCDdEbkJYj6H5qaLQ3ruibtH5kk3jDDONt/zSetl+vNCGGc9gPB/Nd+UpQCnxe0kd59UsNQikmmZC9ju/iah12xvYKKZJRNSYGbjaP7y+mVuWjMbbWZrx1mn0m4mTFKxBnCrmS0Tr+9nZpcDWxOaQa/ISXc58BnwDTO7PPnyjulvifm9HR+nnU9ouhxnZqfF2bMwsxWAvwDLAk+4+z/Le1ekkKx/XmIN/ca4e56ZJdPaYmb9zexc4BhCr/KTyntXRCSLsrq85nDCBBJfIQyXeYXQEziZ+vEMdz8/T7qp8Zob3X18zrlNgUcInYXmAq/TvFziImBnd38sT567EVZa6gfMInzhGrAM4ct5a3d/NU+6I4HfATXAdMISm+sRalPTgC3d/aPy3hEpJuufl7g86P00T1X7MeEe95rx+euBI939ekSk28pcDRvA3WcS7gOeQ+jUsw6hx+7jwN75vnzLyPM5wgQV1xG+ODck9NL+M7B5vi/fmO5+YBPgdkKt+auEL/AbgY3zBeuY7veEuavvB/rG5/uYsDzjZgrWlZP1z4u7zwO2JzSl/5sw/GsDQi1+AvBVBWuR7i+TNWwREZGeJpM1bBERkZ5GAVtERCQDFLBFREQyQAFbREQkAxSwRUREMkABW0REJAMUsEVERDJAAVtERCQDFLBFREQyQAFbREQkAxSwRUREMkABW0REJAP6VLsA1WBmo4B3ilyyCPicsLLTA8CV7v55JxStIDNLVmnZwd3/3s68xhKWm8ynifD6ZwOTgbuBa919YYG8+gGHAN8mrCA1ApgHfBif40Z3/2+BtFNpXuIyrRFYQFh+8jXCClh/dPf5JV9cEWa2AfAf4Dx3/0WZacYDNwAfuPvIPMfzqQdmEpbc/DNwtbsvLvE8pwAXAd9293vM7Gzg58A0dx9VRjmnEt7Lc9z97FLXF8nn/wirmB3p7te0NR8RqTzVsGES8GTO38uEoPN14DzgFTMbU7USdqz/0vK1P0V4T5qA7YArgP+Y2fK5Cc1sNPAqcDWwEzAfeBF4l7A29LEx7VVmVlOkDO/llOFZYAowGNgh5v9yDLhtYmZ9gVsIS5he3NZ8Csj9/LxI+MGxDXA58LSZLVMij10IP5Ta9WOsvdz9X8AdwKXd+DMvkkk9soad4zh3/2e+E7Emeg+wKmG94q06r1idZh93n5rvhJl9D7iJUHP+DXBA6lw/4K/AGMJ7dIy7f5A6PwA4LKb7ITCDUGPM5/p8tcIY5LclBOw1gYfM7OvuXqx1pJAfA+sDB7n7gjakL8jdt8533MzWBe4DNgYuJbwf+a5blvDZejyufV1tpwN7AVcRfoiJSBegGnYRMZD/JO5+3cw2rmJxOp27304IuADfM7MvpU7vSwii7wLfSwfrmHaBu18JnBsPnRwDU2uev8nd/wF8A3gfWJEQRFoltg78lFBrv6W16dvK3ScDx8fdA4u8/h2AvsCDnVKwEuIPopuAHc3sW9Uuj4gECtil3Z16vEXVSlE9yevvDWySOr5p3L5U6P529Ie4HUSo4baau38MnBR3v2Vmm7Uyi1MIzevXuntjW8rQDo/HbT9grQLXJEGxSwTs6Oq4PbuahRCRZmoSL21O6vFS9yFjJ53jCU2awwmdtZ4GLo+1w6WY2RDgKMJ9y3WB5Qj3zN8iNKFe5u6zyylcvM/4OPBl4B/A7u5eV9YrK0+h178obrcws+HuPjNfYnf/0Mw2ivl8kO+aMv2Z0Kw+gtDB7T/lJDKzgYSm6Cbg5iLX7UW4574h0B94Hji/HeVN9E09/qLANd8C3nF3r8DzLZHquFaO1dO3Rtz9OTN7HdjMzDZ392crWTYRaT3VsEtbM/X4vfQJM/sVIVjuTfiS/x+hh/OewKPxPDlp1ozXXQBsCXwa9+uBrxG+YJ82s8GlCmZmaxB6Yn8ZeAjYrcLBGgq//ofidgVCx7JjzGzFfBm4+0vu/o67L8p3vhyxZvx03B3biqQ7AkMJLQEf5rvAzK4CJhI62dURendvRniNB7exyIlvx+0UwqiD3Of+KuHfryNq1++ydIe49F/yWZlDyx9mib/F7fc7oGwi0kqqYZeWNMW26MFrZkcCpwGfAce6+y3xeA3h/u51wGlm9qa7X5fK71pgFeAZ4Dvu/lEq3ThgAmCEQFHwfq2ZrUaoUY8E7ge+W6Jpuq1OiNtPgeeSg+7+dzO7hdARbQ3gSuAKM5sM/Cv+Perun1awLElns1VbkWb7uH0i30kzO4DQKW4RcIi73xqPDwGuAfZpbSFjh7zlgd2AXxN+xB3r7k15Lt8lbisesN39euD6AmU8gNDiUE/oeJivRecJwr//DpUum4i0ngJ2HrEZdW3gCOCgePjSeC81+UI+Jx4/1N2X3OeOX8q3m9kw4LfAOWZ2o7vXx05b68VLD0+CdSrdTXF873bAV4qUbxVCzXo1wj3m77en9pon/8Hx+U+iuTZ7Vp7nOBh4BTiD0FxeQ3h96wFHA41m9i/gTHd/sgJFS5qUh7cizbZxO6nA+TPi9vwkWAO4+2dmNo7QQ96KPUFqjHw+9YSx1Q8VOP8twhCwQuPiVyuRf6vF2zhJIP+Ruz9S4NJX4nY9M/tS8vkXkepQwIbHzIp+H0PoOPWz1P7XgS8RAsg9BdLcQqh1rkxo6v5P/MJb3swG5psExMx6EyZsAagtkO/KhA5BqwN/AfZ19/pSL6CId0q8/gZCMLs694S7NwAXxiblPQi1xW0JTbwQbrmMBf5tZme6e3vvCfeL29YEsNXjdkruiXhLYZ24OyH3vLsvMrPrCBOaFJP7Y6QvMIzQ8tAHuNHMTslpaUlq8VsCjxSZFGYhYax8KZsQbssUZeEf+27Ce3mFu/+2yOVTCK0DvQjvowK2SBUpYIeaV/r+XROhxjOTcG/5L3F4TlrS27kf8K8iAa+B8GW3NqlOUu4+PzZpb0oYx5wEjq8SejND4f4FvwMGxscrEr5Q2+O/hKCQaCLc2/yUMAHIXe4+rVgG7j4XuDX+JR3hvkm4t78DoeZ9npm94O5/K5hRacvFbbkd8gbR/MPns3yXxO0XRV7jS6Wep8g47GHALwhN7n8ws7lxqFxiJ0Lv+2LN4dML5Z/zXFPJP2tc+prl43MNI9yfP7HY9e7eaGafA0MIfRVEpIoUsItMnFJEEjj6U95kKkOSB7GGczVLd5z6HPg3oXa6YZG8BgL3EgLhFsDJtG/mroITp7SVu08h1M5+b2bbEMq7LCFwtSdgJ7Xh18q8fmjqcb7OeMn5YpOVlPXjIB93nwUcEydQGUsI3umA3WH3r3PFiWzuJfw4nExomWkoI+k8wud3aKkLRaRjKWC3TfIF/7y7b1L0yhQzW4HQGWsFQg/ea4AXCL2Sp7p7U+zIVSxg3wgcSri/fDFwrpnd5+6vt/5ltI2ZfY0wj/YwYM1iM4e5++OxyfwnlLgXXOI5BxBuLcDSTdCFpMs1JM/5ZChasWlDBxY5V657CAF7TTNb1t0/j50MdwbecPe3KvAcBcXnuonwA28GYTRBuXPjJ4G6XfO4i0j7KWC3TTJedi0z65PvHnL8khxLmKFrWuywdSghWM8CNnb3GXnyHpnnWNrNsanyUsJwm42BG8xsq06cFGQ2oTMWhKbvB0pcn3Sua0+P8f0Ik68A/KnMNLMJvb/7EcZv50r+HQeZ2VruvtSwK5o7CbZH+t8lmVN9Y8Jn4dalL6+4i4DvEt6Lvcqd2jV2vkxuKej+tUiVaRx22/yLcN97GcJKVfnsTxh29TphGBc0d4Cali9Yx6bTLeNu0R9TsTnzB4ReyEnTeKeIX/hPxd0Lik05ama9CMPcoHRgL5THcaGgSgAAA4tJREFUCjRPYjKx3NaE+B4lnc2W+iEUbwW8EHePzvO8vQg/stpr17h9092T/hJJc/hfK5B/QWZ2FGEedQgjE/IObysg/Z51WguOiOSngN0GcYGGC+LuZWZ2SPxyB8DM9qR5asc7Uk2eyZfehma2d+r6GjPbmXB/N5kZq1Av8XQ5Xqa5B/O5ZrZ2m15Q25xEuC/8FeBZM9szDndbIpbnL8DWwJu0ch5wM+tjZrsSmsBXJCzZeXzxVEtJms+/XuB8Mlf88WZ2QvLvaGa1hFsWmxZIV5KZ9Y+zje0YD/0mdXoXwq2Vx3PTVUqcB/zKuHuWu/+xlVkk/TOmaEiXSPWpSbztLgJGA4cTxrReZGbvEIZdJcOaniDUghPXETpejQHuMrNphGbiVQnNo4uBfxKa0ks1jSfOJfTGNmBCbBovpzNRu7j7s2a2O+GeehKY58XeynWE92DlePlLwN5F7pseambbp/Z7E1ov1qD5HvLrhPHMrZ3e9K+Ef6O8Pa3d/WEzOw34FWFFrdPN7N34mpYhDIHaq9gTmFm+WuuAmEfSjH8D8UecmY0g/BB4oIMmu0ncTngv5wNfM7OHCO9nvh/q18eJVtKS96wrzXEu0mOpht1GcSWpIwhDc+4mNE1vRPiSf4ZQE/xmeqrQGLA2JQSHVwmzYa1P6CF+PeG+ZtIEu6GZlZzRK37hJ3Nlb07nNo3/g/BD4ShCwP6E0Py/YSzPfYTJVTZx97eLZLUKoTaX/G1BuH0wPeZ7ELBBG+fafpDQZ2B1KzD+zt0vIowfvy8eWp9wf3s/4JIynmOrnL+vE4L1+4TOXju4+6GpPgY7Ef7vdXQgTDrTDSRMkbojYeWz3PJuRc7scbGlIWkZuKmDyykiZahpaqroJEoiXY6Z/Zyw6tSv3f2UKhcnE8xsF0Kfg8fcfbtql0dEVMOWnuEyQifBg3Pvs0tBR8TtOUWvEpFOo4At3Z67f0ZYhGN52r/6VrcXOwvuBvzd3TusU5yItI4CtvQUFxKmWj03TlkqhV1M6Dh4eLULIiLNFLClR3D3xcCBhJm7Tq1ycbosM9uWULs+sdJT1opI+6jTmYiISAaohi0iIpIBCtgiIiIZoIAtIiKSAQrYIiIiGaCALSIikgEK2CIiIhmggC0iIpIBCtgiIiIZoIAtIiKSAQrYIiIiGaCALSIikgEK2CIiIhmggC0iIpIBCtgiIiIZ8P8BpDOto6FUNh8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 525x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 525x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 525x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "density = True\n",
    "cumulative = True\n",
    "histtype = 'step'\n",
    "lw = 2\n",
    "if perform_zscore:\n",
    "    bins = {\n",
    "        'theta_energy': np.arange(0, .7, .03),\n",
    "        'theta_peak': np.arange(0, .7, .03),\n",
    "        'theta_freq': np.arange(4, 10, .5),\n",
    "        'theta_half_width': np.arange(0, 15, .5)\n",
    "    }\n",
    "else:\n",
    "    bins = {\n",
    "        'theta_energy': np.arange(0, .008, .0003),\n",
    "        'theta_peak': np.arange(0, .007, .0003),\n",
    "        'theta_freq': np.arange(4, 12, .5),\n",
    "        'theta_half_width': np.arange(0, 15, .5)\n",
    "    }\n",
    "xlabel = {\n",
    "    'theta_energy': 'Theta energy (dB)',\n",
    "    'theta_peak': 'Peak PSD (dB/Hz)',\n",
    "    'theta_freq': '(Hz)',\n",
    "    'theta_half_width': '(Hz)',\n",
    "}\n",
    "# key = 'theta_energy'\n",
    "# key = 'theta_peak'\n",
    "results = {}\n",
    "for key in bins:\n",
    "    results[key] = list()\n",
    "    fig = plt.figure(figsize=(3.5,2))\n",
    "    plt.suptitle(key)\n",
    "    legend_lines = []\n",
    "    for color, query, label in zip(colors, queries, labels):\n",
    "        values = lfp_results_hemisphere.query(query).loc[:,['entity_date_side', key]]\n",
    "        results[key].append(values.rename({key: label}, axis=1))\n",
    "        values[key].hist(\n",
    "            bins=bins[key], density=density, cumulative=cumulative, lw=lw, \n",
    "            histtype=histtype, color=color)\n",
    "        legend_lines.append(matplotlib.lines.Line2D([0], [0], color=color, lw=lw, label=label))\n",
    "        \n",
    "    plt.legend(\n",
    "        handles=legend_lines,\n",
    "        bbox_to_anchor=(1.04,1), borderaxespad=0, frameon=False)\n",
    "    plt.tight_layout()\n",
    "    plt.grid(False)\n",
    "    plt.xlim(right=bins[key].max() - bins[key].max()*0.025)\n",
    "    despine()\n",
    "    plt.xlabel(xlabel[key])\n",
    "    figname = f'lfp-psd-histogram-{key}'\n",
    "    fig.savefig(\n",
    "        output_path / 'figures' / f'{figname}.png', \n",
    "        bbox_inches='tight', transparent=True)\n",
    "    fig.savefig(\n",
    "        output_path / 'figures' / f'{figname}.svg', \n",
    "        bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "density = True\n",
    "cumulative = True\n",
    "histtype = 'step'\n",
    "lw = 2\n",
    "if perform_zscore:\n",
    "    bins = {\n",
    "        'stim_energy': np.arange(0, .7, .01),\n",
    "        'stim_half_width': np.arange(0, 10, .5),\n",
    "        'stim_p_max': np.arange(0, 4, .01),\n",
    "        'stim_strength': np.arange(0, 160, 1)\n",
    "    }\n",
    "else:\n",
    "    bins = {\n",
    "        'stim_energy': np.arange(0, .008, .0001),\n",
    "        'stim_half_width': np.arange(0, 0.5, .001),\n",
    "        'stim_p_max': np.arange(0, .06, .0001),\n",
    "        'stim_strength': np.arange(0, 160, 1)\n",
    "    }\n",
    "xlabel = {\n",
    "    'stim_energy': 'Energy (dB)',\n",
    "    'stim_half_width': '(Hz)',\n",
    "    'stim_p_max': 'Peak PSD (dB/Hz)',\n",
    "    'stim_strength': 'Ratio',\n",
    "}\n",
    "# key = 'theta_energy'\n",
    "# key = 'theta_peak'\n",
    "for key in bins:\n",
    "    results[key] = list()\n",
    "    fig = plt.figure(figsize=(3.2,2))\n",
    "    plt.suptitle(key)\n",
    "    legend_lines = []\n",
    "    for color, query, label in zip(colors[1::2], queries[1::2], labels[1::2]):\n",
    "        values = lfp_results_hemisphere.query(query).loc[:,['entity_date_side', key]]\n",
    "        results[key].append(values.rename({key: label}, axis=1))\n",
    "        values[key].hist(\n",
    "            bins=bins[key], density=density, cumulative=cumulative, lw=lw, \n",
    "            histtype=histtype, color=color)\n",
    "        legend_lines.append(matplotlib.lines.Line2D([0], [0], color=color, lw=lw, label=label))\n",
    "        \n",
    "    plt.legend(\n",
    "        handles=legend_lines,\n",
    "        bbox_to_anchor=(1.04,1), borderaxespad=0, frameon=False)\n",
    "    plt.tight_layout()\n",
    "    plt.grid(False)\n",
    "    plt.xlim(right=bins[key].max() - bins[key].max()*0.025)\n",
    "    despine()\n",
    "    plt.xlabel(xlabel[key])\n",
    "    figname = f'lfp-psd-histogram-{key}'\n",
    "    fig.savefig(\n",
    "        output_path / 'figures' / f'{figname}.png', \n",
    "        bbox_inches='tight', transparent=True)\n",
    "    fig.savefig(\n",
    "        output_path / 'figures' / f'{figname}.svg', \n",
    "        bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import reduce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, val in results.items():\n",
    "    df = reduce(lambda  left,right: pd.merge(left, right, on='entity_date_side', how='outer'), val)\n",
    "    results[key] = df.drop('entity_date_side',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def summarize(data):\n",
    "    return \"{:.1e} ± {:.1e} ({})\".format(data.mean(), data.sem(), sum(~np.isnan(data)))\n",
    "\n",
    "\n",
    "def MWU(df, keys):\n",
    "    '''\n",
    "    Mann Whitney U\n",
    "    '''\n",
    "    Uvalue, pvalue = scipy.stats.mannwhitneyu(\n",
    "        df[keys[0]].dropna(), \n",
    "        df[keys[1]].dropna(),\n",
    "        alternative='two-sided')\n",
    "\n",
    "    return \"{:.2f}, {:.3f}\".format(Uvalue, pvalue)\n",
    "\n",
    "\n",
    "def PRS(df, keys):\n",
    "    '''\n",
    "    Permutation ReSampling\n",
    "    '''\n",
    "    pvalue, observed_diff, diffs = permutation_resampling(\n",
    "        df[keys[0]].dropna(), \n",
    "        df[keys[1]].dropna(), statistic=np.median)\n",
    "\n",
    "    return \"{:.2f}, {:.3f}\".format(observed_diff, pvalue)\n",
    "\n",
    "\n",
    "def wilcoxon(df, keys):\n",
    "    dff = df.loc[:,keys].dropna()\n",
    "    if len(dff[keys].dropna()) == 0:\n",
    "        statistic, pvalue = np.nan, np.nan\n",
    "    else:\n",
    "        statistic, pvalue = scipy.stats.wilcoxon(\n",
    "            dff[keys[0]], \n",
    "            dff[keys[1]],\n",
    "            alternative='two-sided')\n",
    "\n",
    "    return \"{:.2f}, {:.3f}, ({})\".format(statistic, pvalue, len(dff))\n",
    "\n",
    "\n",
    "def paired_t(df, keys):\n",
    "    dff = df.loc[:,[keys[0], keys[1]]].dropna()\n",
    "    statistic, pvalue = scipy.stats.ttest_rel(\n",
    "        dff[keys[0]], \n",
    "        dff[keys[1]])\n",
    "\n",
    "    return \"{:.2f}, {:.3f}\".format(statistic, pvalue)\n",
    "\n",
    "    \n",
    "def normality(df, key):\n",
    "    if len(df[key].dropna()) < 8:\n",
    "        statistic, pvalue = np.nan, np.nan\n",
    "    else:\n",
    "        statistic, pvalue = scipy.stats.normaltest(\n",
    "            df[key].dropna())\n",
    "\n",
    "    return \"{:.2f}, {:.3f}\".format(statistic, pvalue)\n",
    "\n",
    "\n",
    "def shapiro(df, key):\n",
    "    if len(df[key].dropna()) < 8:\n",
    "        statistic, pvalue = np.nan, np.nan\n",
    "    else:\n",
    "        statistic, pvalue = scipy.stats.shapiro(\n",
    "            df[key].dropna())\n",
    "\n",
    "    return \"{:.2f}, {:.3f}\".format(statistic, pvalue)\n",
    "\n",
    "def rename(name):\n",
    "    return name.replace(\"_field\", \"-field\").replace(\"_\", \" \").capitalize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'theta_energy':     Baseline I     11 Hz  Baseline II  30 Hz\n",
       " 0     0.003500       NaN          NaN    NaN\n",
       " 1     0.001237       NaN          NaN    NaN\n",
       " 2     0.003269       NaN     0.004628    NaN\n",
       " 3     0.001465       NaN     0.002155    NaN\n",
       " 4     0.002685       NaN          NaN    NaN\n",
       " 5     0.000772       NaN          NaN    NaN\n",
       " 6     0.002735       NaN     0.002988    NaN\n",
       " 7     0.000872       NaN     0.001177    NaN\n",
       " 8     0.004156       NaN     0.003132    NaN\n",
       " 9     0.001317       NaN     0.001268    NaN\n",
       " 10    0.000423       NaN     0.003200    NaN\n",
       " 11    0.000957       NaN     0.001153    NaN\n",
       " 12    0.002135       NaN     0.001651    NaN\n",
       " 13    0.001649       NaN     0.000871    NaN\n",
       " 14    0.009256       NaN     0.006838    NaN\n",
       " 15    0.003803       NaN     0.003271    NaN\n",
       " 16    0.004999       NaN     0.003724    NaN\n",
       " 17    0.001193       NaN     0.002239    NaN\n",
       " 18    0.004548  0.001780          NaN    NaN\n",
       " 19    0.004548  0.001093          NaN    NaN\n",
       " 20    0.002307  0.001479          NaN    NaN\n",
       " 21    0.002307  0.001122          NaN    NaN\n",
       " 22    0.002577       NaN     0.002665    NaN\n",
       " 23    0.001591       NaN     0.001562    NaN\n",
       " 24    0.001850       NaN     0.002546    NaN\n",
       " 25    0.001388       NaN     0.001375    NaN\n",
       " 26    0.002535       NaN     0.003099    NaN\n",
       " 27    0.001129       NaN     0.001638    NaN\n",
       " 28    0.001581       NaN     0.001302    NaN\n",
       " 29    0.001346       NaN     0.000808    NaN\n",
       " 30    0.002590       NaN     0.001583    NaN\n",
       " 31    0.001029       NaN     0.000921    NaN\n",
       " 32    0.001280       NaN          NaN    NaN\n",
       " 33    0.000644       NaN          NaN    NaN\n",
       " 34    0.002180       NaN          NaN    NaN\n",
       " 35    0.002834       NaN          NaN    NaN\n",
       " 36    0.001891  0.001814          NaN    NaN\n",
       " 37    0.001891  0.001557          NaN    NaN\n",
       " 38    0.002443  0.003549          NaN    NaN\n",
       " 39    0.002443  0.002262          NaN    NaN\n",
       " 40    0.002762       NaN     0.001113    NaN\n",
       " 41    0.002649       NaN     0.003236    NaN\n",
       " 42    0.000662       NaN          NaN    NaN\n",
       " 43    0.003067       NaN          NaN    NaN\n",
       " 44    0.001097       NaN     0.000649    NaN\n",
       " 45    0.002022       NaN     0.001838    NaN\n",
       " 46    0.001199       NaN     0.001475    NaN\n",
       " 47    0.004233       NaN     0.001936    NaN\n",
       " 48    0.002117       NaN     0.004077    NaN\n",
       " 49    0.002906       NaN     0.003994    NaN,\n",
       " 'theta_peak':     Baseline I     11 Hz  Baseline II  30 Hz\n",
       " 0     0.003070       NaN          NaN    NaN\n",
       " 1     0.001036       NaN          NaN    NaN\n",
       " 2     0.003435       NaN     0.003384    NaN\n",
       " 3     0.001416       NaN     0.001389    NaN\n",
       " 4     0.002043       NaN          NaN    NaN\n",
       " 5     0.000472       NaN          NaN    NaN\n",
       " 6     0.002429       NaN     0.003210    NaN\n",
       " 7     0.000625       NaN     0.000961    NaN\n",
       " 8     0.002038       NaN     0.002704    NaN\n",
       " 9     0.000872       NaN     0.001010    NaN\n",
       " 10    0.000342       NaN     0.002434    NaN\n",
       " 11    0.000640       NaN     0.000825    NaN\n",
       " 12    0.001660       NaN     0.001235    NaN\n",
       " 13    0.001190       NaN     0.000620    NaN\n",
       " 14    0.007286       NaN     0.006490    NaN\n",
       " 15    0.002671       NaN     0.002261    NaN\n",
       " 16    0.004189       NaN     0.003590    NaN\n",
       " 17    0.000846       NaN     0.001383    NaN\n",
       " 18    0.003873  0.001374          NaN    NaN\n",
       " 19    0.003873  0.000699          NaN    NaN\n",
       " 20    0.001684  0.001046          NaN    NaN\n",
       " 21    0.001684  0.000645          NaN    NaN\n",
       " 22    0.002021       NaN     0.001967    NaN\n",
       " 23    0.001159       NaN     0.001171    NaN\n",
       " 24    0.001540       NaN     0.002067    NaN\n",
       " 25    0.001135       NaN     0.001147    NaN\n",
       " 26    0.002134       NaN     0.002335    NaN\n",
       " 27    0.000959       NaN     0.001139    NaN\n",
       " 28    0.001189       NaN     0.000792    NaN\n",
       " 29    0.000657       NaN     0.000317    NaN\n",
       " 30    0.001134       NaN     0.001158    NaN\n",
       " 31    0.000427       NaN     0.000362    NaN\n",
       " 32    0.000670       NaN          NaN    NaN\n",
       " 33    0.000301       NaN          NaN    NaN\n",
       " 34    0.001377       NaN          NaN    NaN\n",
       " 35    0.001044       NaN          NaN    NaN\n",
       " 36    0.001698  0.001544          NaN    NaN\n",
       " 37    0.001698  0.000898          NaN    NaN\n",
       " 38    0.002605  0.002939          NaN    NaN\n",
       " 39    0.002605  0.001677          NaN    NaN\n",
       " 40    0.002632       NaN     0.000722    NaN\n",
       " 41    0.002425       NaN     0.003022    NaN\n",
       " 42    0.000412       NaN          NaN    NaN\n",
       " 43    0.003115       NaN          NaN    NaN\n",
       " 44    0.000959       NaN     0.000390    NaN\n",
       " 45    0.002131       NaN     0.001653    NaN\n",
       " 46    0.000515       NaN     0.000891    NaN\n",
       " 47    0.002570       NaN     0.001760    NaN\n",
       " 48    0.001693       NaN     0.002711    NaN\n",
       " 49    0.003029       NaN     0.002933    NaN,\n",
       " 'theta_freq':     Baseline I  11 Hz  Baseline II  30 Hz\n",
       " 0          7.4    NaN          NaN    NaN\n",
       " 1          7.4    NaN          NaN    NaN\n",
       " 2          7.8    NaN          7.8    NaN\n",
       " 3          7.8    NaN          7.8    NaN\n",
       " 4          7.4    NaN          NaN    NaN\n",
       " 5          7.4    NaN          NaN    NaN\n",
       " 6          7.5    NaN          7.8    NaN\n",
       " 7          7.5    NaN          7.8    NaN\n",
       " 8          7.6    NaN          7.9    NaN\n",
       " 9          7.6    NaN          7.9    NaN\n",
       " 10         7.4    NaN          7.7    NaN\n",
       " 11         7.4    NaN          7.7    NaN\n",
       " 12         7.6    NaN          7.6    NaN\n",
       " 13         7.5    NaN          7.6    NaN\n",
       " 14         8.3    NaN          8.5    NaN\n",
       " 15         8.2    NaN          8.5    NaN\n",
       " 16         7.9    NaN          8.1    NaN\n",
       " 17         7.7    NaN          8.2    NaN\n",
       " 18         8.0    8.2          NaN    NaN\n",
       " 19         8.0    8.2          NaN    NaN\n",
       " 20         8.0    8.2          NaN    NaN\n",
       " 21         8.0    8.2          NaN    NaN\n",
       " 22         8.1    NaN          8.2    NaN\n",
       " 23         8.1    NaN          8.2    NaN\n",
       " 24         8.0    NaN          8.2    NaN\n",
       " 25         8.1    NaN          8.2    NaN\n",
       " 26         8.1    NaN          8.2    NaN\n",
       " 27         8.1    NaN          8.2    NaN\n",
       " 28         8.0    NaN          8.2    NaN\n",
       " 29         6.3    NaN          8.3    NaN\n",
       " 30         8.2    NaN          8.6    NaN\n",
       " 31         6.2    NaN          8.3    NaN\n",
       " 32         8.3    NaN          NaN    NaN\n",
       " 33         8.4    NaN          NaN    NaN\n",
       " 34         8.2    NaN          NaN    NaN\n",
       " 35         7.6    NaN          NaN    NaN\n",
       " 36         7.9    7.9          NaN    NaN\n",
       " 37         7.9    7.9          NaN    NaN\n",
       " 38         7.9    8.0          NaN    NaN\n",
       " 39         7.9    7.9          NaN    NaN\n",
       " 40         7.6    NaN          8.1    NaN\n",
       " 41         7.6    NaN          8.1    NaN\n",
       " 42         7.4    NaN          NaN    NaN\n",
       " 43         7.6    NaN          NaN    NaN\n",
       " 44         7.7    NaN          7.8    NaN\n",
       " 45         7.7    NaN          7.8    NaN\n",
       " 46         7.8    NaN          8.0    NaN\n",
       " 47         8.0    NaN          8.0    NaN\n",
       " 48         7.7    NaN          8.3    NaN\n",
       " 49         7.7    NaN          8.0    NaN,\n",
       " 'theta_half_width':     Baseline I     11 Hz  Baseline II  30 Hz\n",
       " 0     0.823669       NaN          NaN    NaN\n",
       " 1     0.845791       NaN          NaN    NaN\n",
       " 2     0.577806       NaN     0.959215    NaN\n",
       " 3     0.653362       NaN     1.056302    NaN\n",
       " 4     1.059779       NaN          NaN    NaN\n",
       " 5     1.147158       NaN          NaN    NaN\n",
       " 6     0.787100       NaN     0.637116    NaN\n",
       " 7     0.964045       NaN     0.633677    NaN\n",
       " 8     1.285535       NaN     0.760866    NaN\n",
       " 9     0.913884       NaN     0.766207    NaN\n",
       " 10    0.776057       NaN     0.996439    NaN\n",
       " 11    0.928426       NaN     0.941342    NaN\n",
       " 12    0.966327       NaN     1.072370    NaN\n",
       " 13    0.984655       NaN     1.106900    NaN\n",
       " 14    0.960731       NaN     0.738833    NaN\n",
       " 15    1.044175       NaN     0.879523    NaN\n",
       " 16    0.911633       NaN     0.641326    NaN\n",
       " 17    0.953731       NaN     0.744352    NaN\n",
       " 18    0.749468  0.930533          NaN    NaN\n",
       " 19    0.749468  0.935671          NaN    NaN\n",
       " 20    0.930580  0.969152          NaN    NaN\n",
       " 21    0.930580  1.049631          NaN    NaN\n",
       " 22    1.009373       NaN     1.085564    NaN\n",
       " 23    1.065057       NaN     0.944394    NaN\n",
       " 24    0.831508       NaN     0.910906    NaN\n",
       " 25    0.825350       NaN     0.831260    NaN\n",
       " 26    0.844569       NaN     0.885471    NaN\n",
       " 27    0.720270       NaN     0.915350    NaN\n",
       " 28    0.873920       NaN     1.122807    NaN\n",
       " 29         NaN       NaN     1.657428    NaN\n",
       " 30    1.224911       NaN     1.120201    NaN\n",
       " 31    6.316287       NaN          NaN    NaN\n",
       " 32    1.015663       NaN          NaN    NaN\n",
       " 33    1.318961       NaN          NaN    NaN\n",
       " 34    1.124338       NaN          NaN    NaN\n",
       " 35    8.080786       NaN          NaN    NaN\n",
       " 36    0.755843  0.620998          NaN    NaN\n",
       " 37    0.755843  1.215511          NaN    NaN\n",
       " 38    0.650319  0.620433          NaN    NaN\n",
       " 39    0.650319  0.745154          NaN    NaN\n",
       " 40    0.677682       NaN     1.000266    NaN\n",
       " 41    0.679729       NaN     0.541226    NaN\n",
       " 42    0.945508       NaN          NaN    NaN\n",
       " 43    0.604237       NaN          NaN    NaN\n",
       " 44    0.583246       NaN     0.870935    NaN\n",
       " 45    0.560840       NaN     0.754164    NaN\n",
       " 46    0.843262       NaN     1.160272    NaN\n",
       " 47    0.969953       NaN     0.680846    NaN\n",
       " 48    0.823915       NaN     0.979878    NaN\n",
       " 49    0.688965       NaN     0.850279    NaN,\n",
       " 'stim_energy':       11 Hz  30 Hz\n",
       " 0  0.002671    NaN\n",
       " 1  0.000532    NaN\n",
       " 2  0.000168    NaN\n",
       " 3  0.000033    NaN\n",
       " 4  0.000513    NaN\n",
       " 5  0.002245    NaN\n",
       " 6  0.000340    NaN\n",
       " 7  0.000240    NaN,\n",
       " 'stim_half_width':        11 Hz  30 Hz\n",
       " 0  10.318750    NaN\n",
       " 1   0.149163    NaN\n",
       " 2   0.156917    NaN\n",
       " 3   0.271082    NaN\n",
       " 4   0.150665    NaN\n",
       " 5   6.113351    NaN\n",
       " 6   0.655030    NaN\n",
       " 7   0.156293    NaN,\n",
       " 'stim_p_max':       11 Hz  30 Hz\n",
       " 0  0.000105    NaN\n",
       " 1  0.004951    NaN\n",
       " 2  0.001566    NaN\n",
       " 3  0.000206    NaN\n",
       " 4  0.004785    NaN\n",
       " 5  0.000374    NaN\n",
       " 6  0.000814    NaN\n",
       " 7  0.002239    NaN,\n",
       " 'stim_strength':       11 Hz  30 Hz\n",
       " 0  0.059252    NaN\n",
       " 1  4.530157    NaN\n",
       " 2  1.059344    NaN\n",
       " 3  0.183414    NaN\n",
       " 4  2.637518    NaN\n",
       " 5  0.239875    NaN\n",
       " 6  0.229359    NaN\n",
       " 7  0.989594    NaN}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/stats/morestats.py:2863: UserWarning: Sample size too small for normal approximation.\n",
      "  warnings.warn(\"Sample size too small for normal approximation.\")\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/stats/stats.py:1450: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=8\n",
      "  \"anyway, n=%i\" % int(n))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Theta energy</th>\n",
       "      <th>Theta peak</th>\n",
       "      <th>Theta freq</th>\n",
       "      <th>Theta half width</th>\n",
       "      <th>Stim energy</th>\n",
       "      <th>Stim half width</th>\n",
       "      <th>Stim p max</th>\n",
       "      <th>Stim strength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11 Hz</th>\n",
       "      <td>1.8e-03 ± 2.8e-04 (8)</td>\n",
       "      <td>1.4e-03 ± 2.6e-04 (8)</td>\n",
       "      <td>8.1e+00 ± 5.3e-02 (8)</td>\n",
       "      <td>8.9e-01 ± 7.4e-02 (8)</td>\n",
       "      <td>8.4e-04 ± 3.6e-04 (8)</td>\n",
       "      <td>2.2e+00 ± 1.4e+00 (8)</td>\n",
       "      <td>1.9e-03 ± 7.0e-04 (8)</td>\n",
       "      <td>1.2e+00 ± 5.6e-01 (8)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30 Hz</th>\n",
       "      <td>nan ± nan (0)</td>\n",
       "      <td>nan ± nan (0)</td>\n",
       "      <td>nan ± nan (0)</td>\n",
       "      <td>nan ± nan (0)</td>\n",
       "      <td>nan ± nan (0)</td>\n",
       "      <td>nan ± nan (0)</td>\n",
       "      <td>nan ± nan (0)</td>\n",
       "      <td>nan ± nan (0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baseline I</th>\n",
       "      <td>2.3e-03 ± 2.1e-04 (50)</td>\n",
       "      <td>1.8e-03 ± 1.8e-04 (50)</td>\n",
       "      <td>7.8e+00 ± 5.9e-02 (50)</td>\n",
       "      <td>1.1e+00 ± 1.8e-01 (49)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baseline II</th>\n",
       "      <td>2.3e-03 ± 2.4e-04 (32)</td>\n",
       "      <td>1.8e-03 ± 2.3e-04 (32)</td>\n",
       "      <td>8.1e+00 ± 4.7e-02 (32)</td>\n",
       "      <td>9.1e-01 ± 3.9e-02 (31)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normality 11 Hz</th>\n",
       "      <td>8.13, 0.017</td>\n",
       "      <td>6.61, 0.037</td>\n",
       "      <td>6.30, 0.043</td>\n",
       "      <td>0.23, 0.890</td>\n",
       "      <td>3.45, 0.178</td>\n",
       "      <td>7.42, 0.024</td>\n",
       "      <td>1.81, 0.405</td>\n",
       "      <td>6.43, 0.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normality 30 Hz</th>\n",
       "      <td>nan, nan</td>\n",
       "      <td>nan, nan</td>\n",
       "      <td>nan, nan</td>\n",
       "      <td>nan, nan</td>\n",
       "      <td>nan, nan</td>\n",
       "      <td>nan, nan</td>\n",
       "      <td>nan, nan</td>\n",
       "      <td>nan, nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normality Baseline I</th>\n",
       "      <td>41.72, 0.000</td>\n",
       "      <td>31.09, 0.000</td>\n",
       "      <td>29.47, 0.000</td>\n",
       "      <td>81.74, 0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normality Baseline II</th>\n",
       "      <td>13.17, 0.001</td>\n",
       "      <td>20.78, 0.000</td>\n",
       "      <td>0.96, 0.618</td>\n",
       "      <td>13.33, 0.001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wilcoxon 11 Hz 30 Hz</th>\n",
       "      <td>nan, nan, (0)</td>\n",
       "      <td>nan, nan, (0)</td>\n",
       "      <td>nan, nan, (0)</td>\n",
       "      <td>nan, nan, (0)</td>\n",
       "      <td>nan, nan, (0)</td>\n",
       "      <td>nan, nan, (0)</td>\n",
       "      <td>nan, nan, (0)</td>\n",
       "      <td>nan, nan, (0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wilcoxon 11 Hz Baseline II</th>\n",
       "      <td>nan, nan, (0)</td>\n",
       "      <td>nan, nan, (0)</td>\n",
       "      <td>nan, nan, (0)</td>\n",
       "      <td>nan, nan, (0)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wilcoxon Baseline I 11 Hz</th>\n",
       "      <td>5.00, 0.069, (8)</td>\n",
       "      <td>2.00, 0.025, (8)</td>\n",
       "      <td>0.00, 0.034, (8)</td>\n",
       "      <td>6.00, 0.093, (8)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wilcoxon Baseline I 30 Hz</th>\n",
       "      <td>nan, nan, (0)</td>\n",
       "      <td>nan, nan, (0)</td>\n",
       "      <td>nan, nan, (0)</td>\n",
       "      <td>nan, nan, (0)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wilcoxon Baseline I Baseline II</th>\n",
       "      <td>264.00, 1.000, (32)</td>\n",
       "      <td>256.00, 0.881, (32)</td>\n",
       "      <td>0.00, 0.000, (32)</td>\n",
       "      <td>203.00, 0.544, (30)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wilcoxon Baseline II 30 Hz</th>\n",
       "      <td>nan, nan, (0)</td>\n",
       "      <td>nan, nan, (0)</td>\n",
       "      <td>nan, nan, (0)</td>\n",
       "      <td>nan, nan, (0)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Theta energy  \\\n",
       "11 Hz                             1.8e-03 ± 2.8e-04 (8)   \n",
       "30 Hz                                     nan ± nan (0)   \n",
       "Baseline I                       2.3e-03 ± 2.1e-04 (50)   \n",
       "Baseline II                      2.3e-03 ± 2.4e-04 (32)   \n",
       "Normality 11 Hz                             8.13, 0.017   \n",
       "Normality 30 Hz                                nan, nan   \n",
       "Normality Baseline I                       41.72, 0.000   \n",
       "Normality Baseline II                      13.17, 0.001   \n",
       "Wilcoxon 11 Hz 30 Hz                      nan, nan, (0)   \n",
       "Wilcoxon 11 Hz Baseline II                nan, nan, (0)   \n",
       "Wilcoxon Baseline I 11 Hz              5.00, 0.069, (8)   \n",
       "Wilcoxon Baseline I 30 Hz                 nan, nan, (0)   \n",
       "Wilcoxon Baseline I Baseline II     264.00, 1.000, (32)   \n",
       "Wilcoxon Baseline II 30 Hz                nan, nan, (0)   \n",
       "\n",
       "                                             Theta peak  \\\n",
       "11 Hz                             1.4e-03 ± 2.6e-04 (8)   \n",
       "30 Hz                                     nan ± nan (0)   \n",
       "Baseline I                       1.8e-03 ± 1.8e-04 (50)   \n",
       "Baseline II                      1.8e-03 ± 2.3e-04 (32)   \n",
       "Normality 11 Hz                             6.61, 0.037   \n",
       "Normality 30 Hz                                nan, nan   \n",
       "Normality Baseline I                       31.09, 0.000   \n",
       "Normality Baseline II                      20.78, 0.000   \n",
       "Wilcoxon 11 Hz 30 Hz                      nan, nan, (0)   \n",
       "Wilcoxon 11 Hz Baseline II                nan, nan, (0)   \n",
       "Wilcoxon Baseline I 11 Hz              2.00, 0.025, (8)   \n",
       "Wilcoxon Baseline I 30 Hz                 nan, nan, (0)   \n",
       "Wilcoxon Baseline I Baseline II     256.00, 0.881, (32)   \n",
       "Wilcoxon Baseline II 30 Hz                nan, nan, (0)   \n",
       "\n",
       "                                             Theta freq  \\\n",
       "11 Hz                             8.1e+00 ± 5.3e-02 (8)   \n",
       "30 Hz                                     nan ± nan (0)   \n",
       "Baseline I                       7.8e+00 ± 5.9e-02 (50)   \n",
       "Baseline II                      8.1e+00 ± 4.7e-02 (32)   \n",
       "Normality 11 Hz                             6.30, 0.043   \n",
       "Normality 30 Hz                                nan, nan   \n",
       "Normality Baseline I                       29.47, 0.000   \n",
       "Normality Baseline II                       0.96, 0.618   \n",
       "Wilcoxon 11 Hz 30 Hz                      nan, nan, (0)   \n",
       "Wilcoxon 11 Hz Baseline II                nan, nan, (0)   \n",
       "Wilcoxon Baseline I 11 Hz              0.00, 0.034, (8)   \n",
       "Wilcoxon Baseline I 30 Hz                 nan, nan, (0)   \n",
       "Wilcoxon Baseline I Baseline II       0.00, 0.000, (32)   \n",
       "Wilcoxon Baseline II 30 Hz                nan, nan, (0)   \n",
       "\n",
       "                                       Theta half width  \\\n",
       "11 Hz                             8.9e-01 ± 7.4e-02 (8)   \n",
       "30 Hz                                     nan ± nan (0)   \n",
       "Baseline I                       1.1e+00 ± 1.8e-01 (49)   \n",
       "Baseline II                      9.1e-01 ± 3.9e-02 (31)   \n",
       "Normality 11 Hz                             0.23, 0.890   \n",
       "Normality 30 Hz                                nan, nan   \n",
       "Normality Baseline I                       81.74, 0.000   \n",
       "Normality Baseline II                      13.33, 0.001   \n",
       "Wilcoxon 11 Hz 30 Hz                      nan, nan, (0)   \n",
       "Wilcoxon 11 Hz Baseline II                nan, nan, (0)   \n",
       "Wilcoxon Baseline I 11 Hz              6.00, 0.093, (8)   \n",
       "Wilcoxon Baseline I 30 Hz                 nan, nan, (0)   \n",
       "Wilcoxon Baseline I Baseline II     203.00, 0.544, (30)   \n",
       "Wilcoxon Baseline II 30 Hz                nan, nan, (0)   \n",
       "\n",
       "                                           Stim energy        Stim half width  \\\n",
       "11 Hz                            8.4e-04 ± 3.6e-04 (8)  2.2e+00 ± 1.4e+00 (8)   \n",
       "30 Hz                                    nan ± nan (0)          nan ± nan (0)   \n",
       "Baseline I                                         NaN                    NaN   \n",
       "Baseline II                                        NaN                    NaN   \n",
       "Normality 11 Hz                            3.45, 0.178            7.42, 0.024   \n",
       "Normality 30 Hz                               nan, nan               nan, nan   \n",
       "Normality Baseline I                               NaN                    NaN   \n",
       "Normality Baseline II                              NaN                    NaN   \n",
       "Wilcoxon 11 Hz 30 Hz                     nan, nan, (0)          nan, nan, (0)   \n",
       "Wilcoxon 11 Hz Baseline II                         NaN                    NaN   \n",
       "Wilcoxon Baseline I 11 Hz                          NaN                    NaN   \n",
       "Wilcoxon Baseline I 30 Hz                          NaN                    NaN   \n",
       "Wilcoxon Baseline I Baseline II                    NaN                    NaN   \n",
       "Wilcoxon Baseline II 30 Hz                         NaN                    NaN   \n",
       "\n",
       "                                            Stim p max          Stim strength  \n",
       "11 Hz                            1.9e-03 ± 7.0e-04 (8)  1.2e+00 ± 5.6e-01 (8)  \n",
       "30 Hz                                    nan ± nan (0)          nan ± nan (0)  \n",
       "Baseline I                                         NaN                    NaN  \n",
       "Baseline II                                        NaN                    NaN  \n",
       "Normality 11 Hz                            1.81, 0.405            6.43, 0.040  \n",
       "Normality 30 Hz                               nan, nan               nan, nan  \n",
       "Normality Baseline I                               NaN                    NaN  \n",
       "Normality Baseline II                              NaN                    NaN  \n",
       "Wilcoxon 11 Hz 30 Hz                     nan, nan, (0)          nan, nan, (0)  \n",
       "Wilcoxon 11 Hz Baseline II                         NaN                    NaN  \n",
       "Wilcoxon Baseline I 11 Hz                          NaN                    NaN  \n",
       "Wilcoxon Baseline I 30 Hz                          NaN                    NaN  \n",
       "Wilcoxon Baseline I Baseline II                    NaN                    NaN  \n",
       "Wilcoxon Baseline II 30 Hz                         NaN                    NaN  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "stat = pd.DataFrame()\n",
    "\n",
    "for key, df in results.items():\n",
    "    Key = rename(key)\n",
    "    stat[Key] = df.agg(summarize)\n",
    "    stat[Key] = df.agg(summarize)\n",
    "\n",
    "    for i, c1 in enumerate(df.columns):\n",
    "        stat.loc[f'Normality {c1}', Key] = normality(df, c1)\n",
    "#         stat.loc[f'Shapiro {c1}', Key] = shapiro(df, c1)\n",
    "        for c2 in df.columns[i+1:]:\n",
    "#             stat.loc[f'MWU {c1} {c2}', Key] = MWU(df, [c1, c2])\n",
    "#             stat.loc[f'PRS {c1} {c2}', Key] = PRS(df, [c1, c2])\n",
    "            stat.loc[f'Wilcoxon {c1} {c2}', Key] = wilcoxon(df, [c1, c2])\n",
    "#             stat.loc[f'Paired T {c1} {c2}', Key] = paired_t(df, [c1, c2])\n",
    "\n",
    "stat.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "stat.to_latex(output_path / \"statistics\" / \"statistics.tex\")\n",
    "stat.to_csv(output_path / \"statistics\" / \"statistics.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot PSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "psd = pd.read_feather(pathlib.Path(\"output\") / (\"stimulus-lfp-response\" + zscore_str) / 'data' / 'psd.feather')\n",
    "freqs = pd.read_feather(pathlib.Path(\"output\") / (\"stimulus-lfp-response\" + zscore_str) / 'data' / 'freqs.feather')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from septum_mec.analysis.plotting import plot_bootstrap_timeseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "freq = freqs.T.iloc[0].values\n",
    "\n",
    "mask = (freq < 49)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/fromnumeric.py:3257: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:154: RuntimeWarning: invalid value encountered in true_divide\n",
      "  ret, rcount, out=ret, casting='unsafe', subok=False)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x300 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, axs = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(5,2))\n",
    "axs = axs.repeat(2)\n",
    "for i, (ax, query) in enumerate(zip(axs.ravel(), queries)):\n",
    "    selection = [\n",
    "        f'{r.action}_{r.channel_group}' \n",
    "        for i, r in lfp_results_hemisphere.query(query).iterrows()]\n",
    "    values = psd.loc[mask, selection].to_numpy()\n",
    "    values = 10 * np.log10(values)\n",
    "    plot_bootstrap_timeseries(freq[mask], values, ax=ax, lw=1, label=labels[i], color=colors[i])\n",
    "#     ax.set_title(titles[i])\n",
    "    ax.set_xlabel('Frequency Hz')\n",
    "    ax.legend(frameon=False)\n",
    "axs[0].set_ylabel('PSD (dB/Hz)')\n",
    "# axs[0].set_ylim(-31, 1)\n",
    "despine()\n",
    "\n",
    "figname = 'lfp-psd'\n",
    "fig.savefig(\n",
    "    output_path / 'figures' / f'{figname}.png', \n",
    "    bbox_inches='tight', transparent=True)\n",
    "fig.savefig(\n",
    "    output_path / 'figures' / f'{figname}.svg', \n",
    "    bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Store results in Expipe action"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "action = project.require_action(\"stimulus-lfp-response-mec\" + zscore_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/statistics.tex',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/statistics.csv',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_energy.png',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_strength.png',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd.png',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_peak.svg',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_p_max.png',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_freq.png',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_energy.svg',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_freq.svg',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_half_width.png',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_half_width.svg',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_half_width.svg',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_energy.png',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_peak.png',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_p_max.svg',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_half_width.png',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd.svg',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/.ipynb_checkpoints/lfp-psd-histogram-theta_energy-checkpoint.png',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_energy.svg',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_strength.svg']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copy_tree(output_path, str(action.data_path()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "septum_mec.analysis.registration.store_notebook(action, \"20_stimulus-lfp-response-mec.ipynb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}