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

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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 statsmodels\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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#############################\n",
    "\n",
    "zscore_str = \"-no-zscore\"\n",
    "# zscore_str = \"\"\n",
    "\n",
    "# stim_loc = 'mec'\n",
    "stim_loc = 'ms'\n",
    "\n",
    "#################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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\" + '-' + stim_loc + 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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_loader = dp.Data()\n",
    "actions = data_loader.actions\n",
    "project = data_loader.project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "identify_neurons = actions['identify-neurons']\n",
    "sessions = pd.read_csv(identify_neurons.data_path('sessions'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "lfp_results = pd.merge(sessions, lfp_results, how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "if stim_loc == 'ms':\n",
    "    lfp_results = lfp_results.query('stim_location!=\"mecl\" and stim_location!=\"mecr\"')\n",
    "elif stim_loc == 'mec':\n",
    "    lfp_results = lfp_results.query('stim_location!=\"ms\"')\n",
    "else:\n",
    "    raise AssertionError('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "lfp_results['stim_strength'] = lfp_results['stim_p_max'] / lfp_results['theta_bandpower']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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_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",
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       "      <td>7</td>\n",
       "      <td>-0.002942</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "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",
       "695  1833-010719-2-0              7         0.004280       1.401239\n",
       "691  1833-010719-2-1              3         0.003974       3.920680\n",
       "583  1833-020719-1-0              7        -0.002942            NaN"
      ]
     },
     "execution_count": 12,
     "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": 13,
   "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": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare pairwise comparison: same animal same side same date different sessions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "lfp_results_hemisphere['entity_date_side'] = lfp_results_hemisphere.apply(make_entity_date_side, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import reduce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "keys = [\n",
    "    'theta_bandpower',\n",
    "    'theta_relpower',\n",
    "    'theta_relpeak',\n",
    "    'theta_peak',\n",
    "    'theta_freq',\n",
    "    'theta_half_width',\n",
    "    'stim_bandpower',\n",
    "    'stim_relpower',\n",
    "    'stim_relpeak',\n",
    "    'stim_half_width',\n",
    "    'stim_p_max',\n",
    "    'stim_strength',\n",
    "]\n",
    "\n",
    "results = {}\n",
    "for key in keys:\n",
    "    results[key] = list()\n",
    "    for query, label in zip(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",
    "        \n",
    "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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  ndim = x[:, None].ndim\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  ndim = x[:, None].ndim\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  ndim = x[:, None].ndim\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  ndim = x[:, None].ndim\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  ndim = x[:, None].ndim\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  ndim = x[:, None].ndim\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n"
     ]
    },
    {
     "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"
    },
    {
     "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"
    },
    {
     "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": [
    "xlabel = {\n",
    "    'theta_bandpower': 'Theta bandpower (V$^2$/Hz)',\n",
    "    'theta_relpower': 'Theta relative power',\n",
    "    'theta_relpeak': 'Theta relative power',\n",
    "    'theta_peak': 'Peak PSD (V$^2$/Hz)',\n",
    "    'theta_freq': '(Hz)',\n",
    "    'theta_half_width': '(Hz)',\n",
    "}\n",
    "\n",
    "for key in xlabel:\n",
    "    fig = plt.figure(figsize=(3.5,2))\n",
    "    plt.suptitle(key)\n",
    "    legend_lines = []\n",
    "    for color, label in zip(colors, labels):\n",
    "        legend_lines.append(matplotlib.lines.Line2D([0], [0], color=color, label=label))\n",
    "    sns.kdeplot(data=results[key].loc[:,labels], cumulative=True, legend=False, palette=colors, common_norm=False)\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",
    "    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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  ndim = x[:, None].ndim\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  ndim = x[:, None].ndim\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  ndim = x[:, None].ndim\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  ndim = x[:, None].ndim\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  ndim = x[:, None].ndim\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  ndim = x[:, None].ndim\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n"
     ]
    },
    {
     "data": {
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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"
    },
    {
     "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": [
    "xlabel = {\n",
    "    'stim_bandpower': 'Energy (V$^2$/Hz)',\n",
    "    'stim_relpower': 'Relative power',\n",
    "    'stim_relpeak': 'Relative power',\n",
    "    'stim_half_width': '(Hz)',\n",
    "    'stim_p_max': 'Peak PSD (V$^2$/Hz)',\n",
    "    'stim_strength': 'Ratio',\n",
    "}\n",
    "for key in xlabel:\n",
    "    fig = plt.figure(figsize=(3.2,2))\n",
    "    plt.suptitle(key)\n",
    "    legend_lines = []\n",
    "    for color, label in zip(colors[1::2], labels[1::2]):\n",
    "        legend_lines.append(matplotlib.lines.Line2D([0], [0], color=color, label=label))\n",
    "    sns.kdeplot(data=results[key].loc[:, labels[1::2]], cumulative=True, legend=False, palette=colors[1::2], common_norm=False)\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",
    "    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": 21,
   "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[0], keys[1]]].dropna()\n",
    "    statistic, pvalue = scipy.stats.wilcoxon(\n",
    "        dff[keys[0]], \n",
    "        dff[keys[1]],\n",
    "        alternative='two-sided')\n",
    "\n",
    "    return \"{:.1e}, {:.1e}, ({})\".format(statistic, pvalue, len(dff))\n",
    "\n",
    "\n",
    "def summarize_wilcoxon(df, keys):\n",
    "    dff = df.loc[:,[keys[0], keys[1]]].dropna()\n",
    "\n",
    "\n",
    "    return\"{:.1e} ± {:.1e}, {:.1e} ± {:.1e} ({})\".format(dff[keys[0]].mean(), dff[keys[0]].sem(), dff[keys[1]].mean(), dff[keys[1]].sem(), 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",
    "    statistic, pvalue = scipy.stats.normaltest(\n",
    "        df[key].dropna())\n",
    "\n",
    "    return \"{:.1e}, {:.1e}\".format(statistic, pvalue)\n",
    "\n",
    "\n",
    "def shapiro(df, key):\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": 22,
   "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>Theta bandpower</th>\n",
       "      <th>Theta relpower</th>\n",
       "      <th>Theta relpeak</th>\n",
       "      <th>Theta peak</th>\n",
       "      <th>Theta freq</th>\n",
       "      <th>Theta half width</th>\n",
       "      <th>Stim bandpower</th>\n",
       "      <th>Stim relpower</th>\n",
       "      <th>Stim relpeak</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>8.5e-04 ± 8.0e-05 (44)</td>\n",
       "      <td>8.6e-02 ± 4.6e-03 (44)</td>\n",
       "      <td>2.3e-01 ± 5.2e-02 (44)</td>\n",
       "      <td>3.2e-04 ± 3.1e-05 (44)</td>\n",
       "      <td>7.7e+00 ± 1.3e-01 (44)</td>\n",
       "      <td>1.7e+00 ± 3.0e-01 (43)</td>\n",
       "      <td>9.6e-04 ± 8.0e-05 (44)</td>\n",
       "      <td>1.1e-01 ± 7.4e-03 (44)</td>\n",
       "      <td>1.6e+01 ± 1.9e+00 (44)</td>\n",
       "      <td>3.4e-01 ± 2.5e-03 (44)</td>\n",
       "      <td>2.1e-03 ± 2.3e-04 (44)</td>\n",
       "      <td>3.3e+00 ± 3.8e-01 (44)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30 Hz</th>\n",
       "      <td>5.3e-04 ± 6.3e-05 (34)</td>\n",
       "      <td>5.3e-02 ± 4.2e-03 (34)</td>\n",
       "      <td>3.0e-02 ± 5.3e-02 (34)</td>\n",
       "      <td>1.9e-04 ± 2.7e-05 (34)</td>\n",
       "      <td>7.4e+00 ± 1.4e-01 (34)</td>\n",
       "      <td>1.2e+01 ± 1.9e+00 (34)</td>\n",
       "      <td>1.8e-03 ± 3.3e-04 (34)</td>\n",
       "      <td>1.6e-01 ± 2.3e-02 (34)</td>\n",
       "      <td>8.6e+01 ± 1.4e+01 (34)</td>\n",
       "      <td>3.0e-01 ± 2.3e-03 (23)</td>\n",
       "      <td>5.3e-03 ± 1.1e-03 (34)</td>\n",
       "      <td>1.3e+01 ± 2.8e+00 (34)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baseline I</th>\n",
       "      <td>2.2e-03 ± 2.2e-04 (46)</td>\n",
       "      <td>2.6e-01 ± 1.6e-02 (46)</td>\n",
       "      <td>6.5e+00 ± 6.7e-01 (46)</td>\n",
       "      <td>1.7e-03 ± 1.8e-04 (46)</td>\n",
       "      <td>7.8e+00 ± 4.0e-02 (46)</td>\n",
       "      <td>7.7e-01 ± 1.8e-02 (46)</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 II</th>\n",
       "      <td>2.2e-03 ± 2.3e-04 (32)</td>\n",
       "      <td>2.7e-01 ± 1.7e-02 (32)</td>\n",
       "      <td>6.3e+00 ± 7.4e-01 (32)</td>\n",
       "      <td>1.6e-03 ± 2.1e-04 (32)</td>\n",
       "      <td>8.1e+00 ± 4.7e-02 (32)</td>\n",
       "      <td>8.4e-01 ± 3.2e-02 (32)</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>Normality 11 Hz</th>\n",
       "      <td>2.1e+01, 2.6e-05</td>\n",
       "      <td>5.6e+00, 6.2e-02</td>\n",
       "      <td>1.2e+01, 2.3e-03</td>\n",
       "      <td>2.4e+01, 5.3e-06</td>\n",
       "      <td>1.9e+00, 3.9e-01</td>\n",
       "      <td>1.0e+01, 5.5e-03</td>\n",
       "      <td>1.7e+01, 1.8e-04</td>\n",
       "      <td>3.9e+00, 1.4e-01</td>\n",
       "      <td>5.8e+00, 5.6e-02</td>\n",
       "      <td>1.6e+01, 4.2e-04</td>\n",
       "      <td>1.5e+01, 5.8e-04</td>\n",
       "      <td>1.2e+01, 2.3e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normality 30 Hz</th>\n",
       "      <td>3.1e+01, 1.9e-07</td>\n",
       "      <td>7.9e+00, 2.0e-02</td>\n",
       "      <td>1.2e+01, 2.3e-03</td>\n",
       "      <td>3.8e+01, 5.3e-09</td>\n",
       "      <td>7.2e+00, 2.8e-02</td>\n",
       "      <td>1.9e+02, 2.2e-41</td>\n",
       "      <td>1.7e+01, 1.7e-04</td>\n",
       "      <td>1.5e+01, 4.8e-04</td>\n",
       "      <td>6.2e+00, 4.5e-02</td>\n",
       "      <td>6.1e-01, 7.4e-01</td>\n",
       "      <td>1.9e+01, 6.3e-05</td>\n",
       "      <td>4.3e+01, 5.1e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normality Baseline I</th>\n",
       "      <td>4.3e+01, 5.8e-10</td>\n",
       "      <td>1.6e+00, 4.6e-01</td>\n",
       "      <td>2.1e+00, 3.4e-01</td>\n",
       "      <td>3.2e+01, 1.3e-07</td>\n",
       "      <td>5.9e+00, 5.3e-02</td>\n",
       "      <td>1.9e+00, 3.8e-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>1.4e+01, 1.1e-03</td>\n",
       "      <td>3.6e-01, 8.3e-01</td>\n",
       "      <td>4.9e+00, 8.8e-02</td>\n",
       "      <td>2.5e+01, 3.4e-06</td>\n",
       "      <td>4.7e+00, 9.7e-02</td>\n",
       "      <td>1.6e+01, 2.8e-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paired summary 11 Hz 30 Hz</th>\n",
       "      <td>8.2e-04 ± 8.8e-05, 5.4e-04 ± 6.6e-05 (32)</td>\n",
       "      <td>8.4e-02 ± 4.5e-03, 5.5e-02 ± 4.2e-03 (32)</td>\n",
       "      <td>2.0e-01 ± 5.0e-02, 4.9e-02 ± 5.4e-02 (32)</td>\n",
       "      <td>3.0e-04 ± 3.1e-05, 1.9e-04 ± 2.8e-05 (32)</td>\n",
       "      <td>7.6e+00 ± 1.5e-01, 7.3e+00 ± 1.4e-01 (32)</td>\n",
       "      <td>1.5e+00 ± 3.5e-01, 1.1e+01 ± 2.0e+00 (31)</td>\n",
       "      <td>9.7e-04 ± 9.3e-05, 1.8e-03 ± 3.4e-04 (32)</td>\n",
       "      <td>1.1e-01 ± 8.6e-03, 1.6e-01 ± 2.4e-02 (32)</td>\n",
       "      <td>1.8e+01 ± 2.4e+00, 8.5e+01 ± 1.4e+01 (32)</td>\n",
       "      <td>3.4e-01 ± 3.6e-03, 3.0e-01 ± 2.3e-03 (21)</td>\n",
       "      <td>2.1e-03 ± 2.7e-04, 5.4e-03 ± 1.1e-03 (32)</td>\n",
       "      <td>3.5e+00 ± 4.9e-01, 1.3e+01 ± 3.0e+00 (32)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paired summary 11 Hz Baseline II</th>\n",
       "      <td>8.2e-04 ± 8.8e-05, 2.2e-03 ± 2.3e-04 (32)</td>\n",
       "      <td>8.4e-02 ± 4.5e-03, 2.7e-01 ± 1.7e-02 (32)</td>\n",
       "      <td>2.0e-01 ± 5.0e-02, 6.3e+00 ± 7.4e-01 (32)</td>\n",
       "      <td>3.0e-04 ± 3.1e-05, 1.6e-03 ± 2.1e-04 (32)</td>\n",
       "      <td>7.6e+00 ± 1.5e-01, 8.1e+00 ± 4.7e-02 (32)</td>\n",
       "      <td>1.5e+00 ± 3.5e-01, 8.6e-01 ± 2.5e-02 (31)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paired summary Baseline I 11 Hz</th>\n",
       "      <td>2.2e-03 ± 2.3e-04, 8.5e-04 ± 8.0e-05 (44)</td>\n",
       "      <td>2.6e-01 ± 1.6e-02, 8.6e-02 ± 4.6e-03 (44)</td>\n",
       "      <td>6.3e+00 ± 6.7e-01, 2.3e-01 ± 5.2e-02 (44)</td>\n",
       "      <td>1.6e-03 ± 1.9e-04, 3.2e-04 ± 3.1e-05 (44)</td>\n",
       "      <td>7.8e+00 ± 4.2e-02, 7.7e+00 ± 1.3e-01 (44)</td>\n",
       "      <td>7.8e-01 ± 1.8e-02, 1.7e+00 ± 3.0e-01 (43)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paired summary Baseline I 30 Hz</th>\n",
       "      <td>2.3e-03 ± 2.8e-04, 5.4e-04 ± 6.6e-05 (32)</td>\n",
       "      <td>2.7e-01 ± 1.8e-02, 5.5e-02 ± 4.2e-03 (32)</td>\n",
       "      <td>6.6e+00 ± 8.1e-01, 4.9e-02 ± 5.4e-02 (32)</td>\n",
       "      <td>1.7e-03 ± 2.3e-04, 1.9e-04 ± 2.8e-05 (32)</td>\n",
       "      <td>7.8e+00 ± 4.4e-02, 7.3e+00 ± 1.4e-01 (32)</td>\n",
       "      <td>7.6e-01 ± 2.0e-02, 1.2e+01 ± 2.0e+00 (32)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paired summary Baseline I Baseline II</th>\n",
       "      <td>2.3e-03 ± 2.8e-04, 2.2e-03 ± 2.3e-04 (32)</td>\n",
       "      <td>2.7e-01 ± 1.8e-02, 2.7e-01 ± 1.7e-02 (32)</td>\n",
       "      <td>6.6e+00 ± 8.1e-01, 6.3e+00 ± 7.4e-01 (32)</td>\n",
       "      <td>1.7e-03 ± 2.3e-04, 1.6e-03 ± 2.1e-04 (32)</td>\n",
       "      <td>7.8e+00 ± 4.4e-02, 8.1e+00 ± 4.7e-02 (32)</td>\n",
       "      <td>7.6e-01 ± 2.0e-02, 8.4e-01 ± 3.2e-02 (32)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paired summary Baseline II 30 Hz</th>\n",
       "      <td>2.2e-03 ± 2.3e-04, 5.4e-04 ± 6.6e-05 (32)</td>\n",
       "      <td>2.7e-01 ± 1.7e-02, 5.5e-02 ± 4.2e-03 (32)</td>\n",
       "      <td>6.3e+00 ± 7.4e-01, 4.9e-02 ± 5.4e-02 (32)</td>\n",
       "      <td>1.6e-03 ± 2.1e-04, 1.9e-04 ± 2.8e-05 (32)</td>\n",
       "      <td>8.1e+00 ± 4.7e-02, 7.3e+00 ± 1.4e-01 (32)</td>\n",
       "      <td>8.4e-01 ± 3.2e-02, 1.2e+01 ± 2.0e+00 (32)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "      <td>nan ± nan, nan ± nan (0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wilcoxon 11 Hz 30 Hz</th>\n",
       "      <td>1.1e+02, 3.3e-03, (32)</td>\n",
       "      <td>4.1e+01, 3.0e-05, (32)</td>\n",
       "      <td>1.3e+02, 1.4e-02, (32)</td>\n",
       "      <td>1.2e+02, 5.6e-03, (32)</td>\n",
       "      <td>1.2e+02, 4.5e-02, (32)</td>\n",
       "      <td>6.7e+01, 3.9e-04, (31)</td>\n",
       "      <td>2.1e+02, 2.9e-01, (32)</td>\n",
       "      <td>2.0e+02, 2.2e-01, (32)</td>\n",
       "      <td>3.0e+01, 1.2e-05, (32)</td>\n",
       "      <td>0.0e+00, 9.5e-07, (21)</td>\n",
       "      <td>1.6e+02, 5.0e-02, (32)</td>\n",
       "      <td>8.8e+01, 1.0e-03, (32)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wilcoxon 11 Hz Baseline II</th>\n",
       "      <td>1.2e+01, 2.5e-06, (32)</td>\n",
       "      <td>2.0e+00, 9.6e-07, (32)</td>\n",
       "      <td>0.0e+00, 8.0e-07, (32)</td>\n",
       "      <td>3.0e+00, 1.1e-06, (32)</td>\n",
       "      <td>1.2e+02, 9.3e-03, (32)</td>\n",
       "      <td>2.3e+02, 6.7e-01, (31)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>3.5e+01, 7.9e-08, (44)</td>\n",
       "      <td>1.0e+00, 8.2e-09, (44)</td>\n",
       "      <td>2.0e+00, 8.7e-09, (44)</td>\n",
       "      <td>3.0e+00, 9.4e-09, (44)</td>\n",
       "      <td>3.6e+02, 4.7e-01, (44)</td>\n",
       "      <td>4.3e+02, 6.2e-01, (43)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>6.0e+00, 1.4e-06, (32)</td>\n",
       "      <td>0.0e+00, 8.0e-07, (32)</td>\n",
       "      <td>0.0e+00, 8.0e-07, (32)</td>\n",
       "      <td>0.0e+00, 8.0e-07, (32)</td>\n",
       "      <td>9.5e+01, 1.6e-03, (32)</td>\n",
       "      <td>7.1e+01, 3.1e-04, (32)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>2.4e+02, 7.1e-01, (32)</td>\n",
       "      <td>2.4e+02, 7.1e-01, (32)</td>\n",
       "      <td>2.4e+02, 5.9e-01, (32)</td>\n",
       "      <td>2.3e+02, 5.5e-01, (32)</td>\n",
       "      <td>6.0e+00, 9.0e-06, (32)</td>\n",
       "      <td>1.4e+02, 2.3e-02, (32)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>1.6e+01, 3.5e-06, (32)</td>\n",
       "      <td>0.0e+00, 8.0e-07, (32)</td>\n",
       "      <td>0.0e+00, 8.0e-07, (32)</td>\n",
       "      <td>3.0e+00, 1.1e-06, (32)</td>\n",
       "      <td>5.0e+01, 9.9e-05, (32)</td>\n",
       "      <td>7.5e+01, 4.1e-04, (32)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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 bandpower  \\\n",
       "11 Hz                                                     8.5e-04 ± 8.0e-05 (44)   \n",
       "30 Hz                                                     5.3e-04 ± 6.3e-05 (34)   \n",
       "Baseline I                                                2.2e-03 ± 2.2e-04 (46)   \n",
       "Baseline II                                               2.2e-03 ± 2.3e-04 (32)   \n",
       "Normality 11 Hz                                                 2.1e+01, 2.6e-05   \n",
       "Normality 30 Hz                                                 3.1e+01, 1.9e-07   \n",
       "Normality Baseline I                                            4.3e+01, 5.8e-10   \n",
       "Normality Baseline II                                           1.4e+01, 1.1e-03   \n",
       "Paired summary 11 Hz 30 Hz             8.2e-04 ± 8.8e-05, 5.4e-04 ± 6.6e-05 (32)   \n",
       "Paired summary 11 Hz Baseline II       8.2e-04 ± 8.8e-05, 2.2e-03 ± 2.3e-04 (32)   \n",
       "Paired summary Baseline I 11 Hz        2.2e-03 ± 2.3e-04, 8.5e-04 ± 8.0e-05 (44)   \n",
       "Paired summary Baseline I 30 Hz        2.3e-03 ± 2.8e-04, 5.4e-04 ± 6.6e-05 (32)   \n",
       "Paired summary Baseline I Baseline II  2.3e-03 ± 2.8e-04, 2.2e-03 ± 2.3e-04 (32)   \n",
       "Paired summary Baseline II 30 Hz       2.2e-03 ± 2.3e-04, 5.4e-04 ± 6.6e-05 (32)   \n",
       "Wilcoxon 11 Hz 30 Hz                                      1.1e+02, 3.3e-03, (32)   \n",
       "Wilcoxon 11 Hz Baseline II                                1.2e+01, 2.5e-06, (32)   \n",
       "Wilcoxon Baseline I 11 Hz                                 3.5e+01, 7.9e-08, (44)   \n",
       "Wilcoxon Baseline I 30 Hz                                 6.0e+00, 1.4e-06, (32)   \n",
       "Wilcoxon Baseline I Baseline II                           2.4e+02, 7.1e-01, (32)   \n",
       "Wilcoxon Baseline II 30 Hz                                1.6e+01, 3.5e-06, (32)   \n",
       "\n",
       "                                                                  Theta relpower  \\\n",
       "11 Hz                                                     8.6e-02 ± 4.6e-03 (44)   \n",
       "30 Hz                                                     5.3e-02 ± 4.2e-03 (34)   \n",
       "Baseline I                                                2.6e-01 ± 1.6e-02 (46)   \n",
       "Baseline II                                               2.7e-01 ± 1.7e-02 (32)   \n",
       "Normality 11 Hz                                                 5.6e+00, 6.2e-02   \n",
       "Normality 30 Hz                                                 7.9e+00, 2.0e-02   \n",
       "Normality Baseline I                                            1.6e+00, 4.6e-01   \n",
       "Normality Baseline II                                           3.6e-01, 8.3e-01   \n",
       "Paired summary 11 Hz 30 Hz             8.4e-02 ± 4.5e-03, 5.5e-02 ± 4.2e-03 (32)   \n",
       "Paired summary 11 Hz Baseline II       8.4e-02 ± 4.5e-03, 2.7e-01 ± 1.7e-02 (32)   \n",
       "Paired summary Baseline I 11 Hz        2.6e-01 ± 1.6e-02, 8.6e-02 ± 4.6e-03 (44)   \n",
       "Paired summary Baseline I 30 Hz        2.7e-01 ± 1.8e-02, 5.5e-02 ± 4.2e-03 (32)   \n",
       "Paired summary Baseline I Baseline II  2.7e-01 ± 1.8e-02, 2.7e-01 ± 1.7e-02 (32)   \n",
       "Paired summary Baseline II 30 Hz       2.7e-01 ± 1.7e-02, 5.5e-02 ± 4.2e-03 (32)   \n",
       "Wilcoxon 11 Hz 30 Hz                                      4.1e+01, 3.0e-05, (32)   \n",
       "Wilcoxon 11 Hz Baseline II                                2.0e+00, 9.6e-07, (32)   \n",
       "Wilcoxon Baseline I 11 Hz                                 1.0e+00, 8.2e-09, (44)   \n",
       "Wilcoxon Baseline I 30 Hz                                 0.0e+00, 8.0e-07, (32)   \n",
       "Wilcoxon Baseline I Baseline II                           2.4e+02, 7.1e-01, (32)   \n",
       "Wilcoxon Baseline II 30 Hz                                0.0e+00, 8.0e-07, (32)   \n",
       "\n",
       "                                                                   Theta relpeak  \\\n",
       "11 Hz                                                     2.3e-01 ± 5.2e-02 (44)   \n",
       "30 Hz                                                     3.0e-02 ± 5.3e-02 (34)   \n",
       "Baseline I                                                6.5e+00 ± 6.7e-01 (46)   \n",
       "Baseline II                                               6.3e+00 ± 7.4e-01 (32)   \n",
       "Normality 11 Hz                                                 1.2e+01, 2.3e-03   \n",
       "Normality 30 Hz                                                 1.2e+01, 2.3e-03   \n",
       "Normality Baseline I                                            2.1e+00, 3.4e-01   \n",
       "Normality Baseline II                                           4.9e+00, 8.8e-02   \n",
       "Paired summary 11 Hz 30 Hz             2.0e-01 ± 5.0e-02, 4.9e-02 ± 5.4e-02 (32)   \n",
       "Paired summary 11 Hz Baseline II       2.0e-01 ± 5.0e-02, 6.3e+00 ± 7.4e-01 (32)   \n",
       "Paired summary Baseline I 11 Hz        6.3e+00 ± 6.7e-01, 2.3e-01 ± 5.2e-02 (44)   \n",
       "Paired summary Baseline I 30 Hz        6.6e+00 ± 8.1e-01, 4.9e-02 ± 5.4e-02 (32)   \n",
       "Paired summary Baseline I Baseline II  6.6e+00 ± 8.1e-01, 6.3e+00 ± 7.4e-01 (32)   \n",
       "Paired summary Baseline II 30 Hz       6.3e+00 ± 7.4e-01, 4.9e-02 ± 5.4e-02 (32)   \n",
       "Wilcoxon 11 Hz 30 Hz                                      1.3e+02, 1.4e-02, (32)   \n",
       "Wilcoxon 11 Hz Baseline II                                0.0e+00, 8.0e-07, (32)   \n",
       "Wilcoxon Baseline I 11 Hz                                 2.0e+00, 8.7e-09, (44)   \n",
       "Wilcoxon Baseline I 30 Hz                                 0.0e+00, 8.0e-07, (32)   \n",
       "Wilcoxon Baseline I Baseline II                           2.4e+02, 5.9e-01, (32)   \n",
       "Wilcoxon Baseline II 30 Hz                                0.0e+00, 8.0e-07, (32)   \n",
       "\n",
       "                                                                      Theta peak  \\\n",
       "11 Hz                                                     3.2e-04 ± 3.1e-05 (44)   \n",
       "30 Hz                                                     1.9e-04 ± 2.7e-05 (34)   \n",
       "Baseline I                                                1.7e-03 ± 1.8e-04 (46)   \n",
       "Baseline II                                               1.6e-03 ± 2.1e-04 (32)   \n",
       "Normality 11 Hz                                                 2.4e+01, 5.3e-06   \n",
       "Normality 30 Hz                                                 3.8e+01, 5.3e-09   \n",
       "Normality Baseline I                                            3.2e+01, 1.3e-07   \n",
       "Normality Baseline II                                           2.5e+01, 3.4e-06   \n",
       "Paired summary 11 Hz 30 Hz             3.0e-04 ± 3.1e-05, 1.9e-04 ± 2.8e-05 (32)   \n",
       "Paired summary 11 Hz Baseline II       3.0e-04 ± 3.1e-05, 1.6e-03 ± 2.1e-04 (32)   \n",
       "Paired summary Baseline I 11 Hz        1.6e-03 ± 1.9e-04, 3.2e-04 ± 3.1e-05 (44)   \n",
       "Paired summary Baseline I 30 Hz        1.7e-03 ± 2.3e-04, 1.9e-04 ± 2.8e-05 (32)   \n",
       "Paired summary Baseline I Baseline II  1.7e-03 ± 2.3e-04, 1.6e-03 ± 2.1e-04 (32)   \n",
       "Paired summary Baseline II 30 Hz       1.6e-03 ± 2.1e-04, 1.9e-04 ± 2.8e-05 (32)   \n",
       "Wilcoxon 11 Hz 30 Hz                                      1.2e+02, 5.6e-03, (32)   \n",
       "Wilcoxon 11 Hz Baseline II                                3.0e+00, 1.1e-06, (32)   \n",
       "Wilcoxon Baseline I 11 Hz                                 3.0e+00, 9.4e-09, (44)   \n",
       "Wilcoxon Baseline I 30 Hz                                 0.0e+00, 8.0e-07, (32)   \n",
       "Wilcoxon Baseline I Baseline II                           2.3e+02, 5.5e-01, (32)   \n",
       "Wilcoxon Baseline II 30 Hz                                3.0e+00, 1.1e-06, (32)   \n",
       "\n",
       "                                                                      Theta freq  \\\n",
       "11 Hz                                                     7.7e+00 ± 1.3e-01 (44)   \n",
       "30 Hz                                                     7.4e+00 ± 1.4e-01 (34)   \n",
       "Baseline I                                                7.8e+00 ± 4.0e-02 (46)   \n",
       "Baseline II                                               8.1e+00 ± 4.7e-02 (32)   \n",
       "Normality 11 Hz                                                 1.9e+00, 3.9e-01   \n",
       "Normality 30 Hz                                                 7.2e+00, 2.8e-02   \n",
       "Normality Baseline I                                            5.9e+00, 5.3e-02   \n",
       "Normality Baseline II                                           4.7e+00, 9.7e-02   \n",
       "Paired summary 11 Hz 30 Hz             7.6e+00 ± 1.5e-01, 7.3e+00 ± 1.4e-01 (32)   \n",
       "Paired summary 11 Hz Baseline II       7.6e+00 ± 1.5e-01, 8.1e+00 ± 4.7e-02 (32)   \n",
       "Paired summary Baseline I 11 Hz        7.8e+00 ± 4.2e-02, 7.7e+00 ± 1.3e-01 (44)   \n",
       "Paired summary Baseline I 30 Hz        7.8e+00 ± 4.4e-02, 7.3e+00 ± 1.4e-01 (32)   \n",
       "Paired summary Baseline I Baseline II  7.8e+00 ± 4.4e-02, 8.1e+00 ± 4.7e-02 (32)   \n",
       "Paired summary Baseline II 30 Hz       8.1e+00 ± 4.7e-02, 7.3e+00 ± 1.4e-01 (32)   \n",
       "Wilcoxon 11 Hz 30 Hz                                      1.2e+02, 4.5e-02, (32)   \n",
       "Wilcoxon 11 Hz Baseline II                                1.2e+02, 9.3e-03, (32)   \n",
       "Wilcoxon Baseline I 11 Hz                                 3.6e+02, 4.7e-01, (44)   \n",
       "Wilcoxon Baseline I 30 Hz                                 9.5e+01, 1.6e-03, (32)   \n",
       "Wilcoxon Baseline I Baseline II                           6.0e+00, 9.0e-06, (32)   \n",
       "Wilcoxon Baseline II 30 Hz                                5.0e+01, 9.9e-05, (32)   \n",
       "\n",
       "                                                                Theta half width  \\\n",
       "11 Hz                                                     1.7e+00 ± 3.0e-01 (43)   \n",
       "30 Hz                                                     1.2e+01 ± 1.9e+00 (34)   \n",
       "Baseline I                                                7.7e-01 ± 1.8e-02 (46)   \n",
       "Baseline II                                               8.4e-01 ± 3.2e-02 (32)   \n",
       "Normality 11 Hz                                                 1.0e+01, 5.5e-03   \n",
       "Normality 30 Hz                                                 1.9e+02, 2.2e-41   \n",
       "Normality Baseline I                                            1.9e+00, 3.8e-01   \n",
       "Normality Baseline II                                           1.6e+01, 2.8e-04   \n",
       "Paired summary 11 Hz 30 Hz             1.5e+00 ± 3.5e-01, 1.1e+01 ± 2.0e+00 (31)   \n",
       "Paired summary 11 Hz Baseline II       1.5e+00 ± 3.5e-01, 8.6e-01 ± 2.5e-02 (31)   \n",
       "Paired summary Baseline I 11 Hz        7.8e-01 ± 1.8e-02, 1.7e+00 ± 3.0e-01 (43)   \n",
       "Paired summary Baseline I 30 Hz        7.6e-01 ± 2.0e-02, 1.2e+01 ± 2.0e+00 (32)   \n",
       "Paired summary Baseline I Baseline II  7.6e-01 ± 2.0e-02, 8.4e-01 ± 3.2e-02 (32)   \n",
       "Paired summary Baseline II 30 Hz       8.4e-01 ± 3.2e-02, 1.2e+01 ± 2.0e+00 (32)   \n",
       "Wilcoxon 11 Hz 30 Hz                                      6.7e+01, 3.9e-04, (31)   \n",
       "Wilcoxon 11 Hz Baseline II                                2.3e+02, 6.7e-01, (31)   \n",
       "Wilcoxon Baseline I 11 Hz                                 4.3e+02, 6.2e-01, (43)   \n",
       "Wilcoxon Baseline I 30 Hz                                 7.1e+01, 3.1e-04, (32)   \n",
       "Wilcoxon Baseline I Baseline II                           1.4e+02, 2.3e-02, (32)   \n",
       "Wilcoxon Baseline II 30 Hz                                7.5e+01, 4.1e-04, (32)   \n",
       "\n",
       "                                                                  Stim bandpower  \\\n",
       "11 Hz                                                     9.6e-04 ± 8.0e-05 (44)   \n",
       "30 Hz                                                     1.8e-03 ± 3.3e-04 (34)   \n",
       "Baseline I                                                         nan ± nan (0)   \n",
       "Baseline II                                                        nan ± nan (0)   \n",
       "Normality 11 Hz                                                 1.7e+01, 1.8e-04   \n",
       "Normality 30 Hz                                                 1.7e+01, 1.7e-04   \n",
       "Normality Baseline I                                                         NaN   \n",
       "Normality Baseline II                                                        NaN   \n",
       "Paired summary 11 Hz 30 Hz             9.7e-04 ± 9.3e-05, 1.8e-03 ± 3.4e-04 (32)   \n",
       "Paired summary 11 Hz Baseline II                        nan ± nan, nan ± nan (0)   \n",
       "Paired summary Baseline I 11 Hz                         nan ± nan, nan ± nan (0)   \n",
       "Paired summary Baseline I 30 Hz                         nan ± nan, nan ± nan (0)   \n",
       "Paired summary Baseline I Baseline II                   nan ± nan, nan ± nan (0)   \n",
       "Paired summary Baseline II 30 Hz                        nan ± nan, nan ± nan (0)   \n",
       "Wilcoxon 11 Hz 30 Hz                                      2.1e+02, 2.9e-01, (32)   \n",
       "Wilcoxon 11 Hz Baseline II                                                   NaN   \n",
       "Wilcoxon Baseline I 11 Hz                                                    NaN   \n",
       "Wilcoxon Baseline I 30 Hz                                                    NaN   \n",
       "Wilcoxon Baseline I Baseline II                                              NaN   \n",
       "Wilcoxon Baseline II 30 Hz                                                   NaN   \n",
       "\n",
       "                                                                   Stim relpower  \\\n",
       "11 Hz                                                     1.1e-01 ± 7.4e-03 (44)   \n",
       "30 Hz                                                     1.6e-01 ± 2.3e-02 (34)   \n",
       "Baseline I                                                         nan ± nan (0)   \n",
       "Baseline II                                                        nan ± nan (0)   \n",
       "Normality 11 Hz                                                 3.9e+00, 1.4e-01   \n",
       "Normality 30 Hz                                                 1.5e+01, 4.8e-04   \n",
       "Normality Baseline I                                                         NaN   \n",
       "Normality Baseline II                                                        NaN   \n",
       "Paired summary 11 Hz 30 Hz             1.1e-01 ± 8.6e-03, 1.6e-01 ± 2.4e-02 (32)   \n",
       "Paired summary 11 Hz Baseline II                        nan ± nan, nan ± nan (0)   \n",
       "Paired summary Baseline I 11 Hz                         nan ± nan, nan ± nan (0)   \n",
       "Paired summary Baseline I 30 Hz                         nan ± nan, nan ± nan (0)   \n",
       "Paired summary Baseline I Baseline II                   nan ± nan, nan ± nan (0)   \n",
       "Paired summary Baseline II 30 Hz                        nan ± nan, nan ± nan (0)   \n",
       "Wilcoxon 11 Hz 30 Hz                                      2.0e+02, 2.2e-01, (32)   \n",
       "Wilcoxon 11 Hz Baseline II                                                   NaN   \n",
       "Wilcoxon Baseline I 11 Hz                                                    NaN   \n",
       "Wilcoxon Baseline I 30 Hz                                                    NaN   \n",
       "Wilcoxon Baseline I Baseline II                                              NaN   \n",
       "Wilcoxon Baseline II 30 Hz                                                   NaN   \n",
       "\n",
       "                                                                    Stim relpeak  \\\n",
       "11 Hz                                                     1.6e+01 ± 1.9e+00 (44)   \n",
       "30 Hz                                                     8.6e+01 ± 1.4e+01 (34)   \n",
       "Baseline I                                                         nan ± nan (0)   \n",
       "Baseline II                                                        nan ± nan (0)   \n",
       "Normality 11 Hz                                                 5.8e+00, 5.6e-02   \n",
       "Normality 30 Hz                                                 6.2e+00, 4.5e-02   \n",
       "Normality Baseline I                                                         NaN   \n",
       "Normality Baseline II                                                        NaN   \n",
       "Paired summary 11 Hz 30 Hz             1.8e+01 ± 2.4e+00, 8.5e+01 ± 1.4e+01 (32)   \n",
       "Paired summary 11 Hz Baseline II                        nan ± nan, nan ± nan (0)   \n",
       "Paired summary Baseline I 11 Hz                         nan ± nan, nan ± nan (0)   \n",
       "Paired summary Baseline I 30 Hz                         nan ± nan, nan ± nan (0)   \n",
       "Paired summary Baseline I Baseline II                   nan ± nan, nan ± nan (0)   \n",
       "Paired summary Baseline II 30 Hz                        nan ± nan, nan ± nan (0)   \n",
       "Wilcoxon 11 Hz 30 Hz                                      3.0e+01, 1.2e-05, (32)   \n",
       "Wilcoxon 11 Hz Baseline II                                                   NaN   \n",
       "Wilcoxon Baseline I 11 Hz                                                    NaN   \n",
       "Wilcoxon Baseline I 30 Hz                                                    NaN   \n",
       "Wilcoxon Baseline I Baseline II                                              NaN   \n",
       "Wilcoxon Baseline II 30 Hz                                                   NaN   \n",
       "\n",
       "                                                                 Stim half width  \\\n",
       "11 Hz                                                     3.4e-01 ± 2.5e-03 (44)   \n",
       "30 Hz                                                     3.0e-01 ± 2.3e-03 (23)   \n",
       "Baseline I                                                         nan ± nan (0)   \n",
       "Baseline II                                                        nan ± nan (0)   \n",
       "Normality 11 Hz                                                 1.6e+01, 4.2e-04   \n",
       "Normality 30 Hz                                                 6.1e-01, 7.4e-01   \n",
       "Normality Baseline I                                                         NaN   \n",
       "Normality Baseline II                                                        NaN   \n",
       "Paired summary 11 Hz 30 Hz             3.4e-01 ± 3.6e-03, 3.0e-01 ± 2.3e-03 (21)   \n",
       "Paired summary 11 Hz Baseline II                        nan ± nan, nan ± nan (0)   \n",
       "Paired summary Baseline I 11 Hz                         nan ± nan, nan ± nan (0)   \n",
       "Paired summary Baseline I 30 Hz                         nan ± nan, nan ± nan (0)   \n",
       "Paired summary Baseline I Baseline II                   nan ± nan, nan ± nan (0)   \n",
       "Paired summary Baseline II 30 Hz                        nan ± nan, nan ± nan (0)   \n",
       "Wilcoxon 11 Hz 30 Hz                                      0.0e+00, 9.5e-07, (21)   \n",
       "Wilcoxon 11 Hz Baseline II                                                   NaN   \n",
       "Wilcoxon Baseline I 11 Hz                                                    NaN   \n",
       "Wilcoxon Baseline I 30 Hz                                                    NaN   \n",
       "Wilcoxon Baseline I Baseline II                                              NaN   \n",
       "Wilcoxon Baseline II 30 Hz                                                   NaN   \n",
       "\n",
       "                                                                      Stim p max  \\\n",
       "11 Hz                                                     2.1e-03 ± 2.3e-04 (44)   \n",
       "30 Hz                                                     5.3e-03 ± 1.1e-03 (34)   \n",
       "Baseline I                                                         nan ± nan (0)   \n",
       "Baseline II                                                        nan ± nan (0)   \n",
       "Normality 11 Hz                                                 1.5e+01, 5.8e-04   \n",
       "Normality 30 Hz                                                 1.9e+01, 6.3e-05   \n",
       "Normality Baseline I                                                         NaN   \n",
       "Normality Baseline II                                                        NaN   \n",
       "Paired summary 11 Hz 30 Hz             2.1e-03 ± 2.7e-04, 5.4e-03 ± 1.1e-03 (32)   \n",
       "Paired summary 11 Hz Baseline II                        nan ± nan, nan ± nan (0)   \n",
       "Paired summary Baseline I 11 Hz                         nan ± nan, nan ± nan (0)   \n",
       "Paired summary Baseline I 30 Hz                         nan ± nan, nan ± nan (0)   \n",
       "Paired summary Baseline I Baseline II                   nan ± nan, nan ± nan (0)   \n",
       "Paired summary Baseline II 30 Hz                        nan ± nan, nan ± nan (0)   \n",
       "Wilcoxon 11 Hz 30 Hz                                      1.6e+02, 5.0e-02, (32)   \n",
       "Wilcoxon 11 Hz Baseline II                                                   NaN   \n",
       "Wilcoxon Baseline I 11 Hz                                                    NaN   \n",
       "Wilcoxon Baseline I 30 Hz                                                    NaN   \n",
       "Wilcoxon Baseline I Baseline II                                              NaN   \n",
       "Wilcoxon Baseline II 30 Hz                                                   NaN   \n",
       "\n",
       "                                                                   Stim strength  \n",
       "11 Hz                                                     3.3e+00 ± 3.8e-01 (44)  \n",
       "30 Hz                                                     1.3e+01 ± 2.8e+00 (34)  \n",
       "Baseline I                                                         nan ± nan (0)  \n",
       "Baseline II                                                        nan ± nan (0)  \n",
       "Normality 11 Hz                                                 1.2e+01, 2.3e-03  \n",
       "Normality 30 Hz                                                 4.3e+01, 5.1e-10  \n",
       "Normality Baseline I                                                         NaN  \n",
       "Normality Baseline II                                                        NaN  \n",
       "Paired summary 11 Hz 30 Hz             3.5e+00 ± 4.9e-01, 1.3e+01 ± 3.0e+00 (32)  \n",
       "Paired summary 11 Hz Baseline II                        nan ± nan, nan ± nan (0)  \n",
       "Paired summary Baseline I 11 Hz                         nan ± nan, nan ± nan (0)  \n",
       "Paired summary Baseline I 30 Hz                         nan ± nan, nan ± nan (0)  \n",
       "Paired summary Baseline I Baseline II                   nan ± nan, nan ± nan (0)  \n",
       "Paired summary Baseline II 30 Hz                        nan ± nan, nan ± nan (0)  \n",
       "Wilcoxon 11 Hz 30 Hz                                      8.8e+01, 1.0e-03, (32)  \n",
       "Wilcoxon 11 Hz Baseline II                                                   NaN  \n",
       "Wilcoxon Baseline I 11 Hz                                                    NaN  \n",
       "Wilcoxon Baseline I 30 Hz                                                    NaN  \n",
       "Wilcoxon Baseline I Baseline II                                              NaN  \n",
       "Wilcoxon Baseline II 30 Hz                                                   NaN  "
      ]
     },
     "execution_count": 22,
     "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",
    "        try:\n",
    "            stat.loc[f'Normality {c1}', Key] = normality(df, c1)\n",
    "        except:\n",
    "            stat.loc[f'Normality {c1}', Key] = np.nan\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",
    "            try:\n",
    "                stat.loc[f'Wilcoxon {c1} {c2}', Key] = wilcoxon(df, [c1, c2])\n",
    "            except:\n",
    "                stat.loc[f'Wilcoxon {c1} {c2}', Key] = np.nan\n",
    "#             stat.loc[f'Paired T {c1} {c2}', Key] = paired_t(df, [c1, c2])\n",
    "            stat.loc[f'Paired summary {c1} {c2}', Key] = summarize_wilcoxon(df, [c1, c2])\n",
    "\n",
    "stat.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stat.to_latex(output_path / \"statistics\" / f\"statistics.tex\")\n",
    "stat.to_csv(output_path / \"statistics\" / f\"statistics.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, result in results.items():\n",
    "    result.to_latex(output_path / \"statistics\" / f\"values_{key}.tex\")\n",
    "    result.to_csv(output_path / \"statistics\" / f\"values_{key}.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot PSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from septum_mec.analysis.plotting import plot_bootstrap_timeseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "freq = freqs.T.iloc[0].values\n",
    "\n",
    "mask = (freq < 49)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/fromnumeric.py:3373: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:170: 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:163: 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": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "action = project.require_action(\"stimulus-lfp-response\" + '-' + stim_loc + zscore_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_bandpower.tex',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_relpeak.tex',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_half_width.csv',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_bandpower.csv',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_relpower.csv',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_half_width.tex',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_energy.csv',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_freq.csv',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_p_max.tex',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_energy.tex',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_strength.csv',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_energy.csv',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_peak.tex',\n",
       " '/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/values_stim_relpower.tex',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_relpeak.tex',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_strength.tex',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_bandpower.csv',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_freq.tex',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_relpeak.csv',\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/statistics/values_stim_half_width.csv',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_relpower.tex',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_bandpower.tex',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_p_max.csv',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_relpeak.csv',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_peak.csv',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_relpower.csv',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_energy.tex',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_half_width.tex',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_relpower.png',\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_bandpower.svg',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_relpeak.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-stim_relpeak.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_bandpower.png',\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_relpower.svg',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_relpeak.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_relpower.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-stim_bandpower.png',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_bandpower.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-histogram-theta_relpower.svg',\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-theta_relpeak.png',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_strength.svg']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copy_tree(output_path, str(action.data_path()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "septum_mec.analysis.registration.store_notebook(action, \"20_stimulus-lfp-response.ipynb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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