septum-mec/actions/stimulus-lfp-response/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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "12:50:36 [I] klustakwik KlustaKwik2 version 0.2.6\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import expipe\n",
    "import pathlib\n",
    "import numpy as np\n",
    "import spatial_maps.stats as stats\n",
    "import septum_mec\n",
    "import septum_mec.analysis.data_processing as dp\n",
    "import septum_mec.analysis.registration\n",
    "import head_direction.head as head\n",
    "import spatial_maps as sp\n",
    "import speed_cells.speed as spd\n",
    "import re\n",
    "import joblib\n",
    "import multiprocessing\n",
    "import shutil\n",
    "import psutil\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "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\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": [
    "%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\"\n",
    "(output_path / \"statistics\").mkdir(exist_ok=True, parents=True)\n",
    "(output_path / \"figures\").mkdir(exist_ok=True, parents=True)\n",
    "output_path.mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_loader = dp.Data()\n",
    "actions = data_loader.actions\n",
    "project = data_loader.project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "identify_neurons = actions['identify-neurons']\n",
    "sessions = pd.read_csv(identify_neurons.data_path('sessions'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "lfp_action = actions['stimulus-lfp-response']\n",
    "lfp_results = pd.read_csv(lfp_action.data_path('results'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "lfp_results = pd.merge(sessions, lfp_results, how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "lfp_results = lfp_results.query('stim_location!=\"mecl\" and stim_location!=\"mecr\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "lfp_results['stim_strength'] = lfp_results['stim_p_max'] / lfp_results['theta_energy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>68</th>\n",
       "      <td>1833-010719-1-0</td>\n",
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       "      <th>694</th>\n",
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       "      <td>6</td>\n",
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       "      <th>691</th>\n",
       "      <td>1833-010719-2-1</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>580</th>\n",
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       "      <td>4</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       action_side_a  channel_group  signal_to_noise  stim_strength\n",
       "68   1833-010719-1-0              4         0.006686            NaN\n",
       "66   1833-010719-1-1              2         0.034550            NaN\n",
       "694  1833-010719-2-0              6         0.004609       7.173297\n",
       "691  1833-010719-2-1              3         0.003974       6.446883\n",
       "580  1833-020719-1-0              4         0.008427            NaN"
      ]
     },
     "execution_count": 11,
     "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",
    ").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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = ['#1b9e77','#d95f02','#7570b3','#e7298a']\n",
    "labels = ['Baseline I', '11 Hz', 'Baseline II', '30 Hz']\n",
    "queries = ['baseline and Hz11', 'frequency==11', 'baseline and Hz30', 'frequency==30']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "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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\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",
    "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",
    "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] = pd.DataFrame()\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)[key]\n",
    "        results[key] = pd.concat([results[key], values.rename(label).reset_index(drop=True)], axis=1)\n",
    "        values.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(-0.05, 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": 14,
   "metadata": {
    "scrolled": false
   },
   "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",
    "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",
    "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] = pd.DataFrame()\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)[key]\n",
    "        results[key] = pd.concat([results[key], values.rename(label).reset_index(drop=True)], axis=1)\n",
    "        values.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(-0.05, bins[key].max() - bins[key].max()*0.02)\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": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def summarize(data):\n",
    "    return \"{:.2f} ± {:.2f} ({})\".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 rename(name):\n",
    "    return name.replace(\"_field\", \"-field\").replace(\"_\", \" \").capitalize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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 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>Baseline I</th>\n",
       "      <td>0.25 ± 0.02 (48)</td>\n",
       "      <td>0.18 ± 0.02 (48)</td>\n",
       "      <td>7.78 ± 0.09 (48)</td>\n",
       "      <td>1.79 ± 0.33 (46)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11 Hz</th>\n",
       "      <td>0.08 ± 0.00 (44)</td>\n",
       "      <td>0.03 ± 0.00 (44)</td>\n",
       "      <td>7.55 ± 0.12 (44)</td>\n",
       "      <td>5.80 ± 0.50 (43)</td>\n",
       "      <td>0.05 ± 0.00 (44)</td>\n",
       "      <td>0.16 ± 0.00 (44)</td>\n",
       "      <td>0.51 ± 0.04 (44)</td>\n",
       "      <td>7.14 ± 0.81 (44)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baseline II</th>\n",
       "      <td>0.24 ± 0.02 (34)</td>\n",
       "      <td>0.17 ± 0.02 (34)</td>\n",
       "      <td>7.96 ± 0.09 (34)</td>\n",
       "      <td>1.23 ± 0.22 (33)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30 Hz</th>\n",
       "      <td>0.04 ± 0.00 (34)</td>\n",
       "      <td>0.02 ± 0.00 (34)</td>\n",
       "      <td>7.74 ± 0.19 (34)</td>\n",
       "      <td>5.88 ± 0.64 (31)</td>\n",
       "      <td>0.16 ± 0.02 (34)</td>\n",
       "      <td>0.14 ± 0.00 (34)</td>\n",
       "      <td>1.54 ± 0.16 (34)</td>\n",
       "      <td>45.30 ± 6.54 (34)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MWU Baseline I 11 Hz</th>\n",
       "      <td>2075.00, 0.000</td>\n",
       "      <td>2102.00, 0.000</td>\n",
       "      <td>1201.50, 0.256</td>\n",
       "      <td>253.00, 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>PRS Baseline I 11 Hz</th>\n",
       "      <td>0.15, 0.000</td>\n",
       "      <td>0.12, 0.000</td>\n",
       "      <td>0.05, 0.686</td>\n",
       "      <td>4.74, 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>MWU Baseline I Baseline II</th>\n",
       "      <td>860.00, 0.682</td>\n",
       "      <td>850.00, 0.753</td>\n",
       "      <td>645.50, 0.108</td>\n",
       "      <td>805.00, 0.651</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRS Baseline I Baseline II</th>\n",
       "      <td>0.00, 0.955</td>\n",
       "      <td>0.01, 0.597</td>\n",
       "      <td>0.30, 0.009</td>\n",
       "      <td>0.05, 0.584</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MWU Baseline I 30 Hz</th>\n",
       "      <td>1629.00, 0.000</td>\n",
       "      <td>1629.00, 0.000</td>\n",
       "      <td>781.50, 0.749</td>\n",
       "      <td>225.00, 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>PRS Baseline I 30 Hz</th>\n",
       "      <td>0.19, 0.000</td>\n",
       "      <td>0.13, 0.000</td>\n",
       "      <td>0.20, 0.416</td>\n",
       "      <td>4.84, 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>MWU 11 Hz Baseline II</th>\n",
       "      <td>22.00, 0.000</td>\n",
       "      <td>10.00, 0.000</td>\n",
       "      <td>524.50, 0.024</td>\n",
       "      <td>1328.00, 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>PRS 11 Hz Baseline II</th>\n",
       "      <td>0.15, 0.000</td>\n",
       "      <td>0.12, 0.000</td>\n",
       "      <td>0.35, 0.020</td>\n",
       "      <td>4.79, 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>MWU 11 Hz 30 Hz</th>\n",
       "      <td>1299.00, 0.000</td>\n",
       "      <td>1275.00, 0.000</td>\n",
       "      <td>657.00, 0.361</td>\n",
       "      <td>664.00, 0.983</td>\n",
       "      <td>248.00, 0.000</td>\n",
       "      <td>1408.00, 0.000</td>\n",
       "      <td>236.00, 0.000</td>\n",
       "      <td>202.00, 0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRS 11 Hz 30 Hz</th>\n",
       "      <td>0.04, 0.000</td>\n",
       "      <td>0.01, 0.000</td>\n",
       "      <td>0.25, 0.510</td>\n",
       "      <td>0.10, 0.909</td>\n",
       "      <td>0.11, 0.000</td>\n",
       "      <td>0.02, 0.000</td>\n",
       "      <td>1.09, 0.000</td>\n",
       "      <td>31.00, 0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MWU Baseline II 30 Hz</th>\n",
       "      <td>1154.00, 0.000</td>\n",
       "      <td>1154.00, 0.000</td>\n",
       "      <td>604.00, 0.754</td>\n",
       "      <td>108.00, 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>PRS Baseline II 30 Hz</th>\n",
       "      <td>0.19, 0.000</td>\n",
       "      <td>0.13, 0.000</td>\n",
       "      <td>0.10, 0.577</td>\n",
       "      <td>4.89, 0.000</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        Theta peak  \\\n",
       "Baseline I                  0.25 ± 0.02 (48)  0.18 ± 0.02 (48)   \n",
       "11 Hz                       0.08 ± 0.00 (44)  0.03 ± 0.00 (44)   \n",
       "Baseline II                 0.24 ± 0.02 (34)  0.17 ± 0.02 (34)   \n",
       "30 Hz                       0.04 ± 0.00 (34)  0.02 ± 0.00 (34)   \n",
       "MWU Baseline I 11 Hz          2075.00, 0.000    2102.00, 0.000   \n",
       "PRS Baseline I 11 Hz             0.15, 0.000       0.12, 0.000   \n",
       "MWU Baseline I Baseline II     860.00, 0.682     850.00, 0.753   \n",
       "PRS Baseline I Baseline II       0.00, 0.955       0.01, 0.597   \n",
       "MWU Baseline I 30 Hz          1629.00, 0.000    1629.00, 0.000   \n",
       "PRS Baseline I 30 Hz             0.19, 0.000       0.13, 0.000   \n",
       "MWU 11 Hz Baseline II           22.00, 0.000      10.00, 0.000   \n",
       "PRS 11 Hz Baseline II            0.15, 0.000       0.12, 0.000   \n",
       "MWU 11 Hz 30 Hz               1299.00, 0.000    1275.00, 0.000   \n",
       "PRS 11 Hz 30 Hz                  0.04, 0.000       0.01, 0.000   \n",
       "MWU Baseline II 30 Hz         1154.00, 0.000    1154.00, 0.000   \n",
       "PRS Baseline II 30 Hz            0.19, 0.000       0.13, 0.000   \n",
       "\n",
       "                                  Theta freq  Theta half width  \\\n",
       "Baseline I                  7.78 ± 0.09 (48)  1.79 ± 0.33 (46)   \n",
       "11 Hz                       7.55 ± 0.12 (44)  5.80 ± 0.50 (43)   \n",
       "Baseline II                 7.96 ± 0.09 (34)  1.23 ± 0.22 (33)   \n",
       "30 Hz                       7.74 ± 0.19 (34)  5.88 ± 0.64 (31)   \n",
       "MWU Baseline I 11 Hz          1201.50, 0.256     253.00, 0.000   \n",
       "PRS Baseline I 11 Hz             0.05, 0.686       4.74, 0.000   \n",
       "MWU Baseline I Baseline II     645.50, 0.108     805.00, 0.651   \n",
       "PRS Baseline I Baseline II       0.30, 0.009       0.05, 0.584   \n",
       "MWU Baseline I 30 Hz           781.50, 0.749     225.00, 0.000   \n",
       "PRS Baseline I 30 Hz             0.20, 0.416       4.84, 0.000   \n",
       "MWU 11 Hz Baseline II          524.50, 0.024    1328.00, 0.000   \n",
       "PRS 11 Hz Baseline II            0.35, 0.020       4.79, 0.000   \n",
       "MWU 11 Hz 30 Hz                657.00, 0.361     664.00, 0.983   \n",
       "PRS 11 Hz 30 Hz                  0.25, 0.510       0.10, 0.909   \n",
       "MWU Baseline II 30 Hz          604.00, 0.754     108.00, 0.000   \n",
       "PRS Baseline II 30 Hz            0.10, 0.577       4.89, 0.000   \n",
       "\n",
       "                                 Stim energy   Stim half width  \\\n",
       "Baseline I                               NaN               NaN   \n",
       "11 Hz                       0.05 ± 0.00 (44)  0.16 ± 0.00 (44)   \n",
       "Baseline II                              NaN               NaN   \n",
       "30 Hz                       0.16 ± 0.02 (34)  0.14 ± 0.00 (34)   \n",
       "MWU Baseline I 11 Hz                     NaN               NaN   \n",
       "PRS Baseline I 11 Hz                     NaN               NaN   \n",
       "MWU Baseline I Baseline II               NaN               NaN   \n",
       "PRS Baseline I Baseline II               NaN               NaN   \n",
       "MWU Baseline I 30 Hz                     NaN               NaN   \n",
       "PRS Baseline I 30 Hz                     NaN               NaN   \n",
       "MWU 11 Hz Baseline II                    NaN               NaN   \n",
       "PRS 11 Hz Baseline II                    NaN               NaN   \n",
       "MWU 11 Hz 30 Hz                248.00, 0.000    1408.00, 0.000   \n",
       "PRS 11 Hz 30 Hz                  0.11, 0.000       0.02, 0.000   \n",
       "MWU Baseline II 30 Hz                    NaN               NaN   \n",
       "PRS Baseline II 30 Hz                    NaN               NaN   \n",
       "\n",
       "                                  Stim p max      Stim strength  \n",
       "Baseline I                               NaN                NaN  \n",
       "11 Hz                       0.51 ± 0.04 (44)   7.14 ± 0.81 (44)  \n",
       "Baseline II                              NaN                NaN  \n",
       "30 Hz                       1.54 ± 0.16 (34)  45.30 ± 6.54 (34)  \n",
       "MWU Baseline I 11 Hz                     NaN                NaN  \n",
       "PRS Baseline I 11 Hz                     NaN                NaN  \n",
       "MWU Baseline I Baseline II               NaN                NaN  \n",
       "PRS Baseline I Baseline II               NaN                NaN  \n",
       "MWU Baseline I 30 Hz                     NaN                NaN  \n",
       "PRS Baseline I 30 Hz                     NaN                NaN  \n",
       "MWU 11 Hz Baseline II                    NaN                NaN  \n",
       "PRS 11 Hz Baseline II                    NaN                NaN  \n",
       "MWU 11 Hz 30 Hz                236.00, 0.000      202.00, 0.000  \n",
       "PRS 11 Hz 30 Hz                  1.09, 0.000       31.00, 0.000  \n",
       "MWU Baseline II 30 Hz                    NaN                NaN  \n",
       "PRS Baseline II 30 Hz                    NaN                NaN  "
      ]
     },
     "execution_count": 17,
     "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",
    "        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",
    "\n",
    "stat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "psd = pd.read_feather(output_path / 'data' / 'psd.feather')\n",
    "freqs = pd.read_feather(output_path / 'data' / 'freqs.feather')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from septum_mec.analysis.plotting import plot_bootstrap_timeseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "freq = freqs.T.iloc[0].values\n",
    "\n",
    "mask = (freq < 49)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "action = project.require_action(\"stimulus-lfp-response\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "copy_tree(output_path, str(action.data_path()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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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