{
 "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": [
      "12:47:23 [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-mec\"\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": [
    "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": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "lfp_results['stim_strength'] = lfp_results['stim_p_max'] / lfp_results['theta_energy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "lfp_results = lfp_results.query('stim_location!=\"ms\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>action_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",
       "      <td>4</td>\n",
       "      <td>0.006686</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>1833-010719-1-1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.034550</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>1833-020719-1-0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.008427</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>1833-020719-1-1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.019033</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>1833-020719-3-0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.001076</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       action_side_a  channel_group  signal_to_noise  stim_strength\n",
       "68   1833-010719-1-0              4         0.006686            NaN\n",
       "66   1833-010719-1-1              2         0.034550            NaN\n",
       "580  1833-020719-1-0              4         0.008427            NaN\n",
       "578  1833-020719-1-1              2         0.019033            NaN\n",
       "372  1833-020719-3-0              4         0.001076            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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/lib/histograms.py:898: RuntimeWarning: invalid value encountered in true_divide\n",
      "  return n/db/n.sum(), bin_edges\n"
     ]
    },
    {
     "data": {
      "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": [
    "\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": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/stats/stats.py:5700: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  z = (bigu - meanrank) / sd\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/fromnumeric.py:3257: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/mikkel/apps/expipe-project/spike-statistics/spike_statistics/core.py:401: RuntimeWarning: invalid value encountered in less\n",
      "  pval = np.sum(diffs > observed_diff) / float(num_samples)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<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>0.20 ± 0.02 (8)</td>\n",
       "      <td>0.14 ± 0.02 (8)</td>\n",
       "      <td>7.99 ± 0.06 (8)</td>\n",
       "      <td>0.99 ± 0.10 (8)</td>\n",
       "      <td>0.15 ± 0.05 (8)</td>\n",
       "      <td>2.42 ± 1.36 (8)</td>\n",
       "      <td>0.40 ± 0.19 (8)</td>\n",
       "      <td>3.09 ± 2.06 (8)</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>nan ± nan (0)</td>\n",
       "      <td>nan ± nan (0)</td>\n",
       "      <td>nan ± nan (0)</td>\n",
       "      <td>nan ± nan (0)</td>\n",
       "      <td>nan ± nan (0)</td>\n",
       "      <td>nan ± nan (0)</td>\n",
       "      <td>nan ± nan (0)</td>\n",
       "      <td>nan ± nan (0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MWU Baseline I 11 Hz</th>\n",
       "      <td>248.00, 0.194</td>\n",
       "      <td>225.00, 0.447</td>\n",
       "      <td>141.50, 0.240</td>\n",
       "      <td>192.00, 0.855</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>0.04, 0.374</td>\n",
       "      <td>0.01, 0.821</td>\n",
       "      <td>0.20, 0.260</td>\n",
       "      <td>0.06, 0.666</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, 0.598</td>\n",
       "      <td>0.30, 0.010</td>\n",
       "      <td>0.05, 0.573</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>0.00, 0.000</td>\n",
       "      <td>0.00, 0.000</td>\n",
       "      <td>0.00, 0.000</td>\n",
       "      <td>0.00, 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>nan, 0.000</td>\n",
       "      <td>nan, 0.000</td>\n",
       "      <td>nan, 0.000</td>\n",
       "      <td>nan, 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>100.00, 0.255</td>\n",
       "      <td>121.00, 0.642</td>\n",
       "      <td>141.00, 0.885</td>\n",
       "      <td>128.00, 0.908</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>0.04, 0.355</td>\n",
       "      <td>0.00, 0.986</td>\n",
       "      <td>0.10, 0.495</td>\n",
       "      <td>0.11, 0.463</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>0.00, 0.000</td>\n",
       "      <td>0.00, 0.000</td>\n",
       "      <td>0.00, 0.000</td>\n",
       "      <td>0.00, 0.000</td>\n",
       "      <td>0.00, 0.000</td>\n",
       "      <td>0.00, 0.000</td>\n",
       "      <td>0.00, 0.000</td>\n",
       "      <td>0.00, 0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRS 11 Hz 30 Hz</th>\n",
       "      <td>nan, 0.000</td>\n",
       "      <td>nan, 0.000</td>\n",
       "      <td>nan, 0.000</td>\n",
       "      <td>nan, 0.000</td>\n",
       "      <td>nan, 0.000</td>\n",
       "      <td>nan, 0.000</td>\n",
       "      <td>nan, 0.000</td>\n",
       "      <td>nan, 0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MWU Baseline II 30 Hz</th>\n",
       "      <td>0.00, 0.000</td>\n",
       "      <td>0.00, 0.000</td>\n",
       "      <td>0.00, 0.000</td>\n",
       "      <td>0.00, 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>nan, 0.000</td>\n",
       "      <td>nan, 0.000</td>\n",
       "      <td>nan, 0.000</td>\n",
       "      <td>nan, 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                        0.20 ± 0.02 (8)   0.14 ± 0.02 (8)   \n",
       "Baseline II                 0.24 ± 0.02 (34)  0.17 ± 0.02 (34)   \n",
       "30 Hz                          nan ± nan (0)     nan ± nan (0)   \n",
       "MWU Baseline I 11 Hz           248.00, 0.194     225.00, 0.447   \n",
       "PRS Baseline I 11 Hz             0.04, 0.374       0.01, 0.821   \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, 0.598   \n",
       "MWU Baseline I 30 Hz             0.00, 0.000       0.00, 0.000   \n",
       "PRS Baseline I 30 Hz              nan, 0.000        nan, 0.000   \n",
       "MWU 11 Hz Baseline II          100.00, 0.255     121.00, 0.642   \n",
       "PRS 11 Hz Baseline II            0.04, 0.355       0.00, 0.986   \n",
       "MWU 11 Hz 30 Hz                  0.00, 0.000       0.00, 0.000   \n",
       "PRS 11 Hz 30 Hz                   nan, 0.000        nan, 0.000   \n",
       "MWU Baseline II 30 Hz            0.00, 0.000       0.00, 0.000   \n",
       "PRS Baseline II 30 Hz             nan, 0.000        nan, 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                        7.99 ± 0.06 (8)   0.99 ± 0.10 (8)   \n",
       "Baseline II                 7.96 ± 0.09 (34)  1.23 ± 0.22 (33)   \n",
       "30 Hz                          nan ± nan (0)     nan ± nan (0)   \n",
       "MWU Baseline I 11 Hz           141.50, 0.240     192.00, 0.855   \n",
       "PRS Baseline I 11 Hz             0.20, 0.260       0.06, 0.666   \n",
       "MWU Baseline I Baseline II     645.50, 0.108     805.00, 0.651   \n",
       "PRS Baseline I Baseline II       0.30, 0.010       0.05, 0.573   \n",
       "MWU Baseline I 30 Hz             0.00, 0.000       0.00, 0.000   \n",
       "PRS Baseline I 30 Hz              nan, 0.000        nan, 0.000   \n",
       "MWU 11 Hz Baseline II          141.00, 0.885     128.00, 0.908   \n",
       "PRS 11 Hz Baseline II            0.10, 0.495       0.11, 0.463   \n",
       "MWU 11 Hz 30 Hz                  0.00, 0.000       0.00, 0.000   \n",
       "PRS 11 Hz 30 Hz                   nan, 0.000        nan, 0.000   \n",
       "MWU Baseline II 30 Hz            0.00, 0.000       0.00, 0.000   \n",
       "PRS Baseline II 30 Hz             nan, 0.000        nan, 0.000   \n",
       "\n",
       "                                Stim energy  Stim half width       Stim p max  \\\n",
       "Baseline I                              NaN              NaN              NaN   \n",
       "11 Hz                       0.15 ± 0.05 (8)  2.42 ± 1.36 (8)  0.40 ± 0.19 (8)   \n",
       "Baseline II                             NaN              NaN              NaN   \n",
       "30 Hz                         nan ± nan (0)    nan ± nan (0)    nan ± nan (0)   \n",
       "MWU Baseline I 11 Hz                    NaN              NaN              NaN   \n",
       "PRS Baseline I 11 Hz                    NaN              NaN              NaN   \n",
       "MWU Baseline I Baseline II              NaN              NaN              NaN   \n",
       "PRS Baseline I Baseline II              NaN              NaN              NaN   \n",
       "MWU Baseline I 30 Hz                    NaN              NaN              NaN   \n",
       "PRS Baseline I 30 Hz                    NaN              NaN              NaN   \n",
       "MWU 11 Hz Baseline II                   NaN              NaN              NaN   \n",
       "PRS 11 Hz Baseline II                   NaN              NaN              NaN   \n",
       "MWU 11 Hz 30 Hz                 0.00, 0.000      0.00, 0.000      0.00, 0.000   \n",
       "PRS 11 Hz 30 Hz                  nan, 0.000       nan, 0.000       nan, 0.000   \n",
       "MWU Baseline II 30 Hz                   NaN              NaN              NaN   \n",
       "PRS Baseline II 30 Hz                   NaN              NaN              NaN   \n",
       "\n",
       "                              Stim strength  \n",
       "Baseline I                              NaN  \n",
       "11 Hz                       3.09 ± 2.06 (8)  \n",
       "Baseline II                             NaN  \n",
       "30 Hz                         nan ± nan (0)  \n",
       "MWU Baseline I 11 Hz                    NaN  \n",
       "PRS Baseline I 11 Hz                    NaN  \n",
       "MWU Baseline I Baseline II              NaN  \n",
       "PRS Baseline I Baseline II              NaN  \n",
       "MWU Baseline I 30 Hz                    NaN  \n",
       "PRS Baseline I 30 Hz                    NaN  \n",
       "MWU 11 Hz Baseline II                   NaN  \n",
       "PRS 11 Hz Baseline II                   NaN  \n",
       "MWU 11 Hz 30 Hz                 0.00, 0.000  \n",
       "PRS 11 Hz 30 Hz                  nan, 0.000  \n",
       "MWU Baseline II 30 Hz                   NaN  \n",
       "PRS Baseline II 30 Hz                   NaN  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 17,
   "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": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "psd = pd.read_feather(pathlib.Path(\"output\") / \"stimulus-lfp-response\" / 'data' / 'psd.feather')\n",
    "freqs = pd.read_feather(pathlib.Path(\"output\") / \"stimulus-lfp-response\" / 'data' / 'freqs.feather')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from septum_mec.analysis.plotting import plot_bootstrap_timeseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "freq = freqs.T.iloc[0].values\n",
    "\n",
    "mask = (freq < 49)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:154: RuntimeWarning: invalid value encountered in true_divide\n",
      "  ret, rcount, out=ret, casting='unsafe', subok=False)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x300 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, axs = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(5,2))\n",
    "axs = axs.repeat(2)\n",
    "for i, (ax, query) in enumerate(zip(axs.ravel(), queries)):\n",
    "    selection = [\n",
    "        f'{r.action}_{r.channel_group}' \n",
    "        for i, r in lfp_results_hemisphere.query(query).iterrows()]\n",
    "    values = psd.loc[mask, selection].to_numpy()\n",
    "    values = 10 * np.log10(values)\n",
    "    plot_bootstrap_timeseries(freq[mask], values, ax=ax, lw=1, label=labels[i], color=colors[i])\n",
    "#     ax.set_title(titles[i])\n",
    "    ax.set_xlabel('Frequency Hz')\n",
    "    ax.legend(frameon=False)\n",
    "axs[0].set_ylabel('PSD (dB/Hz)')\n",
    "axs[0].set_ylim(-31, 1)\n",
    "despine()\n",
    "\n",
    "figname = 'lfp-psd'\n",
    "fig.savefig(\n",
    "    output_path / 'figures' / f'{figname}.png', \n",
    "    bbox_inches='tight', transparent=True)\n",
    "fig.savefig(\n",
    "    output_path / 'figures' / f'{figname}.svg', \n",
    "    bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Store results in Expipe action"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "action = project.require_action(\"stimulus-lfp-response-mec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/statistics/statistics.tex',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/statistics/statistics.csv',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-stim_energy.png',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-stim_strength.png',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd.png',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-theta_peak.svg',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-stim_p_max.png',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-theta_freq.png',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-theta_energy.svg',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-theta_freq.svg',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-stim_half_width.png',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-stim_half_width.svg',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-theta_half_width.svg',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-theta_energy.png',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-theta_peak.png',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-stim_p_max.svg',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-theta_half_width.png',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd.svg',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-stim_energy.svg',\n",
       " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-stim_strength.svg']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copy_tree(output_path, str(action.data_path()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "septum_mec.analysis.registration.store_notebook(action, \"20_stimulus-lfp-response-mec.ipynb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}