{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "13:25:29 [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": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "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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zuUPWLF7Iq6dtVj6hVNWypgH8ZqP8WTYlTZdc3lBzw4h0e404NWnfxOtSVaHcc+u+JdIAYbx1kVMjKy2UiIhIPTViwF5TPslaqjfzi4hIFZnZcOC1IqdbCZNFvU5Y+vNydy/V6tiQ4qIqUwnTxw5NHH+AMBroInc/t/DV9WVm0wiz2D3o7mPrW5rGDNjJoR2lpvfrE7dLy2Xo7gXnS4417zGVF01EpGqeIyw6ktOD8IhwB+BTwGQz29fdn61H4aT+GjVgryAs+1dqDdrcs+t3ql6iMrb60XM096/po/Rub/GSVXDJk2sdO/3UnejbtwcfrFjKv8+ofO2FaftPYqP1+5RPKFJd34id+NYSp2i+jrAu9y1mNjrOSZF1xxIeaXZ+MoNuouECtru3mJkDOwHDC6Uxs56EWc8AZteoaEU19x9CjwEbl0zT0tLK0qUra1Sirm/Feuv+Lgdsuin9+/dizfLFvNu3Z4GrChs6bASDe2spY2lM7r7QzI4D5hFGxRxAWFEs0/JXYJPyGi5gR48RAvYehLWP8+1KKPtywrq4DW/p0pVc+L176l0MEcmgGLSfAz5DaCLPfMCW9mvUgH0TYZa0I83sLHdflHf+a3H7O81yJjnvLV/Cih6rWLS8bLcGkSzKNRt9lH/CzHoARwFfAj5NeJy4GniLMAPlZe6+TmukmR0InALsTnhe/gHhWfpNwLXuvk5TlpkNAL4JfJEwLe16wKuEdcEvd/f3K3kzhTqdJTrh/QvYHDgBOJm2vkbPEdYnn+bu63Q4TqtsjaquAdvMhhGeYSzNax65l7CM5p7AH8xsgrv/K06q8m3gaGAVcHGtyyyNa+xtl7GiR+HJVO4//D8Z1LvwCMCBvcqODJQOGjr17GZgUL3LkZJFbx7/4/aOYkmFmY0k1KxbyKtdm1kfYAZheleAOcCzhFUOt4k/E83s8+4+M3HdacDP4u5bhPW8hxCC6N7ABDPbz93XJK7ZjtBjfThhRM8rhCG22wPnAceZ2UHu/mIn33IT4bn9MYT1yWcTJtTaPf4YYd2K5O+hVmWrm6otr1mhXwEvxO3H4jenYwmzp30emGtm/yQ8w7mYMNzheHd/obbFlawa1Lsvg3v3L/izXlO9/xt0TUOnnj2BEAje6SI/b8X3VBNm1mxmg81sPHAX4e/1FHefm5f0O4RgvQDY1d1HuPsu7j6M8PjwbcI0zv+VyHsj2io8R7n7FvGaEcCBhEA3FpiQuKYfcCchIN4ODHN3c/dPEaalnkGYzOqO+CWiMzYBvkyoLQ9x988Qaty/jufPMLOPOw7VuGx106hN4rj7q2a2M2FFrkOBHQlDuP4EXOLuaSw4UFdnnL03/fqtX+9iZNJ7y5cw9rbL1jq2orkDU5VKNf0C2LDehUjRJoT3dHOV8r/fzEqdvxj4XoHj+xFq3t9398eTJ9z9cTP7H+BCwt/QHCMMm32PsJRx8pq7zWwKoR9Rskn8JEIz85PAEcmat7vPN7MJhArYNoQFnCofqlHYf7v7FYl7LDez0wktrD0IX0Zm1KlsdVHVgO3uw8ucH1vm/ALgW/Gn4ZXqCb5kybrH+/Vbn/79e1W7WF3Sih6rijZ/i2RU/jjsZmADQiDqBfwn0M/MTs8LSHvFkTPFJpHKdepIPvt5jfCMeyAwzcwuc/enE3n+oEA+h8ftjcn7J65ZZma3AGcA4+l8ULyzwD0Wmtm7hC9PG9WxbHXRsDXsLFJP8PZraW3hvRXt7yRWqGOZnlM3nMnAlYQ/rl3BO8CpVcy/2Djs9Qm1wivj/ZuBryfTxAWRBprZ7oShX1vH7c6EZ9mQeATq7u+Y2cWEFsxjgWPNbD6h/9DdwF0FljbeIW4nm9lhRd5DbqGF7cq+2/LmFTme62icjF+1LltdKGBLXb23Yimf/O0PU8kr95xaGsObx//45qFTz74VdTrrlNhT+xoz2xy4ADjZzKa4+xsAZrYBofPYRNp6kkNozn6SMPT1oAL5nmtmTxC+BPwbIaAdHX9Wm9mNwKnunqv15x5v5DqylbJRmfOVKDdxRXKVuFqXrS4UsEWkamKAy9z81w3qdkLAbibUnN9IHN+HUPP8OfAoMAt4Kda8J1MgYAO4+23AbXE41N6EjmYHE2qhEwmB8NCYfEncH+/u01N+b53VyGVLjQK2iEg2JKcjbQKITeC54VzjinTGHZp/IPaU3gZocven3f1DwjPjOwk9sM8GpgDjzWzDWMt2Qkev7YGCQdHMtiEEzrk1XqikkcuWGgXsKivVE7xv367TQzzNZ9EiUtC4uG0FnoivRyTOP0EeM+tLmFAF1v57fzLwU+A5M9upwCQk9xACNoQaPYRgvitwkpldkT9pVZy85XZgNGGo7nEVvq80NHLZUqOAXWXdpSd4ms+iS3UeK0Udy6QrMrMmQi/oc+KhW9z9zfg6OQnIeWb2XXdfFa8bQ+gNnXumm/wPchNwCaGz1uVmdo67L4nXDQEuiukeS8w0eRWhs9sownjmE3MTXsUx0f9LCIgrgZ90/p23SyOXLTUK2NJw1HlMuqmfm9kHecd6EiYDyfW0f4K2qZlx95lmdhNhStIzgElm9hphatJc7fseYH9gAzMb4O4fuvvbZnYCYSKSbwInmtkrhJgwkjBGewFwYuJe75nZoYTa7H7Aa2b2PKGp3ghDz1YTJmKp6RKgjVy2NGmKJxGRxrADYTrm5M9OhOWGpwPHA7u7+8K8675MaOJ+nPA3/ZOEAHUncIi7HwDkpn4en7vI3X9D6GR2C2H6zzGE2cBeIjSHj3b3Wckbufs/YzkvBJ4hfJkYTZj7+1fAZ9z91k79FjqokcuWlqbW1mJj7bs+M5s1atSoMTNmzCifOFr94bu8etpmax3b+or59BiwMYsXr1hnHPZ5P9i/bJN4R5//NpJFy5eyT97MYx319FHnqobdGJrKJxGRWlGTeANI8/lvI9GzaBGR9ChgS9XoWbSISHr0DFtERCQDFLBFREQyQE3iDaqjz38biZ5Fi4ikRwG7Qen5r4iIJKlJXEREJAMUsEVERDJATeLt1NLSyrKmAWsdW7xkFT3WW8GSJcWXby01OYoWwBARkXIUsNtp6bLV/Gaj69c+eMmTZa/rqpOjiIhIbahJXEREJAMUsEVERDJATeLt1drSruTvLV/Cih6r9JxaREQ6RQG7nT5YuXydYw+OeJgFfZoLpr/hjoeKrnlUanIUTToiIiJJCtgpWNm8khU9erb7Ok2OIiIildIzbBERkQxQwBYREckANYmnYNr+kxg6bES7r9NzahEpxMy+AlwNTHb3a9tx3SnAlcDx7j6tnffsAayKu8e4+6/LpD8J+AWwxt0VS2pAv+QUbLR+Hz2LFpFUmNkuwKUduG434OL0SySNQk3iIiINwszGAn8GNmjndf8vXtevCsWSBqEatohInZlZb+Bs4Fyg8BjRwtf1Af4r/qgC1sXpH1hEpI7MbBQwGzg/HjoXmFvBdRavOxdoAb4LzKtSMaUBqIYtIlUze1JzMzCo3uVIyaJtp61ZU4V8hwJbAo8Cp7r7E2Y2uYLrtozXPhKvm2lmp1ahfBXL67hWzv+5+0nVLE9Xo4AtIlUxe1LzBEKP5U3qXZaUvDN7UvOp205bc3PK+b4JjHP3u9p53evAF9z9TymXpzNagYdLnB8CWHxdthVB1qaALSLV8gtgw3oXIkWbEN5TqgHb3V8GXu7AdbMJTeINw93XAHsVOmdmGwAPxd27gR/VqlxdhQK2iIjku97Mrk8rs9hUfjOwE/AC8KUY3KUdFLBFpFom08WaxIG6PiOuodnAu2XSbAqMqjC/q4ADgYXAIe7+QSfK1m0pYItIVWw7bc3Nsyc134o6nWXRD9ox01lJZvYd4GRgJfBFd381nSJ2PwrYIlI1McCVq6lJF2VmE4Apcfcr7v7XepYn6zQOW0REUmdmewC/ApqAi9s7t7msSwFbRERSZWYjgTuA3sAfCJO6SCcpYIuISGrMbBBwF2HM9VPARHdvrW+pugYFbBERSYWZ9SLUqLcF5gAHu/uSuhaqC1GnMxERScs3gc/H1wuAX5hZP6BngbSr3X1srQrWFShgi4hIWgYkXn+2TNruMkQuNQrYIiINxt2Hd/C6oZ2452pCj+5K018LXJt37FzC6mFSBXqGLSIikgEK2CIiIhmggC0iIpIBCtgiIiIZoIAtIiKSAQrYIiIiGaCALSIikgEK2CIiIhmggC0iIpIBCtgiIiIZoIAtIiKSAQrYIiIiGaCALSIikgEK2CIiIhmggC0iIpIBCtgiIiIZ0KPeBRAR6a7MbDjwWpHTrcB7wOvAH4HL3f3dGhUtNWY2CZgKzHP3oYnjDwB7Axe5+7n1KV1pZjYNOA540N3HVnquWhSwRUQaw3PAB4n9HsBAYAfgU8BkM9vX3Z+tR+Gk/hSwRUQawzfc/YH8g2Y2GLgOGAfcYmaj3b2l1oWrgmOBvsCCehckKxSwRUQamLsvNLPjgHnAtsABwJ/qW6rOc/fX612GrFGnswJaWlpZvHhFwZ9ly1bXu3gi0s24+0JCkzmEJnLphlTDLmDp0pVc+L176l0MEZGknnH7Uf4JM+sBHAV8Cfg0MBhYDbwF3Adc5u6zC1x3IHAKsDvhefkHhC8GNwHXuvvKAtcMAL4JfBEYRaj4vQrcSugY934lb6ZQp7NEJ7x/AZsDJwAnA2PiZc8B1wDT3L21WmVrVArYIlI1Z31rejMwqN7lSMmiSy4/ZE09bmxmIwk16xbymsPNrA8wA9gnHpoDPAtsCmwTfyaa2efdfWbiutOAn8Xdt4CngCGEILo3MMHM9nP3NYlrtiP0WB8OrAFeAZYB2wPnAceZ2UHu/mIn33IT4bn9McD7wGxga8IXi90BA87O+z3Uqmx1oyZxEamKs741fQIhELzTRX7eiu+pJsys2cwGm9l44C7C3+sp7j43L+l3CMF6AbCru49w913cfRiwK/A20A/4r0TeGwEXx92j3H2LeM0I4EBCoBsLTEhc0w+4kxAQbweGubu5+6eALQlfGrYC7ohfIjpjE+DLhNryEHf/DKHG/et4/gwz27hOZasbBewCWlq7QgdMkbr7BeEPb1exCeE9Vcv9Ztaa+yE0aS8A7iB0NrsY+F6B6/Yj1Ly/7+6PJ0/E/f+JuzsmThnQmzDO+3d519wNTAFuAZJN4icRmpmfBI5w97cS18wnBPe5hBr9pIrfdXH/7e5X5Gr47r4cOJ0wPr0H4ctIvcpWF2oSL+CDFcvWOTZ92ydY0WMVA1YuY9o/f7nWuT59n6xV0USk68ofh90MbEAIRL2A/wT6mdnpyWZqd9/LzHoSAlkhS+O2b+LYa4QvBAOBaWZ2mbs/ncjzBwXyOTxub0zeP3HNMjO7BTgDGE/bF4WOurPAPRaa2buEL08b1bFsdaGAXaEVPVaxosdqVrasok/rh2uda2pqqlOpRBraZOBKuk4t+x3g1CrmX2wc9vqEWuGV8f7NwNeTadx9lZkNNLPdCbXxreN2Z8KzbEi0qLr7O2Z2MXAOYTz0sWY2H7gXuBu4y93zx0fneqdPNrPDiryHzeJ2u7Lvtrx5RY7nalTJ+FXrstWFAraIVMUllx9y81nfmn4r6nTWKbGn9jVmtjlwAXCymU1x9zcAzGwDQuexibT1JIfQnP0kMBM4qEC+55rZE4QvAf9GCGhHx5/VZnYjcKq752r9G8ZtriNbKRuVOV+JdXqo50nWlGpdtrpQwBaRqokBLnPzXzeo2wkBu5lQc34jcXwfQs3z58CjwCzgpVjznkyBgA3g7rcBt8XhUHsTOpodTKiFTiQEwkNj8iVxf7y7T0/5vXVWI5ctNQrYFbrt4K+xxeANafloAe/9/aq1zm20fmY7HYpIdiR7wzYBxCbw3HCuce5+f4HrhuYfiD2ltwGa3P1pd/+Q8Mz4TkIP7LMJHc/Gm9mGsZbthI5e2wMFg6KZbUMInHNrvFBJI5ctNeolXqGBvfowuHd/Bvbqt865pib9GkWk6sbFbSvwRHw9InH+CfKYWV/ChCqwdgXtZOBp4NdmVqgTTnLmqOa4zXUCO6nQ0Kg4ecvtwOPAT4q/japo5LKlRpFGRKSBmVmTmX2R0EEM4BZ3fzO+Tk4Ccl7sLZ67bgUVJywAABTNSURBVAxhIpHcM91kL/GbCM+IdwAuj+OYc9cNAS6Ku4+5+6L4+irCmO5RhPHMwxLXbBzzHB3zrXVQbOSypUZN4iIijeHnZvZB3rGehMlAcj3tnwC+ljvp7jPN7CbClKRnAJPM7DXC1KS52vc9wP7ABmY2wN0/dPe3zewEwkQk3wRONLNXCDFhJGGM9gLgxMS93jOzQwm12f2A18zseUJTvRGGnq0mTMRS0yVAG7lsaVINW0SkMewA7Jn3sxOwgvBc9nhg97gQSNKXCU3cjxP+pn+SEKDuBA5x9wOA3MpY43MXuftvCJ3MbiFM/zmGMBvYS4Tn16PdfVbyRu7+z1jOC4FnCF8mRhPm/v4V8Bl3v7VTv4UOauSypaWptbXYWPuuz8xmjRo1asyMGTPWOv7GgkX8/KJH1jr2jXM+x5ZDBrH6w3d59bTN1jq39RXz6TFgY0S6GE0wINJAUmsSj50bzgL+g9AU8xGh+ean7v7HDuQ3nDAbTylPx7liRUREurRUAnbssHAvsBuwijDF3mDCQusHmNkF7v79dmb7ybhdBLxQJM1LHSiuiIhI5qRVw76KEKyfAg5NzMBzDPBL4AIze9jd/9KOPHMB+yZ3/1rJlCIiIl1cpzudxXVaJxJ64x2dC9YA7n498OO4e0E7s84F7Mz26BMREUlLGr3EjyEMrP+7uz9f4PzVcbtncmxcBXIB+7nOFE5ERKQrSCNg7xG3DxU66e7zCOuQQpirtiwz609YbQZUwxYREUnlGfaouH2lRJo5hPF921aY506EISVvARub2bcJk933AGYDv3X3hztU2gq0trasc6xl8SJWr7+GNYvzV5wTERGpvjQCdm4GnlKTqecG+g+pMM9cc/hA4Hna5rKFMGPPKWb2S+Cr7r6q0oJWqnXJ++sce//7u7Aibx1sERGRWkkjYOfmp11eIk1uwfG+JdIk5QJ2b+B/CUvGvUxYr/UY4HzgBMIMQF8vlEGSmc0qcmpkheURERGpqzQC9hoqfxZe6bRqf4t5znT3/0kcfx24yMzmEObA/aqZXZU/fZ6IiEhXk0bAXkxouu5dIk1uubOllWQY57j9TanzZnY+YRWawwiLtZfKb/tCx2PNe0wlZRIREamnNAL2AkLAHlwiTe7Z9Tsp3C9nJiFgjyiXMA0Dvv1nttyy8K2a+5d66yIiIp2XRsB+gRA4h5dIkzs3u9JM47quLe6+pkiSXDN86p3OCt6s3yAt8CEiInWTxjjsx+J2j0InzWwokJsw5ZFCafLSDzSzRYSFxg8rkXTnuC00WYuIiEiXkkbAvjlux5qZFTj/1bh90N3nlMvM3d8D5sfdSYXSmNmRhB7eK4FMr28qIiJSiU4HbHd/CbiBMFb6VjPLTaSCmU0EvhN3f5h/rZmNNLPtzGzzvFNT4na8mU0xs16Ja44EpsbdS9z9rc6+BxERkUaX1mpdpwE7xp8XzexZQke0reL5c4qs1HVvTHMdidq0u19vZjsB3wbOJkyU8hKwKbBFTHYtYTy2iIhIl5dKwHb3hWa2B3Am8CVgNKEz2IPAFe7e7mZrdz/TzP4MnEp4Pr4TYW3s6cDV7j4jjbKLiNSbmW1DqJzsT5ggahHwKHCVu99T4rrBwLmE/j5DgfeAhwmtj4+2swxjgfvj7ohyjzDNbBpwHOFx59j23Es6Jq0aNu6+hLCE5gXtuGZ4mfN/AdqzhraISKaY2YHAbYT5KpYSOtJuTAjCh5nZT9z9zALXbUoIziPjdc8QgvbhwKFmdrK7/7I270JqIY1OZyIi0gFmNgT4LSFY3wh8wt0/5e5bAEcTZpL8tpkdUeDy3xGC9T3AUHf/LPAJQk29GbjazEbX4G1IjShgi4jUz0mE/j5zgEnu/kHuhLvfAPwi7n41eVFsvt6bMNPkl+PoGty9xd0vJkzd3BM4p8rllxpSwBYRqZ/XCDXs/3b3FQXOPxO3W+UdnxS3t7t7oTV/r47bfzezPgXOSwal9gxbRCTf/DGXNgOD6l2OlCza7Pkzi8282CHu/jtC03Yxn43bl/KO5yaqeqjIdf8AVgP9Yh5/62gZ2yOv41o5x7v7tOqVputRwBaRqpg/5tIJwJXAJvUuS0remT/m0lM3e/7Mm8sn7Rwz2wj4JnA8IfBenDi3HrB13H2l0PXuvsrM5hFq5ttSo4ANfEDoCFfM1kBu3o3Xq1+crkUBW0Sq5RfAhvUuRIo2IbynqgXs2Lns+8AooBfwBvA1d/9rItlA2v52v1siu4WEgD2kRJpUuftMYK9C58xsDG3TU1/o7vfVqlxdhQK2iEjj2BVILgc8EDjEzP7q7h/FY30T55eXyGtZgfSVeq3wTNMdE4eg3UX4AncT7Rj+K23U6UxEqmUy6S6pW2/vEN5TNf0c6E8YnjWJEHS/CtxnZrkKVnufo7d2oBz/JDRtl/qp6N/WzPoCdxJq+/8AjnP3jpSp21MNW0SqYrPnz7x5/phLb0Wdzirm7m/Gl0uA68zsUeApQsexicA0wlCunN4lssv1Dl/agaJMaMdMZ6XSrEdYa2IXQvP+Ye5eqlVASlDAFpGqiQGu1HNWKcHd3cxuBb4MjKUtYK8gPOMeXOLy3LPrerZyXEaYsW0JcKi7zy+TXkpQk7iISJ2Y2SAz+0yc8ayYuXG7GYTJUQCPx4YXybcnoVkdYHYKRW03M/sGoad7C3C0uz9Vj3J0JQrYIiL18zjhefEJJdLkJk2Zlzj2WNzuQWG7ElpQlwMzO1PAjjCz8cBP4+7Z7n57rcvQFSlgi4jUz91xe1KsFa/FzIYTFvOA0HEr56a4PdLMCvUR+Frc/s7dlxU4XzVm9hnC7G3rAVPd/dJa3r8rU8AWEamfSwk9wbcBbkg2jZvZzsCfCZ3H/goka6n3Enpqbwj8IQ6bwszWM7OzCAuHrCIx4UotmNkwwheLfoSyf6WW9+/q1OlMRKRO3P1VM/sSYXrSI4HxZuaE3t/bxmSPAkckh0K5e6uZHQs8CHwemGtmzwFbEJ51txKm/nyhdu8GCDPb5WYyawLuiMO6mguknenu36hZyboABWwRkTpy9+lm9kngTOAAYDRhKNZDhFW3funuqwpc92qshZ8DHArsGK/7E3CJu1c6p3eaBiReH1Am7epqFqQrampt7b7j181s1qhRo8bMmDFjreNz57zMVT97ca1jp3xzO7YaPqqWxROpt6Z6F0BE2ugZtoiISAYoYIuIiGSAAraIiEgGKGCLiIhkgAK2iIhIBihgi4iIZIACtoiISAYoYIuIiGSAAraIiEgGKGCLiIhkgAK2iIhIBihgi4iIZIACtoiISAYoYIuIiGSAAraIiEgGKGCLiIhkgAK2iIhIBihgi4iIZIACtoiISAYoYIuIiGSAAraIiEgGKGCLiIhkgAK2iIhIBihgi4iIZIACtoiISAYoYIuIiGSAAraIiEgGKGCLiIhkgAK2iIhIBihgi4iIZIACtoiISAYoYIuIiGSAAraIiEgGKGCLiIhkgAK2iIhIBihgi4iIZIACtoiISAYoYIuIiGSAAraIiEgGKGCLiIhkgAK2iIhIBihgi4iIZIACtoiISAYoYIuIiGSAAraIiEgGKGCLiIhkgAK2iIhIBihgi4iIZIACtoiISAYoYIuIiGSAAraIiEgGKGCLiIhkgAK2iIhIBihgi4iIZIACtoiISAYoYIuIiGSAAraIiEgGKGCLiIhkgAK2iIhIBihgi4iIZIACtoiISAYoYIuIiGSAAraIiEgG9EgrIzPrC5wF/AcwAvgIeAL4qbv/sYN5DgPOAw4CNgHeBe4Fprj7C2mUW0REJAtSqWGbWT/gPuB8YGtgFrAEOAC4y8zO70CeBjwJnAj0B54GegPHAE+a2YFplF1ERCQL0moSvwrYDXgKGOnun3b3rYBjgdXABWa2X6WZmVkPYDowGLge2NzddwE2B64kBO4bzWxwSuUXERFpaJ0O2GY2EpgItABHu/sbuXPufj3w47h7QTuynQiMAl4HTnL3ZTG/lcBpwN+AjYBvdbb8IiIiWZBGDfsYoBn4u7s/X+D81XG7Z3wmXYlJcXt9DNIfc/dW4H/j7lHtLKuIiEgmpRGw94jbhwqddPd5wNy4u3e5zMxsPWDXUnkCD8ft1ma2ZYXlFBERyaw0eomPittXSqSZA2wFbFtBflsAfcrk+QawhlCz3zbud9jcOS+vtT//7XmdyU5ERCR1aQTsTeL23RJpFsbtkHbkVzRPd19jZh8AgyrMs6SrfvZiZ7MQERGpqjQCdt+4XV4izbK8tJXkl1qeZjaryKntXn/9df41/5KyhXrquV70XH/9sulEuoqXX375Dnc/tN7lEJEgjYC9hsqfhbdWmF97VJJnMS0rV65k5cp/raJ0kz4fLe7EXaQrGRm3JT8vIiJpSyNgLwYGEsZGF5N7Jr20wvxyelO8ll1xnu6+fbFzudp3qTQiOfq8iEi9pNFLfEHclprEJPec+Z125Fc0zzixyobtyFNERCTT0gjYuTm9h5dIkzs3u1xm7v4W8EGZPLck9BCvKE8REZGsSyNgPxa3exQ6aWZDgdyEKY9UmOc/SuUJfC5u58YALyIi0qWlEbBvjtuxccGOfF+N2wfdfU6Fed4Ut8ebWaGu2bk8p1WYn4iISKZ1OmC7+0vADYQm6lvNLDeRCmY2EfhO3P1h/rVmNtLMtjOzzfNO/ZrQC3dr4AYz2yCmX9/MrgD2IjSb/7yz5RcREcmCptbWzoyKCuKqWfcDOxKGZT1L6Dm+VUxyjrv/qMB1c2Ka69x9Ut65XYB7CJ3LFgMvEgL4IGAlcJC739/pwouIiGRAKstruvtCwvPm7xM6gY0m9PB+EDiiULCuIM/HgU8C/we8H1+3AL8HdlOwFhGR7iSVGraIiIhUVyo1bBEREakuBWwREZEMUMAWERHJAAVsERGRDFDAFhERyYA0VuvKHDPrC5wF/AcwAvgIeAL4qbv/sZ5lk8ZhZl8BrgYmu/u1RdLosyQiNdHtathm1g+4DzifMBHLLGAJcABwl5mdX8fiSYOIE/dcWiaNPksiUjPdLmADVwG7AU8BI9390+6+FXAssBq4wMz2q2cBpb7MbCzwZ2CDMkn1WRKRmulWAdvMRgITCTOmHe3ub+TOufv1wI/j7gW1L53Um5n1NrMLgL8QptYtlVafJRGpqW4VsIFjCIuU/N3dny9w/uq43dPMhhU4L11UXLRmNqF5G+BcYG6JS/RZEpGa6m4BO7e+9kOFTrr7PNr+SO9dkxJJoxgKbAk8Spir/qIy6fVZEpGa6m69xHNLf75SIs0cwgpi21a9NNJI3gTGuftdFabXZ0lEaqq7BexN4vbdEmkWxu2QKpdFGoi7vwy83I5L9FkSkZrqbk3ifeN2eYk0y/LSihSiz5KI1FR3C9hr2pFW645KKfosiUhNdbeAvThue5dI0ydul1a5LJJt+iyJSE11t4C9IG4Hl0iTe974TpXLItmmz5KI1FR3C9gvxO3wEmly52ZXtSSSdfosiUhNdbeA/Vjc7lHopJkNBXKTXDxSkxJJVumzJCI11d0C9s1xO9bMrMD5r8btg+4+pzZFkozSZ0lEaqpbBWx3fwm4gTCl5K1xOkoAzGwi8J24+8M6FE8yRJ8lEam17jZxCsBpwI7x50Uze5aw0MNW8fw57v6XehVOMkWfJRGpmW5VwwZw94WE547fJ3QGGk3o6fsgcIS7/6iOxZMM0WdJRGqpqbVVczqIiIg0um5XwxYREckiBWwREZEMUMAWERHJAAVsERGRDFDAFhERyQAFbBERkQxQwBYREckABWwREZEMUMAWERHJAAVsERGRDFDAFhERyQAFbBERkQxQwJZOM7MpZrY8tya0mU0zs1Yze6DC61vjz6ROluMYM2sxswM7k4+ISCNSwJZOMbO9gLOAK9z95ToX59fAP4BpZjawzmUREUmVArZ0mJn1AK4G3gfqvvazu7cC3wY2A35c5+KIiKRKAVs64yRge+Cn7v5+vQsD4O4PAfcBJ5nZ9vUuj4hIWhSwpUPMrCdwLrAG+L86Fyff1YTP9nn1LoiISFp61LsAkllHAFsAd7v7W2lnbmbTgOMqSevuTXmHbgc+BL5oZlu4+7yUiyciUnOqYUtH/X9xO6NK+c8GHi7xsyamez3/QndfSWgW7wEcWaXyiYjUlGrY0m5m1gzsE3cfqsY93P1HFOnIZmbnAHsCi4HxRbJ4CPh3YH/gZ9Uoo4hILSlgS0fsDGwItADPl0i3t5m1pnljMzsK+EG891Hu/kyRpM8mytDs7muKpBMRyQQFbOmIEXE7z92Xl0j3IW2Bs5Q9K7lpHPM9FWgCvu3u00sk97jtD2wMzK/kHiIijUoBWzpik7gtN5RrpruPLZdZJbVwM9sG+APQC7jW3S8rc0mybJuggC0iGadOZ9IRuVnEltbiZmY2BPgjMBi4H/h6BZctSbzWrGciknkK2NIRuWbwjap9IzPrTRimNRJ4CTjS3VdVcGkySC+rRtlERGpJAVs6Ite8PKSaNzGzJuA64HPAe8Ah7r6owsuTZftX2mUTEak1BWzpiFyHroFm1reK95kCfAlYDUxw99ntuHZo3C6lwFhtEZGsUcCWjniK8Ix4PWD3atzAzCYD3wFagRPd/d52ZpHref5IXBRERCTT1Etc2s3dV5nZvcChwF6EWcVSY2abAv8ddxcSphg9BuhNGNKV7yJ3/2Pesb3i9q40yyYiUi8K2NJR1xMC9heAC1POuw9tn80hwGFl0m+a3DGzAYTn3quB36ZcNhGRumhqbVVrobRfnJ70RWAUsIO7z6pzkT5mZl8HrgKmuvsJ9S6PiEga9AxbOiRO9XlR3J1cz7IUMJmwOMhF5RKKiGSFArZ0xq8Jtezj4+QmdWdm+wGfItSuX6l3eURE0qKALR3m7quBSUA/4Pz6lgbMbD3gJ8CbwJl1Lo6ISKoUsKVT3P0x4MfAV+J83/V0HLATcLy7l5vnXEQkU9TpTEREJANUwxYREckABWwREZEMUMAWERHJAAVsERGRDFDAFhERyQAFbBERkQxQwBYREckABWwREZEMUMAWERHJAAVsERGRDFDAFhERyQAFbBERkQxQwBYREckABWwREZEM+P8BQDW/ugWoRcEAAAAASUVORK5CYII=\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": 31,
   "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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m9vzIkSNH33XXXY0OZamx8KPZvHbsoEWeG37RLN1KaenUo9EBiFRL1wRFRCSzlARFRCSzdE2wztrb2mhtmdvoMGqmtWVOo0MQESmbkmCdtbbMXeyamYiINIa6Q0VEJLOUBEVEJLOUBEVEJLN0TbAbGHr2NJqaBzQ6jJppau7f6BBERPJSEuwGmpoHaDK5iEgDqDtUREQyS0lQREQyS0lQREQyS0lQREQyS0lQREQyS0lQREQyS0lQREQyS0lQREQyS5PlRUS6OTNbGzgF2B4YBLwHPA5c4u73FTmvP3A6sBuwBvA+8Ahwnrs/XmEMY4GH4u4wd59Rovwk4CBgsruPreS16kktQRGRbszMdgSeBQ4B+gMvAK2ExPZXMzu/wHmrAVOB44DVgH8B7cAewBQzO6T20Xd/SoIiIt2UmQ0Afg/0Af4ArO7uG7n7YOAAQjI8wcz2zHP6TcAI4D5gDXffFFid0KJsAi41s3Xr8Gt0a0qCIiLd16HAKsAMYIK7f5gccPcbgSvi7pG5J8Wuy22AFmB/d38/ntPm7ucC1wPLAqfVOP5uT0lQRKT7ep3QEvyNu3+e5/i/4nZop+cnxO0d7j4nz3mXxu3uZtan6iiXYBoYIyINM2v0+U1Av0bHkZL3Br1wYmuaFbr7TYRuzUI2jduXOz0/Jm6nFDjvCWAhsHys4+9djbESnQbXlHKwu0+qXTSBkqCINMSs0efvDVwMrNroWFLy7qzR5x896IUTb6n1C5nZysAPgYMJyezcnGPLAMPj7qv5znf3BWb2FqEFOYo6JUHgQ8Lo1EKGA1+Kj9+ofThKgiLSOFcAKzU6iBStSvidapYE4wCYM4GRwHLAm8BR7v5wTrFV6Phsn12kurmEJFi3O3q7+9PA1vmOmdlo4NG4e5a7P1iPmJQERUSWHJsD6+XsrwLsYmYPu/vH8bm+Occ/K1LXvDzly/W6mXXhtPzidI67CV+KbgYmplZ5CRoYIyKNchjwbqODSNG7hN+pln4NNBOmOkwgJLIjgQfNLGnUVHpdsr0LcfyD0K1Z7Kesv62Z9QX+RGiVPgEc5O5dialL1BKsg/a2Nlpb5gLQ2pJvoJZI9gx64cRbZo0+/1Y0MKZs7v7v+PAT4Bozexx4hjC4ZRwwiTAtItG7SHXJqNBPuxDK3hWsGFOszDLAjcBmhK7d3dy9WOs1dUqCddDaMpfXjh3U6DBEup2YNIpdt5Ii3N3N7FZgf2AsHUnwc8I1w/5FTk+uBTayNf5Lwso3nwC7uvusegeg7lARkW7KzPqZ2SZx5ZhCZsbtIAgT4gGPz61VoN5lCV2qANNTCLViZnYMYYRrG3CAuz/TiDiUBEVEuq8nCdffiq3zmUyUfyvnualxO4b8Nif0BH4GPF1NgF1hZt8GLoi7p7j7HfWOIaEkKCLSff01bg+NrbdFmNlahAWxIQwuSdwct3uZWb5rrkfF7U3uPi/P8Zoxs00Iq+AsA1zt7nkXAK8XJcEGGXr2NIZfNIvhF82iqblYt72IZNj5hBGgawM35naLmtnGwL2EAS4PA7mtqQcIIzRXAm6PUxAws2XM7CTC4tsLyJlkXw9mNoSQrJcnxH5EPV8/Hw2MaZCm5gH0XHFgo8MQkW7M3V8zs30IS6ftBXzbzJww6nNULPY4sGfutAJ3bzez8cBk4GvATDObBgwmXDtsJyxL9mL9fhsgrBCUrAjTA7gzTpFoylP2aXc/ptYBKQmKiHRj7v5nM9sQOBHYAViXMK1hCuFuEFe5+4I8570WW4unAbsC68fz7iHcVLfcNTzTtGLO4x1KlF1Yy0ASPdrb6zYnsdsxs+dHjhw5+q677qrp6yz8aPZiUySGXzRLLUFZ0vVodAAi1dI1QRERySwlQRERySwlQRERySwlQRERySwlQRERySwlQRERySwlQRERySwlQRERySwlQRERySwlQRERySwlQRERySwlQRERySwlQRERySwlQRERySwlQRERySwlQRERySzdWb5K7W1ttLbMLVqmtWVOnaIREZFKKAlWqbVl7mJ3jRcRkSWDukNFRCSzlARFRCSzlARFRCSzdE2wBoaePY2m5gFFyzQ1969TNCIiUoiSYA00NQ+g54oDGx2GiIiUoO5QERHJLCVBERHJLCVBERHJLCVBERHJLCVBERHJLCVBERHJLCVBERHJLCVBERHJLCVBERHJLCVBERHJLCVBERHJLCVBERHJrNQW0DazvsBJwL7AMOBj4CngAnf/SxfqWwt4vUSxZ919o0rrFhERgZSSoJktDzwAbAEsAKYB/YEdgB3MbKK7n1lhtRvG7XvAiwXKvNyFcEVERID0WoKXEBLgM8Cu7v4mgJkdCFwFTDSzR9z9/grqTJLgze5+VEpxioiIfKHqa4JmNgIYB7QBByQJEMDdrwN+EXcnVlh1kgSfqzZGERGRfNIYGHMg0AQ85u4v5Dl+adxuZWZDKqg3SYLTqglORESkkDSS4Ji4nZLvoLu/BcyMu9uUU6GZNQPD465agiIiUhNpXBMcGbevFikzAxgKjCqzzg2AHsDbwEAzOwHYmBDvdOD37v5Il6ItQ3tbG60tc8sq29oyp1ZhiIhIjaWRBFeN29lFyiQZZUCZdSZdoasALxC6WxPbAz8ws6uAI919QbmBlqu1ZS6vHTso7WpFRKSbSSMJ9o3bz4qUmdepbClJEuwNXAb8GngFGES4BnkGcAjwOfD9UpWZ2fMFDo0oMx4REVkKpZEEWyn/2mJ7meX+Hut82t1/m/P8G8DPzWwGcD1wpJld4u6FkpyIiEhBaSTBFkK3Ze8iZfrE7aflVOjuNwA3FDtuZmcAawO7AUWToLuvl+/52EIcXU5MIiKy9EkjCc4hJMH+Rcok1wLfTeH1Ek8TkuCwFOssaOjZ02hqLu+SZlNzsbdCRES6izSS4IuEZLRWkTLJsenlVmpmywJt7t5aoEjSBZv6wJh8mpoH0HPFgfV4KRERqZM05glOjdsx+Q6a2RpAMkn+0VKVmdkqZvYeMJ/Q1VnIxnGbb4K+iIhISWkkwVvidqyZWZ7jR8btZHefUaoyd38fmBV3J+QrY2Z7EUZ2zgdurSRYERGRRNVJ0N1fBm4kzOW71cySyfOY2Tjg5Lj7s87nmtkIM1vHzL7U6dA5cfttMzvHzJbLOWcv4Oq4e567v13t7yAiItmU1l0kjgXWjz8vmdlzhMEyQ+Px0wrcQeKBWOYaclp97n6dmW0AnACcQpgc/zKwGjA4FvsdYb6giIhIl6RyZ3l3n0u4JngmYfDLuoTRopOBPd397C7UeSJhdZg7CJPtNwCWBf4M7OLuh7l7Wxrxi4hINqV2Z3l3/4Rwu6SJFZyzVonj9wOV3INQRESkbKm0BEVERJZESoIiIpJZSoIiIpJZSoIiIpJZSoIiIpJZSoIiIpJZSoIiIpJZSoIiIpJZSoIiIpJZSoIiIpJZSoIiIpJZSoIiIpJZqS2gvaRrb2ujtWUuAK0tcxocjYiI1IOSYNTaMpfXjh3U6DBERKSO1B0qIiKZpSQoIiKZpSQoIiKZpWuCRQw9expNzQMAaGru3+BoREQkbUqCRTQ1D6DnigMbHYaIiNSIukNFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlARFRCSzlASB9rY2Wj+e3egwRESkzpQEgdaWucw8bf1GhyEiInWmJCgiIpmlJCgiIpmlJCgiIpmlJFjA0J8/R1Nz/0aHISIiNaQkWEDTCgPpsYzeHhGRpZk+5UVEJLOUBEVEJLOUBEVEJLOUBEVEJLOUBEVEJLOUBEVEJLOUBEVEJLOUBEVEJLOUBEVEJLOUBEVEJLOUBEVEJLOUBEVEJLN6plWRmfUFTgL2BYYBHwNPARe4+1+6WOcQ4CfATsCqwGzgAeAcd38xjbhFRCS7UmkJmtnywIPAGcBw4HngE2AH4G4zO6MLdRrwT+B7QDPwLNAbOBD4p5ntmEbsIiKSXWl1h14CbAE8A4xw96+4+1BgPLAQmGhm25VbmZn1BP4M9AeuA77k7psBXwIuJiTDP5iZbvgnIiJdVnUSNLMRwDigDTjA3d9Mjrn7dcAv4u7ECqodB4wE3gAOdfd5sb75wLHA34GVgR9VG7+IiGRXGi3BA4Em4DF3fyHP8Uvjdqt4ja8cE+L2upj4vuDu7cBlcXe/CmMVERH5QhpJcEzcTsl30N3fAmbG3W1KVWZmywCbF6sTeCRuh5vZmmXGKSIisog0kuDIuH21SJkZcTuqjPoGA31K1Pkm0FpBnSIiIotJIwmuGrezi5SZG7cDKqivYJ3u3gp8WEGdIiIii0ljnmDfuP2sSJl5ncqWU19qdZrZ8wUOrfPGG2+w6z7jaJu3OQs/fOeLA732GUePptSmUYosdV555ZU73X3XRschUo00PuVbKb9F2V5mfZUop85C2ubPn//Jq6/PeBMYActB0gX7+owqqpUaGBG3xbrdRUQqkkYSbAFWIczdKyS5xvdpmfUlelO4NVh2ne6+XqkySWuxnLJSf/r7iEgtpHFNcE7cFpu4nly3e7eC+grWGSfTr1RBnSIiIotJIwkma3iuVaRMcmx6qcrc/W06Br0UqnNNwtzEsuoUERHJJ40kODVux+Q7aGZrAMkk+UfLrPOJYnUCW8btzJg0RUREKpZGErwlbsfGRa87OzJuJ7v7jDLrvDluDzazXkXqnFRmfSIiIoupOgm6+8vAjYTuyVvNLJk8j5mNA06Ouz/rfK6ZjTCzdczsS50OXU8YBTgcuNHMVojle5nZRcDWhC7TX1cbv4iIZFeP9vZqZhgE8W4ODwHrE6Y4PEcYMTo0FjnN3c/Oc96MWOYad5/Q6dhmwH2EATAtwEuEpNgPmA/s5O4PVR28iIhkViq3UnL3uYTrd2cSBqqsSxjZORnYM18CLKPOJ4ENgSuBD+LjNuCPwBZKgCIiUq1UWoIiIiJLorRuqisiIrLEURIUEZHMUhIUEZHMUhIUEZHMUhIUEZHMyvQN88ysL3ASsC8wDPgYeAq4wN3/0sjYJIjL7h0P7ETHvNPXgT8D/+vuWkBdRLoss1MkzGx54AFgC2ABMI0wtzFZ53Siu5/ZoPAEMLOvAXcCKxMWYXiFsDLRsLidBezo7v9qWJAiskTLcnfoJYQE+Awwwt2/4u5DgfHAQmCimW3XyACzzMxWJiyMsDJwD7Cmu6/j7msDo4BHgEHA7WZW7F6WIiIFZTIJmtkIYBxhBZoD3P3N5Ji7Xwf8Iu5OrH90Ek0ABgJvA/u4+zvJAXd/DdgDeJ/QKtyrEQGKyJIvk0kQOJDQnfaYu7+Q5/ilcbuVmQ3Jc1xq7+tx+2d3/7jzQXefTcetuTarW1QislTJahJM7lM4Jd9Bd38LmBl3t6lLRNLZzwhd01cVKdMjbpuKlBERKSiro0OT2z29WqTMDMJoxFE1j0YWExdQf7LQcTMbAIyNu8/XIyYRWfpktSW4atzOLlJmbtwOqHEs0jUXAn2BTwkDaEREKpbVJNg3bj8rUmZep7LSTZjZ6cD+cfcszRUUka7KahJsraBsNidSdlNmdgbw07h7B3BeA8MRkSVcVq8JthDufF9sflmfuP209uFIKWbWkzC38/D41L3Avu6uLyki0mVZbQnOidv+Rcok1wLV1dZgZrYicDcdCfAmYFd3L9adLSJSUlaT4Itxu1aRMsmx6TWNRIqKa4c+Amwfnzof2M/d5zcuKhFZWmS1O3QqsCsd8wUXET94k0nyj+YrI7VnZqsDfwNGEK7jHu3ulxY9SUSkAlltCd4St2PNzPIcPzJuJ7v7jPqEJLnMrBdh8ewRwHxgbyVAEUlbJpOgu78M3EhYaeRWM61oct8AAA1tSURBVEsmz2Nm44CT4+7PGhCeBCcDm8THP3D32xoZjIgsnbJ8K6X+wEPA+oSutucII0aTe9ad5u5nNyi8TIutwFmEv8dCQvd1MXfrbyUiXZHVa4K4+1wzGwOcCOwDrEu4r+Bk4CJ3v7WR8WXc+oQECOHf6FYlyr9S23BEZGmV2ZagiIhIJq8JioiIgJKgiIhkmJKgiIhklpKgiIhklpKgiIhklpKgiIhklpKgiIhklpKgiIhklpKgiIhklpKgiIhklpKgiIhklpKgiIhkVmbvIpHLzNYCXi9SZD7wETAduAu42N0/qkNoBZlZsvL59u5+f5V1jSXcViqfdsLv/z7wAnAbcIW7f16grl7AwcDuwAbAAOAT4O34Gte4+z8KnDuDjltZ5WoDPgPeA14E/ghc6+7zSv5yRZjZBsATwM/d/adlnjMBuBp4y93XyPN8PguBucBLhNgvdfcFJV7nROA8YHd3v8PMJgJnADPdfa0y4pxBeC/PdPeJpcoXqee/CHdWOcLdL+9qPSLdlVqCi5sGPNLp51nCB/mWwM+B53JvxLuU+QeL/u6PEt6TdmBb4NfAE2Y2sPOJZjYCeB64FNgRmAc8DbwBDAeOjudeYmY9isTwZqcYphJul9QMbB/rfzYmsS4xs2WBG4D/AOd3tZ4COv/7eZqQxLcBLgIeM7MVStSxM+HLR1VfcKrl7g8DNwO/Wor/zUuGqSW4uGPc/W/5DsQW0x3AEOAaSt/nbkm0t7vPyHfAzL4LXEdo4V0AHJBzrBfwF2Ak4T36gbu/lXO8N3BoPO/7wBxCyyafq/K1XmLi/DohCa4N3GtmW7p7sVZ8IScAXwbGu/tnXTi/IHffOt/zZjYa+BOwCfArwvuRr9yKhH9bk939kzRj66JTgD2ASwhfbkSWGmoJViAmx1Pj7pZmtkkDw6k7d7+JkMQAvmtmq+Uc3oeQmN4AvpubAOO5n7n7xcBZ8anj44d9Ja/f7u4PAl8D/g0MInwwVyS2Yv+b0Lq8odLzu8rdXwCOjbsHFvn9tweWBe6uS2AlxC8Z1wE7mNk3Gx2PSJqUBCt3W87jrzYsisZJfv8mYNOc5zeL22cKXS+Mfhe3yxNaYhVz9/8AP4673zSzzSus4kRC1+oV7t7WlRiqMDluewGjCpRJEk23SILRpXE7sZFBiKRN3aGV+zDn8WLXdeJAgmMJ3Vn9CQNKHgMuiq2YxZjZysCRhOtAo4GVCNcgXyV0n13o7u+XE1y8bjMZWB14EPi2u39a1m9WnkK///y4/aqZ9Xf3uflOdve3zWzjWM9b+cqU6Y+ELtUBhEE4T5Rzkpn1IXRDtgPXFym3B+Ea5obAcsBTwNlVxJtYNufxxwXKfBN43d09hdf7Qs7gmnIMy+0Wd/cnzewlYHMz28Ldp6YZm0ijqCVYubVzHr+Ze8DMfkFIQHsSPjj/RRjZuBvwQDxOp3PWjuXOAcYAs+P+QuArhA+tx8ysuVRgZjacMAJzdeBeYJeUEyAU/v3vjdtVCYNffmBmg/JV4O7PuPvr7j4/3/FyxBbcY3F3bAWn7gCsQmixvp2vgJldAtxKGAj0KWFU5+aE3/GgLoac2D1uXyGMNu782hsR/n61aAW+weKDdnJ/kn8rH7Lol53EPXG7bw1iE2kItQQrl3TDLTJyz8yOAE4GPgCOdvcb4vM9CNfLrgRONrOX3f3KnPquANYEHge+4+7v5Jw3DpgEGOHDt+D1LzMbSmj5rQH8GdirRLdkVx0Xt7OBJ5Mn3f1+M7uBMFhmOHAx8GszewF4OP484O6zU4wlGRAzpIJztovbKfkOmtkBhIE784GD3f3G+PzKwOXA3pUGGQcNDQR2Af6H8MXoaHdvz1N857hNPQm6+1XAVQViPIDQMl5IGByVr+dhCuHvv33asYk0ipJgGWIX2jrA4cD4+PSv4rWp5EPuzPj8Ie7+xXXD+EF3k5n1A34DnGlm17j7wjiwZL1Y9LAkAeacd12cf7YtsH6R+NYktACHEq7Z7VtNKytP/c3x9X9MR6vrJ3le4yDgOeA0QldpD8Lvtx5wFNBmZg8Dp7v7IymElnQn9q/gnK/H7bQCx0+L27OTBAjg7h+Y2TjCyFgr9gI5czjzWUiY+3dvgePfJEynKDRvc2iJ+isWu/CT5PhDd7+vQNHn4nY9M1st+fcvsiRTElzcQ2ZFP+MgDO74fzn7WwKrET6U7yhwzg2E1tFgQjfnE/FDZKCZ9ck38dvMmgiT9AH6Fqh3MGHQwjDgdmAfd19Y6hco4vUSv38rIUFc2vmAu7cC58buxF0JrZqvE7r3IHS/jwX+bmanu3u119h6xW0lSWFY3L7S+UDsTl437k7qfNzd55vZlYRJ7MV0TvDLAv0ILeSewDVmdmKnHoGktTkGuK/IQgCfE+ZylrIpoUu+KAt/7NsI7+Wv3f03RYq/QmjFLkN4H5UEZYmnJLi4aSx6PaSd8M18LuFa3e1xqHuuZJRjL+DhIkmklfABsg45AzncfV7sztyMMM8u+TDeiDCKEQpfv/0t0Cc+HkT4kKrGPwgftIl2wrWi2YRJ3//n7jOLVeDuLcCN8ScZrPMNwrXS7QktxJ+b2T/d/Z6CFZW2UtyWO2hoeTq+THyQr0jcflzkd3ym1OsUmSfYD/gpobv1d2bWEqedJHYkjLot1hU6q1D9nV5rBvlX38ktMzC+Vj/C9c4fFSvv7m1m9hGwMuHar8gST0lwcQUnyxeRfBgvR3kT6FdOHsRv4pey+OCOj4C/E1pRGxapqw9wJyG5fBU4nupWQCk4Wb6r3P0VQiviMjPbhhDvioRkUE0STFptL5ZZfpWcx/kGDCXHi01QLyvh5uPu7wE/iJPmxxISYm4SrNn1wM7i4gV3Er5wvUDoQWgt49RPCP9+VylVUGRJoCSYjuRD8yl337RoyRxmtiphwMiqhJF7lwP/JIxGnOHu7XGwSbEkeA1wCOF63fnAWWb2J3d/qfJfo2vM7CuEdTP7AWsXW4HF3SfH7tJTKXFtrcRr9iZ0K8Pi3Y+F5Ma1cp7jybSOYkua9SlyrFx3EJLg2ma2ort/FAdC7QRMd/dXU3iNguJrXUf40jSHMIq43LVwk+RX1bqtIt2FkmA6kvlco8ysZ75rcvGDZyxhpZOZcVDJIYQE+B6wibvPyVP3Gnmey3V97Kb6FWHo+ibA1Wa2VR0ngr9PGDACodvzrhLlkwFA1YwU3Y8w4R7g92We8z5h1GcvwvzCzpK/4/JmNsrdF5vCQMdApmrk/l2SNVQ3IfxbuHHx4qk7D9iL8F7sUe6yc3GAWNKdrOuBslTQPMF0PEy4jrgC4Q4K+exPmMLwEmFKBHQM0piZLwHGbrMxcbfoF5bYlfU9wujDpFu0LuKH6KNx95xiy6GZ2TKEKSNQOlkWqmNVOiau31puqze+R8mAmMW+XMRu4H/G3aPyvO4yhC8u1fpW3L7s7sn156Qr9C8p1F+QmR1JWDcVwojkvFNFCsh9z+rW0yBSS0qCKYiLHJ8Tdy80s4PjByYAZrYbHctO3ZzT3ZV8kGxoZnvmlO9hZjsRrpclK4wUGh2aG8ezdIxcPMvM1unSL9Q1PyZcZ1sfmGpmu8WpI1+I8dwObA28TIXrfppZTzP7FqH7cxDh9kzHFj9rMUnX6ZYFjidrwx5rZsclf0cz60vort6swHklmdlycdWWHeJTF+Qc3pnQrT6583lpiet+Xhx3f+Lu11ZYRXK9+xVNj5ClhbpD03MeMAI4jDDn6jwze50whSGZIjCF0FpLXEkYHDIS+D8zm0noIhxC6BpbAPyN0I1aqls0cRZhFKYBk2K3aDkDHqri7lPN7NuEa5RJsvskjlL8lPAeDI7FnwH2LHId6hAz2y5nv4nQyh5OxzW5lwjz7Spdeu0vhL9R3hGW7v5XMzsZ+AXhTg+nmNkb8XdagTCdYI9iL2Bm+VpXvWMdSRfu1cQvRmY2gJBc76rRAgeJmwjv5TzgK2Z2L+H9zPdl+Ko4uT5X8p51pzVNRaqilmBK4h0ODicMc7+N0C25MeGD83FCi+UbucuYxSSwGeED93nCqiJfJowMvYpwnSjpftvQzEqujBI/RJO1Mbegvt2iDxKS75GEJPguoet3wxjPnwgT6jd199eKVLUmodWR/HyV0HU8K9Y7Htigi2tr3k24BjvMCsxlcffzCPMb/xSf+jLheuF+wC/LeI2tOv1sSUiA/yYMSNne3Q/JuWa7I+H/Yq2TSzLgpw9h+bYdCHfk6BzvVnRahSe2iJMW7HU1jlOkbnq0t6e6+IRIt2dmZxDuhvA/7n5ig8NZIpjZzoRruA+5+7aNjkckLWoJShZdSBjIdFDn65ZS0OFxe2bRUiJLGCVByRx3/4CwkPVAqr8rxFIvDmjaBbjf3Ws2cEekEZQEJavOJSwDd1ZcTk0KO58wuOmwRgcikjYlQckkd18AHEhYAeWkBofTbZnZ1wmtwB+lvZyeSHeggTEiIpJZagmKiEhmKQmKiEhmKQmKiEhmKQmKiEhmKQmKiEhmKQmKiEhmKQmKiEhmKQmKiEhmKQmKiEhmKQmKiEhmKQmKiEhmKQmKiEhmKQmKiEhmKQmKiEhm/X9HK47NCenL7AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 480x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LvkVdW6uG4LvAuoSrEpSSOxf4apkyLWH4OU/S1q+lTl2KSBcUx1BPp8qJSMxsD8L0dOs3oFldWsuFoLuvMTMHPgmMKFYmXij0Y/Hu/E5qWs3a+m3SoZ50IiLF1DoRiZn1Bb4X/1rx9FfTteqbkhs4W+pq1zsRAnwlYfyRiEi3FCcimQ+cGR86A3ihA9tZ3O4MYA1wGuG6n5Kn5fYEoxsIs80caGanFLmA5vFxeb1mixGRbi5/IpIT3P1RMzumA9sNi9s+ELebbWYnNLCdFZlZL8KFkjvi5+5eVcefWjQ1BM1sC8IQh3fc/cW8VXcT5hncBbjRzA5y91fiQPqTgAmEN1IneUW6sPkT29qAjZvdjoy8PmbK6kYMnqx1IpIXgS+4+x0VS3aedsJ3eymbABZvV9zbzUKz9wSvIUyUPQMYn3vQ3dvN7PD4+G7AC2b2JDAEGEx4I49096c7vcUikon5E9sOAi4lTKjdHbw6f2LbCWOmrJ6WZaW1TkTi7vNpsT4T7r4a2LXYOjPrz9qpMv8AnNMZbWp2CJbk7s+Z2Q6EiXf3BbYlDIe4g3BR3UyvCi4ine4KYMNmNyJDgwivKdMQbFHXmtm1WVUWD5NOI3SIfBr4agzMhmtoCLr7iArrx1dYvwT4TvwTEZHWMJ/y01pCuBj66A7WdxnwecJ0mPu4+5t1tK0qLbsnKCLd3jF0s8OhQFM7nnSiH7j7L8oViLPZXFGpIjP7LvB14D3gK3HS9U6jEBSRphgzZfW0+RPbpqOOMckys4OAc+PdY939vs5ug0JQRJomhkZa16ASAMxsHKFzZA/gPHef0ox2tOpgeRER6abMbBRwM2F+6BsJA/mbQiEoIiKdxsw2Bm4jjAmcAxzq7k27GpBCUEREOoWZrUvY8xsDLAT+xd3fbmabdE5QREQ6y78TJkABWAJcYWbrA72LlF1VaRhdFhSCIiLSWTbIu71jhbKd0tO2R3t7uhdmN7O5o0ePHnvrrbdmVueqv7/GcycO/tBjI3+8WJdSku6oR7MbIFIvnRMUEZFkKQRFRCRZOifY3s6qv2c3Vnf18iWZ1SUiIo2VfAi2r1n9kXN4IiKSBh0OFRGRZCkERUQkWQpBERFJVvLnBIsZfs6TtPXbJLP62voNzKwuERHJjkKwiLZ+m2hwu4hIAnQ4VEREkqUQFBGRZCkERUQkWQpBERFJlkJQRESSpRAUEZFkKQRFRCRZCkEREUmWBsuLiLQ4M9sKOBX4Z2Aw8DowC7jM3e8ss91A4AxgP2AosAy4Hzjf3WdV2YbxwB/j3S3dfWGF8lOAI4AZ7j6+mufqTNoTFBFpYWb2eeAx4ChgIPAUsJoQbH8wswtKbLcZ8BDwbWAz4HGgHdgfmGlmRzW+9a1PISgi0qLMbBPgV0Bf4DrgY+6+vbsPASYQwvAkMzugyObXA6OAO4Gh7r4j8DHCHmUbMNnMtumEl9HSFIIiIq3raGAAsBCY6O5v5la4+1Tginj3uPyN4qHL3YHlwCHuvixus8bdzwN+AfQGTm9w+1ueQlBEpHU9T9gT/Im7v1tk/eNxObzg8YlxeZO7Lymy3eS4/LKZ9a27lV2YOsaISNMsHntBG7Bxs9uRkdcHP3Xy6iwrdPfrCYc1S9kxLhcUPD4uLmeW2O5hYBWwfqzjT7W2sRoFnWsqOdLdpzSuNYFCUESaYvHYCw4CLgUGNbstGXl18dgLThj81MnTGv1EZrYR8O/AkYQwOy9vXU9gZLz7bLHt3f19M1tE2IMcQyeFIPAmoXdqKSOBzePtFxvfHIWgiDTPFcCGzW5EhgYRXlPDQjB2gDkLGA2sC7wEHO/u9+UVG8Da7/bXylS3lBCC2V1BvAJ3nw3sWmydmY0FHoh3/8vd7+mMNikERUS6jp2Aj+fdHwDsY2b3uftb8bH18tavLFPXiiLlO+p5M6ths+LicI7bCD+KbgAmZVZ5BeoYIyLNcgzwarMbkaFXCa+pkS4B+hGGOkwkBNlxwD1mltupqfa8ZHsN7XiEcFiz3F+H/m3NbD3gd4S90oeBI9y9ljbVRHuCItIUg586edrisRdMRx1jOszd/xpvvg1cbWazgDmEzi2HAlMIwyJy+pSpLtcr9J0amnJQFTPGlCvTE5gKfIZwaHc/dy+395o5haCINE0MjXLnraQMd3czmw4cAoxnbQi+SzhnOLDM5rlzgc3cG/8fwsw3bwP7uvvizm6ADoeKiLQoM9vYzD4dZ44p5YW4HAxhQDzg8bERJertTTikCjA/g6ZWzcy+RejhugaY4O5zmtEOhaCISOv6M+H8W7l5PnMD5RflPfZQXI6juJ0IRwJXArPraWAtzOxLwEXx7qnuflNntyFHISgi0rr+EJdHx723DzGzEYQJsSF0Lsm5IS4PNLNi51yPj8vr3X1FkfUNY2afJsyC0xO4yt2LTgDeWRSCIiKt6wJCD9CtgKn5h0XNbAfg94QOLvcB+XtTdxN6aG4I3BiHIGBmPc3sFMLk2++TN8i+M5jZFoSwXp/Q9mM78/mLUccYEZEW5e7PmdlXCVOnHQh8ycyc0OtzTCw2Czggf1iBu7eb2eHADGA34AUzexIYQjh32E6Yluzpzns1QJghKDcjTA/g5jhEoq1I2dnu/q1GN0ghKCLSwtz9FjPbDjgZ2AvYhjCsYSbhahBXuvv7RbZ7Lu4tng7sC2wbt7uDcFHdjs7hmaUN8m7vVaHsqkY2JKdHe3unjUlsOWY2d9SWI8ZeNPiuDz0+8seL6bXBpk1qlUiX0aPZDRCpl84JiohIshSCIiKSLIWgiIgkSyEoIiLJUgiKiEiyFIIiIpIshaCIiCRLISgiIslSCIqISLIUgiIikiyFoIiIJEshKCIiyVIIiohIshSCIiKSLIWgiIgkSyEoIiLJUgiuWd3sFoiISJMkH4LvvfpMs5sgIiJNknwIiohIuhSCIiKSLIWgiIgkSyFYYPjZT9DWb2CzmyEiIp1AIVigrf+m9Oipt0VEJAX6thcRkWQpBEVEJFkKQRERSZZCUEREkqUQFBGRZCkERUQkWQpBERFJlkJQRESSpRAUEZFkKQRFRCRZCkEREUmWQlBERJLVK6uKzGw94BTga8CWwFvAo8BF7n57DfWNAJ6vUOwxd9++2rpFREQgoxA0s/WBu4GdgfeBJ4GBwF7AXmY2yd3PqrLa7eLydeDpEmUW1NBcERERILs9wcsIATgH2NfdXwIws8OAK4FJZna/u99VRZ25ELzB3Y/PqJ0iIiIfqPucoJmNAg4F1gATcgEI4O7XAj+KdydVWXUuBJ+ot40iIiLFZNEx5jCgDXjQ3Z8qsn5yXO5iZltUUW8uBJ+sp3EiIiKlZBGC4+JyZrGV7r4IeCHe3b0jFZpZP2BkvKs9QRERaYgszgmOjstny5RZCAwHxnSwzk8CPYC/AZua2UnADoT2zgd+5e7319RaERGRKIsQHBSXr5UpszQuN+lgnblDoQOApwiHW3P+GfimmV0JHOfu73e0oSIiIvmyCMH14nJlmTIrCspWkgvBPsDlwCXAM8BgwjnIM4GjgHeBb1SqzMzmllg1qoPtERGRbiiLEFxNx88ttnew3J9inbPd/ad5j78InG1mC4FfAMeZ2WXuXirkRERESsoiBJcTDlv2KVOmb1y+05EK3f2XwC/LrTezM4GtgP2AsiHo7h8v9njcQxzbkTaJiEj3k0UILiGE4MAyZXLnAl/N4PlyZhNCcMssKht+9hO09d+Utn7lXoaIiHQnWQyRyE1pNqJMmdy6+R2t1Mx6m1lbmSK5tmfSMaat/6b02mBTevTUnOIiIqnI4hv/obgcV2ylmQ0FcoPkH6hUmZkNMLPXgfcIhzpL2SEuiw3QFxERqSiLEJwWl+PNzIqsPy4uZ7j7wkqVufsyYHG8O7FYGTM7kNCz8z1gejWNFRERyak7BN19ATCVMJZvupnlBs9jZocC3413f1i4rZmNMrOtzWzzglXnxuWXzOxcM1s3b5sDgavi3fPd/W/1vgYREUlTVleROBHYNv7NM7MnCJ1lhsf1p5e4gsTdsczV5O31ufu1ZvZJ4CTgVMLg+AXAZsCQWOxnhPGCIiIiNcmkF4i7LyWcEzyL0PllG0Jv0RnAAe5+Tg11nkyYHeYmwmD7TwK9gVuAfdz9GHdfk0X7RUQkTT3a2zs6fr37MbO5w/q+O/Yn27/EyB8vptcGmza7SSJdSY9mN0CkXhoPICIiyVIIiohIshSCIiKSLIWgiIgkSyEoIiLJUgiKiEiyFIIiIpIshaCIiCRLISgiIslSCIqISLIUgiIikiyFoIiIJEshKCIiyVIIiohIshSCIiKSLIWgiIgkSyEoIiLJUgiKiEiyFIIiIpIshaCIiCRLISgiIslSCIqISLIUgiIikiyFoIiIJEshKCIiyVIIiohIshSCIiKSLIWgiIgkSyEoIiLJUgiKiEiyFIIiIpIshaCIiCRLISgiIslSCIqISLIUgiIikiyFoIiIJEshKCIiyVIIiohIshSCIiKSLIWgiIgkSyEoIiLJUgiKiEiyFIIiIpIshaCIiCRLISgiIslSCIqISLIUgiIikiyFoIiIJEshKCIiyVIIiohIshSCIiKSLIWgiIgkSyEoIiLJUgiKiEiyFIIiIpIshaCIiCRLISgiIslSCIqISLIUgiIikiyFoIiIJEshKCIiyVIIiohIshSCIiKSLIWgiIgkSyEoIiLJUgiKiEiyFIIiIpIshaCIiCRLISgiIslSCIqISLIUgiIikiyFoIiIJEshKCIiyVIIiohIshSCIiKSLIWgiIgkSyEoIiLJUgiKiEiyFIIiIpIshaCIiCSrV1YVmdl6wCnA14AtgbeAR4GL3P32GuvcAvg+sDcwCHgNuBs4192fzqLdIiKSrkz2BM1sfeAe4ExgJDAXeBvYC7jNzM6soU4D/gL8G9APeAzoAxwG/MXMPp9F20VEJF1ZHQ69DNgZmAOMcvdPuftw4HBgFTDJzPbsaGVm1gu4BRgIXAts7u6fATYHLiWE4XVmNjCj9ouISILqDkEzGwUcCqwBJrj7S7l17n4t8KN4d1IV1R4KjAZeBI529xWxvveAE4E/ARsB36m3/SIikq4s9gQPA9qAB939qSLrJ8flLvEcX0dMjMtrY/B9wN3bgcvj3YOrbKuIiMgHsgjBcXE5s9hKd18EvBDv7l6pMjPrCexUrk7g/rgcaWbDOthOERGRD8kiBEfH5bNlyiyMyzEdqG8I0LdCnS8Bq6uoU0RE5COyCMFBcflamTJL43KTKuorWae7rwberKJOERGRj8hinOB6cbmyTJkVBWU7Ul9mdZrZ3BKrtn55ZW++MWcY63z1UHq0ZTZsUqTbe+aZZ252932b3Q6RemTxrb+aju9Rtnewvmp0pM5Seq5q77nmpRXrzuP5hXVUI93IqLgsd3hfRLqJLEJwOTCAMHavlNw5vnc6WF9OH0rvDXa4Tnf/eLHHc3uIpdZLevSZEElLFucEl8RluYHrufN2r1ZRX8k642D6DauoU0RE5COyCMHcHJ4jypTJrZtfqTJ3/xtrO72UqnMYYWxih+oUEREpJosQfCguxxVbaWZDgdwg+Qc6WOfD5eoE/iEuX4ihKSIiUrUsQnBaXI6Pk14XOi4uZ7j7wg7WeUNcHmlm65Spc0oH6xMREfmIukPQ3RcAUwmHJ6ebWW7wPGZ2KPDdePeHhdua2Sgz29rMNi9Y9QtC77yRwFQz6x/Lr2NmPwZ2JRwyvaTe9ouISLp6tLfXM8IgiFdz+COwLWGIwxOEHqPDY5HT3f2cItstjGWudveJBes+A9xJ6ACzHJhHCMWNgfeAvd39j3U3XkREkpXJpZTcfSnh/N1ZhI4q2xB6ds4ADigWgB2o88/AdsDPgTfi7TXAb4CdFYAiIlKvTPYERUREuqKsLqorIiLS5SgERUQkWQpBERFJlkJQRESSpRAUEZFkJXcBPTNbDzgF+BqwJfAW8Chwkbvf3sy2SfbM7FhgMnCMu/+sRJmaPhNxfOwZwH7AUGAZcD9wvrvPyvJ1iEhjJLUnaGbrA/cAZxIG3s8F3gb2Am4zszOb2DzJWJxw4YIKZWr6TJjZZoR5c78NbAY8Tri25f7ATDM7KqOXISINlFQIAlMRK/4AAAfsSURBVJcBOwNzgFHu/il3Hw4cDqwCJpnZns1soGTDzMYDvwf6Vyha62fiesIFeO8Ehrr7jsDHgFMJUwhONrNtsngtItI4yYSgmY0CDiXMOjPB3V/KrXP3a4EfxbuTOr91khUz62Nmk4C7CFP3lStb02ciBuzuhOn8DnH3ZXGbNe5+HmHu297A6fW/IhFppGRCEDiM8Av9QXd/qsj6yXG5i5ltUWS9tLg4eft8wqFNCOfrXiizSa2fiYlxeZO7L+Gjctt92cz6dqTtItIcKYVg7tqEM4utdPdFrP3C3L1TWiRZG0q44PIswvyyZ1coX+tnoux2hOthrgLWB3as0AYRaaKUQjB3iadny5RZGJdjGtsUaZC/Al9093Hu/mgHylf9mTCznoQONCW3c/f3gUX524lIa0ppiMSguHytTJmlcblJg9siDeDuzwDPVLFJLZ+JAaz9f1Npu+HosyTS0lLaE1wvLleWKbOioKx0b7V8JvI/G/osiXRxKYXg6irK6vpSaajlM1HNNvnbiUgLSikEl8dlnzJlcj353mlwW6Q11PKZWJ63Tp8lkS4upRDMdWUfWKZM7vzNqw1ui7SGWj4Ty4F3a9hORFpQSiH4dFyOKFMmt25+Q1siraLqz4S7rwG83HZm1pswe8wH24lIa0opBB+Ky3HFVprZUCA3IPqBTmmRNFutn4my2wE7EXqQrgRm19lGEWmglEJwWlyONzMrsv64uJzh7gs7p0nSZLV+Jm6IywPNbOMi2x0fl9e7+4oi60WkRSQTgu6+AJhKmCZrepxiCwAzOxT4brz7wyY0T5qgjs/E3YRLJm0I3BivKIGZ9TSzU4AJwPvAeY19BSJSr5QGywOcCGwb/+aZ2ROEwc/D4/rT3f2uZjVOmqLqz4S7t5vZ4cAMYDfgBTN7EhgCDCYMizjS3Z9GRFpaMnuCAO6+lHAe5yxCh4VtCD38ZgAHuPs5TWyeNEGtnwl3fw7YAbiIMEXatoQhE3cAe7j7LxvfehGpV4/2do3lFRGRNCW1JygiIpJPISgiIslSCIqISLIUgiIikiyFoIiIJEshKCIiyVIIiohIshSCIiKSLIWgiIgkSyEoIiLJUgiKiEiyFIIiIpKs1C6lJHUys0ozrq8C3gKeI1xR4UJ3fyPjNqwHDMq/0K2ZTQSuAha5+9Asn09Eui/tCUqtFhAuLFv4Nyeu/zRwOjDXzIZl9aRmdgjhkkd7ZlWniKRLe4JSq3PcfUqxFWbWEzgEuBL4GGEPLavQOodw8dpCvwVmEa7oLiLSIQpByZy7rwF+YWZjgP8H7GFmW7n7ggY+55vAm42qX0S6Jx0OlUb6Xd7tTzStFSIiJWhPUBppTd7tHoUrzWx74BvAboRDnH2AZcCjwJXu/uu8spOAM/M2v8LMrgDOcvdJlTrGmNke8bn+ARhI2Gt8BLjC3afX8RpFpAvTnqA00r/G5Rrg4fwVZnY8IeyOATYDngGeBTYEvgBMM7Oz8zZ5kdDx5t14/5l4/8VKjTCzS4C7gK8AvQmdd94D9gZ+Y2bXm1nvGl6fiHRxCkHJnJn1MbMTgf+ID13p7n/NW78VcDHh83cGsJm7f8rdtyF0pLkhFj3ZzAYAuPuV7r4rsDiuO8/dd3X3Kyu05T+BEwhDN75JGFqxk7sPAb4KLI/LC+t+4SLS5ehwqNTqe2Z2dMFjPYENgNHAuvGx64ETC8rtRQilx909f28Pd3/dzE4iBFNvwAi9PqtmZn0IIQvwfXf/ScFzTTOzNuBXwDfN7P/njz0Uke5PISi12ir+FbMAuB24zt0fLFzp7pcBl5lZ3xLbv5N3e7062rgbsBEhcC8rUeZ6wl7gEGAf4NI6nk9EuhiFoNTqyNw4wTgucARwMnAc4ZDmgmIBWOA9M9uJ0HN0VPzbFtg6r0w9h+xz9Sxw978XK+Du7Wb2F0IIWh3PJSJdkEJQ6hbHBT4HHG9mrxB6cV5iZn3cvei5NjM7AjgX2Lxg1fPAzwkdZuq1QVxWGj+YC8j+GTyniHQh6hgjWTsLuDPePi8OTfiQGIBTCAF4B3AssAuwsbuPJHRgycJbcblhhXIDCsqLSCK0JyiZiocXjwTmEsLnajMbW3A48rS4vMbdjyhSTVYTYM+Ly63MbINih0Tjodwd4t2GzWgjIq1Je4KSOXdfBJwU7w4Bzi8osmVcPlqiivxep4U/1HID8D8y+L6ImYTB970ovXf5NcIeaTthr1REEqIQlEb5OXBfvP11M9stb11uD+1YM/tgMmwz2yDODHNqXtnC3qHL43J4pQa4+zuE844A/2Vm34h7frnnOwC4PN79X3efX6lOEeleFILSEO7eDnydMMNLD8I0Z7mxg6cT9ujGAs+Z2eNm9jiQ61SzkDB7DHz00OjsuPyumc02szMo70Lgp4S9wcuAV8zsITNbBPwa6BeX367phYpIl6YQlIZxdwdyg+EN+H58/BbgM8CNhBlgtgG2AJ4gnC/cDvhl3O5LBdWeBPwGeDvWuU2FNrS7+zcIA/RvBFaz9hzg74Avu/tB7r6ytlcpIl1Zj/b2ShcKFxER6Z60JygiIslSCIqISLIUgiIikiyFoIiIJEshKCIiyVIIiohIshSCIiKSLIWgiIgkSyEoIiLJUgiKiEiyFIIiIpIshaCIiCRLISgiIslSCIqISLIUgiIikiyFoIiIJEshKCIiyVIIiohIshSCIiKSrP8DE4S87TXIfOYAAAAASUVORK5CYII=\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": 32,
   "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": 33,
   "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/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": [
       "<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>0.20 ± 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, 0.365</td>\n",
       "      <td>0.01, 0.820</td>\n",
       "      <td>0.20, 0.262</td>\n",
       "      <td>0.06, 0.653</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, 0.606</td>\n",
       "      <td>0.30, 0.009</td>\n",
       "      <td>0.05, 0.587</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, 0.354</td>\n",
       "      <td>0.00, 0.985</td>\n",
       "      <td>0.10, 0.492</td>\n",
       "      <td>0.11, 0.470</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, 0.365       0.01, 0.820   \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, 0.606   \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, 0.354       0.00, 0.985   \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, 0.262       0.06, 0.653   \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, 0.587   \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, 0.492       0.11, 0.470   \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": 33,
     "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": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "stat.to_latex(output_path / \"statistics\" / \"statistics.tex\")\n",
    "stat.to_latex(output_path / \"statistics\" / \"statistics.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot PSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from septum_mec.analysis.plotting import plot_bootstrap_timeseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "freq = freqs.T.iloc[0].values\n",
    "\n",
    "mask = (freq < 49)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "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": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "action = project.require_action(\"stimulus-lfp-response-mec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copy_tree(output_path, str(action.data_path()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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
}