{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import seaborn as sns\n",
    "import scipy\n",
    "import pickle\n",
    "import yaml\n",
    "import time\n",
    "\n",
    "import matplotlib\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# define paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You must define the path to the raw data (downloaded from the database)\n",
    "# the raw data to plot example cells is not part of the github directory\n",
    "data_folder = '/Users/kperks/mnt/OneDrive - wesleyan.edu/Research/Manuscripts/GRC_PerksSawtell/AcceptedRevision_CellReports/data_raw'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_dir = Path.cwd().resolve().parents[0] #this is the path to the folder you should be running these notebooks from (Perks_Sawtell_2022)\n",
    "\n",
    "#primary resource folders:\n",
    "script_folder = top_dir / 'scripts'\n",
    "# data_folder = top_dir / 'data_raw'\n",
    "\n",
    "#folders with processed data:\n",
    "df_folder = top_dir / 'data_processed/df_cmdintact'\n",
    "meta_data_folder = top_dir / 'data_processed/GRC_properties_Meta'\n",
    "\n",
    "#where to save any figures that are generated:\n",
    "#change as needed. Default is the location where Perks_Sawtell_2022 lives (if you are running this script from that folder)\n",
    "figure_folder = Path.cwd().resolve().parents[1] / 'Perks_Sawtell_2022_FiguresComponents'\n",
    "\n",
    "#for storing simulation states\n",
    "sim_filename = 'grc_model_initialized.pickle'\n",
    "sim_filepath = top_dir / 'data_processed/grc_model_simulations' / sim_filename "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import custom functions from scripts folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sys.path.append(script_folder)\n",
    "from ClassDef_AmplitudeShift_Stable import AmpShift_Stable #this is the function that imports all of the cell_data structures. Needed if plotting any example cells. \n",
    "from FunctionDefinitions import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# configure figure styles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# configure figure stylessns.set_style(\"ticks\")\n",
    "sns.set_context(\"paper\")\n",
    "rc = set_fig_style()\n",
    "matplotlib.rcParams.update(rc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.colors as colors\n",
    "def truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100):\n",
    "    new_cmap = colors.LinearSegmentedColormap.from_list(\n",
    "        'trunc({n},{a:.2f},{b:.2f})'.format(n=cmap.name, a=minval, b=maxval),\n",
    "        cmap(np.linspace(minval, maxval, n)))\n",
    "    return new_cmap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define datafile pre-processed from Nate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "processed_datafile = top_dir / 'data_processed/Figure6Data/Figure6_summaryforfigure_kp.xlsx'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Panel D and plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.cm.plasma(np.linspace(0.2,0.85,13))\n",
    "newmap = truncate_colormap(plt.cm.plasma, minval=0.2, maxval=0.85, n=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 86.4x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sheet = '20211201_011'\n",
    "df = pd.read_excel(processed_datafile,sheet_name=sheet)\n",
    "\n",
    "df = df.sort_values('stimamp')\n",
    "\n",
    "df['stimamp_percent'] = np.round(100*((df['stimamp']/250)-1))\n",
    "df['peak_latency'] = df['peak_latency']*1000\n",
    "\n",
    "hfig,ax = create_fig_tuning()\n",
    "sns.scatterplot(x='peak_latency',y='peak_amp',hue='stimamp_percent',data=df,\n",
    "               ax=ax,legend=False,palette=newmap)\n",
    "\n",
    "ax.set_ylabel('Mean Peak Amplitude \\n (mV)',linespacing=0.9)\n",
    "ax.set_xlabel('Mean Peak Latency \\n (msec)',linespacing=0.9)\n",
    "# ax.set_xlim(8,13)\n",
    "ax.set_ylim(-1.1,-0.3);\n",
    "\n",
    "sns.despine(hfig)\n",
    "# figsave(figure_folder,'Fig6_scatter'+sheet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12   -30.0\n",
       "10   -25.0\n",
       "8    -20.0\n",
       "6    -15.0\n",
       "4    -10.0\n",
       "2     -5.0\n",
       "0      0.0\n",
       "1      5.0\n",
       "3     10.0\n",
       "5     15.0\n",
       "7     20.0\n",
       "9     25.0\n",
       "11    30.0\n",
       "Name: stimamp_percent, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['stimamp_percent']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Panel E and plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 86.4x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sheet='20211201_007'\n",
    "\n",
    "df = pd.read_excel(processed_datafile,sheet_name=sheet)\n",
    "\n",
    "df = df.sort_values('stimamp')\n",
    "\n",
    "df['stimamp_percent'] = np.round(100*((df['stimamp']/250)-1))\n",
    "df['peak_latency'] = df['peak_latency']*1000\n",
    "\n",
    "hfig,ax = create_fig_tuning()\n",
    "sns.scatterplot(x='peak_latency',y='peak_amp',hue='stimamp_percent',data=df,\n",
    "               ax=ax,legend=False,palette=newmap)\n",
    "\n",
    "ax.set_ylabel('Mean Peak Amplitude \\n (mV)',linespacing=0.9)\n",
    "ax.set_xlabel('Mean Peak Latency \\n (msec)',linespacing=0.9)\n",
    "# ax.set_xlim(8,13)\n",
    "ax.set_ylim(-1.1,-0.3);\n",
    "\n",
    "sns.despine(hfig)\n",
    "figsave(figure_folder,'Fig6_scatter'+sheet)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Panel F and plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 86.4x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sheet='slope_ML'\n",
    "\n",
    "df = pd.read_excel(processed_datafile,sheet_name=sheet)\n",
    "df['slope']=df['slope']/100\n",
    "\n",
    "hfig,ax = create_fig_tuning()\n",
    "sns.scatterplot(x='track',y='slope',data=df,\n",
    "               ax=ax,legend=False,color='black',s=15)\n",
    "ax.set_xticks([0,7])\n",
    "ax.set_xticklabels(['lateral','medial'])\n",
    "ax.set_xlabel('')\n",
    "\n",
    "ax.set_ylabel('Sensitivity (mV/msec)',linespacing=0.9)\n",
    "# ax.set_xlabel('Mean Peak Latency \\n (msec)',linespacing=0.9)\n",
    "# ax.set_xlim(8,13)\n",
    "# ax.set_ylim(-1.1,-0.3);\n",
    "\n",
    "sns.despine(hfig)\n",
    "\n",
    "figsave(figure_folder,'Fig6_scatter'+sheet)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Panel G and plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 86.4x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sheet='sensitivity'\n",
    "\n",
    "df = pd.read_excel(processed_datafile,sheet_name=sheet)\n",
    "df = pd.melt(df,id_vars=['fish'],value_vars=['MZ','DLZ'])\n",
    "\n",
    "df['variable']=df['variable'].astype('string')\n",
    "df['fish']=df['fish'].astype('int')\n",
    "df['value']=df['value'].astype('float')\n",
    "\n",
    "hfig,ax = create_fig_tuning()\n",
    "\n",
    "ax = sns.boxplot(x=\"variable\", y=\"value\", data=df,color = 'grey', showfliers=False)\n",
    "# sns.stripplot(x='variable',y='value',hue='fish',data = df,\n",
    "#              ax=ax,s=5,alpha=0.5,jitter=0.25)\n",
    "sns.stripplot(x='variable',y='value',data = df,\n",
    "             ax=ax,s=3,color='black',jitter=0.25)\n",
    "plt.legend([],[], frameon=False)\n",
    "ax.set_ylabel('Sensitivity (mV/msec)');\n",
    "ax.set_xlabel('')\n",
    "\n",
    "figsave(figure_folder,'Fig6_scatter'+sheet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=4.603638147546896, pvalue=0.0001675916731583152)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sheet='sensitivity'\n",
    "\n",
    "df = pd.read_excel(processed_datafile,sheet_name=sheet)\n",
    "scipy.stats.ttest_ind(df['MZ'], df['DLZ'], equal_var=False, nan_policy='omit')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Panel H and plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 86.4x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sheet='latency_shift'\n",
    "\n",
    "df = pd.read_excel(processed_datafile,sheet_name=sheet)\n",
    "df = pd.melt(df,id_vars=['fish'],value_vars=['MZ','DLZ'])\n",
    "\n",
    "df['variable']=df['variable'].astype('string')\n",
    "df['fish']=df['fish'].astype('int')\n",
    "df['value']=df['value'].astype('float')*1000\n",
    "\n",
    "hfig,ax = create_fig_tuning()\n",
    "\n",
    "ax = sns.boxplot(x=\"variable\", y=\"value\", data=df,color = 'grey', showfliers=False)\n",
    "sns.stripplot(x='variable',y='value',data = df,\n",
    "             ax=ax,s=3,color='black',jitter=0.25)\n",
    "plt.legend([],[], frameon=False)\n",
    "ax.set_ylabel('Latency Shift (msec)');\n",
    "ax.set_xlabel('')\n",
    "\n",
    "figsave(figure_folder,'Fig6_scatter'+sheet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=0.6332764155294383, pvalue=0.5338946348780096)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sheet='latency_shift'\n",
    "\n",
    "df = pd.read_excel(processed_datafile,sheet_name=sheet)\n",
    "scipy.stats.ttest_ind(df['MZ'], df['DLZ'], equal_var=False, nan_policy='omit')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Panel I and plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 86.4x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sheet='amp_change'\n",
    "\n",
    "df = pd.read_excel(processed_datafile,sheet_name=sheet)\n",
    "df = pd.melt(df,id_vars=['fish'],value_vars=['MZ','DLZ'])\n",
    "\n",
    "df['variable']=df['variable'].astype('string')\n",
    "df['fish']=df['fish'].astype('int')\n",
    "df['value']=df['value'].astype('float')\n",
    "\n",
    "hfig,ax = create_fig_tuning()\n",
    "\n",
    "ax = sns.boxplot(x=\"variable\", y=\"value\", data=df,color = 'grey', showfliers=False)\n",
    "sns.stripplot(x='variable',y='value',data = df,\n",
    "             ax=ax,s=3,color='black',jitter=0.25)\n",
    "plt.legend([],[], frameon=False)\n",
    "ax.set_ylabel('Amplitude Shift (mV)');\n",
    "ax.set_xlabel('')\n",
    "\n",
    "figsave(figure_folder,'Fig6_scatter'+sheet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=5.81346178544406, pvalue=1.0175443794799167e-05)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sheet='amp_change'\n",
    "\n",
    "df = pd.read_excel(processed_datafile,sheet_name=sheet)\n",
    "scipy.stats.ttest_ind(df['MZ'], df['DLZ'], equal_var=False, nan_policy='omit')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import waveforms and plot A "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "directory = top_dir / 'data_processed/Figure6Data/waves/'\n",
    "\n",
    "stimamp = np.asarray([int(file.parts[-1].split('(')[0][0:3]) for file in Path(directory).glob('*.txt')])\n",
    "stimamp = np.round(100*((stimamp/250)-1))\n",
    "_ = [file.parts[-1].split('(')[1] for file in Path(directory).glob('*.txt')]\n",
    "cellid = [s.split(')')[0] for s in _]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 10., -15.,  25.,  -5.,  20.,  25., -10.,  -5.,  10., -25., -15.,\n",
       "       -25.,   5.,   0.,   0.,  15., -10., -20., -20.,  20.,   5.,  15.])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stimamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "wavmean = []\n",
    "wavsem = []\n",
    "for file in Path(directory).glob('*.txt'):\n",
    "    data = pd.read_csv(file, sep=\"\\t\", header=None)\n",
    "    data.columns = [\"time\", \"mean\", \"sem\"]\n",
    "    data = data.drop(0,axis=0)\n",
    "    data = data.astype('float')\n",
    "    wavmean.append(data['mean'].values)\n",
    "    wavsem.append(data['sem'].values)\n",
    "    xtime = data['time'].values*1000\n",
    "    \n",
    "wavmean = np.asarray(wavmean).T\n",
    "wavsem = np.asarray(wavsem).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def search(list, ID):\n",
    "    boolarray = []\n",
    "    for i in range(len(list)):\n",
    "        if list[i] == ID:\n",
    "            boolarray.append(True)\n",
    "        if list[i] != ID:\n",
    "            boolarray.append(False)\n",
    "    return boolarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAG0AAABsCAYAAABgpDzzAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAVaElEQVR4nO2de3Bc1X3HP+c+970rafWWH5JlIVsujjEgysOEBic8GhANTEKYtumA23EdkkkwBnew6xAmSVPScbE7JGSa0kwJ0xq7CW2TYgqJMSbIgI2NIwnjh2TJest67Wq1u/fe0z8Ewg7GXsnaldbo85e0e87v/M797jn33PP4XSGllMySVSjT7cAsE2dWtCxkVrQsZFa0LGRWtCxkVrQsRJtohsbGRrZu3YrL5eKBBx6gpKSE3bt389xzz2GaJhs2bMDv96fD11k+QE6Qb3/72zISicjm5ma5efNmKaWU69evl47jyD179sht27ZN1OQsE2TC3WMkEsHr9VJYWEhvby8AjuMghBj/rL6+ni1btlBfXz/lP7JZJtE9GoZBLBajs7OT4uJiAKSUOI5DR0cHxcXF1NbWUltbO+XOzjKGkHJi01hNTU388Ic/RNd15s2bx6233kp7ezs7duzANE02btyIy+VKl7+zMAnRZpl+Jtw9ftKRyQQIgRMbwTrZghyNoeaGUXLCICWKP4gQIq0+zLa085BoeQ/nyCsoQ3uRsQhIC5w4UvUhNBUhQCYtHNuF0BWklcBRi5F6EJSx712XfxrFG0DkVp+1DOnYMHgMkbMwJZ9mRfsYkm1N2L/+NoohkWYekZNBrGgCO2rhREcRMoI1aiAUsNHQvXEQBqoBpn8IzQTNryA8YRzbQJgeVGUI4fVDYhjV5wdvDlgJSIwgHR3t04+k5Nts9/h7OIkRrP/+OnJkiNHcOxh54y3s/iFGIklsSwdPGBnIxy69lKFjQ0Q6YgTDCQpLQGeY+EiS2OAITiSCNTiI4RrEm3sKIwB6OAdlZBTcRcSP9CASJwEdR2pIzUvw06n5ONvS3scZOoHz+j8gR04RGyhl+LiE6AD9vfkca1tEpEtiFAaw4w6qRwdDw8z34gq7SQzEGTnSix2NI20HzVTAkaimgidgEypK4HNOoMQHUFSB4lZB9+FdupjclVfhKisAQCipPTbPigY4zS9gN/wPdvA6YgfeYbglSrTboLWllN6hfJZ+/TKqv1iBaqjntGMnHey4Tf/xKHbSZqQnzlBzhNadx4l3RbBjFl7PCN6ghcsTx+/qR3OGEQgsafIHzzyakr+feNGc5hewj75KIlGJ42gM/uY1+poDNB5dxKfWXU3lHeVo5rnFShUpJT2NQzRuayZytJ9Y2yAKDjJp4fcO8tn/vS8lO2m9px35u7/DsSVCVRkeziHvhj9i3mcq0lnkhJDJKLJ5F8lYCcIXZOBXr3KqNURTx6f4k913YHin9vIIIShYHKTgb5cC4FgOg60jjA4k6T08mLKdtIrWe8TE7R/G5T5F2Gxg5LU++MxDZ0177OUujr7YyTUPVJOM2Zh+jeO/6aZwSZBTRyMIVbDgxqIp9U8e3oFDHslhQXRfPafa/LR0zuO6f/zMlAt2NhRNIafcB0DxspyU86XVs7JwI1ZSxxr0IQ2FgP/AWdOd2NPDsRfaCeh91G9VSPa00bI3zlXfXMaxX3eha9D9bpT51xeg6lOzBChHT+F0vINl/iFW3wFa3/JjJ5IsevizlCzPnZIy0kVaRXOFAkgpwY5DPIpqRs/4XkpJw/Y2+hr6mW8/RSi3k/6TRVjoFH7KRfTlTkYGc0iaQbzVJXTs66esNm9KfHPe3YbUSoi//SqHfxVEyiSVf3M3BdeVTIn9dJJW0cIP/f34361//x38zqvj/zuWw//9zUFy55oE2v6D3IUdjPbPIVTUhe5JIGMW8dhJulzXM3y8i8RxyZGdgsETUWrumntBfsnBZhjqJaEu5lTDuyTsQso/V0DBdVUXZDdTZG67gW7AaVNybz51lJwCgbXnecqXHCDaXYLv0kpk3kqSowtQXQJv/hAFgX2EryhEvPcO8a4ojT9vI9afuCBXnOO/wrb9nPqfl+jvySG0KI/8W66+wApmjoyJpuoGQow9XTi2Q9e+HuL1v2bRVXuI9RUTWDIX3013458LoTvvgdBy9KAXb7mPgH6M3OoATksbi+8oo+d3qY+0fh8pJQz3MHiom9Eem2GrlHk3z8ddnR2tDDI5jeVyQWxMtPY3TiGPHWXJLb8hEc3FXb6AwJ89iBAK7ptXjTk2pxp5ZAccfB1NTxCo9DLUO0j3G904lxVM3o/B48S7+xlqTBCN+Fl8ew7BlTdMRQ0zRuZamts13tKOvdTBosV7EIrEKKsh8OfrEOJMV4TpQcxZgVCimIurMexuSMRQNBjuiE3aD9n1FoONMayIJLRsAaHP3ZDy9NFMIWPeCvPD1exTB3spmN+C7ZQS+NMHPnb9SQTmooRycXkjMNiJaqrIRJLRweR4Gsd2GB1I/R4XPfQWVt8IthEktKwSo6R48pWaJjImmuJ2j7c0ulvRzCS+Lz74kRb2kXxFVciOBhRdxVfswmrvxR61xr9/5TsN/O8392PF7fP6IKUk0fwesaiHnPkuAjesuKA6TRcZE01ze+B90cLeY0ipYMwpP28+JbcaIYcRbhe+OV5kdJD4qVEAjr/YTuT4ENc9vJg3f3SU802jdvzfO9ijNu4CP+acMhRDv/CKTQMZbWkfDPkLy1qx7RQ3tOYvRYhhVI+J7gFdJBntGbunvbWlCU9QJXehD3+Jm12P/e6cpkZ3/wsj/UEMEwK3/8mFVGdayZxovg8HIsH8fkRgTkr5hCuEcLlRjThYo7gCgnjXMPv++ShCQOnVBfQe7KfmzjmYPp3BE9Gz2jl1qJuQ/zCmPwc1lI8WTH2ub6aRudGj6R1vaaYvhrbg8pTzCl8YTYkg7DiK6mCYkra9fZRemUfl7XP53b8dwbEcLruvgrf++dhZbRz7p39lZDCEFu8j58//ciqqNG1kdPQokEhHopoWWsVlqef1hVGUUYhHMXPdhOcbXPXXC4gc6SUZSbLgj+fQtrsL06+jaAJr9MxBScsr3RSE9qFJE/PKG1ADoSmuXWbJ4D3NBQLiQ0kU1UYtWpByXhEsQ4hRZGIEM+RGceJ0vtGDNTzKe//WQOm1hZx8tQuAhTcV859/Uc+OP/0tju2w+3sNHN7yCt7QCJpbxXtj9t7LPiBzoukmANHusZGfapgp5xWBMhTdguQodnMDihWjdWcr+ZcXIS0HJ+EgxNgzW+kVedy6ZTm191ex/Z7fMveaMFrvu7iDNlpuGNUXSkf1MkoGJ4w1BNDTOIhkgps5vcUIxUJIG83nhniM/oY+Ci4vYP7tlbQ8f4TC5WG69/UB4AmblF6Zxxee+UNGDh6lpKIDqQUwF1069fWaBjIo2tgzUV9DP4IJbkvxFiPUJGZhgMBVNcj4CKM9MQZ+20TkcAfRkxHKVhTRuqvzjGyKqtCx4y1yF47gOB70qtQHPzOZzHWPmg5IIm1nH5KfC2H4EDjYLYew3tmFKhwuuaOY0JIiho+OtS7dq5Ecsc7IF2kfZsGlb+ALteEkTNSS1O+jM5nMifb+pGy8e3ji3SOA5iL89YdQDYGqSxIdfZTcsgihjNmSUuItdBNpHxnPcnDDv6O7Qao+XLd+/bxTZtlCxmth9UeYzPkEESrBPrILoWuohoMr7KL7V/tBASNgEO+PU3XnfA49/R5SSvZvbSDP/B2q6cXRw5jl2bNedj4y/9OLT25ZRSmswGk7hOJ2obsleTW5nHx2D5oORkgn2jqEO8+FO9fkN9/cS6jcTU5xG6ZnGK3yyimuxPSS8b38mhWZVPcoXDkIrw81pqJiIRSJqzQHISSaKYi0DpO3tIA/uK+K+ECCo//0n+TljGB4BzCX1019RaaRCYnW2dnJo48+itvt5t5772Xx4sUA7Nq1i1/84hcYhsGqVatYsODjb/iGNji57tFXijD3o2g2vooQeqmHuMhH1UAokmjrMDC2l9AddhFMvISVLMVWFuNyhyde4AxmQt3j888/z9q1a3nsscd45plnxj/fv38/fr+fQCBAaWnpOW14PREQk9hmXbgcRY2iqqOowmbg9Sa8lQUIFayhGMnohwujieE43lAXTkLDc+1nJ17WDGdCLa2trY3S0lJM0ySR+HC1eOXKlVRVVVFfX8/27duprKxk7969XHnllR85MO/1RJFi4r2y0FwIbwjNPoGwTKzWbjR9EXHLIjEyirQ13n36EPP+eAFvr9tB9RIJjsBYcPaDfNnMea/ek08+ydtvvw1AXl4eXV1d5OXl4fV6x9M0NTVRU1NDIBDAcZxzRjdw+UdATX0K63REznyE8y6K1Uv4xmuQ8TjYEqRk8V8tZeBwPwd+8AbeAhXNdLBz5mXd/o9UOK9oq1evHv+7s7OT733ve2iaxurVq9m5cyehUAhVVVm/fj1SSh5++OFz2jPdowjdMylnlfwaRMFhtJOncHwerEgMMAAwgiYFVxRRcEURh9d/FxwF1zW3TKqcmc6E+qmioiI2b948/v/pA466urqUbBjuOMIVmEixH1K4HMX5MUJxzvz8tJFNvLMPH4dw4m6MysWTK2eGk/G+Q3clEO7QpPIKRUN4clCU+PhMyAfsW/tfRFsHOPXSG3gKY9hK0UXZNcI0iKYaFsI3+SG4COSjiATKaQf9dJ+Kp9RHxwvvkugeRNNi6JdcMRXuzkgyL5puI4KFk84vfAWoPg37vf3vfwAMDeAJQVD+nID9CxRhYy67Zkr8nYlkXDRFt1ACk9/WLbxhhEyQbG/90KZrbNnH5z+J6eoCoaEVn397XraSedE0B+G9gO7R8KMGvJjlc5GOg9XXS/xYC7nXViMtUN0JlKJPXTQz+mcj86IpDngv4KSl7kVxaahuE6wkIhlHUWw88/JxIkMouoNx87qpc3gGkvngLwoId3Dy+VVzbIgvJIoqMAv8KK4wQhMIe5Tu5hsoneToNFvIeEsTQiImOSMCgGoghDMWl0qB+P7fAiBHupBJcIKpxZfKZjLf8Qs5uQnjD1ANwEHoBjIWQcaiGIU5JBpexR7VcNfUTJmrM5VpEA1QLuDgg2oCEqGp2P2nEB4/vmULSez5d2zHQ95nlk+VpzOWzHePSIRyYS1NCBCqQI5EyK37HMnGt9HdESxRkvZYizOB7BsXKzooAlRB4mQ7nktrcBp+TjLhgTlXTbd3GSH7RFNNEBIhHOToKGpeLvbQAKpLJXzXF6bbu4yQffEeFX3sp2bbBGsXIge70HNUzOW3oZjZeUhwomRdSxOKOjbb4SRhdAjrnZ+jqv0oNV+cbtcyRtaJBjAWg9ZCKy6D7v3E4+XjBzw+CWSnaIqCwEHx+pDRPhTXzA5ANtVkpWhC1ZBOAuk4yHgMJXRxbZE7H9k3EAFQNbRgELQkii+AWrFkuj3KKFkqmgFDPciRdkQwF7Vo/nR7lFGysntE1bDa30MzhrCdXJTc7Iu6cyFkZUsTioricaMEBWrNXQg1K6sxabKztkJFn1uOEgK1fOl0e5NxsrN7VFSEMwquT+ZrLLNTNKFCrB9heM+f9iIkO0XTDJAxRGD+dHsyLWSnaIYH4dIhePFukzsX2Sma7kIJl4Lv3GfhLlaycvQoEODJvbAV8CwmO1uaY4GSlb+3KSE7a66ZYEzyuNRFQFaKJmr+AiYaqukiIjtFEwImE/XnIiE772mfcNL6psLHH38c00zvNoC2tjbKysqyvgyAeDzO2rVrz59QppEnnngineYvqjImUk5aWlpjYyNbt24lEonw3e9+l5KS9LyTbNeuXfzkJz+huLj4vJGCJoplWbz22mvs27cPr9fLoUOHME2TDRs24PdP3UT16eUkEgn6+voQQrBu3Tpyc8++9yUt97Tt27fz/e9/n0cffZRt27alowhgLFLQ/PnzU4oUNFG6urrYv38/UkqOHz/O5s2bqaur44UXXkhbOcPDw/j9fgoLC8nJ+fgQ9GkRLRKJ4PV6KSwspLe3Nx1FAGORgh555BFWrFjB9u3bp9R2aWkpX/jC2I5lx3EQQqSlPqeXc+edd/LII49QUlLCK6+88rF50iKaYRjEYjE6OjooLk7fVoCmpiZ0XR+PFJQupJQ4jpP2+hw5cgQAv9+PbX/8u3PS8pz25S9/mfXr16Pr+nkj+FwIE4kUdCHcdtttPPjgg5imycaNG9NWTl9fHxs3bkRKec5yPvEvJ89GZh+us5C0irZp0ybuvfdevvSlL/Hwww/z0ksv0dDQcMF2V69ezf79+8/5IHquDmTt2rX09vayY8eOlMtM6aH3NH70ox+llE5KyY9//OMJ2U7r3OOmTZuor6/nxIkT3HXXXezYsQO3282aNWtYtGgRDQ0NlJeX09LSwubNm/nZz37GiRMnSCQSbNiwAf39WP6PPfYYMBZx/LbbbqOtrQ1d12lqaqKpqYmdO3cSiURQVZV77rmHdevW8fnPf567774bgCeeeIJoNIrL5aK2tpampiaam5tpaWlhy5YtRCIR2tvbKSsro729na997Ws8+eSTPP7442zbto25c8de0Xx6XdauXctXv/pVtm7diqZp3HfffVRWVgKwd+9eKioqqK+v5+mnnyY/P59kMonb7ebSSy9lzpw5PPfcc9i2zUMPPYRhGLS2tjJnTmpvupq27nHNmjX4fD7WrFlDVVUVR48e5Ze//CXBYBDLsmhraxtPu3LlSi677DIOHjzIkiVLuOSSS1iyZAnV1dVomsaBAwfw+/10d3cTiURYuHDhuGAAx48fZ8GCBdx4441cffXVVFdXU1RUNP59XV0d119/PbW1tdx9990cOHAgpTr09vZi2za33HLLGQ/Ce/bsYdmyZQDU1tayatUqQqEQGzZs4PXXX6ezsxPDMKirq8M0TZYtW8Zrr72W8rWbFtHcbjdCCBRFwTAMhBBIKSktLeX+++9nxYoVFBSMhWLq7u7m2WefpaKignD4owctLMuipqaG+++/n2uvvRafz0coFDojzVe+8hWqqqr4wQ9+gGVZH7Hh8XjO8MW27fGz2/39/ePpVFXFcRyklAwNDZGXl8c3vvENjh07dkZX29fXRyAwtt7n9XoRQozbtiyL8vJyVq1axZ49e9i9ezfBYJCenp6Ur9+MWZpxu92UlZWxadMmcnNzuemmm4CxC5pMJnn55ZdJJBI0NzeP5/kgJO/AwADf+ta3qKioOKvtF198kVgsxty5c9E0jZ6eHjo7O8+a9gNqamrYvHkzjY2NLF06tiG2qqqKZ599lvb2dlwuF4lEgqeeegpd17n99tvH84bD4TPE/n0GBgb46U9/SjKZpK6ujqGhobP+ID+O2SF/GnjzzTfp6enh5ptvTin9M888w4oVK2b+Pe1i5vLLL6elpSWltFJKotFoyoLBbEvLSmZbWhYyK1oWMitaFjIrWhYyK1oWMitaFvL//vMcSTQD2kEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cell = '20211201_011'\n",
    "\n",
    "thisid = search(cellid,cell)\n",
    "\n",
    "thiswavs = wavmean[:,thisid]\n",
    "thisamp = stimamp[thisid]\n",
    "\n",
    "sortedinds = np.argsort(thisamp)\n",
    "thisamp = thisamp[sortedinds]\n",
    "thiswavs = thiswavs[:,sortedinds]\n",
    "\n",
    "\n",
    "colinds = plt.cm.plasma(np.linspace(0.2,0.85,13))\n",
    "# colinds = np.asarray([c for c in reversed(colinds)])\n",
    "\n",
    "hfig,ax = create_fig()\n",
    "for i,sweep in enumerate(thiswavs.T):\n",
    "    ax.plot(xtime-4.5,sweep,color = colinds[i+1],lw = 0.5);\n",
    "\n",
    "ax.set_xlabel('Time after stimulus (ms)')\n",
    "ax.set_yticks([-0.5,0])\n",
    "ax.set_xticks(np.arange(0,20,5))\n",
    "ax.set_xlim(0,15)\n",
    "ax.set_ylim(-1,0)\n",
    "\n",
    "sns.despine(hfig)\n",
    "ax.set_frame_on(True)\n",
    "yax = ax.spines[\"left\"]\n",
    "yax.set_visible(False)\n",
    "\n",
    "figsave(figure_folder,'Fig6_LFPwavs_'+cell )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cell = '20211201_007'\n",
    "\n",
    "thisid = search(cellid,cell)\n",
    "\n",
    "thiswavs = wavmean[:,thisid]\n",
    "thisamp = stimamp[thisid]\n",
    "\n",
    "sortedinds = np.argsort(thisamp)\n",
    "thisamp = thisamp[sortedinds]\n",
    "thiswavs = thiswavs[:,sortedinds]\n",
    "\n",
    "\n",
    "colinds = plt.cm.plasma(np.linspace(0.2,0.85,13))\n",
    "# colinds = np.asarray([c for c in reversed(colinds)])\n",
    "\n",
    "hfig,ax = create_fig()\n",
    "coloroffset = 1\n",
    "for i,sweep in enumerate(thiswavs.T):\n",
    "    ax.plot(xtime-4.5,sweep,color = colinds[i+1],lw = 0.5);\n",
    "\n",
    "ax.set_xlabel('Time after stimulus (ms)')\n",
    "ax.set_yticks([-0.5,0])\n",
    "ax.set_xticks(np.arange(0,20,5))\n",
    "ax.set_xlim(0,15)\n",
    "ax.set_ylim(-1,0)\n",
    "sns.despine(hfig)\n",
    "ax.set_frame_on(True)\n",
    "yax = ax.spines[\"left\"]\n",
    "yax.set_visible(False)\n",
    "\n",
    "figsave(figure_folder,'Fig6_LFPwavs_'+cell )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-25., -20., -15., -10.,  -5.,   0.,   5.,  10.,  15.,  20.,  25.])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thisamp"
   ]
  }
 ],
 "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": "3.6.12"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "315.797px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}