{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "code_folding": [
     5
    ]
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "from brian2 import *\n",
    "import sympy\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",
    "from scipy import signal\n",
    "from scipy import optimize\n",
    "from scipy import stats\n",
    "import scipy\n",
    "from scipy.stats import gaussian_kde\n",
    "import pickle\n",
    "import yaml\n",
    "import time\n",
    "import random\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "from statsmodels.stats.anova import anova_lm\n",
    "from statsmodels.graphics.factorplots import interaction_plot\n",
    "\n",
    "import matplotlib\n",
    "# matplotlib.rcParams['pdf.fonttype'] = 42\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": [
    "sns.set_style(\"ticks\")\n",
    "sns.set_context(\"paper\")\n",
    "rc = set_fig_style()\n",
    "matplotlib.rcParams.update(rc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load GC metadata from yaml files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(top_dir / 'data_processed/cell_type_metadata.yaml', 'r') as file:\n",
    "    cell_type = yaml.load(file,Loader=yaml.FullLoader)[0]\n",
    "    \n",
    "with open(top_dir / 'data_processed/Vm_offset_metadata.yaml', 'r') as file:\n",
    "    Vm_offset = yaml.load(file,Loader=yaml.FullLoader)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  GC properties (unsubtracted CvsU)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## import dataframe "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = top_dir / 'data_processed' / 'df_cmdintact'\n",
    "files = [e for e in path.iterdir() if e.is_file() and e.suffix=='.csv']\n",
    "keys = [f.name[0:-4] for f in sorted(files)]\n",
    "meta_df = pd.DataFrame(columns = None)\n",
    "for f in files:\n",
    "    thisdf = pd.read_csv(f)\n",
    "    meta_df = meta_df.append(thisdf,ignore_index = True)\n",
    "\n",
    "meta_df['cell_type'] = meta_df['exptname'].map(cell_type)\n",
    "\n",
    "grc_df = meta_df[(meta_df['cell_type']=='s') | (meta_df['cell_type']=='d')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (S2) coupled versus prediction at baseline amp split by cell type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": [
    "hfig,ax = create_fig_tuning()\n",
    "sns.despine(hfig)\n",
    "ax.hlines(0,-0.5,1.5,color = 'gray',lw = 1,linestyle='--')\n",
    "ax.set_xlim(-0.5,1.5)\n",
    "ax.set_ylim(-1,4.2)\n",
    "ax.set_ylabel('Short Delay - Sum \\n (normalized)',linespacing=0.9)\n",
    "ax.set_xticklabels('')\n",
    "sns.stripplot(y=(grc_df['c_epsp_amp']-grc_df['p_epsp_amp'])/grc_df['p_epsp_amp'],\n",
    "              x = grc_df['cell_type'],\n",
    "              palette = sns.color_palette([sns.xkcd_rgb['icky green'],sns.xkcd_rgb['teal blue']]),\n",
    "             jitter=0.25,alpha = 0.75,s = sqrt(10))\n",
    "ax.set_xlabel('')\n",
    "ax.set_xticklabels(['SGC','DGC'])\n",
    "figsave(figure_folder,'S2_baseline_CDcoupleVpredict')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### t-test on difference from 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sgc dgc difference ttest from 0: t(48)= 1.63, p=0.10982\n",
      "sgc ttest from 0: t(19)= 1.47, p=0.15928\n",
      "dgc ttest from 0: t(29)= 1.45, p=0.15763\n"
     ]
    }
   ],
   "source": [
    "# stats.ttest_ind(grc_df[s_ind]['c_epsp_amp']/grc_df[s_ind]['p_epsp_amp'],\n",
    "#                 grc_df[s_ind]['p_epsp_amp']/grc_df[s_ind]['p_epsp_amp'],\n",
    "#                nan_policy='omit')\n",
    "s_ind = grc_df['cell_type'].isin(['s'])\n",
    "sgcresult = (grc_df[s_ind]['c_epsp_amp']-grc_df[s_ind]['p_epsp_amp'])/grc_df[s_ind]['p_epsp_amp']\n",
    "d_ind = grc_df['cell_type'].isin(['d'])\n",
    "dgcresult = (grc_df[d_ind]['c_epsp_amp']-grc_df[d_ind]['p_epsp_amp'])/grc_df[d_ind]['p_epsp_amp']\n",
    "r = stats.ttest_ind(sgcresult.drop(3).values, dgcresult.values,0,nan_policy='omit')\n",
    "print('sgc dgc difference ttest from 0: t(%i)= %.2f, p=%0.5f' % ((sum(s_ind)+sum(d_ind))-2,r[0],r[1]))\n",
    "\n",
    "s_ind = grc_df['cell_type'].isin(['s'])\n",
    "r = stats.ttest_1samp((grc_df[s_ind]['c_epsp_amp'].drop(3)-grc_df[s_ind]['p_epsp_amp'].drop(3))/grc_df[s_ind]['p_epsp_amp'].drop(3)\n",
    "                      ,0,nan_policy='omit')\n",
    "print('sgc ttest from 0: t(%i)= %.2f, p=%0.5f' % (sum(s_ind)-1,r[0],r[1]))\n",
    "\n",
    "d_ind = grc_df['cell_type'].isin(['d'])\n",
    "r = stats.ttest_1samp((grc_df[d_ind]['c_epsp_amp']-grc_df[d_ind]['p_epsp_amp'])/grc_df[d_ind]['p_epsp_amp']\n",
    "                      ,0,nan_policy='omit')\n",
    "print('dgc ttest from 0: t(%i)= %.2f, p=%0.5f' % (sum(d_ind)-1,r[0],r[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     0.143808\n",
       "5     2.503546\n",
       "6     0.321358\n",
       "7     0.431810\n",
       "11   -0.507677\n",
       "16    0.200993\n",
       "23    0.513081\n",
       "26   -0.022511\n",
       "30   -5.329211\n",
       "31    4.007151\n",
       "32    1.173748\n",
       "37    0.875016\n",
       "43    1.632584\n",
       "45         NaN\n",
       "48    1.113503\n",
       "49   -0.196354\n",
       "50    1.007415\n",
       "56    0.586199\n",
       "58    3.730788\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(grc_df[s_ind]['c_epsp_amp'].drop(3)-grc_df[s_ind]['p_epsp_amp'].drop(3))/grc_df[s_ind]['p_epsp_amp'].drop(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    18.000000\n",
       "mean      0.676958\n",
       "std       1.951068\n",
       "min      -5.329211\n",
       "25%       0.158104\n",
       "50%       0.549640\n",
       "75%       1.158686\n",
       "max       4.007151\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgcresult.drop(3).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    28.000000\n",
       "mean      0.070804\n",
       "std       0.257779\n",
       "min      -0.381058\n",
       "25%      -0.057932\n",
       "50%       0.023991\n",
       "75%       0.169794\n",
       "max       0.989362\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dgcresult.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING    /Users/kperks/opt/anaconda3/envs/grc_study/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      " [py.warnings]\n"
     ]
    }
   ],
   "source": [
    "grc_df['normdiff']=(grc_df['c_epsp_amp']-grc_df['p_epsp_amp'])/grc_df['p_epsp_amp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SGC wilcoxon\n",
      "stat = 30.0\n",
      "p = 0.007144927978515625\n",
      "n = 19\n",
      "\n",
      "DGC wilcoxon\n",
      "stat = 141.0\n",
      "p = 0.15800053800961822\n",
      "n = 28\n"
     ]
    }
   ],
   "source": [
    "r = stats.wilcoxon(\n",
    "    grc_df[grc_df['cell_type']=='s'].normdiff.dropna().values,\n",
    "    mode='exact')\n",
    "\n",
    "print('SGC wilcoxon')\n",
    "print ('stat = ' + str(r[0]))\n",
    "print ('p = ' + str(r[1]))\n",
    "print('n = ' + str(len(\n",
    "    grc_df[grc_df['cell_type']=='s'].normdiff.dropna().values)))\n",
    "\n",
    "print('')\n",
    "r = stats.wilcoxon(\n",
    "    grc_df[grc_df['cell_type']=='d'].normdiff.dropna().values)\n",
    "print('DGC wilcoxon')\n",
    "print ('stat = ' + str(r[0]))\n",
    "print ('p = ' + str(r[1]))\n",
    "print('n = ' + str(len(\n",
    "    grc_df[grc_df['cell_type']=='d'].normdiff.dropna().values)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## coupled versus uncoupled ampshift"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SGC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot population results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#superficial grc\n",
    "# meta_df_uc = pd.read_csv('DF_update_sGRCPopulation_UncoupledAmpShift.csv')\n",
    "# meta_df_c = pd.read_csv('DF_sGRCPopulation_CoupledAmpShift.csv')\n",
    "meta_df_uc = pd.read_csv(top_dir / 'data_processed/DF_SGC_UncoupledAmpShift_revised10Sept.csv')\n",
    "meta_df_c = pd.read_csv(top_dir / 'data_processed/DF_SGC_CoupledAmpShift_revised10Sept.csv')\n",
    "\n",
    "R_ampshift_u = []\n",
    "R_ampshift_norm_u = []\n",
    "R_ampshift_c = []\n",
    "R_ampshift_norm_c = []\n",
    "for a in np.unique(meta_df_uc['exptname']):\n",
    "    subdf = meta_df_uc[meta_df_uc['exptname']==a].groupby('ampshift')\n",
    "    r = subdf.peakAmp.mean().values\n",
    "    R_ampshift_u.append(r)\n",
    "    r_scale = np.max(r)#subdf.peakAmp.mean()[40]#np.max(r)\n",
    "    r = r/r_scale\n",
    "    R_ampshift_norm_u.append(r)\n",
    "    \n",
    "    subdf = meta_df_c[meta_df_c['exptname']==a].groupby('ampshift')\n",
    "    r = subdf.peakAmp.mean().values\n",
    "    R_ampshift_c.append(r)\n",
    "    r = r/r_scale\n",
    "    R_ampshift_norm_c.append(r)\n",
    "    \n",
    "R_ampshift_u = np.asarray(R_ampshift_u)\n",
    "R_ampshift_norm_sgrc_u = np.asarray(R_ampshift_norm_u)\n",
    "R_ampshift_c = np.asarray(R_ampshift_c)\n",
    "R_ampshift_norm_sgrc_c = np.asarray(R_ampshift_norm_c)\n",
    "stim_ampshift = np.unique(meta_df_uc.ampshift)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 86.4x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 86.4x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 86.4x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "restrict_inds = stim_ampshift>=-40\n",
    "\n",
    "hfig,ax = create_fig_tuning()\n",
    "\n",
    "for expt_result in R_ampshift_norm_sgrc_u:\n",
    "    ax.plot(stim_ampshift[restrict_inds],expt_result[restrict_inds],linestyle='-',lw=1,color='black')#,marker='o',markersize=6)\n",
    "for expt_result in R_ampshift_norm_sgrc_c:\n",
    "    ax.plot(stim_ampshift[restrict_inds],expt_result[restrict_inds],linestyle='--',lw=1,color='black')#,marker='o',markersize=6)\n",
    "ax.set_ylabel('Peak response (normalized)')\n",
    "ax.set_xlabel('Stimulus amplitude \\n (% change)',linespacing=0.75)\n",
    "ax.set_xlim(-47,47)\n",
    "xticks([-40,-20,0,20,40]);\n",
    "figsave(figure_folder,'Fig4_sgrcTuningCurves_UvC')\n",
    "\n",
    "hfig,ax = create_fig_tuning()\n",
    "plt.errorbar(stim_ampshift[restrict_inds],\n",
    "             np.mean(R_ampshift_norm_sgrc_c[:,restrict_inds],0),\n",
    "             yerr=stats.sem(R_ampshift_norm_sgrc_c[:,restrict_inds],0),\n",
    "             fmt='s', mfc='white',ms = 3, color = sns.xkcd_rgb['icky green'], \n",
    "             ecolor = sns.xkcd_rgb['icky green'], capsize=2)\n",
    "\n",
    "plt.errorbar(stim_ampshift[restrict_inds],\n",
    "             np.mean(R_ampshift_norm_sgrc_u[:,restrict_inds],0),\n",
    "             yerr=stats.sem(R_ampshift_norm_sgrc_u[:,restrict_inds],0),\n",
    "             fmt='o', ms = 3, color = sns.xkcd_rgb['icky green'], \n",
    "             ecolor = sns.xkcd_rgb['icky green'], capsize=2)\n",
    "\n",
    "ax.set_ylabel('Mean Peak Response \\n (normalized)',linespacing=0.9)\n",
    "ax.set_xlabel('Stimulus amplitude \\n (% change)',linespacing=0.9)\n",
    "ax.set_xlim(-47,47)\n",
    "xticks([-40,-20,0,20,40]);\n",
    "ax.set_ylim(-0.03,1.6)\n",
    "sns.despine(hfig)\n",
    "figsave(figure_folder,'Fig3_sgrcTuningCurves_LongVsShort_MeanSem')\n",
    "\n",
    "dv_u = []\n",
    "for r in R_ampshift_u:\n",
    "    dv_u.append(r[restrict_inds][-1] - r[restrict_inds][0])\n",
    "dv_u = np.asarray(dv_u).T\n",
    "                \n",
    "dv_c = []\n",
    "for r in R_ampshift_c:\n",
    "    dv_c.append(r[restrict_inds][-1] - r[restrict_inds][0])\n",
    "dv_c = np.asarray(dv_c).T\n",
    "\n",
    "\n",
    "hfig,ax = create_fig_tuning()\n",
    "sns.boxplot(y=np.round(dv_c - dv_u), whis=np.inf,saturation = 0.5,ax=ax)\n",
    "ax.hlines(0,-1,1,linestyle = '--',color = 'gray')\n",
    "sns.stripplot(y=np.round(dv_c - dv_u),s= sqrt(10),color = 'black',jitter = 0.4,ax=ax)\n",
    "ax.set_ylabel('dv min/max \\n coupled-uncoupled (mV)',linespacing=0.75)\n",
    "ax.set_xlabel('Stimulus amplitude \\n (% change)',linespacing=0.75)\n",
    "figsave(figure_folder,'Fig4_sgrcTuningScatter_UvC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uncoupled: mean mV change 8.28, 5.55 std, 2.77 sem\n",
      "coupled: mean mV change 27.91, 8.75 std, 4.37 sem\n"
     ]
    }
   ],
   "source": [
    "restrict_inds = ((stim_ampshift>=-10)&(stim_ampshift<=10))\n",
    "\n",
    "dv_u = []\n",
    "for r in R_ampshift_u[:,restrict_inds]:\n",
    "    dv_u.append(r[-1] - r[0])\n",
    "dv_u = np.asarray(dv_u).T\n",
    "                \n",
    "dv_c = []\n",
    "for r in R_ampshift_c:\n",
    "    dv_c.append(r[-1] - r[0])\n",
    "dv_c = np.asarray(dv_c).T\n",
    "\n",
    "print('uncoupled: mean mV change %0.2f, %0.2f std, %0.2f sem' %(np.mean(dv_u),np.std(dv_u),stats.sem(dv_u)))\n",
    "print('coupled: mean mV change %0.2f, %0.2f std, %0.2f sem' %(np.mean(dv_c),np.std(dv_c),stats.sem(dv_c)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uncoupled gain: mean percent change 0.36, 0.17 std, 0.09 sem\n",
      "coupled gain: mean percent change 1.28, 0.15 std, 0.07 sem\n",
      "coupled versus uncoupled gain: mean max percent change 0.91, 0.22 std, 0.11 sem\n",
      "coupled versus uncoupled gain: mean max percent change 0.55 min, 1.15 max\n",
      "[103.69901725 102.98215511  54.70491934 115.46014878  79.49255685]\n"
     ]
    }
   ],
   "source": [
    "restrict_inds = ((stim_ampshift>=-10)&(stim_ampshift<=10))\n",
    "\n",
    "dv_u = []\n",
    "for r in R_ampshift_norm_sgrc_u[:,restrict_inds]:\n",
    "    dv_u.append(r[-1] - r[0])\n",
    "dv_u = np.asarray(dv_u).T\n",
    "                \n",
    "dv_c = []\n",
    "for r in R_ampshift_norm_sgrc_c:\n",
    "    dv_c.append(r[-1] - r[0])\n",
    "dv_c = np.asarray(dv_c).T\n",
    "\n",
    "print('uncoupled gain: mean percent change %0.2f, %0.2f std, %0.2f sem' %(np.mean(dv_u),np.std(dv_u),stats.sem(dv_u)))\n",
    "print('coupled gain: mean percent change %0.2f, %0.2f std, %0.2f sem' %(np.mean(dv_c),np.std(dv_c),stats.sem(dv_c)))\n",
    "\n",
    "print('coupled versus uncoupled gain: mean max percent change %0.2f, %0.2f std, %0.2f sem' %(np.mean(dv_c-dv_u),np.std(dv_c-dv_u),stats.sem(dv_c-dv_u)))\n",
    "print('coupled versus uncoupled gain: mean max percent change %0.2f min, %0.2f max' %(np.min(dv_c-dv_u),np.max(dv_c-dv_u)))\n",
    "print(100*(dv_c-dv_u))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### prep a dataframe for t-test and ANOVA on population data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta_df_u = pd.read_csv(top_dir / 'data_processed/DF_SGC_UncoupledAmpShift_revised10Sept.csv')\n",
    "meta_df_c = pd.read_csv(top_dir / 'data_processed/DF_SGC_CoupledAmpShift_revised10Sept.csv')\n",
    "\n",
    "eid = []\n",
    "ramp = []\n",
    "samp = []\n",
    "cond = []\n",
    "\n",
    "for i,n in enumerate(np.unique(meta_df_u['exptname'])):\n",
    "    \n",
    "    exptdf = meta_df_u[meta_df_u['exptname']==n]\n",
    "    subdf = exptdf.groupby('ampshift')\n",
    "    r = subdf.peakAmp.mean().values\n",
    "    # get scaling factor to normalize all values to max uncoupled\n",
    "    r_scale = np.max(r)\n",
    "    for a in np.unique(exptdf['ampshift']):\n",
    "        ampdf = exptdf[exptdf['ampshift']==a]\n",
    "        eid.append(i)\n",
    "        cond.append('u')\n",
    "        samp.append(a)\n",
    "        r = ampdf['peakAmp'].mean()\n",
    "        r = r/r_scale\n",
    "        ramp.append(r)\n",
    "        \n",
    "    exptdf = meta_df_c[meta_df_c['exptname']==n]\n",
    "    for a in np.unique(exptdf['ampshift']):\n",
    "        ampdf = exptdf[exptdf['ampshift']==a]\n",
    "        eid.append(i)\n",
    "        cond.append('c')\n",
    "        samp.append(a)\n",
    "        r = ampdf['peakAmp'].mean()\n",
    "        r = r/r_scale\n",
    "        ramp.append(r)\n",
    "    \n",
    "\n",
    "\n",
    "anal_df = pd.DataFrame({\n",
    "    'eid' : eid,\n",
    "    'cond' : cond,\n",
    "    'samp' : samp,\n",
    "    'ramp' : ramp\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Kruskal wallis for effect of condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KruskalResult(statistic=10.058722448452581, pvalue=0.0015162833569207378)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.kruskal(*[group[\"ramp\"].values for name, group in anal_df.groupby('cond')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### two-factor repeated measures anova with statsmodels on normalized data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>sum_sq</th>\n",
       "      <th>df</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(cond)</th>\n",
       "      <td>1.847661</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55.471215</td>\n",
       "      <td>6.124206e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(samp)</th>\n",
       "      <td>14.720920</td>\n",
       "      <td>10.0</td>\n",
       "      <td>44.195738</td>\n",
       "      <td>4.699054e-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(cond):C(samp)</th>\n",
       "      <td>0.533894</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.602879</td>\n",
       "      <td>1.188818e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>2.931145</td>\n",
       "      <td>88.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    sum_sq    df          F        PR(>F)\n",
       "C(cond)           1.847661   1.0  55.471215  6.124206e-11\n",
       "C(samp)          14.720920  10.0  44.195738  4.699054e-30\n",
       "C(cond):C(samp)   0.533894  10.0   1.602879  1.188818e-01\n",
       "Residual          2.931145  88.0        NaN           NaN"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# formula = 'ramp ~ C(cond) + C(samp) + C(cond):C(samp)'\n",
    "formula = 'ramp ~ C(cond) * C(samp)'\n",
    "model = ols(formula, data = anal_df).fit()\n",
    "aov_table = anova_lm(model, typ=2)\n",
    "\n",
    "aov_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### statsmodels ANOVA by cell to test effect of condition, amp, and interaction on cells with enough trials to test per cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True  True  True]\n",
      " significant effect (p<0.001 ANOVA) of condition in 4 SGC out of 4 with more than 5 trials\n",
      " significant effect (p<0.01 ANOVA) of condition in 4 SGC out of 4 with more than 5 trials\n",
      "p delay: \n",
      "[3.16975317e-04 3.46888542e-26 1.16469027e-25 4.13158913e-28]\n",
      "p amplitdue: \n",
      "[1.4305555068565478e-53, 1.1042288659704157e-55, 3.4111305857942757e-125, 1.933643096760045e-121]\n",
      "p interaction: \n",
      "[5.900924681779239e-07, 2.9288308154991367e-05, 6.591461021377624e-13, 4.54236020139316e-12]\n"
     ]
    }
   ],
   "source": [
    "meta_df_u = pd.read_csv(top_dir / 'data_processed/DF_SGC_UncoupledAmpShift_revised10Sept.csv')\n",
    "meta_df_c = pd.read_csv(top_dir / 'data_processed/DF_SGC_CoupledAmpShift_revised10Sept.csv')\n",
    "\n",
    "p_cond = []\n",
    "p_amp = []\n",
    "p_interact = []\n",
    "ntrials_p = []\n",
    "ntrials_c = []\n",
    "n_tested = 0\n",
    "n_trials_min = 5\n",
    "for expt in np.unique(meta_df_u['exptname']):\n",
    "# expt = '20200718_000'\n",
    "    expt_df_u = meta_df_u[meta_df_u['exptname']==expt]\n",
    "    expt_df_u.insert(1, 'cond', 'u')\n",
    "    expt_df_c = meta_df_c[meta_df_c['exptname']==expt]\n",
    "    expt_df_c.insert(1, 'cond', 'c')    \n",
    "    expt_df = expt_df_u.append(expt_df_c)\n",
    "    \n",
    "  \n",
    "    if (((len(expt_df_u)/11)>=n_trials_min) & ((len(expt_df_c)/11)>=n_trials_min)):\n",
    "        n_tested += 1\n",
    "        # expt_df\n",
    "    #     formula = 'peakAmp ~ C(cond) * C(ampshift)'\n",
    "        formula = 'peakAmp ~ C(cond) + C(ampshift) + C(cond):C(ampshift)'\n",
    "        model = ols(formula, data = expt_df).fit()\n",
    "        aov_table = anova_lm(model) #, typ=2)\n",
    "\n",
    "        p_cond.append(aov_table['PR(>F)'].loc['C(cond)'])\n",
    "#         p_cond.append(aov_table['PR(>F)'].loc['C(cond)'])\n",
    "        p_amp.append(aov_table['PR(>F)'].loc['C(ampshift)'])\n",
    "        p_interact.append(aov_table['PR(>F)'].loc['C(cond):C(ampshift)'])\n",
    "# p_cond = np.asarray(p_cond)\n",
    "p_cond = np.asarray(p_cond)\n",
    "\n",
    "print(p_cond<0.001)\n",
    "\n",
    "print(' significant effect (p<0.001 ANOVA) of condition in ' + \n",
    "     str(np.count_nonzero(p_cond<0.001)) + \n",
    "     ' SGC out of ' +\n",
    "     str(n_tested) + ' with more than ' + str(n_trials_min) + ' trials')\n",
    "\n",
    "print(' significant effect (p<0.01 ANOVA) of condition in ' + \n",
    "     str(np.count_nonzero(p_cond<0.01)) + \n",
    "     ' SGC out of ' +\n",
    "     str(n_tested) + ' with more than ' + str(n_trials_min) + ' trials')\n",
    "\n",
    "\n",
    "print('p delay: ')\n",
    "print(p_cond)\n",
    "print('p amplitdue: ')\n",
    "print(p_amp)\n",
    "print('p interaction: ')\n",
    "print(p_interact)\n",
    "# display(aov_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### statsmodels ANOVA by cell to test effect of condition, amp, and interaction on all cells (use raw mean per cell; not normalized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(cond)</th>\n",
       "      <td>1.0</td>\n",
       "      <td>927.221426</td>\n",
       "      <td>927.221426</td>\n",
       "      <td>12.976687</td>\n",
       "      <td>5.215877e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(ampshift)</th>\n",
       "      <td>10.0</td>\n",
       "      <td>7526.865562</td>\n",
       "      <td>752.686556</td>\n",
       "      <td>10.534030</td>\n",
       "      <td>1.682825e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(cond):C(ampshift)</th>\n",
       "      <td>10.0</td>\n",
       "      <td>260.445823</td>\n",
       "      <td>26.044582</td>\n",
       "      <td>0.364500</td>\n",
       "      <td>9.586452e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>88.0</td>\n",
       "      <td>6287.851723</td>\n",
       "      <td>71.452860</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       df       sum_sq     mean_sq          F        PR(>F)\n",
       "C(cond)               1.0   927.221426  927.221426  12.976687  5.215877e-04\n",
       "C(ampshift)          10.0  7526.865562  752.686556  10.534030  1.682825e-11\n",
       "C(cond):C(ampshift)  10.0   260.445823   26.044582   0.364500  9.586452e-01\n",
       "Residual             88.0  6287.851723   71.452860        NaN           NaN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "KruskalResult(statistic=6.349332142059382, pvalue=0.011742549621692513)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_df_u = pd.read_csv(top_dir / 'data_processed/DF_SGC_UncoupledAmpShift_revised10Sept.csv')\n",
    "meta_df_c = pd.read_csv(top_dir / 'data_processed/DF_SGC_CoupledAmpShift_revised10Sept.csv')\n",
    "meta_df_u.insert(1, 'cond', 'u')\n",
    "\n",
    "meta_df_c.insert(1, 'cond', 'c')    \n",
    "\n",
    "df = meta_df_u.append(meta_df_c)\n",
    "df = df.groupby(['exptname','cond','ampshift']).agg(peakAmp=pd.NamedAgg(column='peakAmp',aggfunc=mean)).reset_index()\n",
    "\n",
    "formula = 'peakAmp ~ C(cond) + C(ampshift) + C(cond):C(ampshift)'\n",
    "model = ols(formula, data = df).fit()\n",
    "aov_table = anova_lm(model) #, typ=2)\n",
    "\n",
    "display(aov_table)\n",
    "\n",
    "\n",
    "scipy.stats.kruskal(*[group[\"peakAmp\"].values for name, group in df.groupby('cond')])\n",
    "#     anal_df.groupby('cond')['ramp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### DGC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot population results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#deep grc\n",
    "meta_df_uc = pd.read_csv(top_dir / 'data_processed/DF_DGC_UncoupledAmpShift_revised10Sept.csv')\n",
    "meta_df_c = pd.read_csv(top_dir / 'data_processed/DF_DGC_CoupledAmpShift_revised10Sept.csv')\n",
    "R_ampshift_u = []\n",
    "R_ampshift_norm_u = []\n",
    "R_ampshift_c = []\n",
    "R_ampshift_norm_c = []\n",
    "for a in np.unique(meta_df_uc['exptname']):\n",
    "    subdf = meta_df_uc[meta_df_uc['exptname']==a].groupby('ampshift')\n",
    "    r = subdf.peakAmp.mean().values\n",
    "    R_ampshift_u.append(r)\n",
    "    r_scale = np.max(r)\n",
    "    r = r/r_scale\n",
    "    R_ampshift_norm_u.append(r)\n",
    "    \n",
    "    subdf = meta_df_c[meta_df_c['exptname']==a].groupby('ampshift')\n",
    "    r = subdf.peakAmp.mean().values\n",
    "    R_ampshift_c.append(r)\n",
    "    r = r/r_scale\n",
    "    R_ampshift_norm_c.append(r)\n",
    "    \n",
    "R_ampshift_u = np.asarray(R_ampshift_u)\n",
    "R_ampshift_norm_dgrc_u = np.asarray(R_ampshift_norm_u)\n",
    "R_ampshift_c = np.asarray(R_ampshift_c)\n",
    "R_ampshift_norm_dgrc_c = np.asarray(R_ampshift_norm_c)\n",
    "stim_ampshift = np.unique(meta_df_uc.ampshift)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 86.4x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 86.4x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 86.4x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "restrict_inds = stim_ampshift>=-40\n",
    "hfig,ax = create_fig_tuning()\n",
    "\n",
    "for expt_result in R_ampshift_norm_dgrc_u:\n",
    "    ax.plot(stim_ampshift[restrict_inds],expt_result[restrict_inds],linestyle='-',lw=1,color='black')#,marker='o',markersize=6)\n",
    "for expt_result in R_ampshift_norm_dgrc_c:\n",
    "    ax.plot(stim_ampshift[restrict_inds],expt_result[restrict_inds],linestyle='--',lw=1,color='black')#,marker='o',markersize=6)\n",
    "ax.set_ylabel('Peak response (normalized)')\n",
    "ax.set_xlabel('Stimulus amplitude \\n (% change)',linespacing=0.75)\n",
    "ax.set_xlim(-17,47)\n",
    "xticks([0,20,40]);\n",
    "figsave(figure_folder,'Fig4_dgrcTuningCurves_UvC')\n",
    "\n",
    "\n",
    "hfig,ax = create_fig_tuning()\n",
    "plt.errorbar(stim_ampshift[restrict_inds],\n",
    "             np.mean(R_ampshift_norm_dgrc_c[:,restrict_inds],0),\n",
    "             yerr=stats.sem(R_ampshift_norm_dgrc_c[:,restrict_inds],0),\n",
    "             fmt='s', mfc='white',ms = 3, color = sns.xkcd_rgb['teal blue'], \n",
    "             ecolor = sns.xkcd_rgb['teal blue'], capsize=2)\n",
    "\n",
    "plt.errorbar(stim_ampshift[restrict_inds],\n",
    "             np.mean(R_ampshift_norm_dgrc_u[:,restrict_inds],0),\n",
    "             yerr=stats.sem(R_ampshift_norm_dgrc_u[:,restrict_inds],0),\n",
    "             fmt='o', ms = 3, color = sns.xkcd_rgb['teal blue'], \n",
    "             ecolor = sns.xkcd_rgb['teal blue'], capsize=2)\n",
    "\n",
    "ax.set_ylabel('Mean Peak Response \\n (normalized)',linespacing=0.9)\n",
    "ax.set_xlabel('Stimulus amplitude \\n (% change)',linespacing=0.9)\n",
    "ax.set_xlim(-47,47)\n",
    "xticks([-40,-20,0,20,40]);\n",
    "ax.set_ylim(-0.03,1.6)\n",
    "sns.despine(hfig)\n",
    "figsave(figure_folder,'Fig3_dgrcTuningCurves_LongVsShort_MeanSem')\n",
    "\n",
    "\n",
    "dv_u = []\n",
    "for r in R_ampshift_u:\n",
    "    dv_u.append(r[restrict_inds][-1] - r[restrict_inds][0])\n",
    "dv_u = np.asarray(dv_u).T\n",
    "                \n",
    "dv_c = []\n",
    "for r in R_ampshift_c:\n",
    "    dv_c.append(r[restrict_inds][-1] - r[restrict_inds][0])\n",
    "dv_c = np.asarray(dv_c).T\n",
    "\n",
    "\n",
    "hfig,ax = create_fig_tuning()\n",
    "sns.boxplot(y=np.round(dv_c - dv_u), whis=np.inf,saturation = 0.5,ax=ax)\n",
    "ax.hlines(0,-1,1,linestyle = '--',color = 'gray')\n",
    "sns.stripplot(y=np.round(dv_c - dv_u),s= sqrt(10),color = 'black',jitter = 0.4,ax=ax)\n",
    "ax.set_ylabel('dv min/max \\n coupled-uncoupled (mV)',linespacing=0.75)\n",
    "ax.set_xlabel('Stimulus amplitude \\n (% change)',linespacing=0.75)\n",
    "figsave(figure_folder,'Fig4_dgrcTuningScatter_UvC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uncoupled: mean mV change 8.11, 8.10 std, 2.34 sem\n",
      "coupled: mean mV change 26.75, 14.63 std, 4.22 sem\n"
     ]
    }
   ],
   "source": [
    "restrict_inds = ((stim_ampshift>=-10)&(stim_ampshift<=10))\n",
    "\n",
    "dv_u = []\n",
    "for r in R_ampshift_u[:,restrict_inds]:\n",
    "    dv_u.append(r[-1] - r[0])\n",
    "dv_u = np.asarray(dv_u).T\n",
    "                \n",
    "dv_c = []\n",
    "for r in R_ampshift_c:\n",
    "    dv_c.append(r[-1] - r[0])\n",
    "dv_c = np.asarray(dv_c).T\n",
    "\n",
    "print('uncoupled: mean mV change %0.2f, %0.2f std, %0.2f sem' %(np.mean(dv_u),np.std(dv_u),stats.sem(dv_u)))\n",
    "print('coupled: mean mV change %0.2f, %0.2f std, %0.2f sem' %(np.mean(dv_c),np.std(dv_c),stats.sem(dv_c)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uncoupled gain: mean percent change 0.27, 0.17 std, 0.05 sem\n",
      "coupled gain: mean percent change 0.95, 0.21 std, 0.06 sem\n",
      "coupled versus uncoupled gain: mean max percent change 0.68, 0.31 std, 0.09 sem\n",
      "coupled versus uncoupled gain: mean max percent change 0.27 min, 1.30 max\n",
      "[ 37.31644447  55.55419922  56.96818982 129.64190493  88.10438634\n",
      "  47.7629604   80.1380699   27.26064484 118.21540492  41.40118969\n",
      "  37.38463404  86.03864647  82.9274071 ]\n"
     ]
    }
   ],
   "source": [
    "restrict_inds = ((stim_ampshift>=-10)&(stim_ampshift<=10))\n",
    "\n",
    "dv_u = []\n",
    "for r in R_ampshift_norm_dgrc_u[:,restrict_inds]:\n",
    "    dv_u.append(r[-1] - r[0])\n",
    "dv_u = np.asarray(dv_u).T\n",
    "                \n",
    "dv_c = []\n",
    "for r in R_ampshift_norm_dgrc_c:\n",
    "    dv_c.append(r[-1] - r[0])\n",
    "dv_c = np.asarray(dv_c).T\n",
    "\n",
    "print('uncoupled gain: mean percent change %0.2f, %0.2f std, %0.2f sem' %(np.mean(dv_u),np.std(dv_u),stats.sem(dv_u)))\n",
    "print('coupled gain: mean percent change %0.2f, %0.2f std, %0.2f sem' %(np.mean(dv_c),np.std(dv_c),stats.sem(dv_c)))\n",
    "\n",
    "print('coupled versus uncoupled gain: mean max percent change %0.2f, %0.2f std, %0.2f sem' %(np.mean(dv_c-dv_u),np.std(dv_c-dv_u),stats.sem(dv_c-dv_u)))\n",
    "print('coupled versus uncoupled gain: mean max percent change %0.2f min, %0.2f max' %(np.min(dv_c-dv_u),np.max(dv_c-dv_u)))\n",
    "print(100*(dv_c-dv_u))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### prep a dataframe for t-test and ANOVA on population data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta_df_u = pd.read_csv(top_dir / 'data_processed/DF_DGC_UncoupledAmpShift_revised10Sept.csv')\n",
    "meta_df_c = pd.read_csv(top_dir / 'data_processed/DF_DGC_CoupledAmpShift_revised10Sept.csv')\n",
    "\n",
    "eid = []\n",
    "ramp = []\n",
    "samp = []\n",
    "cond = []\n",
    "\n",
    "for i,n in enumerate(np.unique(meta_df_u['exptname'])):\n",
    "    \n",
    "    exptdf = meta_df_u[meta_df_u['exptname']==n]\n",
    "    subdf = exptdf.groupby('ampshift')\n",
    "    r = subdf.peakAmp.mean().values\n",
    "    # get scaling factor to normalize all values to max uncoupled\n",
    "    r_scale = np.max(r)\n",
    "    for a in np.unique(exptdf['ampshift']):\n",
    "        ampdf = exptdf[exptdf['ampshift']==a]\n",
    "        eid.append(i)\n",
    "        cond.append('u')\n",
    "        samp.append(a)\n",
    "        r = ampdf['peakAmp'].mean()\n",
    "        r = r/r_scale\n",
    "        ramp.append(r)\n",
    "        \n",
    "    exptdf = meta_df_c[meta_df_c['exptname']==n]\n",
    "    for a in np.unique(exptdf['ampshift']):\n",
    "        ampdf = exptdf[exptdf['ampshift']==a]\n",
    "        eid.append(i)\n",
    "        cond.append('c')\n",
    "        samp.append(a)\n",
    "        r = ampdf['peakAmp'].mean()\n",
    "        r = r/r_scale\n",
    "        ramp.append(r)\n",
    "    \n",
    "\n",
    "\n",
    "anal_df = pd.DataFrame({\n",
    "    'eid' : eid,\n",
    "    'cond' : cond,\n",
    "    'samp' : samp,\n",
    "    'ramp' : ramp\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### two-factor repeated measures anova with statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>sum_sq</th>\n",
       "      <th>df</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(cond)</th>\n",
       "      <td>1.047268</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.368831</td>\n",
       "      <td>5.933956e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(samp)</th>\n",
       "      <td>26.845833</td>\n",
       "      <td>10.0</td>\n",
       "      <td>54.777199</td>\n",
       "      <td>1.158831e-58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(cond):C(samp)</th>\n",
       "      <td>0.184956</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.377391</td>\n",
       "      <td>9.557686e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>12.938412</td>\n",
       "      <td>264.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    sum_sq     df          F        PR(>F)\n",
       "C(cond)           1.047268    1.0  21.368831  5.933956e-06\n",
       "C(samp)          26.845833   10.0  54.777199  1.158831e-58\n",
       "C(cond):C(samp)   0.184956   10.0   0.377391  9.557686e-01\n",
       "Residual         12.938412  264.0        NaN           NaN"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# formula = 'ramp ~ C(cond) + C(samp) + C(cond):C(samp)'\n",
    "formula = 'ramp ~ C(cond) * C(samp)'\n",
    "model = ols(formula, data = anal_df).fit()\n",
    "aov_table = anova_lm(model, typ=2)\n",
    "\n",
    "aov_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Kruskal Wallis on effect of condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KruskalResult(statistic=9.039404707959045, pvalue=0.0026422166233004336)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "scipy.stats.kruskal(*[group[\"ramp\"].values for name, group in anal_df.groupby('cond')])\n",
    "#     anal_df.groupby('cond')['ramp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### statsmodels ANOVA by cell to test effect of condition, amp, and interaction on cells with enough trials to test per cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True  True False  True  True  True False False]\n",
      " significant effect (p<0.001 ANOVA) of condition in 6 DGC out of 9 with more than 5 trials\n",
      " significant effect (p<0.01 ANOVA) of condition in 7 DGC out of 9 with more than 5 trials\n",
      "p delay: \n",
      "[1.01791253e-11 3.32887296e-37 3.31594644e-09 9.92243348e-01\n",
      " 8.08367507e-67 2.97070850e-23 6.30964667e-19 1.59838046e-01\n",
      " 1.09776571e-03]\n",
      "p amplitdue: \n",
      "[1.2764384526413873e-127, 1.802069799331527e-56, 8.088597081222844e-56, 7.316242605894207e-46, 6.993456799906277e-124, 5.006892753621943e-57, 2.8868771122896005e-94, 2.4171278501791707e-98, 6.844910826959184e-154]\n",
      "p interaction: \n",
      "[5.022831693283214e-11, 9.08650984734251e-10, 0.3909765641303829, 0.3365029830107356, 5.639000787159364e-18, 4.3493001352727915e-09, 3.061316669086097e-06, 0.08640885100956942, 1.738906169793234e-44]\n"
     ]
    }
   ],
   "source": [
    "meta_df_u = pd.read_csv(top_dir / 'data_processed/DF_DGC_UncoupledAmpShift_revised10Sept.csv')\n",
    "meta_df_c = pd.read_csv(top_dir / 'data_processed/DF_DGC_CoupledAmpShift_revised10Sept.csv')\n",
    "\n",
    "p_cond = []\n",
    "p_amp = []\n",
    "p_interact = []\n",
    "ntrials_p = []\n",
    "ntrials_c = []\n",
    "n_tested = 0\n",
    "n_trials_min = 5\n",
    "for expt in np.unique(meta_df_u['exptname']):\n",
    "# expt = '20200718_000'\n",
    "    expt_df_u = meta_df_u[meta_df_u['exptname']==expt]\n",
    "    expt_df_u.insert(1, 'cond', 'u')\n",
    "    expt_df_c = meta_df_c[meta_df_c['exptname']==expt]\n",
    "    expt_df_c.insert(1, 'cond', 'c')    \n",
    "    expt_df = expt_df_u.append(expt_df_c)\n",
    "    \n",
    "  \n",
    "    if (((len(expt_df_u)/11)>=n_trials_min) & ((len(expt_df_c)/11)>=n_trials_min)):\n",
    "        n_tested += 1\n",
    "        # expt_df\n",
    "    #     formula = 'peakAmp ~ C(cond) * C(ampshift)'\n",
    "        formula = 'peakAmp ~ C(cond) + C(ampshift) + C(cond):C(ampshift)'\n",
    "        model = ols(formula, data = expt_df).fit()\n",
    "        aov_table = anova_lm(model) #, typ=2)\n",
    "\n",
    "        p_cond.append(aov_table['PR(>F)'].loc['C(cond)'])\n",
    "#         p_cond.append(aov_table['PR(>F)'].loc['C(cond)'])\n",
    "        p_amp.append(aov_table['PR(>F)'].loc['C(ampshift)'])\n",
    "        p_interact.append(aov_table['PR(>F)'].loc['C(cond):C(ampshift)'])\n",
    "# p_cond = np.asarray(p_cond)\n",
    "p_cond = np.asarray(p_cond)\n",
    "\n",
    "print(p_cond<0.001)\n",
    "\n",
    "print(' significant effect (p<0.001 ANOVA) of condition in ' + \n",
    "     str(np.count_nonzero(p_cond<0.001)) + \n",
    "     ' DGC out of ' +\n",
    "     str(n_tested) + ' with more than ' + str(n_trials_min) + ' trials')\n",
    "\n",
    "print(' significant effect (p<0.01 ANOVA) of condition in ' + \n",
    "     str(np.count_nonzero(p_cond<0.01)) + \n",
    "     ' DGC out of ' +\n",
    "     str(n_tested) + ' with more than ' + str(n_trials_min) + ' trials')\n",
    "\n",
    "\n",
    "print('p delay: ')\n",
    "print(p_cond)\n",
    "print('p amplitdue: ')\n",
    "print(p_amp)\n",
    "print('p interaction: ')\n",
    "print(p_interact)\n",
    "# display(aov_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### statsmodels ANOVA by cell to test effect of condition, amp, and interaction on all cells (use raw mean per cell; not normalized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(cond)</th>\n",
       "      <td>1.0</td>\n",
       "      <td>245.716197</td>\n",
       "      <td>245.716197</td>\n",
       "      <td>1.287466</td>\n",
       "      <td>2.575446e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(ampshift)</th>\n",
       "      <td>10.0</td>\n",
       "      <td>24693.148579</td>\n",
       "      <td>2469.314858</td>\n",
       "      <td>12.938339</td>\n",
       "      <td>2.322302e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(cond):C(ampshift)</th>\n",
       "      <td>10.0</td>\n",
       "      <td>43.263568</td>\n",
       "      <td>4.326357</td>\n",
       "      <td>0.022669</td>\n",
       "      <td>9.999998e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>264.0</td>\n",
       "      <td>50385.072321</td>\n",
       "      <td>190.852547</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        df        sum_sq      mean_sq          F        PR(>F)\n",
       "C(cond)                1.0    245.716197   245.716197   1.287466  2.575446e-01\n",
       "C(ampshift)           10.0  24693.148579  2469.314858  12.938339  2.322302e-18\n",
       "C(cond):C(ampshift)   10.0     43.263568     4.326357   0.022669  9.999998e-01\n",
       "Residual             264.0  50385.072321   190.852547        NaN           NaN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "KruskalResult(statistic=1.8163100757335542, pvalue=0.17775312705592403)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_df_u = pd.read_csv(top_dir / 'data_processed/DF_DGC_UncoupledAmpShift_revised10Sept.csv')\n",
    "meta_df_c = pd.read_csv(top_dir / 'data_processed/DF_DGC_CoupledAmpShift_revised10Sept.csv')\n",
    "meta_df_u.insert(1, 'cond', 'u')\n",
    "\n",
    "meta_df_c.insert(1, 'cond', 'c')    \n",
    "\n",
    "df = meta_df_u.append(meta_df_c)\n",
    "df = df.groupby(['exptname','cond','ampshift']).agg(peakAmp=pd.NamedAgg(column='peakAmp',aggfunc=mean)).reset_index()\n",
    "\n",
    "formula = 'peakAmp ~ C(cond) + C(ampshift) + C(cond):C(ampshift)'\n",
    "model = ols(formula, data = df).fit()\n",
    "aov_table = anova_lm(model) #, typ=2)\n",
    "\n",
    "display(aov_table)\n",
    "\n",
    "\n",
    "scipy.stats.kruskal(*[group[\"peakAmp\"].values for name, group in df.groupby('cond')])\n",
    "#     anal_df.groupby('cond')['ramp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### example cells"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SGC 20200718_000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/kperks/mnt/OneDrive - wesleyan.edu/Research/Manuscripts/GRC_PerksSawtell/AcceptedRevision_CellReports/data_raw/20200718_000.smr\n"
     ]
    }
   ],
   "source": [
    "exptname = '20200718_000'\n",
    "colinds = plt.cm.plasma(np.linspace(0.2,0.85,11))\n",
    "expt = AmpShift_Stable()\n",
    "expt.load_expt(exptname, data_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = expt.get_dt('lowgain')\n",
    "expt.set_amps(11,[-40,-30,-20,-10,-5,0,5,10,20,30,40])\n",
    "expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')\n",
    "marker_df = expt.get_marker_table()\n",
    "\n",
    "# bias -65\n",
    "########\n",
    "bout_uc = [expt.get_bout_win('R','Keyboard')[0],\n",
    "          expt.get_bout_win('R','Keyboard')[1]]\n",
    "bout_c = [expt.get_bout_win('N','Keyboard')[0]]\n",
    "\n",
    "sweepdur = 0.05\n",
    "r_onset = int(0.006/dt)\n",
    "\n",
    "bout_df = expt.filter_marker_df_time(marker_df,bout_uc)\n",
    "trial_df = expt.filter_marker_df_code(bout_df,['T'])\n",
    "stim_t = trial_df.time.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "eventDur = 0.001\n",
    "xtime,event_sweeps = expt.get_sweepsmat('SIU',trial_df['time'].values,eventDur)\n",
    "event_Amp = np.asarray([np.max(sweep) for sweep in event_sweeps.T])\n",
    "\n",
    "base_df = expt.filter_marker_df_code(bout_df,['U'])\n",
    "xtime,event_sweeps = expt.get_sweepsmat('SIU',base_df['time'].values,eventDur)\n",
    "event_0_Amp = np.median(np.asarray([np.max(sweep) for sweep in event_sweeps.T]))\n",
    "\n",
    "ampshift = np.asarray([np.round(((A/event_0_Amp)*100)-100) for A in event_Amp]).reshape(-1, 1)\n",
    "trial_df.insert(np.shape(trial_df)[1],'ampshift',ampshift)\n",
    "    \n",
    "trialmat = []\n",
    "for a in np.unique(trial_df['ampshift']):\n",
    "    theseT = trial_df[trial_df['ampshift']==a].time.values - 0.005\n",
    "    xtime, R = expt.get_sweepsmat('lowgain',theseT,sweepdur)\n",
    "    trialmat.append(np.mean(R,1))\n",
    "trialmat = np.asarray(trialmat).T \n",
    "trialmat = np.asarray([sweep-sweep[0] for sweep in trialmat.T]).T\n",
    "\n",
    "stim_ampshift = np.unique(trial_df['ampshift'])\n",
    "restrict_inds = stim_ampshift>=-40\n",
    "\n",
    "Ramp_uc = []\n",
    "for sweep in trialmat[r_onset:,:].T:\n",
    "    Ramp_uc.append(np.max(sweep))\n",
    "Ramp_uc = np.asarray(Ramp_uc)\n",
    "\n",
    "#get CD offset pre-coupled stim\n",
    "bout_df = expt.filter_marker_df_time(marker_df,bout_c)\n",
    "trial_df = expt.filter_marker_df_code(bout_df,['T'])\n",
    "stim_t = trial_df.time.values\n",
    "trial_df = expt.filter_marker_df_code(bout_df,['B'])\n",
    "cd_t = trial_df.time.values\n",
    "cd_offset = np.mean([s-np.max(cd_t[cd_t<s]) for s in stim_t])\n",
    "# offset = np.mean(stim_t-cd_t)\n",
    "\n",
    "#get CD response\n",
    "bout_df = expt.filter_marker_df_time(marker_df,bout_uc)\n",
    "\n",
    "trial_df = expt.filter_marker_df_code(bout_df,['U'])\n",
    "stim_t = trial_df.time.values\n",
    "trial_df = expt.filter_marker_df_code(bout_df,['B'])\n",
    "cd_t_all = trial_df.time.values\n",
    "cd_t = np.asarray([np.max(cd_t_all[cd_t_all<s]) for s in stim_t]) - (0.005 - cd_offset)\n",
    "\n",
    "xtime, R = expt.get_sweepsmat('lowgain',cd_t,sweepdur)\n",
    "cd_R = np.mean(R,1)-np.mean(R,1)[0]\n",
    "\n",
    "Ramp_p = []\n",
    "cd_restrict = cd_R[r_onset:]\n",
    "for sweep in trialmat[r_onset:,:].T:\n",
    "    Ramp_p.append(np.max(sweep+cd_restrict))\n",
    "Ramp_p = np.asarray(Ramp_p)\n",
    "\n",
    "\n",
    "bout_df = expt.filter_marker_df_time(marker_df,bout_c)\n",
    "trial_df = expt.filter_marker_df_code(bout_df,['T'])\n",
    "stim_t = trial_df.time.values\n",
    "\n",
    "eventDur = 0.001\n",
    "xtime,event_sweeps = expt.get_sweepsmat('SIU',trial_df['time'].values,eventDur)\n",
    "event_Amp = np.asarray([np.max(sweep) for sweep in event_sweeps.T])\n",
    "\n",
    "base_df = expt.filter_marker_df_code(bout_df,['C'])\n",
    "xtime,event_sweeps = expt.get_sweepsmat('SIU',base_df['time'].values,eventDur)\n",
    "event_0_Amp = np.median(np.asarray([np.max(sweep) for sweep in event_sweeps.T]))\n",
    "\n",
    "ampshift = np.asarray([np.round(((A/event_0_Amp)*100)-100) for A in event_Amp]).reshape(-1, 1)\n",
    "trial_df.insert(np.shape(trial_df)[1],'ampshift',ampshift)\n",
    "    \n",
    "trialmat = []\n",
    "for a in np.unique(trial_df['ampshift']):\n",
    "    theseT = trial_df[trial_df['ampshift']==a].time.values - 0.005\n",
    "    xtime, R = expt.get_sweepsmat('lowgain',theseT,sweepdur)\n",
    "    trialmat.append(np.mean(R,1))\n",
    "trialmat = np.asarray(trialmat).T \n",
    "trialmat = np.asarray([sweep-sweep[0] for sweep in trialmat.T]).T\n",
    "\n",
    "Ramp_c = []\n",
    "for sweep in trialmat[r_onset:,:].T:\n",
    "    Ramp_c.append(np.max(sweep))\n",
    "Ramp_c = np.asarray(Ramp_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/kperks/mnt/engram/spikedata/data_raw/20200718/20200718_000.smr\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAFgAAABvCAYAAABo6gchAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAATfElEQVR4nO2deVCTx//H3xFigECQGyGKdbyxRcGAR9vvWG08Zkq1aD1anZaOU/BA/XEoqHgCVrEe6FfrVL7a0WLFgvWoHJ16CyYVGeVQECsGJJxiOAQTsr8/AqkB8oDJ84RIfc044D6b3eX9fNj97H52FxYhhOAtjNGnpxvQ23krMMO8FZhh3grMMG8FZhjGBU5KSmK6CqOGcYFLS0uZrsKoMdX1g9euXcOZM2fQ0NCAadOm4fLly+BwONiwYQOsrKzobGPPIRYDaWmAUAgIBLqVQXTkxIkThBBCUlNTyeTJk4lSqSQ3btwgiYmJGvn27dunaxU9i0hEiK0tIYDqq0ikUzE6dxELFy7E5cuXsXv3bnh7e4PFYsHJyQlVVVUAgFu3biEuLg4lJSW6VtGzpKUBNTWq72tqgPR03crR9QUnJycTQgjJzc0lHh4epKWlhVy7do2cOXNGI1+vsWCxWKdidO6DGxsbsWbNGjQ2NiI4OBihoaHgcDiIjIzUtUjjQiAAUlJUlisUAuPG6VYOze+9A2+EBYtEhGzbpnM/S4VWC5bJZDh+/DhKS0vR0tICNpsNPp+PefPmoV+/fjqahREiFgPTp6v62e+/V1mtrh5DJ2gVOCUlBQsXLtQQ8/nz50hLS8PcuXNpa0CP09lgRqPAWr2ICxcu4KeffkJxcbE6zdrauneJC6j6V1tb1fe2tqr/04hWCz527BhycnKQmJgIqVQKLy8vLFiwgNbKjQK6BjMtUPrBo0aNwvjx42FpaQmxWExrxUaFQABERNAuLkBhwZGRkaioqMB//vMfrF69GtbW1rRX/m9Aq8D+/v4YNGgQAECpVEKpVKJPn16wuknH+sJroFXgQYMGITw8HCwWC6Q1LhoTE8N4gxiFYZesMyhncvb29ggODma0AQaFYZesMygFLioqQlBQEDgcDlgsFnbs2MFoYxhHKFRZbk0NIy5ZZ1AKPHr0aCxdupTxRhgMhl2yzqAU+ObNm8jLy4OFhQUAvFkWrG0wEwi61S0UFhaCx+PByclJv3ZoW6QoKyvrNL20tPS1Fjt6ZLFHj8VyhUJB0tPTyZYtW8jt27f1bopWCxaJRMjLy4O9vT14PB5qa2tRWVmJMWPGwMXFRb+3yjQ6DmYymQynT59GbW0tFi9eDDc3N72bolVgX19f+Pr6ori4GNXV1XBwcMCAAQP0rtAgtA5mMoUCz/l8KDw9IS8oAIvFwtChQ9Hc3IwnT57AzMwMHA4HHA4HlpaWaGhogIWFBebNmwcul0tLU7pccHdzc6PlTRqU1sFMlJKCDEJgeucO2Dk54PF4GDp0KGQyGZKTk9Hc3AylUgkAWLx4Md555x3Mnz+f3rbo3cl0QU/0wdnZ2aSxsZEolUrKfEqlksjlclJfX0/kcjkjbaG04MOHD6OoqAgTJkwAh8PBjBkz1M9EIhESEhLQ0tICLy8vZGVl9WjYnhACmUyGnIsXcenBAywaPx5u06ZRfobFYsHU1BSmpp3LIBaLkZaWBqFQCAETYfvo6GgSFxdHCCFk48aNGs8OHTpE5HI5yc/PJ8OGDesQts/MzCT79u0jYWFhjFhGe5KTk8mmTZtITHg4yR8xQq9QOyGEiEQiYmtrSwAQW1tbImIqbP/3338jMTERDQ0NGunffvstpFIpjh07hk8++aRD2N7HxwcrVqwAn8/X7c13A0IIsrKy0NLSgsmTJ+P/TEywJiYGI+7f1y/UDiAtLQ01rZ5ITU0N0nUsi1LgsLAw+Pr6wt7eHtHR0RrPrl69ihMnTmDdunUwMTGBUqlEWVkZ+vfvr1NDdOHSpUtIT0/H8+fPYW1tDatp08CiKTohFAph21qWra0thDqWxSJE+xGCvXv3YurUqYiNjcXAgQOxefNm9bPAwED1GrFQKMSFCxfUYXszMzN1vri4OKxYsUKnxlFx9+5dnD17FosWLdL0csRi2qbCYrEY6enpEAqFGKdjWZSD3IsXL5CXl4dVq1bh3LlzGs8OHjyo8f+PPvpIpwbowsuXL5Geno6pU6d2dCG7ORXuDgKBQPfBrRXKLoLL5SIzMxMsFgvm5uZ6VUQnpqammDJlit4/fBtisRhRUVHMhMWoRsDa2loiFouJSCTSeRSl2w9ubGwkTU1NtJVHl7egDUoL3rhxI4qKivD48WON8H1P8scffyA5ObnLfNqssn06Xd6CVqjUb/OB9YFOC66oqCCbN28mT548ocynzSo7S2+fJtZxk582KAe5Bw8eICgoSO0V9PR68KVLlzB8+PCOi07t1n47s0qBlvSIiAikpKTo7S1ohUr9yspKEh8fT44fP06ePXum0xuky4Lr6+vJd999R8rLyzUfiEREzuMRAhA5j0dyjx4lAQEBhMfjdbBKpq21MygFDg0NJffu3SOZmZlkzZo1OlVAZxfx8uXLDmmSgADVwnrrv00cDgFAeDweCQwM7CCiSCQiUVFRBhGXkC66iL59+2L06NEA0MEPNiQSiQQymQzu7u7/JLZ2C1kVFTAHYAegGsC55mYAqsVzPp/f4VeeDt/2daAU+OOPP0ZQUBCUSiVmzpxpqDZpQAhBeno6nJyc/hFYLIZi6lSYymQQcrkI5nLBa2hABpeLQhMTQCbTa3pLJ5QCu7i4wMnJCRwOR9N6DEhhYSHKyso0dnWWxMeDL5MBAMwaGuDv54dUT0/ECoXqF8LIgKULVP3H8uXLSWVlJamoqCBBQUE69UH69MFKpZIcPHiQpKWlaaQfCQggVa19bhVAjgQG6lwH01BONAYOHAgTExOYm5vD0dFRvUfNUMjlcvD5fEyaNEkj/V1/f8zj8RAO4HMeD+/5+xusTa8L5WpaeHi4KtMr+9OA19ujputqGiEELBZLM/EVf1cMGFdXoA0q887MzCRPnz4lW7duJefOndPpV0TXLiIrK0vzUGM7f5eJAytMQNlFXLhwAQUFBRgzZgxEIpGh3jkIIbhx44bG4n1JfDxMWwc2U5kMkv/9z2Dt0QdKgRsaGpCQkAB3d/cOISMmKSsrQ01NDTw8PNRpaVD5uWj9SvOSDGNQummRkZGQSqXgcrnq/vhVFAoFbt68iaysLHh5eeH06dO0RJbv3buHwXZ2sNy7V72+8K6/P+b9/DMEMhlEPB6+M+KB7VUoBT506BCqqqowadIkNDQ04IsvvtB4Xl5ejjt37oAQgosXL2LPnj3IyMhAamoq5syZo3Oj3Pv0Afnvf4G8PPVGaYFAgJg//kB6ejq+M/aB7RUouwilUgk3NzfMmjULjx496vDc1dUVfn5+6ryvRpZ1PQxOCAFfJMKAvDxVwivRYYFAgIiIiDdGXKALgS0tLZGdnY3du3dDoVBQFkQI0Ygs6xq2P3/+PK4OHszo2TVDQtlF+Pj4YObMmVAoFBg+fDhlQb6+vnofCFcoFMjNzcWsWbMMvlGaKSgFTkhIgFAoRL9+/ZCRkYEJEyZ0yMPn87F69WoA6DDjel0KCwvBYrEwZMgQwNTUIKeAmIZS4A8//BBNTU2QSqUdZ1UMkJOTg5EjR2rdK/YmQvmTzJ49m97aujij5uXlBUtLS3rr7GEMd7Kw7Yza+vWqr+2ivYQQDB48GI6Ojq3ZGdyrYEAoBd6/fz8AID8/X//TRl3cgZOQkIA7d+4AUIk7ffp0rF+/HtOnT3+jRaYU2NPTE/7+/jh79qz+EWWKawPq6+vx8OFDODs7AzDAXgUDolXg0NBQJCcno7q6Gjk5OdiyZYt+NbWdUYuKAlJTNVyvnJwc2NnZqQWma2ejMaB1kNu5cyeAfyYQ9+7d0782LRvzcnJy8O6776o9FYFAwOxeBQNC6UWEhYXh4cOH4PP5aGlpwYEDB2irWKlUQiqVwtnZGe+//36HfcWGjv4yBaXAPB4PgYGBcHZ2xvXr1/WurK6uDllZWXjy5AkkEgkUCgWWLl2KESNG6F22sUIpsI2NDRwdHXHgwAFatq/K5XIUFxdjwIABmDhxIvh8Pjgcjt7lGjVU4Y627aulpaWkpaVFp5DJG3FvGoNQummxsbG4ePEiJBIJtm/fzswbFotVnsUb7OtSQSkwh8OBjY0NfHx88OLFC/pr72J21xugFNjDwwOZmZkICAhgZiCi64ZTI0brIFdeXg4zMzMcOXKEuYGoB24gMTRaLXjjxo1QKBSIiopirnaK2V1vQasF29nZYcaMGcjIyGC2BTQeuzJGtFpwZWUlMjIyUFFRgYyMjC6Fzs/Px7JlyxAcHIynT592uwG9ZVlSG1oFnjFjBqRSKaZNmwapVIry8nLKgn799Vfs2LEDQUFBSExM7DRPezF707KkNrR2Ea8bzaivrweXy4WJiYk6bC8SidRh+zYxa2pq8P333yMlJUXrYZXeBG0Rjb59++LFixdaw/adidmbliW1QteUMD8/n6xcuZKEhISQqqoqdXrbVFnbCR9DH0oxNAa9Uqa3i9kZlBuw6WDdunXqSAUAlJSUdHu3jzHmdXV1xWeffdatzwJg/lKk9rzO6tqblrczDH4hsLe3d6/N2xmMdxFtNDU1wc/PD7/99hsKCwuxf/9+mJmZITg4WOMmwaKiIuzduxcsFguLFi0Cl8vVmhdQTXCongOvf0NWdXU1lixZgtWrV+u/51kv+38N4uLiyDfffEPkcjnZunUrqa+vJ48fPyZ79uzRyHfq1CkilUpJXV0diYyMpMxLCOnyOSFd35DVnu3bt5Ply5eT8PDwLvN2hUG6iCtXrmDkyJGwt7cH8M+k5NVbqtqYO3cu2Gw2du3ahdmzZ1Pm7aqsNrq6IetVEhMTMXPmTHA4nA57nnWBsV12Bw8eRHZ2NgDVwhGXy8Xdu3fxyy+/qCclUqkU/fv318jb1NQENzc3rFy5EnZ2dkhKStLI2572ZXXG1atXkZGRgXXr1mHr1q2UN2RlZWWhoKAAubm5KC0tRXR0tF63aRmsDwaAtWvXYtu2bXj48CEOHToENpuNtWvXws7OTp1n06ZNqKurA5vNhkAggLu7u9a8AHD//n3K50D3b8h6lZCQEMyePRtJSUld5qXCoAL/G+kFf7fBuKFV4MePH2PlypVYu3YtIiIiIJfLsXbtWigUChw+fLjb5SQlJeHmzZt0Nq0DcXFxKC4uxuHDh1FSUoLdu3dT5g8JCdGpHloHucuXL2PBggUYP348bty4gWfPniE3Nxd//vknCgoKUFJSgrCwMHh4eODp06fg8/ng8XhwcHCAs7MzvL29sX79erVzHxISgtjYWOzevRtz585FfHw8TE1N4eLigq+++gqA6uKNLVu2gM1mw9vbGy4uLjh69CgcHBwgl8thbm6O9957DxKJBJWVlZDJZJg3b566zQUFBbCxscGtW7dw/PhxDB48WN2OwMBA7NmzR+39SCQS/Pjjj2CxWPjggw8wZcqULjWh1YLnzJmDrKwsxMTE4M6dO7C2toa7u7vGrYDu7u4IDQ0FIQShoaEoKirqdvnFxcUYPXo0Jk6cqE5jsVjw8/ODh4eH2up9fHywZMkS9OvXDxs2bEBmZiYAlQsYGxuL06dPa5Q7YcIE+Pj4qP8YQBsnT55EeHi4em90QkICrKysYGdn1+3NkLQKnJycjEWLFiE8PBwuLi64evVqhzwWFhbo06cP+vbtC0C1CbDtctHa2lqNvG27LWtra6FQKLBq1So4ODhg165d6jyZmZn466+/MG7cOPWNAFwuFywWC3379gWLxVIfQWOz2SCEaI2St2+HUqlEc3OzutyWlhb4+flhyZIlGDZsWLc0obWL8PT0REREBKysrCCXyxEREYEHDx7g999/p/zc2LFjsX//fty6dUvjAEz//v2xf/9+FBQUwNTUVO1Dv7pHw8HBAampqUhNTYVMJqM8z3f06FHI5XIsWLBAI8ZoY2OD27dvY8aMGYiPj1e34/PPP8fOnTvV92bMnz8fe/bsgYWFBb7++utuafKvcdPi4uLg6+tr8PvojcZN++GHH1BXV4edO3fi5MmTAIAzZ86A6v3T7W3Ex8dDLpfTVh5gJBYskUhw6dIlODo6wtnZGSkpKZg/fz6ePXuGsWPHauQ9ePAgJBIJrK2tMXToUFy5cgVsNhtCoRAjR47Evn371H1leXk5MjMzwePx4OnpiZEjR+LIkSOwsbFBZWUlli1bpuEVODs7o6CggNbja0ZhwRkZGfDw8MCkSZOQlpYGNzc3ZGdno6ioCDt27EBjYyMA1f0VEokE0dHRmDx5MgDV9oJt27bh2rVrMDc3x6xZszBkyBD1FgChUIjQ0FBcv34dp06dwsqVK9X9Z3uvYNSoUbh9+zatP5tRCFxdXQ0rKytYWVkhLCwMo0aNwpgxY/Do0SNMnz5d7Y20tLSoPYu23Z48Hg+mpqaQy+U4f/48JBIJxo4dq+5aLCws1N6BXC4Hm81Gc+vlde29AhaLhZcvX9L6sxnFmVV7e3sNF624uBi+vr4AgJ9//hlBQUEAVGLa2dkhMjISDg4OcHV11SjH1dUV165dQ1VVFR4/ftxhQPv0008RExOjduM68wra3Efa0GkVmWZKSkrIkSNHGK8nPz+f1NbWkrKyMhIVFdXh+f3790lSUhKtdRqFBbu6ukKhUDD+d0OVSiWio6OhVCoREBDQ4fmNGzfw5Zdf0lqnUXgRvRmjGOR6M28FZpi3AjPMW4EZ5q3ADPP/aM73TPzi6/kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 86.4x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hfig,ax = create_fig()\n",
    "colinds = plt.cm.plasma(np.linspace(0.2,0.85,11))\n",
    "colinds = array([c for c in reversed(colinds)])\n",
    "trialmat = trialmat[:,restrict_inds]\n",
    "trialmat = array([sweep for sweep in reversed(trialmat.T)]).T\n",
    "for i,sweep in enumerate(trialmat.T):\n",
    "    ax.plot(xtime-0.5,sweep,color = colinds[i],lw = 0.5);\n",
    "ax.set_xlim(0,50)\n",
    "ax.set_ylim(-5,35)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "# ax.vlines(5,0,5)\n",
    "figsave(figure_folder,'Fig4_grcExample1_uncoupled')\n",
    "\n",
    "hfig,ax = create_fig()\n",
    "ax.plot(xtime-0.5,cd_R,color = 'black',lw = 0.5);\n",
    "ax.set_xlim(0,50)\n",
    "ax.set_ylim(-5,35)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "# ax.vlines((0.005 - cd_offset)*1000,0,3)\n",
    "figsave(figure_folder,'Fig4_grcExample1_cd')\n",
    "\n",
    "\n",
    "\n",
    "hfig,ax = create_fig()\n",
    "# colinds = plt.cm.plasma(np.linspace(0.2,0.85,11))\n",
    "# colinds = array([c for c in reversed(colinds)])\n",
    "# trialmat = trialmat[:,restrict_inds]\n",
    "# trialmat = array([sweep for sweep in reversed(trialmat.T)]).T\n",
    "for i,sweep in enumerate(trialmat.T):\n",
    "    ax.plot(xtime-0.5,sweep+cd_R,color = colinds[i],lw = 0.5);\n",
    "ax.set_xlim(0,50)\n",
    "ax.set_ylim(-5,35)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "# ax.vlines(5,0,5)\n",
    "figsave(figure_folder,'Fig4_grcExample1_predicted')\n",
    "\n",
    "hfig,ax = create_fig()\n",
    "trialmat = trialmat[:,restrict_inds]\n",
    "trialmat = array([sweep for sweep in reversed(trialmat.T)]).T\n",
    "for i,sweep in enumerate(trialmat.T):\n",
    "    ax.plot(xtime-0.5,sweep,color = colinds[i],lw = 0.5);\n",
    "\n",
    "ax.vlines(4.5,10,15)\n",
    "ax.vlines(0.5,10,15)\n",
    "sns.despine(hfig)\n",
    "ax.set_xticks(np.arange(0,55,10))\n",
    "plt.xlim(0,50)\n",
    "ax.set_ylim(-5,35)\n",
    "ax.set_yticks([10,15])\n",
    "ax.set_frame_on(True)\n",
    "yax = ax.spines[\"left\"]\n",
    "yax.set_visible(False)\n",
    "ax.set_xlabel('ms');\n",
    "\n",
    "figsave(figure_folder,'Fig4_grcExample1_coupled')\n",
    "\n",
    "\n",
    "hfig,ax = create_fig_tuning()\n",
    "plt.scatter(stim_ampshift[restrict_inds],[a for a in reversed(Ramp_uc)],\n",
    "            color = 'black',s=10,fmt = 'o')\n",
    "# plt.scatter(stim_ampshift[restrict_inds],[a for a in reversed(Ramp_p)],\n",
    "#             color = 'black',s=10, marker = '*')\n",
    "plt.plot(stim_ampshift[restrict_inds],[a for a in reversed(Ramp_p)],\n",
    "            color = 'gray',linestyle='--')\n",
    "plt.scatter(stim_ampshift[restrict_inds],[a for a in reversed(Ramp_c)],\n",
    "            color = 'red',s=10)\n",
    "ax.set_ylabel('Peak response (mV)')\n",
    "ax.set_xlabel('Stimulus amplitude \\n (% change)',linespacing=0.75)\n",
    "ax.set_xlim(-47,47)\n",
    "xticks([-40,-20,0,20,40]);\n",
    "sns.despine(hfig)\n",
    "figsave(figure_folder,'Fig4_grcExample1_scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": [
    "\n",
    "hfig,ax = create_fig_tuning()\n",
    "plt.scatter(stim_ampshift[restrict_inds],[a for a in Ramp_uc],\n",
    "            color = sns.xkcd_rgb['icky green'],s=10,marker = 'o')\n",
    "# plt.scatter(stim_ampshift[restrict_inds],[a for a in reversed(Ramp_p)],\n",
    "#             color = 'black',s=10, marker = '*')\n",
    "plt.plot(stim_ampshift[restrict_inds],[a for a in Ramp_p],\n",
    "            color = 'gray',linestyle='--')\n",
    "plt.scatter(stim_ampshift[restrict_inds],[a for a in Ramp_c],\n",
    "            edgecolors = sns.xkcd_rgb['icky green'],s=10,marker = 's',color='white')\n",
    "ax.set_ylabel('Mean Peak Response \\n (mV)',linespacing=0.9)\n",
    "ax.set_xlabel('Stimulus amplitude \\n (% change)',linespacing=0.9)\n",
    "ax.set_xlim(-47,47)\n",
    "xticks([-40,-20,0,20,40]);\n",
    "sns.despine(hfig)\n",
    "figsave(figure_folder,'Fig3_grcExample1_scatter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DGC 20200719_004"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/kperks/mnt/engram_share/home/kep2142/spikedata/data_raw/20200719/20200719_004.smr\n"
     ]
    }
   ],
   "source": [
    "exptname = '20200719_004'\n",
    "colinds = plt.cm.plasma(np.linspace(0.2,0.85,11))\n",
    "colinds = array([c for c in reversed(colinds)])\n",
    "expt = AmpShift_Stable()\n",
    "expt.load_expt(exptname, data_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = expt.get_dt('lowgain')\n",
    "expt.set_amps(11,[-40,-30,-20,-10,-5,0,5,10,20,30,40])\n",
    "expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')\n",
    "marker_df = expt.get_marker_table()\n",
    "\n",
    "#hyperpolarized\n",
    "bout_uc = [expt.get_bout_win('R','Keyboard')[3]]\n",
    "bout_c = [expt.get_bout_win('N','Keyboard')[1]]\n",
    "\n",
    "sweepdur = 0.05\n",
    "r_onset = int(0.006/dt)\n",
    "\n",
    "bout_df = expt.filter_marker_df_time(marker_df,bout_uc)\n",
    "trial_df = expt.filter_marker_df_code(bout_df,['T'])\n",
    "stim_t = trial_df.time.values\n",
    "\n",
    "eventDur = 0.001\n",
    "xtime,event_sweeps = expt.get_sweepsmat('SIU',trial_df['time'].values,eventDur)\n",
    "event_Amp = np.asarray([np.max(sweep) for sweep in event_sweeps.T])\n",
    "\n",
    "base_df = expt.filter_marker_df_code(bout_df,['U'])\n",
    "xtime,event_sweeps = expt.get_sweepsmat('SIU',base_df['time'].values,eventDur)\n",
    "event_0_Amp = np.median(np.asarray([np.max(sweep) for sweep in event_sweeps.T]))\n",
    "\n",
    "ampshift = np.asarray([np.round(((A/event_0_Amp)*100)-100) for A in event_Amp]).reshape(-1, 1)\n",
    "trial_df.insert(np.shape(trial_df)[1],'ampshift',ampshift)\n",
    "    \n",
    "trialmat = []\n",
    "for a in np.unique(trial_df['ampshift']):\n",
    "    theseT = trial_df[trial_df['ampshift']==a].time.values - 0.005\n",
    "    xtime, R = expt.get_sweepsmat('lowgain',theseT,sweepdur)\n",
    "    trialmat.append(np.mean(R,1))\n",
    "trialmat = np.asarray(trialmat).T \n",
    "trialmat = np.asarray([sweep-sweep[0] for sweep in trialmat.T]).T\n",
    "\n",
    "stim_ampshift = np.unique(trial_df['ampshift'])\n",
    "restrict_inds = stim_ampshift>=-40\n",
    "\n",
    "\n",
    "\n",
    "Ramp_uc = []\n",
    "for sweep in trialmat[r_onset:,:].T:\n",
    "    Ramp_uc.append(np.max(sweep))\n",
    "Ramp_uc = np.asarray(Ramp_uc)\n",
    "\n",
    "#get CD offset pre-coupled stim\n",
    "bout_df = expt.filter_marker_df_time(marker_df,bout_c)\n",
    "trial_df = expt.filter_marker_df_code(bout_df,['T'])\n",
    "stim_t = trial_df.time.values\n",
    "trial_df = expt.filter_marker_df_code(bout_df,['B'])\n",
    "cd_t = trial_df.time.values\n",
    "cd_offset = np.mean([s-np.max(cd_t[cd_t<s]) for s in stim_t])\n",
    "# offset = np.mean(stim_t-cd_t)\n",
    "\n",
    "#get CD response\n",
    "bout_df = expt.filter_marker_df_time(marker_df,bout_uc)\n",
    "\n",
    "trial_df = expt.filter_marker_df_code(bout_df,['U'])\n",
    "stim_t = trial_df.time.values\n",
    "trial_df = expt.filter_marker_df_code(bout_df,['B'])\n",
    "cd_t_all = trial_df.time.values\n",
    "cd_t = np.asarray([np.max(cd_t_all[cd_t_all<s]) for s in stim_t]) - (0.005 - cd_offset)\n",
    "\n",
    "xtime, R = expt.get_sweepsmat('lowgain',cd_t,sweepdur)\n",
    "cd_R = np.mean(R,1)-np.mean(R,1)[0]\n",
    "\n",
    "\n",
    "\n",
    "Ramp_p = []\n",
    "cd_restrict = cd_R[r_onset:]\n",
    "for sweep in trialmat[r_onset:,:].T:\n",
    "    Ramp_p.append(np.max(sweep+cd_restrict))\n",
    "Ramp_p = np.asarray(Ramp_p)\n",
    "\n",
    "\n",
    "bout_df = expt.filter_marker_df_time(marker_df,bout_c)\n",
    "trial_df = expt.filter_marker_df_code(bout_df,['T'])\n",
    "stim_t = trial_df.time.values\n",
    "\n",
    "eventDur = 0.001\n",
    "xtime,event_sweeps = expt.get_sweepsmat('SIU',trial_df['time'].values,eventDur)\n",
    "event_Amp = np.asarray([np.max(sweep) for sweep in event_sweeps.T])\n",
    "\n",
    "base_df = expt.filter_marker_df_code(bout_df,['C'])\n",
    "xtime,event_sweeps = expt.get_sweepsmat('SIU',base_df['time'].values,eventDur)\n",
    "event_0_Amp = np.median(np.asarray([np.max(sweep) for sweep in event_sweeps.T]))\n",
    "\n",
    "ampshift = np.asarray([np.round(((A/event_0_Amp)*100)-100) for A in event_Amp]).reshape(-1, 1)\n",
    "trial_df.insert(np.shape(trial_df)[1],'ampshift',ampshift)\n",
    "    \n",
    "trialmat = []\n",
    "for a in np.unique(trial_df['ampshift']):\n",
    "    theseT = trial_df[trial_df['ampshift']==a].time.values - 0.005\n",
    "    xtime, R = expt.get_sweepsmat('lowgain',theseT,sweepdur)\n",
    "    trialmat.append(np.mean(R,1))\n",
    "trialmat = np.asarray(trialmat).T \n",
    "trialmat = np.asarray([sweep-sweep[0] for sweep in trialmat.T]).T\n",
    "\n",
    "\n",
    "Ramp_c = []\n",
    "for sweep in trialmat[r_onset:,:].T:\n",
    "    Ramp_c.append(np.max(sweep))\n",
    "Ramp_c = np.asarray(Ramp_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/kperks/mnt/engram/spikedata/data_raw/20200719/20200719_004.smr\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 86.4x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hfig,ax = create_fig()\n",
    "colinds = plt.cm.plasma(np.linspace(0.2,0.85,11))\n",
    "colinds = array([c for c in reversed(colinds)])\n",
    "trialmat = trialmat[:,restrict_inds]\n",
    "trialmat = array([sweep for sweep in reversed(trialmat.T)]).T\n",
    "for i,sweep in enumerate(trialmat.T):\n",
    "    ax.plot(xtime-0.5,sweep,color = colinds[i],lw = 0.5);\n",
    "ax.set_xlim(0,50)\n",
    "ax.set_ylim(-5,25)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "# ax.vlines(5,0,5)\n",
    "figsave(figure_folder,'Fig4_grcExample2_uncoupled')\n",
    "\n",
    "hfig,ax = create_fig()\n",
    "ax.plot(xtime-0.5,cd_R,color = 'black',lw = 0.5);\n",
    "ax.set_xlim(0,50)\n",
    "ax.set_ylim(-5,25)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "# ax.vlines((0.005 - cd_offset)*1000,0,3)\n",
    "figsave(figure_folder,'Fig4_grcExample2_cd')\n",
    "\n",
    "hfig,ax = create_fig()\n",
    "# colinds = plt.cm.plasma(np.linspace(0.2,0.85,11))\n",
    "# colinds = array([c for c in reversed(colinds)])\n",
    "# trialmat = trialmat[:,restrict_inds]\n",
    "# trialmat = array([sweep for sweep in reversed(trialmat.T)]).T\n",
    "for i,sweep in enumerate(trialmat.T):\n",
    "    ax.plot(xtime-0.5,sweep+cd_R,color = colinds[i],lw = 0.5);\n",
    "ax.set_xlim(0,50)\n",
    "ax.set_ylim(-5,25)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "# ax.vlines(5,0,5)\n",
    "figsave(figure_folder,'Fig4_grcExample2_predicted')\n",
    "\n",
    "\n",
    "hfig,ax = create_fig()\n",
    "trialmat = trialmat[:,restrict_inds]\n",
    "trialmat = array([sweep for sweep in reversed(trialmat.T)]).T\n",
    "for i,sweep in enumerate(trialmat.T):\n",
    "    ax.plot(xtime-0.5,sweep,color = colinds[i],lw = 0.5);\n",
    "    \n",
    "ax.vlines(4.5,10,15)\n",
    "ax.vlines(0.5,10,15)\n",
    "sns.despine(hfig)\n",
    "ax.set_xticks(np.arange(0,55,10))\n",
    "plt.xlim(0,50)\n",
    "ax.set_ylim(-5,25)\n",
    "ax.set_yticks([10,15])\n",
    "ax.set_frame_on(True)\n",
    "yax = ax.spines[\"left\"]\n",
    "yax.set_visible(False)\n",
    "ax.set_xlabel('ms');\n",
    "\n",
    "figsave(figure_folder,'Fig4_grcExample2_coupled')\n",
    "\n",
    "hfig,ax = create_fig_tuning()\n",
    "plt.scatter(stim_ampshift[restrict_inds],[a for a in reversed(Ramp_uc)],\n",
    "            color = 'black',s=10)\n",
    "# plt.scatter(stim_ampshift[restrict_inds],[a for a in reversed(Ramp_p)],\n",
    "#             color = 'black',s=10, marker = '*')\n",
    "plt.plot(stim_ampshift[restrict_inds],[a for a in reversed(Ramp_p)],\n",
    "            color = 'gray',linestyle='--')\n",
    "plt.scatter(stim_ampshift[restrict_inds],[a for a in reversed(Ramp_c)],\n",
    "            color = 'red',s=10)\n",
    "ax.set_ylabel('Peak response (mV)')\n",
    "ax.set_xlabel('Stimulus amplitude \\n (% change)',linespacing=0.75)\n",
    "ax.set_xlim(-47,47)\n",
    "xticks([-40,-20,0,20,40]);\n",
    "sns.despine(hfig)\n",
    "figsave(figure_folder,'Fig4_grcExample2_scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": [
    "\n",
    "hfig,ax = create_fig_tuning()\n",
    "plt.scatter(stim_ampshift[restrict_inds],[a for a in Ramp_uc],\n",
    "            color = sns.xkcd_rgb['teal blue'],s=10,marker = 'o')\n",
    "# plt.scatter(stim_ampshift[restrict_inds],[a for a in reversed(Ramp_p)],\n",
    "#             color = 'black',s=10, marker = '*')\n",
    "plt.plot(stim_ampshift[restrict_inds],[a for a in Ramp_p],\n",
    "            color = 'gray',linestyle='--')\n",
    "plt.scatter(stim_ampshift[restrict_inds],[a for a in Ramp_c],\n",
    "            edgecolors = sns.xkcd_rgb['teal blue'],s=10,marker = 's',color='white')\n",
    "ax.set_ylabel('Mean Peak Response \\n (mV)',linespacing=0.9)\n",
    "ax.set_xlabel('Stimulus amplitude \\n (% change)',linespacing=0.9)\n",
    "ax.set_xlim(-47,47)\n",
    "xticks([-40,-20,0,20,40]);\n",
    "sns.despine(hfig)\n",
    "figsave(figure_folder,'Fig3_grcExample2_scatter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DGC 20200607_002"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/kperks/mnt/OneDrive - wesleyan.edu/Research/Manuscripts/GRC_PerksSawtell/AcceptedRevision_CellReports/data_raw/20200607_002.smr\n"
     ]
    }
   ],
   "source": [
    "exptname = '20200607_002'\n",
    "colinds = plt.cm.plasma(np.linspace(0.2,0.85,11))\n",
    "colinds = array([c for c in reversed(colinds)])\n",
    "\n",
    "expt = AmpShift_Stable()\n",
    "expt.load_expt(exptname, data_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = expt.get_dt('lowgain')\n",
    "expt.set_amps(11,[-40,-30,-20,-10,-5,0,5,10,20,30,40])\n",
    "expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')\n",
    "marker_df = expt.get_marker_table()\n",
    "\n",
    "bout_uc = [expt.get_bout_win('R','Keyboard')[0],\n",
    "           expt.get_bout_win('R','Keyboard')[1]]\n",
    "bout_c = [expt.get_bout_win('N','Keyboard')[0]]\n",
    "\n",
    "sweepdur = 0.05\n",
    "r_onset = int(0.006/dt)\n",
    "\n",
    "bout_df = expt.filter_marker_df_time(marker_df,bout_uc)\n",
    "trial_df = expt.filter_marker_df_code(bout_df,['T'])\n",
    "stim_t = trial_df.time.values\n",
    "\n",
    "eventDur = 0.001\n",
    "xtime,event_sweeps = expt.get_sweepsmat('SIU',trial_df['time'].values,eventDur)\n",
    "event_Amp = np.asarray([np.max(sweep) for sweep in event_sweeps.T])\n",
    "\n",
    "base_df = expt.filter_marker_df_code(bout_df,['U'])\n",
    "xtime,event_sweeps = expt.get_sweepsmat('SIU',base_df['time'].values,eventDur)\n",
    "event_0_Amp = np.median(np.asarray([np.max(sweep) for sweep in event_sweeps.T]))\n",
    "\n",
    "ampshift = np.asarray([np.round(((A/event_0_Amp)*100)-100) for A in event_Amp]).reshape(-1, 1)\n",
    "trial_df.insert(np.shape(trial_df)[1],'ampshift',ampshift)\n",
    "    \n",
    "trialmat = []\n",
    "for a in np.unique(trial_df['ampshift']):\n",
    "    theseT = trial_df[trial_df['ampshift']==a].time.values - 0.005\n",
    "    xtime, R = expt.get_sweepsmat('lowgain',theseT,sweepdur)\n",
    "    trialmat.append(np.mean(R,1))\n",
    "trialmat = np.asarray(trialmat).T \n",
    "trialmat = np.asarray([sweep-sweep[0] for sweep in trialmat.T]).T\n",
    "\n",
    "stim_ampshift = np.unique(trial_df['ampshift'])\n",
    "restrict_inds = stim_ampshift>=-40\n",
    "\n",
    "\n",
    "\n",
    "Ramp_uc = []\n",
    "for sweep in trialmat[r_onset:,:].T:\n",
    "    Ramp_uc.append(np.max(sweep))\n",
    "Ramp_uc = np.asarray(Ramp_uc)\n",
    "\n",
    "#get CD offset pre-coupled stim\n",
    "bout_df = expt.filter_marker_df_time(marker_df,bout_c)\n",
    "trial_df = expt.filter_marker_df_code(bout_df,['T'])\n",
    "stim_t = trial_df.time.values\n",
    "trial_df = expt.filter_marker_df_code(bout_df,['B'])\n",
    "cd_t = trial_df.time.values\n",
    "cd_offset = np.mean([s-np.max(cd_t[cd_t<s]) for s in stim_t])\n",
    "# offset = np.mean(stim_t-cd_t)\n",
    "\n",
    "#get CD response\n",
    "bout_df = expt.filter_marker_df_time(marker_df,bout_uc)\n",
    "\n",
    "trial_df = expt.filter_marker_df_code(bout_df,['U'])\n",
    "stim_t = trial_df.time.values\n",
    "trial_df = expt.filter_marker_df_code(bout_df,['B'])\n",
    "cd_t_all = trial_df.time.values\n",
    "cd_t = np.asarray([np.max(cd_t_all[cd_t_all<s]) for s in stim_t]) - (0.005 - cd_offset)\n",
    "\n",
    "xtime, R = expt.get_sweepsmat('lowgain',cd_t,sweepdur)\n",
    "cd_R = np.mean(R,1)-np.mean(R,1)[0]\n",
    "\n",
    "\n",
    "Ramp_p = []\n",
    "cd_restrict = cd_R[r_onset:]\n",
    "for sweep in trialmat[r_onset:,:].T:\n",
    "    Ramp_p.append(np.max(sweep+cd_restrict))\n",
    "Ramp_p = np.asarray(Ramp_p)\n",
    "\n",
    "\n",
    "bout_df = expt.filter_marker_df_time(marker_df,bout_c)\n",
    "trial_df = expt.filter_marker_df_code(bout_df,['T'])\n",
    "stim_t = trial_df.time.values\n",
    "\n",
    "eventDur = 0.001\n",
    "xtime,event_sweeps = expt.get_sweepsmat('SIU',trial_df['time'].values,eventDur)\n",
    "event_Amp = np.asarray([np.max(sweep) for sweep in event_sweeps.T])\n",
    "\n",
    "base_df = expt.filter_marker_df_code(bout_df,['C'])\n",
    "xtime,event_sweeps = expt.get_sweepsmat('SIU',base_df['time'].values,eventDur)\n",
    "event_0_Amp = np.median(np.asarray([np.max(sweep) for sweep in event_sweeps.T]))\n",
    "\n",
    "ampshift = np.asarray([np.round(((A/event_0_Amp)*100)-100) for A in event_Amp]).reshape(-1, 1)\n",
    "trial_df.insert(np.shape(trial_df)[1],'ampshift',ampshift)\n",
    "    \n",
    "trialmat = []\n",
    "for a in np.unique(trial_df['ampshift']):\n",
    "    theseT = trial_df[trial_df['ampshift']==a].time.values - 0.005\n",
    "    xtime, R = expt.get_sweepsmat('lowgain',theseT,sweepdur)\n",
    "    trialmat.append(np.mean(R,1))\n",
    "trialmat = np.asarray(trialmat).T \n",
    "trialmat = np.asarray([sweep-sweep[0] for sweep in trialmat.T]).T\n",
    "\n",
    "\n",
    "Ramp_c = []\n",
    "for sweep in trialmat[r_onset:,:].T:\n",
    "    Ramp_c.append(np.max(sweep))\n",
    "Ramp_c = np.asarray(Ramp_c)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SbI0x5r777jN33HFHyT6vWbPGrF692nzve98ryfasurV0Ok0ymaS1tZWBgYEp2518HPLJp4JMpYwvfvGL9PX18dBDD/HJT36yJNvbbruNzs5O1qxZQ0dHR0m2Gzdu5MYbb8R13ZJ9vu6667j33ntZvnw5zz777JRtz0ocx3HI5XJTP9FjAkyJp4Js2bKFDRs28K1vfQulVEm2mzZt4pprruEHP/gBv/zlL0uy3blzJ08++SR79+7l6aefLsm2q6sL27apra1Faz1l27Mac7q6uli/fj22bfONb3yDpqamKdv29PSwceNGOjo6ePzxx393KkgsdvpatpNZuXIldXV1AFx//fU89dRTU7Z9+OGHeeWVV8hms3R0dPDyyy9P2fa3rFq1iltuuaUknzdt2sT27dsxxrB8+XKeeeaZKdlWp28qmOoMQQVTFaeCKdsP6p1r1q1bRzqdpre3l/b2dnp7e1myZAmHDh0iCAK+853vlNvFkpkx4gDcfPPN7N27l+bmZmKxGCtXruSuu+7isssuK66FnmbflWZUt5ZIJJBS4jgOQgi+9KUvceWVV/Lggw/S399fbvdKZkaJcypaax599FFSqRT19fXldqdkqql0BTOjI2e6UxWngqmKU8FUxalgquJUMFVxKpiqOBXM/wP2PLoCa0IV1wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 86.4x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "hfig,ax = create_fig()\n",
    "colinds = plt.cm.plasma(np.linspace(0.2,0.85,11))\n",
    "colinds = array([c for c in reversed(colinds)])\n",
    "trialmat = trialmat[:,restrict_inds]\n",
    "trialmat = array([sweep for sweep in reversed(trialmat.T)]).T\n",
    "for i,sweep in enumerate(trialmat.T):\n",
    "    ax.plot(xtime-0.5,sweep,color = colinds[i],lw = 0.5);\n",
    "ax.set_xlim(0,50)\n",
    "ax.set_ylim(-5,25)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "# ax.vlines(5,0,5)\n",
    "figsave(figure_folder,'Fig4_grcExample3_uncoupled')\n",
    "\n",
    "hfig,ax = create_fig()\n",
    "ax.plot(xtime-0.5,cd_R,color = 'black',lw = 0.5);\n",
    "ax.set_xlim(0,50)\n",
    "ax.set_ylim(-5,25)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "# ax.vlines((0.005 - cd_offset)*1000,0,3)\n",
    "figsave(figure_folder,'Fig4_grcExample3_cd')\n",
    "\n",
    "hfig,ax = create_fig()\n",
    "colinds = plt.cm.plasma(np.linspace(0.2,0.85,11))\n",
    "colinds = array([c for c in reversed(colinds)])\n",
    "# trialmat = trialmat[:,restrict_inds]\n",
    "# trialmat = array([sweep for sweep in reversed(trialmat.T)]).T\n",
    "for i,sweep in enumerate(trialmat.T):\n",
    "    ax.plot(xtime-0.5,sweep+cd_R,color = colinds[i],lw = 0.5);\n",
    "ax.set_xlim(0,50)\n",
    "ax.set_ylim(-5,25)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "# ax.vlines(5,0,5)\n",
    "figsave(figure_folder,'Fig4_grcExample3_predicted')\n",
    "\n",
    "\n",
    "hfig,ax = create_fig()\n",
    "trialmat = trialmat[:,restrict_inds]\n",
    "trialmat = array([sweep for sweep in reversed(trialmat.T)]).T\n",
    "for i,sweep in enumerate(trialmat.T):\n",
    "    ax.plot(xtime-0.5,sweep,color = colinds[i],lw = 0.5);\n",
    "    \n",
    "ax.vlines(4.5,10,15)\n",
    "ax.vlines(0.5,10,15)\n",
    "sns.despine(hfig)\n",
    "ax.set_xticks(np.arange(0,55,10))\n",
    "plt.xlim(0,50)\n",
    "ax.set_ylim(-5,25)\n",
    "ax.set_yticks([10,15])\n",
    "ax.set_frame_on(True)\n",
    "yax = ax.spines[\"left\"]\n",
    "yax.set_visible(False)\n",
    "ax.set_xlabel('ms');\n",
    "\n",
    "figsave(figure_folder,'Fig4_grcExample3_coupled')\n",
    "\n",
    "hfig,ax = create_fig_tuning()\n",
    "plt.scatter(stim_ampshift[restrict_inds],[a for a in reversed(Ramp_uc)],\n",
    "            color = 'black',s=10)\n",
    "# plt.scatter(stim_ampshift[restrict_inds],[a for a in reversed(Ramp_p)],\n",
    "#             color = 'black',s=10, marker = '*')\n",
    "plt.plot(stim_ampshift[restrict_inds],[a for a in reversed(Ramp_p)],\n",
    "            color = 'gray',linestyle='--')\n",
    "plt.scatter(stim_ampshift[restrict_inds],[a for a in reversed(Ramp_c)],\n",
    "            color = 'red',s=10)\n",
    "ax.set_ylabel('Peak response (mV)')\n",
    "ax.set_xlabel('Stimulus amplitude \\n (% change)',linespacing=0.75)\n",
    "ax.set_xlim(-47,47)\n",
    "xticks([-40,-20,0,20,40]);\n",
    "sns.despine(hfig)\n",
    "figsave(figure_folder,'Fig4_grcExample3_scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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": [
    "hfig,ax = create_fig_tuning()\n",
    "plt.scatter(stim_ampshift[restrict_inds],[a for a in Ramp_uc],\n",
    "            color = sns.xkcd_rgb['teal blue'],s=10,marker = 'o')\n",
    "# plt.scatter(stim_ampshift[restrict_inds],[a for a in reversed(Ramp_p)],\n",
    "#             color = 'black',s=10, marker = '*')\n",
    "plt.plot(stim_ampshift[restrict_inds],[a for a in Ramp_p],\n",
    "            color = 'gray',linestyle='--')\n",
    "plt.scatter(stim_ampshift[restrict_inds],[a for a in Ramp_c],\n",
    "            edgecolors = sns.xkcd_rgb['teal blue'],s=10,marker = 's',color='white')\n",
    "ax.set_ylabel('Mean Peak Response \\n (mV)',linespacing=0.9)\n",
    "ax.set_xlabel('Stimulus amplitude \\n (% change)',linespacing=0.9)\n",
    "ax.set_xlim(-47,47)\n",
    "xticks([-40,-20,0,20,40]);\n",
    "sns.despine(hfig)\n",
    "figsave(figure_folder,'Fig3_grcExample3_scatter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get shift in onset latency of responses\n",
    "> use interactive plotting to go through the responses to each cell and extract onset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# created in 'GRC_properties_Meta_ipynb'\n",
    "pickle_in = open(top_dir / 'data_processed/df_cmdintact/DGC_uncoupled_wavmat.pickle',\"rb\")\n",
    "cell_data = pickle.load(pickle_in)\n",
    "pickle_in.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['20200719_004',\n",
       " '20200719_001',\n",
       " '20200606_005',\n",
       " '20200607_005',\n",
       " '20200607_004',\n",
       " '20200607_002',\n",
       " '20200525_001',\n",
       " '20200525_006',\n",
       " '20200312_002',\n",
       " '20200227_000',\n",
       " '20200226_002',\n",
       " '20200225_000',\n",
       " '20200115_002']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(cell_data.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CQ90gG+PrkHWbYHd8+JqOCU2k9z1kbH4295BQzg33e//k97Ouex1pM51/JerPhrZX8j9eEISCVlBhvS+jj/aAMZHOIsngkhLYlgbkWtapTALd7UHXZOIZGeQkPkUn4dQprq4l2NXJYGqQ/lQ//konwaGZjAD6hIkYzbtznzQ/BxMuHH5tVtksDNPgsd2P5V+Z+iVivLUgCMMKKqz3OXqftYmsyjjtGJblQFEUpAwY6Ti6241TV0lmZJAsvJqFJUto/mLCPd3sHNiBS3VRWVvM4AEta31ibvieHWyFwWZo2h/WTtUJwMudo+gKKRoPvhoxKkQQBKBAw/pocZ1I56aa63YU01LRHbl+66QRw+H24HaqJHObmOOUYyg2oOtYZpbtezcxsWgiJdX7V98D0CdMwAqHMTf/E/x1UDx+xD2/Mv8rPL336bwm7AC58dZNF+T2bRQE4YxXkGF9tECMGyaoErodwTQ1HFquKySViuJ0e/C6NYxs7hqWNYgXB4l0BofLxd6925lcPJmSKs+IbhC1uhrJ7cZ4bWWuC+Mt3tX4LrJ2lrboKIbjjVsEe1fnf7wgCAWrIMPatI68cW4incVSJBxWiGxWxqHlHjImkmEcbg9FbgcZMxfW2UwvPlyEQiECldX0d7UxqXgSxdUeUvEMyWjuoaEky+gNDRg73jhkWFd5qmjwN7Cme03+FWm8AAZ2ifHWgiAUZljnMxrElCUcVoRsVsWhaEiaTCqZ67MudqtYlo1lezGNbnx4cmFdUUmyf4DJxZPxlTpRNHl4bWsAR8M4jPa+Q4Y1wHl15/F0yyhmM5ZOyK0T0jrKYX+CIBScggxrC+uIr0eNLLYiodsxMmkJh6wh6QpGIo7u9lAy1C2SsMrJpLrxmS6CwSAEXLjiElNKpiDLEsVV7pH91uU6RsQJFdMPed9LGi7h1e5XiaVjh3z9kMafkxu3LQjCGa0ww9o8cliHU1lQJVwkyWQUNFlFdqq5sPZ4KFZz46MT2TKyiW58lotQMEjMnaUk5cbv8AO5BZ0Guw8YvucKk446sKVDv60zS2fiUBxs6tuUf2UazoMWMSJEEM50hRnWRxkNEh5qWTtJYhjgkFQkXSZrGOhuD660DapEOFGEJcfx2S4i0SjdagRPQhm+zkEjQtiDmTQxBwYOeV9FVphXOY+13aPYXKDx/Ny+jOH2/M8RBKHgFGRYH63Tet8uMS5SpNMSmqRia7kNb3W3Gy1ugkulLxaAgAO/xwdAa6YPJW2RiuW6MUqqPQx2DoW1ZaEltiLpDoxduw55X4ClNUt5qeOl/OtSPD63VogYby0IZ7SCDOujDd2LGVlkVULFwjBMNFvBVnLnONwe1FgW063SEwkgVXhxB7w4VI22VB9IEO7Nra5XXO0mEUmTimcguAcpHUFvajxoi68DnVt7LjuCO0a3Cl/j+WKdEEE4wxVkWB+tzzqRzqJpEoriJp3OoNkKlpI7R3e5kSMZbLdCR6wIStxoRU68mptMIoO7uIRQTy6sA+UuZEXKdYV0bgB/HfrkqfsXdDqEBn8Dtd7a0bWuG87P9VvnO6FGEISCU5BhbR91nLWJpoKqeHL7L1oKlmSiOV3IioIUTGMFHLwZq8D26yhFOk5bo8guoqSyZrhlLSsygQo3wZ5ELqxr5qJPmnjElvW+Vfhe7BhFt0bjeRBug+Ce/M8RBKGgFGRYW+aRW6CptIlDtVHU3MYDqimTJYPudufOH0xjFTtImDrbzVqUgI5uKlTIFQQqqwj37N9kIFDuItKfhI7XoHY++qRJGG++ecSumHNrz+WVzlfIWtn8KuSvgdKJot9aEM5gBRnWR5vBmEyb6JqNIruHt/TKhbUHgFTQwOVQmaq28HjPTJQiHU/Ggd/2U1RRRbivZ/ha/jInkb4kdL+Ra1lPnowViZDtPXyf9KKqRSTNJG/0v5F/pRrOE/3WgnAGO6awXrlyJV/5ylf4+te/TvNbN4w9hSz7yH3W6YyJrlooSi6stYxMxjJwuN1kDJN0MosfmznKm7zS3UivAsUZL5qhHdSy9pe5iPSEIR2FypmoVVW5XWN27jzs/d2amynFU0a3ZGqj6LcWhDPZMYX1hg0b8Pl8+P1+amtrj1eZjpltHT7QbNsmnbFwqCay4iaTyaAObenldHuID+3+4s+kKQ7oVLpDrOoLU2TlHjD6yiqI9PdimbnWe6DMRbg/BZ5y8Fbkdo2ZNOmIYQ2wbNwytg5szb9SDedBrAf6tud/jiAIBUM9lpOXL1/O5MmTWbNmDQ8++CATJ05k7dq1tLefogkcQxltWodvWadNC8uycalZJCm3zrSalohlEjiGwlp1yHgTUYySWsYrbazvm8h8W8e2bFSPF8s0iQ70E6ioJFDhIpWUMErmDm0QBvqUyaS27zhiUc+uOZtfbfoViUwCt+Y+et285TBuMWx5GCqm5fFmCIJQSI6pZb19+3Y0TcPv92NZFosXL+aGG26grq7ueJXvbbGO0LJOGLkWsUvNIJELayUtkUrHcXo8xEIGnoCONzhIsriWKnc723uCGJqBqqgksyaa7hweEeIvdQE2Edfc4Xu4ZswgtWXLEcs4o3QGLs3FK12j2Lpr8qViE11BOEMdU1grisLNN9/MfffdxxVXXHG8ynTMjjR0L57OjcBwqilshrb0MmWSRgx9qGXtKXLg6+kmXlRFtaeH9sEMCZdBwOkdXn1v31hrRZPxaSFCctPwPZwzZpDeswczFj+4AENUWeWcmnNY3TmK9arHL82NOkkf/rqCIBSmY+oGueqqq7jqqquOU1GOHyt7+G6QfTubu5QUtj20szkKyVSUYs844iEDl5qhqHeQQW8pZ3l6iCRUIpUGfrwEg0EClVVE9o0IsSwCcgdhc/bwPfSJE5E0DWPbVtwLFx62LIuqFvGnbX/Kv2K188HhgV3PwPQr8z9PEIQxrzCH7h1hidS4kQUJXHIS29JwaA4kJBLJME6Pl1jIQDfClMowKKmUuYIA9Ls0/FJuqdRARdVwy5pQCwG5nVDCP3wPSdPQp04leZSukLNrzmZPeA8dsY78KqaoMPkS2PlkfscLglAwCjKsrezhu0ESaRNFlXGQxLIcw1t6xeNhdI+HUE8CV7ST8oCf/rSJz+XCoSUIOnS8WX04rPf1WdO7jWJ3iGD/yHu6Zs4g9caRw3qcbxyTiyfzz73/zL9yky+BN5+EIzxEFQSh8BRkWB+tZS2rMg47jmmqOBTH0MYDURwuD+HeJHrvLirLS+nPZFDVAE7HABHVhTflIBgMUnTgWOuBZopKZUK9yRGzFp15PGQEuKj+Ip5rey7/yk24CBKD0LUh/3MEQRjzCjKs7aO0rFEkNCuWC2tVHQrrBGZWw8xaOHaup6q2mkjWwpQDePR+QpKOJ6ERj8dxl5aRjEZIxWMw2ExRlY90MptbfW+Ic+bMoYeMR94V5vy689nYu5GwEc6vcq6i3LZhO5/K73hBEApCYYb1EVbdi6dzu8RodoxMVslt6eWQwbZJxiV0l4LU1ULNhNzojkGK8TsHGMwo+KzcUD/b4URWFAY72mGgGV9NJbIqETxw15gJE5B0ndTWI098mV46nSK9iJc7R7HP4qR3wsofitmMgnAGKciw5gg7xSQME1uR0Kwo2ayCJqmg5zYeiIcliookJFmmqj43VrzX8lHlidIeNtC9Lty6i0gkQlFlNYMdbTC4G7l8AsWVB2xEAEiqinPq1KP2W8uSzLm1546uK2TSO3N1DLXmf44gCGNaQYZ11jz8anbxdBZLkdCsCJm0hIaKpeXCPdZv4Xek0Kqq0HSdEk2h13RR643SOphAKXUS0H0Eg0FKausYaN0DkQ4omUBJzciwhlxXSD791u+ofwerOlblvwpfxTQonQQb783veEEQxryCDOsjbT6QSJtYioSDNOmMjGYpmEoW3e0h2JPCmxnA0TAegFJNpcdyUe+LkMyYhIsc+IaG75XUjmNw75u5i5Y0UVrrYaBjZP+0c8YMUm8cfWW9JdVLMEyDdT3r8qugJMGEd+TGWwuCcEYoyLDOHmEGY8zIkpUldAzSBqimjGEl8ZaWMdgVxx1uR6uvB6DModKTdVLtDqMpEt0uBV/WSTAYpLR2HIOdHeCtAt1LSY2Xwa63tqxnkN67FzMaPWJ53Zqb8+vO5x97/pF/Jed/HDrWQaQr/3MEQRizCjKssY8c1igSOilSKdCyMqlMDE+glGzaQu/dhWPcUFhrGn1ZBy4pxbhiN52KjSepEQqFKKkdRzgUIVs0AchtnpuKZUhE0sP30puakJxOUlu3HbXIlzZcytN7nyZtpo96LJDrCimZADtGscyqIAhjVkGGtXmEbpBYel9YGxgGqBmJWDKEw1WEosmoXbtRqyqBXMt60HSgWAnGlbjpME28KY1gMEhxTS22DUE19yDSX+pEdcgMdO7vCpFUFee0aXl1hVww7gJs2+bF9jx3g5EkmHY5bPvf/I4XBGFMK8iwPtKqe/G0iS1L6JKNYWRzYR0fRFb9BMpcmL29aJW5sC5SLKL4wM5QV6zSnkrjw0UmkyFjWvicNgNWCQCSLFFW56O/9eB+6+Qbm49aZl3RuaTxEv7e/Pf8Kzr1ityGBMlg/ucIgjAmFWRYm0fYKSaRyU2K8cgyqVQKzZAJR3qxbA/+YhU7nUYdCmunHcNQcmFc67doDSYJFBcjS3LuIaMjyWDKMXztoio3oZ6R/dbu+WeRWJffg8P3THgPL7a/SDCVZ/jWzgdfNaz/Q37HC4IwZhVkWB9pUkxyaAajU1FJpVLoqIRCXWTTLjx6buicWlEBgGwOYiqlIGlU+VLsHUjgqHDjc3gI9ndTqgYZiOwfbldR76OnZeTDROf06Zh9/WS6uzmaOeVzqPHW8Piex/OrqCzDuV+GV+8WE2QEocAVZlgfYZGjZNpEU0Ed2tncYavEEkEyaScuEihFRch6bulUM92NLTvJarVUeWP0xwwyZU78kotQZwuljjgDvftbwZWNfgY7Y6RT+wPcMX48SlkZyfXrj1puSZK4YsIVPNL8SP6VnXE1xPpg1ygWgxIEYcwpyLA2j9BnncqYOFSQJRcADkXDtLOkEjp6OjzcBQKQSrUBEFKbqHAPAtDpUfBmdAb7uigr8RDs7iSbya0JUlrnRXUodO8euc6He95ckpvz28n8iglXsHVgK7uCu/KrrLsE5v0fWP3z/I4XBGFMKsiwtjn80L101sKhWDC0/6LmUNF0J4mwjZ4YQK2sGD62N96GRzLol8fz7Z5ybKfCXtnGn3LSPximvK4W27IZaNsLgKLIlNf76H1rV8is2SQ3v55X2Wu9tSyoXMAju0fRul7yOWhZBZ1iJT5BKFQFGdamefiWtZExcagWDG1vq6oy7qISbAsc4a7hkSAAXbEuKlSTzdZE/pkox3IrPBOKUSL76I2aqOUTKK6ppbdl9/A5lY1+eloiI+7pmjWT5LrXsLP5TSe/vOlyfvfG7zBMI78Kl06AaVfCqp/md7wgCGNOYYX1cEYfus/atm0yWQunYoLtQJVVTDmL01sEEih97agV+8O6M97JJLfCw8mpVMpRxpd62NYXpay8jLQNcW8jFQ1N9LY0D59TPbGIrl0h7AO6YpyzZgEQfzm/zXHf1fguAF7qeCn/up97I2x7FAaaj36sIAhjTmGF9ZDDtayNrIVtg65msWwNXdFIW0kcTj9unwOrp2t4Qkwym2QwNcjZRT4AlihbmFLupXMwSdG4MmQbBrQqKhon0Ltnf8u6uimAkcgS7Nm/XKri9eK54HwSa/ILa7fm5v2T38+Dbz6Yf6Vr5kLTMnj2O/mfIwjCmFGQYW0dpmUdN3LdELqaxrJ0HJJGMhNDcfjxFOlkurvRqqoB6Irn1tz4UO04bq6OcrX9PyxvLCUZMoiWygRsjX7TR0VDE31792ANrUfi9GqU1Hho3z5yrLT3nHOIvZT/Tubvn/x+VnWsois2irU/zvkibPkbxHrzP0cQhDGhIMPaPkzLOm7s29k8iWk60FFJGGEkxYvbo2BFImhDLeuuWBd+h58i3ccnaotRM+0sq/chmTYvZwYotovojphUNk4kY6QIdnYO32fc1BLatw+OuLdn6VKMbdvI9vfnVYdppdOYXjKdh3c9nH/Fm5ZBzTxY+aP8zxEEYUwoqLDeH9GHblnHhlrWHjVOJqOi2xqxRBBsD05taEJMda5l3RHroMZbA4BTrwZsAo4wqq7wcjhMqeWnu7MXp9dLUWU1PbvfHL5P3dRiOnaGRizV6pgwAbW6mtiqVXnX5z0T38P92+8nlU3ld4IkwTu/C6/9DvrfPPrxgiCMGQUV1vtYh5lunkhnkWQJlxQnmQS36SAS7cc03ThJIns8KF4vkOsGqfbkglvTSpBlnVSqi7JSF1sGs5SrJr39vdi2TUXTRLqb94djzeQiMoZJb+v+IXySJOFddgGxZ/PfEeaaSdcQNIJ895Xv5l/5hnNzq/H9YgGkE0c/XhCEMaEgw/pwk2JiRhZFlXFaEZIJcGc0UpkY2bQTRzqKWl01fGxHrINaby2QC1pdryJldDKpykdHRKamOIORTROJRKhsnEDPnv2jMBxOlYrxPjp2jOy39l10MbEXX8SKj1w/5HA0ReMDUz7Aky1PEk0feU3sET44tIPMc9/L/xxBEE5rBRnW9mH2YIwbJrIq4bDDxKMmbksnZoZJxR1oicHhh4uQ67Pe17KGXFeIkepiYW0RiZiEWudGR6Onp4fqiZPp3dM8PJMRYNz0Etq2vqXfesliJFUlvmZN3nX52qKvUeOt4Xdv/C7vcyibBFfdCS//AjqOPs1dEITTX2GG9WF2isntbC7jtMJEo1ncko7idZCImuiR7uFhe5AbY72vzxrA6awhZXRxwfgS5ESWLVVVFFseuls7qZ40FWybrp37Nxmon15KV3N4xDohkqriW76c8COP5l0XTdb40llf4o9b/0hvYhSjPGZ/EEqa4NcXQjKU/3mCIJyWCjKsD7eeddzIYivgJEksIaE7VNyBYrDBMdCKVpnrBkmbafoSfVR797esdWeuZT0lYAI2L9jFlKp+OlvaUR0OaqfNYO/mjcPHVzb4cDjVg1rXRe+9itgzz2CGQnnX58JxFzKzbCY/enUUozxkGT7yUO7P//xW/ucJgnBaKsiwPtxk87iRxVQk3MRJp12oioXDXYTmVKB9N1pdbteXvZG92Ng0+BuGz3Xq1aSMLpzhFvx6ig0DUFNRTWdvbunTcdNn0b5t/07msiLTMKuUPZtGDtVzzZ+PWlVF+LH8t+OSJImbF9/Mky1P8uNXf5z3eZQ0wrVPwmv3wNMisAVhLCvMsD7MCqn98TQZVaZMziDZGhkrgeoI4Ctxkm1vx1E/DoDd4d1UuivxaJ7hc3VnNalUJww2M14PsacvwbiJ44mkY8TjcWqmTKOn+c0R/daNc8vZ83o/ZmZ/gSRZJnDVewj/7eFR1Wly8WQWVC7g91t/n//mBAD1S2Dpl+GlO8QWYIIwhhVkWB+ubd0WTmLrMiUSeCSdiDGAJPvwesDOZHAM7Wq+O7ybpkDTiHOdejXZbBizfxtzPFHCg0n8U2tRbTnXbz1xCgCdOw7ot55RArbN3i0DI65VdNVVpN54g9SOnaOq1a+W/4qJRRP52fqfjeo8Lv42TH4X/Pn/wM6nRneuIAinhYIMa/Mwmw90hVNIThmXpeC1nPQH92JZftxWFKWsDKWsDIA9oT00Fb0lrJ25h42p0DbOLc0gxTLs8siUSD7ad+xFdTiomz6TvZv3L1OqagpNZ1Ww/eWRU8a12lpc8+Yx+LtRjPAAHIqD7y79Lg+++SA/WPOD/E+UpNxwvnFL4M8fged/KHaWEYQxpiDDmkOEtW3btPbH8XhMjLiPYtNDX7ANI+nFFevCOX0akiQBh25Zq6oPRfGSiu/hrHGlSBmbFzrDVPsraN+b26Rg/Ky57H195JrSM8+rpWXzAPHwyOVOiz/0QaLPPIMZGbmc6tHMKJvB2dVnc9/2+/jLjr9g5xu6sgKffBLe+R14/vvw/4rAzBz1NEEQTg8FGdaHGrjXMpAglsxSWhQlHHRSJLlJS0niERd6106c06fnzrVMWiItNAYaD7qGc2hESEVtAx6/g1XN/YxrrKcj1I1t24yfcxY9e5pJRvcHcEWDj6IKFzvX9Iy4lv+yy1ArKhi85/ejrt9/v/O/ubThUr7zynf4wP9+IP91rwEWXweX/Wfuz98pg+ZnR31/QRBOvoIM60NNinl1zyBur0aD9ib9AxoeXaekroGsAerO9ThnzABy46sN0zh0WMvFpNQMVM5g6rgidrSFmXDWFBK2QX9rD2XjxiPLMpuf3d8vLEkS05bWsG1154hWsKQolH/h8wzecw/Z4CgeGA65/YLb+emyn9Id7+Y9D7+HvZG9+Z+88JP7A/uP74VvB+DBT4uukZPASqeJPvscu696L9umTWfb1GlsmzqN3VdcOfznnWefw45Fi9k2dRqRJ57AzmTy/w1KKFjqsZy8bds2fvGLX+B0Ornpppuoqak5+klHYFsWP/jTn9A7w2iBRnyNXla2KsyoL8XMWixvLGe830lRkYbs0sECK5HBXNVBJRI9xtA39Fu+r03L5u5Vu5GrHEygmVCkBBkDX2k9hiGjde/GNdSy3hPeg9/hp9RZelD5dFMl5XaBt5KLpqRY9+g2pMoi/JKb3Rt2UD6+ipop02jfuplF73nf8HmTF1Wy+qFd9LfHKB/nG/6675JL6Lz5G3T/+79T99PR7/Jy8fiLmVcxj0899Sku/9vlfOmsL/HJmZ8c7s45ooWfhLM+Bhv/BI9+CTb/Jfeh++FD9+fWGBGOm9BDf6PrG9847OvGm/vXljEP+OHdceNXDjq29NOfwrd8ObLXi1ZVhex2H9/CCqelYwrrBx98kB/96Ef09/fzwAMPsGTJEtauXUt7e/vbut6tP1vBn3omAqUQAvbm1tB4fneuW+GuVQe3HgNaknDGNeJrb22FPLOth45QioEZ5UxCps/00RPZgdowlRJvFjUQQB36QbOybSVnVZx1yMBzReMM+n0gSXx4Vi0//NsWHtrZS22gktaWVhYDC6+4hsd+/iOymQyqpgHgCeh4i3T+8r1X+dydF9IeTJLKmNy3thXj0uv514d/yve0JuZ+5H2kMiYXT6tEGvqdJxTPMJhIM6cucMgylbpK+csVf+H2V2/nZ+t/xs/W/wxZkvnf9/4v43zjjvyGKyrM/3juo/sNuGspGBG457KRx816PxgxOO8rMG7Rka8pjGCGQrRedx2pTbk9OEuvuw73WfPwnH9+Xj9U0+0dRB5/nNgLK0muew2AgV/fzcCv7z7oWLW8HMswckv91teTaW3NPVw+4N9DybXX4rv4YqxoBKWkJDc5y7bRp0wZsaXdmca2c7+Py/k0dE6RYwrrWCyGx+NBURT6+/tZvHgxixcvZsWKFW/rerd+7noSt/+Erb4SNjXMw6UYJFxekCWwbOSggRxMYwNKXwopmmHQX4QyMLLP9q0zGB/a0EG4zAGaTGRLnEqriN2dqxhXuRTnzrW8NGUpv/zzRhZPtvjLzr/wswsPMTTOtnG376CtIfeWFbk0/NUeHn29i39rHM8rG9Zi2zb1M+dg29C6eSNNZy3M3X99Ow+korwLB4tueZK+EdPha6mrm8f5T/2Ja+1xZBQt7/erqcxDIm1y9oRSJlb8Cz8551JuWv0xLNvi3Q+9mynFUylzlXLnxXcePRiqZsK3h3Zl79mS6xbpHZrks/mB3P93PpH7f91CcHhg5jW5D4fn4Oud4exMhsjjj9N127ewDYPS666j4sYvj/o6jrpayj7zaco+8+nhr5mhEOHHHyfb20v0yaewLZPM3layfX3Dx2RaW4cKMvLfwuBvf8vgb397yHtpNTV4Ljgf38UX45w+HbW4eNTlHStakgZf3tbKK+H8FlVTJDhwmfyLS/3c3FTNVI8TGYiZFhLgVZUTUl44xrB2OBwkk0m6u7uprq4++glHoesO/vObNx/yNdu2SWUsdFViy45+HtvdTeSVjbxR7GXtgtzIDeeTHcPH7jMQM3h6axfWvDK+mfwWg8GZSH1bsLEZ2JVhRscGfvCRbUSNp3h2M5ipKq79rzjwGI1lHn76gbnMHVcErS/j6e8lPd5PJhNB0/zMmVjK2lc7mfLps3hyw/N0b22jekY9kxefw5YXnuXOZgdPbe0hnMyADnPTFh8rKyU5w8fkSh8Tyr1Mq/aTDp9L+/uu4ZHADqRPXc8jGzvZ0BrixuWTaRnIfTM198X41crdI96T3f251/62oeOAr/4HSBm0orXs4FF2BGH2H2ZjGuVkgovxuNNkzSwl9mKyRiWLG4uRJYV/WVBHpd/JQ+vbuXTmOF6a+SdebRmkzKczqzZAlVej7YV7+Gj3f2AO7EFJ9sPu5+GRG47+F6s6Ydwi7IoZWEi0hC38xCjf/icsPYDtLMKO9yOrGoYRJlQ5DXnWv1Ay76OokopkGoSjnZgv/QyvZaLN/VfQvSQySQZlH8m+CC+89iCdTlCULNPd0ymrrmNdYpBnXjNZNOM89vRH8LtVFjaU8481G1jdl8S2NW5cPonLZ9cwzl+GpipYtoU89GuNZdlEU1m8ThVFPvIPOyuVwtjVTGLNGnpvv33464GrrqLq1m8iezzD35uH+sFpWTaSxPBr+76H33qsbdtI/gDFH/oQkiRR8eUvs6c/js+h4FQVvE4VCcgaBklk2gYSuHSVjGlRqoHb60ZPRHObN6fTAJjhMIP33Udq61ZC9/8Pofv/55B11CdNxFZU0tu3H/J1pbyMwLvfTWLjRko/cS2+S96JGQohadrwB+Qmhh3qvdjXN28NjZBSSkpIrF2L7PXhnDoFFAUyGSSH44h/F3Y2i6QeHG1py+KDm3azOhQ75Hl1Tg2forAtnls//oryInrSGdaG4/xLVQn3d+1fOuKfAxH+OXDwSK4iVeH5RVOp0vNvdOVLso/hycX27du566670DSNr3/965SW5vp5V6xYwQ035PGP+DjIpk22belnxe828ZQ7NxTtpkmdfOHaT/H1v7yG6/m7+UPJ5cxe1MbcDT1IaQNP82acgU+h2DrPjP8aO8bZqLbNJfEE3+kbQAP+ysX8Z+pKeijmn46v0ij3sKroclKzX6WFn3DlwuX8dW8/P7x3E3d++Cx2PnwfRrqS58vGY+zaxDv6n+M39R/H63Tw3x9dwIKGYna81MXK+3bwqZ+ej+4a+c0UffZZ2m/4IjU/+D6BK68c1XuwL1Te6AwzqdLLhtYQfVGDP7zcgrt8NXuzj5OVQke9jtG7HDMxATM5/q13yP1PssFWkbRBVLK8O9DHzOiLSHofqrOdlW4XLlPFi8GUdBq3aTM+YRK3ZFwhmZVxP8/MlJnbbNPYAwt32kjYtFTBzBawbQkZm6wsk1ZlOot9xHQNfzKNoSm0lvqxhv6hV4cSWL4KiuUAXQzSEEyRKqqkUWtAS6eQU1EkVzFWtBsz1o1kZpB9lZjpGOFskE6fjlsvweEfj6HJaLKDpuLFmHaWjsQugkYXfe4ImteDs7+T+S0qasVMUHWs4B6CVhBn6+t0uUHNWlRF968dbkoSg1741bskXp8w8hm+ZIEVnkmgaxpKsoyE4sHrc1MysIG+0ih9Ho2s6cdZ+b9IqkF64Fyy8QlIGRdSugQsJw4rSVxxI7u6meJ6FacRoCc1CcP045bSpNDQ9A7mqNvZ4VBpjZ6HYstknZ24pDR2xTOoppNE578wIxukrtFmVSSJFyeGpbCwtYc5LS9haDZuM4ErpXJW6yiW6D1JIhOmorbtIuR2EnbppDSF+oEIQY8TW5JIqwqu+nq6Zy7gjvkX0ndA0t05fTyXlxehHfBD2LJMov39+ErLiA8OoETjdP7pfqSeLlJ1DbS29uJr2w1Lz+cfzYO8PKGJLUtmgWWDLFHUGmNORYD/fsc0AvrbawcfKTuPKazfzg1PhJ5IisXff2b4869M7EAvqefcx77OT6tv5amSAB+SnqZ2cxOKYxIAsixx2acbqR+XATMNz34Xdjx+2HsM2j7epf6aG+b+O0/suZiXOpdgKxJWmY48aPBBu5V6Kc2PMxOYVaFz9tpfcskXbuKsc/c/qDNNi9/c9CLjppbwrs/OOugeA7/5Lb23307lzV+n5GMfO47vUE4qmyKRTRA2wuwO7cav+3m582UebX6U7kT3275uQ7fN8vUWpRGFkNdmXn8jeuNFlJbOJBRtoaxoyiHPs4f+s7CJSwaDUgyX7aAr3cZMbToKEiCRtlIosoMkJp0k2I6DLDZBZDqwcEoJdksx/HKUPWYFPbabaVKUHtvFR5BpQeVlTJbICSpsibNsP9NxIZF7Fj2ATS8WTcjIZJGQ0ZDolkPsUrpJkCYpGQTlOCWWFwnol2LYtozL9NGnpEhZPqrMcrrkMF1mDQ2pXoLOSlYCdchMQSaMzVY7i4bNwkyMFzQ/SJAGdOBIAzBLMkEmJFsodjgZZ0SplyFmR7BVjbDLiW66cdul2JrBNLOerCkTSWfJpvsY5yqhTPezVd7DgJpEtxTcaYmp2kRMLCQkWqM70R1u6pU63Erut4AMJoNSjK3prViyipmNU5ctIpIZYNAVo9KuJRzdQ4svxpvZClr9tYSkInzpBLYdwGXbKLZJGguX5KDGTPBPZyUfjHex0VnEzOhuOlWFpC0T9RUzNZ2gTA0T1m3srI2aSuDRFRzpDOVqMTPVSRTbHhRb5nV7F22JbWCaBCwnfq2UdKAENTJA1Iwyw7+EhJzizvE2L06aCkBVLMVnNnfzzpCHoNFNxs7Sk2whkY3TbUYYVDy40v0YmKRlByV2DCWTwlZywSvZFhWuRgLFk3GmbfYkt1LprMevldBLECsZRk30seRbtzB+QsPb+rd0xoX15+v3cM62H1Oc+X9cXeHDnujms6+kcVhePvr9c/CVOI9+0cQgvHA7eMphzgfBn3sA+cYbNxI2HNz+ypX8y4Jx3N7WQ3h9Hz9dXsWm5x/lyx/5HP5J5Tz1q58TDwV579dGLqD0+nNtvPjnN/nX756Nv8x10G17f/KfDPz61wBM2bQRWdeP4Z0ZnUQmwbqedXz+mc9T660llokRNsLoip4by23BDe5PEXtkFXY2id91FguLpqNIKj6tBFXO/eqXxWY9SVbJA6y0VRxSli4rMHyf+WobIdNHs12EMtRqN5GRsLALczTpqKiYlEpxemx/3udoHpuU341Z48bya/jjMVJbU+BWsC1Q+g001SKTzb2/GlkyefSC+qUkEfvg71MBSuUYQcuNdcD3bL08yHcXOjn/ve9/W9c8UnYeU5/16cru34k28CF+PWuQVLiMOWmTT/778kOG42G5S+DSg6d0BwJzSXQ/yMOf/yEAe0pV/rAtSLfsx6k6eOOfr3HOpEuZd+kV/OFrXyTY3Ulx1f4hjbOW1dG6dZBn/7id93xpLtJb+kIrbvoKViJB8N572X3Z5dT98hc4pxy6ZToa+/oHbdPESiSQdB1ME6N5N87p08Cy0JNZzqteyoYrVrLjif9lw+oX2RmKM8lZj9M2Ge+bRcTtYXv9h7jL1kgBRVKCftsFJIc+DjAU0OXEqJQiw+GzPluHTa7e5gHf6L4ag0jn/r8js9yJHDSQsrn2hFmqY473YjtksMHWZdAVhpvIQw+ic/fO/Wqaa5wPvccZCyltobTGkENpbI+KLUtYFU4UO4MVA7U9gVmmI6VMpKSJHM9iKxKSaWN5VZAl5EgGW5Ww3SqZqQEkwwJVAknCdinYisRkeTs7tRkHvBd27kORcNoJigciGJrKZMdudC1OWA0QoogWaQIAB/Wq2jZSPJsrVziNlDJR2/d3vWTiEko8idKV+ztIMzSJIrZ/PfV9QQ2AX4YDulwl2ca2JPyOCC41RU+iIncdWTpolllTYA+7w7l5CFVFvXQlqsjMLsEqcex/r4fKPDwaJZv7+/iidSeL1WewbYikffgcuZrG0h4w/w1/1QUM7glRqkB7rYeHt/SwLZYi25cko0qQPPRa9fu+P+SggVntBkVCHjSQh+rfFGihL1FKNONjVtkWUlkdh5JhQtEeAlqUgDNCnbeDjKWRyLrpjlfw1N4LKdbDvDEwjbnlr7OxbzZLql9lb2QcqayOxxFjIOodUYap4zZQO/u6w5bxWBRkWGdSFsFJTfwtXINUrfO7jy3Drx2fqvoDc3lz1/cwzSSK4uKiMj+/qnXzgyd28J9TJrJzdzNLDJPy8Y3UTZvBa//7MBd/6nPD50uSxLIPT+V/vrOGLS92MPOCuoPuUXXrNyn5+MdovfaT7HnPVQC4Fy2i7PrP4l64EBRlxIMoMxTCisVIbdtG+JFHcM2eQ7anh2x/P9EnnzxifaKai4Tq4L5pF9MeqGTr8DT7UvBdBb63nPCW38P67QO+WT0yxHMtZd98mz5fNThk2g43EuXAf8ySRArg4N6hET7IfTTbTeyWp/HZ8jiSbdDY912q/GfRH1lJUnLRMOHfKZViPDvQA8FHqJPCRG0ZjxrG0Px4Z8DkSd9AUVyUll6AJMmYZgLLyqDrFUjSwU/0LctCkiR601n6M1lmeF3sThjUOjVWBWPsTRrc19nNG/FcmEj2W+osS7kPICV56CrPdTW8QvlB99LtFIbkZKm9ktlsxEsUCwU8UOFPUle6BZCpuvBKxjd8lmSijYHBFxkYeJa2WAM7BieiW5uYXztIRyjDnMbzwezkH4MGkmSzjZnopAhTxErpohH3TlH7ls8PtvWAY1recrxqZ8hKQw/X9v29SxJouT//XPkc8LncD9EDf8Hd9+doP5QN/TkRhkYn4MSLToqRja1996qx27mSh6ihAx9RSuinsuwSfJ7xaFoxkqxRWnIZkqQSi23DMGrZsfO24etUVl7JwMBmiooW0jD+szid41AUFx/e83NSqU58fh8OxyQy6Xa6uteQTD5AeflyfN5p7N7zNSxrXyNFpqb0FhoaJh3iXTt2BdkN8n+VrbxQkmVN32xuvW4+n2ysOsLZo2NZBs+vnMtZ8/5IUdECspbNtOc2knm6k29dVEv7qkf49JIPUPWuKby5ZjX/uPMOPnvXH9CcI7tetr/SxTP3bOOyz8+mYVbZIe9lWxZdt95K+MGHRnxdcjqxs1l8F19M9B//OGqZfZdcApaJ7PYQfP0NWs67lK172/ll8XmHP0dKUS7FqZCjyNhkdIWNSj3pGOj1DqJ1Rdie0T3xrrHbWKxs4W/WpXy8poT2VIYvN1TR6HKwN5lmolvHr6nEsyZuRc5vcs9p6MBRDrZtkckEURQX8fguZFnH6awhmAohpVpwBJbQnMww1+c6afW1rFxrU5b3N2BWDkb5we4uNh7woPSL9RWsiyRYHYrR6HKwJ5kbPXJxqZ9/DkS4sqKID1aVsKzEl/f45DeiCS5ed/TVJpdIr3K59WfG0YZKlurq99Pf/yxOvYqKindTUrJ0KIhVnHoVtm0hSWO/C+2M6wbJBjy8Fq6lYraHTzQc34H+sqzj800nHNlIUdECVFnioqpi9szM8D9bIrzL4WXdqrVcdslkmuYvxOn18trjf2fJ1R8YcZ0pi6t4+W/NPPbL17nwX6cyfenBsz8lWabme9+j5nvfw4xEGLzn9yRefRX3okUE778fKxrFe+GFYNtUfvObAFixKPrkydiGgeR0IkkS4cFB7n6hmZUvruf1Oeflfv0tzv0aq2FxofYmpXKcLDIeKYMdKOGpGYvYqjnZeoj3YIn9DDF8pG0Hs9hELW3M5HVkLBYveAjNNZmopVBx0PClucAV3HmIa5Y69h/rOYFjVU+GA0NXkmQcjtwoKb9/9vDXy70+8OYmLc3Tjv8wryM5MKT3uaDExwUlb/016vib6XPTfeHcPI6cC3z6aAcNK4SgPpqCDOuNnhjZQZUfXjjnhMxI8vvnEIlsGv78krIAt9VF0FvChMfPY9uuV7jwjT48sys470Mf4+lf/4IZyy7CV7K/BS1JEh/9/jn8/ubVPPfH7WTTJrMvPPyMQ8Xvp/yL+3/ilt/whSOW0ZBVPvPt37E15qJfy/UVn6PVcI0cJSj34JLTlMu5cdpZWWb8eRfyYyVA2Dz08rJ+O8xvA7+jsepSamtvOup7JB5JCcLxVZBh/UaqDsUvcWH1iZmBFfDPpbl5/8SHC0t8DGLx+aUN/G11C5cp8PrTr3L27MuYcvZ5bHr6Ce79xle47s7fj2h1KYrMJ364lKd/s4UX//wmb7zQyfu/vgBNf3sty3TW4st3Ps4/27KkZQ2ohKFG25WONyiRc31rzXUT0csreNBfMfICpsVZ9qvU0M4iXma2R2bB/AdR1X0zFC94W+USBOHYFWRYD8T8NNSduFXK/P45pIxODKMPXS+nSFNZHPCC14VX13goPIvS0C5mvtSBb2ktF3/qc9xz0+fY/OyTzL7o0hHXkiSJd35qJrPfEebBH73Gf39pJcVVbpZ/csaIRZ/eyjQtBjvjPHX3FnZ1vslTngwdzkpAQpY1PiDFQG3FdmZprmviL/WXAOCTM0St/b92l9p9zGU9M7QeFmX+zpxZv6K09BOH/FVZEIRTpyD/RVpJiQX1B6+ad7y4XPVoWgmRyEbKy5cD8O7yAL9u6+Ouf5nDe/9rNf/JBBY/+hoLF1dTWlfPu2/4Ko+v+DH//PV/8ZX/eeSga1Y1Bfg//28J937rFYLdCf7yvVeB3OSdZR+Zyot/2UkmlRtpYNs2mdTLrHU5eLloBpSWAFCMwTsc23ErGfaWVtHStISdrpGBf5X5RxbyMqUM4vVOZ/asu3C5lg+9+p8n6B0TBOFYFWRYA1zQcPCQuONFkiT8/jmEI5uGw/r9lcV8r7mLQY/Cbz++gGvvWce/K73c8/CblL1vCtPOXcYL991DbKCfOz/zET79i9+ivmWNg6JKN5+/6x2kU1mev3cHb77ag2XZPPuH3L6OhhXnVaWZrGLyavW84fMqpTjLHdtoLa/m3hnvHnHNJvtNLudhlkiv4fVOpbHhC5SX//yEvTeCIJwYBRvWM8pO7IphAf9cBgdX7f9cU3l/VTG/be/n3jlNrHjvRL74N5uL1u3gqZ4klZ+fy3X/dQ/bXnyOx3/xE372r1dzwb9+knmXXoFywKIztm2j6Qrv/OQMlrynnC3PP8vr2/fy81ADcdUL7B/DeZ7WzDhHhOamadxd857hr19pP8g5vMg42gCJ6dN+RFXVn8bsUDhBEAo4rOv8J3Y8Qln5xezecweG0YOu54YHfqK2jHe8uoM9CYMrFk9h1da9/HlHhsVtHfz69xIXf3Q20867kJop07jvm19l5R9/w8o//uaga2clhV5HOdu9k9npnURGngkqyNhcrb/OhglTaCmv5mnn+QC47Tgz7NdZwmrO5zlUspxz9gu4XLUHXVsQhLGpYMNaVU7suEuvZwpu9wR6eh+nftwnAJjmdXF2kZfbdnXwx9lN/MfHlxP74W94LFTNp7e1w83tKJLERdMqqL7s66hWGm33a/yzR2WXXHHI+8zDJiCHqdSaWTNlNn+qznVz1CuDuLNRLuFxrnCsZ2LTF6iuWoEsn9wxu4IgnBwFG9YnmiRJVFZeTk/PY8NhDfD9ybW8Y+0Onh+MsKzEzx03fYyZd9/N39t1OsxSorbKU1v3b57rUOtIy/vHNlc6NVxpkyvlViJqFwCmv5x75rybjJwb0nez/W1mZjdTV/dRqio/TiBwiM0SBEEoKIUZ1iepVlWVl7Nnzx0kEntxu3NrQE/1uLi2rowPbtrNhnOmU607uP7669F/+1taWzcAcIWxANX2UooCQ+vsWFisUnewk05w5CYZDnj8/H3+MrKSjAV833UP4xOP4nLVM2PGQwT8c05ORQVBOOUKMqxlx8l5kOZ2NxIInEV7x5+YPOmW4a/fNqGGu9v7mbd6K+0XzEGVJa699lp2797NH/7wBx7V1x32mhYQdnvpuvhKXgjn1mn4aW0n5e03ICVg5oyfU1l52WHPFwShMBVkWDucJ6/fdty4a9m+/WaaGr88PNPPIcs8NHciV2/cxbte28mD8ybiVxWampq45ZZbaGlpYf369Wzbtm34OtOmTWNXbRMrMrmyy+EES3zwxcw3sNt3oOtVLFz4d3THoRd9EgShsBVkWLtOYliXly1nO7ewp+XnTJq4f//Ic4q9vLx4Gmev2cbkFzdzS1M1NbpGo1unfnwj7584kV+09vKrtj4muXXuCschtysZf5rViLrtEsxIHzYwaeIt1Ndfe9LqJAjC6acgw3pBRf47bBwrWVaprf0wbW2/pbHhC6jq/hmDjW6dHefOZMqqN/je7q7DXmMgnOu4/vL4St5n/ZG9r/8KE9C0Es5dulqM8BAEofDCWlZs/uPyGUc/8DhqavwyXV0PsvKFubzjwl0jJp8ENJXuC+fSa2T4r7Ze2lJpHusLs6zYx781VTHP50aSJLLZGC+tPpe92dzGpAsWiAeIgiDsV3BhXVaVotR78vYthFzretq0H7Bp0yd5c9f3Rzxs3KdC1/j2xENPUglHNrFu3dUAeDyTWLL46BsKCIJwZim4sD5VykqXUVHxbtrafsvAwPMsXvQYsuw44jkpo5uXXlo6/Pk5Zz+Hy1V/oosqCMIYVPjbK5xEs2auoLR0GYnEbp57fho7d34H0zx4F7tY/E02bPz4cFDrehXLLtgqgloQhMMquJa1qpzaKs2d8xu6uh5ib+uvaWu/h7b2e3LlUgNks+ERx5aWXsjMGXegqt5DXEkQBGG/ggtrST71K8tVV19NdfXVJBJ7aGv/I+3tvx8OaoejApezlrPOul+M8hAEIW8FF9anE7e7kSmTb2PK5NuOfrAgCMIRFFyftWitCoJQiAourDW16FQXQRAE4bgruLBG7IYiCEIBKriwdp/gTQcEQRBOhYJLttle96kugiAIwnFXUKNBPry4nusvmHCqiyEIgnDcFVRYf+miSVT6nae6GIIgCMddwXWDCIIgFCIR1oIgCGPA2+4G2b59O7/85S/xeDy8733vY8GCBcezXIIgCMIB3nZYb968GV3XcTqdNDU1AbBmzRrWrl1Le3v7cSugIAiCcAzdIPPmzePb3/42H/vYx7j77rsBWLx4MTfccAN1dXXHrYCCIAjCKFvWd955Jxs3bgSgubmZf/zjH/j9fizLOhFlEwRBEIaMKqyvv/764T+/9NJLfO1rX0NVVb7whS8c94IJgiAI+73tPuulS5eydOnSox8oCIIgHDMxdE8QBGEMEGEtCIIwBoiwFgRBGANEWAuCIIwBIqwFQRDGABHWgiAIY4AIa0EQhDFAhLUgCMIYIMJaEARhDBBhLQiCMAaIsBYEQRgDRFgLgiCMASKsBUEQxgAR1oIgCGOACGtBEIQxQIS1IAjCGCDCWhAEYQwQYS0IgjAGiLAWBEEYA0RYC4IgjAEirAVBEMYAEdaCIAhjgAhrQRCEMUCEtSAIwhggwloQBGEMEGEtCIIwBoiwFgRBGANEWAuCIIwBIqwFQRDGABHWgiAIY4AIa0EQhDFAhLUgCMIYIMJaEARhDBBhLQiCMAaMKqwty2Lz5s3cdtttAGzbto3Pf/7z3HTTTXR2dp6QAgqCIAijDOtoNMprr71GIpEA4MEHH+RHP/oRX/ziF3nggQdOSAEFQRCEUYZ1IBDg4x//+PDnsVgMj8dDZWUl/f39rFmzhhUrVtDe3n68y3lEHl3lSxdNwqOrJ/W+giAIJ8tR0+3OO+9k48aNAJSWlvL9739/+DWHw0EymaS7u5vq6moWL17M4sWLWbFixQkr8KF4dZUbl08+qfcUBEE4mY4a1tdff/1hX/vwhz/MzTffjKZpfP3rXz+uBRMEQRD2e1v9Bj/+8Y8BmDp1KnfcccfxLI8gCIJwCGLoniAIwhggwloQBGEMEGEtCIIwBoiwFgRBGANEWAuCIIwBIqwFQRDGgBMy5a+7u/ttT4xpb2+nrq7uOJfo9CbqfGYQdT4zHEudu7u7D/+ifZr5+c9/fqqLcNKJOp8ZRJ3PDCeqzqddN8iiRYtOdRFOOlHnM4Oo85nhRNVZsm3bPiFXfhu2bdvGL37xC5xOJzfddBM1NTWnukjHTTabZfXq1axfv5758+fz17/+FV3XufXWW3nqqad4+eWXcblcfOtb3+I3v/kNLS0tFBUV8bWvfe1UF/1tW7t2Lffffz+maTJ//nzWr19f0HV+8cUXefjhh4nH41xyySU8//zzBV3ffQYGBvj0pz/NjTfeeEZ8X69cuZK///3vOBwOpk2bdtK+r0+rlnUhL7na09PDhg0bsG2bJ554gjvuuIOrrrqKJ598knXr1vHjH/+Y2bNn8+qrr9La2soPfvADHA4HbW1tp7rob9uGDRu4/fbb+dznPsf3v//9gq9zW1sbP/nJT7j66qtZsWJFwdd3n7vvvpva2toz6vva5/Ph9/vZsWPHSavzaRXWb11ytZDU1tZyzTXXALlNHCRJGq7nvl9uKioq6O/vp7i4GIDKykr6+vpOWZmP1XXXXUd3dze///3vueKKKwq+zh/+8Id5/vnn+elPf8qiRYsKvr4ADzzwAO9+97vRdf2M+b5evnw53/zmNzn//PN57rnnTlqdT6uw3rfkaldXF9XV1ae6OCeMbdtYljVcT9M0gdyT4MrKSkKh0PDnY7kr6IUXXuDee+/llltuQVGUgq/zww8/zLJly/jJT37CP/7xj4KvL8D69et55JFH2LJlC48//vgZUeft27ejaRp+vx/Lsk5anU+rPuvt27dz1113DS+5WlpaeqqLdFy1t7fzwAMPsGjRIh566CF0Xee2227jiSeeYO3atXg8Hr7xjW/wm9/8hra2NsrKyvjiF794qov9tl1//fUEAgEA3vnOd/LYY48VdJ3vu+8+Nm3aRCKRYNGiRWzcuLGg63ugr371q7z3ve89I76vH374YdasWYNt25x//vk888wzJ6XOp1VYC4IgCId2WnWDCIIgCIcmwloQBGEMEGEtCIIwBoiwFgRBGANEWAuCIIwBIqwFQRDGABHWgiAIY8D/B7cP+GwUHuBJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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E7R0v99v2bHFzjCAIx4AIayBlOSiK2xfWSsJBze/fAtlPHx7kAzsS1EUTtGdMqqqqaGioorXl2X6tkNkjCmjsTtLYnTyuX4MgCEObCGsgbdpoigvoSJKEFHNy5le/m14TYmTYpFJTeT0cpbKyktaWchzX7Te6rsz3Up7nEa0QQRCOighrIGM5aIqD6+rouo6bsJD92mG3V/waWrGX+a7Ga50RvF4vhYWlqEotra1/y9lWkiTRChEE4aiJsCb7WC9VsXFdHcMwcBIWsu/wYQ2gDw9xbtjmjXAUy3Gpqqqiu2cU4fBSHCeTs+3sEQXiTkZBEI6KCGvAtGx0xcZ1tN6wNpF97/14Sn14kFm7E8RthzWROFVVVdTv03Bdh0hkfc62s4YXsLU1Kh5GIAjCERt0WFuWxZIlS1i4cCFPPPEEt99+O3fccQfh8Ok7cjQtF102sR0VXdf7jaw3vfEay59+HMs8ELZGTQi9JUlt0Mffe/vWbW3dBINT6e5ekXP8CcNCBHSVNXtFK0QQhCMz6LBubW2lrq4O13VZv349wWCQsrIyCgoKjkd9J4RlOWiKiW2r/UbW9e9s4MVf3M+KvzzBX39yD6lY9s5FtdSH5FE439X4e2eEsrIyFEVBYhSR6Iac4yuyxNkjC3l7V+cJ/9oEQRgaBh3WlZWVXHPNNQAsWLCAb3zjG1RUVLBkyRKWL1/OokWLaGhoOOaFHk+W7aDLaSxLOdCz7r3AuO6VF5h8wQf57I8W0d3awht//A2QXdRJrwkxr9NmfSxJp+VQUVFBLF5EJLKh33vMHVXE2+IxX4IgHKGj6lnv2LEDgGAwiG3b1NbWctNNN1FVVXVMijtRbMvBkFPYloKuauC4fSPrxq3vMHzqDPJKh3H5jTezcfErRDs7ADCGh6jaG6PKo/H3cISqqiraWr2k082kMx0573HOqCI2NvYQTYm+tSAIg3dUYd3Z2cndd9/NsmXLOO+8845VTSeUZTu4LhhyCtOU0eXsiFr2aUQ62oh1dlA5YRIAFeMmUFxVw9ZlbwLZi4xmfYyLC0K81pntW+/bl0RVg0TfNbqeVBHCpyliCp8gCEfkvac8HEZVVRU333zzsa7lpEhZDgC6FCeTkQjK2VMie1Ua124mUFRMqLi0b/sxc+ayu24lsz/8MfSqIK7lMF/WubmrlR9MrSEWi+P1TiQSWU9x8YV9+ymyRO2oQt7a2cGFE0oRBEEYjDN+6l669xmJhpwgk5bQUZE8KpIs0bjlHSrHTczZfvi0mTRueQcznUL2qKilPs4O26Rsl62uSjAYxLGH0xOp6/de88YU848d4iKjIAiDd8aH9f6RtUGUTEZCcxVkf3Z03bxtCxXjJ+VsXz5mHLKi0rh1MwB6dRC1PsYlxSEebemkqqqKnp5iIpG1uK6Ts++8McVsbo7QGctdP0QQBOH9iLDuHVn7lATptItmK8g+Dcex6WzcR+nwkTnbK6pG1cTJ1G9cB2TDOlMf5fOVxfy1rZtgZRWNjTqWFSWe2Jmz79jSACVBg2ViCp8gCIMkwro3rL1yglTKRbNlFJ9KpK0N2zQprKrut0/lhMk0bn0HyM4IsdoSnKMZVHt0NoVKaGjoxusdQU/Pmpz9JEni3NFFLN3R0e+YgiAI70WEtemABB45TTLpoFoysk+js7EeTzCEL5TXb5/K8ZNo2bkdyzRRy3zIfo3M7ghXleTzDzMbyqoygZ7uVf32nTemmKWiby0IwiCd8WGdNm1QJAwypNOgmhKyT6WruZHC8spD7lM2egyu49C2eweSJGGMziO9s5sPl+azIpIgVDOcRHI44fBSXDf3YbnzxhSzL5ygPpw4EV+eIAhDxBkf1inLxpUldDI4joKalpB9GrFwB8HikkPuoxkeSkeO7rvIaIzOJ72rm0l+DyO8Oi1Vo2huyiOdaSUe356zb2W+lxFFPt7aKVohgiAM3JAK690dcUbc8Rym7bz/xr3i6eyTzQ1JAiTUNMg+lVg4TKCg8LD7VY6fSNP+vvWoPKy2JE7U5MMl+az3htizJ0wgMJFw+B/99j1XTOETBGGQhlRYr2/oBrLrUw9UNGPhKhLe3jOhJLN3L8a63jusK8ZPonHrZlzXRS32ooR00ruyrZC1aYdux8UwziLc1T+s540uZtnOjn4tEkEQhMMZUmF9JKLmgZG1JEnICSc7su7qxF9YdNj9qiZOIRmN0L53d2/fOp/0zh6mBbyUezS6Ro0nHquhq2s5jpM7r3ru6CI64xm2tkaP95cnCMIQIcI6bYEioSOj6zqkHSSfSjwcJlhw+LD2hfIYNmoMe9Zlp+cZo/NI7ciu+/Gh4nz2lFTS0JB96G539+qcfQv9OpPKQ/xju+hbC4IwMEMyrAfTXohnshcYDSQM3QDAck0sM4O/8PBtEMjeer6v9+YYz9gC7K40VmeKy0vy2Kx42F7fSn7+2YfsW583toQlIqwFQRigIRnWziBawfFMdmStuVJ2eVQgke4BIJD/3mFdPXkajVvewbZMlDwDtcxHemuYs0N+gqrCVk8QQ59JZ+fifvteNKGUt3d2Ek9bAy9WEIQz1pAK623xFAAJyx7wPgnTRlYANHRFA0UiHg1j+PxoHs977lsxfiKu69C8bSsAnvEFpLZ1ocoSlxbn0VI1kp6ekcTiW0kmG3P2PasmH0OTxdNjBEEYkCEV1pHeKXvWIIbWCdNGkV0cJxvWsk8j1t1F4D0uLu6n6QYV4yayb1NvK2RcAeldPbimwxUleewMFbFzTxyfbxSd4Tdy9lUVmfPGFvPGtvZBfIWCIJyphlRY7zeYnnXStJEVF8fW0CQV2acS7wrjf49pewermTyNfRuzTzM3RmRvTU/v6WF+QRBHllkajlJYeP4hWyHnjysRYS0IwoAMqbDeH9KD6VmnTBtVcbFtDR21d45153vOsT5Y9ZTpNG/fiplKIakyxuh8Utu68Coy5xcE2JFfDO4UwuG3sO3cKXzzx5WwtzPBno74wAsWBOGMNKTCen9fOGMNfN2NlOmgKjaWraC5CopPJRYeeFgPGz0WRdNo3LIJyLZCUtuyU/iuLC1kX2kV9Y1eJEmiu3tFzr7leV7GlwXF6FoQhPc1pMLacTMA2M7ALzCmLBtdsTBNBc1RkP29dy8OoGcNoKgqVRMns29TbytkXAFWawKrJ80lxSGiqsaSvW0UFJx76FkhE0t5cWPLgOsVBOHMNKTCen+r2h5EGyRjOqiyhWnKaLbce/dimMB73BDzbtm+dfYio1rkQSn0kN7WRaGmMsvvYbWk4ffV0nGIsP7Q1HKW7+4kHM8MvGhBEM44gw5ry7JYsmQJCxcu5M033+SrX/0qt912G9Hoyb912kUCwBnEBcaM5aDLJmZGRrVkJO/gLjAC1EydQevunaRiMSRJymmFXFVexL7SKrq7q0km95BI7M7Zd3JFiGEhD3/f0jbg9xME4cwz6LBubW2lrq4O13V54YUXWLhwIVdffTUvvfQSy5cvZ9GiRTQ0NByPWg8rlra4/5VtJBJJANKmOeB9M5aDpuxfy1rGwsR1HALvc/fiwUpqRuDxB6jfvAHo7Vtv78a1XS4vzqPdG2D5niiBwETaO17L2VeSJC6ZVCZaIYIgvKdBh3VlZSXXXHMNAI7jIEkSZWVldHR0UFtby0033URVVdUxL/S9xNMW//PadtKZ7N2ApjXwuwIty0GXU6TToGVk0nb24qT/fe5ePJgky1RPnsq+DdlWiDE6H9e0yTREqfEajNMkXo2bFBddQnvbi/32v3JqOUu2tRNNDfyHjCAIZ5aj6lm7rovjODQ3N1NeXn6sajpqtjPwJVIty0GTEqRSoKUhacbw5eWjqGrOduGmOMue2UnLrp5DHqdmygzqey8yyoaCMTxEamsYgOuqStlcXIltz6QnUkcq1ZSz79kjCgl5NdEKEQThsI4qrD/ykY9w66238vzzz3PZZZcdq5qOXO8Ea9Me+GwQ23LwyCkyGQnNVkgku/v1q7ta4jz932uofyfM0/etYfe6/lPtaqZMo7NhH/HubK/aMz7bCgFYUFFCxOvn7/Uufv9Y2tpfytlXliUunSxaIYIgHN4RhXVVVRU333wz8+bN47777uPee+/F8z7raJwIdjrbRsikBz6zwrZdPEoK21bRUYnGu/rNsX7rzzspH53HP90xm9qPjuLvv99CvCf3BpeC8koCBYV9s0KMsQWYDVHsuEmxrjJbsflbJEVp6ZW0tT3fr44rp5Tz+tY2EhmxsJMgCP0Nqal7jpkNOiuTfp8te7d3XFxnf1hraK5KNNqeM8c6Gk6xZ30Hc64ahSRLzPxgDYUVfpY+uSPnWJIkUTNlet+t51q5Hzmgkd6eHWn/c80wNgQKceRz6OlZ068Vcs6oQny6ymubRStEEIT+hlRY75+wlxlgz3r/47+8cnZkbXh0Yt25j/Pas76D/DIfxVUBACRZ4txrxrBjdRuRjiSmY/L09qf505Y/UTZxIvs2rsN13d4pfIV9U/iuHl6BKkk8si1BIDCB1rbncmpRFZkrpgzj2fW5IS4IggBDLKz3p7U5wGcwpsxsb9snp1BlHdVn9N5qfmBkvXt9ByOnFefsVzYiROnwIJvfauYnK3/CD1f+kAfWPcD94YeIhTto2rYFAM+47DohruOiyzKXBXSei2YoyL+Mlpa/9qvnqukVvL61nYiYFSIIwrsMqbB2em9dtAY4zzre2x8OaCa6avQ+1byzrw2STlo0bu1i5PTifvtOPLecDUvr+dPmP/F/l/wff/vY3+iQIzhjilj3cnbU7BlbgJMwyTRkbxi6fuIo9uWXsGpHKbHYO8Ti23OOOWdEIQU+jZc3tR7ZCRAEYcgaUmG9/8ZF0xzYRbpkJjuyDigmuqwheRWS0UjfbJB9mzoxfCplo/L67Tt2dhmpWIaLlY8wtWQqIT3Ed+d9l5cKN7HlrSVEwx3IPi27Ct+m7AMGpof8jNAUnmhxCAbPoqX56ZxjyrLEh6dV8Nd1ohUiCEKuoRXW+x8+kBnYbJBY2sIF/LKFLqk4Snb/YO/Ieve6DkZMLUaWpX77SrpLfeFmZkTP63tt9rDZTJ42l0yxwfpXXgDAO7GI5OZsWEuSxD/XlPFO5SgisbNobvkzjpP7W8BHplewdEcHnbGBXSQVBOHMMLTCundtEMseWM+6O5V9/qIhgS6pWJKJrKh4gyFs22Hvxs5DtkAAljYtZV/RRsxdXpyDFtD+/NQvsKq0kQ1LXsV1XTwTC7Hakpjt2TsjP1tZjKkbPL63EttO0tn5es5xp1XlUVXg5Xkx51oQhIMMqbDen5n2AG+K6UqZoEpojoPuqmScFIHCQiRZpn1vFCttUzXh0Ledv938NhWT8zDTNk29Mz4AppVMwzOphng4TNPWzagFHrTKAMneVkhQVfhCZTFvFQ/HY1xAY9NjOceVJImrplXwt7WiFSIIwgFDK6x7vxzLHljPuidt4ioSup29ezFlxfr61U3buykdEUQzlEPuu6JlBbOrz2L4lCK2r86dG/3xaZ+gtcxi05t/B8A7pZjkho6+z//biHJ6fEFebJ1JZ+eSfnOuPzKjghV7wjR0DfwhCoIgDG1DKqz3R3RmoGGdskCRUW3QLJlEJkqwd9pe0/ZuKsbm920bX7aM7RdcyNaz57DvF//Djs5t1A6rZezsMnatacc+qPVy+YjL2VUZ5523FmNbFr7pJZiNMcy2bPgW6yrX5Pt4mhq83vE0NT+VU9e4siCzhhfwh7f3HvnJEARhSBlSYb3/oQOWNbA2SCSd7VljSWimTCyRfaq547g07+imYmwBAOnt26n/0vWELr+csjvuIPrg7/j8Mg8j80YyfGoRluXQuOVAK8Sn+ZhxzsWYZpo969agFnrQR+YRX3mgD33rxJG0B/NZm7qcpqbHcJzcHzALZlXx3PrmnH64IAhnriEV1q7Ue4FxgHcwRlImsupiZ1RUWybSe6t5Z0MMM21TPjoP17Zp/ubdBC+7jLI7bif/mo/zjxvmcuk/EmR27kTTFUZOK2b7qty50f80+ZPsKo2yZnF2VkhgbjnxVa24vTfiVHoNLlYd/hCbiG3FaO94OWf/K6eUE45nxEp8giAAQyysnb4nxQxsZB1N2yiKSzql4EGjp7uVQEEhTdu7Ka4OontVIs8+S3r3bsruvKNvv2eL6onVTqRt4UIAxp1dxs66dsz0gfcdXzged2Ip+9asJpNM4J1UhKRKJNYd6F3fOWUMe/yF7DH+mfr63+XUlufT+MTsav7vzV1HeDYEQRhKhlZY906Htgb4EMZoxkRVbFIpCQONznAjwaISGrd1UTE2H9d1Cf/+DxR86pOoRdledleqiz2RPRR9+cvE/v46qa1bqZlciKYr7FyTOwq++AMLSCs221YsQ1Jl/GcPI/b2gYuJkwrzOScT47eRs+npqSMSWZ+z/79+YCQr94RZW9995CdFEIQhYUiFtdu7OMhAZ4PE0jaaapFMgqEZ2GaGQFExzTt7qBiTT3LtWlJbtlDwqU/17bOufR1BPcioWRcQvPgiOh74JbIiM2HuMDa/1Zxz/MtHX8HeyiTLXs3eqeivLcdsipGpP/C8yltHV7BNCbI38Jl+o+vqQh8fnlbBz1/PXeFPEIQzz9AK694BtTPAnnUiY6HJGTKmiqFrIEk4to9UzGTY6Dy6/vgwwUsvQSsr69unrq2OmaUzkSWZ4htuIPryy6R37GDiuRU0be+mqyXet21QD1I592x6tu2hq6UJNc/AM7GI2NsHQn3umFHM7Grht4mLaWl7nnQ6t/d944WjeXVzK1tbTv4DiQVBOHmGVlj3XmC03QGGddrCUFNYlo6qyATyC2jdEyOvxIvhJIi8/DKF112Xs8/atrXMLJ0JgGfSJALz59Pxy1+RX+ajcnwBm5bkzpn+xAc+T3tBmrdfzo6uA+eUk1jXjpPI3mYuSRI3lOez2/bwjnEVDY0P5+w/YViIiyeU8cBiMboWhDPZkArr/QY63S2ZsjDUNJatIcsuwaISWnZFGDYqj9jSpaj5+Xhnz+7bPmNn2NixkRklM/peK77xBiLPP096926mzK9ky9vNWJkDFxonFE4gNbGAdxa/hm2ZGKPzUfMN4qsPjKAvPmsm05v38Ih5Nfvq/4BpRnLq/PKFo/nb+mb2dYqbZAThTDWkwnr/2iD2AMM6nbHwaUkk24flpAgWFdO+L0rJ8CCJlSvxzZmDJB1YxGlzeDOO6zCleErfa95p0/Cfey6dv/pfRs4oRtFkti7PXdfjg5d/mkwmxTvL3kCSJfznlBN7uxm3t06v18sXSoI0mQYrlQ9S3/C7nP1n1hRwzqhCHnhj55GcFkEQhoChFdb71wYZYBskk3Hwqwk0108yEyVQVExnU4zSmiCJlavwnX12zvZ1rXVMKpqER8193mTx9V+i57nncDo6mHZhFWtfre8LYoDLxlzBnpEmi5/6Pa7r4p9VhhM3Sa4/8ODdy+ady4zGnTxiXcOOfX/sP7q+YAxPrW6gpSc1mFMiCMIQcVRh/cYbb/C1r32NO+64g507T/6oz+0dBA9w5h626RBQ0xgYxJJdKHoejuWS58uQ2bkT35zcsF7dupqzys7qdxzv7Nl4Jk0k/JsHmXxeJbGuFHt7F24C0BWdGVdcRbItzK66lcheleB5VURe3YfbW2wgEOD6yiIyaZen5U/1G13PHV3E5MoQv1py8s+zIAgn3lGFdV1dHcFgkFAoRGVlJcuXL2fRokU0NDQcq/oGp282yPundcq0cR2yYe2oRGMdSAQJFBhY69egFBejjxzZt73jOqxpW8Ossln9jiVJEsU33EDXY4+jpiJMmFvOutfqc7b51FmfZdfwJC89+r+4rktgXgVOwiRRd2Bu9vlz5zJ/+zr+ap3Pa3sX58wMkSSJr1w8lkeW76MtIkbXgnCmOaqwvuSSS/jGN77B/Pnzeeqpp6itreWmm26iqqrqWNU3KPt71k7/ZwX0s/85hyE1g2GrdEfaME0f+WU+EitW4ps9O6dfvaN7B9FMtG8myLsFzj8ffdRIwr/7HdMvqqZhaxft+w5Mt8v35DP9yquINzSze2MdskcleH41kVf39t2C7vf7+aeJY5nV2cwivsK6HT/LeY8LxpUwuSLET17eOqjzIgjC6e+ownrLli1omkYoFBrw3Objaf942nHff2QdSWZvnAlKCXRbJWnFSCUMCob5Saxeje+gWSAAa1rXMDp/NHlG/0d8Qe/o+t/+ja6HHyGgpRh3dhlLn9yOe1At/3LOl9g33OS53/4U13EInFsOEkSXNPZtM2/ePGp3bSKAh3vbygmHV+S8x3c+OoWn1jSyYnd4oKdFEIQh4KjCWlEU7rzzTh555BGuuuqqY1XTEds/sh5AVtOVNHGBoJvA5+pkSBILy+QXqqS3b8c7Y0bO9nVtdZxV2r9ffbDgBz+IPno0HT/7GXM/NprWPRF2HLTWtU/zMWfBtcRb21m35BUkTSH/Q6OILq7H6s62NjweD5dddBHz161krTSHRZuexrYPTNmbUpnHZ+cO5z+f3kBmgE9xFwTh9HdUYX311Vfz/e9/nx/84Afk5+cfo5KOXN/IegDbNsdSoEoYaROvrOMpzKO7NYkv2YakqnjGjc3Zfm3bWmaUznjPY0qyTNkdt9P12ONonQ3UfmQUbzy6lXj3gecpXjvzM+ye5PD3P/4aM53CM7kIfXiInud3921z1llnMdKjc10qzIPWx3h56//lvM8tl44nlrLEIk+CcAYZUlP39hvINOvWeAZXk5GTFrqi4s8rxEzbGM3bMCZOQNL1g7ZtpSnedNh+9cF8s2YRvPhiWn/8Y6ZfVE1xVZDXfr+5byqfruh85BNfJmrHeP3Pf0CSJPKvGkVyYyep3seDybLMFVdcgbF8GRd6MtzaOpGtrW/0vUfAUPmvj0zmp69tZ29n/JB1CIIwtAypsO67wDiAbVtjadAk7ISDBmjefFRDQdq6Fu/UaTnb1rXXUeItoTJQOaA6Sr9+C/G3lhFfupQPfm4ibXsirH/9wAyZK8d8iJ5ziln37F/pbNiHVuYndFE14T9twY5mn8xeU1PDueeey6RlK6jSNa57J87enn19x7hschnnjS3mrqc3igcUCMIZYEiF9X4DCeu2WBpVdzEzHnAyyEqIgjIfqY0b8U6dkrPt/hbIwbND3oteU0PR5z9Py7e/jc9wuODTE1j29E46G2NA9kLhrZ/+EU3FSZ781Q9wXZfgRTVow/x0/nFz39zriy66iOK8PP5pdz1lqs3VddvYF+/qO8Z3PjqFDY09/H7ZngGfG0EQTk9DKqwHc4GxPZ5G1W1M04OVjuE4fvKLNMz6ejyTJuVsu3+lvcEo/vKNSIZB053/yeiZxYydXcoLv9xAKp6dMlgeKGfGtQvo3L2H1578LZIsUfjpidhdKbqfzd74oigKCxYsoLu1nRsSDhVyJ1et2sCOWDb0K/K9fPfqKXzv+c28Lp4oIwhD2tAK696QHkhToCuextDSYAaIxTrIpLwEpBiSYeTcDJMwE2wNbx10WMuGQfUDvyCxciXtP/0p8z81Ht2r8sqDm3B6H6772bnX03XxMOqe+jPb1i1H8WsU/ctkEqtaiS3Lrt6Xl5fHJz7xCdav2sgdWoDRbOfyVRt5rjV7w8xHpldw22UTuPHhNdTt6zpsPYIgnN6GVlj3din2P97rvfTE03j1FGomj0iig2TMwBdrwhg/HklV+7bb0LEBXdEZXzh+0PXoNTVU/c9COh/8DYlXXuTKG6bR2RTn73/YguO42VbGZ35O0xSVp//7e0Q629ErAxReO57uZ3f1XXCsrq7mYx/7GK+/spofVU7nanUxX3qnge9u24Hjunxx/ij++ZwavvC7lexsjw26TkEQTn1DKqwZxMg6ljQJajH0dJCkFSUZM/A0bcMzYULOdmva1jCleAqarB1RSb6zz6b8v75F05134q5+kw//+3T2berkhV9uwMrYeFUvX/vKIsKhNP97z1fJpJJ4pxSTd+lwOh7aRHJzdo2RSZMmMXfuXJ566jXumHAd9/if5PeNLXxlwzoyjsOdV0zk/HElfPbBFexoEw8qEIShZkiFdV/PegDbplIOeVocr+vFNkBRPKjb1+CZNDFnuzWta973Zpj3k3/NNZTddhsNX/0PlKXP87FbzqKrOc5fFtaRjGWoDlXzTzd/i0g0zE+/9SUs0yR4fjV5V4yk8w+bSazN9qMvvvhiKisreeihJ7h42PUsLK1jcWcbH3x7NbtSaX60YDozqvO58qf/4Nn1Te9TlSAIp5MhFdb7ue/TBnEcFzPtUKDG8bs6csBPXpkXc+f2nJG16Zisa193yMWbBqvwM/9MxQ9/SOu992I9/huuue0sJFniqR+upqslzuxR53LVbXeRbO3g/rs+TyadIviBSgo+PobwY1uJLWtClmU++clPMnnyZH73u9/jb5/KH6p3U5xewcUr3uF7uxv43iemcfvlE/iPP63lnmffIWUO7EnvgiCc2oZUWB+YZ/3eYd0RT4MLRUoPPklH1YvID9jgOBjjD/Sm17Wtw3ZsppdMPyb15X34Q1T97Gd0Pfoo7V+7iQ/9y0jKRoZ4/N6VbFvZwtnjzuND37ybeGcn93/lOprrd+KfPYz8q8fQ/dwuel7YjSLJXH755XzqU59i1apVrF6s8KPqIm6WFvFiwzpmL61jT4nDLz43m5feaeEjP/sHGxt7jkn9giCcPEMsrHP/eziN3UlcIN/qxrAlHDePgBVGHzkS2evt225JwxLmlM/Bp/mOWY2B8z7AyD8/hR3pof7af+IDc2XmLRjL33+/hZcf3MSkYbP51Pfuo9Uf5/f/+VWee+QXeM4qpuQLU0nUtdH287VkGmOMHTuWG264gUAgwGOP7eS8wJd4bHQXn5f/wCNNzfxrYwP/smAM06sL+NgvlrLw1W2kLTHKFoTT1ZAK6wMXGN97ZL21Mw6GjDeVRsokSScD+MK78U7JvRnmjYY3OL/q/GNeplZezog//AHf3HPY+8lPUbL8ET7+/4bT3ZrgsXtWwJ4Cbv/2Q0TPLWPNS3/jf772WZqSuym7eRZaeYC2n68l8upefLqXa6+9liuvvJIXX/w7L7+Y4rMjb+GNCZ18Vvsr9+6t54WSCBdeUMgfV+zj0vuX8I/tHcf86xEE4fhT33+T08f+qXvvN7Le1hkHj4wed7HiETJWFZ7WpXg+clHfNvWRenb17GJ+1fzBFWGmYPcSqF8ObZtBUUHWoHw6hCqgaDSUTkbSdcrvuYfgxR+k41e/JPXQ76n9wHk0zv4Mix/dRl6xh89e/j3qL1rHc7/9H/78nW9QMHEMV19/K8XTJxN+chuxZU34a8uZNm8KI0eO5JVXXuE3v/kdFRUVXDPrw/xbRZwH921iiR2k9exxKDsT/PODy5k2poCbLx7HhSOLB/e1CcIZqm1vhNf/uIV4d5pQsZdQsZeKsflMnFuOop2YMe+QCuv9af1+t5vv7kqgGRZyOp+YGUb1TEXevArvXV/p22ZJ4xLGFoylIlAxsPduXg8vfwP2vQ2KBtVzYNhUsNLQsR02PgVduyEVAdWAURcgDZ9HcEwtgUceIfbGEjp+/nMK7v8iF8w8h9bR17H4j1vQDD8fHPN9NlW+wjtbXiF88/WERlYz+7wrGZM/k/jSZqKL69FrQlxx1ge44APzqduwjpdffhVJkvjIJZfwtdkFNHU8y2L/Tv5WPo6NuxQ+96vlGMUa0ycWce3kGs6vyKdEP7LpiYJwwm17CUacB/qxa1EeTqwrxV//Zy1V4wuYen4VyViGWFealc/tZvULezjno6MYN2cYkjyw5SiO1JAK6wMj6vc+aU3dcfyeJFp3EUk1QUGBjoSLcdBMkDfqB9gCibXBq/8F6/4Ekz4K1/4RRl0Aqn7o7dMx2P5SdvS94Ql45W6k/GqCYy8l+N3PkpFH0PnY07i//ToFrkJk7j8Ra53KsGQtPvsszNLd7I0so/PR/+UN3WDWFVcz9fL5sD1Nz7O7cG2X6WXFnHPZv/B283peeeUVnn/eZMyYMVw481I+M72EnsgSXtq5hUfWl7L6rTQr32zBydMoqVKYPyrAxaOHM7ewmCJ9SP3zEIaSRz4B530dLv7mcX+rpU/toKQmyGVfnJITyPOuGcOq5/ew5E/bWL+4kQuuG09JTfC41TEkvxvd9/kB1xFJkpcXwZMqIaM3USzFMMaORfZkn1renmhnRcsKbpp503sfaMer8MQXoGQ8/OsrUDWAKX5GAKZck/0DkAhng75+OTz7H+iJTspLJzHsux8kERtGz5omYi/fS3V7B5kJc0iddSkN0qdpsdKkrbW89czTrDT/hJafz8gJ0yjxVVKeGo71zG4mZXxMqbyENl+UTZ27efKJJ0CSqKmpobxkBt+eEWLYlS7rW3fxt83drNhTxtObkjytdOCEdGRDwjNMYkSllwlFZZydn0+prjIz5KdYBLlwPPU0QFMddO6EmrlQOSvbUgRI9c5uSnQefv+BcBzYvRj2/AOa1+NGW7DmfBtTm4aSZ6CVeOnsSLFzdRufuGtOv5Gzqiucc/Vopl9czVtP7+Tx769k+OQiLvviFDRDObraDmFIfcf1zQZ5n6Z1JGpSUxIl6IRoc0OEovvwTpnc9/lndjzD6PzRTCmecugDuC4sXQivfRcuuAPOuwXkI/zL8RXC3Buzf1wX2rfAjteQtj6Pv+F3+H1pWDCCpHYO0Y5iEu88S966dYwdNoq2kRfQUX41HekMTjTCjtUNbFW2I2U6wDGpKJxEuTSGomAF59ojmR8fRYMapiucoaOrmc2pd+hJRFAUhRn5+Zw/PEVRRQE9cobl7Z2s2Ouhea3GtvUJGvKaeK4gRDw/hOtX8fpgjNei2uul1JPPSJ+XcX4P1R6dKo+OIQ+ta9fCCZKKwNP/BlufA18xhMrhte9Afg1ceBdMXQDh3gd1KIf57bXX0ie34w3qnHXZ8P6fjDRj/umTbO7egVoykRHBc4ntvIbMEzqSvA4XA5CIyHB2SYodf3qGDXlxyiaMY8K8+cgHfb97gzoXf3YiMy6uZsMbjVgZW4T1+zlwB+Phh9bxtIWZdClVw2imj1SiAH/XCjzXXgpkn2L+1Pan+Nzkzx1+SdQ374M3/xs+/QSMufjYfQGSBKUTs3/O/XewMtC2CepX4t3xKt6Ov8EUFfP8OSStMZS37Ca6+mUye+qxFINYXjUtI8bTFJhKyonSaDm0NjbgWutw7A5kN0WZdwRlHcOZYJQyW5+ApciE5RjJqE00nWZn40bC6R4KgAWGn9EzxtJuBNkZSbKtu5t39iVJWSoOsFOx6RoWxfBbPKOXkNACZDQDWXYZm59kXMBmjM9DlVdjRN44Cg0Pwz0GXkUE+ZD0xOchWA6X33tk+2fi8NCHs4OWG5ZBWe/ql4kwrPg/7L/eRM/bP6OwZl729Xg7yY0doMl4xxf2O9zaV+sB+od1vIMtj3ycWz0p9pQVUp6x+cm6OcRKLPI/GmfS1odxN7/A2+a3SHQbBBSNMrMGOmDzyrdY+sc/MPHCC5l8/kUUlB9Y476oMsAF1w1+DaGBGlJhPZCpe1tbo7hAmRNGjoaQtdEYW1bgPyfb+3py25NEM1E+NOpDhz7AW4tg8Q/guseObVAfiqpDxczsn9ovQToK9SvQNjyJ1rqCkH8PpedEsWcbWL5RWJqHUa1hMt3txJtbcXftxZEUkl6N9kI/Xf4yIlKCNrcVM9OE47p4MhGK1BB5WpA8JUCFmofPqCap2sRMh/pNDWSkDF4pwjQJpijQI3tJuSp73WLUDpfuDgPVzqDaPUhuNoj39P55RUkhF8hkfGtxNAWpRGdYfoZqj8uUgIolBxgeGMacvADV3uxIqUhTkQe4drhwCtn05+x/jySsXRf+8uXsBfl/fQU8oQOf8xXCBbezUEvxux1PsuHtn2dfjzTR+cfNAFT94Lycw/3fv38BM16G5r8k933at/Lc4wv4rl/i0pFX8OiUO4n8ZhsdlREerHmE6mdq8Fku5ZG76Qjvxutu5rpzXYKzriFhfgDPskuYEjmP+jVbeOrZu6g6dzoTzp1PzbQZOaPt4+Gownrz5s387Gc/w+PxcMstt1BRMcCZE8fZe3VB3mrowvUpVMVMUiQo1UxC8+ehDx/Oru5d/Gjlj7hjzh0E9UNcKKh7OPsr2acePf5BfShGMPu++9/bNiHagtK1G6V9K8a+t/GrKyCwD6rAPreAVLcfR/UwKuMDj45pq8TDrSR27ERp7sCWIJJfQiQQosNbwG5fOWlNw5ZlcF1kN41X0RgpewmoKqrkoqsayCqWnkBTPGSUFF1KFzElTVhOkMahB51u10vaVenpMkj0qOwzi2EndEoOCa/FNi+4Wjs9gQgEZRyPjuuR8ag2491ORksxqnGokTzkSUHKbT8hNUgeAfwFHhSfhpJvIOsKTsZG0mRkT/af9P7/CqeJN34IuxbD/3stN6gPsrp7a+4LbZsPe7hIexvQhhG6tO+12N6l3Pf8F3gyoPMfM77MZ4b9M53/twmtxMuUz8zi19rFfHvFt1GjI2nv2EBabqXuCg+SR2Z+3f9S2X4rwwLVJMd8Hk/XTGrkiUSbunjnVy/yavxnMKaKdKiQT3/uc+TnFxyDk5LrqP5FP/XUU/zoRz+io6ODJ554gq9+9atHVcyKVcv4wZL1aJaDK0l4bJlgJk1QilNsJDAzXiJ48AU8jBg9jLIRNQQ8flKZKLrciEMJ8N63m69u7ET3O5R01dBu2oze9gqF93yBpJXka4u/xkU1F3HN2Gv677jpafjbV+Cjv4Cxl/T//MmgaJBfnf0zcj7M+WL29XgnxFpQ2rfit9LQUw/RZujaCw3PgbcHagwcyUuqOUHaWwW6gWWm6HC2Ee7uwW7uQcuoOBkVPW5hqmUkvAE6C4YTl2UkWSEtp0irKVSzi6TmYKleSmQNx+lmlBQG1c/+1nVCzWBqMl1eL2EpQGfaIJrWMWWJzo44YduP6WanDqqyRUsgQ5sPXtMCmF6DlNeD65FAS+BqSTz1CUr0biQZ/G4Gj23TI4WYlmihJpNkeKqHvKSL141hmA6mpSFnPFiJUrC8ROwMMTOFEVSwzAxpKw2KhALIio6m6ei6D4sM8XSUQEkIr9ePI7mousvwMSMI5oWob2wkVFBEaVUVlmWRTCYxTRPbtvH5fCSTSSzLQpZlMpkMXq8Xj8dDOp0mk8lg2zayLKNpGslkEtd1SSQS2LZNe3s78Xgcy7IAcF0XwzAAcBwH13VxXZdUKoVt2+Tn52PbNqlUCsdxSKfT5OXlYRgGPp8PTdOQZRlFUXL+u/+YkiQRCARwey/67P9Y13UymQyKolBWVoYkSci9f7Hq/uWEnUNPmHUdFzNtE+lOITmgqDIev4ZtO3TE0uyzLeyNL2Gu2Yo+5SHct8Cn7UZB6t3XQdFkVF2mZPt4xpkeVhd+EW9iH77SUtTea42tv/1PjLwgaqgQKzT6wPu7QDrGpte/xa0NL6AGC/jDJQ8wvqOG9l+swzu5iIJrxiIpMm0t7QAk5QR56WbOuedrlFi7WNKwhJ/FU6gjRjDTU8r82BPU2osYY+QR0j5Mful0bKua1c4uRrTYmLv3wcxTLKxjsRh+vx9FUejo6GD58uWsWLGChoaG99/5EJZv2MhaqwbHkSDp4DrgqjJSykY6+LmwMaAFWNpy0IsVZDr2z7M+fE/0ncYu8oMRAjuG0ejYVAQ24qut5dvLvo3t2vzX3P/q36ve/Cw8fQNc/gOYfu0RfW0nlL8o+6dscv/P2RZEGqFzO3I6iq9zJ75UD3TvAzNJSSICwQrQxkCyKzsnXPPhutkfgZnEFhra1tIp6aQ0hVZVxYwliGVkvJKOkigi6kKPGiftlBBHx0wX4TP9BFIuRT0+KjPdSHYGGR+mFMcxisHpIuJRiBgaPYpKSzKPhOwhLqfokT1IJLCl3F8zOyQJRXboklW8XhdHTfCmFML0lpDU/TiaAoaMLDvYuooRtPCoKQqdMF7JRHEMpIxDMXFcRyIpBVGVDHG8BFJJvFYXmmXhxjV8NJByPVRnWtBTCer+vgrH1rCs/Re5ev/tOTqOKyHJGWRcZEB3FCTXxcYFCSzZxZVAt6W+3wIdyUV2bBxJQnZdJCQkx0R2ASd7OcP02riahaRnkJU0smMho6A7MXCg2/XhSi5JNU2F6ke2dKyMQtrx0RNV8KAgu5CyTVTZj44P11aJp00yloWCTFc6jl/1kFY1EppOyjGJKSo93gBRr4+Y7sWRZZKagew66LaN6QliyzL2WX/FklSc51dgSaA6LjiQUWXiuorHtJEAW5ZIaQf9XXonwrz9q11ml/fVHBePDT4ZLEC2oGfihwhFMvyHq6JlziGswKTRFhWWxPauK7CjMqYhE0rZFI2YgD+RwO+T+foD97DVrmayfiPj1HlsfSjFlvA6Goo9qLKJ/pctNLTE2L1nH1FPFR41ylQnwTtdI7HN4VxVchnDM+00RsK0dDVwf3eShLSPvHyNoOPFa4ZxURnXWoDPlfAVHFiy4lg6qrDWdZ1kMklLSwvl5eXU1tZSW1vLokWLjuh4N33+i+yfLGdZJh2xTvZEOmlqitDWniKR6EBPt9LsemmyPKQ1mw7dR9IyyDjQ+k72G8Z+V9iu2riaEtXPZqmHtg6TGWNbsWIW5T1JpKuv5s/bn+FvO//Gwx96uP86IDtfhye/ABfcfmDkegRc18VxIWnaNHcniaRM0paDT1dJZCz8uorjuiQzNmnLoTWSotCvoyoSlu2iKTLFAYOWSAoJMG2HlGUzvMiPR1XQVQlNkbEcF02W8egyiiSRNG06YxkMTaY7YaLKEoocwtVn4WqQ1M8jY9sUTTQIeTWau5PE0hYFfh1dkdkbTlDs18nzaWiKTMZyKPTrjFBkgh4VXZHpm9HkutmAVzTQAyDL2Yuk8XYcM4GUjgEukqJj2TaSrxBL8ZBUQvgkm0xPDDraUBWZjOLSlY4STUXR/R7aWnbhdHUQkX04LRaFVpIdCZeOjEYykaEzrtGTkUirGinVQ9xVSXh8JGSdlKuQtGQcJJJAIwHAz4H5+OUASLILuLiODJKbMwfUlcH1qWBPRbJdUCQk20GSXCTZRfPYWCkFO3NgH0l2kWUXFJAcF8uUUSSHkJ7GwCLlqrhIGJKDLjt4FAfVlTEdjaijIKsuCVcmZctoQDIpYWRc5DTovW0qCbAcF11yMaOQtCUqDIuAxyEhqWhph0waUpZLUE0TdTT8ugyyhuW6xJFwNZV0UEfWIerTsVQZS5KzPyEUCcV1KHaTFLoRStQwimsRIkqntxCdCF560N0EqbiBY7vYGQ3JgZ5UiITpwa8liKW8xDI+8owIIOG6oEkZNMfEdbJ/N6arkbI99KSDIIGiOGRsDVlySJs6sqURQ+Lgx2u8dbjvN+MiXL+MZMah9TwkFzYAkF062FUk3KQJm3vXflclKMwDJwReWDFjIry2DSlugS4j+VUUjwKZSiSvgpsZTdh06TJtJC/orsUeo5CkYnKeqjL3yGLiPR1VWF933XXceeedaJrGHXfccaxqAkBVNYblD2NY/jCoee9tWyMpau99re/j/SPrtmiK7/7xV/xt7xguGb+K0pUubuhspibDtKV0ijue46upblreUrkoPp3nX9rJypFeFpxVhVdXYMOT2Yse82/NTs97H67r0tSTYvmuTur2dRNOZNjdHqexO0kiY2HaB7rpqiyhqzKJTO7iSqos4dUVDFUmbTq4gE9X6Elmwz3oUfFoCook0RJJDfyEAoYq47guluPmTG/U1WwIA2iKRIFPpzthkrEddFXGcbL7vJssZa8PFPl1/IaKZbskTRvTdhgW8qAqMq7roioSXXETRZaIpEwkoCthAv1/A5MkkCUJ+6D3y/e56Eol8XQZ8YyNpmQD8eDziQ6678DXAYANGhIhj0bIJ5GyHHy6gqEq2I5LLG1hqDJdiQzjhgUJebTsD0LHpb4zgaJItPSkSFsOkuMgx9M4soqmSWhGmlDQwaMrhHscvIZMsChNwG/jKhC3dFzXxpYc0paMYjvEJZ2wVkibJSGlbVxJAo9y4CKL6SDZ2VG3ZDm4moxku6g+l6SmomkWCUvGYybQrAQqJpajYpkatuOQ9vrIWBo7Iw66aiFnLFTJxMw3MDUDx5HQ40k0zcKVJTTXRHFsFNvEienItoWe0pBthaCaIWV6kHCxXJUuoAsNKD3w94/d13oE8JLA6yZRJRsXCUWy8ZAijoLmJimSYoTcWPYHHNlRt4yDqlrIuICEolj4pCSKbWMpHvwei4yjg+ZSIGVQbQ1ZsgjqSYoMA7/hEJVcHDVOPO0lYnnpcrLbJF0Dv2JTZHRh+BJETB9GQRjZbyFLNrLr4roSNhpWRsNrZ5AkG13JoGCRVA1iBEmnfSTxI/kzxNMhJFwcFExHw3FkZNMh6fGSVr2kJRW/cvaAvh8H66jCesKECSxcuPAYlXLsOCjc/POH0bp+wWue2wBY3HEW02dsgSaJ6XsLKEhtQ7nro3xJTTNqzybGdr+Fv/1vNG4pYfsLITRvkHHpjTTPu4fic7+E5zDv1R5Ns3x3J0t3dPDm9g4aupIUBwzOG1vMsJCHuaOKqCrwEvRkZzhoikxNkQ9NlvHqCo7jkrJsDFXBtB0MVT7klMG0ZeO69Pu867rYjotpu2RsB1kC23FJZGws28WjyRT4dTK9QeW64LjZ7R03O2LXVRnTdkhkbDyajKEquG72eIaa/XU1Zdp923fFM/QkTVoiKWIpC0mSyNhOX68zZdoosoxlO2RsB8dxyffpJDI2xQEdWZLI82kkMzb5Po08r0Y0ZfUFtOO6qLKMqkikLYeOaLovz8pCBmnLIZmxGV7kQ1dlPKqCV1fwaArRlElX3MRyHJKmTVfcpD2W6j13CpGUiWU7mLZLcdAgYzk0diVJWzZN3Uk0Jfu+lfleplfnM7rET5HfIORV+367ORr7f+glHYfWjIkmSaQdF0OWkCWJjOMQUBQ0WSKgyNkR9Ls4joXjJFGUQM7rUu8PuZZoCtN0sl+PKmNaDqoi0ZkwKQ958OkKtuOQ79NRZAnLtoknO3FdC49RjOM4IMl0JWwsxyWZsUiaDvG0RUtPElWW8RsqHbEMBT6NiRVBipreYORL/w/37jCxrk5wIdbViWPZ4DpkYjFi0R6Ka0YgywqSaWL19JBoaSajqriJBK5l4cnLRysuom3fHvKCeXi8PvSCfGKJHr71/M3MKp9DvqnjV4soUycydtdoNk1fSSoeI0/OENDieNbtIRNOMaF6OFY6TVFhCT6PF69m4G02cVLtuH6FDBHkUBJfgYZXKeD1riZsW6Nm4w4cw0Azw2iFETRFwagoIR2K4QuXIhsKpC0kn4FeVoU2dRSSouDi4tgpasqOz0SLIXnJ3EZiWN3PiZTPJR5XWTA8yVN7vazsnkx1VYaPfOqLaPn+d+1kQaSBspUPUbbyf1kenMWN6r/y0qtF+Je8wtiyIB8YU0x5voftrTHe3tVJY3eSaMqiJGgwd1QRN100hvPGllCe5zn8HO13kWUJX+/dgMp7TP3ZH5rvJkkSqiKhKuDlwDb57+rmaL1zmyUJZLLbv/vzed4DwSBJUs57eg7qMZaGPJSGPIwtO3631h6poEcj6Dl11zhRe3tGQVkheJi/0/cjyyqyfOhzr8gSlXnZnumIYv8htzmkd38/ACMGU9SIj8M5H0WSFULF2dF3qKT0fXZ6j8PVnpvzseu6fCjv63x09EcJ6IG+1+xIhqq83Gl7LTu28Y/H/sCCu747qPf01NfTvXUr6sKbGF+3Bskz8O/jE2HIhnWeZy4vV41Ha3X58fUfp/Ifu3hlcysPXnsWWv4hLgAoKhSMQL30W3Dpt5gHzAMSGYvFW9v5w7K9rN7bRcemNMPyPCyYVUV5npeJ5UFGFvtPqb9UQTgpjuM8Y0mS+PTET/d7Tc3r/5vOsDHjBh3UkH0wdXV1Ne6mjUjK8Z0zfSSGZFibjkzH+WF2vlPF+ZODyLLM1+aP4Wvzxwz6WD5d5cqp5Vw5tfw4VCoIwqnmVAxqGKJhHbN9/HrvteQZMos+XHuyyxEEQThqQ3KRBrNTQo+7/OX/zSV0CvcvBUEQBmrIjawl1aUs38+PPzaVEcWB999BEAThNDDkwlr1OLz99QtPdhmCIAjH1JBsgwiCIAw1IqwFQRBOAyKsBUEQTgMirAVBEE4DQy+s3+f5i4IgCKejoRfWgiAIQ5AIa0EQhNPAkAtrsZ6SIAhD0RAM61NzERZBEISjMaTuYHQCKtOmlJ3sMgRBEI65IRXWj3/pHGYWn3oL4guCIBytIdUGGekxMA7xGCRBEITTnUg2QRCE08ARt0G2bNnCz3/+c/x+PwsWLGD27NnHsi5BEAThIEc8st6wYQOGYeDxeBg1atSxrEkQBEF4lyMeWc+cOZMrrriC9vZ2fv3rX3PbbbexfPlyVqxYQUNDw7GsURAE4Yw3qLB+4IEHWLt2LQA7d+7kxRdfJBQK4TgOALW1tdTW1rJo0aJjXqggCMKZbFBhfcMNN/T9/9KlS7n99ttRVZV///d/P+aFCYIgCAcccRtk3rx5zJs371jWIgiCIByGmLonCIJwGhBhLQiCcBoQYS0IgnAaEGEtCIJwGhBhLQiCcBoQYS0IgnAaEGEtCIJwGhBhLQiCcBoQYS0IgnAaEGEtCIJwGhBhLQiCcBoQYS0IgnAaEGEtCIJwGhBhLQiCcBoQYS0IgnAaEGEtCIJwGhBhLQiCcBoQYS0IgnAaEGEtCIJwGhBhLQiCcBoYVFg7jsOGDRu4++67Adi8eTNf/vKXueWWW2hqajouBQqCIAiDDOtoNMrq1atJJBIAPPXUU/zoRz/iK1/5Ck888cRxKVAQBEEYZFjn5eXxuc99ru/jWCyG3++nrKyMjo4Oli9fzqJFi2hoaDjWdb4nv6Hy1YvH4jfUE/q+giAIJ8r7ptsDDzzA2rVrASgqKuLee+/t+5yu6ySTSVpaWigvL6e2tpba2loWLVp03Ao+lIChcvMl407oewqCIJxI7xvWN9xww2E/d91113HnnXeiaRp33HHHMS1MEARBOOCI+gY/+clPAJgwYQILFy48lvUIgiAIhyCm7gmCIJwGRFgLgiCcBkRYC4IgnAZEWAuCIJwGRFgLgiCcBkRYC4IgnAaOyy1/LS0tR3xjTENDA1VVVce4oqMn6hqcU7UuOHVrE3UNzlCsq6Wl5fCfdE8xP/3pT092CYck6hqcU7Uu1z11axN1Dc6ZVtcp1waZM2fOyS7hkERdg3Oq1gWnbm2irsE50+qSXNd1j8uRj8DmzZv52c9+hsfj4ZZbbqGiouKk1PHGG2/wl7/8BV3XmThxImvWrMEwDL75zW/y8ssvs2zZMrxeL9/61rdQ1eO/eJRlWbz11lusWbOGWbNm8eSTTx62ngcffJA9e/aQn5/P7bfffsLqqqysZNWqVUiSxG233cbrr79+0upasWIFjz76KLZtM2vWrPf8+ztZddXW1rJ+/fpT4ny9+eabPPPMM8TjcS677DIWL158Spyvg+uaMmUK9fX1p8T5Aujs7OSLX/wiN9988wn7fjylRtanypKrdXV1BINBQqEQW7duZeHChVx99dW89NJLrFq1ip/85CdMmzaNlStXnpB6Wltbqaurw3VdXnjhhfesZ9++fXz/+99H13Xq6+tPWF3r168nGAxSVlZGQUHBSa2rrq6OH//4x9x4443ce++9p8z5Oriu73znO6fM+aqvr+e+++7j4x//OIsWLTplztfBdf35z38+Zc4XwK9//WsqKytP6PfjKRXW715y9WS55JJL+MY3vsH8+fN5/fXXkSSpr6b9v4iUlpaesBorKyu55pprgOwDIN6rnoKCAgDKyspob28/YXUtWLCAb3zjG1RUVLBkyZKTWtf1119PS0sLDz30EFddddUpc74Oruvxxx8/Zc7Xddddx+LFi7n//vuZM2fOKXO+Dq7r/vvvP2XO1xNPPMGVV16JYRgn9PvxlArr/UuuNjc3U15eftLq2LJlC5qmEQqFcBwHx3H6arJtG6BvWdgTzXXdw9ZTVlZGd3d338cnso20Y8cOAILBILZtn9S6lixZwsMPP8xdd92FoiinzPk6uK5T6Xw988wzXHDBBdx33328+OKLp8z5Oriu/b/Fngrna82aNfz1r39l06ZNPP/88yfsfJ1SPestW7bwy1/+sm/J1aKiopNSxzPPPMPy5ctxXZf58+fz2muvYRgGd999Ny+88AIrVqzA7/fzn//5n8jyifl519DQwBNPPMGcOXP485//fNh6HnzwQerr6ykuLuYrX/nKCavL7/fT0NCA67rcfffdPPvssyetrhtuuIG8vDwALr30Up577rlT4nwdXFdVVRVtbW2nxPl65JFHWLduHYlEgjlz5rB27dpT4nwdXNf48eNPmfO139e//nU+9rGPnbDvx1MqrAVBEIRDO6XaIIIgCMKhibAWBEE4DYiwFgRBOA2IsBYEQTgNiLAWBEE4DYiwFgRBOA2IsBYEQTgN/H//btUZsAsPUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.shape(cell_data['20200719_004'])\n",
    "# plt.plot(cell_data['20200719_004']);\n",
    "\n",
    "for _,c in cell_data.items():\n",
    "    plt.figure()\n",
    "    plt.plot(c);\n",
    "    fs = int(len(c)/0.1)\n",
    "    plt.vlines(int(0.0045*fs),-10,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# created in 'GRC_properties_Meta_ipynb'\n",
    "pickle_in = open(top_dir / 'data_processed/df_cmdintact/SGC_uncoupled_wavmat.pickle',\"rb\")\n",
    "cell_data = pickle.load(pickle_in)\n",
    "pickle_in.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# np.shape(cell_data['20200719_004'])\n",
    "# plt.plot(cell_data['20200719_004']);\n",
    "\n",
    "for _,c in cell_data.items():\n",
    "    plt.figure()\n",
    "    plt.plot(c);\n",
    "    fs = int(len(c)/0.1)\n",
    "    plt.vlines(int(0.0045*fs),-10,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #### enable interactive plotting and create plot\n",
    "\n",
    "# %matplotlib qt\n",
    "\n",
    "# hfig = plt.figure(figsize = (5,8))\n",
    "# ax = hfig.add_axes([0.2,0.2,0.7,0.7])\n",
    "\n",
    "# #### interactive select SGC ES response onsets; save as numpy\n",
    "\n",
    "# # which_cells = cell_meta_df[cell_meta_df['type']=='s'].index\n",
    "# # which_cells = meta_df[meta_df['cell_type']=='s'].exptname.values\n",
    "\n",
    "# # plt.plot(cell_data['20200719_004']);\n",
    "\n",
    "# onset_mat = []\n",
    "# onset = []\n",
    "# i = 0\n",
    "# for _,c in cell_data.items():\n",
    "\n",
    "# #     while i < 3:\n",
    "#     sweepdur = 0.1\n",
    "#     fs = int(len(c)/sweepdur) # when these arrays were made, 100ms was the response window used\n",
    "#     xtime = np.linspace(0,sweepdur*1000,len(c))\n",
    "#     c = np.concatenate([c.T,c[:,10].reshape(1,-1)]).T\n",
    "# #     onset = []\n",
    "#     for sweep in c.T:\n",
    "#         ax.cla()\n",
    "\n",
    "#         ax.plot(xtime,sweep,color='black')\n",
    "#         ax.set_xlim(4.5,20)\n",
    "#         ax.set_ylim(-5,20)\n",
    "\n",
    "#         x = np.asarray(hfig.ginput())\n",
    "#     #     print(x)\n",
    "#         onset.append(x[0][0])\n",
    "#     #     print(x[0][0])\n",
    "\n",
    "# #     onset_mat.append(np.asarray(onset))\n",
    "#     i+=1\n",
    "\n",
    "# onset.append(np.nan)\n",
    "# # ax.cla()\n",
    "# # sweeps_clip = []\n",
    "# # offsetT = -2\n",
    "# # x_clip = np.linspace(offsetT,50,int(50/1000/newdt)-int(offsetT/1000/newdt))\n",
    "# # for o,sweep in zip(onset[1:],sweeps[:,0:-1].T):\n",
    "# #     onsetind = int(o/1000/newdt)\n",
    "# #     startind = onsetind + int((offsetT)/1000/newdt)\n",
    "# #     stopind = onsetind + int((50)/1000/newdt)\n",
    "# #     sweeps_clip.append(sweep[startind:stopind])\n",
    "# #     ax.plot(x_clip,sweep[startind:stopind])\n",
    "# # sweeps_clip = np.asarray(sweeps_clip).T\n",
    "# # ax.set_xlim()\n",
    "\n",
    "# # np.save(meta_data_folder / 'uncoupled_response_pop_sgrc_onset.npy',onset,allow_pickle=False)\n",
    "# # np.save(meta_data_folder / 'uncoupled_response_pop_sgrc_sweeps.npy',sweeps,allow_pickle=False)\n",
    "# # np.save(meta_data_folder / 'uncoupled_response_pop_sgrc_sweeps_clip.npy',sweeps_clip,allow_pickle=False)\n",
    "\n",
    "# len(onset)\n",
    "\n",
    "# print(len(onset))\n",
    "# onset_mat = []\n",
    "# for i in np.arange(2,len(onset),12):\n",
    "#     print(i+11)\n",
    "#     onset_mat.append(onset[i:i+11])\n",
    "    \n",
    "# onset_mat = np.asarray(onset_mat)\n",
    "\n",
    "# # np.save(meta_data_folder / '<your_name>AmpshiftResponseOnset_DGC.npy',onset_mat,allow_pickle=False)\n",
    "\n",
    "# x = [-40,-30,-20,-10,-5,0,5,10,20,30,40]\n",
    "# np.shape(onset_mat)\n",
    "# plt.figure()\n",
    "# for o in onset_mat:\n",
    "#     plt.scatter(x,o)\n",
    "\n",
    "# np.shape(onset_mat)\n",
    "\n",
    "# #### SGC\n",
    "\n",
    "# # created in 'GRC_properties_Meta_ipynb'\n",
    "# pickle_in = open(top_dir / 'data_processed/df_cmdintact/SGC_uncoupled_wavmat.pickle',\"rb\")\n",
    "# cell_data = pickle.load(pickle_in)\n",
    "# pickle_in.close()\n",
    "\n",
    "\n",
    "# onset_mat = []\n",
    "# onset = []\n",
    "# i = 0\n",
    "# for _,c in cell_data.items():\n",
    "\n",
    "# #     while i < 3:\n",
    "#     sweepdur = 0.1\n",
    "#     fs = int(len(c)/sweepdur) # when these arrays were made, 100ms was the response window used\n",
    "#     xtime = np.linspace(0,sweepdur*1000,len(c))\n",
    "#     c = np.concatenate([c.T,c[:,10].reshape(1,-1)]).T\n",
    "# #     onset = []\n",
    "#     for sweep in c.T:\n",
    "#         ax.cla()\n",
    "\n",
    "#         ax.plot(xtime,sweep,color='black')\n",
    "#         ax.set_xlim(4.5,20)\n",
    "#         ax.set_ylim(-5,20)\n",
    "\n",
    "#         x = np.asarray(hfig.ginput())\n",
    "#     #     print(x)\n",
    "#         onset.append(x[0][0])\n",
    "#     #     print(x[0][0])\n",
    "\n",
    "# #     onset_mat.append(np.asarray(onset))\n",
    "#     i+=1\n",
    "\n",
    "# onset.append(np.nan)\n",
    "# # ax.cla()\n",
    "\n",
    "# len(onset)\n",
    "\n",
    "# print(len(onset))\n",
    "# onset_mat = []\n",
    "# for i in np.arange(2,len(onset),12):\n",
    "#     print(i+11)\n",
    "#     onset_mat.append(onset[i:i+11])\n",
    "    \n",
    "# onset_mat = np.asarray(onset_mat)\n",
    "\n",
    "# # np.save(meta_data_folder / '<your_name>AmpshiftResponseOnset_SGC.npy',onset_mat,allow_pickle=False)\n",
    "\n",
    "# x = [-40,-30,-20,-10,-5,0,5,10,20,30,40]\n",
    "# np.shape(onset_mat)\n",
    "# plt.figure()\n",
    "# for o in onset_mat:\n",
    "#     plt.scatter(x,o)\n",
    "\n",
    "# #### close figure and return to inline plotting\n",
    "\n",
    "# plt.close(hfig)\n",
    "\n",
    "# %matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GC scatter of response onset uncoupeld"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "dgc_onset = np.load(meta_data_folder / 'AmpshiftResponseOnset_DGC.npy')\n",
    "sgc_onset = np.load(meta_data_folder / 'AmpshiftResponseOnset_SGC.npy')\n",
    "\n",
    "x = [-40,-30,-20,-10,-5,0,5,10,20,30,40]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "dgc_onset[dgc_onset<6] = np.nan\n",
    "sgc_onset[sgc_onset<6] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(data=[0.586700336832715, 0.4730309197211104,\n",
       "                   0.3294350109080833, 0.2872828669743725,\n",
       "                   0.24973232375754448, 0.22142581886432688,\n",
       "                   0.20021292047959557, 0.16497444976731798,\n",
       "                   0.1436902539182028, 0.1272646696193892,\n",
       "                   0.13571798690881254],\n",
       "             mask=[False, False, False, False, False, False, False, False,\n",
       "                   False, False, False],\n",
       "       fill_value=1e+20)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nanmean(dgc_onset,0)\n",
    "stats.sem(dgc_onset,0,nan_policy = 'omit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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": [
    "hfig,ax = create_fig_tuning()\n",
    "ax.errorbar(x,np.nanmean(dgc_onset,0),\n",
    "            yerr=stats.sem(dgc_onset,0,nan_policy = 'omit'),linestyle=\"None\",\n",
    "            color = sns.xkcd_rgb['teal blue'], capsize=2,fmt='o', mfc=sns.xkcd_rgb['teal blue'],ms=sqrt(10))\n",
    "ax.errorbar(x,np.nanmean(sgc_onset,0),\n",
    "            yerr=stats.sem(sgc_onset,0,nan_policy = 'omit'),linestyle=\"None\",\n",
    "            color = sns.xkcd_rgb['icky green'], capsize=2,fmt='o', mfc=sns.xkcd_rgb['icky green'],ms=sqrt(10))\n",
    "ax.set_ylabel('Response Onset (msec)')\n",
    "ax.set_xlabel('Stimulus amplitude \\n (% change)',linespacing=0.75)\n",
    "ax.set_xlim(-47,47)\n",
    "xticks([-40,-20,0,20,40]);\n",
    "# ax.set_ylim(-0.03,2.25)\n",
    "sns.despine(hfig)\n",
    "figsave(figure_folder,'Fig3_ResponseOnset_GCs')"
   ]
  },
  {
   "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"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 86.4x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hfig,ax = create_fig_tuning()\n",
    "for o in dgc_onset:\n",
    "    plt.scatter(x,o,color = 'black',alpha = 0.5)\n",
    "    ax.set_ylabel('Response Onset (msec)')\n",
    "ax.set_xlabel('Stimulus amplitude \\n (% change)',linespacing=0.75)\n",
    "ax.set_xlim(-47,47)\n",
    "xticks([-40,-20,0,20,40]);\n",
    "# ax.set_ylim(-0.03,2.25)\n",
    "sns.despine(hfig)\n",
    "\n",
    "hfig,ax = create_fig_tuning()\n",
    "for o in sgc_onset:\n",
    "    plt.scatter(x,o,color = 'black',alpha = 0.5)\n",
    "    ax.set_ylabel('Response Onset (msec)')\n",
    "ax.set_xlabel('Stimulus amplitude \\n (% change)',linespacing=0.75)\n",
    "ax.set_xlim(-47,47)\n",
    "xticks([-40,-20,0,20,40]);\n",
    "# ax.set_ylim(-0.03,2.25)\n",
    "sns.despine(hfig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 11)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(sgc_onset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (S4) Output E"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot population results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 86.4x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 86.4x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 86.4x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Output E\n",
    "meta_df_uc = pd.read_csv(top_dir / 'data_processed/DF_OutputEPopulation_SubUncoupledAmpShift.csv')\n",
    "meta_df_c = pd.read_csv(top_dir / 'data_processed/DF_OutputPopulation_CoupledAmpShift.csv')\n",
    "R_ampshift_u = []\n",
    "R_ampshift_norm_u = []\n",
    "R_ampshift_c = []\n",
    "R_ampshift_norm_c = []\n",
    "for a in np.unique(meta_df_uc['exptname']):\n",
    "    subdf = meta_df_uc[meta_df_uc['exptname']==a].groupby('ampshift')\n",
    "    r = subdf.pspamp.mean().values\n",
    "    R_ampshift_u.append(r)\n",
    "    r_scale = np.max(r)\n",
    "    r = r/r_scale\n",
    "    R_ampshift_norm_u.append(r)\n",
    "    \n",
    "    subdf = meta_df_c[meta_df_c['exptname']==a].groupby('ampshift')\n",
    "    r = subdf.pspamp.mean().values\n",
    "    R_ampshift_c.append(r)\n",
    "    r = r/r_scale\n",
    "    R_ampshift_norm_c.append(r)\n",
    "    \n",
    "R_ampshift_u = np.asarray(R_ampshift_u)\n",
    "R_ampshift_norm_sgrc_u = np.asarray(R_ampshift_norm_u)\n",
    "R_ampshift_c = np.asarray(R_ampshift_c)\n",
    "R_ampshift_norm_sgrc_c = np.asarray(R_ampshift_norm_c)\n",
    "stim_ampshift = np.unique(meta_df_uc.ampshift)\n",
    "\n",
    "restrict_inds = stim_ampshift>=-40\n",
    "hfig,ax = create_fig_tuning()\n",
    "# hfig = plt.figure(figsize = (4,6))\n",
    "# ax = hfig.add_axes([0.2,0.2,0.7,0.7])\n",
    "for expt_result in R_ampshift_norm_sgrc_u:\n",
    "    ax.plot(stim_ampshift[restrict_inds],expt_result[restrict_inds],linestyle='-',lw=1,color='black')#,marker='o',markersize=6)\n",
    "for expt_result in R_ampshift_norm_sgrc_c:\n",
    "    ax.plot(stim_ampshift[restrict_inds],expt_result[restrict_inds],linestyle='--',lw=1,color='black')#,marker='o',markersize=6)\n",
    "ax.set_ylabel('Peak response (normalized)')\n",
    "ax.set_xlabel('Stimulus amplitude \\n (% change)',linespacing=0.75)\n",
    "ax.set_xlim(-17,47)\n",
    "xticks([0,20,40]);\n",
    "sns.despine(hfig)\n",
    "figsave(figure_folder,'Fig4_outputTuningCurves_UvC')\n",
    "\n",
    "# # restrict_inds = stim_ampshift>-20\n",
    "# hfig,ax = create_fig_tuning()\n",
    "# ax.errorbar(stim_ampshift[restrict_inds],np.mean(R_ampshift_norm_sgrc_c[:,restrict_inds],0),\n",
    "#             yerr=stats.sem(R_ampshift_norm_sgrc_c[:,restrict_inds],0),linestyle=\"None\",\n",
    "#             color = 'red', capsize=2,fmt='o', mfc='red',ms=sqrt(10))\n",
    "# # ax.scatter(stim_ampshift[restrict_inds],np.mean(R_ampshift_norm_sgrc_c[:,restrict_inds],0),color = 'orange',s = 10)\n",
    "# ax.errorbar(stim_ampshift[restrict_inds],np.mean(R_ampshift_norm_sgrc_u[:,restrict_inds],0),\n",
    "#             yerr=stats.sem(R_ampshift_norm_sgrc_u[:,restrict_inds],0),linestyle=\"None\",\n",
    "#             color = 'black', capsize=2,fmt='o', mfc='black',ms=sqrt(10))\n",
    "# # ax.scatter(stim_ampshift[restrict_inds],np.mean(R_ampshift_norm_sgrc_u[:,restrict_inds],0),color = 'green',s = 10)\n",
    "# ax.set_ylabel('Peak response (normalized)')\n",
    "# ax.set_xlabel('Stimulus amplitude \\n (% change)',linespacing=0.75)\n",
    "# ax.set_xlim(-47,47)\n",
    "# xticks([-40,-20,0,20,40]);\n",
    "# ax.set_ylim(-0.03,2.25)\n",
    "# sns.despine(hfig)\n",
    "# figsave(figure_folder,'Fig4_outputTuningCurves_UvC_mean')\n",
    "hfig,ax = create_fig_tuning()\n",
    "plt.errorbar(stim_ampshift[restrict_inds],\n",
    "             np.mean(R_ampshift_norm_sgrc_c[:,restrict_inds],0),\n",
    "             yerr=stats.sem(R_ampshift_norm_sgrc_c[:,restrict_inds],0),\n",
    "             fmt='s', mfc='white',ms = 3, color = sns.xkcd_rgb['black'], \n",
    "             ecolor = sns.xkcd_rgb['black'], capsize=2)\n",
    "\n",
    "plt.errorbar(stim_ampshift[restrict_inds],\n",
    "             np.mean(R_ampshift_norm_sgrc_u[:,restrict_inds],0),\n",
    "             yerr=stats.sem(R_ampshift_norm_sgrc_u[:,restrict_inds],0),\n",
    "             fmt='o', ms = 3, color = sns.xkcd_rgb['black'], \n",
    "             ecolor = sns.xkcd_rgb['black'], capsize=2)\n",
    "ax.set_ylabel('Mean Peak Response \\n (normalized)',linespacing=0.9)\n",
    "ax.set_xlabel('Stimulus amplitude \\n (% change)',linespacing=0.9)\n",
    "ax.set_xlim(-47,47)\n",
    "xticks([-40,-20,0,20,40]);\n",
    "# ax.set_ylim(-0.03,1.6)\n",
    "sns.despine(hfig)\n",
    "figsave(figure_folder,'Fig3_outputeTuningCurves_LongVsShort_MeanSem')\n",
    "\n",
    "dv_u = []\n",
    "for r in R_ampshift_u:\n",
    "    dv_u.append(r[restrict_inds][-1] - r[restrict_inds][0])\n",
    "dv_u = np.asarray(dv_u).T\n",
    "                \n",
    "dv_c = []\n",
    "for r in R_ampshift_c:\n",
    "    dv_c.append(r[restrict_inds][-1] - r[restrict_inds][0])\n",
    "dv_c = np.asarray(dv_c).T\n",
    "\n",
    "\n",
    "hfig,ax = create_fig_tuning()\n",
    "sns.boxplot(y=np.round(dv_c - dv_u), whis=np.inf,saturation = 0.5,ax=ax)\n",
    "ax.hlines(0,-1,1,linestyle = '--',color = 'gray')\n",
    "sns.stripplot(y=np.round(dv_c - dv_u),s= sqrt(10),color = 'black',jitter = 0.4,ax=ax)\n",
    "ax.set_ylabel('dv min/max \\n coupled-uncoupled (mV)',linespacing=0.75)\n",
    "ax.set_xlabel('Stimulus amplitude \\n (% change)',linespacing=0.75)\n",
    "figsave(figure_folder,'Fig4_outputTuningScatter_UvC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uncoupled: mean mV change 9.23, 3.99 std, 2.30 sem\n",
      "coupled: mean mV change 12.47, 4.81 std, 2.78 sem\n"
     ]
    }
   ],
   "source": [
    "print('uncoupled: mean mV change %0.2f, %0.2f std, %0.2f sem' %(np.mean(dv_u),np.std(dv_u),stats.sem(dv_u)))\n",
    "print('coupled: mean mV change %0.2f, %0.2f std, %0.2f sem' %(np.mean(dv_c),np.std(dv_c),stats.sem(dv_c)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### prep a dataframe for t-test and ANOVA on population data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        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>Unnamed: 0</th>\n",
       "      <th>time</th>\n",
       "      <th>code</th>\n",
       "      <th>ampshift</th>\n",
       "      <th>pspamp</th>\n",
       "      <th>exptname</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1373</td>\n",
       "      <td>181.943123</td>\n",
       "      <td>T</td>\n",
       "      <td>-30.0</td>\n",
       "      <td>0.778198</td>\n",
       "      <td>20200719_005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1375</td>\n",
       "      <td>182.206473</td>\n",
       "      <td>T</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.798340</td>\n",
       "      <td>20200719_005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1377</td>\n",
       "      <td>182.461623</td>\n",
       "      <td>T</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>3.524780</td>\n",
       "      <td>20200719_005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1379</td>\n",
       "      <td>182.718693</td>\n",
       "      <td>T</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.905151</td>\n",
       "      <td>20200719_005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1381</td>\n",
       "      <td>182.971882</td>\n",
       "      <td>T</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>2.044678</td>\n",
       "      <td>20200719_005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>989</td>\n",
       "      <td>95.559200</td>\n",
       "      <td>T</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.876831</td>\n",
       "      <td>20200107_004_ampshift2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>993</td>\n",
       "      <td>96.004540</td>\n",
       "      <td>T</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>0.274658</td>\n",
       "      <td>20200107_004_ampshift2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>997</td>\n",
       "      <td>96.471300</td>\n",
       "      <td>T</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.464844</td>\n",
       "      <td>20200107_004_ampshift2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>1001</td>\n",
       "      <td>96.907720</td>\n",
       "      <td>T</td>\n",
       "      <td>-40.0</td>\n",
       "      <td>0.228882</td>\n",
       "      <td>20200107_004_ampshift2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>1005</td>\n",
       "      <td>97.305280</td>\n",
       "      <td>T</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.631592</td>\n",
       "      <td>20200107_004_ampshift2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>566 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0        time code  ampshift    pspamp                exptname\n",
       "0          1373  181.943123    T     -30.0  0.778198            20200719_005\n",
       "1          1375  182.206473    T      10.0  5.798340            20200719_005\n",
       "2          1377  182.461623    T     -10.0  3.524780            20200719_005\n",
       "3          1379  182.718693    T      20.0  5.905151            20200719_005\n",
       "4          1381  182.971882    T     -20.0  2.044678            20200719_005\n",
       "..          ...         ...  ...       ...       ...                     ...\n",
       "561         989   95.559200    T      10.0  1.876831  20200107_004_ampshift2\n",
       "562         993   96.004540    T     -20.0  0.274658  20200107_004_ampshift2\n",
       "563         997   96.471300    T       5.0  1.464844  20200107_004_ampshift2\n",
       "564        1001   96.907720    T     -40.0  0.228882  20200107_004_ampshift2\n",
       "565        1005   97.305280    T      20.0  3.631592  20200107_004_ampshift2\n",
       "\n",
       "[566 rows x 6 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_df_u"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta_df_u = pd.read_csv(top_dir / 'data_processed/DF_OutputEPopulation_SubUncoupledAmpShift.csv')\n",
    "meta_df_c = pd.read_csv(top_dir / 'data_processed/DF_OutputPopulation_CoupledAmpShift.csv')\n",
    "\n",
    "eid = []\n",
    "ramp = []\n",
    "samp = []\n",
    "cond = []\n",
    "\n",
    "for i,n in enumerate(np.unique(meta_df_u['exptname'])):\n",
    "    \n",
    "    exptdf = meta_df_u[meta_df_u['exptname']==n]\n",
    "    subdf = exptdf.groupby('ampshift')\n",
    "    r = subdf.pspamp.mean().values\n",
    "    # get scaling factor to normalize all values to max uncoupled\n",
    "    r_scale = np.max(r)\n",
    "    for a in np.unique(exptdf['ampshift']):\n",
    "        ampdf = exptdf[exptdf['ampshift']==a]\n",
    "        eid.append(i)\n",
    "        cond.append('u')\n",
    "        samp.append(a)\n",
    "        r = ampdf['pspamp'].mean()\n",
    "        r = r/r_scale\n",
    "        ramp.append(r)\n",
    "        \n",
    "    exptdf = meta_df_c[meta_df_c['exptname']==n]\n",
    "    for a in np.unique(exptdf['ampshift']):\n",
    "        ampdf = exptdf[exptdf['ampshift']==a]\n",
    "        eid.append(i)\n",
    "        cond.append('c')\n",
    "        samp.append(a)\n",
    "        r = ampdf['pspamp'].mean()\n",
    "        r = r/r_scale\n",
    "        ramp.append(r)\n",
    "    \n",
    "\n",
    "\n",
    "anal_df = pd.DataFrame({\n",
    "    'eid' : eid,\n",
    "    'cond' : cond,\n",
    "    'samp' : samp,\n",
    "    'ramp' : ramp\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Kruskal wallis for effect of condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KruskalResult(statistic=29.066260825846584, pvalue=6.9944542279447e-08)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.kruskal(*[group[\"ramp\"].values for name, group in anal_df.groupby('cond')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### two-factor repeated measures anova with statsmodels on normalized data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>sum_sq</th>\n",
       "      <th>df</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(cond)</th>\n",
       "      <td>11.921224</td>\n",
       "      <td>1.0</td>\n",
       "      <td>159.739583</td>\n",
       "      <td>2.747355e-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(samp)</th>\n",
       "      <td>16.641529</td>\n",
       "      <td>10.0</td>\n",
       "      <td>22.298978</td>\n",
       "      <td>1.661574e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(cond):C(samp)</th>\n",
       "      <td>1.066762</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.429418</td>\n",
       "      <td>1.871717e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>4.925522</td>\n",
       "      <td>66.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    sum_sq    df           F        PR(>F)\n",
       "C(cond)          11.921224   1.0  159.739583  2.747355e-19\n",
       "C(samp)          16.641529  10.0   22.298978  1.661574e-17\n",
       "C(cond):C(samp)   1.066762  10.0    1.429418  1.871717e-01\n",
       "Residual          4.925522  66.0         NaN           NaN"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# formula = 'ramp ~ C(cond) + C(samp) + C(cond):C(samp)'\n",
    "formula = 'ramp ~ C(cond) * C(samp)'\n",
    "model = ols(formula, data = anal_df).fit()\n",
    "aov_table = anova_lm(model, typ=2)\n",
    "\n",
    "aov_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### statsmodels ANOVA by cell to test effect of condition, amp, and interaction on cells with enough trials to test per cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True  True  True]\n",
      " significant effect (p<0.001 ANOVA) of condition in 4 output cells out of 4 with more than 5 trials\n",
      " significant effect (p<0.01 ANOVA) of condition in 4 output cells out of 4 with more than 5 trials\n",
      "p delay: \n",
      "[1.60610445e-095 5.80383237e-138 2.17899561e-184 2.42426450e-141]\n",
      "p amplitdue: \n",
      "[9.549752098907673e-69, 1.683186018954298e-157, 4.0066002869483264e-259, 1.5816343373585823e-137]\n",
      "p interaction: \n",
      "[1.7343123031219945e-12, 5.770737055822015e-61, 1.6396264990329006e-82, 6.031141419129658e-30]\n"
     ]
    }
   ],
   "source": [
    "meta_df_u = pd.read_csv(top_dir / 'data_processed/DF_OutputEPopulation_SubUncoupledAmpShift.csv')\n",
    "meta_df_c = pd.read_csv(top_dir / 'data_processed/DF_OutputPopulation_CoupledAmpShift.csv')\n",
    "\n",
    "p_cond = []\n",
    "p_amp = []\n",
    "p_interact = []\n",
    "ntrials_p = []\n",
    "ntrials_c = []\n",
    "n_tested = 0\n",
    "n_trials_min = 5\n",
    "for expt in np.unique(meta_df_u['exptname']):\n",
    "# expt = '20200718_000'\n",
    "    expt_df_u = meta_df_u[meta_df_u['exptname']==expt]\n",
    "    expt_df_u.insert(1, 'cond', 'u')\n",
    "    expt_df_c = meta_df_c[meta_df_c['exptname']==expt]\n",
    "    expt_df_c.insert(1, 'cond', 'c')    \n",
    "    expt_df = expt_df_u.append(expt_df_c)\n",
    "    \n",
    "  \n",
    "    if (((len(expt_df_u)/11)>=n_trials_min) & ((len(expt_df_c)/11)>=n_trials_min)):\n",
    "        n_tested += 1\n",
    "        # expt_df\n",
    "    #     formula = 'peakAmp ~ C(cond) * C(ampshift)'\n",
    "        formula = 'pspamp ~ C(cond) + C(ampshift) + C(cond):C(ampshift)'\n",
    "        model = ols(formula, data = expt_df).fit()\n",
    "        aov_table = anova_lm(model) #, typ=2)\n",
    "\n",
    "        p_cond.append(aov_table['PR(>F)'].loc['C(cond)'])\n",
    "#         p_cond.append(aov_table['PR(>F)'].loc['C(cond)'])\n",
    "        p_amp.append(aov_table['PR(>F)'].loc['C(ampshift)'])\n",
    "        p_interact.append(aov_table['PR(>F)'].loc['C(cond):C(ampshift)'])\n",
    "# p_cond = np.asarray(p_cond)\n",
    "p_cond = np.asarray(p_cond)\n",
    "\n",
    "print(p_cond<0.001)\n",
    "\n",
    "print(' significant effect (p<0.001 ANOVA) of condition in ' + \n",
    "     str(np.count_nonzero(p_cond<0.001)) + \n",
    "     ' output cells out of ' +\n",
    "     str(n_tested) + ' with more than ' + str(n_trials_min) + ' trials')\n",
    "\n",
    "print(' significant effect (p<0.01 ANOVA) of condition in ' + \n",
    "     str(np.count_nonzero(p_cond<0.01)) + \n",
    "     ' output cells out of ' +\n",
    "     str(n_tested) + ' with more than ' + str(n_trials_min) + ' trials')\n",
    "\n",
    "\n",
    "print('p delay: ')\n",
    "print(p_cond)\n",
    "print('p amplitdue: ')\n",
    "print(p_amp)\n",
    "print('p interaction: ')\n",
    "print(p_interact)\n",
    "# display(aov_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### statsmodels ANOVA by cell to test effect of condition, amp, and interaction on all cells (use raw mean per cell; not normalized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(cond)</th>\n",
       "      <td>1.0</td>\n",
       "      <td>704.886021</td>\n",
       "      <td>704.886021</td>\n",
       "      <td>43.351230</td>\n",
       "      <td>8.829933e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(ampshift)</th>\n",
       "      <td>10.0</td>\n",
       "      <td>1350.394701</td>\n",
       "      <td>135.039470</td>\n",
       "      <td>8.305069</td>\n",
       "      <td>1.479983e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(cond):C(ampshift)</th>\n",
       "      <td>10.0</td>\n",
       "      <td>71.857422</td>\n",
       "      <td>7.185742</td>\n",
       "      <td>0.441931</td>\n",
       "      <td>9.201862e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>66.0</td>\n",
       "      <td>1073.152429</td>\n",
       "      <td>16.259885</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       df       sum_sq     mean_sq          F        PR(>F)\n",
       "C(cond)               1.0   704.886021  704.886021  43.351230  8.829933e-09\n",
       "C(ampshift)          10.0  1350.394701  135.039470   8.305069  1.479983e-08\n",
       "C(cond):C(ampshift)  10.0    71.857422    7.185742   0.441931  9.201862e-01\n",
       "Residual             66.0  1073.152429   16.259885        NaN           NaN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "KruskalResult(statistic=18.114495310613847, pvalue=2.0801188345153102e-05)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_df_u = pd.read_csv(top_dir / 'data_processed/DF_OutputEPopulation_SubUncoupledAmpShift.csv')\n",
    "meta_df_c = pd.read_csv(top_dir / 'data_processed/DF_OutputPopulation_CoupledAmpShift.csv')\n",
    "\n",
    "meta_df_u.insert(1, 'cond', 'u')\n",
    "\n",
    "meta_df_c.insert(1, 'cond', 'c')    \n",
    "\n",
    "df = meta_df_u.append(meta_df_c)\n",
    "df = df.groupby(['exptname','cond','ampshift']).agg(pspamp=pd.NamedAgg(column='pspamp',aggfunc=mean)).reset_index()\n",
    "\n",
    "formula = 'pspamp ~ C(cond) + C(ampshift) + C(cond):C(ampshift)'\n",
    "model = ols(formula, data = df).fit()\n",
    "aov_table = anova_lm(model) #, typ=2)\n",
    "\n",
    "display(aov_table)\n",
    "\n",
    "stats.kruskal(*[group[\"pspamp\"].values for name, group in df.groupby('cond')])\n",
    "#     anal_df.groupby('cond')['ramp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### output cell 20200614_002 (_ampshift)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/kperks/mnt/OneDrive - wesleyan.edu/Research/Manuscripts/GRC_PerksSawtell/AcceptedRevision_CellReports/data_raw/20200614_002_ampshift.smr\n"
     ]
    }
   ],
   "source": [
    "# ax.set_ylim(-5,25)\n",
    "\n",
    "exptname = '20200614_002_ampshift'\n",
    "colinds = plt.cm.plasma(np.linspace(0.2,0.85,11))\n",
    "colinds = array([c for c in reversed(colinds)])\n",
    "\n",
    "expt = AmpShift_Stable()\n",
    "expt.load_expt(exptname, data_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = expt.get_dt('lowgain')\n",
    "expt.set_amps(11,[-40,-30,-20,-10,-5,0,5,10,20,30,40])\n",
    "expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')\n",
    "marker_df = expt.get_marker_table()\n",
    "\n",
    "bout_uc = [expt.get_bout_win('R','Keyboard')[0]]\n",
    "bout_c = [expt.get_bout_win('N','Keyboard')[0]]\n",
    "bout_cmd = [expt.get_bout_win('R','Keyboard')[0]]\n",
    "\n",
    "sweepdur = 0.05\n",
    "r_onset = int(0.006/dt)\n",
    "\n",
    "filtwin = int(0.003/dt)\n",
    "if filtwin % 2 == 0:\n",
    "    filtwin +=1\n",
    "\n",
    "bout_df = expt.filter_marker_df_time(marker_df,bout_uc)\n",
    "trial_df = expt.filter_marker_df_code(bout_df,['T'])\n",
    "stim_t = trial_df.time.values\n",
    "\n",
    "eventDur = 0.001\n",
    "xtime,event_sweeps = expt.get_sweepsmat('SIU',trial_df['time'].values,eventDur)\n",
    "event_Amp = np.asarray([np.max(sweep) for sweep in event_sweeps.T])\n",
    "\n",
    "base_df = expt.filter_marker_df_code(bout_df,['U'])\n",
    "xtime,event_sweeps = expt.get_sweepsmat('SIU',base_df['time'].values,eventDur)\n",
    "event_0_Amp = np.median(np.asarray([np.max(sweep) for sweep in event_sweeps.T]))\n",
    "\n",
    "ampshift = np.asarray([np.round(((A/event_0_Amp)*100)-100) for A in event_Amp]).reshape(-1, 1)\n",
    "trial_df.insert(np.shape(trial_df)[1],'ampshift',ampshift)\n",
    "    \n",
    "trialmat = []\n",
    "for a in np.unique(trial_df['ampshift']):\n",
    "    theseT = trial_df[trial_df['ampshift']==a].time.values - 0.005\n",
    "    xtime, R = expt.get_sweepsmat('lowgain',theseT,sweepdur)\n",
    "    R = np.asarray([sweep-sweep[0] for sweep in R.T]).T\n",
    "    R = np.asarray([signal.medfilt(sweep,filtwin) for sweep in R.T]).T\n",
    "    trialmat.append(np.mean(R,1))\n",
    "trialmat = np.asarray(trialmat).T \n",
    "\n",
    "stim_ampshift = np.unique(trial_df['ampshift'])\n",
    "restrict_inds = stim_ampshift>=-40\n",
    "\n",
    "\n",
    "\n",
    "Ramp_uc = []\n",
    "for sweep in trialmat[r_onset:,:].T:\n",
    "    Ramp_uc.append(np.max(sweep))\n",
    "Ramp_uc = np.asarray(Ramp_uc)\n",
    "\n",
    "#get CD offset pre-coupled stim\n",
    "bout_df = expt.filter_marker_df_time(marker_df,bout_c)\n",
    "trial_df = expt.filter_marker_df_code(bout_df,['T'])\n",
    "stim_t = trial_df.time.values\n",
    "trial_df = expt.filter_marker_df_code(bout_df,['B'])\n",
    "cd_t = trial_df.time.values\n",
    "cd_offset = np.mean([s-np.max(cd_t[cd_t<s]) for s in stim_t])\n",
    "# offset = np.mean(stim_t-cd_t)\n",
    "\n",
    "#get CD response\n",
    "bout_df = expt.filter_marker_df_time(marker_df,bout_uc)\n",
    "\n",
    "trial_df = expt.filter_marker_df_code(bout_df,['U'])\n",
    "stim_t = trial_df.time.values\n",
    "trial_df = expt.filter_marker_df_code(bout_df,['B'])\n",
    "cd_t_all = trial_df.time.values\n",
    "cd_t = np.asarray([np.max(cd_t_all[cd_t_all<s]) for s in stim_t]) - (0.005 - cd_offset)\n",
    "\n",
    "xtime, R = expt.get_sweepsmat('lowgain',cd_t,sweepdur)\n",
    "cd_R = np.mean(R,1)-np.mean(R,1)[0]\n",
    "\n",
    "\n",
    "Ramp_p = []\n",
    "cd_restrict = cd_R[r_onset:]\n",
    "for sweep in trialmat[r_onset:,:].T:\n",
    "    Ramp_p.append(np.max(sweep+cd_restrict))\n",
    "Ramp_p = np.asarray(Ramp_p)\n",
    "\n",
    "\n",
    "bout_df = expt.filter_marker_df_time(marker_df,bout_c)\n",
    "trial_df = expt.filter_marker_df_code(bout_df,['T'])\n",
    "stim_t = trial_df.time.values\n",
    "\n",
    "eventDur = 0.001\n",
    "xtime,event_sweeps = expt.get_sweepsmat('SIU',trial_df['time'].values,eventDur)\n",
    "event_Amp = np.asarray([np.max(sweep) for sweep in event_sweeps.T])\n",
    "\n",
    "base_df = expt.filter_marker_df_code(bout_df,['C'])\n",
    "xtime,event_sweeps = expt.get_sweepsmat('SIU',base_df['time'].values,eventDur)\n",
    "event_0_Amp = np.median(np.asarray([np.max(sweep) for sweep in event_sweeps.T]))\n",
    "\n",
    "ampshift = np.asarray([np.round(((A/event_0_Amp)*100)-100) for A in event_Amp]).reshape(-1, 1)\n",
    "trial_df.insert(np.shape(trial_df)[1],'ampshift',ampshift)\n",
    "    \n",
    "trialmat = []\n",
    "for a in np.unique(trial_df['ampshift']):\n",
    "    theseT = trial_df[trial_df['ampshift']==a].time.values - 0.005\n",
    "    xtime, R = expt.get_sweepsmat('lowgain',theseT,sweepdur)\n",
    "    R = np.asarray([sweep-sweep[0] for sweep in R.T]).T\n",
    "    R = np.asarray([signal.medfilt(sweep,filtwin) for sweep in R.T]).T\n",
    "    trialmat.append(np.mean(R,1))\n",
    "trialmat = np.asarray(trialmat).T \n",
    "\n",
    "\n",
    "\n",
    "Ramp_c = []\n",
    "for sweep in trialmat[r_onset:,:].T:\n",
    "    Ramp_c.append(np.max(sweep))\n",
    "Ramp_c = np.asarray(Ramp_c)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 86.4x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "hfig,ax = create_fig()\n",
    "colinds = plt.cm.plasma(np.linspace(0.2,0.85,11))\n",
    "colinds = array([c for c in reversed(colinds)])\n",
    "trialmat = trialmat[:,restrict_inds]\n",
    "trialmat = array([sweep for sweep in reversed(trialmat.T)]).T\n",
    "for i,sweep in enumerate(trialmat.T):\n",
    "    ax.plot(xtime-0.5,sweep,color = colinds[i],lw = 0.5);\n",
    "ax.set_xlim(0,50)\n",
    "ax.set_ylim(-5,25)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "# ax.vlines(5,0,5)\n",
    "figsave(figure_folder,'Fig4_outputcellExample2_uncoupled')\n",
    "\n",
    "hfig,ax = create_fig()\n",
    "ax.plot(xtime-0.5,cd_R,color = 'black',lw = 0.5);\n",
    "ax.set_xlim(0,50)\n",
    "ax.set_ylim(-5,25)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "# ax.vlines((0.005 - cd_offset)*1000,0,3)\n",
    "figsave(figure_folder,'Fig4_outputcellExample2_cd')\n",
    "\n",
    "hfig,ax = create_fig()\n",
    "# colinds = plt.cm.plasma(np.linspace(0.2,0.85,11))\n",
    "# colinds = array([c for c in reversed(colinds)])\n",
    "# trialmat = trialmat[:,restrict_inds]\n",
    "# trialmat = array([sweep for sweep in reversed(trialmat.T)]).T\n",
    "for i,sweep in enumerate(trialmat.T):\n",
    "    ax.plot(xtime-0.5,sweep+cd_R,color = colinds[i],lw = 0.5);\n",
    "ax.set_xlim(0,50)\n",
    "ax.set_ylim(-5,25)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "# ax.vlines(5,0,5)\n",
    "figsave(figure_folder,'Fig4_outputcellExample2_predicted')\n",
    "\n",
    "\n",
    "hfig,ax = create_fig()\n",
    "trialmat = trialmat[:,restrict_inds]\n",
    "trialmat = array([sweep for sweep in reversed(trialmat.T)]).T\n",
    "for i,sweep in enumerate(trialmat.T):\n",
    "    ax.plot(xtime-0.5,sweep,color = colinds[i],lw = 0.5);\n",
    "    \n",
    "ax.vlines(4.5,10,15)\n",
    "ax.vlines(0.5,10,15)\n",
    "sns.despine(hfig)\n",
    "ax.set_xticks(np.arange(0,55,10))\n",
    "plt.xlim(0,50)\n",
    "ax.set_ylim(-5,25)\n",
    "ax.set_yticks([10,15])\n",
    "ax.set_frame_on(True)\n",
    "yax = ax.spines[\"left\"]\n",
    "yax.set_visible(False)\n",
    "ax.set_xlabel('ms');\n",
    "\n",
    "figsave(figure_folder,'Fig4_outputcellExample2_coupled')\n",
    "\n",
    "hfig,ax = create_fig_tuning()\n",
    "plt.scatter(stim_ampshift[restrict_inds],[a for a in reversed(Ramp_uc)],\n",
    "            color = 'black',s=10)\n",
    "# plt.scatter(stim_ampshift[restrict_inds],[a for a in reversed(Ramp_p)],\n",
    "#             color = 'black',s=10, marker = '*')\n",
    "plt.plot(stim_ampshift[restrict_inds],[a for a in reversed(Ramp_p)],\n",
    "            color = 'gray',linestyle='--')\n",
    "plt.scatter(stim_ampshift[restrict_inds],[a for a in reversed(Ramp_c)],\n",
    "            color = 'red',s=10)\n",
    "ax.set_ylabel('Peak response (mV)')\n",
    "ax.set_xlabel('Stimulus amplitude \\n (% change)',linespacing=0.75)\n",
    "ax.set_xlim(-47,47)\n",
    "xticks([-40,-20,0,20,40]);\n",
    "sns.despine(hfig)\n",
    "figsave(figure_folder,'Fig4_outputcellExample2_scatter')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sncn19fW1aeuqVatITk6mqKio17L28Bjhbzfc7uNvV1wi/Llz51ixYgVr1qwhKysLk8nEmjVrMJvN5OXlOVxPUVERX3311SBa2pE+fv78efLy8mhoaGD79u2y5VetWtWvdlwyuH7xxRcsWrSI6dOnU1JSQktLCzU1NXz22WfodDoaGhpYvXo10dHRNDY2EhkZSUBAACEhIYSFhREbG8uf//xnadKyatUqtm7dyvbt20lNTWXv3r34+PgQERHBU089BXS8Sum1115DoVAQGxtLREQE+/btIyQkBJPJxPDhw5kyZQp6vZ4ffviBtra2biunOp2OoKAgysrK+Pe//83Pf/5zyY709HRycnKkSEyv1/P3v/8dQRBISEhg9uzZvWrikh6/cOFCysvL2bRpExUVFQQGBjJx4sRuL+aaOHEiGRkZiKJIRkYGdXV1Dtd//vx5Jk2axIwZM6TvCYJASkoK0dHR0l9JXFwcS5cuZeTIkbz88stotVqgI0zdunWrdD9+J/Hx8cTFxUkXZXTy7rvvkpmZyXPPdaQJFhQU4O/vT3BwsMMZdy4R/v3332fJkiVkZmYSERFh8w07fn5+eHl5MWzYMKDj9La3tzdWq5XW1tZuZTuP5rS2tmI2m1m5ciUhISFs27ZNKqPVavnmm2+YNm0anfGDSqVCEASGDRuGIAiYzWag49iRKIp2jx3dbIfVauXGjRtSvRaLhZSUFJYuXcq9997rkCYucTUPPPAAWVlZ+Pv7YzKZyMrK4rvvvpOWm+0xdepUdu7cSVlZWbfjOeHh4ezcuROdToePj48U/3d9uUBISAhHjhzhyJEjtLW1SSLbYt++fZhMJhYtWkRpaan0/aCgIL799luSkpLYu3evZEdaWhpbtmzhZz/7Gd7e3jzxxBPk5OTg5+fH008/7ZAmt304mZuby7x584iKinJpux4fTu7evRuj0ciWLVt49913Afjggw+Q6y/Ojn727t2LyWRyWn3g4T1er9fz+eefExoaSlhYGIcPH+aJJ56gpaWFqVOndiu7a9cu9Ho9gYGB3HPPPXz55ZcoFArmzJnDhAkT2LFjh+SLm5qa0Gq1BAQE8MADDzBhwgT27NlDUFAQP/zwA88//3y3KCUsLAydTkdycrLTfjeP7vGlpaVER0czc+ZMjh49SlRUFJWVldTV1bF582auXeu42am9vR29Xs/GjRul+42TkpJYv349Go2G4cOHs2DBAsaNG8fx48eBjuWBjIwMiouLOXDgACtWrJD8881Ryv3338+3337r1N/No4Vvbm7G398ff39/Vq9ezf33388vfvEL6uvrefTRR6XoqPPuS+h4rwl0pJj7+PhgMpk4ePAger2eqVOnSi7Kz89PilZMJhMKhUK6I//mKEUQBOn6W2fh0Sd5R40a1S2UPH/+vPSWzfz8fP70pz8BHSIHBweTnZ1NSEgIarW6Wz1qtRqNRsPly5c5d+5cj4F0/vz5bNq0SQo3bUUpnWGu0+jzCr4LaWhoEPfs2TPo7dTW1oqtra3ipUuXxA0bNvR4furUKbGoqMipbXp0j1er1ZjNZqxWq3ST92BgtVrZuHEjVquVP/7xjz2el5SUOP09hx4d1QxlPHpwHcrcEd5N3BHeTdwR3k3cEd5N3BHeTfw/E2XhNCWjcHsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 86.4x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hfig,ax = create_fig_tuning()\n",
    "plt.scatter(stim_ampshift[restrict_inds],[a for a in Ramp_uc],\n",
    "            color = sns.xkcd_rgb['icky green'],s=10,marker = 'o')\n",
    "# plt.scatter(stim_ampshift[restrict_inds],[a for a in reversed(Ramp_p)],\n",
    "#             color = 'black',s=10, marker = '*')\n",
    "plt.plot(stim_ampshift[restrict_inds],[a for a in Ramp_p],\n",
    "            color = 'gray',linestyle='--')\n",
    "plt.scatter(stim_ampshift[restrict_inds],[a for a in Ramp_c],\n",
    "            edgecolors = sns.xkcd_rgb['icky green'],s=10,marker = 's',color='white')\n",
    "ax.set_ylabel('Mean Peak Response \\n (mV)',linespacing=0.9)\n",
    "ax.set_xlabel('Stimulus amplitude \\n (% change)',linespacing=0.9)\n",
    "ax.set_xlim(-47,47)\n",
    "xticks([-40,-20,0,20,40]);\n",
    "sns.despine(hfig)\n",
    "figsave(figure_folder,'Fig3_OutputE_Example1_scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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    "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": "263.344px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}