PSP_properties.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import math\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.style\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "from scipy import stats, ndimage, misc\n",
    "import csv\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "import opto_util as opto\n",
    "mpl.style.use('classic')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams.update({\n",
    "    'font.sans-serif': 'arial',\n",
    "    'font.family': 'sans-serif'\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/travish/opt/miniconda3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3441: DtypeWarning: Columns (57) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    }
   ],
   "source": [
    "full_df=pd.read_csv('data/full_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "standard_df=full_df[full_df.internal=='standard']\n",
    "standard_df=standard_df[standard_df.notes.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "RorbAi167=standard_df[(standard_df.presynapticCre=='Rorb') & (standard_df.presynapticEffector=='Ai167')]\n",
    "RorbKv=standard_df[(standard_df.presynapticCre=='Rorb') & (standard_df.presynapticEffector=='ChrimsonR_kv_eyfp_AAV')]\n",
    "SstAi167=standard_df[(standard_df.presynapticCre=='Sst') & (standard_df.presynapticEffector=='Ai167')]\n",
    "SstKv=standard_df[(standard_df.presynapticCre=='Sst') & (standard_df.presynapticEffector=='ChrimsonR_kv_eyfp_AAV')]\n",
    "PvalbKv=standard_df[(standard_df.presynapticCre=='Pvalb') & (standard_df.presynapticEffector=='ChrimsonR_kv_eyfp_AAV')]\n",
    "PenkKv=standard_df[(standard_df.presynapticCre=='Penk') & (standard_df.presynapticEffector=='ChrimsonR_kv_eyfp_AAV')]\n",
    "Tlx3Kv=standard_df[(standard_df.presynapticCre=='Tlx3') & (standard_df.presynapticEffector=='ChrimsonR_kv_eyfp_AAV')]\n",
    "Scnn1aAi167=standard_df[(standard_df.presynapticCre=='Scnn1a') & (standard_df.presynapticEffector=='Ai167')]\n",
    "\n",
    "Sst=standard_df[standard_df.presynapticCre=='Sst']\n",
    "Ntsr1=standard_df[standard_df.presynapticCre=='Ntsr1']\n",
    "Rorb=standard_df[standard_df.presynapticCre=='Rorb']\n",
    "Scnn1a=standard_df[standard_df.presynapticCre=='Scnn1a']\n",
    "Sst=standard_df[standard_df.presynapticCre=='Sst']\n",
    "\n",
    "\n",
    "exc_L23_pyr=standard_df[(standard_df.post_class=='pyramidal') & (standard_df.post_layer==2)]\n",
    "exc_L23_pyr=exc_L23_pyr[exc_L23_pyr.presynapticCre.isin(['Penk', 'Scnn1a', 'Rorb', 'Tlx3', 'Ntsr1'])]\n",
    "exc_L23_fsi=standard_df[(standard_df.Kcluster==1) & (standard_df.post_layer==2)]\n",
    "exc_L23_fsi=exc_L23_fsi[exc_L23_fsi.presynapticCre.isin(['Penk', 'Scnn1a', 'Rorb', 'Tlx3', 'Ntsr1'])]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "Penk_L23pyr=PenkKv[(PenkKv.post_class=='pyramidal') & (PenkKv.post_layer==2)]\n",
    "Penk_L23int=PenkKv[(PenkKv.post_class=='interneuron') & (PenkKv.post_layer==2)]\n",
    "Penk_L23fsi=PenkKv[(PenkKv.Kcluster==1) & (PenkKv.post_layer==2)]\n",
    "Penk_L23vip=Penk_L23int[Penk_L23int.Kcluster==2]\n",
    "Rorb_L23pyr=Rorb[(Rorb.post_class=='pyramidal') & (Rorb.post_layer==2)]\n",
    "Rorb_L23fsi=Rorb[(Rorb.Kcluster==1) & (Rorb.post_layer==2)]\n",
    "Tlx3_L23pyr=Tlx3Kv[(Tlx3Kv.post_class=='pyramidal') & (Tlx3Kv.post_layer==2)]\n",
    "Tlx3_L23fsi=Tlx3Kv[(Tlx3Kv.Kcluster==1) & (Tlx3Kv.post_layer==2)]\n",
    "Ntsr1_L23pyr=Ntsr1[(Ntsr1.post_class=='pyramidal') & (Ntsr1.post_layer==2)]\n",
    "Scnn1a_L23pyr=Scnn1a[(Scnn1a.post_class=='pyramidal') & (Scnn1a.post_layer==2)]\n",
    "Sst_L23pyr=Sst[(Sst.post_class=='pyramidal') & (Sst.post_layer==2)]\n",
    "Sst_L5pyr=Sst[(Sst.post_class=='pyramidal') & (Sst.post_layer==5)]\n",
    "\n",
    "\n",
    "Sst_L23fsi=Sst[(Sst.Kcluster==1) & (Sst.post_layer==2)]\n",
    "Pvalb_L23pyr=PvalbKv[(PvalbKv.post_class=='pyramidal') & (PvalbKv.post_layer==2)]\n",
    "Pvalb_Pvalb=PvalbKv[(PvalbKv.Kcluster==1) & (PvalbKv.post_layer==2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dress_up(ax,tick_n=4):\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.yaxis.set_ticks_position('left')\n",
    "    ax.xaxis.set_ticks_position('bottom')\n",
    "    ax.yaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "    ax.xaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "    ax.yaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "    ax.yaxis.set_major_locator(plt.MaxNLocator(tick_n))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s=15\n",
    "tick_size=4\n",
    "fs=10\n",
    "Pvalb_Pvalb_sub=Pvalb_Pvalb[Pvalb_Pvalb.xyz_dist>100] ##exclude Pvalb to Pvalb connections within 100 microns to avoid overlap of direct stim artifact with IPSP\n",
    "df_list=[Pvalb_L23pyr,Sst_L23pyr,Pvalb_Pvalb_sub,Sst_L23fsi]\n",
    "tick_labels=['Pvalb to PC', 'Sst to PC', 'Pvalb to Pvalb','Sst to FSI']\n",
    "markers=['^','^','o','o',]\n",
    "colors=['purple',  'green', 'purple','green']\n",
    "colors2=['purple',  'green','purple', 'green']\n",
    "fig, ax = plt.subplots()\n",
    "master_dists=[]\n",
    "\n",
    "for i in range(len(df_list)):\n",
    "    df=df_list[i]\n",
    "    df=df[df.cnx=='inhibitory']\n",
    "    df=df.dropna(subset=['peakOfAvg'])\n",
    "    y=np.array(df.peakOfAvg*-1)\n",
    "    description=tick_labels[i]\n",
    "\n",
    "    y=np.log10(y)\n",
    "    x=np.random.normal(i,0.1, size=len(y))\n",
    "    mark=markers[i]\n",
    "    color=colors[i]\n",
    "    color2=colors2[i]\n",
    "    master_dists.append(y)\n",
    "    \n",
    "    ax.scatter(i+1,np.median(y),facecolor=color2,marker=mark,s=s*4,edgecolor='white')\n",
    "violin_parts=ax.violinplot(master_dists,showmeans=False, showmedians=False,\n",
    "        showextrema=False,points=1000)\n",
    "i=0\n",
    "for pc in violin_parts['bodies']:\n",
    "    mark=markers[i]\n",
    "    color=colors[i]\n",
    "    pc.set_facecolor(color)\n",
    "    pc.set_edgecolor(color)\n",
    "    \n",
    "    i+=1    \n",
    "\n",
    "    dress_up(ax)\n",
    "plt.xticks(range(1,len(df_list)+1), tick_labels, rotation='vertical')\n",
    "plt.yticks([-2,-1,0,1],['0.01','0.1','1','10'])\n",
    "plt.ylim([-2.2,0.5])\n",
    "plt.ylabel('IPSP amplitude (mV)')\n",
    "fig.set_figheight(1.5)\n",
    "fig.set_figwidth(1.5)\n",
    "fig.autofmt_xdate()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s=15\n",
    "SstAi_L23pyr=SstAi167[(SstAi167.post_layer==2) & (SstAi167.post_class=='pyramidal')]\n",
    "SstKv_L23pyr=SstKv[(SstKv.post_layer==2) & (SstKv.post_class=='pyramidal')]\n",
    "\n",
    "df_list=[SstKv_L23pyr,SstAi_L23pyr,Pvalb_L23pyr]\n",
    "tick_labels=['Sst:AAV','Sst:Ai167','Pvalb:AAV']\n",
    "markers=['^','^','^']\n",
    "colors=['green','magenta', 'purple']\n",
    "colors2=['green','magenta', 'purple']\n",
    "fig, ax = plt.subplots()\n",
    "master_dists=[]\n",
    "for i in range(len(df_list)):\n",
    "    df=df_list[i]\n",
    "    df=df[df.cnx=='inhibitory']\n",
    "    df=df.dropna(subset=['peakOfAvg'])\n",
    "    y=np.array(df.peakOfAvg*-1)\n",
    "    y=np.log10(y)\n",
    "    x=np.random.normal(i,0.1, size=len(y))\n",
    "    mark=markers[i]\n",
    "    color=colors[i]\n",
    "    color2=colors2[i]\n",
    "    master_dists.append(y)\n",
    "    ax.scatter(i+1,np.median(y),facecolor=color2,marker=mark,s=s*4,edgecolor='white')\n",
    "\n",
    "violin_parts=ax.violinplot(master_dists,showmeans=False, showmedians=False,\n",
    "        showextrema=False,points=1000)\n",
    "i=0\n",
    "for pc in violin_parts['bodies']:\n",
    "    mark=markers[i]\n",
    "    color=colors[i]\n",
    "    pc.set_facecolor(color)\n",
    "    pc.set_edgecolor(color)\n",
    "    \n",
    "    i+=1    \n",
    "\n",
    "dress_up(ax)\n",
    "\n",
    "plt.xticks(range(1,len(df_list)+1), tick_labels, rotation='vertical')\n",
    "plt.yticks([-2,-1,0,1],['0.01','0.1','1','10'])\n",
    "\n",
    "plt.ylim([-2.2,0.5])\n",
    "plt.ylabel('IPSP amplitude (mV)')\n",
    "fig.set_figheight(1.5)\n",
    "fig.set_figwidth(1.5)\n",
    "fig.autofmt_xdate()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAIYAAACFCAYAAACe7YA/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAXDUlEQVR4nO2deZxcVZXHv7+q6uo96aydQBIIQQxBZJEICUZAAUEFHARHBEVHZXQQPm64jSAIoyiKC3wQRZEBRAZBHAVk3AiExaAQiRLCYvalk17Snd6rq+rMH/dVd3V1dbpredUL75dPfdJvqXfuq/d759x7zrnnyswIECATobFuQIDxiYAYAbIiIEaArAiIESArAmIEyIqAGAGywjdiSApLulXSE5Iek7Qo4/h5klZLelLSzZICko4j+PkwzgAws+OBK4DrUwckVQLXACeZ2XJgKvBOH9sSIEdE/Lqwmf1K0gPe5gHArrTDvcByM+tKa0ePX20JkDt8IwaAmcUl/TfwL8A5afuTeESRdAlQA/zez7YEyA0qhUtc0hxgNbDEzDq9fSHgm8AhwHvTtEf6904ETkzb9ZXAhV9UaNgDfv3Qkt4PzDOzr0uaAjwHHGpmPd7xW3Am5VJPg4zmmhYQo6gYE2JUAz8F5gBlwLVANc5s/NX7rAJSDfiemd0/wjUDYhQXpSeGHwiIUXQMS4zAdxAgKwJiBMiKgBgBsiIgRoCsCIgRICsCYgTIioAYAbIiIEaArAiIESArAmIEyIqAGAGywhdiSCqTdIekVZKelnTmMOf9SNK1frQhQGHwS2NcADSb2QrgdODGzBMk/TtwuE/yAxQIvzK4fgHcm7YdTz8oaRlwHPBDYLFPbQhQAHzRGGbWYWbtkmpxBPly6pikucCVwMV+yB5zNAK3AlvHuiGFwbecT0nzgfuBm8zsrrRD5wIzgYdwSTxVktab2W1ZrnEig1P7xj9iwG6gb6wbUhh8SdSRVA+sBD5hZn/cx3kfBBab2RdGed3xn6izA7gDR/+DxrgtI6PkiTpfAqYBl0ta6X3Ol3SRT/LGD0JpnwmMILWv2NgN3ImbLLFgjNsyMoLUvpIhBISZ8L/sBG/+OITSPhMYATH8wAQnBQTEKD6MgZkyExgBMYqNJM7PmxjrhhSGgBjFRh/OyRUf6cTxjYAYxUYMNyO3e6wbUhgmPTH6En3c9fe7aOhoKI3ALtxwta004vzCpCdGPBmnraeNnniJ6rK0ArUExBjvkNzYUaUaQ7YAU4A9pRHnF/ZJDElflTT+Hbv7gBAhhfoJ4juaccTYy4QemYykMVqA/5X0kKSzcq2sJ+lYSSuz7F/qpf09LuleSRW5XDcXWOpfKWIsMVwlsai33em/SL+wzwdtZt81s6NwVfdOBf4u6erRaBFJnwN+DFRk7BdwC/AhM3sT8DCueJsvSCQTxJNxElaC17djhO0JhFFpADP7q5ldDByNc/i+OIqv/RM4O8v+Q3AK95OSHgWmm9lorpcXYokYPfGe0nQ+X23EkDRf0heBPwOLgHeM9B0zu4/seUwzgeXATcDJwFslvXXULc4R3fFuYokYrT2tfokYQBsDcZIoLs3PZ/R19bHpkU0kYsXViPtM7ZP0IeD9wCzgJ8DJZtZcoMxm4BUzW+fJeBh4AzAk06sYqX3tve1EQ1GaupoKuczo0MiA4aykJMSIdcZofqWZucfMJRwNF+26I+V8ngT8AbjOzPokrZB0mJndXIDMDUCNpIPN7BVgBY50Q2BmK3EpggBI+kquwho6GphWOY3dnbsxM39HJ004QoAjSDMuoOb3gMiHwN1IpmQDrl9R5m1vBU6VdHmugiS9T9JFZhYDPgzcJekvwFYzezDX640WOzt2UhutJZ6I0xHz0egncL6LlMaI4Ayp365xw5cR10ga43TguFQ+nZltkvSvwJPA1SNd3Mw24eaPkJ4pbmZ/At6YZ5tHjb5EH609rexXsx8I9vbupba81h9he8muHdqAKn9EAiQTSSxuJBOjKpU6aoykMTozkyzNrA9oL2orfEJHrANswPvZ1uujn7qN7OrcZw9osi9JvDdOsq+0xOiSNCgJ3tueEKko3fHuflJEQ1F/RyY7gPKMfVX4PvGor6ePvq4+4j3FjfOPZEo+D/xK0h9x/Y0FwNuAC4vaCp/Ql+jDPA5HwhG6+oaUKy8etuLqHqejFtiOS97xKSrVu7eXZCJJX3dxZziN5Pl8HjdqWIO77WeB481sTVFb4RP6kgM/VlhheuO9/gjqxgUPMvsSEZyb3EcL1ru3l7KqMnrbi3tvI05RNLM24PaiSi0ReuO9/VHVSCjin/dzJH/Fbtz0Kx8Qa49RVl1G797iEmNSh92bu5spDzvDHw27PoYvwbQtDO1fpFCNCw74hFhHjGhVlL6OEpqSiQwzY3PrZmqiNYDTGL64xpPAy0DdMMen4PofseKKBbCkEeuIUVZTRk9bcbXhpCVGQ0cD7bF2yiMDr3I4FOal5peKK6gJF2ovG+Z4CEceHzILYx0xLGlEyiPEOmJFjZdMSmIkLclT256iNjrYmTWrahZrGtawt3dv8YRtYOSeWiVQZD4CdLekuVWVsV0gJiUx1jeuZ2f7Tuoq6gbtj4QiRENRHt/8eHH6GglgHTBjhPPqcP2MIvd992zYQ6TSsTIUDtG6ubVo1550xGjtaWXVllXMqZmT9fis6llsbN3Ii01FSAHZyb7NSAphnDnZUrjIFOI9cVpebqGizgVnKqdX0vh8Y9Fc434uyBvyFtp9yquPcXDG8fMlPSvpL5I+XgyZSUuycuNKysPlRMPR/v3x5GCv4JyaOTy2+bHCTcp6ssdBsjkhpwL/KExcOlo3tZJMJgmF3SMMR8PEe+K0by9OtMJPjfEuoMLMlgFfAL6dcfxbuESd44HPSCp4pP9S00tsb9/OjKoB3R5PxjGzQeSIhqNEQhGe2PpE/ialF2ce6jL2x3EBg0xy1OAWFC1C98bM2Pm3nVROrxy0P1obZddzu4b5Vm7wkxipfE7M7M/AMRnH1+LeowpcTLIgox9LxHhy25PUV9cP2p+wBNesumZIzufMqplsaNmQ/0SkXbgWZ+bGJHBrUGcOEFKlEXbkJy4dXU1d9LT0UFY12IaVTy2nbWtbUbygfhJjCoOdwQlJ6f33fwDPAM8DD5hZayHCtrRuoTfeO2h4Gk/GWb1tNWsa1vD0tqcHaQ1J1ERreG7Xc/kJ3MpANni/QNzqsmuApxmqNaqBTfmJS8eeDXsIlQ19dJJQSLRtKdwH7ycx9uLCSP2yzCwOIOn1uLzRhcCBwGxJ52ZeQNKJkq5MffYlbGPrRqrLBkexEpbgzrV3AnDH2juGaI26ijo2t24mkcxj/N/A0KBZAldmCVyBtszLVlOwP8PMaF7fTMW07DMuyqeW07S+8DRGP4nxBPB2AEnHAX9PO9aGCz11m1mCYaIJZrbSzK5MffYlLNOZldIWDZ3uSTR0NgzRGiGFMIxYIg+3ZAeDRyMpbZF68A0M1RpluLsuwGh2NXUR64oRKc/uPCmrLqNzV2fB5sRPYtwP9Eh6EvgO8Km09L7NuKrAj0t6HNeFu60QYTXRGnoTAz9GurZIIVNrJC2J0KARzKgRYvADTtcW/QIZrDWKkP/ZtL5piBlJxgeGqJJQWLS80lKQHN8KwHrLcn8sY/f6tOM3A4UkFQ/CommLeKX5FaZVTCOejLOucR1Jksyunt1/TpIk6xrXcfjsw4mEIuzp3sPCaQsJh/LIrp6K0xpRnFZYh/NVzE47J+ntP5yBEHwteZOjd28vu/+xm5q5NQMi4knMjGQ8SSjiCFM1s4odz+xg1qGziFTk94h9I0apsWDqAqrLq+nq6yIcCrN4xmJuOO2GrOcmLEHIQnT2dXLknCPzEzgf+AvuQSdwFdGzi3PHI7he18HDnDMKbFu9jXA03O+7ALCEseprq1jxpRX9TzMcDWMJY+ezO5m/fH5esiaN57MsXMYJC06gsauRaChKZVnlsJ/ycDm7OnfxutmvG6RRcsJ8BvoP5bh4yHCfVNenh7yrBbfvbKf5pWaqZg141JLxJNtWb6NhTQPbnt42yKRU11fT8FwD3Xvyi59MGmIAHDjtQJbMWtLf4RwOHbEOKiIVHDvv2PyFzcDNqRuto7EHNyqZm5+4hr81EK2NDpoXYwlj7Z1rAVh7x1osMdDpCYVDhMpC7H5+d17yJhUxAJbPX05VWRXtvdmfWDwZp6W7hVMXnUpFpIBJ9gKW4gqljAZNOBdfHt0ZM2Pv1r1UTB1ob0pbdDa4KfWdDZ1DtEZFXQVtm/PzaUw6YlREKjjloFPY07Mnq3+ioaOB4+YdN2yQLSccAExnZK3RgzMph+QvSmFhyQGNkK4tUsjUGpa0Qf2RXDDpiAFQX1PP0v2WDjEpbT1tzKiawRFzjiiOoBBuevZIc0d2e+eNFIUdBpKY/prpdDW7LPdkPEnjukZIQvXs6v4PSWhc19ivNXr29DDjkJFyArJj0oxKMnHU3KN4oekFuvq6qCqrwsxo623jnEXnEAkV8bbnA/NwCcF1WY534rTKosLEzDliDk3rmkj0JSAJMxbP4LQbTst6riWMeDxOKBJi5qEz85I3KTUGuFHKsvnLaO5yk/NbultYNH0R9TX1I3wzRwgXH47iTErmJwm8mbz6FumomFrBvGXz6NjZQbg8TFll2bCfUDRE565ODjzhQMoq81NTk1ZjACysW0h5pJy+RB9d8S6OqC+SCcnETNzygD6j/vB62ra00dXYNWjYmomOhg5mLp7JtEX5ZzJMWo0BTmu8ZsZr2Ni6kWgoWnxtUWIoJBaetBBL2rAzz3rbeymrLGPBmxYUVPJhUmsMgCWzliDE7OrZhHKrLTcuEa2JsvDkhbz8wMtMmT8FhQYefjKepGdPD0vevSRvV3gKY7bCkVcB8CbgCFw+1Ee8Qir7+s74X+GoRNj06CYa1jQQqRogQLwrzoIVC9jvDfuN9jLDqpSx1Bjvwkv988Ly3wbOGsP2TCjMXzafyhmVg72dkRAzX5vfKCQTY6kxrgeeNrO7ve3tZrb/CN8JNEZxMS7XRBsp9S/AGGIsH8SwqX8pZKvaV7LSz68OmJll/0HNbEw+wLuB27y/jwN+66OsK0t8bxNe3lhqjPuBU7zUPwEfGsO2BMjAmBHDsqf+BRgnmPgen9FhZSAvN0yoJbwDlA6vFo0RIEcExAiQFQExJiBUAmfOpCGGV4/jJEk+FQsfIu+9kt5WClmevBpJV0mqsxJ0DCcNMXA529ebma91ziW9WdJvgLso7SKaC3GJhCXRGJMmNmFmqyVtk7TCzFb5IUPS94ElwH8CL+Dz7ydpupmlJqFuB44Cqs2sVT5HFCecxki9LZJukPRRSbO97Rm4iptFXVdIUpmkk7zN683sZDNbDbwOb257sd9gSVXeoj3flzQVwCPII8AJ3rav5mTCEQP3QAD+hHt7fyApYm5Jrrm4VZmK8rAknYErZnCmpE8yuLzaBrwAX7EekqSDJS0zsy7gRtyC4DdJOtw7JYYrCZdKdPINE4YYkqKSbgTulnQm8JCZfQqXoP99SYtxy3bOlRQq9GF5P/zZwAeA63FFkmak2oIjSbE1xjeA2yTd6BH948BzwOckHYsj4znQH1LwDROGGOaWzKrAlSc5EviRd+gi3KoIVwHn4ZbSSkrKOWFf0kxJs7zN2TjSnQLcChwG/EHSEV5bEsB78r+jIbKFWznqMuAASVcAx5jZN4GfA1/DlXiL5XNvOaOU4eEcQ8mRtL+j3v/nAN/z/v498FPc0lwAb8EVfHusAJnfAX7t/V2NK4XyU+Agb98XgJWpNgEbgf3zlLUgy75vAp9Pa8sW4Ehv+/XAr4GfleL3H5caQ9LZuGo774F+bZFCs6TzcNqjG7jXq9/1KHAGjiC5yNo/7f9lwAmSTjWzTuBB3IzTed7pPwA2SJrqtekwM9uex/19BHhE0rkavHz5ncBySTfhNNZDwGWS7jaztcDZZnZ+rvLywXgdrrbgqm9fJmkK8Bsz2wVsw62d8gPgPDPbJukF3IOrMFfCaVTwVPdJuOlC9+BSDb+EqwV2DfA7M/u5pKXA+yUdiktWftbcGi54ncR8sB1H6rcCx0q63sx24MrJ7g/cZ2a3e+08BGfOwM1rKw3G2mSkqVGl/X0BcC1uqa2rcA9uhnfsFuDwtHPDOcqJMBBVrsVNHjza2673/n8UuNT7ez/cg7kZOL8Y94gzC1fhZrVeies3HYFLWHoJmJNq61g9jzE1JXKoAi/5cCAZ+CEGlt+M4MzDtyRditMOsdRIwMxGXYsxZaKAc73vtuO0wCclvd7TSgCfAy6WVGtmO8zs92b2MTP7WUE3PDDteQpudeo6HPFiuP7Fp3GZbUu99hV3BbwcMNZ9jKPw1KSkw4CLJE3HvTm7gXtxJdAOwjl3zsJ1+DaY90rliHQTdZGkalySy4t4pSfBeVFxBWrfm99tDYWko4Hvetd/HKcNf4Hr7K4ArsAR9n4z+02x5OaNMTYZK3Blpb+Ie4NSKjSKWzr8HenfA2oLlJduor6KM1G1OEfVtcDpaeeW5Xl/bwJ+mLb9DqAu45xK4DrgLd52KLOtY/0pqcaQFM540xO4DPFFZvaMmTV4MYAYrvDyCu97ModRBchGaaLCuM7ft3E197bgOoJl3vdyWmRM0gGSvoPrL5wu6QJJtwOXAl+T9NHUuWbW7cl8Q2pXqq25yPQTvhNDUljSIknVZpaQVCHpcknfwNUSfyfQIClVBSQs6c24at0PQl4/2GhN1EJgFW6t+SbgmlwJkYYW3INegSPDdcAvzOxtwAPA0ZIOlTRd0vk407E6z/vzHX5HB8/DTQtoAJbIrQv/LeBvOAfVMlxp6aOB4yW14zpkfcAt3ps1Wlnp0cZq4OOSlgC/A35pZi2eK3smcJWZPeide4ekX5s3BM3x/sLmdX7NrF3StcDlZnaSF1up8059BjdXt9Xbjts4X7vWN2JI+iAuEniJmb3oRT/3wz2Yh4H/wJmSmbi39hLg34DLbCDUPFpZ/Q/IQ8pEbTOzZ7xzZGYxSSkT9WCaicorryIl09MAL+FGPG+XdDHwGeAWSY/i+h2LgKR3b/+Tj7xSwrcscbnVAmRmX5F0JE5zvIB7i3bhyHAUcIaZfcAzNZ05XD+MW7mgwcw6PQ/iZbh4whW4Id9pwONm9rDXz1iOi7OssTxzNtI1k6SjgP/CEXEznkse+DFulHMjrojjr4DbzWx9tmuOR/hJjDfiev2P4Gzvn3F1MC7AeS534qYp3mqely+Haw8yUUC6iXoCV7P3CVx0chZOQ9XhTNSqXEzUMPLrgHpc9a0NwFO4gtHC9S3OBg4FLgaONbM/FiJvLODrvBJJ9bhy7HvMrNHb9xOcZ7EeuMdycGN73/8gzkRdm2GibgI+y4CJegi3ONUluF5/XiYK14ndaGYvevuqcSOZu3BD3QNwpmkVbimvrThyHG9m495kDAe/RyUxnG0tl7RY0i9xoey7zey6XEnh4UBgi0eKI3Fm43jcCGYJcDXwW+BMr4N3iZl9OA9SvBun7d4D3Cfp05IO8sxdI3CK14F9LU4zrMH5X/4J7JrIpAD/g2gxXCfwE8Ac4LtmdkeB13wIuEfSAgZM1GEMmKgaPBMFkEu/BUAuVfA+XD/oEjN7TtLVwJdxI6cbgP9jwAcxF2equnHxlTzX0hpf8JUY3kP5iqT5uE5iwSvTm9nTctlMmSaqCrceSj1wYZ7aCAZ8Hfd4pKjH9U/eiQuFn4kj+yPe+RcC883Mh7WYxw4Tcu6q3FKcqeW0anDZTduAz9rg3I18ri3caOZCXMfySOBJM7vGO16P00bTgLNSxJxsmKjEqMZFQI+heCYq/fq1wNdxMZRjU+ZILuk4Lmke0GEFrvw4njEhiZFCMU1UlmsvxfWNvm5m6yWV+SFnvGJCE8NPeO7zi4GlZva+sW5PqTFeU/vGHJ77/AFgR1pS0KvmLQo0RoCsGOsMrgDjFAExAmRFQIwAWREQI0BWBMQIkBUBMQJkRUCMAFnx/wWh1IKGcUFqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s=15\n",
    "SstAi_L23pyr=SstAi167[(SstAi167.post_layer==2) & (SstAi167.post_class=='pyramidal')]\n",
    "SstKv_L23pyr=SstKv[(SstKv.post_layer==2) & (SstKv.post_class=='pyramidal')]\n",
    "\n",
    "df_list=[SstKv_L23pyr,SstAi_L23pyr,Pvalb_L23pyr]\n",
    "tick_labels=['Sst:AAV','Sst:Ai167','Pvalb:AAV']\n",
    "markers=['^','^','^']\n",
    "colors=['green','magenta', 'purple']\n",
    "colors2=['green','magenta', 'purple']\n",
    "fig, ax = plt.subplots()\n",
    "master_dists=[]\n",
    "for i in range(len(df_list)):\n",
    "    df=df_list[i]\n",
    "    df=df[df.cnx=='inhibitory']\n",
    "    df=df.dropna(subset=['peakOfAvg'])\n",
    "    y=np.array(df.CV)\n",
    "\n",
    "    x=np.random.normal(i,0.1, size=len(y))\n",
    "    mark=markers[i]\n",
    "    color=colors[i]\n",
    "    color2=colors2[i]\n",
    "    master_dists.append(y)\n",
    "\n",
    "    ax.scatter(i+1,np.median(y),facecolor=color2,marker=mark,s=s*4,edgecolor='white')\n",
    "violin_parts=ax.violinplot(master_dists,showmeans=False, showmedians=False,\n",
    "        showextrema=False,points=1000)\n",
    "i=0\n",
    "for pc in violin_parts['bodies']:\n",
    "    mark=markers[i]\n",
    "    color=colors[i]\n",
    "    pc.set_facecolor(color)\n",
    "    pc.set_edgecolor(color)\n",
    "    \n",
    "    i+=1    \n",
    "\n",
    "dress_up(ax)\n",
    "\n",
    "plt.xticks(range(1,len(df_list)+1), tick_labels, rotation='vertical')\n",
    "\n",
    "plt.ylabel('CV')\n",
    "fig.set_figheight(1.5)\n",
    "fig.set_figwidth(1.5)\n",
    "fig.autofmt_xdate()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Penk to PC\n",
      "16 connections measured\n",
      "Rorb to PC\n",
      "245 connections measured\n",
      "Tlx3 to PC\n",
      "85 connections measured\n",
      "Penk to FSI\n",
      "28 connections measured\n",
      "Rorb to FSI\n",
      "24 connections measured\n",
      "Tlx3 to FSI\n",
      "9 connections measured\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 162x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s=15\n",
    "fs=10\n",
    "tick_size=4\n",
    "df_list=[Penk_L23pyr,Rorb_L23pyr, Tlx3_L23pyr,Penk_L23fsi,Rorb_L23fsi,  Tlx3_L23fsi]\n",
    "tick_labels=['Penk to PC', 'Rorb to PC', 'Tlx3 to PC','Penk to FSI','Rorb to FSI',  'Tlx3 to FSI']\n",
    "markers=['^','^','^','o','o','o']\n",
    "colors=['magenta',  'blue', 'darkorange', 'magenta',  'blue', 'darkorange',]\n",
    "fig, ax = plt.subplots()\n",
    "master_dists=[]\n",
    "\n",
    "for i in range(len(df_list)):\n",
    "    df=df_list[i]\n",
    "    df=df[df.cnx=='excitatory']\n",
    "    df=df.dropna(subset=['peakOfAvg'])\n",
    "    y=np.array(df.peakOfAvg)\n",
    "    description=tick_labels[i]\n",
    "\n",
    "    print (description)\n",
    "    print (np.shape(y)[0], 'connections measured') \n",
    "    y=np.log10(y)\n",
    "    x=np.random.normal(i,0.1, size=len(y))\n",
    "    master_dists.append(y)\n",
    "    mark=markers[i]\n",
    "    color=colors[i]\n",
    "    quartile1, median, quartile3 = np.percentile(y, [25, 50, 75])\n",
    "\n",
    "    ax.scatter(i+1,np.median(y),facecolor=color,marker=mark,s=s*4,edgecolor='white')\n",
    "\n",
    "violin_parts=ax.violinplot(master_dists,showmeans=False, showmedians=False,\n",
    "        showextrema=False,points=1000)\n",
    "\n",
    "i=0\n",
    "for pc in violin_parts['bodies']:\n",
    "    mark=markers[i]\n",
    "    color=colors[i]\n",
    "    pc.set_facecolor(color)\n",
    "    pc.set_edgecolor(color)\n",
    "    \n",
    "    i+=1\n",
    "plt.xticks(range(1,len(df_list)+1), tick_labels, rotation='vertical')\n",
    "plt.yticks([-3,-2,-1,0,1],['0.001','0.01','0.1','1','10'])\n",
    "\n",
    "plt.ylim([-2,1])\n",
    "dress_up(ax)\n",
    "\n",
    "plt.ylabel('EPSP amplitude (mV)')\n",
    "fig.set_figheight(1.5)\n",
    "fig.set_figwidth(2.25)\n",
    "fig.autofmt_xdate()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s=15\n",
    "fs=10\n",
    "tick_size=4\n",
    "RorbAi_L23pyr=RorbAi167[(RorbAi167.post_layer==2) & (RorbAi167.post_class=='pyramidal')]\n",
    "RorbKv_L23pyr=RorbKv[(RorbKv.post_layer==2) & (RorbKv.post_class=='pyramidal')]\n",
    "df_list=[RorbKv_L23pyr,RorbAi_L23pyr,Penk_L23pyr]\n",
    "tick_labels=['Rorb:AAV','Rorb:Ai167','Penk:AAV' ]\n",
    "markers=['^','^','^']\n",
    "colors=['green','magenta','black']\n",
    "fig, ax = plt.subplots()\n",
    "master_dists=[]\n",
    "for i in range(len(df_list)):\n",
    "    df=df_list[i]\n",
    "    df=df[df.cnx=='excitatory']\n",
    "    df=df.dropna(subset=['peakOfAvg'])\n",
    "    y=np.array(df.peakOfAvg)\n",
    "    y=np.log10(y)\n",
    "    x=np.random.normal(i,0.1, size=len(y))\n",
    "    master_dists.append(y)\n",
    "    mark=markers[i]\n",
    "    color=colors[i]\n",
    "    quartile1, median, quartile3 = np.percentile(y, [25, 50, 75])\n",
    "    ax.scatter(i+1,np.median(y),facecolor=color,marker=mark,s=s*4,edgecolor='white')\n",
    "\n",
    "violin_parts=ax.violinplot(master_dists,showmeans=False, showmedians=False,\n",
    "        showextrema=False,points=1000)\n",
    "\n",
    "i=0\n",
    "for pc in violin_parts['bodies']:\n",
    "    mark=markers[i]\n",
    "    color=colors[i]\n",
    "    pc.set_facecolor(color)\n",
    "    pc.set_edgecolor(color)\n",
    "    i+=1\n",
    "plt.xticks(range(1,len(df_list)+1), tick_labels, rotation='vertical')\n",
    "plt.yticks([-3,-2,-1,0,1],['0.001','0.01','0.1','1','10'])\n",
    "plt.ylim([-2,1])\n",
    "dress_up(ax)\n",
    "plt.ylabel('EPSP amplitude (mV)')\n",
    "fig.set_figheight(1.5)\n",
    "fig.set_figwidth(1.5)\n",
    "fig.autofmt_xdate()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s=15\n",
    "fs=10\n",
    "tick_size=4\n",
    "RorbAi_L23pyr=RorbAi167[(RorbAi167.post_layer==2) & (RorbAi167.post_class=='pyramidal')]\n",
    "RorbKv_L23pyr=RorbKv[(RorbKv.post_layer==2) & (RorbKv.post_class=='pyramidal')]\n",
    "df_list=[RorbKv_L23pyr,RorbAi_L23pyr,Penk_L23pyr]\n",
    "tick_labels=['Rorb:AAV','Rorb:Ai167','Penk:AAV' ]\n",
    "markers=['^','^','^']\n",
    "colors=['green','magenta','black']\n",
    "fig, ax = plt.subplots()\n",
    "master_dists=[]\n",
    "for i in range(len(df_list)):\n",
    "    df=df_list[i]\n",
    "    df=df[df.cnx=='excitatory']\n",
    "    df=df.dropna(subset=['CV'])\n",
    "    y=np.array(df.CV)\n",
    "    x=np.random.normal(i,0.1, size=len(y))\n",
    "    master_dists.append(y)\n",
    "    mark=markers[i]\n",
    "    color=colors[i]\n",
    "    quartile1, median, quartile3 = np.percentile(y, [25, 50, 75])\n",
    "    ax.scatter(i+1,np.median(y),facecolor=color,marker=mark,s=s*4,edgecolor='white')\n",
    "\n",
    "violin_parts=ax.violinplot(master_dists,showmeans=False, showmedians=False,\n",
    "        showextrema=False,points=1000)\n",
    "\n",
    "i=0\n",
    "for pc in violin_parts['bodies']:\n",
    "    mark=markers[i]\n",
    "    color=colors[i]\n",
    "    pc.set_facecolor(color)\n",
    "    pc.set_edgecolor(color)\n",
    "    \n",
    "    i+=1\n",
    "plt.xticks(range(1,len(df_list)+1), tick_labels, rotation='vertical')\n",
    "\n",
    "dress_up(ax)\n",
    "plt.ylabel('CV')\n",
    "fig.set_figheight(1.5)\n",
    "fig.set_figwidth(1.5)\n",
    "fig.autofmt_xdate()\n",
    "#plt.savefig('Rorb_Ai_v_AAV_CV.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s=15\n",
    "fs=10\n",
    "tick_size=4\n",
    "df_list=[RorbKv_L23pyr,RorbAi_L23pyr,Penk_L23pyr]\n",
    "tick_labels=['Rorb:AAV','Rorb:Ai167','Penk:AAV' ]\n",
    "markers=['^','^','^']\n",
    "colors=['green','magenta','black']\n",
    "fig, ax = plt.subplots()\n",
    "master_dists=[]\n",
    "for i in range(len(df_list)):\n",
    "    df=df_list[i]\n",
    "    df=df[df.cnx=='excitatory']\n",
    "    df=df.dropna(subset=['rise2080'])\n",
    "    df=df[df.peakOfAvg>0.1]\n",
    "    y=df.rise2080\n",
    "    master_dists.append(np.array(y))\n",
    "    mark=markers[i]\n",
    "    color=colors[i]\n",
    "    ax.scatter(i+1,np.mean(y),facecolor=color,marker=mark,s=s*4,edgecolor='white')\n",
    "\n",
    "violin_parts=ax.violinplot(master_dists,showmeans=False, showmedians=False,\n",
    "        showextrema=False,points=100)\n",
    "\n",
    "i=0\n",
    "for pc in violin_parts['bodies']:\n",
    "    mark=markers[i]\n",
    "    color=colors[i]\n",
    "    pc.set_facecolor(color)\n",
    "    pc.set_edgecolor(color)\n",
    "    \n",
    "    i+=1\n",
    "    \n",
    "\n",
    "plt.xticks(range(1,len(df_list)+1), tick_labels, rotation='vertical')\n",
    "\n",
    "dress_up(ax)\n",
    "plt.ylabel('20-80% rise time (ms)')\n",
    "fig.set_figheight(1.5)\n",
    "fig.set_figwidth(1.5)\n",
    "fig.autofmt_xdate()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 162x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s=15\n",
    "fs=10\n",
    "tick_size=4\n",
    "df_list=[Penk_L23pyr,Rorb_L23pyr, Tlx3_L23pyr,Penk_L23fsi,Rorb_L23fsi,  Tlx3_L23fsi]\n",
    "tick_labels=['Penk to PC', 'Rorb to PC', 'Tlx3 to PC','Penk to FSI','Rorb to FSI',  'Tlx3 to FSI']\n",
    "markers=['^','^','^','o','o','o']\n",
    "colors=['magenta',  'blue', 'darkorange', 'magenta',  'blue', 'darkorange',]\n",
    "fig, ax = plt.subplots()\n",
    "master_dists=[]\n",
    "for i in range(len(df_list)):\n",
    "    df=df_list[i]\n",
    "    df=df[df.cnx=='excitatory']\n",
    "    df=df.dropna(subset=['rise2080'])\n",
    "    df=df[df.peakOfAvg>0.1]\n",
    "    y=df.rise2080\n",
    "    description=tick_labels[i]\n",
    "    x=np.random.normal(i,0.1, size=len(y))\n",
    "    master_dists.append(np.array(y))\n",
    "    mark=markers[i]\n",
    "    color=colors[i]\n",
    "    ax.scatter(i+1,np.mean(y),facecolor=color,marker=mark,s=s*4,edgecolor='white')\n",
    "\n",
    "violin_parts=ax.violinplot(master_dists,showmeans=False, showmedians=False,\n",
    "        showextrema=False,points=100)\n",
    "\n",
    "i=0\n",
    "for pc in violin_parts['bodies']:\n",
    "    mark=markers[i]\n",
    "    color=colors[i]\n",
    "    pc.set_facecolor(color)\n",
    "    pc.set_edgecolor(color)\n",
    "    i+=1\n",
    "    \n",
    "plt.xticks(range(1,len(df_list)+1), tick_labels, rotation='vertical')\n",
    "plt.ylim(0.0,6)\n",
    "dress_up(ax)\n",
    "plt.ylabel('20-80% rise time (ms)')\n",
    "fig.set_figheight(1.5)\n",
    "fig.set_figwidth(2.25)\n",
    "fig.autofmt_xdate()\n",
    "#plt.savefig('EPSP_rise_short_scale.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s=15\n",
    "fs=10\n",
    "tick_size=4\n",
    "Pvalb_Pvalb_sub=Pvalb_Pvalb[Pvalb_Pvalb.xyz_dist>100]\n",
    "df_list=[Pvalb_L23pyr,Sst_L23pyr,Pvalb_Pvalb_sub,Sst_L23fsi]\n",
    "tick_labels=['Pvalb to PC', 'Sst to PC', 'Pvalb to Pvalb','Sst to FSI']\n",
    "markers=['^','^','o','o',]\n",
    "colors=['purple',  'green', 'purple','green']\n",
    "master_dists=[]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "for i in range(len(df_list)):\n",
    "    df=df_list[i]\n",
    "    df=df[df.cnx=='inhibitory']\n",
    "    df=df.dropna(subset=['rise2080'])\n",
    "    df=df[df.peakOfAvg<-0.1]\n",
    "    description=tick_labels[i]\n",
    "    y=df.rise2080\n",
    "    \n",
    "    master_dists.append(np.array(y))\n",
    "    x=np.random.normal(i,0.1, size=len(y))\n",
    "    mark=markers[i]\n",
    "    color=colors[i]\n",
    "\n",
    "    ax.scatter(i+1,np.median(y),facecolor=color,marker=mark,s=s*4,edgecolor='white')\n",
    "\n",
    "violin_parts=ax.violinplot(master_dists,showmeans=False, showmedians=False,\n",
    "        showextrema=False,points=1000)\n",
    "\n",
    "plt.xticks(range(1,len(df_list)+1), tick_labels, rotation='vertical')\n",
    "\n",
    "i=0\n",
    "for pc in violin_parts['bodies']:\n",
    "    mark=markers[i]\n",
    "    color=colors[i]\n",
    "    pc.set_facecolor(color)\n",
    "    pc.set_edgecolor(color)\n",
    "    \n",
    "    i+=1\n",
    "\n",
    "plt.ylim(0.0,15)\n",
    "dress_up(ax)\n",
    "plt.ylabel('20-80% rise time (ms)')\n",
    "fig.set_figheight(1.5)\n",
    "fig.set_figwidth(1.5)\n",
    "fig.autofmt_xdate()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s=15\n",
    "fs=10\n",
    "tick_size=4\n",
    "\n",
    "df_list=[SstKv_L23pyr,SstAi_L23pyr,Pvalb_L23pyr]\n",
    "tick_labels=['Sst:AAV','Sst:Ai167','Pvalb:AAV']\n",
    "markers=['^','^','^','o',]\n",
    "colors=['green',  'magenta', 'purple']\n",
    "master_dists=[]\n",
    "fig, ax = plt.subplots()\n",
    "for i in range(len(df_list)):\n",
    "    df=df_list[i]\n",
    "    df=df[df.cnx=='inhibitory']\n",
    "    df=df.dropna(subset=['rise2080'])\n",
    "    df=df[df.peakOfAvg<-0.1]\n",
    "    y=df.rise2080\n",
    "    master_dists.append(np.array(y))\n",
    "    x=np.random.normal(i,0.1, size=len(y))\n",
    "    mark=markers[i]\n",
    "    color=colors[i]\n",
    "\n",
    "    ax.scatter(i+1,np.median(y),facecolor=color,marker=mark,s=s*4,edgecolor='white')\n",
    "\n",
    "    \n",
    "violin_parts=ax.violinplot(master_dists,showmeans=False, showmedians=False,\n",
    "        showextrema=False,points=1000)\n",
    "\n",
    "plt.xticks(range(1,len(df_list)+1), tick_labels, rotation='vertical')\n",
    "\n",
    "i=0\n",
    "for pc in violin_parts['bodies']:\n",
    "    mark=markers[i]\n",
    "    color=colors[i]\n",
    "    pc.set_facecolor(color)\n",
    "    pc.set_edgecolor(color)\n",
    "    \n",
    "    i+=1\n",
    "\n",
    "plt.ylim(0.0,15)\n",
    "dress_up(ax)\n",
    "plt.ylabel('20-80% rise time (ms)')\n",
    "fig.set_figheight(1.5)\n",
    "fig.set_figwidth(1.5)\n",
    "fig.autofmt_xdate()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "color_dict={'Scnn1a':'blue', 'Rorb':'blue', 'Tlx3':'darkorange', 'Ntsr1':'red', 'Penk':'magenta', 'Sst':'green','Pvalb':'purple'}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SpearmanrResult(correlation=-0.7557812676309871, pvalue=4.528616272290246e-67)\n",
      "SpearmanrResult(correlation=-0.5765203595980961, pvalue=1.1594341822763673e-06)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 180x108 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gskw = dict(width_ratios=[1,1], wspace=0.2)\n",
    "\n",
    "gs = matplotlib.gridspec.GridSpec(1,2, **gskw)\n",
    "mp=20\n",
    "x_max=5\n",
    "y_max=2.5\n",
    "\n",
    "fig = plt.figure(figsize=(2.5,1.5))\n",
    "ax0=fig.add_subplot(gs[0])\n",
    "ax1=fig.add_subplot(gs[1])\n",
    "df_list=[exc_L23_pyr, exc_L23_fsi]\n",
    "axes=[ax0, ax1]\n",
    "\n",
    "for df, ax in zip(df_list,axes):\n",
    "\n",
    "    df=df[df.cnx=='excitatory'].dropna(subset=['peakOfAvg','CV'])\n",
    "    print (stats.spearmanr(df.peakOfAvg, df.CV))\n",
    "    ax.scatter(df.peakOfAvg, df.CV, facecolor=[color_dict[i] for i in df.presynapticCre],alpha=0.5,s=5,edgecolors='none')\n",
    "    ax.set_xlim([-0.1,x_max])\n",
    "    ax.set_ylim([0,y_max])\n",
    "    dress_up(ax,3)\n",
    "    ax.xaxis.set_major_locator(plt.MaxNLocator(3))\n",
    "    ax.xaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "\n",
    "ax1.plot([0,0],[0,3],ls='solid',color='grey')\n",
    "\n",
    "ax0.set_xlabel('EPSP amplitude (mV)')\n",
    "ax0.xaxis.set_label_coords(1.15, -0.3)\n",
    "ax0.set_ylabel('EPSP CV')\n",
    "ax1.yaxis.set_ticks_position('none')\n",
    "ax1.spines['left'].set_visible(False)\n",
    "ax1.axes.yaxis.set_visible(False)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 180x108 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gskw = dict(width_ratios=[1,1], wspace=0.2)\n",
    "\n",
    "gs = matplotlib.gridspec.GridSpec(1,2, **gskw)\n",
    "mp=20\n",
    "fig = plt.figure(figsize=(2.5,1.5))\n",
    "\n",
    "ax0=fig.add_subplot(gs[0])\n",
    "ax1=fig.add_subplot(gs[1])\n",
    "axes=[ax0,ax0,ax1,ax1]\n",
    "colors=['green','purple','green','purple']\n",
    "df_list=[Sst_L23pyr,Pvalb_L23pyr, Sst_L23fsi, Pvalb_Pvalb]\n",
    "for df, ax, color in zip(df_list,axes,colors):\n",
    "    df=df[df.cnx=='inhibitory']\n",
    "    ax.scatter(df.peakOfAvg*-1,df.CV,c=color,alpha=0.5,s=5,edgecolors='None')\n",
    "    ax.set_xlim([-0.1,2.5])\n",
    "    ax.set_ylim([0,2.5])\n",
    "    dress_up(ax,5)\n",
    "    ax.xaxis.set_major_locator(plt.MaxNLocator(4))\n",
    "    \n",
    "    \n",
    "ax1.plot([0,0],[0,3],ls='solid',color='grey')\n",
    "ax0.set_xlabel('IPSP amplitude (mV)')\n",
    "ax0.xaxis.set_label_coords(1.15, -0.3)\n",
    "ax0.set_ylabel('IPSP CV')\n",
    "ax1.yaxis.set_ticks_position('none')\n",
    "ax1.spines['left'].set_visible(False)\n",
    "ax1.axes.yaxis.set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "L23=standard_df[standard_df.post_layer==2]\n",
    "L23pyr=L23[L23.post_class=='pyramidal']\n",
    "L23pyr_exc=L23pyr[L23pyr.presynapticCre.isin(['Scnn1a', 'Rorb','Tlx3','Ntsr1','Penk'])]\n",
    "L23int=L23[L23.post_class=='interneuron']\n",
    "L23int_exc=L23int[L23int.presynapticCre.isin(['Scnn1a', 'Rorb', 'Tlx3', 'Ntsr1','Penk'])]\n",
    "L23fsi_exc=L23int_exc[L23int_exc.Kcluster==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def low_quant(arr):\n",
    "    return np.quantile(arr,0.25)\n",
    "def high_quant(arr):\n",
    "    return np.quantile(arr,0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x122.4 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_lim=[-500,500]\n",
    "y_lim=[500,0]\n",
    "fs=10\n",
    "tick_size=5\n",
    "vmax=1\n",
    "vmin=0\n",
    "font='arial'\n",
    "size=10\n",
    "mp=20\n",
    "\n",
    "gskw = dict(width_ratios=[5,5,2], wspace=0.25)\n",
    "gs = matplotlib.gridspec.GridSpec(1,3, **gskw)\n",
    "\n",
    "fig = plt.figure(figsize=(6,1.7))\n",
    "\n",
    "ax0=fig.add_subplot(gs[0], aspect='equal', adjustable='box')\n",
    "ax1=fig.add_subplot(gs[1], aspect='equal', adjustable='box')\n",
    "\n",
    "df_list=[L23pyr_exc,L23fsi_exc]\n",
    "map_axes=[ax0,ax1]\n",
    "cm_names=['Blues','Oranges']\n",
    "for df, ax, cm_name in zip(df_list,map_axes,cm_names):\n",
    "\n",
    "    df=df.dropna(subset=['y_pia'])\n",
    "    exc=df[df.cnx=='excitatory']\n",
    "    exc= exc.sort_values(['peakOfAvg'], ascending=1)\n",
    "    cells=exc.groupby(['exp_id', 'headstage']).toPia.mean()\n",
    "    avg_post_cell=np.mean(cells)\n",
    "    ax.scatter(0,avg_post_cell, marker='^', facecolor='black',s=size,zorder=2,edgecolors='white')\n",
    "    nocnx=df[df.cnx=='no cnx']\n",
    "    ax.scatter(nocnx.x_ap, nocnx.y_pia, s=15, color='white', alpha=0.1, marker='^', edgecolors='grey')\n",
    "    ax.tick_params(axis='both', which='major', labelsize=10)\n",
    "    ax.xaxis.set_ticks([-400,0,400])\n",
    "    ax.axis([-600,600,1000,0])\n",
    "    full_cmp = plt.get_cmap(cm_name, 512)\n",
    "    newcmp = matplotlib.colors.ListedColormap(full_cmp(np.linspace(0.25, 1, 256)))\n",
    "    heat_cnxs=ax.scatter(exc.x_ap, exc.y_pia, c=exc.peakOfAvg, vmin=vmin, vmax=vmax, s=size, cmap=newcmp, alpha=0.9\n",
    "                , marker='^', edgecolors='None',zorder=1)\n",
    "    ax.set_xlabel('horizontal distance (' r'$\\mu$'+'m)', fontsize=10, fontname='arial')\n",
    "    cb=fig.colorbar(heat_cnxs,ticks=[0,0.5,1], ax=ax,fraction=0.046, pad=0.04)\n",
    "    cb.ax.set_yticklabels(['0\\nmV', '0.5\\nmV','>1\\nmV'])\n",
    "    cb.ax.tick_params(labelsize=10)\n",
    "\n",
    "\n",
    "ax0.set_ylabel('distance from pia (' r'$\\mu$'+'m)', fontsize=10, fontname='arial')\n",
    "ax1.axes.yaxis.set_visible(False)\n",
    "ax1.spines['left'].set_visible(False)\n",
    "ax2=fig.add_subplot(gs[2])\n",
    "\n",
    "colors=['blue', 'darkorange']\n",
    "colors2=['blue','orange']\n",
    "\n",
    "min_cnx=3\n",
    "bins=[50,330,480,750]\n",
    "\n",
    "for df, color,color2 in zip(df_list, colors,colors2):\n",
    "    df=df.dropna(subset=['peakOfAvg','y_pia'])\n",
    "    df=df[df.cnx=='excitatory']\n",
    "    ax2.scatter(df.peakOfAvg,df.y_pia, facecolor=color,edgecolors='none',alpha=0.5,s=10)\n",
    "    binned_median=stats.binned_statistic(df.y_pia,df.peakOfAvg, bins=bins, statistic='median')[0]\n",
    "    binned_up=stats.binned_statistic(df.y_pia,df.peakOfAvg,bins=bins,statistic=high_quant)[0]\n",
    "    binned_low=stats.binned_statistic(df.y_pia,df.peakOfAvg,bins=bins,statistic=low_quant)[0]\n",
    "    count=stats.binned_statistic(df.y_pia,df.peakOfAvg,  bins=bins, statistic='count')[0]\n",
    "    thresh=count>min_cnx\n",
    "    x=binned_median[thresh]\n",
    "    y=opto.midPoints(bins)[thresh]\n",
    "    xerr=[x-binned_low[thresh],binned_up[thresh]-x]\n",
    "    ax2.errorbar(x, y, xerr=xerr, color=color2,  marker='o', ms=6,elinewidth=2, capsize=1)\n",
    "\n",
    "\n",
    "for ax in [ax0, ax1, ax2]:\n",
    "    opto.layer_borders(ax)\n",
    "    dress_up(ax)\n",
    "\n",
    "ax2.axis([0.01,5,1000,0])\n",
    "ax2.set_xscale('log')\n",
    "ax2.set_xlabel('EPSP amp. (mV)', fontsize=10, fontname='arial')\n",
    "ax2.xaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "ax2.spines['left'].set_visible(False)\n",
    "ax2.axes.yaxis.set_visible(False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(77, 81)\n",
      "(162, 81)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x122.4 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_lim=[-500,500]\n",
    "y_lim=[500,0]\n",
    "fs=10\n",
    "tick_size=5\n",
    "vmax=1\n",
    "vmin=0\n",
    "font='arial'\n",
    "size=10\n",
    "mp=20\n",
    "\n",
    "gskw = dict(width_ratios=[5,5,2], wspace=0.25)\n",
    "gs = matplotlib.gridspec.GridSpec(1,3, **gskw)\n",
    "\n",
    "fig = plt.figure(figsize=(6,1.7))\n",
    "\n",
    "ax0=fig.add_subplot(gs[0], aspect='equal', adjustable='box')\n",
    "ax1=fig.add_subplot(gs[1], aspect='equal', adjustable='box')\n",
    "\n",
    "df_list=[Pvalb_L23pyr,Sst_L23pyr]\n",
    "map_axes=[ax0,ax1]\n",
    "cm_names=['Purples','Greens']\n",
    "for df, ax, cm_name in zip(df_list,map_axes,cm_names):\n",
    "\n",
    "    df=df.dropna(subset=['y_pia'])\n",
    "    inh=df[df.cnx=='inhibitory']\n",
    "    print (np.shape(inh))\n",
    "    inh= inh.sort_values(['peakOfAvg'], ascending=0)\n",
    "    cells=inh.groupby(['exp_id', 'headstage']).toPia.mean()\n",
    "    avg_post_cell=np.mean(cells)\n",
    "    ax.scatter(0,avg_post_cell, marker='^', facecolor='black',s=size,zorder=2,edgecolors='white')\n",
    "    nocnx=df[df.cnx=='no cnx']\n",
    "    ax.scatter(nocnx.x_ap, nocnx.y_pia, s=15, color='white', alpha=0.1, marker='o', edgecolors='grey')\n",
    "    ax.tick_params(axis='both', which='major', labelsize=10)\n",
    "    ax.xaxis.set_ticks([-400,0,400])\n",
    "    ax.axis([-600,600,1000,0])\n",
    "    full_cmp = plt.get_cmap(cm_name, 512)\n",
    "    newcmp = matplotlib.colors.ListedColormap(full_cmp(np.linspace(0.25, 1, 256)))\n",
    "    heat_cnxs=ax.scatter(inh.x_ap, inh.y_pia, c=inh.peakOfAvg*-1, vmin=vmin, vmax=vmax, s=size, cmap=newcmp, alpha=1\n",
    "                , marker='o', edgecolors='None',zorder=1)\n",
    "    ax.set_xlabel('horizontal distance (' r'$\\mu$'+'m)', fontsize=10, fontname='arial')\n",
    "    cb=fig.colorbar(heat_cnxs,ticks=[0,0.5,1], ax=ax,fraction=0.046, pad=0.04)\n",
    "    cb.ax.set_yticklabels(['0\\nmV', '0.5\\nmV','>1\\nmV'])\n",
    "    cb.ax.tick_params(labelsize=10)\n",
    "\n",
    "\n",
    "ax0.set_ylabel('distance from pia (' r'$\\mu$'+'m)', fontsize=10, fontname='arial')\n",
    "ax1.axes.yaxis.set_visible(False)\n",
    "ax1.spines['left'].set_visible(False)\n",
    "ax2=fig.add_subplot(gs[2])\n",
    "\n",
    "colors=['mediumorchid','limegreen']\n",
    "colors2=['purple','green']\n",
    "\n",
    "min_cnx=3\n",
    "bins=[50,330,480,750]\n",
    "\n",
    "for df, color,color2 in zip(df_list, colors,colors2):\n",
    "    df=df.dropna(subset=['peakOfAvg','y_pia'])\n",
    "    df=df[df.cnx=='inhibitory']\n",
    "    ax2.scatter(df.peakOfAvg*-1,df.y_pia, facecolor=color,edgecolors='none',alpha=0.5,s=10)\n",
    "    binned_median=stats.binned_statistic(df.y_pia,df.peakOfAvg*-1, bins=bins, statistic='median')[0]\n",
    "    binned_up=stats.binned_statistic(df.y_pia,df.peakOfAvg*-1,bins=bins,statistic=high_quant)[0]\n",
    "    binned_low=stats.binned_statistic(df.y_pia,df.peakOfAvg*-1,bins=bins,statistic=low_quant)[0]\n",
    "    count=stats.binned_statistic(df.y_pia,df.peakOfAvg*-1,  bins=bins, statistic='count')[0]\n",
    "    thresh=count>min_cnx\n",
    "    x=binned_median[thresh]\n",
    "    y=opto.midPoints(bins)[thresh]\n",
    "    xerr=[x-binned_low[thresh],binned_up[thresh]-x]\n",
    "    ax2.errorbar(x, y, xerr=xerr, color=color2,  marker='o', ms=6,elinewidth=2, capsize=1)\n",
    "\n",
    "\n",
    "for ax in [ax0, ax1, ax2]:\n",
    "    opto.layer_borders(ax)\n",
    "    dress_up(ax)\n",
    "\n",
    "ax2.axis([0.01,5,1000,0])\n",
    "ax2.set_xscale('log')\n",
    "ax2.set_xlabel('IPSP amp. (mV)', fontsize=10, fontname='arial')\n",
    "ax2.xaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "ax2.spines['left'].set_visible(False)\n",
    "ax2.axes.yaxis.set_visible(False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'horizontal distance ($\\\\mu$m)')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 108x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gskw = dict(height_ratios=[1,1,1], wspace=0.2)\n",
    "\n",
    "gs = matplotlib.gridspec.GridSpec(3,1, **gskw)\n",
    "mp=20\n",
    "fig = plt.figure(figsize=(1.5,4))\n",
    "\n",
    "ax0=fig.add_subplot(gs[0])\n",
    "ax1=fig.add_subplot(gs[1])\n",
    "ax2=fig.add_subplot(gs[2])\n",
    "\n",
    "df_list=[exc_L23_pyr, exc_L23_fsi]\n",
    "colors=['royalblue', 'orange']\n",
    "colors2=['blue','darkorange']\n",
    "off=0\n",
    "min_cnx=3\n",
    "x_bins=np.arange(0,601,100)\n",
    "y_bins=[50,330,480,750]\n",
    "min_cnx=2\n",
    "\n",
    "for df, color,color2 in zip(df_list, colors,colors2):\n",
    "    df=df.dropna(subset=['peakOfAvg'])\n",
    "    df=df[df.cnx=='excitatory']\n",
    "    L2=df[df.y_pia<330]\n",
    "    L4=df[(df.y_pia>330) & (df.y_pia<480)]\n",
    "    L5=df[(df.y_pia>480)]\n",
    "    ec='none'\n",
    "    ax0.scatter(L2.abs_x, L2.peakOfAvg,facecolor=color,edgecolors=ec,alpha=0.9,s=10)\n",
    "    ax1.scatter(L4.abs_x, L4.peakOfAvg, facecolor=color, edgecolors=ec, alpha=0.9, s=10)\n",
    "    ax2.scatter(L5.abs_x, L5.peakOfAvg, facecolor=color, edgecolors=ec, alpha=0.9, s=10)\n",
    "    for Ldf, ax in zip([L2,L4,L5],[ax0,ax1,ax2]):\n",
    "        binned_median=stats.binned_statistic(Ldf.abs_x,Ldf.peakOfAvg, bins=x_bins, statistic='median')[0]\n",
    "        binned_up=stats.binned_statistic(Ldf.abs_x, Ldf.peakOfAvg,bins=x_bins,statistic=high_quant)[0]\n",
    "        binned_low=stats.binned_statistic(Ldf.abs_x, Ldf.peakOfAvg, bins=x_bins, statistic=low_quant)[0]\n",
    "        count=stats.binned_statistic(Ldf.abs_x,Ldf.peakOfAvg,  bins=x_bins, statistic='count')[0]\n",
    "        thresh=count>min_cnx\n",
    "   \n",
    "        y=binned_median[thresh]\n",
    "        x=opto.midPoints(x_bins)[thresh]\n",
    "        yerr=[y-binned_low[thresh],binned_up[thresh]-y]\n",
    "        ax.errorbar(x, y, yerr=yerr, color=color2,  marker='o', ms=6,elinewidth=2, capsize=1,xlolims=True)\n",
    "    \n",
    "for ax in [ax0,ax1,ax2]:\n",
    "    ax.set_xlim([-10,610])\n",
    "    ax.set_ylim([0.005,10])\n",
    "    ax.set_yscale('log')\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.tick_params(axis='both', which='both', labelsize=10, direction='out')\n",
    "    ax.yaxis.set_ticks_position('left')\n",
    "    ax.xaxis.set_ticks_position('bottom')\n",
    "\n",
    "    ax.yaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "\n",
    "ax0.axes.xaxis.set_visible(False)\n",
    "ax1.axes.xaxis.set_visible(False)\n",
    "ax2.xaxis.set_major_locator(plt.MaxNLocator(4))\n",
    "ax1.set_ylabel('PSP amplitude (mV)')\n",
    "ax2.set_xlabel('horizontal distance (' r'$\\mu$'+'m)')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'horizontal distance ($\\\\mu$m)')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 54x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gskw = dict(height_ratios=[1,1,1], wspace=0.2)\n",
    "\n",
    "gs = matplotlib.gridspec.GridSpec(3,1, **gskw)\n",
    "mp=20\n",
    "fig = plt.figure(figsize=(0.75,4))\n",
    "\n",
    "ax0=fig.add_subplot(gs[0])\n",
    "ax1=fig.add_subplot(gs[1])\n",
    "ax2=fig.add_subplot(gs[2])\n",
    "\n",
    "df_list=[Pvalb_L23pyr, Sst_L23pyr]\n",
    "colors=['mediumorchid', 'limegreen']\n",
    "colors2=['purple','green']\n",
    "off=0\n",
    "min_cnx=3\n",
    "x_bins=np.arange(0,601,100)\n",
    "y_bins=[50,330,480,750]\n",
    "min_cnx=2\n",
    "\n",
    "for df, color,color2 in zip(df_list, colors,colors2):\n",
    "    df=df.dropna(subset=['peakOfAvg'])\n",
    "    df=df[df.cnx=='inhibitory']\n",
    "    L2=df[df.y_pia<330]\n",
    "    L4=df[(df.y_pia>330) & (df.y_pia<480)]\n",
    "    L5=df[(df.y_pia>480)]\n",
    "    ec='none'\n",
    "    ax0.scatter(L2.abs_x, abs(L2.peakOfAvg),facecolor=color,edgecolors=ec,alpha=0.9,s=10)\n",
    "    ax1.scatter(L4.abs_x, abs(L4.peakOfAvg), facecolor=color, edgecolors=ec, alpha=0.9, s=10)\n",
    "    ax2.scatter(L5.abs_x, abs(L5.peakOfAvg), facecolor=color, edgecolors=ec, alpha=0.9, s=10)\n",
    "    for Ldf, ax in zip([L2,L4,L5],[ax0,ax1,ax2]):\n",
    "        binned_median=stats.binned_statistic(Ldf.abs_x,abs(Ldf.peakOfAvg), bins=x_bins, statistic='median')[0]\n",
    "        binned_up=stats.binned_statistic(Ldf.abs_x, abs(Ldf.peakOfAvg),bins=x_bins,statistic=high_quant)[0]\n",
    "        binned_low=stats.binned_statistic(Ldf.abs_x, abs(Ldf.peakOfAvg), bins=x_bins, statistic=low_quant)[0]\n",
    "        count=stats.binned_statistic(Ldf.abs_x,abs(Ldf.peakOfAvg),  bins=x_bins, statistic='count')[0]\n",
    "        thresh=count>min_cnx\n",
    "   \n",
    "        y=binned_median[thresh]\n",
    "        x=opto.midPoints(x_bins)[thresh]\n",
    "        yerr=[y-binned_low[thresh],binned_up[thresh]-y]\n",
    "        ax.errorbar(x, y, yerr=yerr, color=color2,  marker='o', ms=6,elinewidth=2, capsize=1,xlolims=True)\n",
    "\n",
    "        \n",
    "for ax in [ax0,ax1,ax2]:\n",
    "    ax.set_xlim([-10,310])\n",
    "    ax.set_ylim([0.005,10])\n",
    "    ax.set_yscale('log')\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.tick_params(axis='both', which='both', labelsize=10, direction='out')\n",
    "    ax.yaxis.set_ticks_position('left')\n",
    "    ax.xaxis.set_ticks_position('bottom')\n",
    "\n",
    "    ax.yaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "\n",
    "ax0.axes.xaxis.set_visible(False)\n",
    "ax1.axes.xaxis.set_visible(False)\n",
    "ax2.xaxis.set_major_locator(plt.MaxNLocator(4))\n",
    "\n",
    "ax2.set_xlabel('horizontal distance (' r'$\\mu$'+'m)')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def PSP_population_params(df, cnx_call,label):\n",
    "    cnxs=df[df.cnx==cnx_call]\n",
    "    cnxs=cnxs.dropna(subset=['peakOfAvg'])\n",
    "    median=np.median(abs(cnxs.peakOfAvg))\n",
    "    mean=np.mean(abs(cnxs.peakOfAvg))\n",
    "    sd=np.std(abs(cnxs.peakOfAvg))\n",
    "    n=len(cnxs.peakOfAvg)\n",
    "    skew=stats.skew(abs(cnxs.peakOfAvg))\n",
    "    CV=np.nanmean(cnxs.CV)\n",
    "    rise_sub=cnxs[abs(cnxs.peakOfAvg)>0.1]\n",
    "    rise_mean=np.nanmean(rise_sub.rise2080)\n",
    "    cnxs=cnxs.dropna(subset=['abs_x','y_pia'])\n",
    "    XspearmanR,XspearmanP=stats.spearmanr(cnxs.abs_x,cnxs.peakOfAvg)\n",
    "    YspearmanR,YspearmanP=stats.spearmanr(cnxs.y_pia,cnxs.peakOfAvg)\n",
    "    PSP_dict={'connection class':label,'amp. median (mV)':median,'amp. mean (mV)':mean,\n",
    "              'amp. sd (mV)':sd,'amp. skew':skew,'n':n,'CV':CV,'20-80% rise (ms)':rise_mean}\n",
    "    return PSP_dict\n",
    "def PSP_dist_corr(df,cnx_call,label):\n",
    "    cnxs=df[df.cnx==cnx_call]\n",
    "    cnxs=cnxs.dropna(subset=['peakOfAvg','y_pia','abs_x'])\n",
    "    amps=cnxs.peakOfAvg\n",
    "    x=cnxs.abs_x\n",
    "    y=cnxs.y_pia\n",
    "    XspearmanR,XspearmanP=stats.spearmanr(x,amps)\n",
    "    YspearmanR,YspearmanP=stats.spearmanr(y,amps)\n",
    "    n=len(amps)\n",
    "    corr_dict={'connection class':label,'horizontal Spearman R':XspearmanR,'horizontal Spearman p':XspearmanP,\n",
    "             'presyn. pia Spearman R':YspearmanR,'presyn. pia Spearman p':YspearmanP,'n':n}\n",
    "    return corr_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_17623/1692640933.py:11: RuntimeWarning: Mean of empty slice\n",
      "  rise_mean=np.nanmean(rise_sub.rise2080)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>connection class</th>\n",
       "      <th>amp. median (mV)</th>\n",
       "      <th>amp. mean (mV)</th>\n",
       "      <th>amp. sd (mV)</th>\n",
       "      <th>amp. skew</th>\n",
       "      <th>n</th>\n",
       "      <th>CV</th>\n",
       "      <th>20-80% rise (ms)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Penk to L2/3 PC</td>\n",
       "      <td>0.146486</td>\n",
       "      <td>0.174925</td>\n",
       "      <td>0.104434</td>\n",
       "      <td>1.297460</td>\n",
       "      <td>16</td>\n",
       "      <td>0.797779</td>\n",
       "      <td>1.918923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rorb to L2/3 PC</td>\n",
       "      <td>0.250468</td>\n",
       "      <td>0.383338</td>\n",
       "      <td>0.416290</td>\n",
       "      <td>3.023495</td>\n",
       "      <td>245</td>\n",
       "      <td>0.633395</td>\n",
       "      <td>2.014566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tlx3 to L2/3 PC</td>\n",
       "      <td>0.304859</td>\n",
       "      <td>0.455875</td>\n",
       "      <td>0.513689</td>\n",
       "      <td>3.201072</td>\n",
       "      <td>85</td>\n",
       "      <td>0.538308</td>\n",
       "      <td>1.723459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ntsr1 to L2/3 PC</td>\n",
       "      <td>0.092858</td>\n",
       "      <td>0.092858</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.085600</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Penk to L2/3 FSI</td>\n",
       "      <td>0.811742</td>\n",
       "      <td>1.002615</td>\n",
       "      <td>0.985359</td>\n",
       "      <td>1.407496</td>\n",
       "      <td>28</td>\n",
       "      <td>0.896457</td>\n",
       "      <td>1.524869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Rorb to L2/3 FSI</td>\n",
       "      <td>0.404168</td>\n",
       "      <td>0.547148</td>\n",
       "      <td>0.382470</td>\n",
       "      <td>1.303851</td>\n",
       "      <td>24</td>\n",
       "      <td>0.754512</td>\n",
       "      <td>1.231490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Tlx3 to L2/3 FSI</td>\n",
       "      <td>0.439667</td>\n",
       "      <td>0.396489</td>\n",
       "      <td>0.217038</td>\n",
       "      <td>0.179792</td>\n",
       "      <td>9</td>\n",
       "      <td>0.749269</td>\n",
       "      <td>1.159951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Pvalb to L2/3 PC</td>\n",
       "      <td>0.272244</td>\n",
       "      <td>0.406684</td>\n",
       "      <td>0.445330</td>\n",
       "      <td>1.969450</td>\n",
       "      <td>77</td>\n",
       "      <td>0.584943</td>\n",
       "      <td>3.035219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sst to L2/3 PC</td>\n",
       "      <td>0.114605</td>\n",
       "      <td>0.159684</td>\n",
       "      <td>0.127290</td>\n",
       "      <td>2.350467</td>\n",
       "      <td>162</td>\n",
       "      <td>0.849414</td>\n",
       "      <td>4.512205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Pvalb to L2/3 FSI</td>\n",
       "      <td>0.155071</td>\n",
       "      <td>0.205219</td>\n",
       "      <td>0.179378</td>\n",
       "      <td>2.608240</td>\n",
       "      <td>34</td>\n",
       "      <td>0.862443</td>\n",
       "      <td>2.029468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Sst to L2/3 FSI</td>\n",
       "      <td>0.194480</td>\n",
       "      <td>0.337991</td>\n",
       "      <td>0.420714</td>\n",
       "      <td>2.384198</td>\n",
       "      <td>18</td>\n",
       "      <td>0.751002</td>\n",
       "      <td>2.734192</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     connection class  amp. median (mV)  amp. mean (mV)  amp. sd (mV)  \\\n",
       "0     Penk to L2/3 PC          0.146486        0.174925      0.104434   \n",
       "1     Rorb to L2/3 PC          0.250468        0.383338      0.416290   \n",
       "2     Tlx3 to L2/3 PC          0.304859        0.455875      0.513689   \n",
       "3    Ntsr1 to L2/3 PC          0.092858        0.092858      0.000000   \n",
       "4    Penk to L2/3 FSI          0.811742        1.002615      0.985359   \n",
       "5    Rorb to L2/3 FSI          0.404168        0.547148      0.382470   \n",
       "6    Tlx3 to L2/3 FSI          0.439667        0.396489      0.217038   \n",
       "7    Pvalb to L2/3 PC          0.272244        0.406684      0.445330   \n",
       "8      Sst to L2/3 PC          0.114605        0.159684      0.127290   \n",
       "9   Pvalb to L2/3 FSI          0.155071        0.205219      0.179378   \n",
       "10    Sst to L2/3 FSI          0.194480        0.337991      0.420714   \n",
       "\n",
       "    amp. skew    n        CV  20-80% rise (ms)  \n",
       "0    1.297460   16  0.797779          1.918923  \n",
       "1    3.023495  245  0.633395          2.014566  \n",
       "2    3.201072   85  0.538308          1.723459  \n",
       "3    0.000000    1  1.085600               NaN  \n",
       "4    1.407496   28  0.896457          1.524869  \n",
       "5    1.303851   24  0.754512          1.231490  \n",
       "6    0.179792    9  0.749269          1.159951  \n",
       "7    1.969450   77  0.584943          3.035219  \n",
       "8    2.350467  162  0.849414          4.512205  \n",
       "9    2.608240   34  0.862443          2.029468  \n",
       "10   2.384198   18  0.751002          2.734192  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnx_classes=['Penk to L2/3 PC', 'Rorb to L2/3 PC',  'Tlx3 to L2/3 PC', 'Ntsr1 to L2/3 PC',\n",
    "             'Penk to L2/3 FSI','Rorb to L2/3 FSI', 'Tlx3 to L2/3 FSI',\n",
    "             'Pvalb to L2/3 PC', 'Sst to L2/3 PC', 'Pvalb to L2/3 FSI', 'Sst to L2/3 FSI']\n",
    "df_list=[Penk_L23pyr,Rorb_L23pyr,Tlx3_L23pyr,Ntsr1_L23pyr,Penk_L23fsi,Rorb_L23fsi,Tlx3_L23fsi,\n",
    "         Pvalb_L23pyr, Sst_L23pyr, Pvalb_Pvalb, Sst_L23fsi]\n",
    "calls=['excitatory','excitatory','excitatory','excitatory','excitatory','excitatory','excitatory',\n",
    "      'inhibitory','inhibitory','inhibitory','inhibitory']\n",
    "rows=[]\n",
    "corr_rows=[]\n",
    "for df,label,call in zip(df_list, cnx_classes,calls):\n",
    "    PSP_dict=PSP_population_params(df,call,label)\n",
    "    corr_dict=PSP_dist_corr(df,call,label)\n",
    "    rows.append(PSP_dict)\n",
    "    corr_rows.append(corr_dict)\n",
    "PSP_df=pd.DataFrame.from_dict(rows)\n",
    "corr_df=pd.DataFrame.from_dict(corr_rows)\n",
    "PSP_df.to_csv('outputs/PSP_props.csv')\n",
    "corr_df.to_csv('outputs/PSP_dist_corr.csv')\n",
    "PSP_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>connection class</th>\n",
       "      <th>horizontal Spearman R</th>\n",
       "      <th>horizontal Spearman p</th>\n",
       "      <th>presyn. pia Spearman R</th>\n",
       "      <th>presyn. pia Spearman p</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Penk to L2/3 PC</td>\n",
       "      <td>-0.526471</td>\n",
       "      <td>0.036167</td>\n",
       "      <td>-0.505882</td>\n",
       "      <td>0.045582</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rorb to L2/3 PC</td>\n",
       "      <td>0.079746</td>\n",
       "      <td>0.214513</td>\n",
       "      <td>0.029029</td>\n",
       "      <td>0.651834</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tlx3 to L2/3 PC</td>\n",
       "      <td>0.184835</td>\n",
       "      <td>0.090363</td>\n",
       "      <td>-0.012859</td>\n",
       "      <td>0.907015</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ntsr1 to L2/3 PC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Penk to L2/3 FSI</td>\n",
       "      <td>-0.351396</td>\n",
       "      <td>0.066712</td>\n",
       "      <td>-0.443350</td>\n",
       "      <td>0.018128</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Rorb to L2/3 FSI</td>\n",
       "      <td>-0.274783</td>\n",
       "      <td>0.193781</td>\n",
       "      <td>-0.054783</td>\n",
       "      <td>0.799308</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Tlx3 to L2/3 FSI</td>\n",
       "      <td>0.716667</td>\n",
       "      <td>0.029818</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>0.111633</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Pvalb to L2/3 PC</td>\n",
       "      <td>0.075556</td>\n",
       "      <td>0.513697</td>\n",
       "      <td>0.170461</td>\n",
       "      <td>0.138288</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sst to L2/3 PC</td>\n",
       "      <td>0.185061</td>\n",
       "      <td>0.018393</td>\n",
       "      <td>0.044322</td>\n",
       "      <td>0.575454</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Pvalb to L2/3 FSI</td>\n",
       "      <td>-0.073186</td>\n",
       "      <td>0.680829</td>\n",
       "      <td>0.043545</td>\n",
       "      <td>0.806824</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Sst to L2/3 FSI</td>\n",
       "      <td>0.399381</td>\n",
       "      <td>0.100600</td>\n",
       "      <td>0.126935</td>\n",
       "      <td>0.615727</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     connection class  horizontal Spearman R  horizontal Spearman p  \\\n",
       "0     Penk to L2/3 PC              -0.526471               0.036167   \n",
       "1     Rorb to L2/3 PC               0.079746               0.214513   \n",
       "2     Tlx3 to L2/3 PC               0.184835               0.090363   \n",
       "3    Ntsr1 to L2/3 PC                    NaN                    NaN   \n",
       "4    Penk to L2/3 FSI              -0.351396               0.066712   \n",
       "5    Rorb to L2/3 FSI              -0.274783               0.193781   \n",
       "6    Tlx3 to L2/3 FSI               0.716667               0.029818   \n",
       "7    Pvalb to L2/3 PC               0.075556               0.513697   \n",
       "8      Sst to L2/3 PC               0.185061               0.018393   \n",
       "9   Pvalb to L2/3 FSI              -0.073186               0.680829   \n",
       "10    Sst to L2/3 FSI               0.399381               0.100600   \n",
       "\n",
       "    presyn. pia Spearman R  presyn. pia Spearman p    n  \n",
       "0                -0.505882                0.045582   16  \n",
       "1                 0.029029                0.651834  244  \n",
       "2                -0.012859                0.907015   85  \n",
       "3                      NaN                     NaN    1  \n",
       "4                -0.443350                0.018128   28  \n",
       "5                -0.054783                0.799308   24  \n",
       "6                 0.566667                0.111633    9  \n",
       "7                 0.170461                0.138288   77  \n",
       "8                 0.044322                0.575454  162  \n",
       "9                 0.043545                0.806824   34  \n",
       "10                0.126935                0.615727   18  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_17623/1878958519.py:4: 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",
      "  sub['horizontal BH corrected p']=horiz_bh\n",
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_17623/1878958519.py:5: 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",
      "  sub['pia BH corrected p']=pia_bh\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>connection class</th>\n",
       "      <th>horizontal Spearman R</th>\n",
       "      <th>horizontal Spearman p</th>\n",
       "      <th>presyn. pia Spearman R</th>\n",
       "      <th>presyn. pia Spearman p</th>\n",
       "      <th>n</th>\n",
       "      <th>horizontal BH corrected p</th>\n",
       "      <th>pia BH corrected p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Penk to L2/3 PC</td>\n",
       "      <td>-0.526471</td>\n",
       "      <td>0.036167</td>\n",
       "      <td>-0.505882</td>\n",
       "      <td>0.045582</td>\n",
       "      <td>16</td>\n",
       "      <td>0.120555</td>\n",
       "      <td>0.227908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rorb to L2/3 PC</td>\n",
       "      <td>0.079746</td>\n",
       "      <td>0.214513</td>\n",
       "      <td>0.029029</td>\n",
       "      <td>0.651834</td>\n",
       "      <td>244</td>\n",
       "      <td>0.268141</td>\n",
       "      <td>0.896471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tlx3 to L2/3 PC</td>\n",
       "      <td>0.184835</td>\n",
       "      <td>0.090363</td>\n",
       "      <td>-0.012859</td>\n",
       "      <td>0.907015</td>\n",
       "      <td>85</td>\n",
       "      <td>0.167667</td>\n",
       "      <td>0.907015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Penk to L2/3 FSI</td>\n",
       "      <td>-0.351396</td>\n",
       "      <td>0.066712</td>\n",
       "      <td>-0.443350</td>\n",
       "      <td>0.018128</td>\n",
       "      <td>28</td>\n",
       "      <td>0.166779</td>\n",
       "      <td>0.181276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Rorb to L2/3 FSI</td>\n",
       "      <td>-0.274783</td>\n",
       "      <td>0.193781</td>\n",
       "      <td>-0.054783</td>\n",
       "      <td>0.799308</td>\n",
       "      <td>24</td>\n",
       "      <td>0.268141</td>\n",
       "      <td>0.896471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Tlx3 to L2/3 FSI</td>\n",
       "      <td>0.716667</td>\n",
       "      <td>0.029818</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>0.111633</td>\n",
       "      <td>9</td>\n",
       "      <td>0.120555</td>\n",
       "      <td>0.345720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Pvalb to L2/3 PC</td>\n",
       "      <td>0.075556</td>\n",
       "      <td>0.513697</td>\n",
       "      <td>0.170461</td>\n",
       "      <td>0.138288</td>\n",
       "      <td>77</td>\n",
       "      <td>0.570775</td>\n",
       "      <td>0.345720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sst to L2/3 PC</td>\n",
       "      <td>0.185061</td>\n",
       "      <td>0.018393</td>\n",
       "      <td>0.044322</td>\n",
       "      <td>0.575454</td>\n",
       "      <td>162</td>\n",
       "      <td>0.120555</td>\n",
       "      <td>0.896471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Pvalb to L2/3 FSI</td>\n",
       "      <td>-0.073186</td>\n",
       "      <td>0.680829</td>\n",
       "      <td>0.043545</td>\n",
       "      <td>0.806824</td>\n",
       "      <td>34</td>\n",
       "      <td>0.680829</td>\n",
       "      <td>0.896471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Sst to L2/3 FSI</td>\n",
       "      <td>0.399381</td>\n",
       "      <td>0.100600</td>\n",
       "      <td>0.126935</td>\n",
       "      <td>0.615727</td>\n",
       "      <td>18</td>\n",
       "      <td>0.167667</td>\n",
       "      <td>0.896471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     connection class  horizontal Spearman R  horizontal Spearman p  \\\n",
       "0     Penk to L2/3 PC              -0.526471               0.036167   \n",
       "1     Rorb to L2/3 PC               0.079746               0.214513   \n",
       "2     Tlx3 to L2/3 PC               0.184835               0.090363   \n",
       "4    Penk to L2/3 FSI              -0.351396               0.066712   \n",
       "5    Rorb to L2/3 FSI              -0.274783               0.193781   \n",
       "6    Tlx3 to L2/3 FSI               0.716667               0.029818   \n",
       "7    Pvalb to L2/3 PC               0.075556               0.513697   \n",
       "8      Sst to L2/3 PC               0.185061               0.018393   \n",
       "9   Pvalb to L2/3 FSI              -0.073186               0.680829   \n",
       "10    Sst to L2/3 FSI               0.399381               0.100600   \n",
       "\n",
       "    presyn. pia Spearman R  presyn. pia Spearman p    n  \\\n",
       "0                -0.505882                0.045582   16   \n",
       "1                 0.029029                0.651834  244   \n",
       "2                -0.012859                0.907015   85   \n",
       "4                -0.443350                0.018128   28   \n",
       "5                -0.054783                0.799308   24   \n",
       "6                 0.566667                0.111633    9   \n",
       "7                 0.170461                0.138288   77   \n",
       "8                 0.044322                0.575454  162   \n",
       "9                 0.043545                0.806824   34   \n",
       "10                0.126935                0.615727   18   \n",
       "\n",
       "    horizontal BH corrected p  pia BH corrected p  \n",
       "0                    0.120555            0.227908  \n",
       "1                    0.268141            0.896471  \n",
       "2                    0.167667            0.907015  \n",
       "4                    0.166779            0.181276  \n",
       "5                    0.268141            0.896471  \n",
       "6                    0.120555            0.345720  \n",
       "7                    0.570775            0.345720  \n",
       "8                    0.120555            0.896471  \n",
       "9                    0.680829            0.896471  \n",
       "10                   0.167667            0.896471  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub=corr_df.dropna()\n",
    "horiz_bh= sm.stats.multipletests(sub['horizontal Spearman p'], alpha=0.1, method='fdr_bh', is_sorted=False, returnsorted=False)[1]\n",
    "pia_bh=sm.stats.multipletests(sub['presyn. pia Spearman p'], alpha=0.1, method='fdr_bh', is_sorted=False, returnsorted=False)[1]\n",
    "sub['horizontal BH corrected p']=horiz_bh\n",
    "sub['pia BH corrected p']=pia_bh\n",
    "sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub.to_csv('outputs/PSP_dist_corr_BH.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnx_classes=['Penk to L2/3 PC', 'Rorb to L2/3 PC',  'Tlx3 to L2/3 PC','Penk to L2/3 FSI','Rorb to L2/3 FSI',\n",
    "            'Tlx3 to L2/3 FSI']\n",
    "measures=['amp. median', 'amp. mean', 'amp. sd', 'skew', 'CV', 'rise2080']\n",
    "df_list=[Penk_L23pyr,Rorb_L23pyr,Tlx3_L23pyr,Penk_L23fsi,Rorb_L23fsi,Tlx3_L23fsi]\n",
    "i=0\n",
    "amp_master=[]\n",
    "rise_master=[]\n",
    "summary_table=np.zeros([len(cnx_classes),len(measures)])\n",
    "for df in df_list:\n",
    "    exc_cnxs=df[df.cnx=='excitatory']\n",
    "    exc_cnxs=exc_cnxs.dropna(subset=['peakOfAvg'])\n",
    "    class_name=cnx_classes[i]\n",
    "    \n",
    "    median=np.median(exc_cnxs.peakOfAvg)\n",
    "    mean=np.mean(exc_cnxs.peakOfAvg)\n",
    "    sd=np.std(exc_cnxs.peakOfAvg)\n",
    "    n=len(exc_cnxs.peakOfAvg)\n",
    "    skew=stats.skew(exc_cnxs.peakOfAvg)\n",
    "    CV=np.nanmean(exc_cnxs.CV)\n",
    "    rise=np.nanmean(exc_cnxs.rise2080)\n",
    "    summary_table[i,:]=np.array([median, mean, sd, skew,CV,rise])\n",
    "    amp_master.append(np.array(exc_cnxs.peakOfAvg))\n",
    "    exc_cnxs_rise=exc_cnxs.dropna(subset=['rise2080'])\n",
    "    rise_master.append(np.array(exc_cnxs_rise.rise2080))\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.14648558, 0.17492532, 0.10443425, 1.29745982, 0.79777858,\n",
       "        1.90934975],\n",
       "       [0.25046769, 0.38333844, 0.41629013, 3.0234951 , 0.63339455,\n",
       "        1.98709483],\n",
       "       [0.30485916, 0.45587488, 0.51368922, 3.20107168, 0.53830818,\n",
       "        1.81302675],\n",
       "       [0.81174159, 1.0026152 , 0.98535925, 1.40749633, 0.89645704,\n",
       "        1.50738839],\n",
       "       [0.40416814, 0.54714776, 0.38246963, 1.30385141, 0.7545124 ,\n",
       "        1.23340415],\n",
       "       [0.43966651, 0.39648871, 0.21703757, 0.17979232, 0.74926864,\n",
       "        1.15995129]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KruskalResult(statistic=9.468106895411525, pvalue=0.00879076581446018)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.kruskal(np.array(amp_master[0]),np.array(amp_master[1]),np.array(amp_master[2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KruskalResult(statistic=2.478265188284695, pvalue=0.28963534040160543)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.kruskal(np.array(rise_master[0]),np.array(rise_master[1]),np.array(rise_master[2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KruskalResult(statistic=29.257936648730862, pvalue=2.0636327469436655e-05)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "args=[l for l in amp_master]\n",
    "stats.kruskal(*args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scikit_posthocs as sp\n",
    "def Cohens_d(pop1,pop2):\n",
    "    u1=np.mean(pop1)\n",
    "    u2=np.mean(pop2)\n",
    "    s=np.std(np.append(pop1,pop2))\n",
    "    s1=np.std(pop1)\n",
    "    s2=np.std(pop2)\n",
    "    d=(u2-u1)/((0.5*(s1**2+s2**2)))**0.5\n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_array=np.zeros([6,6])\n",
    "y_array=range(6)\n",
    "x_array=range(6)\n",
    "i=0\n",
    "for y in y_array:\n",
    "    x_array=range(0,6)\n",
    "    for x in x_array:\n",
    "        #print (cnx_classes[y], cnx_classes[x])\n",
    "        d=Cohens_d(args[x],args[y])\n",
    "        #print (d)\n",
    "        out_array[x,y]=d\n",
    "    i+=1"
   ]
  },
  {
   "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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.028032</td>\n",
       "      <td>0.011027</td>\n",
       "      <td>0.000208</td>\n",
       "      <td>0.000771</td>\n",
       "      <td>0.036764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.028032</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.264882</td>\n",
       "      <td>0.001488</td>\n",
       "      <td>0.012435</td>\n",
       "      <td>0.409824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.011027</td>\n",
       "      <td>0.264882</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.028032</td>\n",
       "      <td>0.092089</td>\n",
       "      <td>0.690034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000208</td>\n",
       "      <td>0.001488</td>\n",
       "      <td>0.028032</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.696673</td>\n",
       "      <td>0.409824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000771</td>\n",
       "      <td>0.012435</td>\n",
       "      <td>0.092089</td>\n",
       "      <td>0.696673</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.544623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.036764</td>\n",
       "      <td>0.409824</td>\n",
       "      <td>0.690034</td>\n",
       "      <td>0.409824</td>\n",
       "      <td>0.544623</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          1         2         3         4         5         6\n",
       "1  1.000000  0.028032  0.011027  0.000208  0.000771  0.036764\n",
       "2  0.028032  1.000000  0.264882  0.001488  0.012435  0.409824\n",
       "3  0.011027  0.264882  1.000000  0.028032  0.092089  0.690034\n",
       "4  0.000208  0.001488  0.028032  1.000000  0.696673  0.409824\n",
       "5  0.000771  0.012435  0.092089  0.696673  1.000000  0.544623\n",
       "6  0.036764  0.409824  0.690034  0.409824  0.544623  1.000000"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exc_dunn=sp.posthoc_dunn(args,p_adjust='fdr_bh')\n",
    "exc_dunn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x7ffbda1217f0>,\n",
       "  <matplotlib.axis.YTick at 0x7ffbda11bfa0>,\n",
       "  <matplotlib.axis.YTick at 0x7ffbda17bf70>,\n",
       "  <matplotlib.axis.YTick at 0x7ffbda192c10>,\n",
       "  <matplotlib.axis.YTick at 0x7ffbda1983a0>,\n",
       "  <matplotlib.axis.YTick at 0x7ffbda198af0>],\n",
       " [Text(0, 0.5, 'Penk to L2/3 PC'),\n",
       "  Text(0, 1.5, 'Rorb to L2/3 PC'),\n",
       "  Text(0, 2.5, 'Tlx3 to L2/3 PC'),\n",
       "  Text(0, 3.5, 'Penk to L2/3 FSI'),\n",
       "  Text(0, 4.5, 'Rorb to L2/3 FSI'),\n",
       "  Text(0, 5.5, 'Tlx3 to L2/3 FSI')])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##excitatory amp comparison table\n",
    "sns.heatmap(np.transpose(out_array),cmap='vlag',square=True, annot=exc_dunn, cbar_kws={'label': \"Cohen's d\"})\n",
    "plt.xticks([0.5,1.5,2.5,3.5,4.5,5.5], cnx_classes, rotation='vertical')\n",
    "plt.yticks([0.5,1.5,2.5,3.5,4.5,5.5], cnx_classes, rotation='horizontal')\n",
    "#plt.savefig('exc_amp_comparison_table.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KruskalResult(statistic=35.290930963181836, pvalue=1.3163029875176739e-06)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x7ffbda43e220>,\n",
       "  <matplotlib.axis.YTick at 0x7ffbda437a60>,\n",
       "  <matplotlib.axis.YTick at 0x7ffbda252bb0>,\n",
       "  <matplotlib.axis.YTick at 0x7ffbda4a5e20>,\n",
       "  <matplotlib.axis.YTick at 0x7ffbda4ab5b0>,\n",
       "  <matplotlib.axis.YTick at 0x7ffbda4abd00>],\n",
       " [Text(0, 0.5, 'Penk to L2/3 PC'),\n",
       "  Text(0, 1.5, 'Rorb to L2/3 PC'),\n",
       "  Text(0, 2.5, 'Tlx3 to L2/3 PC'),\n",
       "  Text(0, 3.5, 'Penk to L2/3 FSI'),\n",
       "  Text(0, 4.5, 'Rorb to L2/3 FSI'),\n",
       "  Text(0, 5.5, 'Tlx3 to L2/3 FSI')])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "args=[l for l in rise_master]\n",
    "print (stats.kruskal(*args))\n",
    "out_array=np.zeros([6,6])\n",
    "y_array=range(6)\n",
    "x_array=range(6)\n",
    "i=0\n",
    "for y in y_array:\n",
    "    x_array=range(0,6)\n",
    "    for x in x_array:\n",
    "        #print cnx_classes[y], cnx_classes[x]\n",
    "        d=Cohens_d(args[x],args[y])\n",
    "        #print d\n",
    "        out_array[x,y]=d\n",
    "    i+=1\n",
    "    \n",
    "out_array[out_array == 0] ='nan'\n",
    "exc_dunn_rise=sp.posthoc_dunn(args,p_adjust='fdr_bh')\n",
    "sns.heatmap(np.transpose(out_array),cmap='vlag',square=True, annot=exc_dunn_rise, cbar_kws={'label': \"Cohen's d\"})\n",
    "plt.xticks([0.5,1.5,2.5,3.5,4.5,5.5], cnx_classes, rotation='vertical')\n",
    "plt.yticks([0.5,1.5,2.5,3.5,4.5,5.5], cnx_classes, rotation='horizontal')\n",
    "#plt.savefig('exc_rise_comparison_table.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pvalb to L2/3 PC 0.27224353 0.40668365346753255 0.44533027803224023 1.96944969542753 0.5849427918181819 2.9804102412987015\n",
      "Sst to L2/3 PC 0.11460514 0.15968361426543207 0.12729008641993256 2.3504668055929354 0.8494139724691359 4.027286719135803\n",
      "Pvalb to L2/3 Pvalb 0.15507073999999998 0.2209873559642857 0.18831560270146208 2.5472037579825466 0.7616326335714287 2.1017170464285715\n",
      "Sst to L2/3 FSI 0.194480255 0.33799078427777773 0.4207139294598545 2.3841976129283746 0.751002473888889 2.726183394444445\n"
     ]
    }
   ],
   "source": [
    "cnx_classes=['Pvalb to L2/3 PC', 'Sst to L2/3 PC',  'Pvalb to L2/3 Pvalb','Sst to L2/3 FSI']\n",
    "measures=['amp. median', 'amp. mean', 'amp. sd', 'skew', 'CV', 'rise2080']\n",
    "Pvalb_Pvalb_sub=Pvalb_Pvalb[Pvalb_Pvalb.xyz_dist>100]\n",
    "df_list=[Pvalb_L23pyr,Sst_L23pyr,Pvalb_Pvalb_sub,Sst_L23fsi]\n",
    "i=0\n",
    "amp_master=[]\n",
    "rise_master=[]\n",
    "summary_table=np.zeros([len(cnx_classes),len(measures)])\n",
    "for df in df_list:\n",
    "    inh_cnxs=df[df.cnx=='inhibitory']\n",
    "    inh_cnxs=inh_cnxs.dropna(subset=['peakOfAvg'])\n",
    "    class_name=cnx_classes[i]\n",
    "    \n",
    "    median=np.median(inh_cnxs.peakOfAvg*-1)\n",
    "    mean=np.mean(inh_cnxs.peakOfAvg*-1)\n",
    "    sd=np.std(inh_cnxs.peakOfAvg*-1)\n",
    "    n=len(inh_cnxs.peakOfAvg)\n",
    "    skew=stats.skew(inh_cnxs.peakOfAvg*-1)\n",
    "    CV=np.nanmean(inh_cnxs.CV)\n",
    "    rise=np.nanmean(inh_cnxs.rise2080)\n",
    "    print (class_name, median, mean ,sd, skew,CV,rise)\n",
    "    summary_table[i,:]=np.array([median, mean, sd, skew,CV,rise])\n",
    "    amp_master.append(np.array(inh_cnxs.peakOfAvg*-1))\n",
    "    inh_cnxs_rise=inh_cnxs.dropna(subset=['rise2080'])\n",
    "    rise_master.append(np.array(inh_cnxs_rise.rise2080))\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KruskalResult(statistic=30.18137868393046, pvalue=1.2639814212761591e-06)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "args=[l for l in amp_master]\n",
    "stats.kruskal(*args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KruskalResult(statistic=41.793097339190126, pvalue=4.438931766124997e-09)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "args=[l for l in rise_master]\n",
    "stats.kruskal(*args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_array=np.zeros([4,4])\n",
    "y_array=range(4)\n",
    "x_array=range(4)\n",
    "i=0\n",
    "for y in y_array:\n",
    "    x_array=range(0,4)\n",
    "    for x in x_array:\n",
    "        \n",
    "        d=Cohens_d(args[x],args[y])\n",
    "        out_array[x,y]=d\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.612595e-04</td>\n",
       "      <td>4.776047e-03</td>\n",
       "      <td>0.771579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000561</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.941899e-08</td>\n",
       "      <td>0.025462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.004776</td>\n",
       "      <td>3.941899e-08</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.059018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.771579</td>\n",
       "      <td>2.546237e-02</td>\n",
       "      <td>5.901849e-02</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          1             2             3         4\n",
       "1  1.000000  5.612595e-04  4.776047e-03  0.771579\n",
       "2  0.000561  1.000000e+00  3.941899e-08  0.025462\n",
       "3  0.004776  3.941899e-08  1.000000e+00  0.059018\n",
       "4  0.771579  2.546237e-02  5.901849e-02  1.000000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inh_dunn=sp.posthoc_dunn(args,p_adjust='fdr_bh')\n",
    "inh_dunn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x7ffbda5106d0>,\n",
       "  <matplotlib.axis.YTick at 0x7ffbda4be3d0>,\n",
       "  <matplotlib.axis.YTick at 0x7ffbdab6c910>,\n",
       "  <matplotlib.axis.YTick at 0x7ffbda625e80>],\n",
       " [Text(0, 0.5, 'Pvalb to L2/3 PC'),\n",
       "  Text(0, 1.5, 'Sst to L2/3 PC'),\n",
       "  Text(0, 2.5, 'Pvalb to L2/3 Pvalb'),\n",
       "  Text(0, 3.5, 'Sst to L2/3 FSI')])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.heatmap(np.transpose(out_array),cmap='vlag',square=True, annot=inh_dunn, cbar_kws={'label': \"Cohen's d\"})\n",
    "plt.xticks([0.5,1.5,2.5,3.5], cnx_classes, rotation='vertical')\n",
    "plt.yticks([0.5,1.5,2.5,3.5], cnx_classes, rotation='horizontal')\n",
    "#plt.savefig('inh_amp_comparison_table.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x7ffbdb0a82e0>,\n",
       "  <matplotlib.axis.YTick at 0x7ffbda688b20>,\n",
       "  <matplotlib.axis.YTick at 0x7ffbdb140e20>,\n",
       "  <matplotlib.axis.YTick at 0x7ffbdb159490>],\n",
       " [Text(0, 0.5, 'Pvalb to L2/3 PC'),\n",
       "  Text(0, 1.5, 'Sst to L2/3 PC'),\n",
       "  Text(0, 2.5, 'Pvalb to L2/3 Pvalb'),\n",
       "  Text(0, 3.5, 'Sst to L2/3 FSI')])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "args=[l for l in rise_master]\n",
    "stats.kruskal(*args)\n",
    "out_array=np.zeros([4,4])\n",
    "y_array=range(4)\n",
    "x_array=range(4)\n",
    "i=0\n",
    "for y in y_array:\n",
    "    x_array=range(0,4)\n",
    "    for x in x_array:\n",
    "        \n",
    "        d=Cohens_d(args[x],args[y])\n",
    "        \n",
    "        out_array[x,y]=d\n",
    "    i+=1\n",
    "    \n",
    "out_array[out_array == 0] ='nan'\n",
    "inh_dunn_rise=sp.posthoc_dunn(args,p_adjust='fdr_bh')\n",
    "sns.heatmap(np.transpose(out_array),cmap='vlag',square=True, annot=inh_dunn_rise, cbar_kws={'label': \"Cohen's d\"})\n",
    "plt.xticks([0.5,1.5,2.5,3.5], cnx_classes, rotation='vertical')\n",
    "plt.yticks([0.5,1.5,2.5,3.5], cnx_classes, rotation='horizontal')\n",
    "#plt.savefig('inh_rise_comparison_table.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tL1HHS8Rga0kHE4S+qYttvE/SEQBmtongsbbWUMdwSXtK6g0cB7wI/JYQudYG/B0hZvgMYKiZnUsIEtqDIGqA24DLCc2p3kX1dxDaqAXKBrlIOogwm2ES8EVCU63gJPeJdXWKnuBxnwW+L2kdsBy4BziV4G2XSepL+HmsFCH1PWC2pLMJH/QlZvZRiMAryQhJ7UX7yq7IY2YfSjoH+FH8gI3wcDabMPMjDR8SZmQcCDxoZr+R9I/Av0aPvgfwLUIz5zpJKwhfjFeAj3tazOwxSV8Cro7/d4F2whfiyRS2vAEMlrSK4NVn2I7Y6eMIX5hO0e2iwxQm/w01s+ubbUujkHQooedhdLVjO3GN/oR5fl/oRB2DgLvNbEJn7elWTQVJ4wheZUmzbelumNkWQhOiMysyX0EXeFvohh7X6Rl0K4/r9BxcuE4uceE6ucSF6+QSF66TS1y4Ti75fxm7eMg7SaAyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=standard_df\n",
    "df=df[df.cnx=='excitatory']\n",
    "df=df.dropna(subset=['jitter'])\n",
    "fig, ax= plt.subplots()\n",
    "ax.hist(df.jitter,edgecolor='darkblue',facecolor='skyblue',alpha=0.8,density=True)\n",
    "ax.set_xlabel('jitter of EPSP peak (ms)')\n",
    "ax.set_ylabel('prob. density')\n",
    "ax.tick_params(axis='both', which='both', labelsize=10, direction='out')\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_visible(True)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "ax.xaxis.set_major_locator(plt.MaxNLocator(3))\n",
    "ax.yaxis.set_major_locator(plt.MaxNLocator(3))\n",
    "ax.axes.xaxis.set_visible(True)\n",
    "\n",
    "fig.set_figheight(2)\n",
    "fig.set_figwidth(2)\n",
    "#plt.savefig('EPSP_jitter_hist.svg')\n",
    "plt.show()\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "bins=np.arange(0,45,1)\n",
    "ax.hist(df.timeOfPeak,bins=bins)\n",
    "ax.set_xlabel('latency of EPSP peak (ms)')\n",
    "ax.set_ylabel('# of connections')\n",
    "ax.tick_params(axis='both', which='both', labelsize=10, direction='out')\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_visible(True)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "ax.xaxis.set_major_locator(plt.MaxNLocator(3))\n",
    "ax.axes.xaxis.set_visible(True)\n",
    "ax.set_xlim(0,40)\n",
    "fig.set_figheight(2)\n",
    "fig.set_figwidth(2)\n",
    "#plt.savefig('EPSP_latency_hist.svg')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}