draw_converge.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 matplotlib.gridspec\n",
    "import math\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.style\n",
    "import matplotlib as mpl\n",
    "import matplotlib.colors as colors\n",
    "import matplotlib.cm as cmx\n",
    "import seaborn as sns\n",
    "from scipy import stats, ndimage, misc\n",
    "import csv\n",
    "import opto_util as opto\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "mpl.style.use('classic')\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/travish/opt/miniconda3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3441: DtypeWarning: Columns (5,24,56) 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('20210415_full_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1., nan,  0.,  2.])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intKclusters=pd.read_csv('interneuron_Kclusters_1203.csv')\n",
    "full_df = pd.merge(full_df, intKclusters,  how='left', left_on=['exp_id','headstage'], right_on = ['experiment','headstage'])\n",
    "full_df.Kcluster.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16286, 85)\n"
     ]
    }
   ],
   "source": [
    "Cs_internal=full_df[full_df.internal=='Cs']\n",
    "\n",
    "standard_df=full_df[full_df.internal=='standard']\n",
    "standard_df=standard_df[standard_df.notes.isnull()]\n",
    "print (np.shape(standard_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=PvalbKv\n",
    "df=df[(df.post_layer==2) & (df.post_class=='pyramidal')]\n",
    "df=df.dropna(subset=['y_pia','toPia'])\n",
    "df=df.sort_values(by=['toPia'])\n",
    "fig, ax = plt.subplots()\n",
    "grouped=df.groupby(['exp_id', 'headstage'], sort=False)\n",
    "x_offset=100\n",
    "count=0\n",
    "\n",
    "for name, group in grouped:\n",
    "    group=group.reset_index()\n",
    "    cnxs=group[group.cnx=='inhibitory']\n",
    "    if len(cnxs.index)>1:\n",
    "        min_x=min(cnxs.x_ap)\n",
    "        max_x=max(cnxs.x_ap)\n",
    "        post_pia=np.mean(cnxs.toPia)\n",
    "        count+=1\n",
    "        nametag=\"cell\"+str(count)+\"_\"\n",
    "        if min_x<0:\n",
    "            x_offset+=abs(min_x)\n",
    "        for cnx in cnxs.index:\n",
    "            plt.plot([cnxs.x_ap[cnx]+x_offset, x_offset], [cnxs.y_pia[cnx], post_pia], lw=2, alpha=0.7, c='purple',zorder=0)\n",
    "        plt.scatter([x_offset],[post_pia],marker='^', s=10,c='white',zorder=1)    \n",
    "        if max_x>0:\n",
    "            x_offset=x_offset+100+max_x\n",
    "        else:\n",
    "            x_offset=x_offset+100\n",
    "            \n",
    "        \n",
    "plt.ylim([1050,0])\n",
    "x_lims=ax.get_xlim()\n",
    "\n",
    "opto.layer_borders(ax, max_x=x_lims[1],lw=1,alpha=0.6, ls=(0,(2,1)))\n",
    "plt.plot([200,400], [630,630], c='black', lw=2.5)\n",
    "ax.set_xlim(x_lims)\n",
    "width_fact=(x_lims[1]-x_lims[0])/1050\n",
    "ax.set_aspect(aspect='equal',adjustable='box')\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(True)\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.yaxis.set_tick_params(direction='out')\n",
    "ax.xaxis.set_ticks_position('none')\n",
    "plt.xticks([])\n",
    "plt.yticks([0,215,405,630,890], (' ', 'L2/3', 'L4', 'L5', 'L6'), fontname = \"arial\")\n",
    "\n",
    "\n",
    "fig.set_figheight(1)\n",
    "fig.set_figwidth(width_fact)\n",
    "\n",
    "#plt.savefig('Pvalb_pyr_converge.svg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 240x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=PvalbKv\n",
    "df=df[(df.post_layer==2) & (df.Kcluster==1)]\n",
    "df=df.dropna(subset=['y_pia','toPia'])\n",
    "df=df.sort_values(by=['toPia'])\n",
    "fig, ax = plt.subplots()\n",
    "grouped=df.groupby(['exp_id', 'headstage'], sort=False)\n",
    "x_offset=100\n",
    "count=0\n",
    "for name, group in grouped:\n",
    "    group=group.reset_index()\n",
    "    cnxs=group[group.cnx=='inhibitory']\n",
    "    if len(cnxs.index)>1:\n",
    "        min_x=min(cnxs.x_ap)\n",
    "        max_x=max(cnxs.x_ap)\n",
    "        post_pia=np.mean(cnxs.toPia)\n",
    "        count+=1\n",
    "        if min_x<0:\n",
    "            x_offset+=abs(min_x)\n",
    "        for cnx in cnxs.index:\n",
    "            plt.plot([cnxs.x_ap[cnx]+x_offset, x_offset], [cnxs.y_pia[cnx], post_pia], lw=2, alpha=0.7, c='purple',zorder=0)\n",
    "        plt.scatter([x_offset],[post_pia],marker='o', s=10,c='white',zorder=1)    \n",
    "        if max_x>0:\n",
    "            x_offset=x_offset+100+max_x\n",
    "        else:\n",
    "            x_offset=x_offset+100\n",
    "            \n",
    "        \n",
    "plt.ylim([1050,0])\n",
    "x_lims=ax.get_xlim()\n",
    "\n",
    "opto.layer_borders(ax, max_x=x_lims[1],lw=1,alpha=0.6, ls=(0,(2,1)))\n",
    "plt.plot([200,400], [630,630], c='black', lw=2.5)\n",
    "ax.set_xlim(x_lims)\n",
    "width_fact=(x_lims[1]-x_lims[0])/1050\n",
    "ax.set_aspect(aspect='equal',adjustable='box')\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(True)\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.yaxis.set_tick_params(direction='out')\n",
    "ax.xaxis.set_ticks_position('none')\n",
    "plt.xticks([])\n",
    "plt.yticks([0,215,405,630,890], (' ', 'L2/3', 'L4', 'L5', 'L6'), fontname = \"arial\")\n",
    "\n",
    "\n",
    "fig.set_figheight(1)\n",
    "fig.set_figwidth(width_fact)\n",
    "\n",
    "#plt.savefig('Pvalb_fsi_converge.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 822.857x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=Sst\n",
    "df=df[(df.post_layer==2) & (df.post_class=='pyramidal')]\n",
    "df=df.dropna(subset=['y_pia','toPia'])\n",
    "df=df.sort_values(by=['toPia'])\n",
    "fig, ax = plt.subplots()\n",
    "grouped=df.groupby(['exp_id', 'headstage'], sort=False)\n",
    "x_offset=100\n",
    "count=0\n",
    "\n",
    "for name, group in grouped:\n",
    "    group=group.reset_index()\n",
    "    cnxs=group[group.cnx=='inhibitory']\n",
    "    if len(cnxs.index)>1:\n",
    "        min_x=min(cnxs.x_ap)\n",
    "        max_x=max(cnxs.x_ap)\n",
    "        post_pia=np.mean(cnxs.toPia)\n",
    "        count+=1\n",
    "        if min_x<0:\n",
    "            x_offset+=abs(min_x)\n",
    "        for cnx in cnxs.index:\n",
    "            plt.plot([cnxs.x_ap[cnx]+x_offset, x_offset], [cnxs.y_pia[cnx], post_pia], lw=2, alpha=0.7, c='green',zorder=0)\n",
    "        plt.scatter([x_offset],[post_pia],marker='^', s=10,c='white',zorder=1)    \n",
    "        if max_x>0:\n",
    "            x_offset=x_offset+100+max_x\n",
    "        else:\n",
    "            x_offset=x_offset+100\n",
    "            \n",
    "        \n",
    "plt.ylim([1050,0])\n",
    "x_lims=ax.get_xlim()\n",
    "\n",
    "opto.layer_borders(ax, max_x=x_lims[1],lw=1,alpha=0.6, ls=(0,(2,1)))\n",
    "plt.plot([200,400], [630,630], c='black', lw=2.5)\n",
    "ax.set_xlim(x_lims)\n",
    "width_fact=(x_lims[1]-x_lims[0])/1050\n",
    "ax.set_aspect(aspect='equal',adjustable='box')\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(True)\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.yaxis.set_tick_params(direction='out')\n",
    "ax.xaxis.set_ticks_position('none')\n",
    "plt.xticks([])\n",
    "plt.yticks([0,215,405,630,890], (' ', 'L2/3', 'L4', 'L5', 'L6'), fontname = \"arial\")\n",
    "\n",
    "\n",
    "fig.set_figheight(1)\n",
    "fig.set_figwidth(width_fact)\n",
    "\n",
    "#plt.savefig('Sst_pyr_converge.svg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 82.2857x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=Sst\n",
    "df=df[(df.post_layer==2) & (df.Kcluster==1)]\n",
    "df=df.dropna(subset=['y_pia','toPia'])\n",
    "df=df.sort_values(by=['toPia'])\n",
    "fig, ax = plt.subplots()\n",
    "grouped=df.groupby(['exp_id', 'headstage'], sort=False)\n",
    "x_offset=100\n",
    "count=0\n",
    "for name, group in grouped:\n",
    "    group=group.reset_index()\n",
    "    cnxs=group[group.cnx=='inhibitory']\n",
    "    if len(cnxs.index)>1:\n",
    "\n",
    "        min_x=min(cnxs.x_ap)\n",
    "        max_x=max(cnxs.x_ap)\n",
    "        post_pia=np.mean(cnxs.toPia)\n",
    "        count+=1\n",
    "        if min_x<0:\n",
    "            x_offset+=abs(min_x)\n",
    "        for cnx in cnxs.index:\n",
    "            plt.plot([cnxs.x_ap[cnx]+x_offset, x_offset], [cnxs.y_pia[cnx], post_pia], lw=2, alpha=0.7, c='green',zorder=0)\n",
    "        plt.scatter([x_offset],[post_pia],marker='o', s=10,c='white',zorder=1)    \n",
    "        if max_x>0:\n",
    "            x_offset=x_offset+100+max_x\n",
    "        else:\n",
    "            x_offset=x_offset+100\n",
    "            \n",
    "        \n",
    "plt.ylim([1050,0])\n",
    "x_lims=ax.get_xlim()\n",
    "\n",
    "opto.layer_borders(ax, max_x=x_lims[1],lw=1,alpha=0.6, ls=(0,(2,1)))\n",
    "plt.plot([200,400], [630,630], c='black', lw=2.5)\n",
    "ax.set_xlim(x_lims)\n",
    "width_fact=(x_lims[1]-x_lims[0])/1050\n",
    "ax.set_aspect(aspect='equal',adjustable='box')\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(True)\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.yaxis.set_tick_params(direction='out')\n",
    "ax.xaxis.set_ticks_position('none')\n",
    "plt.xticks([])\n",
    "plt.yticks([0,215,405,630,890], (' ', 'L2/3', 'L4', 'L5', 'L6'), fontname = \"arial\")\n",
    "\n",
    "\n",
    "fig.set_figheight(1)\n",
    "fig.set_figwidth(width_fact)\n",
    "\n",
    "#plt.savefig('Sst_fsi_converge.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 68.5714x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=PenkKv\n",
    "df=df[(df.post_layer==2) & (df.post_class=='pyramidal')]\n",
    "df=df.dropna(subset=['y_pia','toPia'])\n",
    "df=df.sort_values(by=['toPia'])\n",
    "fig, ax = plt.subplots()\n",
    "grouped=df.groupby(['exp_id', 'headstage'], sort=False)\n",
    "x_offset=100\n",
    "count=0\n",
    "\n",
    "\n",
    "for name, group in grouped:\n",
    "    group=group.reset_index()\n",
    "\n",
    "    cnxs=group[group.cnx=='excitatory']\n",
    "    if len(cnxs.index)>1:\n",
    "        #cnxs=cnxs.reset_index()\n",
    "        min_x=min(cnxs.x_ap)\n",
    "        max_x=max(cnxs.x_ap)\n",
    "        post_pia=np.mean(cnxs.toPia)\n",
    "        count+=1\n",
    "        if min_x<0:\n",
    "            x_offset+=abs(min_x)\n",
    "        for cnx in cnxs.index:\n",
    "            plt.plot([cnxs.x_ap[cnx]+x_offset, x_offset], [cnxs.y_pia[cnx], post_pia], lw=2, alpha=0.7, c='magenta',zorder=0)\n",
    "        plt.scatter([x_offset],[post_pia],marker='^', s=10,c='white',zorder=1)    \n",
    "        if max_x>0:\n",
    "            x_offset=x_offset+100+max_x\n",
    "        else:\n",
    "            x_offset=x_offset+100\n",
    "            \n",
    "        \n",
    "plt.ylim([1050,0])\n",
    "x_lims=ax.get_xlim()\n",
    "\n",
    "opto.layer_borders(ax, max_x=x_lims[1],lw=1,alpha=0.6, ls=(0,(2,1)))\n",
    "plt.plot([200,400], [630,630], c='black', lw=2.5)\n",
    "ax.set_xlim(x_lims)\n",
    "width_fact=(x_lims[1]-x_lims[0])/1050\n",
    "ax.set_aspect(aspect='equal',adjustable='box')\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(True)\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.yaxis.set_tick_params(direction='out')\n",
    "ax.xaxis.set_ticks_position('none')\n",
    "plt.xticks([])\n",
    "plt.yticks([0,215,405,630,890], (' ', 'L2/3', 'L4', 'L5', 'L6'), fontname = \"arial\")\n",
    "\n",
    "\n",
    "fig.set_figheight(1)\n",
    "fig.set_figwidth(width_fact)\n",
    "\n",
    "#plt.savefig('Penk_pyr_converge.svg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 82.2857x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=PenkKv\n",
    "df=df[(df.post_layer==2) & (df.Kcluster==1)]\n",
    "df=df.dropna(subset=['y_pia','toPia'])\n",
    "df=df.sort_values(by=['toPia'])\n",
    "fig, ax = plt.subplots()\n",
    "grouped=df.groupby(['exp_id', 'headstage'], sort=False)\n",
    "x_offset=100\n",
    "count=0\n",
    "for name, group in grouped:\n",
    "    group=group.reset_index()\n",
    "\n",
    "    cnxs=group[group.cnx=='excitatory']\n",
    "    if len(cnxs.index)>1:\n",
    "\n",
    "        min_x=min(cnxs.x_ap)\n",
    "        max_x=max(cnxs.x_ap)\n",
    "        post_pia=np.mean(cnxs.toPia)\n",
    "        count+=1\n",
    "        if min_x<0:\n",
    "            x_offset+=abs(min_x)\n",
    "        for cnx in cnxs.index:\n",
    "            plt.plot([cnxs.x_ap[cnx]+x_offset, x_offset], [cnxs.y_pia[cnx], post_pia], lw=2, alpha=0.7, c='magenta',zorder=0)\n",
    "        plt.scatter([x_offset],[post_pia],marker='o', s=10,c='white',zorder=1)    \n",
    "        if max_x>0:\n",
    "            x_offset=x_offset+100+max_x\n",
    "        else:\n",
    "            x_offset=x_offset+100\n",
    "            \n",
    "        \n",
    "plt.ylim([1050,0])\n",
    "x_lims=ax.get_xlim()\n",
    "\n",
    "opto.layer_borders(ax, max_x=x_lims[1],lw=1,alpha=0.6, ls=(0,(2,1)))\n",
    "plt.plot([200,400], [630,630], c='black', lw=2.5)\n",
    "ax.set_xlim(x_lims)\n",
    "width_fact=(x_lims[1]-x_lims[0])/1050\n",
    "ax.set_aspect(aspect='equal',adjustable='box')\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(True)\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.yaxis.set_tick_params(direction='out')\n",
    "ax.xaxis.set_ticks_position('none')\n",
    "plt.xticks([])\n",
    "plt.yticks([0,215,405,630,890], (' ', 'L2/3', 'L4', 'L5', 'L6'), fontname = \"arial\")\n",
    "\n",
    "fig.set_figheight(1)\n",
    "fig.set_figwidth(width_fact)\n",
    "\n",
    "#plt.savefig('Penk_fsi_converge.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1714.29x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=Rorb\n",
    "df=df[(df.post_layer==2) & (df.post_class=='pyramidal')]\n",
    "df=df.dropna(subset=['y_pia','toPia'])\n",
    "df=df.sort_values(by=['toPia'])\n",
    "fig, ax = plt.subplots()\n",
    "grouped=df.groupby(['exp_id', 'headstage'], sort=False)\n",
    "x_offset=100\n",
    "count=0\n",
    "\n",
    "for name, group in grouped:\n",
    "    group=group.reset_index()\n",
    "\n",
    "    cnxs=group[group.cnx=='excitatory']\n",
    "    if len(cnxs.index)>1:\n",
    "\n",
    "        min_x=min(cnxs.x_ap)\n",
    "        max_x=max(cnxs.x_ap)\n",
    "        post_pia=np.mean(cnxs.toPia)\n",
    "        count+=1\n",
    "        nametag=\"cell\"+str(count)+\"_\"\n",
    "\n",
    "        if min_x<0:\n",
    "            x_offset+=abs(min_x)\n",
    "        for cnx in cnxs.index:\n",
    "            plt.plot([cnxs.x_ap[cnx]+x_offset, x_offset], [cnxs.y_pia[cnx], post_pia], lw=2, alpha=0.7, c='blue',zorder=0)\n",
    "        plt.scatter([x_offset],[post_pia],marker='^', s=10,c='white',zorder=1)    \n",
    "        if max_x>0:\n",
    "            x_offset=x_offset+100+max_x\n",
    "        else:\n",
    "            x_offset=x_offset+100\n",
    "            \n",
    "        \n",
    "plt.ylim([1050,0])\n",
    "x_lims=ax.get_xlim()\n",
    "\n",
    "opto.layer_borders(ax, max_x=x_lims[1],lw=1,alpha=0.6, ls=(0,(2,1)))\n",
    "plt.plot([200,400], [630,630], c='black', lw=2.5)\n",
    "ax.set_xlim(x_lims)\n",
    "width_fact=(x_lims[1]-x_lims[0])/1050\n",
    "ax.set_aspect(aspect='equal',adjustable='box')\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(True)\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.yaxis.set_tick_params(direction='out')\n",
    "ax.xaxis.set_ticks_position('none')\n",
    "plt.xticks([])\n",
    "plt.yticks([0,215,405,630,890], (' ', 'L2/3', 'L4', 'L5', 'L6'), fontname = \"arial\")\n",
    "\n",
    "\n",
    "fig.set_figheight(1)\n",
    "fig.set_figwidth(width_fact)\n",
    "\n",
    "#plt.savefig('Rorb_pyr_converge.svg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 171.429x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=Rorb\n",
    "df=df[(df.post_layer==2) & (df.Kcluster==1)]\n",
    "df=df.dropna(subset=['y_pia','toPia'])\n",
    "df=df.sort_values(by=['toPia'])\n",
    "fig, ax = plt.subplots()\n",
    "grouped=df.groupby(['exp_id', 'headstage'], sort=False)\n",
    "x_offset=100\n",
    "count=0\n",
    "for name, group in grouped:\n",
    "    group=group.reset_index()\n",
    "\n",
    "    cnxs=group[group.cnx=='excitatory']\n",
    "    if len(cnxs.index)>1:\n",
    "\n",
    "        min_x=min(cnxs.x_ap)\n",
    "        max_x=max(cnxs.x_ap)\n",
    "        post_pia=np.mean(cnxs.toPia)\n",
    "        count+=1\n",
    "        if min_x<0:\n",
    "            x_offset+=abs(min_x)\n",
    "        for cnx in cnxs.index:\n",
    "            plt.plot([cnxs.x_ap[cnx]+x_offset, x_offset], [cnxs.y_pia[cnx], post_pia], lw=2, alpha=0.7, c='blue',zorder=0)\n",
    "        plt.scatter([x_offset],[post_pia],marker='o', s=10,c='white',zorder=1)    \n",
    "        if max_x>0:\n",
    "            x_offset=x_offset+100+max_x\n",
    "        else:\n",
    "            x_offset=x_offset+100\n",
    "            \n",
    "        \n",
    "plt.ylim([1050,0])\n",
    "x_lims=ax.get_xlim()\n",
    "\n",
    "opto.layer_borders(ax, max_x=x_lims[1],lw=1,alpha=0.6, ls=(0,(2,1)))\n",
    "plt.plot([200,400], [630,630], c='black', lw=2.5)\n",
    "ax.set_xlim(x_lims)\n",
    "width_fact=(x_lims[1]-x_lims[0])/1050\n",
    "ax.set_aspect(aspect='equal',adjustable='box')\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(True)\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.yaxis.set_tick_params(direction='out')\n",
    "ax.xaxis.set_ticks_position('none')\n",
    "plt.xticks([])\n",
    "plt.yticks([0,215,405,630,890], (' ', 'L2/3', 'L4', 'L5', 'L6'), fontname = \"arial\")\n",
    "\n",
    "\n",
    "fig.set_figheight(1)\n",
    "fig.set_figwidth(width_fact)\n",
    "\n",
    "#plt.savefig('Rorb_fsi_converge.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 548.571x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=Tlx3Kv\n",
    "df=df[(df.post_layer==2) & (df.post_class=='pyramidal')]\n",
    "df=df.dropna(subset=['y_pia','toPia'])\n",
    "df=df.sort_values(by=['toPia'])\n",
    "fig, ax = plt.subplots()\n",
    "grouped=df.groupby(['exp_id', 'headstage'], sort=False)\n",
    "x_offset=100\n",
    "count=0\n",
    "\n",
    "for name, group in grouped:\n",
    "    group=group.reset_index()\n",
    "    count_test=0\n",
    "    cnxs=group[group.cnx=='excitatory']\n",
    "    if len(cnxs.index)>1:\n",
    "\n",
    "        min_x=min(cnxs.x_ap)\n",
    "        max_x=max(cnxs.x_ap)\n",
    "        post_pia=np.mean(cnxs.toPia)\n",
    "        count+=1\n",
    "\n",
    "\n",
    "        if min_x<0:\n",
    "            x_offset+=abs(min_x)\n",
    "        for cnx in cnxs.index:\n",
    "            plt.plot([cnxs.x_ap[cnx]+x_offset, x_offset], [cnxs.y_pia[cnx], post_pia], lw=2, alpha=0.7, c='darkorange',zorder=0)\n",
    "        plt.scatter([x_offset],[post_pia],marker='^', s=10,c='white',zorder=1)    \n",
    "        if max_x>0:\n",
    "            x_offset=x_offset+100+max_x\n",
    "        else:\n",
    "            x_offset=x_offset+100\n",
    "            \n",
    "        \n",
    "plt.ylim([1050,0])\n",
    "x_lims=ax.get_xlim()\n",
    "\n",
    "opto.layer_borders(ax, max_x=x_lims[1],lw=1,alpha=0.6, ls=(0,(2,1)))\n",
    "plt.plot([200,400], [830,830], c='black', lw=2.5)\n",
    "ax.set_xlim(x_lims)\n",
    "width_fact=(x_lims[1]-x_lims[0])/1050\n",
    "ax.set_aspect(aspect='equal',adjustable='box')\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(True)\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.yaxis.set_tick_params(direction='out')\n",
    "ax.xaxis.set_ticks_position('none')\n",
    "plt.xticks([])\n",
    "plt.yticks([0,215,405,630,890], (' ', 'L2/3', 'L4', 'L5', 'L6'), fontname = \"arial\")\n",
    "\n",
    "\n",
    "fig.set_figheight(1)\n",
    "fig.set_figwidth(width_fact)\n",
    "\n",
    "#plt.savefig('Tlx3_pyr_converge.svg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 54.8571x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=Tlx3Kv\n",
    "df=df[(df.post_layer==2) & (df.Kcluster==1)]\n",
    "df=df.dropna(subset=['y_pia','toPia'])\n",
    "df=df.sort_values(by=['toPia'])\n",
    "fig, ax = plt.subplots()\n",
    "grouped=df.groupby(['exp_id', 'headstage'], sort=False)\n",
    "x_offset=100\n",
    "count=0\n",
    "for name, group in grouped:\n",
    "    group=group.reset_index()\n",
    "    count_test=0\n",
    "    cnxs=group[group.cnx=='excitatory']\n",
    "    if len(cnxs.index)>1:\n",
    "\n",
    "        min_x=min(cnxs.x_ap)\n",
    "        max_x=max(cnxs.x_ap)\n",
    "        post_pia=np.mean(cnxs.toPia)\n",
    "        count+=1\n",
    "        if min_x<0:\n",
    "            x_offset+=abs(min_x)\n",
    "        for cnx in cnxs.index:\n",
    "            plt.plot([cnxs.x_ap[cnx]+x_offset, x_offset], [cnxs.y_pia[cnx], post_pia], lw=2, alpha=0.7, c='darkorange',zorder=0)\n",
    "        plt.scatter([x_offset],[post_pia],marker='o', s=10,c='white',zorder=1)    \n",
    "        if max_x>0:\n",
    "            x_offset=x_offset+100+max_x\n",
    "        else:\n",
    "            x_offset=x_offset+100\n",
    "            \n",
    "        \n",
    "plt.ylim([1050,0])\n",
    "x_lims=ax.get_xlim()\n",
    "\n",
    "opto.layer_borders(ax, max_x=x_lims[1],lw=1,alpha=0.6, ls=(0,(2,1)))\n",
    "plt.plot([200,400], [830,830], c='black', lw=2.5)\n",
    "ax.set_xlim(x_lims)\n",
    "width_fact=(x_lims[1]-x_lims[0])/1050\n",
    "ax.set_aspect(aspect='equal',adjustable='box')\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(True)\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.yaxis.set_tick_params(direction='out')\n",
    "ax.xaxis.set_ticks_position('none')\n",
    "plt.xticks([])\n",
    "plt.yticks([0,215,405,630,890], (' ', 'L2/3', 'L4', 'L5', 'L6'), fontname = \"arial\")\n",
    "\n",
    "\n",
    "fig.set_figheight(1)\n",
    "fig.set_figwidth(width_fact)\n",
    "\n",
    "#plt.savefig('Tlx3_fsi_converge.svg')"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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}