draw_diverge.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": [
    "\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",
    "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": 7,
   "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=PvalbKv\n",
    "df=df[(df.post_layer==2)]\n",
    "df=df.dropna(subset=['y_pia','toPia'])\n",
    "df=df.sort_values(by=['y_pia'])\n",
    "fig, ax = plt.subplots()\n",
    "grouped=df.groupby(['exp_id', 'MP_ID'], 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=='inhibitory']\n",
    "    if len(cnxs.index)>1:\n",
    "\n",
    "        min_x=min(cnxs.x_ap)\n",
    "        max_x=max(cnxs.x_ap)\n",
    "        pre_pia=np.mean(cnxs.y_pia)\n",
    "        count+=1\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.toPia[cnx], pre_pia], lw=2, alpha=0.7, c='purple',zorder=0)\n",
    "        plt.scatter([x_offset],[pre_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_diverge.svg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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=Sst\n",
    "df=df[(df.post_layer==2)]\n",
    "df=df.dropna(subset=['y_pia','toPia'])\n",
    "df=df.sort_values(by=['y_pia'])\n",
    "fig, ax = plt.subplots()\n",
    "grouped=df.groupby(['exp_id', 'MP_ID'], 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=='inhibitory']\n",
    "    if len(cnxs.index)>1:\n",
    "        count+=1\n",
    "        min_x=min(cnxs.x_ap)\n",
    "        max_x=max(cnxs.x_ap)\n",
    "        pre_pia=np.mean(cnxs.y_pia)\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.toPia[cnx], pre_pia], lw=2, alpha=0.7, c='green',zorder=0)\n",
    "        plt.scatter([x_offset],[pre_pia],marker='o', s=10,c='white',zorder=1)    \n",
    "    \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.xaxis.set_ticks_position('none')\n",
    "plt.xticks([])\n",
    "plt.yticks([215,405,630,890], ('L2/3', 'L4', 'L5', 'L6'), fontname = \"arial\")\n",
    "ax.yaxis.set_tick_params(direction='out')\n",
    "fig.set_figheight(1)\n",
    "fig.set_figwidth(width_fact)\n",
    "\n",
    "#plt.savefig('Sst_diverge.svg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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)]\n",
    "df=df.dropna(subset=['y_pia','toPia'])\n",
    "df=df.sort_values(by=['y_pia'])\n",
    "fig, ax = plt.subplots()\n",
    "grouped=df.groupby(['exp_id', 'MP_ID'], 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=='excitatory']\n",
    "    if len(cnxs.index)>1:\n",
    "        count+=1\n",
    "        min_x=min(cnxs.x_ap)\n",
    "        max_x=max(cnxs.x_ap)\n",
    "        pre_pia=np.mean(cnxs.y_pia)\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.toPia[cnx], pre_pia], lw=2, alpha=0.7, c='magenta',zorder=0)\n",
    "        plt.scatter([x_offset],[pre_pia],marker='^', s=10,c='white',zorder=1)    \n",
    "    \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",
    "\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.xaxis.set_ticks_position('none')\n",
    "plt.xticks([])\n",
    "plt.yticks([215,405,630,890], ('L2/3', 'L4', 'L5', 'L6'), fontname = \"arial\")\n",
    "ax.yaxis.set_tick_params(direction='out')\n",
    "fig.set_figheight(1)\n",
    "fig.set_figwidth(width_fact)\n",
    "\n",
    "#plt.savefig('Penk_diverge.svg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "\n",
    "df=Rorb\n",
    "df=df[(df.post_layer==2)]\n",
    "df=df.dropna(subset=['y_pia','toPia'])\n",
    "df=df.sort_values(by=['y_pia'])\n",
    "fig, ax = plt.subplots()\n",
    "grouped=df.groupby(['exp_id', 'MP_ID'], 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=='excitatory']\n",
    "    if len(cnxs.index)>1:\n",
    "        count+=1\n",
    "        min_x=min(cnxs.x_ap)\n",
    "        max_x=max(cnxs.x_ap)\n",
    "        pre_pia=np.mean(cnxs.y_pia)\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.toPia[cnx], pre_pia], lw=2, alpha=0.7, c='blue',zorder=0)\n",
    "        plt.scatter([x_offset],[pre_pia],marker='^', s=10,c='white',zorder=1)    \n",
    "   \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",
    "\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",
    "\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.xaxis.set_ticks_position('none')\n",
    "plt.xticks([])\n",
    "plt.yticks([215,405,630,890], ('L2/3', 'L4', 'L5', 'L6'), fontname = \"arial\")\n",
    "plt.plot([200,400], [630,630], c='black', lw=2.5)\n",
    "ax.yaxis.set_tick_params(direction='out')\n",
    "\n",
    "fig.set_figheight(1)\n",
    "fig.set_figwidth(width_fact)\n",
    "\n",
    "\n",
    "#plt.savefig('Rorb_diverge.svg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 205.714x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=Tlx3Kv\n",
    "df=df[(df.post_layer==2)]\n",
    "df=df.dropna(subset=['y_pia','toPia'])\n",
    "df=df.sort_values(by=['y_pia'])\n",
    "fig, ax = plt.subplots()\n",
    "grouped=df.groupby(['exp_id', 'MP_ID'], 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",
    "        count+=1\n",
    "        min_x=min(cnxs.x_ap)\n",
    "        max_x=max(cnxs.x_ap)\n",
    "        pre_pia=np.mean(cnxs.y_pia)\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.toPia[cnx], pre_pia], lw=2, alpha=0.7, c='orange',zorder=0)\n",
    "        plt.scatter([x_offset],[pre_pia],marker='^', s=10,c='white',zorder=1)    \n",
    "    \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",
    "opto.layer_borders(ax, max_x=x_lims[1],lw=1,alpha=0.6, ls=(0,(2,1)))\n",
    "plt.plot([200,400], [880,880], c='black', lw=2.5)\n",
    "ax.set_xlim(x_lims)\n",
    "width_fact=(x_lims[1]-x_lims[0])/1050\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([215,405,630,890], ('L2/3', 'L4', 'L5', 'L6'), fontname = \"arial\")\n",
    "fig.set_figheight(1)\n",
    "fig.set_figwidth(width_fact)\n",
    "#plt.savefig('Tlx3_diverge.svg')\n"
   ]
  }
 ],
 "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
}