connectivity_figs.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",
    "from ast import literal_eval\n",
    "import opto_util as opto\n",
    "\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "import importlib\n",
    "\n",
    "\n",
    "mpl.style.use('classic')\n",
    "%matplotlib inline\n",
    "plt.rcParams.update({\n",
    "    'font.sans-serif': 'arial',\n",
    "    'font.family': 'sans-serif'\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 (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": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1., nan,  0.,  2.])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_df.Kcluster.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16286, 81)\n"
     ]
    }
   ],
   "source": [
    "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": 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",
    "Cux2Kv=standard_df[(standard_df.presynapticCre=='Cux2') & (standard_df.presynapticEffector=='ChrimsonR_kv_eyfp_AAV')]\n",
    "Cux2ERKv=standard_df[(standard_df.presynapticCre=='Cux2-ERT2') & (standard_df.presynapticEffector=='ChrimsonR_kv_eyfp_AAV')]\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": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/travish/Desktop/2p_opto_nbs/opto_util.py:50: RuntimeWarning: invalid value encountered in true_divide\n",
      "  exc_frac=exc_hist.astype(float)/probed_hist.astype(float)\n",
      "/Users/travish/opt/miniconda3/lib/python3.9/site-packages/statsmodels/stats/proportion.py:89: RuntimeWarning: invalid value encountered in true_divide\n",
      "  q_ = count * 1. / nobs\n",
      "/Users/travish/Desktop/2p_opto_nbs/opto_util.py:52: RuntimeWarning: invalid value encountered in true_divide\n",
      "  direct_frac=direct_hist.astype(float)/probed_hist.astype(float)\n",
      "/Users/travish/Desktop/2p_opto_nbs/opto_util.py:54: RuntimeWarning: invalid value encountered in true_divide\n",
      "  inh_frac=inh_hist.astype(float)/probed_hist.astype(float)\n"
     ]
    }
   ],
   "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=Penk_L23int[Penk_L23int.Kcluster==1]\n",
    "Penk_L23vip=Penk_L23int[Penk_L23int.Kcluster==2]\n",
    "\n",
    "bins=np.arange(0,501,100)\n",
    "Penk_L23pyr_con=opto.connectivity_profile(Penk_L23pyr, 'xyz_dist', bins=bins)\n",
    "Penk_L23int_con=opto.connectivity_profile(Penk_L23int, 'xyz_dist', bins=bins)\n",
    "Penk_L23fsi_con=opto.connectivity_profile(Penk_L23fsi, 'xyz_dist', bins=bins)\n",
    "Penk_L23vip_con=opto.connectivity_profile(Penk_L23vip, 'xyz_dist', bins=bins)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 23\n"
     ]
    }
   ],
   "source": [
    "## count the number of patch L23 pyr's in Penk experiments that were Penk+\n",
    "patched=Penk_L23pyr.groupby(['exp_id', 'headstage'])\n",
    "pos=0\n",
    "neg=0\n",
    "for name, group in patched:\n",
    "    cell_reporter=group.post_reporter.unique()\n",
    "    if cell_reporter[0]=='positive':\n",
    "        pos+=1\n",
    "    else:\n",
    "        neg+=1\n",
    "print (pos, neg)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 590.4x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes = plt.subplots(1,3)\n",
    "x_lim=[-500,500]\n",
    "y_lim=[500,0]\n",
    "fs=10\n",
    "tick_size=5\n",
    "font='arial'\n",
    "\n",
    "ax=axes[0]\n",
    "opto.graph_map_subplot(ax, Penk_L23pyr, 'excitatory', 'red', '^', 'blue', '^')\n",
    "opto.graph_selected(ax,Penk_L23pyr,'direct','green','o',pre_size=50)    \n",
    "ax.set_ylabel(r'distance from pia ($\\mu$m)', fontsize=fs, fontname=font)\n",
    "ax.yaxis.set_ticks([0,200,400])\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.yaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "\n",
    "ax1=axes[1]\n",
    "opto.graph_map_subplot(ax1, Penk_L23fsi, 'excitatory','red', '^', 'blue', 'o')\n",
    "ax1.axes.yaxis.set_visible(False)\n",
    "ax1.spines['left'].set_visible(False)\n",
    "ax1.yaxis.set_ticks_position('none')\n",
    "\n",
    "ax2=axes[2]\n",
    "opto.graph_map_subplot(ax2, Penk_L23vip, 'excitatory','red', '^', 'blue', 'o')\n",
    "ax2.axes.yaxis.set_visible(False)\n",
    "ax2.spines['left'].set_visible(False)\n",
    "ax2.yaxis.set_ticks_position('none')\n",
    "\n",
    "for ax in axes:\n",
    "    opto.layer_borders(ax)\n",
    "    ax.set_xlim(x_lim)\n",
    "    ax.set_ylim(y_lim)\n",
    "    ax.set_xlabel(r'horizontal offset ($\\mu$m)', fontsize=fs, fontname=font)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.xaxis.set_ticks_position('bottom')\n",
    "    ax.set_aspect(aspect='equal',adjustable='box')\n",
    "    ax.xaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "\n",
    "fig.set_figwidth(8.2)\n",
    "#plt.savefig('Penk_L23_maps.svg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "895  probed cnxs\n",
      "16  exc cnxs\n",
      "0  inh cnxs\n",
      "25 postsyanptic cells\n",
      "125  probed cnxs\n",
      "28  exc cnxs\n",
      "0  inh cnxs\n",
      "3 postsyanptic cells\n",
      "43  probed cnxs\n",
      "7  exc cnxs\n",
      "0  inh cnxs\n",
      "2 postsyanptic cells\n"
     ]
    }
   ],
   "source": [
    "df_list=[Penk_L23pyr, Penk_L23fsi, Penk_L23vip]\n",
    "for df in df_list:\n",
    "    opto.print_counts(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "## MP.npz contains MultiPatch/paired recording data pulled from the AIBS multipatch database\n",
    "## data contain Euclidian distances measured in 3 dimensions with multipatch connection calls\n",
    "MP=np.load('data/MP.npz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "probed 1:  [135  48   0   2   0]\n",
      "called 1:  [8 2 0 0 0]\n",
      "probed 2:  [205 312 175 103  56]\n",
      "called 2:  [12  3  1  0  0]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 126x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "probed1=MP['l23pyr_l23pyr_d']*1e6\n",
    "cnx1=MP['l23pyr_l23pyr_d'][MP['l23pyr_l23pyr_syn']]*1e6\n",
    "\n",
    "df=Penk_L23pyr\n",
    "probed2, cnx2 = opto.return_probed_cnx(df,'xyz_dist','excitatory')\n",
    "\n",
    "bins=np.arange(0,501,100)\n",
    "summary_dict=opto.plotFrac_wHist2(probed1,cnx1,'paired\\ncount','black',probed2,cnx2,'2P\\ncount','red',r'distance ($\\mu$m)',bins,5,ymax=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "probed 1:  [28 27  3  0  0]\n",
      "called 1:  [13  8  0  0  0]\n",
      "probed 2:  [41 60 23  1  0]\n",
      "called 2:  [19  9  0  0  0]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJwAAADZCAYAAADVLVmSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAjZklEQVR4nO2deXyU1dn3v9dMZrJNQhZICAn7vlsVUKwKWrGKPsXtqVpbca9V32rbp31arbV9W2v7qba1tW7Vom9rbZWKgqKoyKoouACyiBh2CCEJgRBCtrneP849MAkzyZDcmS339/OZT+bezrkGL8+5zznX9Tuiqjg4RAtXrA1w6F44DucQVRyHc4gqjsM5RBXH4RyiSkqsDbAbEVFn5B0XSKiT3bKF27dvH6tWrcJxzOjTLR3u8OHDvPPOO6xevTrWpnQ7uqXDAWRkZLBs2TI2b94ca1O6FTFxOBFxichjIvKeiCwSkSGtrk8QkaUiskxEXhSRNLttcLvd9O7dmzfffJM9e/bYXbxDGGLVws0A0lT1dOB/gQcDF0REgCeB61T1y8DrQH87K3cdOEBmVRWpqank5uby6quvUlVVZWcVDmHo8ChVRP4GhHzrVtXr23k84Eio6goROTXo2jCgErhTRMYCr6rqZx21MxRSU0NBaSkNo0eTmZlJY2Mjr776Kpdccgk+n8/Oqhxa0Zlpkeetv7cC7wLLgQnAxAiezQYOBB03i0iKqjYBPYHJwB3A58A8EflQVd9uXYiITAGmdMT4rOpqDh04QH1uLjk5Oezbt4/XX3+diy++mNTU1I4U6RABHXY4VX0DQES+r6q/tU4vF5E3I3j8IJAVdOyynA1M67ZZVddb5b8OnAIc53CqughYFHxORH4Wif3uxkZ827dTn5sLQK9evdizZw9vv/02559/Pm63O5JiHE4QO97hfCJyjohkicj5gDeCZ5YDFwKIyGnA2qBrpVaZgYHEmcA6G+xsQX1GBpl79uCurz96rqioiG3btrF8+XJnjq6LsMPhrsd0qyuAm4GvR/DMS8AREXkX+D1wl4hcLSI3q2oDcAPwnIisBHao6qs22NkCFQFV0svKWpzv06cPa9as4aOPPrK7SgdsWNpS1Y0icjcwBFgD7I3gGT/w7VanNwZdX0hk74KdotHnI3vLFg716wdiVmJcLhclJSW89957+Hw+hg8f3tVmdCs63cKJyO3Ao8CvgMuAP3W2zGjhT00l5fBhUqurW5x3u90UFRXx1ltvsWPHjtgYl6TY0aVeCXwFqFbVPwKTbCgzavi9XjK3bz/uvNfrpWfPnsyfP5+KiooYWJac2OFwgTICb9n14W6MRxp9PrJ278ZVf7zZGRkZZGZmMnfuXA4ePBgD65IPOxzun8ASYIiIvAbMsaHM6OFyoUD63tCvntnZ2YgIr732GkeOHImubUmIHQ73KGZ0+n3MMtVfbSgzqjT6fPTYsgXCTIXk5+dz6NAhFixYQGNjY5fYcOTIETZv3sycOXPYsGFDl9QRD3Rmaas3ZsXgWeCbwGrADSwgCiNMO/GnppJWUUFqdfXRieDWFBYWsnPnTpYsWcLUqVNxuTr//6rf76esrIyNGzfy+eefo6o0NDTQr1+/Tpcdr3RmWuQ04LvAcOBxTISnH3jDBruijt/jIXPHjrAOB1BcXMzGjRvx+XxMmtTxsdH+/fspLS1l7dq1HD58mPT0dAoLC3G73ZS1mhdMNjqztDUHmCMiFwKLVPWwiPRR1d22WRdFGrKy8O3eTfXw4fjDrKWKCMXFxaxcuZLMzEzGjBkTcfl1dXVs376dtWvXUl5eTkpKCnl5eeTn59v1ExICO3IaJgDnYt7h/igiq1T1NzaUG11cLrPyUF5Obd++YW9zu9306dOHxYsX4/P5GDBgQNh7m5ub2bNnD+vXr6e0tBRVJScnh75tlJ/s2OFw/6WqpwCo6hUishxIPIfDGjyUllJbUnJ05SEUHo+HgoIC5s+fz6WXXkphYeHRa6pKVVUVmzdvZt26ddTX15Oenk5RUZEt732Jjh0O5xcRr6o2iIiHBA5bDwwevNXVNLTxLgeQlpZGTk4O8+bN47LLLsPj8bB9+3bWrFlDVVUVHo+HvLw8PB5PlKxPDOxwuMeAT0VkLTCCBG3dAvg9Hnw7d1LVjsMB+Hw+GhsbeeGFF2hqasLlctGjRw9KSkqiYGliYsfi/VMi8gowCPhCVeN6Hcjv97PsuefoVVsb8npDVha+XbuoHjYs7OAhmNzcXOrr6/F4PE6XGQGddjgRGY1p5XKAf4jIp6o6r7PldhWLr7qKLS+8wFIR9i1eTFN2Nv977bXHbnC5wO9vd/AQjBMhHDl2/C/5MHAdUAE8BdxnQ5ldxshbbkFUecDv556aGrwh1kgbs7LaXHlw6Di29AGqutn80X1AjR1ldhW9zzmHw6mpuIAsv5+6hobj7vGnpuI5dAhvq7Alh85jh8NVicgtQKaIXAlU21Bml1Lety+PiPAM8EVjI6tDxLwFBg8O9mKHw90ADMR0qadax3HNPU8/zXUlJdyYnc0ZWVn8ZeFCtrfKSw0MHlwhWkCHjtNph1PVg8BDmLyGh4H4T+x0udg7YAA5GRlc73ZzpLGR3y9YwIHDh1vcg2rYsCWHjmFHiPlfgPcxear/4li+alvPtCn1EHTfEyLyQGdtDEVVURFNGRmMTklhus9HVW0tv3/zTeqbmo7e017YksOJY0eXOhEYrKqTVfV0VZ0cwTMzCCP1EMB6Lxxrg30hUbebqtGjERG+m5lJL5+PLRUVPLF4MX7LwY4OHg4caKc0h0ixw+E2AycqNtNC6gHz7ncUETkdE/70uA32haVqxAj8KSnklZfz0wkTSPd4WLV1K7NXrTp6j9/jweck0tiGHQ7XD9hmdY/vWbmm7RFS6gFARIowc3m3tVeIiEwRkfuCPydiuD81lf1WGuCobdu4/dxzcYkwb80almzaBDiDB7uxYy31qg4805bUwxUYfZHXgN5AhohsVNVZrQvpjNRDgMrRo8lbv54eW7bwpQkT+NbkycxavpxZy5bRy+djZJ8+iN9P+t69Ea88OITHjhbOj8m2vzbo0x5hpR5U9WFVPUVVpwAPAM+Fcja7aMzK4sDAgYgq+Z9+ytQRI/jqmDE0q/Lw22+zp7raGTzYiB0O9wKmi9wb9GmPsFIPNthzwlSOGwdAzmef4aqv5+sTJ3Jyv34cbmjgoQULqAZn8GATdnSpNap6z4k80J7UQ9B9szphV8Qcyc/nUJ8++HbvJm/jRirGj+eWKVO4f948tlVV8ae33uLuyZNN2FJOTjRMSlrsaOE+FZErRWS4iAwTkWE2lBl1KseaGZj8deuQ5mbSPB7unDaN3IwMPtu7l8dWryZzxw5n8NBJ7HC4k4BbMCFKj1t/E45DxcUcyc0lpa6OHl98AUBeZiZ3TZuG1+3m3dJS5qxfT3p5eYwtTWzsWNqaClwO/Ai4QlXP6bRVsUCECutdLn/t2qMDhP75+Xxn6lQE+PfGjWxYuNAZPHQCO5a2rsBIrv4EWCEi13TaqhhxcOBAGjMySKuubjHZ+6X+/blq0iTU5eLJpUvZuXZtG6U4tIUdXer3gFNUdQbwJUxydEKibjeVo0cD0LOVU00bPZqpI0bQ4Pfz9KxZVFZWxsLEhMeWeThVPQSgqjVAQiu+7B8xgmaPh8yyMtL27Tt6XkS45vTTGd2vH4cPHOCpJ56grq4uhpYmJnY43Bci8qCIfE1EHgS+sKHMmOH3etk/YgRwfCuX4nJx27nnUpKVRfmuXTzzzDM0NzfHwsyExS6N31LgPIyz3WRDmV2KpqUhfn/Yl//K0aNREbK3bsXTKuchw+vlrmnTyBVh02efMXv2bEeA+gSww+FOAdyqejtwBhC54EaM8PfsSXXv3mFXDpoyMzkwePDR5a7W5PXsyQ8mTsQjwooVK1i8eHFXm5w02OFwfwLesr7/FPijDWV2OeUDBiB+PxIUcBlMhTURnLtpE+4QQoRDCgu56cwzqays5Mknn+T+++/nueee61KbkwE7HK4psImHqpZiFvPjngbrXa21oHSA+rw8akpKcDU3kxtCILAhO5tz0tPpkZ1NQ0MDmzZtcjaJiwA7HG6biNwvIheLyP8FdtlQZpdSWFhIr169KE1NpTEzE3eY0WalJceVv3490npw4HIhqpRYa6u1tbU0OMte7WKHw10HlGPCjfZhBhFxTVpaGhdddBE98vPZWFCAp6Ym5ACitk8f6vLySDlyhJzPPz/uemNmJiMzMyksLMTj8XA4OAnHISR2LG0dUdU/qOqtVixbQqiYp6enM336dNKHDGFnevpxo1EARI6GLgUvdwVoTkvj9rFj+fl3v0vv3r2pr6931M7boVurr6SlpTF9+nR00iRqKiuP7zaBAwMH0piZSerBg2SF2M/B7/EwtLGRsWPH0tTUxDvvvBMN0xOWbu1wYIRozrvsMnTCBOq2bj3+Bpfr6LtczzVrjrvckJ2Nb/t2vnrGGQC8++67HDp0qCtNTmjsWLzvKyI/FJF7Ax87DIsmXq+X02+6iayiIg6GGGnuHz6cZq+XjPLy4xOjXS78Xi+n7trFyCFDaGxsZMmSJVGyPPGIVYh53OHNzGTMrbdS5PVSsW9fi9UDv8dD1ciRwPHLXWASpqW5masKC8HvZ9myZc46axjscLgaVb1HVR8PfGwoMyZ4Bg9m+AUX0C8zk8rKyhZOVzVqFOpykbVtW8gVisbsbEZmZTE2O5sjdXUsW7YsmqYnDDEJMW9P6kFErhKR90XkXeu+6LxripBy5pmMHDSIPoWFLZyuKSOD6sGDEQi53AXQkJPDpYMHk3L4MIsXLaI+xP5d3Z1YhZjPIIzUg4ikA78EplqyET2Ai2ywMzJycnBPmsTYggKKi4upCHK6wHJXzuefh50sHjpiBEOzs2morGTFu5HkhHcvYhVi3pbUQz0wWVUDs6gpRDvGbtw43JmZjBk0iL4lJVRUVOD3+2nIzaWmb19czc3krV8f8lERYfrEibgaG1k6bx6NTivXgliFmIeVelBVv6rutcq+AyP/9WZn7TwhvF446yzc+/czZswY+vfvT2VlJX6//2grl7dhQ9iF/5P69aOkoIBD+/ez7uWXnRyIIOzISw2EmB8SkSxgIfD3dp5pS+oB653tt8Aw4DINE3AmIlOAKR22vC0GDIB+/XBVVjJq1ChEhK1bt0JBAXU9e5JeUUHOpk3sHzUqlF1cPH48jyxcyFtLljB53DgOW0Gd3Z1YhZiHlXqweByjyDQjqGs9DlVdpKr3BX868gNCIgJnnAGHD+NSZeTIkQwaNIiKykrKAxPBn34atvU6deBAinJyKG9oYMPbb+Pbts020xKZWIWYh5V6EJGTMbKtY4GF1ij2EhvsPHHy8uDkk2HvXlwuFyNGjGDo0KGU+nzU+3x4a2rIDrU6AbhEuGj8eBBh9pYt5K1ZQ8auuA+k6XLs6FKvx4xSzwPWY0adbRKB1EP8LLmddBJs2AB1dUh6OsOGDUNE2FJayvB16+i5Zg0HBwwIuTfXaYMHM+ejjyirqWHpwYOc9cknlHs81BUURP1nxAsd/g8rIoGR5TnA58ArGHHCqTbYFT+kpcGZZ4KVwSUiDB06lJxp0zjicpG2bx8ZYXSAU1wupo8fD8Ara9dSn51NwapVpHbjFMPOtCTnWn+vavW5srNGxR2DBkFxMezfDxinGzxyJDp5Mk1NTeStXh320S8PHUpuRgY79u/no7IyGrKyKFy5stsqMXXY4YL2RF2uqtcFPsAntlgWT7hc8OUvw6FDEBTC1OuSS3ihsZH/rFnD4lmzWBAimcbjdnOBNZXyyief0Oz10pSeTuEHH5jAz25GZ7rUq0Tkn8AvReQ56/MvzPtc8tGzJ4wfD8FiNllZaEkJ3/H7uausjNQwI9Gzhw8nOy2N0ooK1u/eTXN6Ov6UFAo++ICUbhYl3Jku9XXM9MUaji1r/RkzeEhOTj4Z3G4IXj3o2RNXbi4uEdL37sVXWnrcY2keD9MsCYmXP/kEMKmIAhSsXBkyKyxZ6UyXut/S2P0OUKyqi4HpgNcm2+KP9HTTtQa1ct6iIp4YOZInBwwg1eul6K23yAgRGfyVUaPI8Hr5rKyMTWVlgJF7dTU2UrBqVbfRnbNj+uEZIBC1+BpmR8HkZehQKCwEK71w5tVXc/P3v88tv/sdN1x+OWkeD8VvvEH67t0tHkv3ejnPWpWYGzTIaMzOJqW2loKPPkIaG6P2M2KFXbsJLrL+LrGrzLjF5TLTJDU14A9KwRXBNWMGqVOmkOp2U/zaa6S1mi6ZNno0qSkprNm5ky0Vx/YxbsjNxVtdTc/Vq0PmVSQTdjhHtbVCMFZEbiDOt6+0hYICGDOm5QACjNNdcQVpZ5xBmstF8auvkhY05+ZLS+McK3J4rvUuF6A+L4+M8nJ6b9rU0pGTDDscbiYwCrPYPooEyEu1hVNPNasLrd+9XC5cV19N2oQJpKnSZ+5cvNb8HcAFY8bgcbv5cNs2dgWdByNunbtnD6lhlsuSATvi4fYBv8FMhzwMDO1smQlBRgZMngyhVhncblzf+hZpJ51EWlMTxXPnHs177ZGRwdnDTFB061YOEZo9HlxJHENnRzzc08DbwFJgJWYxvnswfDjk50Oo5GePB/cNN5A+ZgzpDQ0Uv/IKKbW1AFw4bhxuEd7fsoW93WzFwY4udQQwGngD06V2n0kltxvOPhsOHIBQI0yvF/ctt5A+fDjpdXWUvPIK7ro68n0+Jg8Zgl+VeSFyXZMZu7K2FMhU1QqSeR4uFL17m7m5igrYs6fF0hcAqam4b72V9MGDSaupoWTuXFz19Vw0fjwuEZZv3kxlN0qctsPhPhSRHwC7ReR5wG1DmYnFuHFwzTXm75495r0ueKSZkUHKbbeRMWAA6dXVFM+bR5/0dCYOHEiz389r3aiVs2vi93Hgx8A/gP+yoczEIyMDTjsNvvENGDwYdu6EyspjEcFZWaTcfjsZJSVkVFTQZ/58vmYtdy3etKnl9udJjB0O95Sq1qhqk6rODSTAdFuys2HqVLjySjNft2OHeccDyMkh5Y47yCgqIrOsjAkrVzKhb18am5t5PUyua7Jhh8PVisjvReTb1gRwTHYEjDvy8+HCC+HSS80a7I4dJrwpPx/PHXeQUVCAb+dOflRbi1uVhRs2cKgbLOJ3Jjyph/X1XaAaKASKrI9DgKIiuOQS43yqxvF69MBz++2k5+VRXF7OfUB9YyML1q2LtbVdTmdyGuYCZwG9VfVWm+w5ipUq+BdgPCY5+kZV3Wx3PVHB5TJph337wubN8N57AHhvuQUefZQz9+3jpqYm/r5uHVMnT46trV1MZxyuTkRWAkNFZHzwBUuiobPMwJKDsFIJHwS+ZkO5scPtNpPFAwfC+vWwciXeyy8n9/nnmb5nD/X797No2za+FWs7u5DOONwFQB/MCPU79pjTghZyEEFJO4mP12uywYYPhzVr8DY20uvZZ5leVcW8deuoT+LYOInXXVRE5K/AbFWdbx1vBwYFZ+iHeS5con78cvAg+te/svmee3ixro4dqakcKipC+vfnmUWLYm1dRzk+bxJ78lK7ijblICC81IOEyBFNKOrrYetW2LqVZxP3t6iqHmd8PLdwlwEXq+pM6x3uZ6p6Qaztcugc8exwgVHqOEzzfJ2qbmz7KYd4J24dziE5Se78A4e4w3E4h6gS81GqiLiBJ4HhQDNm7y4BZgEKfArcZikuOSQ48dDCXQygqmcA9wIPWZ97VPVMjPMl9gqDw1Fi7nCqOgcIRJj0x2wscgoQUIaZD3wl+pY5dAUxdzgAVW0SkWcwu0u/iBk9B4bPNRjpfIckIObvcAFU9VoR+RHwPpAedCkLE/50HGFWGn7mTPXEBSGXSGI+Dyci3wRKVPXXIpINrMYoaf5KVReJyGPAO6r6rwjLS7y11OQkbh0uE/gb0BvwAA8AGzAjV6/1/SZVjUh0Q0T02muvtcW2fv368Ytf/MKWsrohJ754LyL9wl1T1eM1qTqAqtYC/x3i0tkdLXPAgAEdtieYl156ie0hpLc6iuPA7b/DBbqxfMy71KeYZOe9wMldaFdcUFtba5vzAmZjkW5Om6NUVT3d2oBtHTBMVc/D7A6zM9T9InJjq+P/Y5ehDslBpKPUEmuXGVS1VkT6BF8Ukasw+ahTRSSwuZsbGIMRuHFwACJ3uAUishhYBUwE/l+r669jVDDzMSHnAH4i25XGoRsRkcOp6t0iMhojWvOsqq5udX0/sAhYJCIFmH2yIi7fofsQkUOISDFGyqEX8KKIpKnq+yHuewQjLL0bMyxWILnz3hxOiEhboCcwaXo/BZZg9EROC3HfJEyiixPZ4RCSSNdS01R1ISYx4jPCa8Bt5lh3ekKIyCQRWWR9P1lEdlk7CS4Ska93pEyH+CPSFq5eRM4H3FZCSziH6wdsE5FAhrxGkhQtIj8EvgnUWqdOBh5S1QcjtM8hQYjU4W4Gfgf0BH4AhJN2uKqDdnwBXMqx0e8pwHAR+Rpmp8I7A9MyDolNpA73PVWNZJfAUIuY7a7lqOpsERkQdOoD4K+q+qGI3A38DOPoDglOpA43UkRyVLW6nfsC2nCC6RY7Gm/3UlBdL2Hi5I6jS/e8d+gSInW4UUCliOzDTHWoqvZpfZOqPh58LCLzO2jXGyJyh6p+gNmX9cNQN1k74CxqVefPOlinQxSIdOK3fyT3iciwoMMizCCiI9wK/FlEGoAyjoWgR8TPf/7zDlbbEre7+8kVdzXthSfdo6q/tPZFbRE4p6pXh3gkuIU7wgm8d6nqVqy5PVX9iDiYMG5O8n2vYkF7Ldxc6+9jkRSmqlNFJB8YDJRaMvoODkdpLzwpsGa6FqMF1x8YQJjWR0SuwEiw/gRYISLX2GapQ1IQ6aDhRWATMBbTVYbTeP8ecIqqHhKRLGAh8PdOW+mQNESU0yAiC1X1HGtfrRuBJar65RD3LbcSmgPHS61k5qghInGbQZOWlkZdXV2szYgWnRMkFJE0IBMzePCFue0LEXkQs8B/Jk48XAuOdANZ/PaIdGL2EcyIcy0mvDycTtsTQBVwHkYj5M+dNdAhuYi0hVNMV7ofI2H/tzD3PQTMVNX1IvIQRpDmrM4a6ZA8ROpwPwUmqmq5iBRipkveCHFfk6quB1DVUhHpcFxcUu3TEMTMmTNtKSdRUw4jdbhKVS0HUNW9IhJiR1rAhCbdD7yHyX3Y1QnbZpBs+zRgX85soqYcRvoOd1BE3hCRn4jIbCBDRO63nCuY64By4EJgH3B9J2xrsU8DkDz7NHRjIp0WCaudoKrP2GrRsTrb3achnJhNV9jjcOIkmmz+Q8AKVf23dbxTVUtibJZDJ4kLfbgwLMd0zVjvcGtja46DHcRzC+fs05CExK3DOSQn8dylOiQhjsM5RJW4cDgR+bGIvCciH4rIDSIyRESWichSEXnUep9zSAJi/h/SmkubDJyBUb3si7NPQ9ISc4cDzsdMebyEWaOdh7NPQ9ISD3JaPTGh6xcBA4FXMJvxtrtPQ1fL5v/h3nuptlHjN6dfP+5MwAX3DhK3O0JXAhtVtQH4TESOYLrVAGH3aejqvNTq7du5z0aN3/sSdMHdTuKhS10GfFUMfTBRxW9brRfABcDSGNnmYDMxb+FUdZ6InIXRE3EBtwFbgCdFJLBPw4sxNNHBRmLucACq+sMQpzu8T4ND/GJrl+rI5ju0hy0tnCOb7xApdnWpjmy+Q0TY4nCObL5DpNjqEI5svkN72N0CxYVs/n02peKt//hjsHHi18F+hwvI5ocTuzkOEXFj9kYdDjRjMr8Ek0StmB0MbzsRJ7ZrdWDGsmW2lONwjLDTIiIyUkRmi8gsK/k5EgKy+e9Zn3cjeOZiAEsE515MpIgTLZKktNXCPQb8GsgDfktohfLWnLBsvqrOEZF51mF/jDD1dFpGi0zDRJM4JDhtOVyzqr4OICKRJjR3VDa/SUSeAS4BLgcuiiRaxCHxiPQdLtIViQ7L5qvqtSLyI+B9ID3oUthoEUc2P/Foy+HyRWQaxnkC3wFQ1QWhHuiIbL6IfBOzAfCvMYMNP7BKRKZY4UcXAO+EqW8Rjmx+QtGWw30EXBn0PfB+lgaEdLgOyub/B/ibiCwBPMCdmAgRJ1okCWnL4f6DERRsAu5W1efByK+28cwJy+arai3w3yEuJV20yOqPP7ZtjjBRo4fbcri7gS9hutQXRCTVEq4JGToMjmx+e2htrW1zhIkaPdyWwzWoahWAtavfQkvBKGzCgCWb/0tMNzhGRO5T1airmItNO9H0cHaisZ22HG6rpWD0U1WtEZFLMaqXOW08Exey+XaJV4izE43ttDVtcT2wBuu/n6ruAKYC/27jGb+qHrLuryH8Rr4O3ZSwLZwl/Der1bm9mFFkOBzZfIc2sTtry5HNd2gTu6NFkko2PxX7BiDgDELAfodLKtn8euwbgIAzCAH7Hc6RzXdoE7vf4RzZfIc2sbWFU9UjwB9sKi4bOBB03CwiKcGy+d0ZO5fJIHpLZXGr8RuJbL6zT0N8E2qfBlQ1Lj/AZcAs6/tpwPwIn7svBrZ2izrtqDee80ZfAs6z8iIE837okODErcOpydL6dqztcLCXeNCHc+hGJKPDLXLqjN9643aU6pCcJGML5xDHOA7nEFUch3OIKknhcCLiEpHHLD2TRSIypIvqmSQii6zvJ4vILqu+RSLydev8TSKySkRWiMhFnajLLSJPi8hyEVkiIoPDbQllV51BdReIyA4RGWH774zFbHUXzH5fSstViZe7oI4fYnbMWWEd3wh8v9U9va17UjHyFGuB1A7WNwN42vo+BXgZs2nKFOvcYxhpDNvqtMr1YCbdNwEj7P6dSdHCEZ3Iki8wjh3gFGC61fo8ZSUNTQSWq2q9qh7AyJeN60hlqjoHuNk6DIj8hNoSyrY6LX6Hcebd1rGtvzNZHC5kZImdFajqbKAx6NQHwP+o6llAKSZooLUdnRLi0WMiP3/CqA+IHpvHCpRtW50iMhPYp6pvBJ229Xcmi8MdxIjeBHBp14cxvaSqHwa+Y5LGW9sRVognUlT1WmAYRrQxlMiPnXVej1m/XgScBDyLCZqw7Xcmi8MtxwR9YkUHr41CnW+IyETr+7nAh5jW4EwRSRORHsBIjILnCSMi3xSRH1uHLUR+rHOBLaFsq1NVz1LVs1V1CvAJ8C3gZTt/Z9wu3p8gsYgsuRX4s4g0AGXAzap6UEQexjiCC6PJ0tHc3IhEflS12cY6Q2Hr73SWthyiSrJ0qQ4JguNwDlHFcTiHqOI4nENUcRzOIao4DucQVRyHc4gqjsM5RBXH4SLAWsLZKiJfFZGb27jnxlDXutCufBF5vP07wz7fW0SiquHnONwJoKqvq+oTYS73xsSORZNfAo909GFVLQNqRCRqWxQ4DhcGEfGJyMvWWuZfrHMzReQBERkmIu+KyGIReVtEijHbDIwSkXtFJFtE/i0iC0TkIxG5Nej5f4vIPBHZYB2ni8jzVrTyKhE5XUQ8VuzZEivCd0oI+7KBCaq6xjruIyJfsr6fLiI5VvmzReQ1EfnYOn5JRD4Xo0wP8Bzw3a7+9zxKNCNzE+kD3A78yvo+CdgKzAQeAG7DxKh5gHOAMcAAjkUDnwxcan3vA3xufZ8JvGF9HwpsBO4CHrDOjcEs0t8K/MY6lw+sC2HfNOAfQcf/A5xhfX8JE8o0E1hgnbsSs4+ZYMTB51jn3cDeaP27Oi1ceEZjwnBQ1fdpGXz5FFCBiTK+HbNbTzBlwAwR+TtwD8YxA3xi/d2B2UZqOEbAEVX9VFX/AIwFLrTi0mYDKWI2XAmmJ8c20wPj5O9bkSQuVa2zzn9s/a0GNqjxsv1W3ahqM9AUyI/oahyHC89G4HQAq6sKdpqvAUtV9VzgBeBHmHi1wL/nD4D3VPUa63qwbFXr8JwNwASrnkEi8pxV9z/VxKVdYJWxv9Vz5Vh7ZljRzb3VBJ1ejHG8QCJRm+FAIiIYqdyobBufLPFwXcEjmHi0ZRgHqA+6tgr4u4g0YRztLowDeEXkN8Bc4FER+QZQiWlBUsPU8zjwtIgsxnRvd2ICSJ+0zmUDfwnhECuA31jfTwX2ish0TDRuGZE3JmOxWtho4MTDJTAi8hjGYb8KvKWqKztQxm+Buaq61G77QuF0qYnNvcB3MJvpfXKiD4tIbyA7Ws4GTgvnEGWcFs4hqjgO5xBVHIdziCqOwzlEFcfhHKKK43AOUcVxOIeo8v8BVIiOwFplYeoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 126x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "probed1=MP['l23pyr_l23pv_d']*1e6\n",
    "cnx1=MP['l23pyr_l23pv_d'][MP['l23pyr_l23pv_syn']]*1e6\n",
    "\n",
    "df=Penk_L23fsi\n",
    "probed2, cnx2 = opto.return_probed_cnx(df,'xyz_dist','excitatory')\n",
    "\n",
    "summary_dict=opto.plotFrac_wHist2(probed1,cnx1,'paired\\ncount','black',probed2,cnx2,'2P\\ncount','red',r'distance ($\\mu$m)',bins,2,ymax=0.65)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "probed 1:  [25 28  4  0  0]\n",
      "called 1:  [6 4 0 0 0]\n",
      "probed 2:  [24 11  8  0  0]\n",
      "called 2:  [7 0 0 0 0]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 126x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "probed1=MP['l23pyr_l23vip_d']*1e6\n",
    "cnx1=MP['l23pyr_l23vip_d'][MP['l23pyr_l23vip_syn']]*1e6\n",
    "\n",
    "df=Penk_L23vip\n",
    "probed2, cnx2 = opto.return_probed_cnx(df,'xyz_dist','excitatory')\n",
    "\n",
    "summary_dict=opto.plotFrac_wHist2(probed1,cnx1,'paired\\ncount','black',probed2,cnx2,'2P\\ncount','red',r'distance ($\\mu$m)',bins,2,ymax=0.65)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "probed 1:  [38 22  1  2  0]\n",
      "called 1:  [9 6 0 0 0]\n",
      "probed 2:  [102 167  63  40  18]\n",
      "called 2:  [45 39  4  0  0]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 126x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "probed1=MP['l23sst_l23pyr_d']*1e6\n",
    "cnx1=MP['l23sst_l23pyr_d'][MP['l23sst_l23pyr_syn']]*1e6\n",
    "\n",
    "df=Sst\n",
    "df=df[(df.post_layer==2) & (df.post_class=='pyramidal')]\n",
    "df=df[df.y_pia<=330]\n",
    "probed2, cnx2 = opto.return_probed_cnx(df,'xyz_dist','inhibitory')\n",
    "\n",
    "summary_dict=opto.plotFrac_wHist2(probed1,cnx1,'paired\\ncount','black',probed2,cnx2,'2P\\ncount','red',r'distance ($\\mu$m)',bins,4,ymax=0.75)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "probed 1:  [38 22  1  2  0]\n",
      "called 1:  [9 6 0 0 0]\n",
      "probed 2:  [ 64 136  47  32  14]\n",
      "called 2:  [25 32  4  0  0]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 126x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "probed1=MP['l23sst_l23pyr_d']*1e6\n",
    "cnx1=MP['l23sst_l23pyr_d'][MP['l23sst_l23pyr_syn']]*1e6\n",
    "\n",
    "df=SstKv\n",
    "df=df[(df.post_layer==2) & (df.post_class=='pyramidal')]\n",
    "df=df[df.y_pia<=330]\n",
    "probed2, cnx2 = opto.return_probed_cnx(df,'xyz_dist','inhibitory')\n",
    "\n",
    "summary_dict=opto.plotFrac_wHist2(probed1,cnx1,'paired\\ncount','black',probed2,cnx2,'2P\\ncount','green',r'distance ($\\mu$m)',bins,4,ymax=0.75)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "probed 1:  [38 22  1  2  0]\n",
      "called 1:  [9 6 0 0 0]\n",
      "probed 2:  [38 31 16  8  4]\n",
      "called 2:  [20  7  0  0  0]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 126x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "probed1=MP['l23sst_l23pyr_d']*1e6\n",
    "cnx1=MP['l23sst_l23pyr_d'][MP['l23sst_l23pyr_syn']]*1e6\n",
    "\n",
    "df=SstAi167\n",
    "df=df[(df.post_layer==2) & (df.post_class=='pyramidal')]\n",
    "df=df[df.y_pia<=330]\n",
    "probed2, cnx2 = opto.return_probed_cnx(df,'xyz_dist','inhibitory')\n",
    "\n",
    "summary_dict=opto.plotFrac_wHist2(probed1,cnx1,'paired\\ncount','black',probed2,cnx2,'2P\\ncount','magenta',r'distance ($\\mu$m)',bins,4,ymax=0.75)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "probed 1:  [36 35  9  0  0]\n",
      "called 1:  [8 4 0 0 0]\n",
      "probed 2:  [12 26  8  2  2]\n",
      "called 2:  [2 4 0 0 0]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 126x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "probed1=MP['l23sst_l23pv_d']*1e6\n",
    "cnx1=MP['l23sst_l23pv_d'][MP['l23sst_l23pv_syn']]*1e6\n",
    "\n",
    "df=Sst\n",
    "df=df[(df.post_layer==2) & (df.post_class=='interneuron') & (df.Kcluster==1)]\n",
    "df=df[df.y_pia<=330]\n",
    "\n",
    "probed2, cnx2 = opto.return_probed_cnx(df,'xyz_dist','inhibitory')\n",
    "\n",
    "summary_dict=opto.plotFrac_wHist2(probed1,cnx1,'paired\\ncount','black',probed2,cnx2,'2P\\ncount','red',r'distance ($\\mu$m)',bins,4,ymax=0.75)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "probed 1:  [31 30  4  0  0]\n",
      "called 1:  [17  9  0  0  0]\n",
      "probed 2:  [50 95 40 18  7]\n",
      "called 2:  [17 17  3  1  0]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 126x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "probed1=MP['l23pv_l23pyr_d']*1e6\n",
    "cnx1=MP['l23pv_l23pyr_d'][MP['l23pv_l23pyr_syn']]*1e6\n",
    "\n",
    "df=PvalbKv\n",
    "df=df[(df.post_layer==2) & (df.post_class=='pyramidal')]\n",
    "df=df[df.y_pia<=330]\n",
    "probed2, cnx2 = opto.return_probed_cnx(df,'xyz_dist','inhibitory')\n",
    "\n",
    "summary_dict=opto.plotFrac_wHist2(probed1,cnx1,'paired\\ncount','black',probed2,cnx2,'2P\\ncount','red',r'distance ($\\mu$m)',bins,4,ymax=0.75)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "probed 1:  [ 82 102   9   2   0]\n",
      "called 1:  [30 32  0  0  0]\n",
      "probed 2:  [16 35 14  4  3]\n",
      "called 2:  [ 5 14  3  0  0]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 126x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "probed1=MP['l23pv_l23pv_d']*1e6\n",
    "cnx1=MP['l23pv_l23pv_d'][MP['l23pv_l23pv_syn']]*1e6\n",
    "\n",
    "df=PvalbKv\n",
    "df=df[(df.post_layer==2) & (df.post_reporter=='positive')]\n",
    "df=df[df.y_pia<=330]\n",
    "probed2, cnx2 = opto.return_probed_cnx(df,'xyz_dist','inhibitory')\n",
    "\n",
    "\n",
    "summary_dict=opto.plotFrac_wHist2(probed1,cnx1,'paired\\ncount','black',probed2,cnx2,'2P\\ncount','red',r'distance ($\\mu$m)',bins,4,ymax=0.75)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig,axes = plt.subplots(1,4)\n",
    "x_lim=[-500,500]\n",
    "y_lim=[1100,0]\n",
    "fs=10\n",
    "tick_size=5\n",
    "font='arial'\n",
    "\n",
    "df_list=[RorbAi167,RorbKv,Tlx3Kv,Ntsr1]\n",
    "colors=['magenta', 'green','darkorange','red']\n",
    "i=0\n",
    "\n",
    "for df in df_list:\n",
    "    \n",
    "    ax=axes[i]\n",
    "    color=colors[i]\n",
    "    df=df[(df.post_layer==2) & (df.post_class=='pyramidal')]\n",
    "    cnx=df[df.cnx=='excitatory']\n",
    "    opto.graph_map_subplot(ax, df, 'excitatory', color, '^', 'blue', '^')\n",
    "    opto.layer_borders(ax)\n",
    "    ax.set_xlim(x_lim)\n",
    "\n",
    "    ax.set_ylim(y_lim)\n",
    "\n",
    "    \n",
    "    ax.set_xlabel(r'horizontal offset ($\\mu$m)', fontsize=fs, fontname=font)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    if i == 0:\n",
    "        ax.yaxis.set_ticks([0,200,400,600,800,1000])\n",
    "        ax.yaxis.set_ticks_position('left')\n",
    "        ax.set_ylabel(r'distance from pia ($\\mu$m)', fontsize=fs, fontname=font)\n",
    "    else:\n",
    "        ax.yaxis.set_ticks_position('none') \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.set_aspect(aspect='equal',adjustable='box')\n",
    "    i+=1\n",
    "\n",
    "\n",
    "\n",
    "fig.set_figwidth(7)\n",
    "\n",
    "fig.tight_layout(pad=0.1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/travish/Desktop/2p_opto_nbs/opto_util.py:50: RuntimeWarning: invalid value encountered in true_divide\n",
      "  exc_frac=exc_hist.astype(float)/probed_hist.astype(float)\n",
      "/Users/travish/opt/miniconda3/lib/python3.9/site-packages/statsmodels/stats/proportion.py:89: RuntimeWarning: invalid value encountered in true_divide\n",
      "  q_ = count * 1. / nobs\n",
      "/Users/travish/Desktop/2p_opto_nbs/opto_util.py:52: RuntimeWarning: invalid value encountered in true_divide\n",
      "  direct_frac=direct_hist.astype(float)/probed_hist.astype(float)\n",
      "/Users/travish/Desktop/2p_opto_nbs/opto_util.py:54: RuntimeWarning: invalid value encountered in true_divide\n",
      "  inh_frac=inh_hist.astype(float)/probed_hist.astype(float)\n"
     ]
    }
   ],
   "source": [
    "RorbAi167_L23pyr=RorbAi167[(RorbAi167.post_layer==2) & (RorbAi167.post_class=='pyramidal')]\n",
    "RorbKv_L23pyr=RorbKv[(RorbKv.post_layer==2) & (RorbKv.post_class=='pyramidal')]\n",
    "Rorb_L23pyr=Rorb[(Rorb.post_layer==2) & (Rorb.post_class=='pyramidal')]\n",
    "Tlx3Kv_L23pyr=Tlx3Kv[(Tlx3Kv.post_layer==2) & (Tlx3Kv.post_class=='pyramidal')]\n",
    "Ntsr1Ai_L23pyr=Ntsr1[(Ntsr1.post_layer==2) & (Ntsr1.post_class=='pyramidal')]\n",
    "Ntsr1Ai_L23fsi=Ntsr1[(Ntsr1.post_layer==2) & (Ntsr1.Kcluster==1)]\n",
    "Scnn1a_L23pyr=Scnn1a[(Scnn1a.post_layer==2) & (Scnn1a.post_class=='pyramidal')]\n",
    "\n",
    "bins=np.arange(50,1101,100)\n",
    "RorbAi_L23pyr_con=opto.connectivity_profile(RorbAi167_L23pyr[RorbAi167_L23pyr.abs_x<300], 'y_pia', bins=bins)\n",
    "RorbKv_L23pyr_con=opto.connectivity_profile(RorbKv_L23pyr[RorbKv_L23pyr.abs_x<300], 'y_pia', bins=bins)\n",
    "Rorb_L23pyr_con=opto.connectivity_profile(Rorb_L23pyr[Rorb_L23pyr.abs_x<300], 'y_pia', bins=bins)\n",
    "Tlx3Kv_L23pyr_con=opto.connectivity_profile(Tlx3Kv_L23pyr[Tlx3Kv_L23pyr.abs_x<300], 'y_pia', bins=bins)\n",
    "Ntsr1Ai_L23pyr_con=opto.connectivity_profile(Ntsr1Ai_L23pyr[Ntsr1Ai_L23pyr.abs_x<300], 'y_pia', bins=bins)\n",
    "PenkKv_L23pyr_con=opto.connectivity_profile(Penk_L23pyr[Penk_L23pyr.abs_x<300],'y_pia', bins=bins)\n",
    "Scnn1a_L23pyr_con=opto.connectivity_profile(Scnn1a_L23pyr[Scnn1a_L23pyr.abs_x<300],'y_pia', bins=bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x144 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gskw = dict(width_ratios=[2,2,2,2], wspace=0.1)\n",
    "\n",
    "gs = matplotlib.gridspec.GridSpec(1,4, **gskw)\n",
    "mp=20\n",
    "fig = plt.figure(figsize=(9,2))\n",
    "\n",
    "ax1=fig.add_subplot(gs[0], aspect='equal', adjustable='box')\n",
    "opto.graph_map_subplot(ax1, RorbAi167_L23pyr, 'excitatory', 'magenta', '^', 'black', '^')\n",
    "ax1.axis([-600,600,1100,0])\n",
    "ax1.set_ylabel(r'distance from pia ($\\mu$m)', fontsize=fs, fontname=font)\n",
    "ax1.set_xlabel(r'horizontal offset ($\\mu$m)', fontsize=fs, fontname=font)\n",
    "ax1.yaxis.set_ticks([0,200,400,600,800,1000])\n",
    "ax1.yaxis.set_ticks_position('left')\n",
    "ax1.yaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax1.xaxis.set_ticks([-500,0,500])\n",
    "\n",
    "ax2=fig.add_subplot(gs[1], aspect='equal', adjustable='box')\n",
    "opto.graph_map_subplot(ax2, RorbKv_L23pyr, 'excitatory', 'green', '^', 'black', '^')\n",
    "ax2.axis([-600,600,1100,0])\n",
    "ax2.axes.yaxis.set_visible(False)\n",
    "ax2.set_xlabel(r'horizontal offset ($\\mu$m)', fontsize=fs, fontname=font)\n",
    "ax2.spines['left'].set_visible(False)\n",
    "ax2.yaxis.set_ticks_position('none')\n",
    "ax2.xaxis.set_ticks([-500,0,500])\n",
    "\n",
    "ax3=fig.add_subplot(gs[2])\n",
    "opto.rate_ci_probed_cnx_y(ax3,'magenta', RorbAi_L23pyr_con['midX'], RorbAi_L23pyr_con['probed_hist'], RorbAi_L23pyr_con['exc_hist'], min_probed=mp)\n",
    "opto.rate_ci_probed_cnx_y(ax3,'green', RorbKv_L23pyr_con['midX'], RorbKv_L23pyr_con['probed_hist'], RorbKv_L23pyr_con['exc_hist'], min_probed=mp)\n",
    "ax3.plot([0,0],[1100,0], lw=1,c='darkgrey')\n",
    "ax3.axis([0,0.2,1100,0])\n",
    "ax3.set_xlabel('fraction connected', fontsize=fs, fontname=font)\n",
    "ax3.spines['left'].set_visible(False)\n",
    "ax3.yaxis.set_ticks_position('none')\n",
    "ax3.xaxis.set_ticks([0,0.1,0.2])\n",
    "ax3.xaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "ax3.axes.yaxis.set_visible(False)\n",
    "\n",
    "ax4=fig.add_subplot(gs[3])\n",
    "df1=RorbAi167_L23pyr.dropna(subset=['y_pia'])\n",
    "df1=df1[df1.abs_x<300]\n",
    "df2=RorbKv_L23pyr.dropna(subset=['y_pia'])\n",
    "df2=df2[df2.abs_x<300]\n",
    "ax4.hist(df1.y_pia, color='magenta', bins=bins,alpha=0.9,density=False,cumulative=False,orientation='horizontal')\n",
    "ax4.hist(df2.y_pia, color='green', bins=bins,alpha=0.9,density=False,cumulative=False,orientation='horizontal')\n",
    "ax4.axis([0,800,1100,0])\n",
    "ax4.set_xlabel('connections probed', fontsize=fs, fontname=font)\n",
    "ax4.spines['left'].set_visible(False)\n",
    "ax4.yaxis.set_ticks_position('none')\n",
    "ax4.axes.yaxis.set_visible(False)\n",
    "ax4.xaxis.set_major_locator(plt.MaxNLocator(3))\n",
    "\n",
    "for ax in [ax1,ax2,ax3,ax4]:\n",
    "    opto.layer_borders(ax,max_x=1000)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.xaxis.set_ticks_position('bottom')\n",
    "    ax.xaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tlx3\n",
      "1724 connections tested\n",
      "86 connections found\n",
      "connection prob.:  0.04988399071925754\n",
      "L5 Tlx3_win300\n",
      "1052 connections tested\n",
      "61 connections found\n",
      "connection prob.:  0.05798479087452472\n",
      "L5 Rorb_win300\n",
      "1028 connections tested\n",
      "71 connections found\n",
      "connection prob.:  0.06906614785992218\n"
     ]
    }
   ],
   "source": [
    "df1=Tlx3Kv_L23pyr\n",
    "df2=Tlx3Kv_L23pyr[(Tlx3Kv_L23pyr.abs_x<300) & (Tlx3Kv_L23pyr.y_pia>480) & (Tlx3Kv_L23pyr.y_pia<750)]\n",
    "df3=Rorb_L23pyr[(Rorb_L23pyr.abs_x<300) & (Rorb_L23pyr.y_pia>480) & (Rorb_L23pyr.y_pia<750)]\n",
    "\n",
    "df_list=[df1,df2,df3]\n",
    "name_list=['Tlx3', 'L5 Tlx3_win300', 'L5 Rorb_win300']\n",
    "i,j=0,0\n",
    "table=np.zeros([2,3])\n",
    "for df in df_list:\n",
    "    \n",
    "    probed=df[df.cnx.isin(opto.probed_calls)]\n",
    "    exc=df[df.cnx=='excitatory']\n",
    "    p_num=np.shape(probed)[0]\n",
    "    cnx_num=np.shape(exc)[0]\n",
    "    print (name_list[i])\n",
    "    print (p_num, \"connections tested\")\n",
    "    print (cnx_num, \"connections found\")\n",
    "    print (\"connection prob.: \",float(cnx_num)/p_num)\n",
    "    table[0,i]=cnx_num\n",
    "    table[1,i]=p_num-cnx_num\n",
    "    i+=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1724  probed cnxs\n",
      "86  exc cnxs\n",
      "0  inh cnxs\n",
      "46 postsyanptic cells\n",
      "1145  probed cnxs\n",
      "1  exc cnxs\n",
      "0  inh cnxs\n",
      "23 postsyanptic cells\n"
     ]
    }
   ],
   "source": [
    "df_list=[Tlx3Kv_L23pyr, Ntsr1Ai_L23pyr]\n",
    "for df in df_list:\n",
    "    opto.print_counts(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "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'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x144 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gskw = dict(width_ratios=[2,2,2], wspace=0.01)\n",
    "gs = matplotlib.gridspec.GridSpec(1,3, **gskw)\n",
    "mp=20\n",
    "fig = plt.figure(figsize=(7,2))\n",
    "\n",
    "ax1=fig.add_subplot(gs[0], aspect='equal', adjustable='box')\n",
    "opto.graph_map_subplot(ax1, Tlx3Kv_L23pyr, 'excitatory', 'darkorange', '^', 'black', '^')\n",
    "ax1.set_ylabel(r'distance from pia ($\\mu$m)', fontsize=fs, fontname=font)\n",
    "ax1.yaxis.set_ticks([0,200,400,600,800,1000])\n",
    "ax1.yaxis.set_ticks_position('left')\n",
    "ax1.yaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "\n",
    "ax2=fig.add_subplot(gs[1], aspect='equal', adjustable='box')\n",
    "opto.graph_map_subplot(ax2, Ntsr1Ai_L23pyr, 'excitatory', 'red', '^', 'black', '^')\n",
    "ax2.axes.yaxis.set_visible(False)\n",
    "ax2.spines['left'].set_visible(False)\n",
    "ax2.yaxis.set_ticks_position('none')\n",
    "\n",
    "ax3=fig.add_subplot(gs[2])\n",
    "opto.rate_ci_probed_cnx_y(ax3, 'blue', Rorb_L23pyr_con['midX'], Rorb_L23pyr_con['probed_hist'], Rorb_L23pyr_con['exc_hist'], min_probed=mp)\n",
    "opto.rate_ci_probed_cnx_y(ax3,'darkorange', Tlx3Kv_L23pyr_con['midX'], Tlx3Kv_L23pyr_con['probed_hist'], Tlx3Kv_L23pyr_con['exc_hist'], min_probed=mp)\n",
    "opto.rate_ci_probed_cnx_y(ax3,'red', Ntsr1Ai_L23pyr_con['midX'], Ntsr1Ai_L23pyr_con['probed_hist'], Ntsr1Ai_L23pyr_con['exc_hist'], min_probed=mp)\n",
    "opto.rate_ci_probed_cnx_y(ax3, 'magenta', PenkKv_L23pyr_con['midX'], PenkKv_L23pyr_con['probed_hist'], PenkKv_L23pyr_con['exc_hist'], min_probed=mp)\n",
    "opto.rate_ci_probed_cnx_y(ax3, 'cyan', Scnn1a_L23pyr_con['midX'], Scnn1a_L23pyr_con['probed_hist'], Scnn1a_L23pyr_con['exc_hist'], min_probed=mp)\n",
    "ax3.plot([0,0],[1100,0], lw=1,c='darkgrey')\n",
    "ax3.axis([0,0.2,1100,0])\n",
    "ax3.set_xlabel('fraction connected', fontsize=fs, fontname=font)\n",
    "ax3.spines['left'].set_visible(False)\n",
    "ax3.yaxis.set_ticks_position('none')\n",
    "ax3.xaxis.set_ticks([0,0.1,0.2])\n",
    "ax3.xaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "ax3.axes.yaxis.set_visible(False)\n",
    "\n",
    "for ax in [ax1, ax2, ax3]:\n",
    "    opto.layer_borders(ax)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.xaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "    ax.xaxis.set_ticks_position('bottom')\n",
    "    \n",
    "for ax in [ax1, ax2]:\n",
    "    ax.xaxis.set_ticks([-500,0,500])\n",
    "    ax.set_xlabel(r'horizontal offset ($\\mu$m)', fontsize=fs, fontname=font)\n",
    "    ax.axis([-600,600,1100,0])\n",
    "#plt.savefig('Tlx3Ntrs1_L23_maps.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/travish/Desktop/2p_opto_nbs/opto_util.py:50: RuntimeWarning: invalid value encountered in true_divide\n",
      "  exc_frac=exc_hist.astype(float)/probed_hist.astype(float)\n",
      "/Users/travish/opt/miniconda3/lib/python3.9/site-packages/statsmodels/stats/proportion.py:89: RuntimeWarning: invalid value encountered in true_divide\n",
      "  q_ = count * 1. / nobs\n",
      "/Users/travish/Desktop/2p_opto_nbs/opto_util.py:52: RuntimeWarning: invalid value encountered in true_divide\n",
      "  direct_frac=direct_hist.astype(float)/probed_hist.astype(float)\n",
      "/Users/travish/Desktop/2p_opto_nbs/opto_util.py:54: RuntimeWarning: invalid value encountered in true_divide\n",
      "  inh_frac=inh_hist.astype(float)/probed_hist.astype(float)\n"
     ]
    }
   ],
   "source": [
    "binsize=100\n",
    "bins=np.arange(50,1001,binsize)\n",
    "\n",
    "exc_L23_int=standard_df[(standard_df.post_class=='interneuron') & (standard_df.post_layer==2)]\n",
    "exc_L23_int=exc_L23_int[exc_L23_int.presynapticCre.isin(['Penk', 'Scnn1a', 'Rorb', 'Tlx3', 'Ntsr1'])]\n",
    "\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",
    "exc_L23_sst=standard_df[(standard_df.Kcluster==0) & (standard_df.post_layer==2)]\n",
    "exc_L23_sst=exc_L23_sst[exc_L23_sst.presynapticCre.isin(['Penk', 'Scnn1a', 'Rorb', 'Tlx3', 'Ntsr1'])]\n",
    "\n",
    "exc_L23_vip=standard_df[(standard_df.Kcluster==2) & (standard_df.post_layer==2)]\n",
    "exc_L23_vip=exc_L23_vip[exc_L23_vip.presynapticCre.isin(['Penk', 'Scnn1a', 'Rorb', 'Tlx3', 'Ntsr1'])]\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",
    "\n",
    "color_dict={'Scnn1a':'blue', 'Rorb':'blue', 'Tlx3':'darkorange', 'Ntsr1':'red', 'Penk':'magenta', 'Sst':'green','Pvalb':'purple'}\n",
    "\n",
    "exc_L23int_con=opto.connectivity_profile(exc_L23_int[exc_L23_int.abs_x<=300],'y_pia',bins)\n",
    "exc_L23fsi_con=opto.connectivity_profile(exc_L23_fsi[exc_L23_fsi.abs_x<=300],'y_pia',bins)\n",
    "exc_L23sst_con=opto.connectivity_profile(exc_L23_sst[exc_L23_sst.abs_x<=300],'y_pia',bins)\n",
    "exc_L23vip_con=opto.connectivity_profile(exc_L23_vip[exc_L23_vip.abs_x<=300],'y_pia',bins)\n",
    "exc_L23pyr_con=opto.connectivity_profile(exc_L23_pyr[exc_L23_pyr.abs_x<=300],'y_pia',bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "gskw = dict(width_ratios=[4,3], wspace=0.1)\n",
    "gs = matplotlib.gridspec.GridSpec(1,2, **gskw)\n",
    "fs=10\n",
    "mp=10\n",
    "fig = plt.figure(figsize=(3,2))\n",
    "\n",
    "ax1=fig.add_subplot(gs[0], aspect='equal', adjustable='box')\n",
    "opto.graph_map_subplot_color_dict(ax1, exc_L23_fsi, 'excitatory', color_dict, '^', 'black', 'o')\n",
    "ax1.axis([-450,450,1100,0])\n",
    "ax1.set_ylabel(r'distance from pia ($\\mu$m)', fontsize=fs, fontname=font)\n",
    "ax1.set_xlabel(r'horiz. offset ($\\mu$m)', fontsize=fs, fontname=font)\n",
    "ax1.yaxis.set_ticks([0,200,400,600,800,1000])\n",
    "ax1.yaxis.set_ticks_position('left')\n",
    "ax1.yaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax1.xaxis.set_ticks([-300,0,300])\n",
    "\n",
    "ax2=fig.add_subplot(gs[1])\n",
    "opto.rate_ci_probed_cnx_y(ax2,'black', exc_L23pyr_con['midX'], exc_L23pyr_con['probed_hist'], exc_L23pyr_con['exc_hist'], min_probed=mp)\n",
    "opto.rate_ci_probed_cnx_y(ax2,'blue', exc_L23fsi_con['midX'], exc_L23fsi_con['probed_hist'], exc_L23fsi_con['exc_hist'], min_probed=mp)\n",
    "ax2.plot([0,0],[1100,0], lw=1,c='darkgrey')\n",
    "ax2.axis([0,0.4,1100,0])\n",
    "ax2.set_xlabel('fraction connected', fontsize=fs, fontname=font)\n",
    "ax2.spines['left'].set_visible(False)\n",
    "ax2.yaxis.set_ticks_position('none')\n",
    "ax2.xaxis.set_ticks([0,0.2,0.4])\n",
    "ax2.xaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "ax2.axes.yaxis.set_visible(False)\n",
    "\n",
    "for ax in [ax1, ax2]:\n",
    "    opto.layer_borders(ax)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.xaxis.set_ticks_position('bottom')\n",
    "    ax.xaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18.759   4.2188  1.4062  1.5        nan]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "gskw = dict(width_ratios=[4,2], wspace=0.1)\n",
    "gs = matplotlib.gridspec.GridSpec(1,2, **gskw)\n",
    "mp=20\n",
    "fig = plt.figure(figsize=(3,2))\n",
    "\n",
    "ax1=fig.add_subplot(gs[0], aspect='equal', adjustable='box')\n",
    "opto.graph_map_subplot_color_dict(ax1, exc_L23_sst, 'excitatory', color_dict, '^', 'blue', 'o')\n",
    "opto.layer_borders(ax1)\n",
    "ax1.axis([-450,450,1100,0])\n",
    "ax1.set_ylabel(r'distance from pia ($\\mu$m)', fontsize=fs, fontname=font)\n",
    "ax1.set_xlabel(r'lateral offset ($\\mu$m)', fontsize=fs, fontname=font)\n",
    "ax1.spines['right'].set_visible(False)\n",
    "ax1.spines['top'].set_visible(False)\n",
    "ax1.yaxis.set_ticks([0,200,400,600,800,1000])\n",
    "ax1.yaxis.set_ticks_position('left')\n",
    "ax1.xaxis.set_ticks_position('bottom')\n",
    "ax1.yaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax1.xaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax1.xaxis.set_ticks([-300,0,300])\n",
    "\n",
    "\n",
    "ax2=fig.add_subplot(gs[1])\n",
    "opto.rate_ci_probed_cnx_y(ax2,'black', exc_L23pyr_con['midX'], exc_L23pyr_con['probed_hist'], exc_L23pyr_con['exc_hist'], min_probed=mp)\n",
    "opto.rate_ci_probed_cnx_y(ax2,'red', exc_L23sst_con['midX'], exc_L23sst_con['probed_hist'], exc_L23sst_con['exc_hist'], min_probed=mp)\n",
    "\n",
    "opto.layer_borders(ax2)\n",
    "ax2.axis([0,0.4,1100,0])\n",
    "ax2.set_xlabel('fraction connected', fontsize=fs, fontname=font)\n",
    "ax2.spines['left'].set_visible(False)\n",
    "ax2.spines['right'].set_visible(False)\n",
    "ax2.spines['top'].set_visible(False)\n",
    "ax2.yaxis.set_ticks_position('none')\n",
    "ax2.xaxis.set_ticks_position('bottom')\n",
    "ax2.xaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax2.xaxis.set_ticks([0,0.2,0.4])\n",
    "ax2.xaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "ax2.axes.yaxis.set_visible(False)\n",
    "\n",
    "print (exc_L23_sst.oneP.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[30.781   12.675   27.681    9.25     0.09375]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "gskw = dict(width_ratios=[4,2], wspace=0.1)\n",
    "gs = matplotlib.gridspec.GridSpec(1,2, **gskw)\n",
    "mp=20\n",
    "fig = plt.figure(figsize=(3,2))\n",
    "\n",
    "ax1=fig.add_subplot(gs[0], aspect='equal', adjustable='box')\n",
    "opto.graph_map_subplot_color_dict(ax1, exc_L23_vip, 'excitatory', color_dict, '^', 'blue', 'o')\n",
    "opto.layer_borders(ax1)\n",
    "ax1.axis([-450,450,1100,0])\n",
    "ax1.set_ylabel(r'distance from pia ($\\mu$m)', fontsize=fs, fontname=font)\n",
    "ax1.set_xlabel(r'lateral offset ($\\mu$m)', fontsize=fs, fontname=font)\n",
    "ax1.spines['right'].set_visible(False)\n",
    "ax1.spines['top'].set_visible(False)\n",
    "ax1.yaxis.set_ticks([0,200,400,600,800,1000])\n",
    "ax1.yaxis.set_ticks_position('left')\n",
    "ax1.xaxis.set_ticks_position('bottom')\n",
    "ax1.yaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax1.xaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax1.xaxis.set_ticks([-300,0,300])\n",
    "\n",
    "\n",
    "ax2=fig.add_subplot(gs[1])\n",
    "opto.rate_ci_probed_cnx_y(ax2,'black', exc_L23pyr_con['midX'], exc_L23pyr_con['probed_hist'], exc_L23pyr_con['exc_hist'], min_probed=mp)\n",
    "opto.rate_ci_probed_cnx_y(ax2,'red', exc_L23vip_con['midX'], exc_L23vip_con['probed_hist'], exc_L23vip_con['exc_hist'], min_probed=mp)\n",
    "\n",
    "opto.layer_borders(ax2)\n",
    "ax2.axis([0,0.4,1100,0])\n",
    "ax2.set_xlabel('fraction connected', fontsize=fs, fontname=font)\n",
    "ax2.spines['left'].set_visible(False)\n",
    "ax2.spines['right'].set_visible(False)\n",
    "ax2.spines['top'].set_visible(False)\n",
    "ax2.yaxis.set_ticks_position('none')\n",
    "ax2.xaxis.set_ticks_position('bottom')\n",
    "ax2.xaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax2.xaxis.set_ticks([0,0.2,0.4])\n",
    "ax2.xaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "ax2.axes.yaxis.set_visible(False)\n",
    "\n",
    "print (exc_L23_vip.oneP.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/travish/Desktop/2p_opto_nbs/opto_util.py:50: RuntimeWarning: invalid value encountered in true_divide\n",
      "  exc_frac=exc_hist.astype(float)/probed_hist.astype(float)\n",
      "/Users/travish/opt/miniconda3/lib/python3.9/site-packages/statsmodels/stats/proportion.py:89: RuntimeWarning: invalid value encountered in true_divide\n",
      "  q_ = count * 1. / nobs\n",
      "/Users/travish/Desktop/2p_opto_nbs/opto_util.py:52: RuntimeWarning: invalid value encountered in true_divide\n",
      "  direct_frac=direct_hist.astype(float)/probed_hist.astype(float)\n",
      "/Users/travish/Desktop/2p_opto_nbs/opto_util.py:54: RuntimeWarning: invalid value encountered in true_divide\n",
      "  inh_frac=inh_hist.astype(float)/probed_hist.astype(float)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "bins=np.arange(0,601,100)\n",
    "mp=14\n",
    "Penk_L23pyr_lat=opto.connectivity_profile(Penk_L23pyr, 'abs_x',bins)\n",
    "RorbL4_L23pyr_lat=opto.connectivity_profile(Rorb_L23pyr[(Rorb_L23pyr.y_pia<480) & (Rorb_L23pyr.y_pia>330)],'abs_x',bins)\n",
    "RorbL5_L23pyr_lat=opto.connectivity_profile(Rorb_L23pyr[(Rorb_L23pyr.y_pia>480) & (Rorb_L23pyr.y_pia<750)],'abs_x',bins)\n",
    "Tlx3L5_L23pyr_lat=opto.connectivity_profile(Tlx3Kv_L23pyr[(Tlx3Kv_L23pyr.y_pia>480)],'abs_x',bins)\n",
    "\n",
    "Rorb_L23fsi=Rorb[(Rorb.Kcluster==1) & (Rorb.post_layer==2)]\n",
    "Tlx3_L23fsi=Tlx3Kv[(Tlx3Kv.Kcluster==1) & (Tlx3Kv.post_layer==2)]\n",
    "Penk_L23fsi_lat=opto.connectivity_profile(Penk_L23fsi, 'abs_x',bins)\n",
    "RorbL4_L23fsi_lat=opto.connectivity_profile(Rorb_L23fsi[(Rorb_L23fsi.y_pia<480) & (Rorb_L23fsi.y_pia>330)],'abs_x',bins)\n",
    "RorbL5_L23fsi_lat=opto.connectivity_profile(Rorb_L23fsi[(Rorb_L23fsi.y_pia>480) & (Rorb_L23fsi.y_pia<750)],'abs_x',bins)\n",
    "Tlx3L5_L23fsi_lat=opto.connectivity_profile(Tlx3_L23fsi[(Tlx3_L23fsi.y_pia>480)],'abs_x',bins)\n",
    "\n",
    "gskw = dict(width_ratios=[1,1], wspace=0.2) #\n",
    "\n",
    "gs = matplotlib.gridspec.GridSpec(1,2, **gskw)\n",
    "fig = plt.figure(figsize=(4,2))\n",
    "\n",
    "ax1=fig.add_subplot(gs[0])\n",
    "opto.rate_ci_probed_cnx_x(ax1, 'magenta', Penk_L23pyr_lat['midX'], Penk_L23pyr_lat['probed_hist'], Penk_L23pyr_lat['exc_hist'], min_probed=mp)\n",
    "opto.rate_ci_probed_cnx_x(ax1, 'blue', RorbL4_L23pyr_lat['midX'], RorbL4_L23pyr_lat['probed_hist'], RorbL4_L23pyr_lat['exc_hist'], min_probed=mp)\n",
    "opto.rate_ci_probed_cnx_x(ax1, 'skyblue', RorbL5_L23pyr_lat['midX'], RorbL5_L23pyr_lat['probed_hist'], RorbL5_L23pyr_lat['exc_hist'], min_probed=mp)\n",
    "opto.rate_ci_probed_cnx_x(ax1, 'orange', Tlx3L5_L23pyr_lat['midX'], Tlx3L5_L23pyr_lat['probed_hist'], Tlx3L5_L23pyr_lat['exc_hist'], min_probed=mp)\n",
    "ax1.yaxis.set_ticks([0,0.2,0.4])\n",
    "ax1.yaxis.set_ticks_position('left')\n",
    "ax1.yaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax1.yaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "ax1.set_ylabel('fraction connected', fontsize=fs, fontname=font)\n",
    "\n",
    "ax2=fig.add_subplot(gs[1])\n",
    "opto.rate_ci_probed_cnx_x(ax2, 'magenta', Penk_L23fsi_lat['midX'], Penk_L23fsi_lat['probed_hist'], Penk_L23fsi_lat['exc_hist'], min_probed=mp)\n",
    "opto.rate_ci_probed_cnx_x(ax2, 'blue', RorbL4_L23fsi_lat['midX'], RorbL4_L23fsi_lat['probed_hist'], RorbL4_L23fsi_lat['exc_hist'], min_probed=mp)\n",
    "opto.rate_ci_probed_cnx_x(ax2, 'skyblue', RorbL5_L23fsi_lat['midX'], RorbL5_L23fsi_lat['probed_hist'], RorbL5_L23fsi_lat['exc_hist'], min_probed=mp)\n",
    "opto.rate_ci_probed_cnx_x(ax2, 'orange', Tlx3L5_L23fsi_lat['midX'], Tlx3L5_L23fsi_lat['probed_hist'], Tlx3L5_L23fsi_lat['exc_hist'], min_probed=mp)\n",
    "ax2.axes.yaxis.set_visible(False)\n",
    "ax2.spines['left'].set_visible(False)\n",
    "\n",
    "for ax in [ax1, ax2]:\n",
    "    ax.axis([0,600,0,0.60])\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.xaxis.set_ticks_position('bottom')\n",
    "    ax.xaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "    ax.xaxis.set_ticks([0,300,600])\n",
    "    ax.set_xlabel(r'horizontal distance ($\\mu$m)', fontsize=fs, fontname=font)\n",
    "#plt.savefig('exc_cnx_lat_off.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_17510/1991973026.py:22: RuntimeWarning: invalid value encountered in true_divide\n",
      "  H_prob=H_cnx/H\n",
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_17510/1991973026.py:26: MatplotlibDeprecationWarning: You are modifying the state of a globally registered colormap. This has been deprecated since 3.3 and in 3.6, you will not be able to modify a registered colormap in-place. To remove this warning, you can make a copy of the colormap first. cmap = mpl.cm.get_cmap(\"plasma\").copy()\n",
      "  current_cmap.set_bad(color='black')\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=exc_L23_pyr.dropna(subset=['y_pia'])\n",
    "df_probed=df[df.cnx.isin(opto.probed_calls)]\n",
    "df_cnx=df[df.cnx=='excitatory']\n",
    "\n",
    "min_x=-600\n",
    "max_x=600\n",
    "min_y=80\n",
    "max_y=1080\n",
    "binsize=100\n",
    "\n",
    "xedges = range(min_x,max_x+1,binsize)\n",
    "yedges = range(min_y,max_y+1,binsize)\n",
    "\n",
    "H, xedges, yedges = np.histogram2d(df_probed.x_ap, df_probed.y_pia, bins=(xedges, yedges))\n",
    "\n",
    "threshold=H>4 ##at least 5 tested connections to draw color\n",
    "H=H*threshold\n",
    "H = H.T\n",
    "H_cnx, xedges, yedges = np.histogram2d(df_cnx.x_ap,df_cnx.y_pia, bins=(xedges, yedges))\n",
    "H_cnx=H_cnx*threshold\n",
    "H_cnx = H_cnx.T\n",
    "H_prob=H_cnx/H\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "current_cmap = matplotlib.cm.plasma\n",
    "current_cmap.set_bad(color='black')\n",
    "plt.imshow(H_prob, interpolation='nearest',filterrad=1, origin='lower',extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]], cmap=current_cmap)\n",
    "\n",
    "plt.xlabel(r'horizontal offset ($\\mu$m)', fontsize=12, fontname='arial')\n",
    "plt.ylabel(r'distance from pia ($\\mu$m)', fontsize=12, fontname='arial')\n",
    "ax.axis([min_x, max_x, 1040, 0])\n",
    "ax.set_facecolor('black')\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "plt.tick_params(axis='both', which='major', labelsize=12, direction='out')\n",
    "plt.yticks(fontname = \"arial\")\n",
    "plt.xticks([-500,-250,0,250,500],fontname= \"arial\")\n",
    "layer_bs=[80,330,480,750,1000]\n",
    "for b in layer_bs:\n",
    "    plt.plot([-600,600],[b,b], c='white', ls='dashed', lw=3, alpha=0.7)\n",
    "\n",
    "cb=plt.colorbar(fraction=0.04, pad=0.05)\n",
    "cb.set_label('fraction connected', fontsize=12, fontname='arial')\n",
    "cb.ax.tick_params(labelsize=12)\n",
    "\n",
    "cells=df.groupby(['exp_id', 'headstage']).toPia.mean()\n",
    "for cell in cells:\n",
    "    plt.scatter(0,cell, marker='^', color='white', alpha=0.05, s=100)\n",
    "fig.set_figheight(3)\n",
    "fig.set_figwidth(3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/travish/Desktop/2p_opto_nbs/opto_util.py:50: RuntimeWarning: invalid value encountered in true_divide\n",
      "  exc_frac=exc_hist.astype(float)/probed_hist.astype(float)\n",
      "/Users/travish/opt/miniconda3/lib/python3.9/site-packages/statsmodels/stats/proportion.py:89: RuntimeWarning: invalid value encountered in true_divide\n",
      "  q_ = count * 1. / nobs\n",
      "/Users/travish/Desktop/2p_opto_nbs/opto_util.py:52: RuntimeWarning: invalid value encountered in true_divide\n",
      "  direct_frac=direct_hist.astype(float)/probed_hist.astype(float)\n",
      "/Users/travish/Desktop/2p_opto_nbs/opto_util.py:54: RuntimeWarning: invalid value encountered in true_divide\n",
      "  inh_frac=inh_hist.astype(float)/probed_hist.astype(float)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "SstAi167_L23pyr=SstAi167[(SstAi167.post_layer==2) & (SstAi167.post_class=='pyramidal')]\n",
    "SstKv_L23pyr=SstKv[(SstKv.post_layer==2) & (SstKv.post_class=='pyramidal')]\n",
    "PvalbKv_L23pyr=PvalbKv[(PvalbKv.post_layer==2) & (PvalbKv.post_class=='pyramidal')]\n",
    "\n",
    "\n",
    "bins=np.arange(80,1001,100)\n",
    "#bins=[100,330,480,780,1100]\n",
    "lat_lim=200\n",
    "SstAi_L23pyr_con=opto.connectivity_profile(SstAi167_L23pyr[SstAi167_L23pyr.abs_x<=lat_lim],'y_pia',bins)\n",
    "SstKv_L23pyr_con=opto.connectivity_profile(SstKv_L23pyr[SstKv_L23pyr.abs_x<=lat_lim],'y_pia',bins)\n",
    "PvalbKv_L23pyr_con=opto.connectivity_profile(PvalbKv_L23pyr[PvalbKv_L23pyr.abs_x<=lat_lim],'y_pia',bins)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x144 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gskw = dict(width_ratios=[1,1,1,0.7], wspace=0.05) #\n",
    "font='arial'\n",
    "gs = matplotlib.gridspec.GridSpec(1,4, **gskw)\n",
    "fs=10\n",
    "min_probed=9\n",
    "fig = plt.figure(figsize=(9,2))\n",
    "\n",
    "ax1=fig.add_subplot(gs[0], aspect='equal', adjustable='box')\n",
    "opto.graph_map_subplot(ax1, PvalbKv_L23pyr, 'inhibitory', 'darkblue', 'o', 'black', '^')\n",
    "ax1.set_ylabel(r'distance from pia ($\\mu$m)', fontsize=fs, fontname=font)\n",
    "ax1.yaxis.set_ticks([0,200,400,600,800,1000])\n",
    "ax1.yaxis.set_ticks_position('left')\n",
    "ax1.yaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "\n",
    "ax2=fig.add_subplot(gs[1], aspect='equal', adjustable='box')\n",
    "opto.graph_map_subplot(ax2, SstKv_L23pyr, 'inhibitory', 'green', 'o', 'black', '^')\n",
    "\n",
    "ax3=fig.add_subplot(gs[2], aspect='equal', adjustable='box')\n",
    "opto.graph_map_subplot(ax3, SstAi167_L23pyr, 'inhibitory', 'magenta', 'o', 'black', '^')\n",
    "\n",
    "ax4=fig.add_subplot(gs[3])\n",
    "opto.rate_ci_probed_cnx_y(ax4,'magenta', SstAi_L23pyr_con['midX'], SstAi_L23pyr_con['probed_hist'], SstAi_L23pyr_con['inh_hist'], min_probed=min_probed)\n",
    "opto.rate_ci_probed_cnx_y(ax4,'green', SstKv_L23pyr_con['midX'], SstKv_L23pyr_con['probed_hist'], SstKv_L23pyr_con['inh_hist'], min_probed=min_probed)\n",
    "opto.rate_ci_probed_cnx_y(ax4,'darkblue', PvalbKv_L23pyr_con['midX'], PvalbKv_L23pyr_con['probed_hist'], PvalbKv_L23pyr_con['inh_hist'], min_probed=min_probed)\n",
    "ax4.plot([0,0],[1100,0], lw=1,c='darkgrey')\n",
    "ax4.axis([0,0.7,1100,0])\n",
    "ax4.set_xlabel('fraction connected', fontsize=fs, fontname=font)\n",
    "ax4.xaxis.set_ticks([0,0.3,0.6])\n",
    "ax4.xaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "\n",
    "for ax in [ax1, ax2, ax3, ax4]:\n",
    "    opto.layer_borders(ax)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.xaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "    ax.xaxis.set_ticks_position('bottom')\n",
    "    \n",
    "for ax in [ax1, ax2, ax3]:\n",
    "    ax.axis([-600,600,1100,0])\n",
    "    ax.set_xlabel(r'horizontal offset ($\\mu$m)', fontsize=fs, fontname=font)\n",
    "\n",
    "    ax.xaxis.set_ticks([-500,0,500])\n",
    "\n",
    "for ax in [ax2,ax3,ax4]:\n",
    "    ax.axes.yaxis.set_visible(False)\n",
    "    ax.spines['left'].set_visible(False)\n",
    "    ax.yaxis.set_ticks_position('none')\n",
    "#plt.savefig('inh_L23_maps.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/travish/Desktop/2p_opto_nbs/opto_util.py:50: RuntimeWarning: invalid value encountered in true_divide\n",
      "  exc_frac=exc_hist.astype(float)/probed_hist.astype(float)\n",
      "/Users/travish/opt/miniconda3/lib/python3.9/site-packages/statsmodels/stats/proportion.py:89: RuntimeWarning: invalid value encountered in true_divide\n",
      "  q_ = count * 1. / nobs\n",
      "/Users/travish/Desktop/2p_opto_nbs/opto_util.py:52: RuntimeWarning: invalid value encountered in true_divide\n",
      "  direct_frac=direct_hist.astype(float)/probed_hist.astype(float)\n",
      "/Users/travish/Desktop/2p_opto_nbs/opto_util.py:54: RuntimeWarning: invalid value encountered in true_divide\n",
      "  inh_frac=inh_hist.astype(float)/probed_hist.astype(float)\n"
     ]
    }
   ],
   "source": [
    "SstAi167_L23fsi=SstAi167[(SstAi167.post_layer==2) & (SstAi167.Kcluster==1)]\n",
    "SstKv_L23fsi=SstKv[(SstKv.post_layer==2) & (SstKv.Kcluster==1)]\n",
    "PvalbKv_L23fsi=PvalbKv[(PvalbKv.post_layer==2) & (PvalbKv.Kcluster==1)]\n",
    "\n",
    "SstAi_L23fsi_con=opto.connectivity_profile(SstAi167_L23fsi[SstAi167_L23fsi.abs_x<=lat_lim],'y_pia',bins)\n",
    "SstKv_L23fsi_con=opto.connectivity_profile(SstKv_L23fsi[SstKv_L23fsi.abs_x<=lat_lim],'y_pia',bins)\n",
    "PvalbKv_L23fsi_con=opto.connectivity_profile(PvalbKv_L23fsi[PvalbKv_L23fsi.abs_x<=lat_lim],'y_pia',bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x144 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gskw = dict(width_ratios=[1,1,0.7], wspace=0.05) #\n",
    "\n",
    "gs = matplotlib.gridspec.GridSpec(1,3, **gskw)\n",
    "\n",
    "fig = plt.figure(figsize=(7,2))\n",
    "\n",
    "min_probed=1\n",
    "\n",
    "ax1=fig.add_subplot(gs[0], aspect='equal', adjustable='box')\n",
    "opto.graph_map_subplot(ax1, PvalbKv_L23fsi, 'inhibitory', 'darkblue', 'o', 'black', 'o')\n",
    "opto.graph_selected(ax1,PvalbKv_L23fsi,'direct','orange','o',pre_size=25)\n",
    "ax1.set_ylabel(r'distance from pia ($\\mu$m)', fontsize=fs, fontname=font)\n",
    "ax1.yaxis.set_ticks([0,200,400,600,800,1000])\n",
    "ax1.yaxis.set_ticks_position('left')\n",
    "ax1.yaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "\n",
    "ax2=fig.add_subplot(gs[1], aspect='equal', adjustable='box')\n",
    "opto.graph_map_subplot(ax2, SstKv_L23fsi, 'inhibitory', 'green', 'o', 'black', 'o')\n",
    "ax2.axes.yaxis.set_visible(False)\n",
    "ax2.spines['left'].set_visible(False)\n",
    "ax2.yaxis.set_ticks_position('none')\n",
    "\n",
    "ax3=fig.add_subplot(gs[2])\n",
    "opto.rate_ci_probed_cnx_y(ax3,'green', SstKv_L23fsi_con['midX'], SstKv_L23fsi_con['probed_hist'], SstKv_L23fsi_con['inh_hist'], min_probed=min_probed)\n",
    "opto.rate_ci_probed_cnx_y(ax3,'darkblue', PvalbKv_L23fsi_con['midX'], PvalbKv_L23fsi_con['probed_hist'], PvalbKv_L23fsi_con['inh_hist'], min_probed=min_probed)\n",
    "ax3.plot([0,0],[1100,0], lw=1,c='darkgrey')\n",
    "ax3.axis([0,0.8,1100,0])\n",
    "ax3.set_xlabel('fraction connected', fontsize=fs, fontname=font)\n",
    "ax3.spines['left'].set_visible(False)\n",
    "ax3.yaxis.set_ticks_position('none')\n",
    "ax3.xaxis.set_ticks([0,0.4,0.8])\n",
    "ax3.xaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "ax3.axes.yaxis.set_visible(False)\n",
    "\n",
    "for ax in [ax1, ax2]:\n",
    "    ax.set_xlabel(r'horizontal offset ($\\mu$m)', fontsize=fs, fontname=font)\n",
    "    ax.xaxis.set_ticks([-500,0,500])\n",
    "    ax.axis([-600,600,1100,0])\n",
    "\n",
    "for ax in [ax1, ax2, ax3]:\n",
    "    opto.layer_borders(ax)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.xaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "    ax.xaxis.set_ticks_position('bottom')\n",
    "\n",
    "#plt.savefig('inh_L23fsi_maps.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins=np.arange(0,301,100)\n",
    "Sst_L23fsi=SstKv[(SstKv.Kcluster==1) & (SstKv.post_layer==2)]\n",
    "Pvalb_L23fsi=PvalbKv[(PvalbKv.Kcluster==1) & (PvalbKv.post_layer==2)]\n",
    "\n",
    "L23Sst_L23fsi=Sst_L23fsi[Sst_L23fsi.y_pia<330]\n",
    "L23Pvalb_L23fsi=Pvalb_L23fsi[Pvalb_L23fsi.y_pia<330]\n",
    "L4Sst_L23fsi=Sst_L23fsi[(Sst_L23fsi.y_pia>330) & (Sst_L23fsi.y_pia<480)]\n",
    "L4Pvalb_L23fsi=Pvalb_L23fsi[(Pvalb_L23fsi.y_pia>330) & (Pvalb_L23fsi.y_pia<480)]\n",
    "L5Sst_L23fsi=Sst_L23fsi[(Sst_L23fsi.y_pia>480) & (Sst_L23fsi.y_pia<750)]\n",
    "L5Pvalb_L23fsi=Pvalb_L23fsi[(Pvalb_L23fsi.y_pia>480) & (Pvalb_L23fsi.y_pia<750)]\n",
    "\n",
    "\n",
    "L23Sst_L23fsi_lat=opto.connectivity_profile(L23Sst_L23fsi, 'abs_x',bins)\n",
    "L23Pvalb_L23fsi_lat=opto.connectivity_profile(L23Pvalb_L23fsi, 'abs_x',bins)\n",
    "L4Sst_L23fsi_lat=opto.connectivity_profile(L4Sst_L23fsi, 'abs_x',bins)\n",
    "L4Pvalb_L23fsi_lat=opto.connectivity_profile(L4Pvalb_L23fsi, 'abs_x',bins)\n",
    "L5Sst_L23fsi_lat=opto.connectivity_profile(L5Sst_L23fsi, 'abs_x',bins)\n",
    "L5Pvalb_L23fsi_lat=opto.connectivity_profile(L5Pvalb_L23fsi, 'abs_x',bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "mp=4\n",
    "fs=10\n",
    "y_max=0.6\n",
    "\n",
    "gskw = dict(width_ratios=[1,1], wspace=0.2) #\n",
    "gs = matplotlib.gridspec.GridSpec(1,2, **gskw)\n",
    "fig = plt.figure(figsize=(4,2))\n",
    "\n",
    "ax1=fig.add_subplot(gs[0])\n",
    "opto.rate_ci_probed_cnx_x(ax1, 'midnightblue', L23Pvalb_L23fsi_lat['midX'], L23Pvalb_L23fsi_lat['probed_hist'], L23Pvalb_L23fsi_lat['inh_hist'], min_probed=mp)\n",
    "opto.rate_ci_probed_cnx_x(ax1, 'blue', L4Pvalb_L23fsi_lat['midX'], L4Pvalb_L23fsi_lat['probed_hist'], L4Pvalb_L23fsi_lat['inh_hist'], min_probed=mp)\n",
    "opto.rate_ci_probed_cnx_x(ax1, 'cyan', L5Pvalb_L23fsi_lat['midX'], L5Pvalb_L23fsi_lat['probed_hist'], L5Pvalb_L23fsi_lat['inh_hist'], min_probed=mp)\n",
    "ax1.axis([0,300,0,y_max])\n",
    "ax1.yaxis.set_ticks([0,0.25,0.5])\n",
    "ax1.yaxis.set_ticks_position('left')\n",
    "ax1.yaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax1.yaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "ax1.set_ylabel('fraction connected', fontsize=fs, fontname=font)\n",
    "\n",
    "ax2=fig.add_subplot(gs[1])\n",
    "opto.rate_ci_probed_cnx_x(ax2, 'darkolivegreen', L23Sst_L23fsi_lat['midX'], L23Sst_L23fsi_lat['probed_hist'], L23Sst_L23fsi_lat['inh_hist'], min_probed=mp)\n",
    "opto.rate_ci_probed_cnx_x(ax2, 'green', L4Sst_L23fsi_lat['midX'], L4Sst_L23fsi_lat['probed_hist'], L4Sst_L23fsi_lat['inh_hist'], min_probed=mp)\n",
    "opto.rate_ci_probed_cnx_x(ax2, 'lightgreen', L5Sst_L23fsi_lat['midX'], L5Sst_L23fsi_lat['probed_hist'], L5Sst_L23fsi_lat['inh_hist'], min_probed=mp)\n",
    "ax2.axis([0,300,0,y_max])\n",
    "ax2.axes.yaxis.set_visible(False)\n",
    "ax2.spines['left'].set_visible(False)\n",
    "\n",
    "for ax in [ax1, ax2]:\n",
    "    ax.set_xlabel(r'horizontal distance ($\\mu$m)', fontsize=fs, fontname=font)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.xaxis.set_ticks_position('bottom')\n",
    "    ax.xaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "    ax.xaxis.set_ticks([0,150,300])\n",
    "#plt.savefig('inh_cnx_toFSI_lat_off.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "x_max=400\n",
    "bins=np.arange(0,x_max+1,100)\n",
    "SstAi_L23pyr=SstAi167[(SstAi167.post_class=='pyramidal') & (SstAi167.post_layer==2)]\n",
    "SstKv_L23pyr=SstKv[(SstKv.post_class=='pyramidal') & (SstKv.post_layer==2)]\n",
    "Pvalb_L23pyr=PvalbKv[(PvalbKv.post_class=='pyramidal') & (PvalbKv.post_layer==2)]\n",
    "Sst_L23int=Sst[(Sst.post_class=='interneuron') & (Sst.post_layer==2)]\n",
    "Pvalb_L23int=PvalbKv[(PvalbKv.post_class=='interneuron') & (PvalbKv.post_layer==2)]\n",
    "\n",
    "L23SstAi_L23pyr=SstAi_L23pyr[SstAi_L23pyr.y_pia<330]\n",
    "L23SstKv_L23pyr=SstKv_L23pyr[SstKv_L23pyr.y_pia<330]\n",
    "L23Pvalb_L23pyr=Pvalb_L23pyr[Pvalb_L23pyr.y_pia<330]\n",
    "\n",
    "L4SstAi_L23pyr=SstAi_L23pyr[(SstAi_L23pyr.y_pia>330) & (SstAi_L23pyr.y_pia<480)]\n",
    "L4SstKv_L23pyr=SstKv_L23pyr[(SstKv_L23pyr.y_pia>330) & (SstKv_L23pyr.y_pia<480)]\n",
    "L4Pvalb_L23pyr=Pvalb_L23pyr[(Pvalb_L23pyr.y_pia>330) & (Pvalb_L23pyr.y_pia<480)]\n",
    "\n",
    "L5SstAi_L23pyr=SstAi_L23pyr[(SstAi_L23pyr.y_pia>480) & (SstAi_L23pyr.y_pia<750)]\n",
    "L5SstKv_L23pyr=SstKv_L23pyr[(SstKv_L23pyr.y_pia>480) & (SstKv_L23pyr.y_pia<750)]\n",
    "L5Pvalb_L23pyr=Pvalb_L23pyr[(Pvalb_L23pyr.y_pia>480) & (Pvalb_L23pyr.y_pia<750)]\n",
    "\n",
    "Pvalb_L23pyr_lat=opto.connectivity_profile(Pvalb_L23pyr,'abs_x',bins)\n",
    "SstKv_L23pyr_lat=opto.connectivity_profile(SstKv_L23pyr,'abs_x',bins)\n",
    "\n",
    "L23SstAi_L23pyr_lat=opto.connectivity_profile(L23SstAi_L23pyr, 'abs_x',bins)\n",
    "L23SstKv_L23pyr_lat=opto.connectivity_profile(L23SstKv_L23pyr, 'abs_x',bins)\n",
    "L23Pvalb_L23pyr_lat=opto.connectivity_profile(L23Pvalb_L23pyr, 'abs_x',bins)\n",
    "\n",
    "L4SstAi_L23pyr_lat=opto.connectivity_profile(L4SstAi_L23pyr, 'abs_x',bins)\n",
    "L4SstKv_L23pyr_lat=opto.connectivity_profile(L4SstKv_L23pyr, 'abs_x',bins)\n",
    "L4Pvalb_L23pyr_lat=opto.connectivity_profile(L4Pvalb_L23pyr, 'abs_x',bins)\n",
    "\n",
    "L5SstAi_L23pyr_lat=opto.connectivity_profile(L5SstAi_L23pyr, 'abs_x',bins)\n",
    "L5SstKv_L23pyr_lat=opto.connectivity_profile(L5SstKv_L23pyr, 'abs_x',bins)\n",
    "L5Pvalb_L23pyr_lat=opto.connectivity_profile(L5Pvalb_L23pyr, 'abs_x',bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x144 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "mp=9\n",
    "fs=10\n",
    "y_max=0.6\n",
    "gskw = dict(width_ratios=[1,1,1], wspace=0.2) #\n",
    "gs = matplotlib.gridspec.GridSpec(1,3, **gskw)\n",
    "\n",
    "fig = plt.figure(figsize=(8,2))\n",
    "\n",
    "ax1=fig.add_subplot(gs[0])\n",
    "opto.rate_ci_probed_cnx_x(ax1, 'darkblue', L23Pvalb_L23pyr_lat['midX'], L23Pvalb_L23pyr_lat['probed_hist'], L23Pvalb_L23pyr_lat['inh_hist'], min_probed=mp)\n",
    "opto.rate_ci_probed_cnx_x(ax1, 'blue', L4Pvalb_L23pyr_lat['midX'], L4Pvalb_L23pyr_lat['probed_hist'], L4Pvalb_L23pyr_lat['inh_hist'], min_probed=mp)\n",
    "opto.rate_ci_probed_cnx_x(ax1, 'cyan', L5Pvalb_L23pyr_lat['midX'], L5Pvalb_L23pyr_lat['probed_hist'], L5Pvalb_L23pyr_lat['inh_hist'], min_probed=mp)\n",
    "ax1.yaxis.set_ticks([0,0.25,0.5])\n",
    "ax1.yaxis.set_ticks_position('left')\n",
    "ax1.yaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax1.yaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "ax1.set_ylabel('fraction connected', fontsize=fs, fontname=font)\n",
    "\n",
    "\n",
    "ax2=fig.add_subplot(gs[1])\n",
    "opto.rate_ci_probed_cnx_x(ax2, 'darkolivegreen', L23SstKv_L23pyr_lat['midX'], L23SstKv_L23pyr_lat['probed_hist'], L23SstKv_L23pyr_lat['inh_hist'], min_probed=mp)\n",
    "opto.rate_ci_probed_cnx_x(ax2, 'green', L4SstKv_L23pyr_lat['midX'], L4SstKv_L23pyr_lat['probed_hist'], L4SstKv_L23pyr_lat['inh_hist'], min_probed=mp)\n",
    "opto.rate_ci_probed_cnx_x(ax2, 'lightgreen', L5SstKv_L23pyr_lat['midX'], L5SstKv_L23pyr_lat['probed_hist'], L5SstKv_L23pyr_lat['inh_hist'], min_probed=mp)\n",
    "ax2.axes.yaxis.set_visible(False)\n",
    "ax2.spines['left'].set_visible(False)\n",
    "\n",
    "ax3=fig.add_subplot(gs[2])\n",
    "opto.rate_ci_probed_cnx_x(ax3, 'magenta', L23SstAi_L23pyr_lat['midX'], L23SstAi_L23pyr_lat['probed_hist'], L23SstAi_L23pyr_lat['inh_hist'], min_probed=mp)\n",
    "opto.rate_ci_probed_cnx_x(ax3, 'red', L4SstAi_L23pyr_lat['midX'], L4SstAi_L23pyr_lat['probed_hist'], L4SstAi_L23pyr_lat['inh_hist'], min_probed=mp)\n",
    "opto.rate_ci_probed_cnx_x(ax3, 'lightcoral', L5SstAi_L23pyr_lat['midX'], L5SstAi_L23pyr_lat['probed_hist'], L5SstAi_L23pyr_lat['inh_hist'], min_probed=mp)\n",
    "ax3.axes.yaxis.set_visible(False)\n",
    "ax3.spines['left'].set_visible(False)\n",
    "\n",
    "for ax in [ax1, ax2, ax3]:\n",
    "    ax.set_xlabel(r'horizontal distance ($\\mu$m)', fontsize=fs, fontname=font)\n",
    "    ax.axis([0,x_max,0,y_max])\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.xaxis.set_ticks([0,x_max/2,x_max])\n",
    "    ax.xaxis.set_ticks_position('bottom')\n",
    "    ax.xaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "    ax.set_xlabel(r'horizontal distance ($\\mu$m)', fontsize=fs, fontname=font)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_17510/2932944282.py:25: MatplotlibDeprecationWarning: You are modifying the state of a globally registered colormap. This has been deprecated since 3.3 and in 3.6, you will not be able to modify a registered colormap in-place. To remove this warning, you can make a copy of the colormap first. cmap = mpl.cm.get_cmap(\"plasma\").copy()\n",
      "  current_cmap.set_bad(color='black')\n",
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_17510/2932944282.py:26: RuntimeWarning: invalid value encountered in true_divide\n",
      "  H_prob=H_cnx/H\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 180x180 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=standard_df\n",
    "df=df[df.presynapticCre.isin(['Pvalb', 'Sst'])]\n",
    "df=df[(df.post_layer==2) & (df.post_class=='pyramidal')]\n",
    "inh_L23pyr=df\n",
    "df_probed=df[df.cnx.isin(opto.probed_calls)]\n",
    "df_cnx=df[df.cnx=='inhibitory']\n",
    "\n",
    "min_x=-600\n",
    "max_x=600\n",
    "min_y=80\n",
    "max_y=1080\n",
    "binsize=100\n",
    "xedges = range(min_x,max_x+1,binsize)\n",
    "yedges = range(min_y,max_y+1,binsize)\n",
    "\n",
    "H, xedges, yedges = np.histogram2d(df_probed.x_ap, df_probed.y_pia, bins=(xedges, yedges))\n",
    "threshold=H >4\n",
    "H=H*threshold\n",
    "H = H.T\n",
    "H_cnx, xedges, yedges = np.histogram2d(df_cnx.x_ap,df_cnx.y_pia, bins=(xedges, yedges))\n",
    "H_cnx=H_cnx*threshold\n",
    "H_cnx = H_cnx.T\n",
    "fig, ax = plt.subplots()\n",
    "current_cmap = matplotlib.cm.plasma\n",
    "current_cmap.set_bad(color='black')\n",
    "H_prob=H_cnx/H\n",
    "\n",
    "plt.imshow(H_prob, interpolation='nearest',filterrad=1, origin='lower',extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]], cmap=current_cmap)\n",
    "plt.xlabel(r'horizontal offset ($\\mu$m)', fontsize=10, fontname='arial')\n",
    "plt.ylabel(r'distance from pia ($\\mu$m)', fontsize=10, fontname='arial')\n",
    "plt.axis([min_x, max_x, 1040, 0])\n",
    "ax.set_facecolor('black')\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",
    "plt.tick_params(axis='both', which='major', labelsize=10, direction='out')\n",
    "plt.yticks(fontname = \"arial\")\n",
    "plt.xticks([-500,-250,0,250,500],fontname= \"arial\")\n",
    "layer_bs=[80,330,480,750,1000]\n",
    "for b in layer_bs:\n",
    "    plt.plot([-600,600],[b,b], c='white', ls='dashed', lw=3, alpha=0.7)\n",
    "\n",
    "cb=plt.colorbar(fraction=0.04, pad=0.05)\n",
    "cb.set_label('fraction connected', fontsize=10, fontname='arial')\n",
    "cb.ax.tick_params(labelsize=10)\n",
    "\n",
    "tick_locator = mpl.ticker.MaxNLocator(nbins=5)\n",
    "cb.locator = tick_locator\n",
    "cb.update_ticks()\n",
    "cells=df.groupby(['exp_id', 'headstage']).toPia.mean()\n",
    "for cell in cells:\n",
    "    plt.scatter(0,cell, marker='^', color='white', alpha=0.05, s=100)\n",
    "\n",
    "fig.set_figheight(2.5)\n",
    "fig.set_figwidth(2.5)\n",
    "#plt.savefig('inh_cnx_prob_heat2x2.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/travish/Desktop/2p_opto_nbs/opto_util.py:50: RuntimeWarning: invalid value encountered in true_divide\n",
      "  exc_frac=exc_hist.astype(float)/probed_hist.astype(float)\n",
      "/Users/travish/opt/miniconda3/lib/python3.9/site-packages/statsmodels/stats/proportion.py:89: RuntimeWarning: invalid value encountered in true_divide\n",
      "  q_ = count * 1. / nobs\n",
      "/Users/travish/Desktop/2p_opto_nbs/opto_util.py:52: RuntimeWarning: invalid value encountered in true_divide\n",
      "  direct_frac=direct_hist.astype(float)/probed_hist.astype(float)\n",
      "/Users/travish/Desktop/2p_opto_nbs/opto_util.py:54: RuntimeWarning: invalid value encountered in true_divide\n",
      "  inh_frac=inh_hist.astype(float)/probed_hist.astype(float)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 172.8x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=np.arange(-601,601,100)\n",
    "mp=9\n",
    "\n",
    "L23inh_connect_lat=opto.connectivity_profile(inh_L23pyr,'abs_x',bins)\n",
    "L23exc_connect_lat=opto.connectivity_profile(exc_L23_pyr,'abs_x',bins)\n",
    "\n",
    "fig,ax=plt.subplots()\n",
    "ax2=ax.twinx()\n",
    "opto.rate_ci_probed_cnx_x(ax, 'darkblue', L23exc_connect_lat['midX'], L23exc_connect_lat['probed_hist'], L23exc_connect_lat['exc_hist'], min_probed=mp)\n",
    "opto.rate_ci_probed_cnx_x(ax2, 'red', L23inh_connect_lat['midX'], L23inh_connect_lat['probed_hist'], L23inh_connect_lat['inh_hist'], min_probed=mp)\n",
    "ax.set_ylim([0,0.095])\n",
    "ax2.tick_params(axis='y', which='both', labelsize=10, direction='out',colors='red')\n",
    "ax.tick_params(axis='y', which='both', labelsize=10, direction='out',colors='darkblue')\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_visible(True)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.yaxis.label.set_color('darkblue')\n",
    "ax2.yaxis.label.set_color('red')\n",
    "ax2.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",
    "ax2.yaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "ax.xaxis.set_major_locator(plt.MaxNLocator(5))\n",
    "ax.set_ylabel('exc. fraction connected')\n",
    "ax2.set_ylabel('inh. fraction connected')\n",
    "ax.set_xlabel('horizontal distance (' r'$\\mu$'+'m)')\n",
    "ax.axes.xaxis.set_visible(True)\n",
    "fig.set_figheight(2)\n",
    "fig.set_figwidth(2.4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 172.8x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=np.arange(-601,601,100)\n",
    "mp=9\n",
    "\n",
    "L23inh_connect_lat=opto.connectivity_profile(inh_L23pyr,'x_ap',bins)\n",
    "L23exc_connect_lat=opto.connectivity_profile(exc_L23_pyr,'x_ap',bins)\n",
    "\n",
    "fig,ax=plt.subplots()\n",
    "opto.rate_ci_probed_cnx_x(ax, 'darkblue', L23exc_connect_lat['midX'], L23exc_connect_lat['probed_hist'], L23exc_connect_lat['exc_hist'], min_probed=mp)\n",
    "opto.rate_ci_probed_cnx_x(ax, 'red', L23inh_connect_lat['midX'], L23inh_connect_lat['probed_hist'], L23inh_connect_lat['inh_hist'], min_probed=mp)\n",
    "ax.set_ylabel('fraction connected')\n",
    "ax.set_xlabel('horizontal offset (' r'$\\mu$'+'m)')\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(5))\n",
    "ax.axes.xaxis.set_visible(True)\n",
    "fig.set_figheight(2)\n",
    "fig.set_figwidth(2.4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "MP_2P_comp=pd.read_csv('data/MP_2P_comp_table.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "781\n",
      "1238\n"
     ]
    }
   ],
   "source": [
    "print (np.sum(MP_2P_comp.MP_probed))\n",
    "print (np.sum(MP_2P_comp.twoP_probed))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "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=MP_2P_comp\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(df.MP_prob, df.twoP_prob)\n",
    "\n",
    "ax.plot([0,1],[0,1], c='black', ls='dashed')\n",
    "plt.xlabel('paired fraction connected', fontsize=12)\n",
    "plt.ylabel('2P fraction connected', fontsize=12)\n",
    "plt.axis([0,1,0,1])\n",
    "fig.set_figheight(2)\n",
    "fig.set_figwidth(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>cnx_class</th>\n",
       "      <th>MP_probed</th>\n",
       "      <th>MP_cnx</th>\n",
       "      <th>twoP_probed</th>\n",
       "      <th>twoP_cnx</th>\n",
       "      <th>MP_prob</th>\n",
       "      <th>twoP_prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L23exc_L23exc</td>\n",
       "      <td>135</td>\n",
       "      <td>8</td>\n",
       "      <td>205</td>\n",
       "      <td>12</td>\n",
       "      <td>0.059259</td>\n",
       "      <td>0.058537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L23exc_L23exc200</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "      <td>312</td>\n",
       "      <td>3</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.009615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>L23exc_L23pv</td>\n",
       "      <td>28</td>\n",
       "      <td>13</td>\n",
       "      <td>41</td>\n",
       "      <td>19</td>\n",
       "      <td>0.464286</td>\n",
       "      <td>0.463415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L23exc_L23pv200</td>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "      <td>60</td>\n",
       "      <td>9</td>\n",
       "      <td>0.296296</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>L23exc_L23vip</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.291667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>L23exc_L23vip200</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>L23sst_L23exc</td>\n",
       "      <td>38</td>\n",
       "      <td>9</td>\n",
       "      <td>101</td>\n",
       "      <td>45</td>\n",
       "      <td>0.236842</td>\n",
       "      <td>0.445545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>L23sst_L23exc200</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>167</td>\n",
       "      <td>38</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.227545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>L23sst_L23pv</td>\n",
       "      <td>36</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>L23sst_L23pv200</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>0.114286</td>\n",
       "      <td>0.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>L23sst_L23sst</td>\n",
       "      <td>94</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.053191</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>L23sst_L23sst200</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.032787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>L23pv_L23pyr</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>50</td>\n",
       "      <td>17</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>0.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>L23pv_L23pyr200</td>\n",
       "      <td>30</td>\n",
       "      <td>9</td>\n",
       "      <td>96</td>\n",
       "      <td>18</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.187500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>L23pv_L23pv</td>\n",
       "      <td>82</td>\n",
       "      <td>30</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>0.365854</td>\n",
       "      <td>0.352941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>L23pv_L23pv200</td>\n",
       "      <td>102</td>\n",
       "      <td>32</td>\n",
       "      <td>35</td>\n",
       "      <td>14</td>\n",
       "      <td>0.313725</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           cnx_class  MP_probed  MP_cnx  twoP_probed  twoP_cnx   MP_prob  \\\n",
       "0      L23exc_L23exc        135       8          205        12  0.059259   \n",
       "1   L23exc_L23exc200         48       2          312         3  0.041667   \n",
       "2       L23exc_L23pv         28      13           41        19  0.464286   \n",
       "3    L23exc_L23pv200         27       8           60         9  0.296296   \n",
       "4      L23exc_L23vip         25       6           24         7  0.240000   \n",
       "5   L23exc_L23vip200         28       4           11         0  0.142857   \n",
       "6      L23sst_L23exc         38       9          101        45  0.236842   \n",
       "7   L23sst_L23exc200         22       6          167        38  0.272727   \n",
       "8       L23sst_L23pv         36       8           12         2  0.222222   \n",
       "9    L23sst_L23pv200         35       4           26         4  0.114286   \n",
       "10     L23sst_L23sst         94       5           20         1  0.053191   \n",
       "11  L23sst_L23sst200         20       1           61         2  0.050000   \n",
       "12      L23pv_L23pyr         31      17           50        17  0.548387   \n",
       "13   L23pv_L23pyr200         30       9           96        18  0.300000   \n",
       "14       L23pv_L23pv         82      30           17         6  0.365854   \n",
       "15    L23pv_L23pv200        102      32           35        14  0.313725   \n",
       "\n",
       "    twoP_prob  \n",
       "0    0.058537  \n",
       "1    0.009615  \n",
       "2    0.463415  \n",
       "3    0.150000  \n",
       "4    0.291667  \n",
       "5    0.000000  \n",
       "6    0.445545  \n",
       "7    0.227545  \n",
       "8    0.166667  \n",
       "9    0.153846  \n",
       "10   0.050000  \n",
       "11   0.032787  \n",
       "12   0.340000  \n",
       "13   0.187500  \n",
       "14   0.352941  \n",
       "15   0.400000  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MP_2P_comp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.7908931692211648, 0.0002642297981838401)\n",
      "SpearmanrResult(correlation=0.7941176470588236, pvalue=0.0002391909276256312)\n"
     ]
    }
   ],
   "source": [
    "print (stats.pearsonr(df.MP_prob, df.twoP_prob))\n",
    "print (stats.spearmanr(df.MP_prob, df.twoP_prob))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_vals=[]\n",
    "min_probed=[]\n",
    "for row in df.index:\n",
    "    MP_cnx=df['MP_cnx'][row]\n",
    "    MP_nocnx=df['MP_probed'][row]-df['MP_cnx'][row]\n",
    "    twoP_cnx=df['twoP_cnx'][row]\n",
    "    twoP_nocnx=df['twoP_probed'][row]-df['twoP_cnx'][row]\n",
    "    cnx_fisher=stats.fisher_exact([[MP_cnx, MP_nocnx],[twoP_cnx, twoP_nocnx]])\n",
    "    p_vals=np.append(p_vals,cnx_fisher[1])\n",
    "    \n",
    "    min_probed_i=np.min([df['MP_probed'][row], df['twoP_probed'][row]])\n",
    "    min_probed=np.append(min_probed, min_probed_i)\n",
    "\n",
    "df['fisher_p']=p_vals\n",
    "df['min_probed']=min_probed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>cnx_class</th>\n",
       "      <th>MP_probed</th>\n",
       "      <th>MP_cnx</th>\n",
       "      <th>twoP_probed</th>\n",
       "      <th>twoP_cnx</th>\n",
       "      <th>MP_prob</th>\n",
       "      <th>twoP_prob</th>\n",
       "      <th>fisher_p</th>\n",
       "      <th>min_probed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L23exc_L23exc</td>\n",
       "      <td>135</td>\n",
       "      <td>8</td>\n",
       "      <td>205</td>\n",
       "      <td>12</td>\n",
       "      <td>0.059259</td>\n",
       "      <td>0.058537</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L23exc_L23exc200</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "      <td>312</td>\n",
       "      <td>3</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>0.133801</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>L23exc_L23pv</td>\n",
       "      <td>28</td>\n",
       "      <td>13</td>\n",
       "      <td>41</td>\n",
       "      <td>19</td>\n",
       "      <td>0.464286</td>\n",
       "      <td>0.463415</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L23exc_L23pv200</td>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "      <td>60</td>\n",
       "      <td>9</td>\n",
       "      <td>0.296296</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.145391</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>L23exc_L23vip</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.291667</td>\n",
       "      <td>0.753614</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>L23exc_L23vip200</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.309115</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>L23sst_L23exc</td>\n",
       "      <td>38</td>\n",
       "      <td>9</td>\n",
       "      <td>101</td>\n",
       "      <td>45</td>\n",
       "      <td>0.236842</td>\n",
       "      <td>0.445545</td>\n",
       "      <td>0.031458</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>L23sst_L23exc200</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>167</td>\n",
       "      <td>38</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.227545</td>\n",
       "      <td>0.600734</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>L23sst_L23pv</td>\n",
       "      <td>36</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>L23sst_L23pv200</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>0.114286</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.713381</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>L23sst_L23sst</td>\n",
       "      <td>94</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.053191</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>L23sst_L23sst200</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.032787</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>L23pv_L23pyr</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>50</td>\n",
       "      <td>17</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.104213</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>L23pv_L23pyr200</td>\n",
       "      <td>30</td>\n",
       "      <td>9</td>\n",
       "      <td>96</td>\n",
       "      <td>18</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.208089</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>L23pv_L23pv</td>\n",
       "      <td>82</td>\n",
       "      <td>30</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>0.365854</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>L23pv_L23pv200</td>\n",
       "      <td>102</td>\n",
       "      <td>32</td>\n",
       "      <td>35</td>\n",
       "      <td>14</td>\n",
       "      <td>0.313725</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.408287</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           cnx_class  MP_probed  MP_cnx  twoP_probed  twoP_cnx   MP_prob  \\\n",
       "0      L23exc_L23exc        135       8          205        12  0.059259   \n",
       "1   L23exc_L23exc200         48       2          312         3  0.041667   \n",
       "2       L23exc_L23pv         28      13           41        19  0.464286   \n",
       "3    L23exc_L23pv200         27       8           60         9  0.296296   \n",
       "4      L23exc_L23vip         25       6           24         7  0.240000   \n",
       "5   L23exc_L23vip200         28       4           11         0  0.142857   \n",
       "6      L23sst_L23exc         38       9          101        45  0.236842   \n",
       "7   L23sst_L23exc200         22       6          167        38  0.272727   \n",
       "8       L23sst_L23pv         36       8           12         2  0.222222   \n",
       "9    L23sst_L23pv200         35       4           26         4  0.114286   \n",
       "10     L23sst_L23sst         94       5           20         1  0.053191   \n",
       "11  L23sst_L23sst200         20       1           61         2  0.050000   \n",
       "12      L23pv_L23pyr         31      17           50        17  0.548387   \n",
       "13   L23pv_L23pyr200         30       9           96        18  0.300000   \n",
       "14       L23pv_L23pv         82      30           17         6  0.365854   \n",
       "15    L23pv_L23pv200        102      32           35        14  0.313725   \n",
       "\n",
       "    twoP_prob  fisher_p  min_probed  \n",
       "0    0.058537  1.000000       135.0  \n",
       "1    0.009615  0.133801        48.0  \n",
       "2    0.463415  1.000000        28.0  \n",
       "3    0.150000  0.145391        27.0  \n",
       "4    0.291667  0.753614        24.0  \n",
       "5    0.000000  0.309115        11.0  \n",
       "6    0.445545  0.031458        38.0  \n",
       "7    0.227545  0.600734        22.0  \n",
       "8    0.166667  1.000000        12.0  \n",
       "9    0.153846  0.713381        26.0  \n",
       "10   0.050000  1.000000        20.0  \n",
       "11   0.032787  1.000000        20.0  \n",
       "12   0.340000  0.104213        31.0  \n",
       "13   0.187500  0.208089        30.0  \n",
       "14   0.352941  1.000000        17.0  \n",
       "15   0.400000  0.408287        35.0  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([False, False, False, False, False, False, False, False, False,\n",
      "       False, False, False, False, False, False, False]), array([1.        , 0.58156245, 1.        , 0.58156245, 1.        ,\n",
      "       0.82430609, 0.50332374, 1.        , 1.        , 1.        ,\n",
      "       1.        , 1.        , 0.58156245, 0.66588467, 1.        ,\n",
      "       0.93322822]), 0.017819451444741063, 0.015625)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print (sm.stats.multipletests(p_vals, alpha=0.25, method='fdr_bh', is_sorted=False, returnsorted=False))\n",
    "multi_correct_p=sm.stats.multipletests(p_vals, alpha=0.25, method='fdr_bh', is_sorted=False, returnsorted=False)[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['p_multiple_comp']=multi_correct_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('outputs/MP_2P_comparisons_with_pvals.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_1: 0.8261353794368266 +/- 0.17083951458889685\n",
      "x_2: 0.01596988019641115 +/- 0.046939249968617466\n"
     ]
    }
   ],
   "source": [
    "#np.polyfit(df.MP_prob, df.twoP_prob, 1, full=True)\n",
    "p, V = np.polyfit(df.MP_prob, df.twoP_prob, 1, cov=True)\n",
    "\n",
    "print(\"x_1: {} +/- {}\".format(p[0], np.sqrt(V[0][0])))\n",
    "print(\"x_2: {} +/- {}\".format(p[1], np.sqrt(V[1][1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8261353794368266 0.01596988019641115\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 180x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "m, b = np.polyfit(df.MP_prob, df.twoP_prob, 1)\n",
    "print (m, b)\n",
    "probs=ax.scatter(df.MP_prob, df.twoP_prob, c=df.p_multiple_comp, cmap='plasma_r', vmin=0, vmax=1,s=30) ##, s=df.min_probed\n",
    "\n",
    "cb=plt.colorbar(probs, ticks=[0,0.5,1])\n",
    "\n",
    "cb.set_label('BH p-value', fontsize=10, fontname='arial')\n",
    "cb.ax.tick_params(labelsize=10)\n",
    "ax.plot([0,1],[0,1], c='black', ls='dashed')\n",
    "ax.plot(df.MP_prob, m*df.MP_prob + b, lw=2, alpha=0.5)\n",
    "plt.xlabel('paired fraction connected')\n",
    "plt.ylabel('2P fraction connected')\n",
    "plt.axis([0,0.6,0,0.6])\n",
    "\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_major_formatter(FormatStrFormatter('%g'))\n",
    "ax.yaxis.set_major_locator(plt.MaxNLocator(4))\n",
    "ax.xaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "ax.xaxis.set_major_locator(plt.MaxNLocator(4))\n",
    "plt.tick_params(axis='both', which='both', labelsize=10, direction='out')\n",
    "fig.set_figheight(2)\n",
    "fig.set_figwidth(2.5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "Sst_L23pyr=Sst[(Sst.post_class=='pyramidal') & (Sst.post_layer==2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "df1=Penk_L23pyr.dropna(subset=['oneP'])\n",
    "df1=df1[df1.post_reporter=='negative']\n",
    "df2=Penk_L23fsi.dropna(subset=['oneP'])\n",
    "df3=Rorb_L23pyr.dropna(subset=['oneP'])\n",
    "df4=Rorb_L23fsi.dropna(subset=['oneP'])\n",
    "df5=Tlx3Kv_L23pyr.dropna(subset=['oneP'])\n",
    "df6=Tlx3_L23fsi.dropna(subset=['oneP'])\n",
    "df7=Ntsr1Ai_L23pyr.dropna(subset=['oneP'])\n",
    "df8=Ntsr1Ai_L23fsi\n",
    "df9=Sst_L23pyr.dropna(subset=['oneP'])\n",
    "df10=Sst_L23int\n",
    "df10=df10[df10.Kcluster==1].dropna(subset=['oneP'])\n",
    "df11=PvalbKv_L23pyr.dropna(subset=['oneP'])\n",
    "df_list=[df1,df2,df3,df4,df5,df6,df7,df8,df9,df10,df11]\n",
    "color=['magenta','magenta','blue','blue','darkorange','darkorange','red','red','green','green','purple']\n",
    "markers=['^','o','^','o','^','o','^','o','^','o','^']\n",
    "tick_labels=['Penk to L2/3 pyr', 'Penk to L2/3 FSI', 'Rorb to L2/3 pyr', 'Rorb to L2/3 fsi',\n",
    "            'Tlx3 to L2/3 pyr', 'Tlx3 to L2/3 fsi', 'Ntsr1 to L2/3 pyr']\n",
    "x_loc=[1,2,5,6,9,10,13,14,17,18,21,22]\n",
    "i=0\n",
    "small_s=20\n",
    "big_s=8\n",
    "fig,axes=plt.subplots(2,1,sharex=True)\n",
    "ax0=axes[0]\n",
    "ax1=axes[1]\n",
    "for df in df_list:\n",
    "    grouped=df.groupby(['exp_id', 'headstage'])\n",
    "    amps=[]\n",
    "    spikes=[]\n",
    "    for name, group in grouped:\n",
    "        amps.append(group.oneP.mean())\n",
    "        spikes.append(group.oneP_spike.iloc[0])\n",
    "        \n",
    "    spike_count=np.nansum(spikes)\n",
    "    total= np.shape(amps)[0]\n",
    "    marker=np.ones(total)*x_loc[i]\n",
    "    \n",
    "    if total>=1:\n",
    "        frac_spike=float(spike_count)/total\n",
    "        ax0.scatter(marker[0],frac_spike,c=color[i], marker=markers[i],s=small_s*4,edgecolors='None')\n",
    "        ax1.scatter(marker,amps,c=color[i], marker=markers[i],alpha=0.4,s=small_s,edgecolors='None')\n",
    "        avg=np.mean(amps)\n",
    "        sd=np.std(amps)\n",
    "        #print (i,avg,sd)\n",
    "        ax1.errorbar(x_loc[i]+0.5,avg,yerr=sd, color=color[i], marker=markers[i],elinewidth=3, capsize=0, markersize=big_s)\n",
    "    i+=1\n",
    "\n",
    "\n",
    "\n",
    "plt.xticks([1.5,5.5,9.5,13.5,17.5,21.5], ['Penk','Rorb', 'Tlx3', 'Ntsr1', 'Sst','Pvalb'], fontname= \"arial\")\n",
    "\n",
    "\n",
    "\n",
    "ax0.set_ylabel(\"fraction of\\ncells spiking\")\n",
    "ax0.set_ylim([-0.05,0.4])\n",
    "ax0.yaxis.set_major_locator(plt.MaxNLocator(4))\n",
    "\n",
    "ax0.yaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "ax1.set_ylim([-15,40])\n",
    "ax1.set_ylabel(\"one-photon-evoked\\nPSP amplitude (mV)\")\n",
    "\n",
    "for ax in axes:\n",
    "    ax.tick_params(axis='both', which='both', labelsize=12, direction='out')\n",
    "    ax.plot([0,23],[0,0],ls='dashed', c='black')\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",
    "\n",
    "fig.set_figheight(4)\n",
    "fig.set_figwidth(6)\n",
    "\n",
    "#plt.savefig('oneP_amplitudes_revis.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 259.2x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s_factor=60\n",
    "no_cnx_s=20\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "df_list=[Rorb_L23pyr,Tlx3Kv_L23pyr,Penk_L23pyr,Ntsr1Ai_L23pyr,Pvalb_L23pyr,Sst_L23pyr]\n",
    "colors=['blue','darkorange','magenta','red','purple','green']\n",
    "cnx_calls=['excitatory','excitatory','excitatory','excitatory','inhibitory','inhibitory']\n",
    "for df,color,call in zip(df_list,colors,cnx_calls):    \n",
    "    cnx=df[df.cnx==call]\n",
    "    cnx=cnx.dropna(subset=['peakOfAvg'])\n",
    "    no_cnx=df[df.cnx=='no cnx']\n",
    "    plt.scatter(no_cnx.x_ap, no_cnx.y_pia,c='grey', alpha=0.2,edgecolor='none',s=no_cnx_s,zorder=0)\n",
    "    plt.scatter(cnx.x_ap, cnx.y_pia,c=color,alpha=0.8,edgecolor='none',s=abs(cnx.peakOfAvg*s_factor),zorder=1)\n",
    "plt.scatter([0],[200],marker='^',c='black',edgecolor='white',s=100,zorder=2)\n",
    "opto.layer_borders(ax)\n",
    "ax.axis([-600,600,1000,0])\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.set_xlabel(r'horizontal distance ($\\mu$m)', fontsize=10, fontname='arial')\n",
    "ax.set_ylabel(r'distance from pia ($\\mu$m)', fontsize=10, fontname='arial')\n",
    "fig.set_figheight(3)\n",
    "fig.set_figwidth(3.6)\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
}