converge_measures.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d166df4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib.gridspec\n",
    "import math\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.style\n",
    "import matplotlib as mpl\n",
    "import matplotlib.colors as colors\n",
    "import matplotlib.cm as cmx\n",
    "import seaborn as sns\n",
    "from scipy import stats, ndimage, misc\n",
    "import csv\n",
    "import opto_util as opto\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "import importlib\n",
    "mpl.style.use('classic')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "623c9673",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams.update({\n",
    "    'font.sans-serif': 'arial',\n",
    "    'font.family': 'sans-serif'\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0c7d732f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/travish/opt/miniconda3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3441: DtypeWarning: Columns (5,24,56) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    }
   ],
   "source": [
    "full_df=pd.read_csv('20210415_full_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "013c57ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1., nan,  0.,  2.])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intKclusters=pd.read_csv('interneuron_Kclusters_1203.csv')\n",
    "full_df = pd.merge(full_df, intKclusters,  how='left', left_on=['exp_id','headstage'], right_on = ['experiment','headstage'])\n",
    "full_df.Kcluster.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e6382054",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16286, 85)\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": 6,
   "id": "20bf0456",
   "metadata": {},
   "outputs": [],
   "source": [
    "RorbAi167=standard_df[(standard_df.presynapticCre=='Rorb') & (standard_df.presynapticEffector=='Ai167')]\n",
    "RorbKv=standard_df[(standard_df.presynapticCre=='Rorb') & (standard_df.presynapticEffector=='ChrimsonR_kv_eyfp_AAV')]\n",
    "SstAi167=standard_df[(standard_df.presynapticCre=='Sst') & (standard_df.presynapticEffector=='Ai167')]\n",
    "SstKv=standard_df[(standard_df.presynapticCre=='Sst') & (standard_df.presynapticEffector=='ChrimsonR_kv_eyfp_AAV')]\n",
    "PvalbKv=standard_df[(standard_df.presynapticCre=='Pvalb') & (standard_df.presynapticEffector=='ChrimsonR_kv_eyfp_AAV')]\n",
    "PenkKv=standard_df[(standard_df.presynapticCre=='Penk') & (standard_df.presynapticEffector=='ChrimsonR_kv_eyfp_AAV')]\n",
    "Tlx3Kv=standard_df[(standard_df.presynapticCre=='Tlx3') & (standard_df.presynapticEffector=='ChrimsonR_kv_eyfp_AAV')]\n",
    "Scnn1aAi167=standard_df[(standard_df.presynapticCre=='Scnn1a') & (standard_df.presynapticEffector=='Ai167')]\n",
    "Sst=standard_df[standard_df.presynapticCre=='Sst']\n",
    "Ntsr1=standard_df[standard_df.presynapticCre=='Ntsr1']\n",
    "Rorb=standard_df[standard_df.presynapticCre=='Rorb']\n",
    "Scnn1a=standard_df[standard_df.presynapticCre=='Scnn1a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "84d034c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "Rorb_L23pyr=Rorb[(Rorb.post_layer==2) & (Rorb.post_class=='pyramidal')]\n",
    "Penk_L23pyr=PenkKv[(PenkKv.post_layer==2) & (PenkKv.post_class=='pyramidal')]\n",
    "Tlx3_L23pyr=Tlx3Kv[(Tlx3Kv.post_layer==2) & (Tlx3Kv.post_class=='pyramidal')]\n",
    "Sst_L23pyr=Sst[(Sst.post_layer==2) & (Sst.post_class=='pyramidal')]\n",
    "Pvalb_L23pyr=PvalbKv[(PvalbKv.post_layer==2) & (PvalbKv.post_class=='pyramidal')]\n",
    "\n",
    "Rorb_L23fsi=Rorb[(Rorb.post_layer==2) & (Rorb.Kcluster==1)]\n",
    "Penk_L23fsi=PenkKv[(PenkKv.post_layer==2) & (PenkKv.Kcluster==1)]\n",
    "Tlx3_L23fsi=Tlx3Kv[(Tlx3Kv.post_layer==2) & (Tlx3Kv.Kcluster==1)]\n",
    "Sst_L23fsi=Sst[(Sst.post_layer==2) & (Sst.Kcluster==1)]\n",
    "Pvalb_L23fsi=PvalbKv[(PvalbKv.post_layer==2) & (PvalbKv.Kcluster==1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6667e35b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/travish/Desktop/2p_opto_nbs/opto_util.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
      "  H_prob=H_cnx/H\n",
      "/Users/travish/opt/miniconda3/lib/python3.9/site-packages/pandas/core/indexing.py:1684: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[key] = infer_fill_value(value)\n",
      "/Users/travish/opt/miniconda3/lib/python3.9/site-packages/pandas/core/indexing.py:1817: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(loc, value, pi)\n"
     ]
    }
   ],
   "source": [
    "min_probed=0\n",
    "x_bins=np.arange(0,701,100)\n",
    "y_bins=np.arange(50,1051,100)\n",
    "prob_matrix=opto.cnx_prob_2d(Rorb_L23pyr, ['excitatory'], x_bins, y_bins, 0)\n",
    "Rorb_L23pyr=opto.assign_est_cnx_prob_2d(Rorb_L23pyr, x_bins, y_bins, prob_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "76c986c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "prob_matrix=opto.cnx_prob_2d(Penk_L23pyr, ['excitatory'], x_bins, y_bins, min_probed)\n",
    "Penk_L23pyr=opto.assign_est_cnx_prob_2d(Penk_L23pyr, x_bins, y_bins, prob_matrix)\n",
    "prob_matrix=opto.cnx_prob_2d(Tlx3_L23pyr, ['excitatory'], x_bins, y_bins, min_probed)\n",
    "Tlx3_L23pyr=opto.assign_est_cnx_prob_2d(Tlx3_L23pyr, x_bins, y_bins, prob_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "17d7b160",
   "metadata": {},
   "outputs": [],
   "source": [
    "prob_matrix=opto.cnx_prob_2d(Rorb_L23fsi, ['excitatory'], x_bins, y_bins, min_probed)\n",
    "Rorb_L23fsi=opto.assign_est_cnx_prob_2d(Rorb_L23fsi, x_bins, y_bins, prob_matrix)\n",
    "prob_matrix=opto.cnx_prob_2d(Penk_L23fsi, ['excitatory'], x_bins, y_bins, min_probed)\n",
    "Penk_L23fsi=opto.assign_est_cnx_prob_2d(Penk_L23fsi, x_bins, y_bins, prob_matrix)\n",
    "prob_matrix=opto.cnx_prob_2d(Tlx3_L23fsi, ['excitatory'], x_bins, y_bins, min_probed)\n",
    "Tlx3_L23fsi=opto.assign_est_cnx_prob_2d(Tlx3_L23fsi, x_bins, y_bins, prob_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "df6cefb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "prob_matrix=opto.cnx_prob_2d(Pvalb_L23pyr, ['inhibitory'], x_bins, y_bins, min_probed)\n",
    "Pvalb_L23pyr=opto.assign_est_cnx_prob_2d(Pvalb_L23pyr, x_bins, y_bins, prob_matrix)\n",
    "prob_matrix=opto.cnx_prob_2d(Sst_L23pyr, ['inhibitory'], x_bins, y_bins, min_probed)\n",
    "Sst_L23pyr=opto.assign_est_cnx_prob_2d(Sst_L23pyr, x_bins, y_bins, prob_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4aa4ae3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "prob_matrix=opto.cnx_prob_2d(Pvalb_L23fsi, ['inhibitory'], x_bins, y_bins, min_probed)\n",
    "Pvalb_L23fsi=opto.assign_est_cnx_prob_2d(Pvalb_L23fsi, x_bins, y_bins, prob_matrix)\n",
    "prob_matrix=opto.cnx_prob_2d(Sst_L23fsi, ['inhibitory'], x_bins, y_bins, min_probed)\n",
    "Sst_L23fsi=opto.assign_est_cnx_prob_2d(Sst_L23fsi, x_bins, y_bins, prob_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "efd0238f",
   "metadata": {},
   "outputs": [],
   "source": [
    "##create a dataframe with all ordered pairs of tested presynaptic cells converging to a postsynaptic cell\n",
    "##these functions may take a little time\n",
    "Rorb_L23pyr_ordered_conv=opto.make_ordered_conv_df(Rorb_L23pyr) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "379aa7cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "Penk_L23pyr_ordered_conv=opto.make_ordered_conv_df(Penk_L23pyr)\n",
    "Tlx3_L23pyr_ordered_conv=opto.make_ordered_conv_df(Tlx3_L23pyr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "17b2dbca",
   "metadata": {},
   "outputs": [],
   "source": [
    "Rorb_L23fsi_ordered_conv=opto.make_ordered_conv_df(Rorb_L23fsi)\n",
    "Penk_L23fsi_ordered_conv=opto.make_ordered_conv_df(Penk_L23fsi)\n",
    "Tlx3_L23fsi_ordered_conv=opto.make_ordered_conv_df(Tlx3_L23fsi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a8b50f44",
   "metadata": {},
   "outputs": [],
   "source": [
    "Pvalb_L23pyr_ordered_conv=opto.make_ordered_conv_df(Pvalb_L23pyr)\n",
    "Sst_L23pyr_ordered_conv=opto.make_ordered_conv_df(Sst_L23pyr)\n",
    "Pvalb_L23fsi_ordered_conv=opto.make_ordered_conv_df(Pvalb_L23fsi)\n",
    "Sst_L23fsi_ordered_conv=opto.make_ordered_conv_df(Sst_L23fsi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b737e81d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted convergence motifs:  13.757340614968236\n",
      "observed convergeence motifs:  12\n",
      "predicted convergence motifs:  681.9943025218652\n",
      "observed convergeence motifs:  888\n",
      "predicted convergence motifs:  190.72398843850948\n",
      "observed convergeence motifs:  256\n"
     ]
    }
   ],
   "source": [
    "df_list=[Penk_L23pyr_ordered_conv, Rorb_L23pyr_ordered_conv, Tlx3_L23pyr_ordered_conv]\n",
    "for df in df_list:\n",
    "    print ('predicted convergence motifs: ',np.nansum(df.conv_prob))\n",
    "    conv=df[(df.cnx_i=='excitatory') & (df.cnx_j=='excitatory')]\n",
    "    print ('observed convergeence motifs: ',np.shape(conv)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cb5437d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'opto_util' from '/Users/travish/Desktop/2p_opto_nbs/opto_util.py'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importlib.reload(opto)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6edd97d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'MP_i', 'MP_j', 'prepre_dx', 'prepre_dy', 'prepre_dz',\n",
       "       'prepre_dxyz', 'pia_i', 'pia_j', 'x_ap_i', 'x_ap_j', 'amp_i', 'amp_j',\n",
       "       'cnx_i', 'cnx_j', 'post_pia', 'post_layer', 'post_class', 'conv_prob',\n",
       "       'post_depth_i', 'pre_depth_i', 'pre_depth_j', 'xyz_dist_i',\n",
       "       'xyz_dist_j', 'abs_x'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Penk_L23pyr_ordered_conv.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d8c006a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs=10\n",
    "tick_size=5\n",
    "font='arial'\n",
    "df_list=[Penk_L23pyr_ordered_conv, Rorb_L23pyr_ordered_conv, Tlx3_L23pyr_ordered_conv]\n",
    "tags=['Penk_L23PC','Rorb_L23PC','Tlx3_L23PC']\n",
    "colors=['magenta','blue','darkorange']\n",
    "i=0\n",
    "fig,ax=plt.subplots()\n",
    "bins=np.arange(0,601,100)\n",
    "\n",
    "for df in df_list:\n",
    "    color=colors[i]\n",
    "    tag=tags[i]\n",
    "    df['abs_x']=abs(df['prepre_dx'])\n",
    "    i_cnx=df[df.cnx_i=='excitatory']\n",
    "    conv=i_cnx[i_cnx.cnx_j=='excitatory']\n",
    "    ij_dist=opto.frac_ci_j_give_i(i_cnx,'abs_x',['excitatory'],bins)\n",
    "    opto.rate_ci_probed_cnx_x(ax, color, opto.midPoints(bins), ij_dist['probed'], ij_dist['conv'], min_probed=20,alpha=0.3, ls='solid')\n",
    "    i+=1\n",
    "    \n",
    "ax.axis([0,600,0,0.15])\n",
    "ax.set_xlabel(r'i to j hor. distance ($\\mu$m)', fontsize=fs, fontname=font)\n",
    "ax.set_ylabel('prob. j|i', fontsize=fs, fontname=font)\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.xaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax.yaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax.yaxis.set_major_locator(plt.MaxNLocator(4))\n",
    "ax.xaxis.set_major_locator(plt.MaxNLocator(3))\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "fig.set_figheight(1.5)\n",
    "fig.set_figwidth(1.5)\n",
    "#plt.savefig('exc_inter_x_converge.svg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1240e32f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs=10\n",
    "tick_size=5\n",
    "font='arial'\n",
    "df_list=[Penk_L23pyr_ordered_conv, Rorb_L23pyr_ordered_conv, Tlx3_L23pyr_ordered_conv]\n",
    "\n",
    "colors=['magenta','blue','darkorange']\n",
    "i=0\n",
    "fig,ax=plt.subplots()\n",
    "bins=np.arange(0,601,100)\n",
    "tags=['Penk_L23PC','Rorb_L23PC','Tlx3_L23PC']\n",
    "\n",
    "for df in df_list:\n",
    "    color=colors[i]\n",
    "    df['abs_xj']=abs(df['x_ap_j'])\n",
    "    i_cnx=df[df.cnx_i=='excitatory']\n",
    "    div=i_cnx[i_cnx.cnx_j=='excitatory']\n",
    "    jx_dist=opto.frac_ci_j_give_i(i_cnx,'abs_xj',['excitatory'],bins)\n",
    "    opto.rate_ci_probed_cnx_x(ax, color, opto.midPoints(bins), jx_dist['probed'], jx_dist['conv'], min_probed=20,alpha=0.3, ls='solid')\n",
    "    i+=1\n",
    "    \n",
    "ax.axis([0,600,0,0.15])\n",
    "ax.set_xlabel(r'post to j hor. distance ($\\mu$m)', fontsize=fs, fontname=font)\n",
    "ax.set_ylabel('prob. j|i', fontsize=fs, fontname=font)\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.xaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax.yaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax.yaxis.set_major_locator(plt.MaxNLocator(4))\n",
    "ax.xaxis.set_major_locator(plt.MaxNLocator(3))\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "fig.set_figheight(1.5)\n",
    "fig.set_figwidth(1.5)\n",
    "#plt.savefig('exc_postToj_x_converge.svg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "54c2f6f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_68625/2386247425.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['abs_x']=abs(df['prepre_dx'])\n",
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_68625/2386247425.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['abs_x']=abs(df['prepre_dx'])\n",
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_68625/2386247425.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['abs_x']=abs(df['prepre_dx'])\n",
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_68625/2386247425.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['abs_x']=abs(df['prepre_dx'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs=10\n",
    "tick_size=5\n",
    "font='arial'\n",
    "dfP=Pvalb_L23pyr_ordered_conv\n",
    "dfS=Sst_L23pyr_ordered_conv\n",
    "dfPL23=dfP[(dfP.pia_i<330) & (dfP.pia_j<330)]\n",
    "dfSL23=dfS[(dfS.pia_i<330) & (dfS.pia_j<330)]\n",
    "dfPtrans=dfP[(dfP.pia_i>330) & (dfP.pia_j>330)]\n",
    "dfStrans=dfS[(dfS.pia_i>330) & (dfS.pia_j>330)]\n",
    "df_list=[dfPL23,dfSL23,dfPtrans,dfStrans]\n",
    "colors=['purple','green','purple','green']\n",
    "styles=['solid','solid','dashed','dashed']\n",
    "tags=['L23Pvalb','L23Sst','transPvalb','transSst']\n",
    "i=0\n",
    "fig,ax=plt.subplots()\n",
    "bins=np.arange(0,501,100)\n",
    "\n",
    "for df in df_list:\n",
    "    df['abs_x']=abs(df['prepre_dx'])\n",
    "    color=colors[i]\n",
    "    tag=tags[i]\n",
    "    style=styles[i]\n",
    "    i_cnx=df[df.cnx_i=='inhibitory']\n",
    "    div=i_cnx[i_cnx.cnx_j=='inhibitory']\n",
    "    ij_dist=opto.frac_ci_j_give_i(i_cnx,'abs_x',['inhibitory'],bins)\n",
    "    opto.rate_ci_probed_cnx_x(ax, color, opto.midPoints(bins), ij_dist['probed'], ij_dist['conv'], min_probed=12,alpha=0.3, ls=style)\n",
    "    i+=1\n",
    "    \n",
    "ax.axis([0,600,0,0.5])\n",
    "ax.set_xlabel(r'i to j hor. distance ($\\mu$m)', fontsize=fs, fontname=font)\n",
    "ax.set_ylabel('prob. j|i', fontsize=fs, fontname=font)\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.xaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax.yaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax.yaxis.set_major_locator(plt.MaxNLocator(4))\n",
    "ax.xaxis.set_major_locator(plt.MaxNLocator(3))\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "fig.set_figheight(1.5)\n",
    "fig.set_figwidth(1.5)\n",
    "#plt.savefig('inh_inter_x_converge.svg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "722f5314",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_68625/2447483905.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['abs_xj']=abs(df['x_ap_j'])\n",
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_68625/2447483905.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['abs_xj']=abs(df['x_ap_j'])\n",
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_68625/2447483905.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['abs_xj']=abs(df['x_ap_j'])\n",
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_68625/2447483905.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['abs_xj']=abs(df['x_ap_j'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs=10\n",
    "tick_size=5\n",
    "font='arial'\n",
    "dfP=Pvalb_L23pyr_ordered_conv\n",
    "dfS=Sst_L23pyr_ordered_conv\n",
    "dfPL23=dfP[(dfP.pia_i<330) & (dfP.pia_j<330)]\n",
    "dfSL23=dfS[(dfS.pia_i<330) & (dfS.pia_j<330)]\n",
    "dfPtrans=dfP[(dfP.pia_i>330) & (dfP.pia_j>330)]\n",
    "dfStrans=dfS[(dfS.pia_i>330) & (dfS.pia_j>330)]\n",
    "df_list=[dfPL23,dfSL23,dfPtrans,dfStrans]\n",
    "colors=['purple','green','purple','green']\n",
    "styles=['solid','solid','dashed','dashed']\n",
    "tags=['L23Pvalb','L23Sst','transPvalb','transSst']\n",
    "i=0\n",
    "fig,ax=plt.subplots()\n",
    "bins=np.arange(0,601,100)\n",
    "\n",
    "\n",
    "for df in df_list:\n",
    "    df['abs_xj']=abs(df['x_ap_j'])\n",
    "    color=colors[i]\n",
    "    style=styles[i]\n",
    "    i_cnx=df[df.cnx_i=='inhibitory']\n",
    "    ij_dist=opto.frac_ci_j_give_i(i_cnx,'abs_xj',['inhibitory'],bins)\n",
    "    opto.rate_ci_probed_cnx_x(ax, color, opto.midPoints(bins), ij_dist['probed'], ij_dist['conv'], min_probed=20,alpha=0.3, ls=style)\n",
    "    i+=1\n",
    "    \n",
    "ax.axis([0,600,0,0.5])\n",
    "ax.set_xlabel(r'post to j hor. distance ($\\mu$m)', fontsize=fs, fontname=font)\n",
    "ax.set_ylabel('prob. j|i', fontsize=fs, fontname=font)\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.xaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax.yaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax.yaxis.set_major_locator(plt.MaxNLocator(4))\n",
    "ax.xaxis.set_major_locator(plt.MaxNLocator(3))\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "fig.set_figheight(1.5)\n",
    "fig.set_figwidth(1.5)\n",
    "#plt.savefig('inh_xj_converge.svg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1817853a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Penk to L2/3pyr\n",
      "12 pairs\n",
      "pearson stats:  (0.5316249873722099, 0.07526031663670033)\n",
      "spearman stats:  SpearmanrResult(correlation=0.6395759717314488, pvalue=0.025119356617596143)\n"
     ]
    },
    {
     "data": {
      "image/png": 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W8L46uR6SNFfSXo76Fi2xvVPSt4F3smc9skXA+c3OT1kKFUfSMLC5SVNdK+P0gO1vjfNe+wNftX1qs+Oq3PwlivM54J2t4lQF+DAtrBQkS5XoAslSJUonKVWidJJSJUonKVWidJJSJUonKVWidP4fPG2kHjFvrUYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rorb to L2/3pyr\n",
      "850 pairs\n",
      "pearson stats:  (0.010640243051104337, 0.7567389163940921)\n",
      "spearman stats:  SpearmanrResult(correlation=0.01345231094961112, pvalue=0.6953246294532902)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tlx3 to L2/3pyr\n",
      "246 pairs\n",
      "pearson stats:  (-0.014151208214348801, 0.8252219761619224)\n",
      "spearman stats:  SpearmanrResult(correlation=-0.013547460466076508, pvalue=0.8325665026823816)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Penk to L2/3fsi\n",
      "258 pairs\n",
      "pearson stats:  (0.07464935771789079, 0.23212930764432516)\n",
      "spearman stats:  SpearmanrResult(correlation=0.14122021335974233, pvalue=0.02328661226152429)\n"
     ]
    },
    {
     "data": {
      "image/png": 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ra5FUE3MWfiroZ0zVgZbjdyKDuNRt/BTKxRuJokLL6Y5NaB0Ruqa9g5jpbpTO3o6WX7ah5ZepSHLFFPthYSzptPeFSHq9Fkm8R5BeGo8CA+tYUK44+zOz6Wb2UzOba2blauL2azQ8+8sLm0mm6aUZzJDUh4qMUA5rSEoErSm5tg5JqPWhj2VobRFkwMeCHOtC/+m095eQ9GtHxjvhHMgVUU9AIdVtqmuBLyKB+xVUnL9p0Kv1qXajh1duXW8AeqiHUD5M92hkiI+hsoN1EpJmcREaZD/tRu6OEeG9HRnkOxATnR7aDg7XhyAptymMJWY3DyHZOmQKmgEeUeH7ZEA1pup099sAzOzy+ro/CLAZJWTuQ9P2tE/rEeA6JCHmkET2x7Sow1EkwQDEVJX0xho0k2tFDNCO0r46kAG+JfR1LFKxhDF1hvGdRWK/nRDOTQp0YyWauOgc1WI6K6hGZI01zFRz4aBE3MNmL7JD0kz1IEkAXyeSRMcgPxAobGU5UjUtKPEz7YOKuBv4P/SwpyKGeBypslgQ7YXQ9wmIydaiegxTEBPGAvz3hHteQsZ5VJVnIZ10L5J0g1A4dHfZ2WVQjanGmdmFyPofG44BcPff107qAMVKlB+9F3mwz0hdW4bcB9tJpu7nkqiwdpKkhGj7lGOqx0gyhzcidTcv9HsI8iBuRSpsR+hzfaAfHaIdof9YkjFdaC2OJZ4ntO0kd6Z6BO1LA/ovxmMHGmIqM5sD/Aca8g3u/qWS69OA7yE/71Xu/tVG6PUoXkZ2iqMHmsbw8Iop69uRmjkSPdDJSHo8i6TESMpjGGLE6G/ahhhrOGKyo8I4jkHl6HajMOaZYVyjAq2WcG4jkngxVT6d93cKkl4TyebtL4NatmYbjeysSnOUrP0MQJlqF6D/+YPAW2IWdGhzGPoZ/hLYXCtT9apLYS3awWc38Ha6esifBz6BHtIING1/N5VrmFfCfcCvkJ3z3nDuWqQ2X4mk2GDgPSTrdYtRHcRhwPtoZNeKXPemORn9XKehAIhrkLb+mLv/qu4Rapu3Nnd/Q/j8CQB3/2KZtm3A9n7NVCBV4ZT/Z+9BEmwwkgp1eqlpp2vNg06SGp8dSAqV9t0R2jdW2yc/PxXweeCd7r4HJZRehBKwr6xvbH/CJJLVL5C0ymmnuT7CQCqrioFISgymfoaChCkjBpBM+Vsr9N1KnxSLqkayxd0XmtlEYJi7PwxgZo3u91eO8/uJ+7tAHqjKVOF9DkoEwsxaaTy4diUqVRFxJIkft2b0WNGzvLACqb/xlN93Jis2IYM6Sqd25Kkfy/5riRFbkATNcYPILKjGVLeb2R8RA1xiZlORXfXjBmk+CBwXdj5dBVwKvLXezioVPesX24gsQFbpevQrno+SSWtd9FqEjP50YY75JMXOYt2EM0kWnJehNK0W5IMaXf/XqBXd7fY+HZWc3hiY6iR3/3nDRM0uQgmqA4Ab3f3zZnYFgLtfa2YTUDbbSJKA3hnuninItd8sKN8M/IIk7+8SNFubVmM/d5Ok8p6OmCrGaK0nWWJJL1g/RLJX80loLl0fcp39DQP+Hj3QHzTT3sn9JkXrJeRtW4PW5F6J/EC1rqmtR9JqFPCqcO5R5K+aglTsQJLoTpBKfCzQOplGJgm5MtVPkFtuNLDJ3T9Z97B6Gf1GUh0YyNWlMN7drwTej3xVTYV+E/pyEKKapPqDu59XetwMKCRVrshVUrWY2cDgRojHg8yszhWh3kUhqfoO1STVcpI17Vgc0GiSWgqFpMoNRYw69KPZ34GBXGd/n4iLvGb2ene/Ixxf4+7/0NAwexiFpMoVudpUF6SOr0od1+q6K5AV7aha/W+pvnNXd1iNyv/OQ/6sX4f3SngR+A2KCo07oG5EZR/rQDWmsgrHTSECmtJQX0OyMWQjVVWXo9CYrSjs2NGKa6VSdc8j63kzSZnHF+m6sXcNqLb25xWOmwL9Zrf3WjAeeb730lhRkIkogG8ISfp7OnO5XPtYITlm9EygfEpZBlSzqZai3UgN+Gjq+CPu3q9VYFPbVDG/r1HHTQfJtnLtaOG5mnUUY9bTums3MChfQ/3qSje5+2dqJdSbaGqm6n8oXApQuBRyRsFUUEiqnJGrS6FAgbpQcfZnZqe6+0PpJNIUdgOPuvvWnhtagWZFNZfC61H8YLnNHAeisLNTemJQBZobddtUZnZlaWZxf0FhU+WK3rOp+itDRTSlR/0AQTH7K9Ad8iskW8FAB4qqLwWqo5qhXmm39YarvhQ4sFFtmabi6lOxNdtBhVwN9aWodm76Fc9lG43ZHDNbambPmtl+hT1M+Ea4vjBUmonXbjSzdWa2KPO3KdA/4O6ZXiiIwmpoPwAlXx+D1twfR1nG6TYXoZA0QzXo7k9dOwelQS7KSjN1rxfIDTX99u7evUvBzM4J0uKPwGfM7F0Z+fU04Fl3f86lLn8EvKmkzZtQ9rO7+33AaDM7InDFXagsRYEmQxY/1eeQ1FgDfAEll2ZBljpUfVarqpIPq/R8+nO54/R7LX6xvqZfaQyl50JVnZqQhan2ufsmpFLaqVxCvhRZ6lD1Wa2qvn6ofU2/0hjKnJtdU6dk20XrBlQi4iLgJmC6u3dbqD9LGUYzuw6Y5+43hc9Lgdnuvjp8ngL82t1nVqEzm/2/eMUAwwK1w91rmwF2Z3QhX9YVqN7tPwKDshhr4b7n0J4G0VA/oaTNX9DVUH+g5PoU6jPU2+oxMPN6Hez0s6i/UwIjfQCV7Dqhm/aRWfcGJvwd2ijjf919sZldEWtRoUSi51B1me+QstfM7CZUNux4M1tZwwShQB8jS5nRbwJ/F47/BZiLDPdu4e63IsZJn7s2dexo0+9y91by6Bfo58giqfZ6qHHu7s+R1FcoUKAsskiqF8zsC0gVnUbd2WC9inkF/b5DltnfYGSoH49so+vcvc6d4AocDDgg46kK9C2KbJoCuaOpmKo/RT1kGMs0M1tgZh1m9rE8aGYYU/+I7OhLJ1mNDr0+i3qocyyHob18Po82ieqN3yi379jIq5kkVX+Keuh2LO6+zt0fpO6CPLUj5+9YN5qJqfpT1MOBtxNYjmgmpupPUQ/FTmBV0ExMlWX3rVx36GpwLActmomp/rT7VkjKuBS4paTNLcA7wizwDGCrhzCaPhjLwYu+ntXVOLu5CO2/vAxt2A3y9l8Rjg2F6CwDngBOTd17EyqxugdJmnf18FgmBDrbUIHElcDIHv59cv2O9b4Kj3qB3NFM6q9Ak6BgqgK5o2CqArmjYKoCuaNgqgK5o2CqArnjoGAqM1tuZoPNbK6ZzSm5dqWZnVZyrs3MnjazeanXVam+7jKzO81svpn9zMxGpPq63cx+b2a/M7NTwvm5IRRnXrjvbjPLlJWU0/dvNbP/MrOqz9vMWszsOTObWnL+l2Z2vpn9q5nN6JZgXzs0e+OFtgAajDKB5mRo30ZwYlbqK/X5y8CHgBnAvSTRtK8CHg/HXegCfw78rBe//z8DF2ds+ylSeYPA4ajSj6FN2n/TXR9ZEh/6JcxsCPA9YDKqlvxBVE35WuA4JIU/5e7zuulnLvAjd695I7Lwzx+NSiytA44CLjez29z9sVIJmMJYYHtJX0cC1yDmHwf8q7v/wsyeAO4CTgx01qK4qQ7k1b8KbZd3GDAG+KC735Pq14C3A68On7vr73tog7i20MU7gLkuDttiZu1mdpK7L6z0uzSz+rsCWO7us1Be4unAu4EN7n4Oim/6VgP9f7RE/aX3P/y9md0J3I42M/uBu28ALgHOAhaY2VPAG1P3fCX0cwcwB0mPNKYBX3P3C1ASbsyHHAH8MHyns4F7w/EgksTena6N099W5jsfh9ZA92Tpz91XAUvN7KzQ/jLEaBEL6aa+QtNKKpTd81sAd18ELDKzbwNnm9npoc0hZjauzv7/3VOJryW40FWs5E8ws2OBbe5+efh8KnBrYD6Aj3cjDVcDnwqZ2I6kb8Qj4X0L8GQ43oykGkiy4MoAn1DS73gkjdLorr/voIX5TuAZd0/fv5puYseaWVItQeG6mNkxZvZDpPtvcvfZyG65mWRbxJ7GScA1IaUNtNi8FW20lgWfRRLv7cCd1LZxZ5wQzGT/vMx1SEWn0V1/t6ISB+8Eri+5NoZu9k1tZkl1HXCjmc1HMeMfRpEJ3wnnRgLfdvd9MitqxkfN7NLU56Xu/r5Kjd39Z2Y2HbjfzLajP+w/ufvWjPRvBr5hZmtQVOn4Gsb66qBWhwHvKRnXs2Z2mJkd4qpv0S3cvdPMfgm8mf3rkZ0OfLLa/UWUQpPDzNqANVVUdSzj9JS7/7xBWmOB77v7xdXaNbP6K5AdXwfe3J2fKgM+QjdSCgpJVaAHUEiqArmjYKoCuaNgqgK5o2CqArmjYKoCuaNgqgK54/8Bw5a1uUARdTsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rorb to L2/3fsi\n",
      "64 pairs\n",
      "pearson stats:  (-0.20589733781820257, 0.10262569677810274)\n",
      "spearman stats:  SpearmanrResult(correlation=-0.17307913173308834, pvalue=0.17140907370552716)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tlx3 to L2/3fsi\n",
      "22 pairs\n",
      "pearson stats:  (-0.19894821258534534, 0.37475593542233826)\n",
      "spearman stats:  SpearmanrResult(correlation=-0.13538640325392215, pvalue=0.5480213653503823)\n"
     ]
    },
    {
     "data": {
      "image/png": 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TX3O4xNxiJe8i6XSg08xuCI7PBVYGygfwxSLWsBW4NZiJbTjrm+XF4Lsd2Bik9+OsGjjLgrkZ4JNzyp2Is0ZhipV3H+7FfA/wv2YWvr+VIrFjabZUr+HCdZE0XdIPcG3/CjNrxvktj+B+rGpwJnB3MKUN3MvmDqAn4v234SzedcCzlLZxZ7ZDMJdj52W24ZroMMXKW4lb4mAx8E8518YFZeYlzZbqXuABSatxMeM34iIT7gvOjQbuMrNe51aUzOclXR063mxmf5Yvs5k9Kmk28EtJB3D/sH9lZh0R638E+J6kN3FRpRNLkPWsoFkdBXwyR66tkiZJGmZufYuimFmPpJ8AV3HsemTzgJsL3e+jFFKOpAzwZoGmOruM0yYz+/EA6xoPPGhmiwrlS3Pz54nOd4Crio1TReBzFLFS4C2VpwJ4S+WJHa9UntjxSuWJHa9UntjxSuWJHa9Untj5f+xAX4YG193TAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs=10\n",
    "tick_size=5\n",
    "font='arial'\n",
    "df_list=[Penk_L23pyr_ordered_conv,Rorb_L23pyr_ordered_conv,Tlx3_L23pyr_ordered_conv,\n",
    "        Penk_L23fsi_ordered_conv,Rorb_L23fsi_ordered_conv,Tlx3_L23fsi_ordered_conv]\n",
    "tags=['Penk to L2/3pyr', 'Rorb to L2/3pyr', 'Tlx3 to L2/3pyr', 'Penk to L2/3fsi', 'Rorb to L2/3fsi', 'Tlx3 to L2/3fsi']\n",
    "colors=['magenta','blue','darkorange','magenta','blue','darkorange']\n",
    "i=0\n",
    "\n",
    "for df in df_list:\n",
    "    color=colors[i]\n",
    "\n",
    "    df=df.dropna(subset=['amp_i', 'amp_j'])\n",
    "    i_cnx=df[df.cnx_i=='excitatory']\n",
    "    conv=i_cnx[i_cnx.cnx_j=='excitatory']\n",
    "    print (tags[i])\n",
    "    print (np.shape(conv)[0], 'pairs')\n",
    "    print ('pearson stats: ', stats.pearsonr(np.log(conv.amp_i),np.log(conv.amp_j)))\n",
    "    print ('spearman stats: ', stats.spearmanr(np.log(conv.amp_i),np.log(conv.amp_j)))\n",
    "    fig,ax=plt.subplots()\n",
    "    ax.scatter(conv.amp_i, conv.amp_j, facecolor=color,alpha=0.3,edgecolor='none',s=10)\n",
    "    ax.axis([0.01,4,0.01,4])\n",
    "    ax.set_xlabel('cell i EPSP amp (mV)', fontsize=fs, fontname=font)\n",
    "    ax.set_ylabel('cell j EPSP amp (mV)', fontsize=fs, fontname=font)\n",
    "    plt.xscale('log')\n",
    "    plt.yscale('log')\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.xaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "    ax.yaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "\n",
    "    ax.yaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "    ax.xaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "\n",
    "    fig.set_figheight(1.5)\n",
    "    fig.set_figwidth(1.5)\n",
    "    plt.show()\n",
    "    i+=1\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9f1a7488",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sst to L2/3pyr\n",
      "454 pairs\n",
      "pearson stats:  (0.18368266494605665, 8.266807204769129e-05)\n",
      "spearman stats:  SpearmanrResult(correlation=0.19062843577369654, pvalue=4.348769125617107e-05)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pvalb to L2/3pyr\n",
      "192 pairs\n",
      "pearson stats:  (0.0019116287290572992, 0.9790058039826837)\n",
      "spearman stats:  SpearmanrResult(correlation=0.013500114925273128, pvalue=0.8525623726195516)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sst to L2/3fsi\n",
      "56 pairs\n",
      "pearson stats:  (0.04265404383964907, 0.7549367773775951)\n",
      "spearman stats:  SpearmanrResult(correlation=0.29091658650542807, pvalue=0.029614762430267432)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pvalb to L2/3fsi\n",
      "106 pairs\n",
      "pearson stats:  (0.02575863163536894, 0.7932440706687126)\n",
      "spearman stats:  SpearmanrResult(correlation=0.00488016593573523, pvalue=0.9604021548783236)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs=10\n",
    "tick_size=5\n",
    "font='arial'\n",
    "df_list=[Sst_L23pyr_ordered_conv, Pvalb_L23pyr_ordered_conv, Sst_L23fsi_ordered_conv, Pvalb_L23fsi_ordered_conv]\n",
    "tags=['Sst to L2/3pyr', 'Pvalb to L2/3pyr', 'Sst to L2/3fsi', 'Pvalb to L2/3fsi']\n",
    "colors=['green','purple','green', 'purple']\n",
    "i=0\n",
    "\n",
    "\n",
    "\n",
    "for df in df_list:\n",
    "    color=colors[i]\n",
    "\n",
    "    df=df.dropna(subset=['amp_i', 'amp_j'])\n",
    "    i_cnx=df[df.cnx_i=='inhibitory']\n",
    "    conv=i_cnx[i_cnx.cnx_j=='inhibitory']\n",
    "    print (tags[i])\n",
    "    print (np.shape(conv)[0], 'pairs')\n",
    "    print ('pearson stats: ', stats.pearsonr(np.log(conv.amp_i*-1),np.log(conv.amp_j*-1)))\n",
    "    print ('spearman stats: ', stats.spearmanr(np.log(conv.amp_i*-1),np.log(conv.amp_j*-1)))\n",
    "    fig,ax=plt.subplots()\n",
    "    ax.scatter(conv.amp_i*-1, conv.amp_j*-1, facecolor=color,alpha=0.3,edgecolor='none',s=10)\n",
    "    ax.axis([0.01,3,0.01,3])\n",
    "    ax.set_xlabel('cell i IPSP amp (mV)', fontsize=fs, fontname=font)\n",
    "    ax.set_ylabel('cell j IPSP amp (mV)', fontsize=fs, fontname=font)\n",
    "    plt.xscale('log')\n",
    "    plt.yscale('log')\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.xaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "    ax.yaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "\n",
    "    ax.yaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "    ax.xaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "\n",
    "    fig.set_figheight(1.5)\n",
    "    fig.set_figwidth(1.5)\n",
    "    plt.show()\n",
    "    i+=1\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "124220da",
   "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": 5
}