{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "import os\n",
    "\n",
    "import scipy.io\n",
    "from scipy import stats\n",
    "from scipy.signal import savgol_filter\n",
    "import octopus as oct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import data\n",
    "Rinput_TM_all_sub = np.load('Rinput_TM_all_sub.npy')\n",
    "Rinput_PM_all_sub = np.load('Rinput_PM_all_sub.npy')\n",
    "Rinput_TM_Split_all_sub = np.load('Rinput_TM_Split_all_sub.npy')\n",
    "Rinput_PM_Split_all_sub = np.load('Rinput_PM_Split_all_sub.npy')\n",
    "Rinput_TM_Split12_all_sub = np.load('Rinput_TM_Split12_all_sub.npy')\n",
    "Rinput_PM_Split12_all_sub = np.load('Rinput_PM_Split12_all_sub.npy')\n",
    "Rinput_PM_Split12_all_sw1_sub = np.load('Rinput_PM_Split12_all_sw1_sub.npy')\n",
    "\n",
    "time = np.linspace(0, 7.99, 799)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sum different parts od visual arena\n",
    "Rinput_PM_sum = (Rinput_PM_all_sub[:,:,0]) + (Rinput_PM_all_sub[:,:,1])\n",
    "Rinput_PM_Split_sum = (Rinput_PM_Split_all_sub[:,:,0]) + (Rinput_PM_Split_all_sub[:,:,1])\n",
    "Rinput_PM_Split12_sum = (Rinput_PM_Split12_all_sub[:,:,0]) + (Rinput_PM_Split12_all_sub[:,:,1])\n",
    "Rinput_PM_Split12_sw1_sum = (Rinput_PM_Split12_all_sw1_sub[:,:,0]) + (Rinput_PM_Split12_all_sw1_sub[:,:,1])\n",
    "\n",
    "#Merge identical stimuli\n",
    "Rinput_PM_Split12_avg = (Rinput_PM_Split12_sum + Rinput_PM_Split12_sw1_sum) / 2\n",
    "Rinput_TM_Split_avg = ((Rinput_TM_Split_all_sub[:,:,0]) + (Rinput_TM_Split_all_sub[:,:,1])) / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate average ND and PD conductances for split screen stimulus\n",
    "Rinput_PM_Split12_all_ND = (Rinput_PM_Split12_all_sub[:,:,0] + Rinput_PM_Split12_all_sw1_sub[:,:,0]) / 2\n",
    "Rinput_PM_Split12_all_PD = (Rinput_PM_Split12_all_sub[:,:,1] + Rinput_PM_Split12_all_sw1_sub[:,:,1]) / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate mean conductances\n",
    "Rinput_TM_all_sub_mean = np.mean(Rinput_TM_all_sub[300:600,:],0)\n",
    "Rinput_PM_all_sub_mean = np.mean(Rinput_PM_all_sub[300:600,:,:],0)\n",
    "Rinput_PM_sum_mean = np.mean(Rinput_PM_sum[300:600],0)\n",
    "\n",
    "Rinput_TM_Split12_all_sub_mean = np.mean(Rinput_TM_Split12_all_sub[300:600,:],0)\n",
    "Rinput_PM_Split12_sum_mean = np.mean(Rinput_PM_Split12_sum[300:600],0)\n",
    "Rinput_PM_Split12_avg_mean = np.mean(Rinput_PM_Split12_avg[300:600],0)\n",
    "\n",
    "Rinput_PM_Split12_all_ND_mean = np.mean(Rinput_PM_Split12_all_ND[300:600],0)\n",
    "Rinput_PM_Split12_all_PD_mean = np.mean(Rinput_PM_Split12_all_PD[300:600],0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\gammer\\AppData\\Local\\Continuum\\anaconda2\\lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:187: RankWarning: Polyfit may be poorly conditioned\n",
      "  xx_edge, polyorder)\n",
      "C:\\Users\\gammer\\AppData\\Local\\Continuum\\anaconda2\\lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:187: RankWarning: Polyfit may be poorly conditioned\n",
      "  xx_edge, polyorder)\n",
      "C:\\Users\\gammer\\AppData\\Local\\Continuum\\anaconda2\\lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:187: RankWarning: Polyfit may be poorly conditioned\n",
      "  xx_edge, polyorder)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460.8x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (6.4,3))\n",
    "\n",
    "plt.subplots_adjust(wspace = 0.1)\n",
    "\n",
    "ax = fig.add_subplot(131)\n",
    "ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1.5)\n",
    "plt.plot(time, savgol_filter(np.mean(Rinput_PM_all_sub[:,:,1],1),15,1), color = '0.4', linewidth = 2)\n",
    "#plt.plot(time, savgol_filter((Rinput_PM_all_sub[:,:,1]),15,1), color = '0.4', alpha=0.1, linewidth = 1)\n",
    "#plt.plot(time, savgol_filter(np.mean(voltage_PM_Split12_all_sub[:,:,1],1),15,1), color = 'C4', linewidth = 2)\n",
    "\n",
    "plt.axvspan(3, 6, facecolor='0.2', alpha=0.2)\n",
    "plt.xlim([1.5,7.5])\n",
    "plt.ylim([-0.0002, 0.0005])\n",
    "plt.axis('off')\n",
    "\n",
    "ax = fig.add_subplot(132)\n",
    "ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1.5)\n",
    "plt.plot(time, savgol_filter(np.mean(Rinput_PM_all_sub[:,:,0],1),15,1), color = '0.4', linewidth = 2)\n",
    "#plt.plot(time, savgol_filter(np.mean(voltage_PM_Split12_all_sub[:,:,0],1),15,1), color = 'C4', linewidth = 2)\n",
    "\n",
    "plt.axvspan(3, 6, facecolor='0.2', alpha=0.2)\n",
    "plt.xlim([1.5,7.5])\n",
    "plt.ylim([-0.0002, 0.0005])\n",
    "plt.axis('off')\n",
    "\n",
    "ax = fig.add_subplot(133)\n",
    "ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1.5)\n",
    "plt.plot(time, savgol_filter(np.mean(Rinput_TM_all_sub,1),15,1), color = '0.4', linewidth = 2)\n",
    "#plt.plot(time, savgol_filter(np.mean(voltage_TM_Split12_all_sub,1),15,1), color = 'C4', linewidth = 2)\n",
    "\n",
    "plt.axvspan(3, 6, facecolor='0.2', alpha=0.2)\n",
    "plt.xlim([1.5,7.5])\n",
    "plt.ylim([-0.0002, 0.0005])\n",
    "plt.axis('off') \n",
    "\n",
    "bbox_inches = 'tight'\n",
    "#plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\\n",
    "#Fig7_VS_TM_local_ephys_conductance_traces.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\gammer\\AppData\\Local\\Continuum\\anaconda2\\lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:187: RankWarning: Polyfit may be poorly conditioned\n",
      "  xx_edge, polyorder)\n",
      "C:\\Users\\gammer\\AppData\\Local\\Continuum\\anaconda2\\lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:187: RankWarning: Polyfit may be poorly conditioned\n",
      "  xx_edge, polyorder)\n",
      "C:\\Users\\gammer\\AppData\\Local\\Continuum\\anaconda2\\lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:187: RankWarning: Polyfit may be poorly conditioned\n",
      "  xx_edge, polyorder)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460.8x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (6.4,3))\n",
    "\n",
    "plt.subplots_adjust(wspace = 0.1)\n",
    "\n",
    "ax = fig.add_subplot(131)\n",
    "ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1.5)\n",
    "#plt.plot(time, savgol_filter(np.mean(Rinput_PM_all_sub[:,:,1],1),11,1), color = '0.4', linewidth = 2)\n",
    "plt.plot(time, savgol_filter(np.mean(Rinput_PM_Split12_all_PD,1),15,1), color = 'C4', linewidth = 2)\n",
    "\n",
    "plt.axvspan(3, 6, facecolor='0.2', alpha=0.2)\n",
    "plt.xlim([1.5,7.5])\n",
    "plt.ylim([-0.0002, 0.0005])\n",
    "plt.axis('off')\n",
    "\n",
    "ax = fig.add_subplot(132)\n",
    "ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1.5)\n",
    "#plt.plot(time, savgol_filter(np.mean(Rinput_PM_all_sub[:,:,0],1),11,1), color = '0.4', linewidth = 2)\n",
    "plt.plot(time, savgol_filter(np.mean(Rinput_PM_Split12_all_ND,1),15,1), color = 'C4', linewidth = 2)\n",
    "\n",
    "plt.axvspan(3, 6, facecolor='0.2', alpha=0.2)\n",
    "plt.xlim([1.5,7.5])\n",
    "plt.ylim([-0.0002, 0.0005])\n",
    "plt.axis('off')\n",
    "\n",
    "ax = fig.add_subplot(133)\n",
    "ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1.5)\n",
    "#plt.plot(time, savgol_filter(np.mean(Rinput_TM_all_sub,1),11,1), color = '0.4', linewidth = 2)\n",
    "plt.plot(time, savgol_filter(np.mean(Rinput_TM_Split12_all_sub,1),15,1), color = 'C4', linewidth = 2)\n",
    "\n",
    "plt.axvspan(3, 6, facecolor='0.2', alpha=0.2)\n",
    "plt.xlim([1.5,7.5])\n",
    "plt.ylim([-0.0002, 0.0005])\n",
    "plt.axis('off') \n",
    "\n",
    "bbox_inches = 'tight'\n",
    "#plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\\n",
    "#Fig7_VS_split_global_ephys_conductance_traces.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\gammer\\AppData\\Local\\Continuum\\anaconda2\\lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:187: RankWarning: Polyfit may be poorly conditioned\n",
      "  xx_edge, polyorder)\n",
      "C:\\Users\\gammer\\AppData\\Local\\Continuum\\anaconda2\\lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:187: RankWarning: Polyfit may be poorly conditioned\n",
      "  xx_edge, polyorder)\n"
     ]
    },
    {
     "data": {
      "image/png": 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aB0xvqOgHf5bzQCg00Kb1BxKmOQQwYBijAFHNWVcX7A6l3bVnd0fCqWFDvecXsrk/AIhpfSHAFmdWuCplmrA84Row1BTAhFLp8jaPmOax4P6oUkMAjTBv6mFhFq0MUCoCYGbG1yQVt7Lzo9qKNBVad291Qk3b3WdNnFrsuvIJAX/Ias6MjABoK7pTm6F2gNDQ00+6S5XyQ9oIdQAVlxmdacTiWgf8mCk/+LMSXwyH+58yzSmA71rmKa2wI1pve3k+f0Ow3WAFaykWsLjKM00ELbbnDNfiGjPmClfCNEuc8xPKFbmQ59gXFsaJtnrBwzqvnMKaBL8pOzcMYDds3arDjXsBzKmBRYULwMyMPaScwkjLY3e/wz8WGnvezbBqhs4CTOXkhxqOHz4F0Jzs8637Nc3Mu1zE4loHTFwraLSC6FRi1DAKBdRECL0l7znNC56QHDHMOfdoDKw7DOvSDBTtWoeVxnQU+VHDKAAMlVl+n4yE/9JWqrQPns/LquBfE+biRFq8lDZ61f1bPqqQHSPSQiG263JtWO3Ksccbjn530aEiQNtD//AprQyCEwTWxPFptM76lqKy88P+udBwagz3b4l19vTF+nu7J+be9cwhwrXGXJsvdPjBpScMY3qx9j42pEOwJQqtAKeUehBgwlAl2TLDWqtmLzIeIFwmNLscp9G936xonjSMEuH6jmWdLn/+KaMoXGJxLQEnEivmm1+rZxQDSSMtlwIY6dFHqrHuymc1lXZABVLgFHJjwXPKzg1o9/O3G3gCoLOn7zzgV4CP9fd2P7uCP2dFyFBxjbk5lysWqThmGHOGaPNR8CytiNbu9Pc8/rGrCnb7gYJdTGnSoHWHqTVvTWde9Npstmuboz3hmk3dHCw9NmgYD5ZbWwD9XhS9qXWjLPtZHCfU4A4V11C4/EBSH2t6cMXCYWQmigHQqpAuWY6kChl/xjIYjnMHcCnw/jM54yjCtcZEoLiUp5oF0gUvb1YY2qA0BuxL4fDf+Nu3ZbN/HLyuAb37tbncRRfa9qtemC/8Rpt2hSuYcx5gWqnnAIa9/FvlTClla4WtwNjvOM17bDt6daEgjvp50FZDsVDGWj3DyE2OB/et8eNL8m8tROyHn/6Mv63sXMlnxMhND3ibewA6e/rOYjZJoQLOWJyXCNcaMw1HAZ4xzS9Xc13By9UVKlpcs8L11XDo2FHTmFM+LK/UuNKY59iOuyYN1E7H2QFQUKXC9R/h0Kcfs8xP/2M08p35+uDPZr4mm3v5mzPZ22/PZP/0unxewiMqoK1ISdrmtcCcGQn6mZzwUHJ43sZLvWdmPG/kZ54CiAw8nig9N+YLl58AsTzv/Q6Azp6+dXc5iXCtMX4M1eOm+WQ11+XLItkzZTFgD5tWyfKRYcN4OO2tN9yqdXFt407tdAFMo0qWhXwrFBr8SDT64EIVq20vbKJJ621+6MVP5Asbcnr8TKPNsD9UXDOLy5o4XrS4lJ0fXmh9YjXEHvnXj7U88YX/Fxn4UYkQGukx/3l+HrDyYOQdnT19vwf8/86evopFhtcKEa41xndun/JiqJZKTpUKlx+e4JMwjZIPmQP2lFIDAI1a7/WPtzr6YoAhwygJMF0Kj5rm3QAaikPcqJdVQihFmyGvws8aWlyBpT3KKazaLJ+ZHs1VSmpoZMZ9K91/z/2hof/s24CX4E4IzZuFdS0Q4VpD9th2NKR1q1YUniyLk1qMRl38lUMrCveFrOeD54+aZomzPg/pMUMNMA/PGcbJ+c7Nx4wnnr61BbOTBUIpuljhx167cIjgrOAaDkl9rOlB/8fSF66zvNd7KzRftArRaiLCtYZcZds7ATKoUwsNySpxzDCKWSJyqDE/Bms+PhsOf+OHpvlMpXMa9Pcsc15Rm48nTHOO8zcMc5YUCYBXKAPHXnNBAXdN4Vo/w7PwbCDa2dMXAvzCxQ8B3ytrvq+zp2/d9KQuhCvRFf9Qoiu+6mW/FuMcr+jqlFLHq732rkj4+/52pSSDAF8Lhz4wbqjHPxcO/3XSMqe+FQpVXHCbU+r0YsJXiUcsayznlTPzCaO3JLriG7aIRmdP32c6e/oqVrxZS7RhrbnFBRA98YOPGLnpJ5qe+mpVkz3LwbPw/BCMFmaFawj4mrf9HW8/wqwTf1l09vT9+lLb1oVwAXuBK9f7oVu0mxJ5VKmq/Uu2Uvh5s7Kq8hDwi+Fw/x82Nv7jN8Kh4jCw3zS+Wt4uraja2vKZUKokVsibadw2T/ONQCPwi+v9UL9QBk5hTS2uptQ9T2357oc/Gho/uuRg5hXiC1cbrnNeA8O4VtfvAO/FmzkHlj3j3NnT91vAzy21fT1Fzq/o12A5RHH9QcOGWta09Z3RyPtvzeZu6AuH7l/qNSPKGOykdFQaLMJRLZlZR2yQncCKY4jWks6ePrO/t3v91tl5+ebVOg0V15Fx3Diu/bixWyP9vd1579wzAJ09fb5Vviw3ghfI+tOLNgxQT8K17r4ZS7vLbybKcl0tladMc+rPGxs+X801mUC819Om+cXzbPtnvxWy+ha6ZiHSXg6vMmohELUFqLqizrLxS5MFKvxsEvz3/7e910rZVke915LPhbc86FzgG/293QsFX1dtqYlwrSGWl2t+qYurV4P/tqznrizYpwYN9dgHo5FvNsB/TSm1bMsjUxa46lELDvpW1lG4ihV+nPxms7jKK/7MWdcK+BZXUbg6e/pM4I9xV460Ap9d4Bn+bOWSrfh68XHBGfiy+ZlLTxnGuglXv2mm39bU+J7ehoav2EqxEtECmA6I7oxX5ZrZgMSNzJwc6muKn7bZ3nTC9YOy/Up/3xzhwvUp+5NKt3hCNh++xfXYAm1KqCeLa0kf5ERXPAroeDJRtcmf6IqHgZf/kWFcsdtxy4kBnKhicfVGYzwQ+HrSMH6437bPYWMPFYdwvzDrKq7aK2Vv5KbWy2m+XnwZNzvE9d7+/1RoU8nHdVNguxE3ePW5eZ7hLyVa8iRWPVlci6Zn8YTnw8D7El3x5fxvrgN+PShawIqtnjNJGuU7YnnKdFNKU5otYKPhfzmWZXF19vRd4y0mrg6vIrSRHttUwtXf2z3T39v9PuBW3KHfQxWa+T6udig62/3KVD/yXjsXeIxf5Pj5BdqUUE8W11LySl3B7Hh7L/P/QhRJdMXjuPmJ/gvXEVmCX7GnVrk3ZB1/YaHwvRGljj9oWSdfnstrYHeiK27Fk4mqY8PWgedx0w1XLVydPX2XA38CTAOvqeZavyJ0INp8U9Hf2z0D/HCe077F1dbZ06eAl+LOQJ7AHWpegitOc2bHO3v6XgD4ZdCeWGp/6sXiKgARz6JaiBcFti9e4r3fAMSB3wCKaZZtpaa/Fg594K5o5F+r6ukGw1aKdzQ2fPL9DdEHBg0jZ7szpBZznbYbBd95vByL6wrvtamajAdOuMnysog65iKlwjYj/b3dOVyxt3CH6X5h2THATwYw3yLsV3mvD/b3di/ZWq0Xi2sa94PczOyvQwmJrvge4MWBQ9fiju/nJdEVb8aNbylhQqnkf4ZDX3ggFFp24OdGxUtIGMH9X44u0vxM4C+FqUq4PEsh+P5fwBItgELzjkYApZ2ZldROrHGGmF1sfZV37A7c98MB9nT29LX093ZPeo76v6H0u/O31TysXiwuPxZlofzpB3DNW39oN6c+YQUqpXiZ6WlqPLQZRQug4P4IwMa1uMpTsSyVFgJJH5m1vhbFbtzqfq4ce1P5t6rkW97r64Aw8Hh/b/dpzxr7Ae5364WdPX2XAl+gVLSmq7G2oH6Ey/c7LPRl2+W9fg/IA1sTXfGGBdrDrLh9Hvgr3CHp+5bbyVogkJBwowqXH7tV7VCxPAhyyeEzTiTWBKCcwqb0by2R/y7bvy+w7YdU7ANuqXBt1T/y9SJcvsW1kIPeXxJ0jNlp2cWsLv+aZ+PJxLfjycRN8WTiwWX2sSbIzxbd2KjCVYzi9oZ/S6V8/WUbQGdP3ys6e/ruXChRnhNucj9X9S1cJ5lND56jNGyi33t9OXC5t/39wPklzyb61ItwLWhxJbriMWanb5/DFS9YfH2jf77q7A+1So4Nb3HNABkgCnOL4y6Ab3H5qYH8oeav4oZ/3NHZ01fRCtNW1LO48nU7VPSW9PwacBfQUzb0669wyTuBZ3H9X/9Z7fPqTbjms7hegvtBfySeTJxkVojmFa5EV1wxGzi3oRccrybZ2XWXG1K4vC+Qv6i9PNXwQvhhMH6K7TYvHik4E10xy6e2oi0Ayq5f4QLo7+2e7O/t/nx/b/fTZcfLc4d93Huf/gj4zf7e7gRVUi+ziosJly9Qh71X3+IqDhUTXfGtwFQ8mfCXPDTiil0mcP9NT2bjW1zgCtfZQHtnT99R4Ebv2C3AIPDuCot+feFKAq/AFb19ZW1egOtYLkFbYVe4CpkNVTR1g/E3wO952/8B4FllyxL7erG4FvNx+Y5537dVIlyJrvh+4BPAGwPXFJOqxZOJuik8OLPKFleiK74z0RW/fPGWVRG0uC4CXg+8HTd6+2pKZw99fOE6gvtDZDE7rf9tXL9N3JsVK0Gb4RiAkZsW4Zqf7+LOPH7Ym2lcEXUhXG8fGf7zY4XCbqAlFouFYrHYN2Ox2C8DxGKxxvvT6V87USjsAk7EYrHW60+e+NjJQmEHsGt7a2vH3dNT956yC9uBG2Kx2I5YLPbNuyYnXgVw2rZnvPu9zLvfvlQqdejUqVNXAgwPD+9NpVKHBgYGLgUYGhran0qlDg0ODl4EcPr06QtSqdSh06dPXwAwODh4USqVOjQ0NLQfYGBg4NJUKnVoeHh4L8CpU6euTKVSh0ZHR88GOHny5NWpVOrQ+Pj4doATJ05cm0qlDk1OTrZ7+y9KpVKHpqam2gCOHz9+fSqVOjQzM9MMcOzYsRtSqdShbDYbBTh69OjPpFKpQ4VCwfL2X5lKpQ75/8uvj45c84Ns9sfxhCsWi/1mLBYr+ihisdhbY7HYlwL7vx+Lxf49sN8Ti8U+DZDoip+TyOW+9qNc7suJrvjrvPPvisVi/xRo/1fVvNexWOyb2SfujwPoQnb71Nc/+Cl7fOBsAO3YZv5E4trp+//hdq9taywW+2aste1VwHZdyIVHP/ZrHy8MPd8MoHPpm/InEtdmnrg3DDys85lo/kTivl23vuu9AK3t2/ennj/20cL0xHkAmePJ5s30Xh85cuTGWCxWLIO3kve6v7c7M3Lna6Mjd772lwPnl/1e14VwTTuOv1ZwzhT5KxsaQ62GEdKuk3AAYEZrJ6v1NBD+9Lazbt1vhZoD7ZsVcI5lbQeYcpxj5ffczIxr7S8+X41qP28KK+X7kH4h0RVflYwOTmbCHbor85zIJTfsKj9vtHTEyvajQAPojM5O2zqXdi0nZTQDZBMPPAHc7bc32/dcCdDyqnfe3PRTv3MuDTHXsT91um5cBmcapeugvHqiK3428PfAcDyZuL3sXCfwd8CJeDLxhsDxXuZf9nM/rn/rWuCOeDLxQPDkwYMHr6p41Sbg8kKh7fcy2d8BRuLJxOuWex8vb/2/Uer8fmc8mXh4pX3s7Om7FtfxG+TDuJMpNwP/0t/b/W+B9vuADwDP9/d2/1ZnT98bgW7v9FB/b/eveO1CwOdw0xe/Grhb5aaLYtv24EfebmYnN+L6zWVz+PDhFb8fa0FdWFy4MSZZoD3RFS/3zQTjt4KUp9j4RGD7elzfCVSe6t20PGuYxZJV3szqcvk7XNEaAb7oHVutElflAY0P9vd2f5XZ7J3lMVt+KIRfbCR4fbHakpey+HncKPByxz2bTbQ2MnUhXPFkwmFWYMrXFs4XixUUrm/jfrmC07ytuL+8R6kjJgzlfzkt4EvebGtVeILnp8Z5gNnYqdUSrmBWjyf7e7vf7W37C7C3AXT29L24s6fvnczmO/eFKxg8+Z2ye/vBku8JHjTTY5s68HijURfC5eF/mF9SdtwPeSi3uIIpap+IJxP5eDLxNuDrgeP3eaJYzxxYvMkcgoGc/wL4eb5WRbi8UIc34s5ivT9wyo+329PZ09eBO9N4FbOzhwPe9YO4ifDe1N/b/c2y25ekdrGmBu4JjR35XMsPP7VQamJhlamXOC5wfzlfDpRPZ89ncQUr8wSzBPxiHT+0AAAE0ElEQVQzkAYeZv78RJudDzJbPGE5pcr8wN2n48lEPtEVP44bD9eR6Iq/E3hvPJlYUTBnf2/3cdzSWUGO407CnAV8tMJlxbqU/b3dBeb+mAHcA/yWt/391u9/4isr6aewPOrJ4noUNxZnW6Ir3gTgZTk93ztf/iF9Cnd4+TizQxniycR4PJn4x3gy8YN4MlGzmU1XQjyZuAcviJDlCZc/TDzu3c/B/T+Da/28p9JFK8UTIz+vuf+jHUxzVLGgbtk9HNyJnh/gpm0RzgB1Y3HFkwk70RV/HleozsVNKXsbrqOVeDIxXtY+B7xlvftZQzwK/CyzgZvV4FssweH43+EKQhTYm+iK/yXwjjXIsnoncCiw/7vMTrwsyV/Z39vdB/QBHDz4Z6vaOWFp1JPFBbMO+k7v1c+n9dSclsJi+KJTlXAluuLBPFnFCjLxZGI4nkzcwmw+rQPMszZwJfT3dp/AnVTx90eAPwX+oL+3u+6yl9Yq9Spc/8eLI/IDSz90ZrpT0/ghA2ctNSzCi5n7F283FU8mKpWjujuwvRzH/1Lwl5w4AP293Y/093YvOd+5cOapN+HyZ68acIcr7bgzTUfmvUKoiOc8n8Ed2i2lEAm4efl9HpinTbCgwrmJrng1GR6Wyqe818+swb2FdaDehCvBbBzOy7zXT9Wrk30V8J3Ziw4XvdULQQuqv1K7eDIxAfjr2fYBH19+9+blc7jhEv+2WENhY1I3znmAeDKhE13xO3AdwT7z/fILi3Ma11+4ncDMaxBvpUIr8BHv0DjwFRYOJZlgNhngquPFedVN8sfNSL1ZXMSTiX7gD3GzL/6uWFsrwvdzVQyJSHTFt+PGvX0kcPiT8WTikwsF7nppgh5dtV4Km466srh84snEj4C3nul+bALmHSomuuJR3IXK5Z+x+8rbzsN9wDXL75qwmalL4RJWjYVCIt6IW9E4yOvjycRSQw4O4yaf25Rl3oSVIcIlrARfuCrN/JWLFsxmZ1gULwD4L5bTKWHzU3c+LmFV8VMVlyQA9GLkyhmopxTXwtoiFpewEuYrd+8PHYdwY7duwi20KwirggiXsBLSuNW7I4mueCSeTPhpnYvFR+LJRB6JlxJWGRkqCsvGG/pVKnlfXjVJEFYVES5hpVTyc4lwCWuKCJewUnw/VzDrg+/jqpsK38L6Ij4uYaUcAa4AfinRFX8WtzLS1d654XmvEoQVIMIlrJTvAD+PW4TkLkqt+JGKVwjCCpGhorBSgpWPyj9Po+vZEaF+EOESVoQX4f6bFU59QhawC2uFCJewYuLJxFHcQNOHcYto3BhPJqRcl7BmiI9LWBXiycRJ4J1nuh9CfSAWlyAINYcIlyAINYcIlyAINYcIlyAINYcIlyAINYcIlyAINYcIlyAINYcIlyAINYcIlyAINYcIlyAINYcIlyAINYcIlyAINYcIlyAINYfSWmp0CoJQW4jFJQhCzSHCJQhCzSHCJQhCzSHCJQhCzSHCJQhCzSHCJQhCzSHCJQhCzSHCJQhCzSHCJQhCzSHCJQhCzSHCJQhCzSHCJQhCzSHCJQhCzSHCJQhCzSHCJQhCzSHCJQhCzSHCJQhCzSHCJQhCzSHCJQhCzSHCJQhCzSHCJQhCzSHCJQhCzSHCJQhCzfG/3hP7JKQHC9cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 460.8x165.6 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (6.4,2.3))\n",
    "\n",
    "plt.subplots_adjust(wspace = 0.1)\n",
    "\n",
    "ax = fig.add_subplot(131)\n",
    "ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1.5)\n",
    "plt.plot(time, savgol_filter(np.mean(Rinput_PM_all_sub[:,:,1],1),15,1), color = 'C3', alpha = 0.7, linewidth = 2)\n",
    "#plt.plot(time, savgol_filter((Rinput_PM_all_sub[:,:,1]),15,1), color = '0.4', alpha=0.1, linewidth = 1)\n",
    "#plt.plot(time, savgol_filter(np.mean(voltage_PM_Split12_all_sub[:,:,1],1),15,1), color = 'C4', linewidth = 2)\n",
    "\n",
    "plt.axvspan(3, 6, facecolor='0.2', alpha=0.2)\n",
    "plt.xlim([1.5,7.5])\n",
    "plt.ylim([-0.0002, 0.0004])\n",
    "plt.axis('off')\n",
    "\n",
    "ax = fig.add_subplot(132)\n",
    "ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1.5)\n",
    "plt.plot(time, savgol_filter(np.mean(Rinput_PM_all_sub[:,:,0],1),15,1), color = 'C0', alpha = 0.7, linewidth = 2)\n",
    "#plt.plot(time, savgol_filter(np.mean(voltage_PM_Split12_all_sub[:,:,0],1),15,1), color = 'C4', linewidth = 2)\n",
    "\n",
    "plt.axvspan(3, 6, facecolor='0.2', alpha=0.2)\n",
    "plt.xlim([1.5,7.5])\n",
    "plt.ylim([-0.0002, 0.0004])\n",
    "plt.axis('off')\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "#plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\\n",
    "#FigSX_VS_PDvsND_local_ephys_conductance_traces.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.6802077293395996, 0.0001029854392982088)\n",
      "(0.7855320572853088, 0.001761077088303864)\n",
      "LeveneResult(statistic=3.108508757666642, pvalue=0.08806934777135457)\n",
      "WilcoxonResult(statistic=9.0, pvalue=0.002282193441519148)\n"
     ]
    }
   ],
   "source": [
    "# local PD+ND sum vs. local TM\n",
    "\n",
    "print(stats.shapiro(Rinput_TM_all_sub_mean))\n",
    "print(stats.shapiro(Rinput_PM_sum_mean))\n",
    "print(stats.levene(Rinput_TM_all_sub_mean, Rinput_PM_sum_mean))\n",
    "print(stats.wilcoxon(Rinput_TM_all_sub_mean, Rinput_PM_sum_mean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.7493762969970703, 0.0006243873503990471)\n",
      "(0.896782636642456, 0.07139323651790619)\n",
      "LeveneResult(statistic=0.13706975937676733, pvalue=0.7138127397468177)\n",
      "WilcoxonResult(statistic=53.0, pvalue=0.43796657516602056)\n"
     ]
    }
   ],
   "source": [
    "# global PD+ND sum vs. global TM\n",
    "\n",
    "print(stats.shapiro(Rinput_TM_Split12_all_sub_mean))\n",
    "print(stats.shapiro(Rinput_PM_Split12_avg_mean))\n",
    "print(stats.levene(Rinput_TM_Split12_all_sub_mean, Rinput_PM_Split12_avg_mean))\n",
    "print(stats.wilcoxon(Rinput_TM_Split12_all_sub_mean, Rinput_PM_Split12_avg_mean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WilcoxonResult(statistic=22.0, pvalue=0.017378363446698287)\n"
     ]
    }
   ],
   "source": [
    "# local TM vs. global TM\n",
    "print(stats.wilcoxon(Rinput_TM_all_sub_mean, Rinput_TM_Split12_all_sub_mean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Figure parameters\n",
    "fw = 0.4 # width of figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 57.6x162 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make Scatter Plot of conductance for local vs. global opponent motion\n",
    "\n",
    "pos = [0,1]\n",
    "\n",
    "fig = plt.figure(figsize = (fw*len(pos),2.25))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "w = 0.6\n",
    "wb = 0.6\n",
    "data = [Rinput_TM_all_sub_mean, Rinput_TM_Split12_all_sub_mean]\n",
    "colors = ['0.4', 'C4']\n",
    "\n",
    "ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1)\n",
    "\n",
    "for j in range (len(data)):\n",
    "    ax.bar(pos[j], height = np.mean(data[j]), yerr=[stats.sem(data[j])], color=colors[j])\n",
    "    ax.errorbar(pos[j],np.mean(data[j]),yerr=[stats.sem(data[j])], color='k',zorder = 0)    \n",
    "    for i in range(len(data[j])):\n",
    "        ax.scatter(pos[j] + np.random.random(1) * w/2 - w/4, data[j][i], s=25, color='0.3', alpha=0.4, zorder = 2) \n",
    "   \n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines['left'].set_position(('outward', 7))\n",
    "ax.spines['bottom'].set_position(('outward', 7))\n",
    "ax.set_xticks(pos)\n",
    "ax.set_xticklabels(('local TM','global TM'), rotation=45, size=10)\n",
    "ax.set_ylabel('$\\Delta$ conductance [nS]', size=10)\n",
    "\n",
    "yticks = np.array([0, 0.0002, 0.0004])\n",
    "ax.set_yticklabels([0.0, 0.2, 0.4], size=10)\n",
    "ax.set_yticks(yticks)\n",
    "plt.ylim([-0.00005,0.0004])\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "#plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\\n",
    "#Fig7_Conductance_local_global_ephys_barplot_zoom.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 57.6x162 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make Scatter Plot of conductance for local vs. global opponent motion\n",
    "\n",
    "pos = [0,1]\n",
    "\n",
    "fig = plt.figure(figsize = (fw*len(pos),2.25))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "w = 0.6\n",
    "wb = 0.6\n",
    "data = [Rinput_TM_all_sub_mean, Rinput_TM_Split12_all_sub_mean]\n",
    "colors = ['0.4', 'C4']\n",
    "\n",
    "ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1)\n",
    "\n",
    "for j in range (len(data)):\n",
    "    ax.bar(pos[j], height = np.mean(data[j]), yerr=[stats.sem(data[j])], color=colors[j])\n",
    "    ax.errorbar(pos[j],np.mean(data[j]),yerr=[stats.sem(data[j])], color='k',zorder = 0)    \n",
    "    for i in range(len(data[j])):\n",
    "        ax.scatter(pos[j] + np.random.random(1) * w/2 - w/4, data[j][i], s=25, color='0.3', alpha=0.4, zorder = 2) \n",
    "   \n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines['left'].set_position(('outward', 7))\n",
    "ax.spines['bottom'].set_position(('outward', 7))\n",
    "ax.set_xticks(pos)\n",
    "ax.set_xticklabels(('local TM','global TM'), rotation=45, size=10)\n",
    "ax.set_ylabel('$\\Delta$ conductance [nS]', size=10)\n",
    "\n",
    "yticks = np.array([0, 0.0005, 0.0010])\n",
    "ax.set_yticklabels([0.0, 0.5, 1.0], size=10)\n",
    "ax.set_yticks(yticks)\n",
    "plt.ylim([-0.00005,0.0010])\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "#plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\\n",
    "#Fig7_Conductance_local_global_ephys_barplot_all.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5987327183955766"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(Rinput_TM_all_sub_mean) / np.mean(Rinput_TM_Split12_all_sub_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 57.6x162 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make Scatter Plot of conductnace for local PD+ND sum vs. local opponent motion\n",
    "\n",
    "pos = [0,1]\n",
    "\n",
    "fig = plt.figure(figsize = (fw*len(pos),2.25))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "w = 0.6\n",
    "wb = 0.6\n",
    "data = [Rinput_PM_sum_mean, Rinput_TM_all_sub_mean]\n",
    "colors = ['0.7', '0.4']\n",
    "\n",
    "ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1)\n",
    "\n",
    "for j in range (len(data)):\n",
    "    ax.bar(pos[j], height = np.mean(data[j]), yerr=[stats.sem(data[j])], color=colors[j])\n",
    "    ax.errorbar(pos[j],np.mean(data[j]),yerr=[stats.sem(data[j])], color='k',zorder = 0)    \n",
    "    for i in range(len(data[j])):\n",
    "        ax.scatter(pos[j] + np.random.random(1) * w/2 - w/4, data[j][i], s=25, color='0.3', alpha=0.4, zorder = 2) \n",
    "   \n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines['left'].set_position(('outward', 7))\n",
    "ax.spines['bottom'].set_position(('outward', 7))\n",
    "ax.set_xticks(pos)\n",
    "ax.set_xticklabels(('PD+ND','local TM'), rotation=45, size=10)\n",
    "ax.set_ylabel('$\\Delta$ conductance [nS]', size=10)\n",
    "yticks = np.array([0, 0.0005, 0.001, 0.0015])\n",
    "ax.set_yticklabels([0.0, 0.5, 1.0, 1.5], size=10)\n",
    "ax.set_yticks(yticks)\n",
    "plt.ylim([-0.000075,0.001])\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "#plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\\n",
    "#Fig7_Conductance_TM_PDND_ephys_barplot_zoom.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 57.6x162 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make Scatter Plot of conductnace for local PD+ND sum vs. local opponent motion\n",
    "\n",
    "pos = [0,1]\n",
    "\n",
    "fig = plt.figure(figsize = (fw*len(pos),2.25))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "w = 0.6\n",
    "wb = 0.6\n",
    "data = [Rinput_PM_sum_mean, Rinput_TM_all_sub_mean]\n",
    "colors = ['0.7', '0.4']\n",
    "\n",
    "ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1)\n",
    "\n",
    "for j in range (len(data)):\n",
    "    ax.bar(pos[j], height = np.mean(data[j]), yerr=[stats.sem(data[j])], color=colors[j])\n",
    "    ax.errorbar(pos[j],np.mean(data[j]),yerr=[stats.sem(data[j])], color='k',zorder = 0)    \n",
    "    for i in range(len(data[j])):\n",
    "        ax.scatter(pos[j] + np.random.random(1) * w/2 - w/4, data[j][i], s=25, color='0.3', alpha=0.4, zorder = 2) \n",
    "   \n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines['left'].set_position(('outward', 7))\n",
    "ax.spines['bottom'].set_position(('outward', 7))\n",
    "ax.set_xticks(pos)\n",
    "ax.set_xticklabels(('PD+ND','local TM'), rotation=45, size=10)\n",
    "ax.set_ylabel('$\\Delta$ conductance [nS]', size=10)\n",
    "yticks = np.array([0, 0.0005, 0.001, 0.0015])\n",
    "ax.set_yticklabels([0.0, 0.5, 1.0, 1.5], size=10)\n",
    "ax.set_yticks(yticks)\n",
    "plt.ylim([-0.000075,0.0015])\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "#plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\\n",
    "#Fig7_Conductance_TM_PDND_ephys_barplot_all.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 57.6x162 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make Scatter Plot of conductance for PDND vs. Split (global opponent) motion\n",
    "\n",
    "pos = [0,1]\n",
    "\n",
    "fig = plt.figure(figsize = (fw*len(pos),2.25))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "w = 0.6\n",
    "wb = 0.6\n",
    "data = [Rinput_PM_Split12_avg_mean, Rinput_TM_Split12_all_sub_mean]\n",
    "colors = ['C4', 'C4']\n",
    "alphas = ([0.5, 1])\n",
    "\n",
    "ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1)\n",
    "\n",
    "for j in range (len(data)):\n",
    "    ax.bar(pos[j], height = np.mean(data[j]), yerr=[stats.sem(data[j])], color=colors[j], alpha=alphas[j])\n",
    "    ax.errorbar(pos[j],np.mean(data[j]),yerr=[stats.sem(data[j])], color='k',zorder = 0)    \n",
    "    for i in range(len(data[j])):\n",
    "        ax.scatter(pos[j] + np.random.random(1) * w/2 - w/4, data[j][i], s=25, color='0.3', alpha=0.4, zorder = 2) \n",
    "   \n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines['left'].set_position(('outward', 7))\n",
    "ax.spines['bottom'].set_position(('outward', 7))\n",
    "ax.set_xticks(pos)\n",
    "ax.set_xticklabels(('PD+ND','global TM'), rotation=45, size=10)\n",
    "ax.set_ylabel('$\\Delta$ conductance [nS]', size=10)\n",
    "yticks = np.array([0, 0.0005, 0.001])\n",
    "ax.set_yticklabels([0.0, 0.5, 1.0], size=10)\n",
    "ax.set_yticks(yticks)\n",
    "plt.ylim([-0.000066,0.001])\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "#plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\\n",
    "#Fig7_Conductance_split_PDND_ephys_barplot.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 604.8x165.6 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make Scatter Plot of modelling conductance (Split global: PD+ND vs. Split)\n",
    "\n",
    "pos = [0,1,3,4,6,7]\n",
    "\n",
    "fig = plt.figure(figsize = (1.4 * len(pos),2.3))\n",
    "plt.subplots_adjust(wspace = 0.4)\n",
    "\n",
    "ax = fig.add_subplot(121)\n",
    "w = 0.6\n",
    "wb = 0.6\n",
    "data = [Rinput_TM_all_sub_mean, Rinput_TM_Split12_all_sub_mean,\n",
    "        Rinput_PM_sum_mean, Rinput_TM_all_sub_mean,\n",
    "        Rinput_PM_Split12_avg_mean, Rinput_TM_Split12_all_sub_mean]\n",
    "\n",
    "colors = ['0.4', 'C4', '0.7', '0.4', '0.7', 'C4']\n",
    "\n",
    "ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1)\n",
    "\n",
    "for j in range (len(data)):\n",
    "    ax.bar(pos[j], height = np.mean(data[j]), color=colors[j])\n",
    "    #ax.errorbar(pos[j],np.mean(data[j]),yerr=[stats.sem(data[j])], color='k',zorder = 0)    \n",
    "    #for i in range(len(data[j])):\n",
    "        #ax.scatter(pos[j] + np.random.random(1) * w/2 - w/4, data[j][i], s=25, color='0.3', alpha=0.4, zorder = 2)\n",
    "   \n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines['left'].set_position(('outward', 7))\n",
    "ax.spines['bottom'].set_position(('outward', 7))\n",
    "ax.set_xticks(pos)\n",
    "ax.set_xticklabels(('loc \\nTM','glob \\nTM', 'PDND \\nsum', 'loc \\nTM', 'PDND \\nsum', 'glob \\nTM'), size=10)\n",
    "ax.set_ylabel(r'$\\Delta$'+ ' conductance [nS]', size=11)\n",
    "yticks = np.array([0, 0.0001, 0.0002, 0.0003, 0.0004])\n",
    "ax.set_yticklabels([0, 0.1, 0.2, 0.3, 0.4], size=11)\n",
    "ax.set_yticks(yticks)\n",
    "plt.ylim([-0.000005,0.0004])\n",
    "plt.title('Ephys')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WilcoxonResult(statistic=49.0, pvalue=0.32587002429697964)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.wilcoxon(Rinput_PM_all_sub_mean[:,0], Rinput_PM_all_sub_mean[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 57.6x162 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make Scatter Plot of conductance for PDND vs. Split (global opponent) motion\n",
    "\n",
    "pos = [0,1]\n",
    "\n",
    "fig = plt.figure(figsize = (fw*len(pos), 2.25))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "w = 0.6\n",
    "wb = 0.6\n",
    "data = [Rinput_PM_all_sub_mean[:,1], Rinput_PM_all_sub_mean[:,0]]\n",
    "colors = ['C3', 'C0']\n",
    "alphas = ([0.7, 0.7])\n",
    "\n",
    "ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1)\n",
    "\n",
    "for j in range (len(data)):\n",
    "    ax.bar(pos[j], height = np.mean(data[j]), yerr=[stats.sem(data[j])], color=colors[j], alpha=alphas[j])\n",
    "    ax.errorbar(pos[j],np.mean(data[j]),yerr=[stats.sem(data[j])], color='k',zorder = 0)    \n",
    "    for i in range(len(data[j])):\n",
    "        ax.scatter(pos[j] + np.random.random(1) * w/2 - w/4, data[j][i], s=25, color='0.3', alpha=0.4, zorder = 2) \n",
    "   \n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines['left'].set_position(('outward', 7))\n",
    "ax.spines['bottom'].set_position(('outward', 7))\n",
    "ax.set_xticks(pos)\n",
    "ax.set_xticklabels(('PD','ND'), size=10)\n",
    "ax.set_ylabel('$\\Delta$ conductance [nS]', size=10)\n",
    "yticks = np.array([0, 0.00025, 0.0005, 0.00075])\n",
    "ax.set_yticklabels(['0.00', '0.25', '0.50', '0.75'], size=10)\n",
    "ax.set_yticks(yticks)\n",
    "#plt.ylim([-0.000066,0.00035])\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "#plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\\n",
    "#Fig3_Conductance_PD_vs_ND_local_barplot.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 57.6x160.56 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make Scatter Plot of conductance for PDND vs. Split (global opponent) motion\n",
    "\n",
    "pos = [0,1]\n",
    "\n",
    "fig = plt.figure(figsize = (fw*len(pos), 2.23))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "w = 0.6\n",
    "wb = 0.6\n",
    "data = [Rinput_PM_all_sub_mean[:,1], Rinput_PM_all_sub_mean[:,0]]\n",
    "colors = ['C3', 'C0']\n",
    "alphas = ([0.7, 0.7])\n",
    "\n",
    "ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1)\n",
    "\n",
    "for j in range (len(data)):\n",
    "    ax.bar(pos[j], height = np.mean(data[j]), yerr=[stats.sem(data[j])], color=colors[j], alpha=alphas[j])\n",
    "    ax.errorbar(pos[j],np.mean(data[j]),yerr=[stats.sem(data[j])], color='k',zorder = 0)    \n",
    "    for i in range(len(data[j])):\n",
    "        ax.scatter(pos[j] + np.random.random(1) * w/2 - w/4, data[j][i], s=25, color='0.3', alpha=0.4, zorder = 2) \n",
    "   \n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines['left'].set_position(('outward', 7))\n",
    "ax.spines['bottom'].set_position(('outward', 7))\n",
    "ax.set_xticks(pos)\n",
    "ax.set_xticklabels(('PD local','ND local'), rotation=45, size=10)\n",
    "ax.set_ylabel('$\\Delta$ conductance [nS]', size=10)\n",
    "yticks = np.array([0, 0.00025, 0.0005, 0.00075])\n",
    "ax.set_yticklabels(['0.00', '0.25', '0.50', '0.75'], size=10)\n",
    "ax.set_yticks(yticks)\n",
    "#plt.ylim([-0.000066,0.001])\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "#plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\\n",
    "#FigS3_Conductance_PD_vs_ND_local_barplot.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WilcoxonResult(statistic=49.0, pvalue=0.32587002429697964)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.wilcoxon(Rinput_PM_all_sub_mean[:,1], Rinput_PM_all_sub_mean[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.2571577031198893\n",
      "10.862052957918708\n"
     ]
    }
   ],
   "source": [
    "NDPD_local_g_ratio = np.mean(Rinput_PM_all_sub_mean[:,0]) / np.mean(Rinput_PM_all_sub_mean[:,1])\n",
    "gi_syn_ratio_local = NDPD_local_g_ratio * (2065.0 / 239.0)\n",
    "print(NDPD_local_g_ratio)\n",
    "print(gi_syn_ratio_local)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 57.6x162 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make Scatter Plot of conductance for PDND vs. Split (global opponent) motion\n",
    "\n",
    "pos = [0,1]\n",
    "\n",
    "fig = plt.figure(figsize = (fw*len(pos), 2.25))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "w = 0.6\n",
    "wb = 0.6\n",
    "data = [Rinput_PM_Split12_all_PD_mean, Rinput_PM_Split12_all_ND_mean]\n",
    "colors = ['C3', 'C0']\n",
    "alphas = ([0.7, 0.7])\n",
    "\n",
    "ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1)\n",
    "\n",
    "for j in range (len(data)):\n",
    "    ax.bar(pos[j], height = np.mean(data[j]), yerr=[stats.sem(data[j])], color=colors[j], alpha=alphas[j])\n",
    "    ax.errorbar(pos[j],np.mean(data[j]),yerr=[stats.sem(data[j])], color='k',zorder = 0)    \n",
    "    for i in range(len(data[j])):\n",
    "        ax.scatter(pos[j] + np.random.random(1) * w/2 - w/4, data[j][i], s=25, color='0.3', alpha=0.4, zorder = 2) \n",
    "   \n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines['left'].set_position(('outward', 7))\n",
    "ax.spines['bottom'].set_position(('outward', 7))\n",
    "ax.set_xticks(pos)\n",
    "ax.set_xticklabels(('PD global','ND global'), rotation=45, size=10)\n",
    "ax.set_ylabel('$\\Delta$ conductance [nS]', size=10)\n",
    "yticks = np.array([0, 0.00025, 0.0005])\n",
    "ax.set_yticklabels(['0.00', '0.25', '0.50'], size=10)\n",
    "ax.set_yticks(yticks)\n",
    "#plt.ylim([-0.000066,0.001])\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\\n",
    "FigS3_Conductance_PD_vs_ND_global_barplot.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WilcoxonResult(statistic=59.0, pvalue=0.6416601266046645)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.wilcoxon(Rinput_PM_Split12_all_PD_mean, Rinput_PM_Split12_all_ND_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8853369086395647\n",
      "7.649459064187035\n"
     ]
    }
   ],
   "source": [
    "NDPD_global_g_ratio = np.mean(Rinput_PM_Split12_all_ND_mean) / np.mean(Rinput_PM_Split12_all_PD_mean)\n",
    "gi_syn_ratio_global = NDPD_global_g_ratio * (2065.0 / 239.0)\n",
    "print(NDPD_global_g_ratio)\n",
    "print(gi_syn_ratio_global)"
   ]
  }
 ],
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