{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import data\n",
    "voltage_TM_all_sub = np.load('voltage_TM_all_sub.npy')\n",
    "voltage_TM_Split12_all_sub = np.load('voltage_TM_Split12_all_sub.npy')\n",
    "voltage_PM_all_sub = np.load('voltage_PM_all_sub.npy')\n",
    "voltage_PM_Split12_all_sub = np.load('voltage_PM_Split12_all_sub.npy')\n",
    "\n",
    "time = np.linspace(0, 7.95, 795)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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(voltage_PM_all_sub[:,:,1],1),7,1), color = '0.4', linewidth = 2)\n",
    "#plt.plot(time, savgol_filter(np.mean(voltage_PM_Split12_all_sub[:,:,1],1),7,1), color = 'C4', linewidth = 2)\n",
    "plt.axvspan(3, 6, facecolor='0.2', alpha=0.2)\n",
    "plt.xlim([1.5,7.5])\n",
    "plt.ylim([-2.8,4.8])\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(voltage_PM_all_sub[:,:,0],1),7,1), color = '0.4', linewidth = 2)\n",
    "#plt.plot(time, savgol_filter(np.mean(voltage_PM_Split12_all_sub[:,:,0],1),7,1), color = 'C4', linewidth = 2)\n",
    "plt.axvspan(3, 6, facecolor='0.2', alpha=0.2)\n",
    "plt.xlim([1.5,7.5])\n",
    "plt.ylim([-2.8,4.8])\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(voltage_TM_all_sub,1),7,1), color = '0.4', linewidth = 2)\n",
    "#plt.plot(time, savgol_filter(np.mean(voltage_TM_Split12_all_sub,1),7,1), color = 'C4', linewidth = 2)\n",
    "plt.axvspan(3, 6, facecolor='0.2', alpha=0.2)\n",
    "plt.xlim([1.5,7.5])\n",
    "plt.ylim([-2.8,4.8])\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_traces.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7PR/3vRj4Ucn5ZcD3RrmuHdhYOF7yc+/Vq1fb6tWrzcxs6dKl1tHRYZs2bbL3v//9ZmZ2/fXX26233mpmZm1tbbZnzx57/PHH7bzzzjMzs1WrVtmaNWvMzGzWrFmWTqdt7dq1tnLlSjMzu+KKK+zuu+82M7Pm5mYzM7v77rvtiiuuMDOzlStX2tq1ay2dTtv06dOtvb3dzjrrLFu6dKldeeWVNm/ePDvnnHPsoosustbWVvvYxz5m73vf+2zZsmXW3t5uc+fOtYsvvtguvfRSmz17trW3t9tpp51mp512mrW3t9vs2bMn9De9/e1vt9tvv92uueYaO/HEE+2qq66yJUuW2CWXXGI33nijNTc32z333GM33HCDrVixwvbt2/emv2nWrFlmZrZmzRpbtWqVFfD1fx+3inlYSFoJfNjMriqcXwacamaryvzOlNvzfmQFdW+cVPq+S2LatGmHtdR9IhXUu7q6uP/++2ltbaW3t5edO3eyd+9ejjnmGF5//XXmz5/PggULkERXVxenn346CxcuHO+2wVQxl/Q24BZgrpmdKekk4HQzu81PA2V4BTiu5PxYKiiCUQ5v7Zk3ePcyxqJipJN59uzZnHLKKcyYMYNnnnmGuXPnhhbt8DOG+yFwP/D5wvkL5JeYT1RwG4C2gotlN3Ap8IkJ3nNK4O394AnNS7q2QlgrbMZyMkN+IhFmtMOP4OaY2W2SrgYws6GA6sNlJX0BeJD8JGSNmW2b6H0nO14oyatL5wltcHAQILKFn2M5mcOOdvgRXHaEoUcTUIkGy8dhqyo05qUlesnXXvHDZDJJJpMphpfiWkgQdrTDj+B+Jel2oLmQ2/B5YE0o1lQB3uoUT1SewLzHan9//5smFJW23N1PLPXbkj4JHA2cD9xqZlNy2/HJgqRiBXVPbN4APZfLFas4WYzL3cPCb9n8u4C7vHNJbzOzfaFZVQWMtvDTm0zEvdw9TMqGtiQdI+m9klKF85mSbgG2R2JdBeM9WpuammhoaKCxsZHa2tqip7/U+19JtU7G/EskXQn8L/DfwGZJHwE6yVca9+N8dYyB5xLxVth6EwYvaD4wMMDg4CDZbDaW5e5hUu6Rei3wHjPbVoilPgp8wszujca0ysQT1MhHqVd2wdsGPZvNFvdmrZQJA5QXXMbzi5nZekm7nNgmzmh5sF4uKLwRWB85jqsUygmuVtK7eMPnlis9N7OKXtURFqPlwZZGGrzogyfMaurhGnmrU9Y7N+D4UCyqcEbLg/XcJAMDA0UBplKp4qYkXoJyJZR9HVNwZjY/QjuqhtHcIbW1tTz55JPMmDGDhQsXFkU1NDREX19f0SVSCU7gcDMmHG9hrDxYL67q9WKee6S+vr7on6sEJ7ATXAyMlgfr9WbeGK50CZP3OzD1ncBOcJOInp4empqaillV/f39xZ9VihM4thW/R8JUXPHrlxUrVgDw2GOPAZMj9+EwCWbFryMeoszEjxInuElMVJn4UeIEFxM2otplteDGcDEwBcdnfvBleCxTHknflvSCpD9Kuq+wbL1qKK126R1BVwufrMQ1x34IOMHMTgI6gOtjsiMWylW7hPhLQYRJLGM4M1tXcvoU+Sz8qqFctcsKfdwWmQyThs+S34t1VCS180YxxHDKQUZMa2srixYtYuvWrcUVISeffDKtra2RFJeOk9AeqZJ+L+nZUY4LS675Gvk0xLvGuo+Z3WFmywqThYoa5Iy25m28x+1UJ7Qezsw+VO7nym+hdAFwtlXSIMUH3d3d7Nixg3nz5hV7sR07dtDW1kZLSwu5XI50Os3g4CB1dXXkcrmKcZ3E8kiVdC7wFeADVgFbmR8u5XqxBQsWYGZs2LChKMYlS5YwY8aMmK0OhrjGcN8D6oCHCm/6U2b2tzHZEjnlJg379+9HEqeeeioDAwPU19dz6NAh9u/fXxFjuLhmqYviaHeyUK5E/q5du4pbo3tbhAe9wVqcTIZZatVRLjAfxwYhUeIEFxNjBeaj3iAkalwsNSZGBu9Llx6V+9kkxpeBTnAxUKHRBLcAc7JS6dGEckztBfJTlEqPJpTD9XAxUOnRhHI4wcXAjBkzKjqaUA4nuBio9GhCOdwYLgbS6XQxmjBz5kyam5tJJBJVMYZzgouB0mgCUHHRhHK4R2oMVHo0oRzO8RsTUzSaUA4XaXBEyuRNE3RUL24MFxMV+Ej1hRNcDFRo8N4XTnAx4IL3MSHpy5JMUuX7A0qo5uB9bIKTdBxwDvByXDbERTU7fuPs4f4FuI4qdGF4jt+urq7iUS2O37jyUj8K7DazreMNkkeUeqj328QEzAsdSdx8880CjiNfvqIb+NNvf/vbiv/wheb4lfR74JhRfvQ14AbgL8ysR9JLwDIzq6gyDo7RiTzSIOlE4GHAy7g/FtgDnGpmr0VqjCNyYg9tuR6uunChLUekxN7DOaoL18M5IsUJzhEpTnCOSHGCc0RKRa4WkfQAR16A2vP8x0GcbU+0/W4zO3e8i9wsdQSHsYy9otqOqn33SHVEihOcI1Kc4N7KHVXadiTtuzGcI1JcD+eIFCc4R6RUreAkvVTYs3VrYQ+wSyWtkHRI0mZJ2wrHLZKmB9zus5ISI147QdJPJL1SaL9T0h8kXRZg209L2iLpOUnZwvdbJD1aSGb6zxHX/7Tw+glB2VBM4Ki2A3iJ/J6tAKcA/eS30dxYck0z8ENgE5AMsN2XgMtH2gL8BPhCyetLge3AtQH/7fPJO2pLz/cDLwDTC69NA3YAr3jvUxBH1fZwpZjZZuAgsGDE6weBz5P3wI/rRT8MbgJuklQ7jl1bgC8CX1H4GdIG3AN8vHC+EvgN+d0eA8MJDpD0QfIJOp0jf2ZmGWAzsCTAJjcWjs/5uPZp4G1AFBnSPwE+Xfj+8sJ5oFRkLPUwuFfSAJAG/gbIjHFdGL3L14FHJf14nOsiy0Azs12SBiWdDzSa2bNBd6zV3sNdbGZLzexMM3totAsk1ZAfSz0bZMNmth1YC1w7zqV/DuwDuoJsvww/BX5WOAKn2nu4skiaBnyH/AqKB0No4ibgGcb4P0g6Cfgu8E2zyDz095B/hI+5S/dEcIJ7K++WtAWoIf84e5D8rtXDQTdkZq9I+jnwpZKXvyrpKqCRfM92s5mF0tuMYVMv8I2w7u9CW45IqfYxnCNinOAckeIE54gUJzhHpDjBOSLFCc4RKU5wjkj5f6oSC1SLza93AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 119.952x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make Bar Plot of Responses\n",
    "\n",
    "fig = plt.figure(figsize = (1.666,3))\n",
    "plt.subplots_adjust(wspace=0.5)\n",
    "ax = fig.add_subplot(111)\n",
    "w=0.7\n",
    "\n",
    "ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1)\n",
    "\n",
    "ax.bar(0,height=[np.mean(np.mean(voltage_PM_all_sub[300:600,:,1],0))],width=w,color='0.4',yerr=[stats.sem(np.mean(voltage_PM_all_sub[300:600,:,1],0))])\n",
    "for i in range(len(np.mean(voltage_PM_all_sub[300:600,:,1],0))):\n",
    "    ax.scatter(0 + np.random.random(1) * w/2 - w/4, np.mean(voltage_PM_all_sub[300:600,i,1],0), s=25, color='0.3', alpha=0.4, zorder = 2)\n",
    "    \n",
    "ax.bar(1,height=[np.mean(np.mean(voltage_PM_all_sub[300:600,:,0],0))],width=w,color='0.4',yerr=[stats.sem(np.mean(voltage_PM_all_sub[300:600,:,0],0))])\n",
    "for i in range(len(np.mean(voltage_PM_all_sub[300:600,:,0],0))):\n",
    "    ax.scatter(1 + np.random.random(1) * w/2 - w/4, np.mean(voltage_PM_all_sub[300:600,i,0],0), s=25, color='0.3', alpha=0.4, zorder = 2)\n",
    "    \n",
    "    \n",
    "ax.bar(2,height=[np.mean(np.mean(voltage_TM_all_sub[300:600],0))],width=w,color='0.4',yerr=[stats.sem(np.mean(voltage_TM_all_sub[300:600],0))])\n",
    "for i in range(len(np.mean(voltage_TM_all_sub[300:600],0))):\n",
    "    ax.scatter(2 + np.random.random(1) * w/2 - w/4, np.mean(voltage_TM_all_sub[300:600,i],0), 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', 10))\n",
    "ax.spines['bottom'].set_position(('outward', 10))\n",
    "ax.set_xticks([0,1,2])\n",
    "ax.set_xticklabels(('PD', 'ND', 'TM'), size=11)\n",
    "ax.set_ylabel('Response [mV]', size=11)\n",
    "plt.ylim([-4,8.1])\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "#plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\\n",
    "#Fig7_VS_TM_local_voltage_barplot.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "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(voltage_PM_all_sub[:,:,1],1),7,1), color = '0.4', linewidth = 2)\n",
    "plt.plot(time, savgol_filter(np.mean(voltage_PM_Split12_all_sub[:,:,1],1),7,1), color = 'C4', linewidth = 2)\n",
    "plt.axvspan(3, 6, facecolor='0.2', alpha=0.2)\n",
    "plt.xlim([1.5,7.5])\n",
    "plt.ylim([-1.9,3.1])\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(voltage_PM_all_sub[:,:,0],1),7,1), color = '0.4', linewidth = 2)\n",
    "plt.plot(time, savgol_filter(np.mean(voltage_PM_Split12_all_sub[:,:,0],1),7,1), color = 'C4', linewidth = 2)\n",
    "plt.axvspan(3, 6, facecolor='0.2', alpha=0.2)\n",
    "plt.xlim([1.5,7.5])\n",
    "plt.ylim([-1.9,3.1])\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(voltage_TM_all_sub,1),7,1), color = '0.4', linewidth = 2)\n",
    "plt.plot(time, savgol_filter(np.mean(voltage_TM_Split12_all_sub,1),7,1), color = 'C4', linewidth = 2)\n",
    "plt.axvspan(3, 6, facecolor='0.2', alpha=0.2)\n",
    "plt.xlim([1.5,7.5])\n",
    "plt.ylim([-1.9,3.1])\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_traces.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 119.952x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make Bar Plot of Responses\n",
    "\n",
    "fig = plt.figure(figsize = (1.666,3))\n",
    "plt.subplots_adjust(wspace=0.5)\n",
    "ax = fig.add_subplot(111)\n",
    "w=0.7\n",
    "\n",
    "ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1)\n",
    "\n",
    "ax.bar(0,height=[np.mean(np.mean(voltage_PM_Split12_all_sub[300:600,:,1],0))],width=w,color='C4',yerr=[stats.sem(np.mean(voltage_PM_Split12_all_sub[300:600,:,1],0))])\n",
    "for i in range(len(np.mean(voltage_PM_Split12_all_sub[300:600,:,1],0))):\n",
    "    ax.scatter(0 + np.random.random(1) * w/2 - w/4, np.mean(voltage_PM_Split12_all_sub[300:600,i,1],0), s=25, color='0.3', alpha=0.4, zorder = 2)\n",
    "    \n",
    "ax.bar(1,height=[np.mean(np.mean(voltage_PM_Split12_all_sub[300:600,:,0],0))],width=w,color='C4',yerr=[stats.sem(np.mean(voltage_PM_Split12_all_sub[300:600,:,0],0))])\n",
    "for i in range(len(np.mean(voltage_PM_Split12_all_sub[300:600,:,0],0))):\n",
    "    ax.scatter(1 + np.random.random(1) * w/2 - w/4, np.mean(voltage_PM_Split12_all_sub[300:600,i,0],0), s=25, color='0.3', alpha=0.4, zorder = 2)\n",
    "    \n",
    "    \n",
    "ax.bar(2,height=[np.mean(np.mean(voltage_TM_Split12_all_sub[300:600],0))],width=w,color='C4',yerr=[stats.sem(np.mean(voltage_TM_Split12_all_sub[300:600],0))])\n",
    "for i in range(len(np.mean(voltage_TM_Split12_all_sub[300:600],0))):\n",
    "    ax.scatter(2 + np.random.random(1) * w/2 - w/4, np.mean(voltage_TM_Split12_all_sub[300:600,i],0), 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', 10))\n",
    "ax.spines['bottom'].set_position(('outward', 10))\n",
    "ax.set_xticks([0,1,2])\n",
    "ax.set_xticklabels(('PD', 'ND', 'TM'), size=11)\n",
    "ax.set_ylabel('Response [mV]', size=11)\n",
    "plt.ylim([-4,6])\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "#plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\\n",
    "#FigSX_VS_Split_global_voltage_barplot.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
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 },
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