{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/usr/bin/env python3\n",
    "# -*- coding: utf-8 -*-\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#import blindschleiche_py3 as bs\n",
    "from scipy import ndimage\n",
    "\n",
    "from scipy import stats\n",
    "from scipy.signal import savgol_filter\n",
    "import octopus as oct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import slope for all coherences\n",
    "coh = np.array([-0.5, -0.375, -0.25, -0.125, 0, 0.125, 0.25, 0.375, 0.5])*2\n",
    "\n",
    "slope_coh_ctrl = np.load('slope_coh_ctrl_300rep.npy')\n",
    "slope_coh_feedback_block = np.load('slope_coh_feedback_block_300rep.npy')\n",
    "slope_coh_LT_ctrl = np.load('slope_coh_LT_ctrl_300rep.npy')\n",
    "slope_coh_LL_ctrl = np.load('slope_coh_LL_ctrl_300rep.npy')\n",
    "slope_coh_LV_ctrl = np.load('slope_coh_LV_ctrl_300rep.npy')\n",
    "slope_coh_LT_block = np.load('slope_coh_LT_block_300rep.npy')\n",
    "slope_coh_LL_block = np.load('slope_coh_LL_block_300rep.npy')\n",
    "slope_coh_LV_block = np.load('slope_coh_LV_block_300rep.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import VS mV responses for all coherences\n",
    "VS_mV_ctrl = np.load('VS_mV_ctrl_300rep.npy')\n",
    "VS_mV_feedback_block = np.load('VS_mV_feedback_block_300rep.npy')\n",
    "VS_mV_LT_ctrl = np.load('VS_mV_LT_ctrl_300rep.npy')\n",
    "VS_mV_LL_ctrl = np.load('VS_mV_LL_ctrl_300rep.npy')\n",
    "VS_mV_LV_ctrl = np.load('VS_mV_LV_ctrl_300rep.npy')\n",
    "VS_mV_LT_block = np.load('VS_mV_LT_block_300rep.npy')\n",
    "VS_mV_LL_block = np.load('VS_mV_LL_block_300rep.npy')\n",
    "VS_mV_LV_block = np.load('VS_mV_LV_block_300rep.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import VS conductance changes for all coherences\n",
    "\n",
    "delta_g_ctrl = np.load('delta_g_ctrl_300rep.npy')\n",
    "delta_g_feedback_block = np.load('delta_g_feedback_block_300rep.npy')\n",
    "delta_g_LT_ctrl = np.load('delta_g_LT_ctrl_300rep.npy')\n",
    "delta_g_LL_ctrl = np.load('delta_g_LL_ctrl_300rep.npy')\n",
    "delta_g_LV_ctrl = np.load('delta_g_LV_ctrl_300rep.npy')\n",
    "delta_g_LT_block = np.load('delta_g_LT_block_300rep.npy')\n",
    "delta_g_LL_block = np.load('delta_g_LL_block_300rep.npy')\n",
    "delta_g_LV_block = np.load('delta_g_LV_block_300rep.npy')\n",
    "\n",
    "delta_ge_ctrl = np.load('delta_ge_ctrl_300rep.npy')\n",
    "delta_ge_feedback_block = np.load('delta_ge_feedback_block_300rep.npy')\n",
    "delta_ge_LT_ctrl = np.load('delta_ge_LT_ctrl_300rep.npy')\n",
    "delta_ge_LL_ctrl = np.load('delta_ge_LL_ctrl_300rep.npy')\n",
    "delta_ge_LV_ctrl = np.load('delta_ge_LV_ctrl_300rep.npy')\n",
    "delta_ge_LT_block = np.load('delta_ge_LT_block_300rep.npy')\n",
    "delta_ge_LL_block = np.load('delta_ge_LL_block_300rep.npy')\n",
    "delta_ge_LV_block = np.load('delta_ge_LV_block_300rep.npy')\n",
    "\n",
    "delta_gi_ctrl = np.load('delta_gi_ctrl_300rep.npy')\n",
    "delta_gi_feedback_block = np.load('delta_gi_feedback_block_300rep.npy')\n",
    "delta_gi_LT_ctrl = np.load('delta_gi_LT_ctrl_300rep.npy')\n",
    "delta_gi_LL_ctrl = np.load('delta_gi_LL_ctrl_300rep.npy')\n",
    "delta_gi_LV_ctrl = np.load('delta_gi_LV_ctrl_300rep.npy')\n",
    "delta_gi_LT_block = np.load('delta_gi_LT_block_300rep.npy')\n",
    "delta_gi_LL_block = np.load('delta_gi_LL_block_300rep.npy')\n",
    "delta_gi_LV_block = np.load('delta_gi_LV_block_300rep.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "slope_int_ctrl = np.load('slope_int_ctrl.npy')\n",
    "slope_int_feedback_block = np.load('slope_int_feedback_block.npy')\n",
    "slope_int_LT_block = np.load('slope_int_LT_block.npy')\n",
    "slope_int_LL_block = np.load('slope_int_LL_block.npy')\n",
    "slope_int_LV_block = np.load('slope_int_LV_block.npy')\n",
    "\n",
    "slope_int_ctrl_PD = np.load('slope_int_ctrl_PD.npy')\n",
    "slope_int_feedback_block_PD = np.load('slope_int_feedback_block_PD.npy')\n",
    "slope_int_LT_block_PD = np.load('slope_int_LT_block_PD.npy')\n",
    "slope_int_LL_block_PD = np.load('slope_int_LL_block_PD.npy')\n",
    "slope_int_LV_block_PD = np.load('slope_int_LV_block_PD.npy')\n",
    "\n",
    "slope_int_ctrl_ND = np.load('slope_int_ctrl_ND.npy')\n",
    "slope_int_feedback_block_ND = np.load('slope_int_feedback_block_ND.npy')\n",
    "slope_int_LT_block_ND = np.load('slope_int_LT_block_ND.npy')\n",
    "slope_int_LL_block_ND = np.load('slope_int_LL_block_ND.npy')\n",
    "slope_int_LV_block_ND = np.load('slope_int_LV_block_ND.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate motion opponent suppression indices\n",
    "MOSI_ctrl = (VS_mV_ctrl[8,:] - VS_mV_ctrl[4,:]) / VS_mV_ctrl[8,:]\n",
    "MOSI_feedback_block = (VS_mV_feedback_block[8,:] - VS_mV_feedback_block[4,:]) / VS_mV_feedback_block[8,:]\n",
    "MOSI_LT_block = (VS_mV_LT_block[8,:] - VS_mV_LT_block[4,:]) / VS_mV_LT_block[8,:]\n",
    "MOSI_LL_block = (VS_mV_LL_block[8,:] - VS_mV_LL_block[4,:]) / VS_mV_LL_block[8,:]\n",
    "MOSI_LV_block = (VS_mV_LV_block[8,:] - VS_mV_LV_block[4,:]) / VS_mV_LV_block[8,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate motion opponent suppression indices for global TM\n",
    "MOSI_glob_ctrl = (VS_mV_ctrl[8,:] - VS_mV_ctrl[9,:]) / VS_mV_ctrl[8,:]\n",
    "MOSI_glob_feedback_block = (VS_mV_feedback_block[8,:] - VS_mV_feedback_block[9,:]) / VS_mV_feedback_block[8,:]\n",
    "MOSI_glob_LT_block = (VS_mV_LT_block[8,:] - VS_mV_LT_block[9,:]) / VS_mV_LT_block[8,:]\n",
    "MOSI_glob_LL_block = (VS_mV_LL_block[8,:] - VS_mV_LL_block[9,:]) / VS_mV_LL_block[8,:]\n",
    "MOSI_glob_LV_block = (VS_mV_LV_block[8,:] - VS_mV_LV_block[9,:]) / VS_mV_LV_block[8,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "slope_LTsumLLblock = slope_int_LT_block + slope_int_LL_block - slope_int_ctrl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "slope_coh_ctrl = np.zeros([9,300])\n",
    "for i in range(len(VS_mV_ctrl[0,:])):\n",
    "    slope, intercept, r_value, p_value, std_err = stats.linregress(coh[0:2], VS_mV_ctrl[0:2,i])\n",
    "    slope_coh_ctrl[0,i] = slope\n",
    "    slope, intercept, r_value, p_value, std_err = stats.linregress(coh[7:9], VS_mV_ctrl[7:9,i])\n",
    "    slope_coh_ctrl[8,i] = slope   \n",
    "    for j in [1,2,3,4,5,6,7]:   \n",
    "        slope, intercept, r_value, p_value, std_err = stats.linregress(coh[j-1:j+2], VS_mV_ctrl[j-1:j+2,i])\n",
    "        slope_coh_ctrl[j,i] = slope       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12.07854737, 12.08397343, 11.67246063, 10.61328156,  9.6578325 ,\n",
       "        9.35114149,  9.49109926,  9.3436325 ,  9.06272582])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(slope_coh_ctrl,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "slope_coh_feedback_block = np.zeros([9,300])\n",
    "for i in range(len(VS_mV_feedback_block[0,:])):\n",
    "    slope, intercept, r_value, p_value, std_err = stats.linregress(coh[0:2], VS_mV_feedback_block[0:2,i])\n",
    "    slope_coh_feedback_block[0,i] = slope\n",
    "    slope, intercept, r_value, p_value, std_err = stats.linregress(coh[7:9], VS_mV_feedback_block[7:9,i])\n",
    "    slope_coh_feedback_block[8,i] = slope   \n",
    "    for j in [1,2,3,4,5,6,7]:   \n",
    "        slope, intercept, r_value, p_value, std_err = stats.linregress(coh[j-1:j+2], VS_mV_feedback_block[j-1:j+2,i])\n",
    "        slope_coh_feedback_block[j,i] = slope   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9.85579056,  8.83555531,  9.30029234,  6.2070023 ,  4.06816263,\n",
       "        5.94143182,  5.21381888,  7.86416476, 10.67597012])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slope_coh_feedback_block[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "fw = 0.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 151.2x165.6 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (2.1, 2.3))\n",
    "\n",
    "ax = fig.add_subplot(111) #PLOT coherence tuning\n",
    "\n",
    "ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1)\n",
    "plt.plot(coh, np.mean(VS_mV_ctrl,1)[0:9], linewidth = 2.5, color = '0.35')\n",
    "plt.errorbar(coh, np.mean(VS_mV_ctrl,1)[0:9], yerr = stats.sem(VS_mV_ctrl,1)[0:9],\n",
    "             linewidth = 2.5, color = '0.35')\n",
    "\n",
    "plt.plot(coh, np.mean(VS_mV_feedback_block,1)[0:9], linewidth = 2.5, color = 'C6')\n",
    "plt.errorbar(coh, np.mean(VS_mV_feedback_block,1)[0:9], yerr = stats.sem(VS_mV_feedback_block,1)[0:9],\n",
    "             linewidth = 2.5, color = 'C6')\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_xlabel('Coherence', fontsize = 10)\n",
    "ax.set_ylabel('Response [mV]', fontsize = 10)\n",
    "plt.ylim([-10,12.5])\n",
    "\n",
    "\n",
    "#plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\Modelling\\\\\\\n",
    "#FigS7_Coherence_tuning_VS_feedback_block_mV_300rep.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 151.2x165.6 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (2.1, 2.3))\n",
    "\n",
    "ax = fig.add_subplot(111) #PLOT coherence tuning\n",
    "\n",
    "ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1)\n",
    "plt.plot(coh, np.mean(delta_g_ctrl,1)[0:9], linewidth = 2.5, color = '0.35')\n",
    "plt.errorbar(coh, np.mean(delta_g_ctrl,1)[0:9], yerr = stats.sem(delta_g_ctrl,1)[0:9],\n",
    "            linewidth = 2.5, color = '0.35')\n",
    "\n",
    "ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1)\n",
    "plt.plot(coh, np.mean(delta_g_feedback_block,1)[0:9], linewidth = 2.5, color = 'C6')\n",
    "plt.errorbar(coh, np.mean(delta_g_feedback_block,1)[0:9], yerr = stats.sem(delta_g_feedback_block,1)[0:9],\n",
    "            linewidth = 2.5, color = 'C6')\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_xlabel('Coherence', fontsize = 10)\n",
    "ax.set_ylabel(r'$\\Delta$' + ' conductance [a.u.]', fontsize = 10)\n",
    "plt.ylim([0,0.8])\n",
    "\n",
    "#plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\Modelling\\\\\\\n",
    "#FigS7_Coherence_tuning_VS_feedback_block_only_total_300rep.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 151.2x165.6 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (2.1, 2.3))\n",
    "\n",
    "ax = fig.add_subplot(111) #PLOT slopes of coherence tuning\n",
    "\n",
    "ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1)\n",
    "plt.plot(coh, np.mean(slope_coh_ctrl,1), linewidth = 2.5, color = '0.35')\n",
    "plt.errorbar(coh, np.mean(slope_coh_ctrl,1), yerr = stats.sem(slope_coh_ctrl,1),\n",
    "             linewidth = 2.5, color = '0.35')\n",
    "\n",
    "plt.plot(coh, np.mean(slope_coh_feedback_block,1), linewidth = 2.5, color = 'C6')\n",
    "plt.errorbar(coh, np.mean(slope_coh_feedback_block,1), yerr = stats.sem(slope_coh_feedback_block,1),\n",
    "             linewidth = 2.5, color = 'C6')\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_xlabel('Coherence', fontsize = 10)\n",
    "ax.set_ylabel('Response gain [mV]', fontsize = 10)\n",
    "#xticks = np.array([-0.5, -0.25, 0, 0.25, 0.5])\n",
    "#ax.set_xticklabels([-0.5, -0.25, 0, 0.25, 0.5], size=10)\n",
    "plt.ylim([0,13.5])\n",
    "#ax.set_xticks(xticks)\n",
    "#ax.set_yticks([-5, 0, 5])\n",
    "#ax.set_yticklabels([-5, 0, 5], size=10)\n",
    "\n",
    "plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\Modelling\\\\\\\n",
    "FigS8_Coherence_tuning_VS_ctrl_block_gain.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "slope_coh_ctrl_min_block = slope_coh_ctrl - slope_coh_feedback_block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 165.6x165.6 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (2.3, 2.3))\n",
    "\n",
    "ax = fig.add_subplot(111) #PLOT slopes of coherence tuning\n",
    "\n",
    "ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1)\n",
    "plt.plot(coh, np.mean(slope_coh_ctrl_min_block,1), linewidth = 2.5, color = '0.35')\n",
    "plt.errorbar(coh, np.mean(slope_coh_ctrl_min_block,1), yerr = stats.sem(slope_coh_ctrl_min_block,1),\n",
    "             linewidth = 2.5, color = '0.35')\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_xlabel('Coherence', fontsize = 10)\n",
    "ax.set_ylabel('Diff. gain [mV/coh]', fontsize = 10)\n",
    "plt.ylim([0,5])\n",
    "\n",
    "#ax.set_xticks(xticks)\n",
    "#ax.set_yticks([-5, 0, 5])\n",
    "#ax.set_yticklabels([-5, 0, 5], size=10)\n",
    "\n",
    "#plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\\\\n",
    "#FigS7_Coherence_tuning_VS_LL_block_mV.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "delta_ge_ctrl_min_block = delta_ge_ctrl - delta_ge_feedback_block\n",
    "delta_gi_ctrl_min_block = delta_gi_ctrl - delta_gi_feedback_block\n",
    "delta_g_ctrl_min_block = delta_g_ctrl - delta_g_feedback_block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 165.6x165.6 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (2.3, 2.3))\n",
    "\n",
    "ax = fig.add_subplot(111) #PLOT coherence tuning\n",
    "\n",
    "ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1)\n",
    "plt.plot(coh, np.mean(delta_g_ctrl_min_block,1)[0:9], linewidth = 2.5, color = 'C2')\n",
    "plt.errorbar(coh, np.mean(delta_g_ctrl_min_block,1)[0:9], yerr = stats.sem(delta_g_ctrl_min_block,1)[0:9],\n",
    "        linewidth = 2.5, color = 'C2')\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_xlabel('Coherence', fontsize = 10)\n",
    "ax.set_ylabel(r'$\\Delta$' + ' conductance [a.u.]', fontsize = 10)\n",
    "plt.ylim([-0.25,0])\n",
    "\n",
    "#plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\Modelling\\\\\\\n",
    "#FigS7_Coherence_tuning_VS_LV_block_minus_ctrl_only_total_300rep.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 151.2x165.6 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (2.1, 2.3))\n",
    "\n",
    "ax = fig.add_subplot(111) #PLOT slopes of coherence tuning\n",
    "\n",
    "#ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1)\n",
    "plt.plot(coh, np.mean(slope_coh_ctrl_min_block,1), linewidth = 2.5, color = '0.35')\n",
    "plt.errorbar(coh, np.mean(slope_coh_ctrl_min_block,1), yerr = stats.sem(slope_coh_ctrl_min_block,1),\n",
    "             linewidth = 2.5, color = '0.35')\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_xlabel('Coherence', fontsize = 10)\n",
    "ax.set_ylabel('Diff. gain [mV/coh]', fontsize = 10)\n",
    "plt.ylim([0,5])\n",
    "#ax.set_xticks([-1, -0.5, 0, 0.5, 1])\n",
    "#ax.set_xticklabels([-1, -0.5, 0, 0.5, 1], size=10)\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(coh, np.mean(delta_g_ctrl_min_block,1)[0:9], linewidth = 2.5, linestyle='--', color = 'C2')\n",
    "#ax2.errorbar(coh, np.mean(delta_g_ctrl_min_block,1)[0:9], yerr = stats.sem(delta_g_ctrl_min_block,1)[0:9],\n",
    "#        linewidth = 2.5, color = 'C2')\n",
    "\n",
    "ax2.tick_params(axis='y', colors='C2')\n",
    "ax2.spines['right'].set_color('C2')\n",
    "ax2.spines[\"top\"].set_visible(False)\n",
    "ax2.spines['left'].set_position(('outward', 7))\n",
    "ax2.spines['bottom'].set_position(('outward', 7))\n",
    "ax2.spines['right'].set_position(('outward', 7))\n",
    "ax2.set_ylim([-0.21, 0])\n",
    "ax2.set_yticks([-0.2, -0.1, 0.0])\n",
    "ax2.set_yticklabels([-0.2, -0.1, 0.0], size=10)\n",
    "ax2.set_ylabel('Diff. conductance [a.u.]', fontsize = 10, color = 'C2')\n",
    "\n",
    "#plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\Modelling\\\\\\\n",
    "#FigS7_Diff_Gain_tuning_VS_feedback_block_300rep.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 151.2x165.6 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (2.1, 2.3))\n",
    "\n",
    "ax = fig.add_subplot(111) #PLOT slopes of coherence tuning\n",
    "\n",
    "ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1)\n",
    "\n",
    "plt.plot(np.mean(delta_g_ctrl_min_block,1)[0:5], np.mean(slope_coh_ctrl_min_block[0:5],1), linewidth=2.5, color='C0', alpha=0.7)\n",
    "plt.errorbar(np.mean(delta_g_ctrl_min_block,1)[0:5], np.mean(slope_coh_ctrl_min_block[0:5],1), \n",
    "             xerr = stats.sem(delta_g_ctrl_min_block,1)[0:5],\n",
    "             yerr = stats.sem(slope_coh_ctrl_min_block,1)[0:5], fmt='none', color = 'C0', alpha=0.7)\n",
    "\n",
    "plt.plot(np.mean(delta_g_ctrl_min_block,1)[4:9], np.mean(slope_coh_ctrl_min_block[4:9],1), linewidth=2.5, color='C3', alpha=0.7)\n",
    "plt.errorbar(np.mean(delta_g_ctrl_min_block,1)[4:9], np.mean(slope_coh_ctrl_min_block[4:9],1), \n",
    "             xerr = stats.sem(delta_g_ctrl_min_block,1)[4:9],\n",
    "             yerr = stats.sem(slope_coh_ctrl_min_block,1)[4:9], fmt='none', color = 'C3', alpha=0.7)\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",
    "\n",
    "ax.set_xlabel('Diff. conductance [a.u.]', fontsize = 10)\n",
    "ax.set_ylabel('Diff. gain [mV/coh]', fontsize = 10)\n",
    "plt.ylim([0,5])\n",
    "plt.xlim([-0.21,-0.04])\n",
    "ax.set_xticks([-0.2, -0.15, -0.1, -0.05])\n",
    "#ax.set_yticklabels([-5, 0, 5], size=10)\n",
    "\n",
    "#plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\Modelling\\\\\\\n",
    "#FigS7_Gain_vs_conductance_tuning_VS_feedback_block_300rep.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.9787791435219251, 0.003699056467477023)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.pearsonr(np.mean(delta_g_ctrl_min_block,1)[0:5], np.mean(slope_coh_ctrl_min_block[0:5],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinregressResult(slope=-15.565925804820125, intercept=1.3637289070446577, rvalue=-0.9787791435219253, pvalue=0.003699056467476963, stderr=1.8815238957486269)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.linregress(np.mean(delta_g_ctrl_min_block,1)[0:5], np.mean(slope_coh_ctrl_min_block[0:5],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.9889516610453779, 0.001391740601458019)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.pearsonr(np.mean(delta_g_ctrl_min_block,1)[4:9], np.mean(slope_coh_ctrl_min_block[4:9],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinregressResult(slope=-26.01448414745178, intercept=-0.9963915810861312, rvalue=-0.9889516610453781, pvalue=0.0013917406014579768, stderr=2.251335066089426)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.linregress(np.mean(delta_g_ctrl_min_block,1)[4:9], np.mean(slope_coh_ctrl_min_block[4:9],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+G7+/n2AkQNv0KG2HNxLvSTJneTuHL2zx7B60R9s5ou0I0j1p9qf3o0P8jYDPmFY4EKYl3MJAZmDijFZVHwNWiEgLZiBkb9HXd1REC0fVERFmLWkj2hhi0+O7iU2LEGkIokA2nadlWkNJ/uxgdpDTZ53O7ObZ/KnrT3Qnu0lmTJw26A/SEe2gPdJOS6iFgC9AY7ByoX3PHc2qOvVeWHWIISK0z2xk9mAbe18eoD9lBqK0z2r03H1boCnUhM/vY/XM1SzvXM62vm1s6t1E1B8lFo6xL7WPkD9EOpfmyPYj6YhULk3TTYs0BRmaDF5O8ndx7oEgKMqilkUAbOrdxGB2kFQuxRFtR3DWnLPwDT+vblmhCme0jrIpZHkNZAZoDDYeqE2Hlo1yQ1THaEWkAfg/wFxVfa99Z+4RqvqLcg5YDs5oD1mqNgHddzCjcE+x29uBG8o5mMNRCbwY7SJVvRHIAKhqgjLvEIejEniJHqRFJIp9DIvIIormPygHEdmKiffmMK98GunR73AchBej/QzwG2COiPwAOA34uwoc+6xS37frcIC3zoX7ROQJ4GSMW/AhZ2yOWjKmTysipwFJVf0l0Ap8UkTG+2JWBe4Vkcfti/UcDs94aYh9EzPk5ljgo8BLwHfHedzTVPUE4HzgOhF57dAdRORqEVknIuuAyTnU1VETvBhtVk0w92Lgq6r6FWBcL7NS1R32czfwM+Cgd8Cr6s2qeqJtpDl3xHEAL0YbF5HrgSuBX4qIn3FM/SkijSLSXFgHXg88U648x9TDi9FegQlxXaWqO4FZwH+M45jTgYdEZD3wKPBLVf3NOOQ5phgu98BRS6rTjSsibxGRF0Rkv4j0iUhcRPrKOZjDUQm8dC7cCFykqu6Vg45JgRefdpczWMdkwktNu05Efgz8nFe/c+GnVdPK4RgFL0Ybw7ws5PVFZQo4o3XUBC+5B++aCEUcDq94iR7MFpGfichuEdklIv8rIrMnQjmHYzi8jly4C5iJ6Vi425Y5HDXBi9FOU9XvqGrWLrcB06qsl8MxIl6MtltErhQRv12uBNxMiY6a4cVo3w1cDuy0y6W2zOGoCV6iB9uAN06ALg6HJ7xEDxaKyN0issdGEO4UkYUToZzDMRxe3IMfYiaam4GJIPwE+FE1lXI4RsOL0Yqqfq8oevB9XDqgo4Z46cZ9UEQ+AfwPxlivwIxgaAdQ1b1V1M/hOAgvc3m9OMrXqqpV929dEvghS1lJ4F6iBwvKEexwVAsv0YPLigYi/qOI/FREjq++ag7H8HhpiH1aVeMicjpwLnA78K3qquVwjIwXoy28tvoC4Juqeicw8itQHI4q48VoXxaRmzBdub8SkbDH3zkcVcGL8V0O3AOcZ99s046ZHsnhqAljGq2qDgK7gdNtURZ4oZpKORyj4SV68Bng48D1tigIfL+aSjkco+HFPXgzJstrAA5MHjeuCegcjvHgxWjTdtbEwvT1lXv1nsNRBl6M9g4bPWgVkfcC/w+4pbpqORwj42kCOhE5BzPvgQD3qOp91VZsyPFd7sGhycS8sdHOT7tGVX9QzgHLwRntIUtlZ00UkZiIXC8iXxOR14vhA8AWTOzW4agJo2V5fQ/YBzwMvAfToRACLlbVJydAN4djWEYz2oWqugJARG7BvPdgrqrGJ0Qzh2MERoseZAorqpoDXnQG65gMjFbTHls047cAUbstmBELsapr53AMw4hGq6r+iVTE4fCKSzF01B3OaB11hzNaR93hjNZRdzijddQdzmgddUdNjFZEzhORjSKyyU655HB4ZsKN1maJfR04H1gOvFVElk+0Ho76pRY17Spgk6puUdU0ZmK7i2ugh6NOqYXRzgL+WrS93ZY5HJ7wMtVnpRku8fegRG4RuRq42m5GSpTnOISphdFuB+YUbc8GdgzdSVVvBm6eKKUc9UMt3IPHgCUiskBEQsAazMv1HA5PTHhNq6pZO2znHsAPfFtV/zLRejjql5IHNjoctcb1iDnqDme0jrqjFtGDiiIivwE6S/hJJ2aQZqVxckuX262q55V6gCnn044x8YeTO0nlFuPcA0fd4YzWUXdMRaOtVi+bk1tduQeYcj6to/6ZijWto86ZskYrIofEfxeRqmW5DZVdqWONV84hceFKQUROFZETVDU/kYZbTeOy8iuek2xfW4CInCgiAR2nL1k4B0Vyp5UjZ8oZLXAiZkr+4yfacAFE5LUiMq8CcuaJiKiq2lfA3iIivkrcHCIyV0Sa7PqJwCfHK9MyzcoUEbkE+Fw553/KGG3RXf5V4AbgtomocUVktYi0W+OaAfwLRTNSlilzGuY1WYVBoXGgS1XzjCMp3hpTDPgucLU9Z92YeYqxb+ss2bWycjuBF0TkClvT9gB/tee/pHnjpoTRFmoku3418DxwE3DrBNS4VwG/FZFOVe0CBoG01cVX5nHjwAPALBH5IBADNgFYwy0bVe3DvDPuXOBaYC6wX1Wzqpoq7FaG3G7gHcBXReRi7Cu+7He5EX84DFMq5CUiFwHXAFer6g6b1/se4O9U9UkR8Y33ohcd64AsEfkKsBrzspVPA99Q1RftdzFrKF5kFt98IeAc4GxgBbAU+BwwH9gLPFPKC12GyBbgSMyo6d2YMXx7gF1AHtgPfMqLj1ss126fA/wYeMjquR5jwFHgOVW9dyyZdZ8wMxpDLsSRwHsxI4F32O++JiJ54Oci8kZVfaqCxy0Y7AJV/ZCY11o9jXlN6wwRiWCS4LtE5GOq2j+W3KL/8g8YF+M7VsbhmHcWp4A+IIwZ1uSZItnXAMeq6rUishb4ItAF/ARIAE3Aw6UarIhcZY9zq4hcCvwM2AzcCZyE8Xfv9KrsIbkADcAFdv1MzLCej2Lu8POH7PtezHT9ldZhLXAH0GG3Pwu8DBwPLLIXa3aJMq/FvAfjaLsdwgzB/yKwdpz6Xomp+RYVlR0L3Atch30ylyH3PcBTxefYXpNdwOtKlldr46rGApxgT/bXgd/Zi+yzd/N1mK7Gc6usw4WY8XDThpR/DXiyYMglyhTgVuAYux2xnw3ApcCXxiHXB3wBuNSWhQGfXV9pa8GSZFu5TcBPgdNsWbBI7hsxb0tqAfxe5R5yDTERuQC4HTgGUwstBzaoal5V9wC/whjNO61/VS1mAr9X1T0iEhSRAICqfgD4NeZCjcow4asQxnc9zcpK2vJlwM+BT6tqjxflimWrIY/xX18rIq2qmlLTQH0zxj241IvsYeT2YyIFTbYsY+Weqqp3YZ4Y+7WUxthE1oDVXoAzMK3ok+12hy37KvBfvNLwPB14J3B4hY570GMTuABTo7cVla0BzitVJnAKcDTG0E8GfgNcYr/7W+A5htToJcg+E7gE04A7F1Nbvx0zzP8y4A+YtxqVKvdYTGMuBPw9prKYbr+7HHiQMp4KqnpoRQ9E5MNATlW/IiIRVU3aO/9YjFvQDzyBqYX/VVV7K3DM4sbG2zE3imIaLt8EHsE0OEKYIP2Fqrq5xP90Gcb/G8S4On/BuAm/xbhCb1MPI5qLOiN8amq7D2L8+ccwPvY3gBkYA16Bebz/g47RQC2SW/i8zsrdACzAzNv2EeA4TKRgDiaCU17Dt9a1YyVrOkxtesMwd71gLu7XgXXAURU8dsE/ezemEXMZ5sa4HtP79ingv4EfYRtPY8hrB0J2fQHwJ6DRlp+BuRlOwEQM5lLC0wJYXLQ+FzOMv1D7vQXzou5Vdns60OJR7vSi9ZX2HM+y2/8KPGr/wzxMhTFrXOe81gZXYeM9G/OW9JUFg8I6+JhW9xKvF8LDsVYB8+x6A2YivfPtdiNmApL/W7R/1IPMhZhOj3MxNfNcawBB+30L8B/AVSXqKpiG1U7g34vK7sK6GbbsM8D3CzeiV4O1ct5mt2dhetRiRZXJbcBHK3WdD7WG2COYkNYVIrJSTeMrJyJXYMIuSVXdX6FjXYCJ785T1UFgKzBfRBpUdQB4H+ZdbE12/+QIcg6gqlswQfyLMK3tbZha+xsiErS6JzCPcs9JOGpIYTo41ojIDWqs6XfAMhFZbXd9AdNoKqUrOIEJ671FRC7DdPvOBt5sjwGmBzJbgszRqXXtWOkFc6f/E+aCfAGTZ7ABD4/mEo8TtMd5CPOoPgcTFjoTUyNeiulq9VLDCkW1G/CPGJ/1TExj5ivA4xiXYyOwtAQ9C+5L4YkzDzN32scxtfmNmED/jzGdH8eUIteuxzCNzLsxLsxSTITmi/YaPAEsq9S5P6QaYgVEJIrxrf4GE655UFWfr4DcV6XW2bIbMNGIizENjjdiwjsdwPvVQ2OjqGE0T1VfsmUfAo7C1GK/xUQJssA6Vd3oUd+Aqmbt+nJMI3WjiMzE+Jn/qapfEJGl9liPq6ndPelr12NAVlUHRWQN8DaMC/MCxl2bA/ysEuf/ALWuGetlAVqL1s/EPMJjmNrqUxjD6sDUmovw0EACXgecbtc/iKmRvgvcWFR2M+bm8xx8t789AvisXS9ECJ4C/g0Tu56OcWm+UKLcZcCpdn0t5mnyNKamPRwTzvo5cFm1rsWh5tNWBRFZAnxURI4WkXdjohDXYHp6LrDbD2AMd66qblbVnR5EzwN+b/v7l2G6Ub8EzBSRW9SkUXbZY4RLVHsuMF1EvozpnVuNiWzswSQI7cLcfBeIyDQv/rHd513Au0TkHZib7nJMiuR7gHNU9Q6M0b5JRGJe/e6SqHUNVg8Lpmb6MiZ887/YCASmBrsNWGK3PwHML1H22zCNmVvtdgBos8c51pZ1lqGzzxrV1zBx3UZbvhjj7xc6JwIlyg1h2gk/B24rKj8DE51YjommNFfreria1gOq+izmMe3H1Ih/Y8v/GxMV+LDd/ryqbvUis8g//iGmlnqXiKxSk7e6D5PFNd/uU9L0RUU+5wOY+PBzwMdFpEVVN2EadbPt7p5TMW0EI41J/Hkc6BSR00UkrKq/A36BcaMGVTVeis6l4Ix2BKwrcERhW1Wfw8RQfwEcJyJn2q8eA9KlJnOrmh4ku/4DjLvxkIh8xOb9LsfUkF71DRbJzlvDVeCPwLcw/vbPbG/VmZj8B3SM/GEReY2IfNXumxERv6pmgM8Dz2C6fD8mIldiGl4HzepecWr96J2MC/AGTMz3iGG+W24v2B8wRvwQsKIE2TLSNiazP4/p2ZtTgsxzMf71p4CzisoL4S7BJNncg8nB9ZyGiamRnwLOHkZuCBOeexKTgL5kQq5PrQ1ksi3WADZjY6EM0zuE6bn6hjXeMfNhMS354p6n0Qz3LcPdLGPo+ygmt+LzwEeGfB8oHAM4Cw/+Ma/0ZAXt7z4FfKD4fBR9Bq3hViT5yNN/rrWRTKYFE2fdZGu7Qihq2C5NTAB9zMwqjB/8YUzyzJuKDaPIOMTuV1KSNSYHIV2oBYG32ifAu4G/LdahRLlDc4AvxLxGa8mQcs/dvZVcnE9rEZHjMQ2MC4FTgbtF5M1q/MODwjaq+rya/NxRUZMneiumMXSGiLzFliuvdJc2q2rOlnnVN4AZXnMncJKYYelrMUk7IeDD1n8t6OBV7hzgThH5J7FzKajqL3hlhO6B12NphcbTlUyta7fJsmDSF48q2r4IM/DuTXa73KEmhcdzDGNUX8aODrDlH8QkxYS8HsPq9s/2N8dg/NQ+4O+L9rkM+FIZ+jZjbtr7MBGTW2zZuZh4dNN4zkdFrlWtjWWyLbw6M+xCa7hvLOVCYeKj7y/aHs5wT8eEujYAJ5Sg3zmYhs/5RWVHYrLMirPKPoHpAi41Dlv47y0Y9+P7mAbcx4Be4HM1v0a1VqCmf/5g45Kh65hIQh54QwlyTwJymETnoYbbYmvXu+wN4SlBxf72bEwie6GROB+43K4fi8nb/RymZ+1hYHmZ5yEw5PtLMG/P7AHuL9S2NbtutTacmv754Y3rQAOpqOx8SmjR29+sxMzM8j677Ssy3NWY1n5JI4AxrkAOM3+CDxODvbZI/gqMj9vt1WDHOA/BIfudACyo+XWrtQK1XkYwrkI450igYRyyT7Syry0q+wAmplpy16z9/UlWZjewxpZJke7LKCHGOxHnoeLXrNYKTIZlBON6P6bXqL1Csi/B+MgvAsePU+YKK7NgtH5KDGtN9Hmo6PWqtQKTZRnBuI6roOzyiwF3AAAAgUlEQVQ8ZmCiZx92DJmFGvd99XIeKrUckkng5SJmWstHMUkwJ2uFpkmyspcBefWYwO1R5kpM7sNVqvqdCsqt2nmoBM5oh1AN46omtlNksNL6Tubz4IzWUXe4blxH3eGM1lF3OKN11B3OaB11hzNaR93hjNZRdzijddQdzmgddcf/ByZLSaKBz9v/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x162 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make Scatter Plot Gain (slope)\n",
    "\n",
    "pos = [0, 1, 2, 3, 4]\n",
    "fw = 0.4\n",
    "fig = plt.figure(figsize = (fw*len(pos), 2.25))\n",
    "ax = fig.add_subplot(111)\n",
    "w = 0.6\n",
    "wb = 0.6\n",
    "\n",
    "data = [slope_int_ctrl[0,:], slope_int_LT_block[0,:], slope_int_LL_block [0,:],\n",
    "        slope_int_feedback_block[0,:], slope_int_LV_block[0,:]]\n",
    "colors = ['0.4', 'C3', 'C1', 'C4', 'C2']\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.plot([pos[j]-w/2, pos[j]+w/2],[np.mean(data[j]),np.mean(data[j])],\n",
    "            color=colors[j], linestyle='-', linewidth = 4)\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=colors[j], alpha=0.3, 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(('Ctrl','LT block', 'LL block', 'FB block', 'LV block'), rotation=45, size=10)\n",
    "ax.set_ylabel('Response gain [mV]', size=10)\n",
    "yticks = np.array([0, 5, 10])\n",
    "ax.set_yticklabels([0, 5, 10], size=10)\n",
    "ax.set_yticks(yticks)\n",
    "plt.ylim([0, 12])\n",
    "#plt.title('Gain', size=10)\n",
    "    \n",
    "bbox_inches = 'tight'\n",
    "#plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\\\\n",
    "#Fig7_Conductance_global_model_ctrl_block_barplot.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 Gain (slope)\n",
    "\n",
    "pos = [0, 1]\n",
    "fw = 0.4\n",
    "fig = plt.figure(figsize = (fw*len(pos), 2.25))\n",
    "ax = fig.add_subplot(111)\n",
    "w = 0.6\n",
    "wb = 0.6\n",
    "\n",
    "data = [slope_int_ctrl[0,:], slope_int_feedback_block[0,:]]\n",
    "colors = ['0.4', 'C3']\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.plot([pos[j]-w/2, pos[j]+w/2],[np.mean(data[j]),np.mean(data[j])],\n",
    "            color=colors[j], linestyle='-', linewidth = 4)\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=colors[j], alpha=0.3, 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(('Ctrl','FB block', 'LL block', 'FB block', 'LV block'), rotation=45, size=10)\n",
    "ax.set_ylabel('Response gain [mV]', size=10)\n",
    "yticks = np.array([0, 5, 10])\n",
    "ax.set_yticklabels([0, 5, 10], size=10)\n",
    "ax.set_yticks(yticks)\n",
    "plt.ylim([0, 12.5])\n",
    "#plt.title('Gain', size=10)\n",
    "    \n",
    "bbox_inches = 'tight'\n",
    "#plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\\\\n",
    "#FigRX_Response_gain_model_fb_block.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=6.087118395660161, pvalue=1.2545169391346318e-06)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_rel(slope_int_ctrl[0,:], slope_int_LL_block[0,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x162 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make Scatter MOSI (Motion Opponent Suppression)\n",
    "\n",
    "pos = [0, 1, 2, 3, 4]\n",
    "fw = 0.4\n",
    "fig = plt.figure(figsize = (fw*len(pos), 2.25))\n",
    "ax = fig.add_subplot(111)\n",
    "w = 0.6\n",
    "wb = 0.6\n",
    "\n",
    "data = [MOSI_ctrl, MOSI_LT_block, MOSI_LL_block, MOSI_feedback_block, MOSI_LV_block]\n",
    "colors = ['0.4', 'C3', 'C1', 'C6', 'C2']\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.plot([pos[j]-w/2, pos[j]+w/2],[np.mean(data[j]),np.mean(data[j])],\n",
    "            color=colors[j], linestyle='-', linewidth = 4)\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=colors[j], alpha=0.03, 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(('Ctrl','LT block', 'LL block', 'FB block', 'LV block'), rotation=45, size=10)\n",
    "ax.set_ylabel('Suppression', size=10)\n",
    "#yticks = np.array([0, 5, 10, 15])\n",
    "#ax.set_yticklabels([0, 5, 10, 15], size=10)\n",
    "#ax.set_yticks(yticks)\n",
    "plt.ylim([0, 1.05])\n",
    "#plt.title('Gain', size=10)\n",
    "    \n",
    "bbox_inches = 'tight'\n",
    "plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\Modelling\\\\\\\n",
    "FigS7_MOSI_local_ctrl_block_scatter.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=26.854249532878274, pvalue=1.1375963595758143e-81)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_rel(MOSI_ctrl, MOSI_feedback_block)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x162 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make Scatter MOSI_glob (Motion Opponent Suppression)\n",
    "\n",
    "pos = [0, 1, 2, 3, 4]\n",
    "fw = 0.4\n",
    "fig = plt.figure(figsize = (fw*len(pos), 2.25))\n",
    "ax = fig.add_subplot(111)\n",
    "w = 0.6\n",
    "wb = 0.6\n",
    "\n",
    "data = [MOSI_glob_ctrl, MOSI_glob_LT_block, MOSI_glob_LL_block, MOSI_glob_feedback_block, MOSI_glob_LV_block]\n",
    "colors = ['0.4', 'C3', 'C1', 'C6', 'C2']\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.plot([pos[j]-w/2, pos[j]+w/2],[np.mean(data[j]),np.mean(data[j])],\n",
    "            color=colors[j], linestyle='-', linewidth = 4)\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=colors[j], alpha=0.03, 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(('Ctrl','LT block', 'LL block', 'FB block', 'LV block'), rotation=45, size=10)\n",
    "ax.set_ylabel('Suppression', size=10)\n",
    "#yticks = np.array([0, 5, 10, 15])\n",
    "#ax.set_yticklabels([0, 5, 10, 15], size=10)\n",
    "#ax.set_yticks(yticks)\n",
    "plt.ylim([0, 1.05])\n",
    "#plt.title('Gain', size=10)\n",
    "    \n",
    "bbox_inches = 'tight'\n",
    "plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\Modelling\\\\\\\n",
    "FigS7_MOSI_global_ctrl_block_scatter.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=116.67321744030016, pvalue=2.219276526721781e-251)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_rel(MOSI_glob_ctrl, MOSI_glob_LV_block)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x162 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make Scatter Plot zero-crossing (x-intercept)\n",
    "\n",
    "pos = [0, 1, 2, 3, 4]\n",
    "fw = 0.4\n",
    "fig = plt.figure(figsize = (fw*len(pos), 2.25))\n",
    "ax = fig.add_subplot(111)\n",
    "w = 0.6\n",
    "wb = 0.6\n",
    "\n",
    "data = [slope_int_ctrl[1,:], slope_int_LT_block[1,:], slope_int_LL_block [1,:],\n",
    "        slope_int_feedback_block[1,:], slope_int_LV_block[1,:]]\n",
    "colors = ['0.4', 'C3', 'C1', 'C4', 'C2']\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.plot([pos[j]-w/2, pos[j]+w/2],[np.mean(data[j]),np.mean(data[j])],\n",
    "            color=colors[j], linestyle='-', linewidth = 4)\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=colors[j], alpha=0.3, 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(('Ctrl','LT block', 'LL block', 'FB block', 'LV block'), rotation=45, size=10)\n",
    "ax.set_ylabel('Intercept [coh]', size=10)\n",
    "yticks = np.array([-1, 0, 1])\n",
    "ax.set_yticklabels([-1, 0, 1], size=10)\n",
    "ax.set_yticks(yticks)\n",
    "plt.ylim([-1, 0])\n",
    "#plt.title('Gain', size=10)\n",
    "    \n",
    "bbox_inches = 'tight'\n",
    "#plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\\\\n",
    "#Fig7_Conductance_global_model_ctrl_block_barplot.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x162 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make Scatter Plot Gain (slope)\n",
    "\n",
    "pos = [0, 1, 2, 3, 4]\n",
    "fw = 0.4\n",
    "fig = plt.figure(figsize = (fw*len(pos), 2.25))\n",
    "ax = fig.add_subplot(111)\n",
    "w = 0.6\n",
    "wb = 0.6\n",
    "\n",
    "data = [slope_int_ctrl_PD[0,:], slope_int_LT_block_PD[0,:], slope_int_LL_block_PD[0,:],\n",
    "        slope_int_feedback_block_PD[0,:], slope_int_LV_block_PD[0,:]]\n",
    "colors = ['0.4', 'C3', 'C1', 'C4', 'C2']\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.plot([pos[j]-w/2, pos[j]+w/2],[np.mean(data[j]),np.mean(data[j])],\n",
    "            color=colors[j], linestyle='-', linewidth = 4)\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=colors[j], alpha=0.3, 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(('Ctrl','LT block', 'LL block', 'FB block', 'LV block'), rotation=45, size=10)\n",
    "ax.set_ylabel('Gain PD [mV]', size=10)\n",
    "yticks = np.array([0, 5, 10, 15])\n",
    "ax.set_yticklabels([0, 5, 10, 15], size=10)\n",
    "ax.set_yticks(yticks)\n",
    "plt.ylim([0, 15])\n",
    "#plt.title('Gain', size=10)\n",
    "    \n",
    "bbox_inches = 'tight'\n",
    "#plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\\\\n",
    "#Fig7_Conductance_global_model_ctrl_block_barplot.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x162 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make Scatter Plot Gain (slope)\n",
    "\n",
    "pos = [0, 1, 2, 3, 4]\n",
    "fw = 0.4\n",
    "fig = plt.figure(figsize = (fw*len(pos), 2.25))\n",
    "ax = fig.add_subplot(111)\n",
    "w = 0.6\n",
    "wb = 0.6\n",
    "\n",
    "data = [slope_int_ctrl_ND[0,:], slope_int_LT_block_ND[0,:], slope_int_LL_block_ND[0,:],\n",
    "        slope_int_feedback_block_ND[0,:], slope_int_LV_block_ND[0,:]]\n",
    "colors = ['0.4', 'C3', 'C1', 'C4', 'C2']\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.plot([pos[j]-w/2, pos[j]+w/2],[np.mean(data[j]),np.mean(data[j])],\n",
    "            color=colors[j], linestyle='-', linewidth = 4)\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=colors[j], alpha=0.3, 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(('Ctrl','LT block', 'LL block', 'FB block', 'LV block'), rotation=45, size=10)\n",
    "ax.set_ylabel('Gain ND [mV]', size=10)\n",
    "yticks = np.array([0, 5, 10, 15])\n",
    "ax.set_yticklabels([0, 5, 10, 15], size=10)\n",
    "ax.set_yticks(yticks)\n",
    "plt.ylim([0, 15])\n",
    "#plt.title('Gain', size=10)\n",
    "    \n",
    "bbox_inches = 'tight'\n",
    "#plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\\\\n",
    "#Fig7_Conductance_global_model_ctrl_block_barplot.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 151.2x165.6 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coh = np.array([-0.5, -0.375, -0.25, -0.125, 0, 0.125, 0.25, 0.375, 0.5])*2\n",
    "\n",
    "fig = plt.figure(figsize = (2.1, 2.3))\n",
    "\n",
    "ax = fig.add_subplot(111) #PLOT coherence tuning\n",
    "\n",
    "ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1)\n",
    "plt.plot(coh, np.mean(VS_mV_ctrl,1)[0:9] - np.mean(VS_mV_feedback_block,1)[0:9], linewidth = 2.5, color = '0.35')\n",
    "#plt.plot(coh, 2*np.mean(VS_mV_ctrl,1)[0:9] - np.mean(VS_mV_LT_block,1)[0:9] - np.mean(VS_mV_LL_block,1)[0:9],\n",
    "#         linewidth = 2.5, color = 'C8')\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_xlabel('Coherence', fontsize = 10)\n",
    "ax.set_ylabel('Diff. response [mV]', fontsize = 10)\n",
    "#xticks = np.array([-0.5, -0.25, 0, 0.25, 0.5])\n",
    "#ax.set_xticklabels([-0.5, -0.25, 0, 0.25, 0.5], size=10)\n",
    "plt.ylim([-4,4])\n",
    "#ax.set_xticks(xticks)\n",
    "#ax.set_yticks([-5, 0, 5])\n",
    "#ax.set_yticklabels([-5, 0, 5], size=10)\n",
    "\n",
    "#plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\Modelling\\\\\\\n",
    "#FigS8_Coherence_tuning_VS_ctrl_min_block_mV.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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 Ephys conductance (TM vs. Split)\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",
    "\n",
    "data = [delta_g_ctrl[4], delta_g_ctrl[9]]\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]), 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=0.05, 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(r'$\\Delta$'+ ' conductance [a.u.]', size=10)\n",
    "yticks = np.array([0, 0.5, 1, 1.5, 2])\n",
    "ax.set_yticklabels([0.0, 0.5, 1.0, 1.5, 2], size=10)\n",
    "ax.set_yticks(yticks)\n",
    "plt.ylim([-0.1, 1.2])\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\\\\n",
    "Fig7_Conductance_local_global_model_barplot.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=-60.415835843653966, pvalue=1.310787710150531e-169)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_rel(delta_g_ctrl[4], delta_g_ctrl[9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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 modelling conductance (Split global: PD+ND vs. Split)\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 = [delta_g_ctrl[9], delta_g_ctrl[9]]\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]), 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=0.05, 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(r'$\\Delta$'+ ' conductance [a.u.]', size=10)\n",
    "yticks = np.array([0, 0.5, 1, 1.5, 2])\n",
    "ax.set_yticklabels([0.0, 0.5, 1.0, 1.5, 2.0], size=10)\n",
    "ax.set_yticks(yticks)\n",
    "plt.ylim([-0.1,1.2])\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\\\\n",
    "Fig7_Conductance_Split_PDND_model_barplot.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": true
   },
   "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 modelling conductance (Split global: PD+ND vs. Split)\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",
    "\n",
    "data = [delta_g_feedback_block[4], delta_g_feedback_block[9]]\n",
    "\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]), 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=0.05, 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(r'$\\Delta$'+ ' conductance [a.u.]', size=10)\n",
    "yticks = np.array([0, 0.5, 1, 1.5, 2])\n",
    "ax.set_yticklabels([0.0, 0.5, 1.0, 1.5, 2.0], size=10)\n",
    "ax.set_yticks(yticks)\n",
    "plt.ylim([-0.1, 1.2])\n",
    "#plt.title('Feedback block')\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\\\\n",
    "Fig7_Conductance_model_barplot_comp_ctrl_block.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=0.617147352187203, pvalue=0.5376072081657177)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_rel(delta_g_feedback_block[4], delta_g_feedback_block[9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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 modelling conductance (Split global: PD+ND vs. Split)\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",
    "\n",
    "data = [delta_g_feedback_block[9], delta_g_feedback_block[9]]\n",
    "\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]), 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=0.05, 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(r'$\\Delta$'+ ' conductance [a.u.]', size=10)\n",
    "yticks = np.array([0, 0.5, 1, 1.5, 2])\n",
    "ax.set_yticklabels([0.0, 0.5, 1.0, 1.5, 2.0], size=10)\n",
    "ax.set_yticks(yticks)\n",
    "plt.ylim([-0.1,1.2])\n",
    "#plt.title('Feedback block')\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\\\\n",
    "Fig7_Conductance_model_Split_PDND_model_block_barplot.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 modelling conductance (TM local: PD+ND vs. TM)\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 = [delta_g_ctrl[0] + delta_g_ctrl[8], delta_g_ctrl[4]]\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]), 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=0.05, 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(r'$\\Delta$'+ ' conductance [a.u.]', size=10)\n",
    "yticks = np.array([0, 0.5, 1, 1.5, 2])\n",
    "ax.set_yticklabels([0.0, 0.5, 1.0, 1.5, 2.0], size=10)\n",
    "ax.set_yticks(yticks)\n",
    "plt.ylim([-0.1, 1.2])\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\\\\n",
    "Fig7_Conductance_TM_PDND_model_barplot.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=117.00934359047282, pvalue=9.563787391582376e-252)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_rel(delta_g_ctrl[0] + delta_g_ctrl[8], delta_g_ctrl[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AJCXGgETEGJCIOAkws3eY2Qf9KMDQxLqyHxV+Ys8Mf7hLUmSNAmZ2CPAZ4E+AvWb2C0n9YeJIUuIomNk5uHkhn8V5mbzJzC6KKn45h4f/hRvGeSXwdu80PzBJShwBM1sGXAv8taQHzOxY4HTgSjMrSrprDHE3y6/2A/wTbnLSTOAaoN7M7vITj0YlEfEgmNlRuMHMN3oBTdJmM9uDSzVnm9n9knqriPvPgA1mdiPwmKT7zOxZYCuwHvgHoGBmd0sadeWDJDsdBl/A2APcDBxuZqeWphP4yT0P4lLk/CriPgr4U5xgJwL/bGYfBhqAm4CngRuATwOBGsITEQ/A/8hrgEacQ94OnAP7U0ph5LxLPsHL184KEvc5uMmzP8N56Pot7n17JM5/7NG4WWH34go7TwSJNxHRUzGZtRm3kvmncKPlbga2AZeWhDSzS3CzuvpeGdNB4387cD3wabnp8v8B7MCtlL4T+DpwEfB/AJLuVkAf60mzm8fMZknq9vvHACtwU+uuBYq4qehzgEHgTOBSSU8FjPss3AzoJknNFecNuAR4A84V9w8l7Q9re5ISATM7A7jPzD7hCx1bcYWa54HrcCnyFpyAFxFOwFOBG3Hv0JvMbJuZzQG3qAjwHeBh3B/jXeYJZX+SEsuzltfh5lA+iEsZt+GWoZ+Jm95+nQ+ekbQnRNzn4BoIHvXH/wKcD5xUmgHtRXsf8HNJu0M/QDVzxOO44fz0bMWVHM8DPoZz/3I/Lju9Hv+nrzL+TMX+l4A/Aq2R2D7RP95Eb0C6Yv8anPOHE/3xYcAJuELHMSHiXAjM8PtWcT5Vsf8FoAtoSUQML1rlj1o3zPWPA1uAN1YZ/wJc1eTKAEJeDxw11meadgUblX5Vs7/COVrCH19tZjMlrcFldz8xsxOqiP8FXP3uKOC9ZjZDkkqFFUlF75gJSX8naetYn2laNrv5H3EmsMjM3gKcC5wCfAtA0tfMbBC36GfQOEsiSdK3zewvcUvaY2bfl9Trm+4kqTBiZGGfx/8xY03px/P7KUlFv/8B4GpcVeutkvrMrEEB2itHiP/Vknb6/eXAW3AV+NsrhYzu6aZJSqz80UoCepbimtfuBd7gx4iG6surjN/MrgLON7OHgackrTezIvBmYIW5dUQCt/IEJfYi+qay1+E6dXdJut2fvxLXAH0qcCGwHCfoT6u8zwqcy5YP4LLlN5nZfEn/6vsHjwdCOzYMxESXFmu54Tw6/g7XtbMG1wLzFX/tMKDR7zfinCDNDRF3ZYkzg6tXzgVW4uqWFwE/x63qA9Bcs+ec6B+6hgKeB/wv8OaKc4fiKvBfrjhXX0XclQK+3f8h6r2IP+SlssZ9uAb0SCr100ZEXCGlEdiFK0yAa/ts8PvzgOeACyK41yeAB4Cj/fGrgd8Dp+FWtftBmNSdiPjSD1vvP48EngU+U3Et6z/XAZeM8T6vB/4HmFX6o/jPy3wO8BDw+vF45lgVbMzsbbh+v03A3bhCy6NmNiTpBkk5H7QbyB0snoBkgKGKeFJAQdLNZnYrLuXvO+i3IyQ2LTa+z241rheiAfgk7vlOAz5hZqt8uA/iun1+FSJuq9jP+uMtuPfr6WZ2iKSCmX3I3DK9jJeAEJPKvrkV5Npx77mfmNlhuAbmOyXdYWZLgF/glkjKAh+V9HTAuCsr8h/HVRUOxZV4zwYOwRVoNgGXAudL2hzl841GLFKipL240uiNvv2zDdeB22pmaUm/B87AFTyuCiqgj7sk4IW4XvjrcC0w78a5r74D1w8JcN54CwgxSYkl/DjRNbgK+6uB90vKmVmdnL/VOklDAeM6E1fq/Jo/XoXrlfiUP/4YriP3LEn9tWhOC8xElyZrUDr9C1wn7qGVJdLKEmTAeE7CjUD7qD8+GzeU4tiKMHcBrwkbd1I6HQVJ9/shEQ+Y2RmSXqy4FjilSPqNmZ0M3O97NG7BDatYbmbP+GBH450qTFgqJKZtp5Lu8QOA7zWzE/E9RFXE86gfavgz3BJLf4Mb9fZu3Cjw91T+SSaKWL0TD8R3yIYeZj9MPCfhhLxa0nejjDsKYi1ilPgU/TBwuaSbJ9qeShIRQ2BmxwP9kn430bZUkogYA2JR2Z/uJCLGgETEGJCIGAMSEWNAImIMSESMAf8PaHLgT/YhO7cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 57.6x162 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]\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",
    "\n",
    "data = [delta_g_feedback_block[0] + delta_g_feedback_block[8], delta_g_feedback_block[4]]\n",
    "\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]), 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=0.05, 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(r'$\\Delta$'+ ' conductance [a.u.]', size=10)\n",
    "yticks = np.array([0, 0.5, 1, 1.5, 2])\n",
    "ax.set_yticklabels([0.0, 0.5, 1.0, 1.5, 2.0], size=10)\n",
    "ax.set_yticks(yticks)\n",
    "plt.ylim([-0.1, 1.2])\n",
    "#plt.title('Feedback block')\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\\\\n",
    "Fig7_Conductance_model_TM_PDND_model_block_barplot.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=63.1312819035642, pvalue=6.640823013510619e-175)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_rel(delta_g_feedback_block[0] + delta_g_feedback_block[8], delta_g_feedback_block[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}