{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analysis of w+; UAS-ArcLight/ +; R24E09-Gal4/ VT50384-lexA, lexAop-TNT Flies (Block flies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python_version 2.7.15\n",
    "\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",
    "import scipy.io\n",
    "from scipy import stats\n",
    "%matplotlib inline\n",
    "import octopus as oct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "filenames =['R24E09-ArcLight_50384-TNT001_2020-05-26_11.58VT50384lexA-TNT_Ftrace_mean',\n",
    "            'R24E09-ArcLight_50384-TNT004_2020-05-26_13.12VT50384lexA-TNT_Ftrace_mean',\n",
    "            'R24E09-ArcLight_50384-TNT_SING001_2020-05-27_11.59VT50384lexA-TNT_Ftrace_mean',\n",
    "            'R24E09-ArcLight_50384-TNT_SING005_2020-05-27_15.46VT50384lexA-TNT_Ftrace_mean',\n",
    "            'R24E09-ArcLight_50384-TNT_SING001_2020-05-28_11.36VT50384lexA-TNT_Ftrace_mean',\n",
    "            'R24E09-ArcLight_50384-TNT_SING002_2020-05-30_12.36VT50384lexA-TNT_Ftrace_mean',\n",
    "            'R24E09-ArcLight_50384-TNT_SING004_2020-05-30_13.26VT50384lexA-TNT_Ftrace_mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Data\n",
    "time = np.load('time.npy')\n",
    "\n",
    "for i in range(len(filenames)):\n",
    "    a = np.load(filenames[i] + '.npy')\n",
    "    if(i==0):\n",
    "        Ftrace_all = a\n",
    "    if(i==1):\n",
    "        Ftrace_all = np.stack((Ftrace_all,a), axis=2)   \n",
    "    if(i>1):\n",
    "        a = a[:,:,np.newaxis]\n",
    "        Ftrace_all = np.concatenate((Ftrace_all,a), axis=2)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "R24E09_TNT_Ctrl_Ftrace_all = np.load('R24E09_TNT_Ctrl_Ftrace_all.npy')\n",
    "R24E09_TNT_Ctrl_Ftrace_mean = np.load('R24E09_TNT_Ctrl_Ftrace_mean.npy')\n",
    "R24E09_TNT_Ctrl_MOI = np.load('R24E09_TNT_Ctrl_MOI.npy')\n",
    "R24E09_TNT_Ctrl_LDir = np.load('R24E09_TNT_Ctrl_LDir.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (5,3))\n",
    "\n",
    "ax = fig.add_subplot(121)\n",
    "plt.plot(time,Ftrace_all[:,6,:], color = 'C2', alpha = 0.3, linewidth = 1)\n",
    "plt.plot(time,np.mean(Ftrace_all[:,6,:], axis=1), color = 'C2', linewidth = 2.5)\n",
    "plt.axvspan(0, 1, facecolor='0.5', alpha=0.2)\n",
    "plt.xlim([-1.5,2.5])\n",
    "plt.ylim([-0.14,0.14])\n",
    "plt.axis('off') \n",
    "\n",
    "ax = fig.add_subplot(122)\n",
    "plt.plot(time,Ftrace_all[:,2,:], color = 'C2', alpha = 0.3, linewidth = 1)\n",
    "plt.plot(time,np.mean(Ftrace_all[:,2,:], axis=1), color = 'C2', linewidth = 2.5)\n",
    "plt.axvspan(0, 1, facecolor='0.5', alpha=0.2)\n",
    "plt.xlim([-1.5,2.5])\n",
    "plt.ylim([-0.14,0.14])\n",
    "plt.axis('off')\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "#plt.savefig('Fig2_R24E09_50384-TNT_Ftraces.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x273.6 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (4, 3.8))\n",
    "\n",
    "plt.subplots_adjust(wspace=0.1)\n",
    "\n",
    "data = [R24E09_TNT_Ctrl_Ftrace_all, Ftrace_all]\n",
    "colors = ['0.3', 'C2']\n",
    "vsize = 0.14\n",
    "alpha = 0.45\n",
    "\n",
    "ax = fig.add_subplot(121)\n",
    "plt.plot(time, data[0][:,6,:] + vsize*1.2, color = colors[0], alpha = alpha, linewidth = 1)\n",
    "plt.plot(time, np.mean(data[0][:,6,:], axis=1)+ vsize*1.2, colors[0], linewidth = 2.5)\n",
    "\n",
    "plt.plot(time, data[1][:,6,:], color = colors[1], alpha = alpha, linewidth = 1)\n",
    "plt.plot(time, np.mean(data[1][:,6,:], axis=1), color = colors[1], linewidth = 2.5)\n",
    "\n",
    "plt.axvspan(0, 1, facecolor='0.2', alpha=0.2)\n",
    "plt.xlim([-1.5,2.5])\n",
    "plt.ylim([-vsize,+vsize*2.2])\n",
    "plt.axis('off') \n",
    "\n",
    "\n",
    "ax = fig.add_subplot(122)\n",
    "plt.plot(time, data[0][:,2,:] + vsize*1.2, color = colors[0], alpha = alpha, linewidth = 1)\n",
    "plt.plot(time, np.mean(data[0][:,2,:], axis=1)+ vsize*1.2, colors[0], linewidth = 2.5)\n",
    "\n",
    "plt.plot(time,data[1][:,2,:], color = colors[1], alpha = alpha, linewidth = 1)\n",
    "plt.plot(time,np.mean(data[1][:,2,:], axis=1), color = colors[1], linewidth = 2.5)\n",
    "\n",
    "plt.axvspan(0, 1, facecolor='0.2', alpha=0.2)\n",
    "plt.xlim([-1.5,2.5])\n",
    "plt.ylim([-vsize,+vsize*2.2])\n",
    "plt.axis('off')\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "#plt.savefig('Fig2_R24E09_50384-TNT_Ftraces.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "rotations = [0,45,90,135,180,225,270,315,360]\n",
    "Ftrace_mean = np.zeros([len(rotations),len(filenames)])\n",
    "Ftrace_sem = np.zeros([len(rotations)])\n",
    "for i in range(len(rotations)):\n",
    "    Ftrace_sem[i] = stats.sem(np.mean(Ftrace_all[38:51,i,:],0))\n",
    "    for j in range(len(filenames)):\n",
    "        Ftrace_mean[i,j] = np.mean(Ftrace_all[np.int(np.where(time == 0)[0]):51,i,j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "R24E09_TNT_Ctrl_Ftrace_sem= stats.sem(R24E09_TNT_Ctrl_Ftrace_mean,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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 directional tuning\n",
    "\n",
    "\n",
    "ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1)\n",
    "plt.plot(rotations, R24E09_TNT_Ctrl_Ftrace_mean, color='0.3', linewidth = 1, zorder = 0, alpha=0.35)\n",
    "plt.plot(rotations, np.mean(R24E09_TNT_Ctrl_Ftrace_mean,1), linewidth = 2.5, color = '0.3')\n",
    "plt.errorbar(rotations, np.mean(R24E09_TNT_Ctrl_Ftrace_mean,1), yerr = R24E09_TNT_Ctrl_Ftrace_sem, linewidth = 2.5, color = '0.3')\n",
    "\n",
    "plt.plot(rotations, Ftrace_mean, color='C2', linewidth = 1, zorder = 0, alpha=0.35)\n",
    "plt.plot(rotations, np.mean(Ftrace_mean,1), linewidth = 2.5, color = 'C2')\n",
    "plt.errorbar(rotations, np.mean(Ftrace_mean,1), yerr = Ftrace_sem, linewidth = 2.5, color = 'C2')\n",
    "\n",
    "\n",
    "plt.xticks(rotations)\n",
    "ax.set_xticklabels(rotations)\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",
    "for axis in ['top','bottom','left','right']:\n",
    "  ax.spines[axis].set_linewidth(0.5)\n",
    "ax.set_xlabel(u'Direction [°]', fontsize = 10)\n",
    "ax.set_ylabel('-$\\Delta F/F$ (%)', fontsize = 10)\n",
    "yticks = np.array([-0.1,-0.05,0,0.05,0.1])\n",
    "ax.set_yticks(yticks)\n",
    "ax.set_yticklabels(('-10','-5','0','5','10'), size=10)\n",
    "xticks = np.array([0,90,180,270,360])\n",
    "ax.set_xticklabels(xticks, size=10)\n",
    "ax.set_xticks(xticks)\n",
    "plt.ylim([-0.1,0.11])\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "#plt.savefig('Fig2_R24E09_50384-TNT_DirTuning.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Input to MOI function\n",
    "rot_rad = np.radians(rotations[0:8])\n",
    "radius = np.mean(Ftrace_mean[0:8,:],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D_magn =0.08751722320026867   D_deg = 245.19059812664943\n",
      "H_magn =0.0   H_deg = 0.0\n",
      "MOI = -0.0\n",
      "D_magn =0.04265430963924522   D_deg = 265.7143955096024\n",
      "H_magn =0.0   H_deg = 0.0\n",
      "MOI = -0.0\n",
      "D_magn =0.12928256478480457   D_deg = 232.76823060915765\n",
      "H_magn =0.0   H_deg = 0.0\n",
      "MOI = -0.0\n",
      "D_magn =0.16829774273835227   D_deg = 219.78327310923095\n",
      "H_magn =0.0007103821815685293   H_deg = 180.0\n",
      "MOI = 0.003243701577899881\n",
      "D_magn =0.0972462812523271   D_deg = 259.5023025609638\n",
      "H_magn =0.0   H_deg = 0.0\n",
      "MOI = -0.0\n",
      "D_magn =0.1417451401575515   D_deg = 253.54054364141243\n",
      "H_magn =0.0   H_deg = 0.0\n",
      "MOI = -0.0\n",
      "D_magn =0.18887236584537712   D_deg = 248.79050875800834\n",
      "H_magn =0.00426868825139668   H_deg = 231.26044340730516\n",
      "MOI = 0.021551306346784534\n"
     ]
    }
   ],
   "source": [
    "MOI_flies = np.empty(len(Ftrace_mean[0,:]))\n",
    "for i in range(len(Ftrace_mean[0,:])):\n",
    "    MOI_flies[i] = oct.MOI(rot_rad, Ftrace_mean[0:8,i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_magn =0.08751722320026867   R_deg = 245.19059812664943\n",
      "R_indiv_magn = 0.187827942049026\n",
      "LDir = 0.46594357711391693\n",
      "R_magn =0.04265430963924522   R_deg = 265.7143955096024\n",
      "R_indiv_magn = 0.21922353945849488\n",
      "LDir = 0.19456993416220647\n",
      "R_magn =0.12928256478480457   R_deg = 232.76823060915765\n",
      "R_indiv_magn = 0.457963653382702\n",
      "LDir = 0.2822987453914127\n",
      "R_magn =0.1688442622814986   R_deg = 219.6290210049509\n",
      "R_indiv_magn = 0.31204227543644447\n",
      "LDir = 0.5410941900271078\n",
      "R_magn =0.0972462812523271   R_deg = 259.5023025609638\n",
      "R_indiv_magn = 0.29489265645439455\n",
      "LDir = 0.3297684059737389\n",
      "R_magn =0.1417451401575515   R_deg = 253.54054364141243\n",
      "R_indiv_magn = 0.323711352799213\n",
      "LDir = 0.43787509746521347\n",
      "R_magn =0.19294709610003247   R_deg = 248.40869993366852\n",
      "R_indiv_magn = 0.3319408292394428\n",
      "LDir = 0.5812695489799228\n"
     ]
    }
   ],
   "source": [
    "LDir_flies = np.empty(len(Ftrace_mean[0,:]))\n",
    "for i in range(len(Ftrace_mean[0,:])):\n",
    "    LDir_flies[i] = oct.LDir(rot_rad, Ftrace_mean[0:8,i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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 MOI\n",
    "\n",
    "pos = [0,1]\n",
    "\n",
    "fig = plt.figure(figsize = (0.4*len(pos), 2.25))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "w = 0.6\n",
    "wb = 0.6\n",
    "data = [R24E09_TNT_Ctrl_MOI, MOI_flies]\n",
    "colors = ['0.35', '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=0.5, 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', 'TNT'), size=10)\n",
    "ax.set_ylabel('Motion opponency index', size=10)\n",
    "ax.tick_params(axis='both', which='major', labelsize=10)\n",
    "plt.ylim([-0.05,1])\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "#plt.savefig('Fig2_R24E09_50384-TNT_MOI.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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 LDir\n",
    "\n",
    "pos = [0,1]\n",
    "\n",
    "fig = plt.figure(figsize = (0.4*len(pos), 2.25))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "w = 0.6\n",
    "wb = 0.6\n",
    "data = [R24E09_TNT_Ctrl_LDir, LDir_flies]\n",
    "colors = ['0.35', '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=0.5, 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', 'TNT'), size=10)\n",
    "ax.set_ylabel('Direction selectivity index', size=10)\n",
    "ax.tick_params(axis='both', which='major', labelsize=10)\n",
    "plt.ylim([-0.05,1])\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "#plt.savefig('Fig2_R24E09_50384-TNT_LDir.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.8736805319786072, 0.24127419292926788)\n",
      "(0.5364698171615601, 4.6927529183449224e-05)\n",
      "LeveneResult(statistic=10.03938244774426, pvalue=0.008942504137061803)\n",
      "Ttest_indResult(statistic=7.167349408225972, pvalue=0.0008134355965976256)\n"
     ]
    }
   ],
   "source": [
    "print(stats.shapiro(R24E09_TNT_Ctrl_MOI))\n",
    "print(stats.shapiro(MOI_flies))\n",
    "print(stats.levene(MOI_flies, R24E09_TNT_Ctrl_MOI))\n",
    "print(stats.ttest_ind(R24E09_TNT_Ctrl_MOI, MOI_flies, axis=0, equal_var=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.9245194792747498, 0.5384910106658936)\n",
      "(0.9603171944618225, 0.8214513659477234)\n",
      "LeveneResult(statistic=6.51655480352424, pvalue=0.02686301659797771)\n",
      "Ttest_indResult(statistic=6.336405513245997, pvalue=0.0004719748566017207)\n"
     ]
    }
   ],
   "source": [
    "print(stats.shapiro(R24E09_TNT_Ctrl_LDir))\n",
    "print(stats.shapiro(LDir_flies))\n",
    "print(stats.levene(LDir_flies, R24E09_TNT_Ctrl_LDir))\n",
    "print(stats.ttest_ind(R24E09_TNT_Ctrl_LDir, LDir_flies, axis=0, equal_var=False))"
   ]
  },
  {
   "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": 2
}