{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "filenames =['R24E09_ArcLight_SING_002_2020-01-28_14.24_Ftrace_mean',\n",
    "            'R24E09_ArcLight_SING_004_2020-01-28_15.34_Ftrace_mean',\n",
    "            'R24E09_ArcLight_SING_005_2020-01-29_12.53_Ftrace_mean',\n",
    "            'R24E09_ArcLight_SING_004_2020-01-30_14.28_Ftrace_mean',\n",
    "            'R24E09_ArcLight_SING_007_2020-01-30_15.13_Ftrace_mean',\n",
    "            'R24E09_ArcLight_SING_009_2020-01-30_18.28_Ftrace_mean',\n",
    "            'R24E09_ArcLight_SING_002_2020-01-31_13.39_Ftrace_mean',\n",
    "            'R24E09_ArcLight_SING_001_2020-02-27_15.37_Ftrace_mean',\n",
    "            'R24E09_ArcLight_SING_004_2020-08-06_16.28_Ftrace_mean',\n",
    "            'R24E09_ArcLight_SING_001_2020-08-07_14.57_Ftrace_mean',\n",
    "            'R24E09_ArcLight_SING_001_2020-08-12_12.29_Ftrace_mean',\n",
    "            'R24E09_ArcLight_SING_007_2020-08-13_17.38_Ftrace_mean',\n",
    "            'R24E09_ArcLight_SING_001_2020-08-17_15.21_Ftrace_mean',\n",
    "            'R24E09_ArcLight_SING_004_2020-08-17_17.10_Ftrace_mean',\n",
    "            'R24E09_ArcLight_SING_001_2020-08-18_12.25_Ftrace_mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x187.2 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (4, 2.6))\n",
    "\n",
    "plt.subplots_adjust(wspace=0.1)\n",
    "\n",
    "ax = fig.add_subplot(121)\n",
    "plt.plot(time,Ftrace_all[:,6,:], color = 'C2', alpha = 0.35, linewidth = 1)\n",
    "plt.plot(time,np.mean(Ftrace_all[:,6,:], axis=1), color = 'C2', linewidth = 2.5)\n",
    "plt.axvspan(0, 1, facecolor='0.2', alpha=0.2)\n",
    "plt.xlim([-1.5,2.5])\n",
    "plt.ylim([-0.16,0.16])\n",
    "plt.axis('off') \n",
    "\n",
    "ax = fig.add_subplot(122)\n",
    "plt.plot(time,Ftrace_all[:,2,:], color = 'C2', alpha = 0.35, linewidth = 1)\n",
    "plt.plot(time,np.mean(Ftrace_all[:,2,:], axis=1), color = 'C2', linewidth = 2.5)\n",
    "plt.axvspan(0, 1, facecolor='0.2', alpha=0.2)\n",
    "plt.xlim([-1.5,2.5])\n",
    "plt.ylim([-0.16,0.16])\n",
    "plt.axis('off')\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "#plt.savefig('Fig1_R24E09_Ftraces.pdf',bbox_inches='tight', transparent=True, dpi=600)\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": [
    {
     "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",
    "ax.axhline(y=0,xmin=0.0001,xmax=0.9999, color='0.05', linestyle=':', linewidth = 1)\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([-0.14,0.14])\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "#plt.savefig('Fig1_R24E09_DirTuning.pdf',bbox_inches='tight', transparent=True, dpi=600)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D_magn =0.14747146650092405   D_deg = 245.83255450361096\n",
      "H_magn =0.0485664420045872   H_deg = 248.00681104367374\n",
      "MOI = 0.3290906261519682\n",
      "D_magn =0.22782514412120713   D_deg = 231.39585928416244\n",
      "H_magn =0.15003048098997288   H_deg = 228.35813407483667\n",
      "MOI = 0.6576081299263051\n",
      "D_magn =0.08497971175356166   D_deg = 232.03213714602816\n",
      "H_magn =0.20537886918599987   H_deg = 248.18041393764952\n",
      "MOI = 0.39744522097192936\n",
      "D_magn =0.24733234693476025   D_deg = 236.5674892621486\n",
      "H_magn =0.22511099994869282   H_deg = 235.78586182020769\n",
      "MOI = 0.91007123047475\n",
      "D_magn =0.1767364771683282   D_deg = 245.62741051829374\n",
      "H_magn =0.1814484669909651   H_deg = 255.0292820090213\n",
      "MOI = 0.960946919281725\n",
      "D_magn =0.12795333095555567   D_deg = 232.93591821886162\n",
      "H_magn =0.2113503965042534   H_deg = 237.55383631608834\n",
      "MOI = 0.6034432130338433\n",
      "D_magn =0.23221011583158155   D_deg = 249.35723030442978\n",
      "H_magn =0.168101432472577   H_deg = 268.8238787633275\n",
      "MOI = 0.6825371101380773\n",
      "D_magn =0.2283651330990831   D_deg = 231.2499766612704\n",
      "H_magn =0.13856180854051667   H_deg = 237.3885299898792\n",
      "MOI = 0.603276529837088\n",
      "D_magn =0.15752787049124736   D_deg = 241.13044693042954\n",
      "H_magn =0.0553466700828643   H_deg = 244.15667002474925\n",
      "MOI = 0.3508552976081251\n",
      "D_magn =0.21866561759447087   D_deg = 228.22843298369753\n",
      "H_magn =0.10374203722830493   H_deg = 225.98489250827552\n",
      "MOI = 0.47406865119792646\n",
      "D_magn =0.24445974248249444   D_deg = 238.15517475509859\n",
      "H_magn =0.11254559685744549   H_deg = 241.91040528191613\n",
      "MOI = 0.4593965227856189\n",
      "D_magn =0.23420322042170205   D_deg = 233.491889703574\n",
      "H_magn =0.1499796212875535   H_deg = 232.25225532539358\n",
      "MOI = 0.640232527069197\n",
      "D_magn =0.21011362976579348   D_deg = 227.76843415638237\n",
      "H_magn =0.1618670538059932   H_deg = 236.30813786139862\n",
      "MOI = 0.7618376128535269\n",
      "D_magn =0.197234251075825   D_deg = 233.01753198703858\n",
      "H_magn =0.14295945816265132   H_deg = 235.92466577574478\n",
      "MOI = 0.7238878427023391\n",
      "D_magn =0.1857423762546125   D_deg = 212.25130261690035\n",
      "H_magn =0.1291590570343336   H_deg = 209.08373551477914\n",
      "MOI = 0.694304287268733\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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_magn =0.19601160415660415   R_deg = 246.37115586576974\n",
      "R_indiv_magn = 0.2565684184931398\n",
      "LDir = 0.7639740124985225\n",
      "R_magn =0.37772849503553335   R_deg = 230.18977729943848\n",
      "R_indiv_magn = 0.49631221390389757\n",
      "LDir = 0.7610703191533265\n",
      "R_magn =0.2879772415198952   R_deg = 243.4727321309921\n",
      "R_indiv_magn = 0.3967556550895859\n",
      "LDir = 0.7258302126906573\n",
      "R_magn =0.47243238082542427   R_deg = 236.1950577276256\n",
      "R_indiv_magn = 0.6129877758302302\n",
      "LDir = 0.7707044079069312\n",
      "R_magn =0.3569802362449551   R_deg = 250.39032706778627\n",
      "R_indiv_magn = 0.4708739906308943\n",
      "LDir = 0.7581226471367847\n",
      "R_magn =0.339044897950098   R_deg = 235.81268243356698\n",
      "R_indiv_magn = 0.4719882689158863\n",
      "LDir = 0.7183333152089839\n",
      "R_magn =0.3946980248130172   R_deg = 257.517018064643\n",
      "R_indiv_magn = 0.5203166331517525\n",
      "LDir = 0.7585727606326625\n",
      "R_magn =0.3664321435635305   R_deg = 233.5673887517002\n",
      "R_indiv_magn = 0.4592906860239019\n",
      "LDir = 0.797821847283141\n",
      "R_magn =0.21281741782429633   R_deg = 241.91712486759909\n",
      "R_indiv_magn = 0.269805906776975\n",
      "LDir = 0.7887796837606447\n",
      "R_magn =0.3223537157688854   R_deg = 227.50656712561823\n",
      "R_indiv_magn = 0.42108608948352605\n",
      "LDir = 0.7655292440656525\n",
      "R_magn =0.356839836938387   R_deg = 239.33879312200665\n",
      "R_indiv_magn = 0.46848758231645893\n",
      "LDir = 0.7616847284915762\n",
      "R_magn =0.38416144267037877   R_deg = 233.0079587837118\n",
      "R_indiv_magn = 0.5026391646258163\n",
      "LDir = 0.7642887178446653\n",
      "R_magn =0.37096562413374856   R_deg = 231.48346824039683\n",
      "R_indiv_magn = 0.48176674256976043\n",
      "LDir = 0.7700108607642884\n",
      "R_magn =0.34008702539461655   R_deg = 234.23914708855207\n",
      "R_indiv_magn = 0.423887447510898\n",
      "LDir = 0.802305016087727\n",
      "R_magn =0.31478501873466197   R_deg = 210.95217265954764\n",
      "R_indiv_magn = 0.4093916956622028\n",
      "LDir = 0.7689091451293075\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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "R_deg_flies = np.empty(len(Ftrace_mean[0,:]))\n",
    "for i in range(len(Ftrace_mean[0,:])):\n",
    "    R_deg_flies[i] = oct.R_deg(rot_rad, Ftrace_mean[0:8,i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save Ftrace as numpy array\n",
    "\n",
    "np.save('R24E09_Ftrace_mean.npy', Ftrace_mean)\n",
    "np.save('R24E09_Ftrace_all.npy', Ftrace_all)\n",
    "np.save('R24E09_MOI.npy', MOI_flies)\n",
    "np.save('R24E09_LDir.npy', LDir_flies)\n",
    "np.save('R24E09_R_deg.npy', R_deg_flies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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