{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis of w+; UAS-ArcLight/ +; R38G02-Gal4/ lexAop-TNT (Control Flies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
    "\n",
    "import octopus as oct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "filenames =['R38G02-ArcLight_TNT-Ctrl_SING001_2020-05-29_11.17-TNT-Ctrl_Ftrace_mean',\n",
    "            'R38G02-ArcLight_TNT-Ctrl_SING008_2020-05-29_18.58-TNT-Ctrl_Ftrace_mean',\n",
    "            'R38G02-ArcLight_TNT-Ctrl_SING006_2020-05-30_14.45-TNT-Ctrl_Ftrace_mean',\n",
    "            'R38G02-ArcLight_TNT-Ctrl_SING002_2020-05-31_20.48-TNT-Ctrl_Ftrace_mean',\n",
    "            'R38G02-ArcLight_TNT-Ctrl002_2020-06-02_14.32-TNT-Ctrl_Ftrace_mean',\n",
    "            'R38G02-ArcLight-TNT-Ctrl005_2020-06-05_15.25-TNT-Ctrl_Ftrace_mean',\n",
    "            'R38G02-ArcLight-TNT-Ctrl003_2020-06-07_17.40-TNT-Ctrl_Ftrace_mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": 31,
   "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 = '0.3', alpha = 0.5, linewidth = 1)\n",
    "plt.plot(time,np.mean(Ftrace_all[:,6,:], axis=1), color = '0.3', 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 = '0.3', alpha = 0.5, linewidth = 1)\n",
    "plt.plot(time,np.mean(Ftrace_all[:,2,:], axis=1), color = '0.3', 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_R38G02_TNT_Ctrl_Ftraces.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 34,
   "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": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D_magn =0.1679509127633958   D_deg = 221.21961303967788\n",
      "H_magn =0.12581119458091466   H_deg = 228.42129670638568\n",
      "MOI = 0.7431854359439941\n",
      "D_magn =0.10475705171567355   D_deg = 259.76864387408705\n",
      "H_magn =0.11352155846442998   H_deg = 255.36158822254876\n",
      "MOI = 0.9200659121964134\n",
      "D_magn =0.14260543878966014   D_deg = 273.1646614399937\n",
      "H_magn =0.06461303657213113   H_deg = 275.8341936967848\n",
      "MOI = 0.4525978669915969\n",
      "D_magn =0.11474321631825352   D_deg = 260.56469196196326\n",
      "H_magn =0.08582159219811916   H_deg = 269.16903688997985\n",
      "MOI = 0.7395267119062255\n",
      "D_magn =0.1802540334099572   D_deg = 247.64384034348595\n",
      "H_magn =0.0711684293604298   H_deg = 254.94392221902393\n",
      "MOI = 0.39162261899261225\n",
      "D_magn =0.17256288210209722   D_deg = 228.67588571771248\n",
      "H_magn =0.10048761765176209   H_deg = 232.95948857026713\n",
      "MOI = 0.5806979423237993\n",
      "D_magn =0.17127020381082528   D_deg = 234.3079487256125\n",
      "H_magn =0.12277954907728074   H_deg = 248.98762283466547\n",
      "MOI = 0.6934758131608302\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": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_magn =0.2931941092037885   R_deg = 224.3032526243332\n",
      "R_indiv_magn = 0.39769433217106165\n",
      "LDir = 0.7372348195238445\n",
      "R_magn =0.21811746458024087   R_deg = 257.4765945642735\n",
      "R_indiv_magn = 0.27986838354491517\n",
      "LDir = 0.7793572886564941\n",
      "R_magn =0.207170214523388   R_deg = 273.9969734133165\n",
      "R_indiv_magn = 0.2774158114159057\n",
      "LDir = 0.7467858932265237\n",
      "R_magn =0.20001144260709344   R_deg = 264.2453431571527\n",
      "R_indiv_magn = 0.27317781895811727\n",
      "LDir = 0.732165749656851\n",
      "R_magn =0.25100854072952083   R_deg = 249.70848328451544\n",
      "R_indiv_magn = 0.3625268652426782\n",
      "LDir = 0.6923860403048857\n",
      "R_magn =0.27287304037891447   R_deg = 230.25208542388367\n",
      "R_indiv_magn = 0.3700028653486125\n",
      "LDir = 0.7374889924752788\n",
      "R_magn =0.2917060539996603   R_deg = 240.43091860483358\n",
      "R_indiv_magn = 0.4138885184882163\n",
      "LDir = 0.704793781342754\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": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save Ftrace as numpy array\n",
    "\n",
    "np.save('R38G02_TNT_Ctrl_Ftrace_all.npy', Ftrace_all)\n",
    "np.save('R38G02_TNT_Ctrl_Ftrace_mean.npy', Ftrace_mean)\n",
    "np.save('R38G02_TNT_Ctrl_MOI.npy', MOI_flies)\n",
    "np.save('R38G02_TNT_Ctrl_LDir.npy', LDir_flies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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