{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python_version 2.7.15\n",
    "\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "import os\n",
    "import scipy.io\n",
    "from scipy import stats\n",
    "import octopus as oct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "filenames =['R38G02_ArcLight_SING_1s3p_006_2020-01-09_15.21_Ftrace_mean',\n",
    "            'R38G02_ArcLight_SING_1s3p_009_2020-01-09_16.42_Ftrace_mean',\n",
    "            'R38G02_ArcLight_SING_013_2020-01-09_18.17_Ftrace_mean',\n",
    "            'R38G02_ArcLight_SING_001_2020-01-10_10.56_Ftrace_mean',\n",
    "            'R38G02_ArcLight_SING_006_2020-01-10_15.20_Ftrace_mean',\n",
    "            'R38G02_ArcLight_SING_004_2020-01-13_17.03_Ftrace_mean',\n",
    "            'R38G02_ArcLight_SING_006_2020-01-24_18.04_Ftrace_mean',\n",
    "            'R38G02_ArcLight_SING_009_2020-08-11_18.56_Ftrace_mean',\n",
    "            'R38G02_ArcLight_SING_001_2020-08-13_14.44_Ftrace_mean',\n",
    "            'R38G02_ArcLight_SING_001_2020-08-28_15.35_Ftrace_mean',\n",
    "            'R38G02_ArcLight_SING_001_2020-09-04_14.55_Ftrace_mean',\n",
    "            'R38G02_ArcLight_SING_004_2020-09-07_14.06_Ftrace_mean',\n",
    "            'R38G02_ArcLight_SING_008_2020-09-07_16.34_Ftrace_mean',\n",
    "            'R38G02_ArcLight_SING_012_2020-09-07_18.14_Ftrace_mean',\n",
    "            'R38G02_ArcLight_SING_003_2020-09-09_12.32_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": [
    {
     "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 = 'C0', alpha = 0.35, linewidth = 1)\n",
    "plt.plot(time,np.mean(Ftrace_all[:,6,:], axis=1), color = 'C0', 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 = 'C0', alpha = 0.35, linewidth = 1)\n",
    "plt.plot(time,np.mean(Ftrace_all[:,2,:], axis=1), color = 'C0', linewidth = 2.5)\n",
    "plt.axvspan(0, 1, facecolor='0.2', alpha=0.2)\n",
    "plt.xlim([-1.5,2.5])\n",
    "plt.ylim([-0.135,0.135])\n",
    "plt.axis('off')\n",
    "\n",
    "bbox_inches = 'tight'\n",
    "#plt.savefig('Fig1_R38G02_Ftraces.pdf',bbox_inches='tight', dpi=600, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "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": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D_magn =0.1201234090257115   D_deg = 234.1188998487069\n",
      "H_magn =0.18607850228295927   H_deg = 247.17137415252589\n",
      "MOI = 0.6288735954784855\n",
      "D_magn =0.2383859370178115   D_deg = 225.0877563442411\n",
      "H_magn =0.2818756267783029   H_deg = 234.99000656229242\n",
      "MOI = 0.8331142448579973\n",
      "D_magn =0.19402102120753228   D_deg = 246.31279276384674\n",
      "H_magn =0.19814996046658798   H_deg = 249.50749312437114\n",
      "MOI = 0.9776408593815311\n",
      "D_magn =0.18503379756447313   D_deg = 241.7779190687433\n",
      "H_magn =0.15693339662474876   H_deg = 244.48776567987818\n",
      "MOI = 0.8471852722231108\n",
      "D_magn =0.13797204447653508   D_deg = 234.77101351007036\n",
      "H_magn =0.17038308848657652   H_deg = 238.26392323666425\n",
      "MOI = 0.8082711805720193\n",
      "D_magn =0.12930094244556126   D_deg = 230.90191234244244\n",
      "H_magn =0.14477549688181376   H_deg = 231.86199804448106\n",
      "MOI = 0.8929880590107621\n",
      "D_magn =0.15968839901590695   D_deg = 241.3295569352785\n",
      "H_magn =0.10498808120826335   H_deg = 250.96266316672776\n",
      "MOI = 0.6481854574352042\n",
      "D_magn =0.13068131959728244   D_deg = 249.71675362168796\n",
      "H_magn =0.04685190057294022   H_deg = 238.5152625281817\n",
      "MOI = 0.3516904841615086\n",
      "D_magn =0.1456967462608236   D_deg = 234.6645309198902\n",
      "H_magn =0.10613135426460348   H_deg = 246.28735955644962\n",
      "MOI = 0.7135034885275715\n",
      "D_magn =0.17519019681981274   D_deg = 239.11900909172832\n",
      "H_magn =0.08186384447909033   H_deg = 255.5390842001007\n",
      "MOI = 0.44822728766049447\n",
      "D_magn =0.15655429625752748   D_deg = 238.84700657204894\n",
      "H_magn =0.16201489886669207   H_deg = 229.85483794107603\n",
      "MOI = 0.9544196253460214\n",
      "D_magn =0.12703348228090652   D_deg = 251.16956281829897\n",
      "H_magn =0.11821462022661158   H_deg = 248.957256583751\n",
      "MOI = 0.9298848353444703\n",
      "D_magn =0.14167543649924072   D_deg = 235.67388237928307\n",
      "H_magn =0.1752829531085473   H_deg = 228.92449008148094\n",
      "MOI = 0.8026655258212022\n",
      "D_magn =0.1466393141842915   D_deg = 213.2581184456033\n",
      "H_magn =0.16297598854151887   H_deg = 215.2871139245225\n",
      "MOI = 0.899196121809608\n",
      "D_magn =0.10325769617463061   D_deg = 245.25590237668465\n",
      "H_magn =0.13379707093667362   H_deg = 244.22727025636937\n",
      "MOI = 0.7716241870015996\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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_magn =0.30431004219184793   R_deg = 242.05669695524523\n",
      "R_indiv_magn = 0.42435320543661964\n",
      "LDir = 0.7171149841527448\n",
      "R_magn =0.518333892426749   R_deg = 230.45378176788293\n",
      "R_indiv_magn = 0.6977142593570821\n",
      "LDir = 0.7429028222877005\n",
      "R_magn =0.3920186030025494   R_deg = 247.926964869355\n",
      "R_indiv_magn = 0.5637981661100627\n",
      "LDir = 0.695317272326886\n",
      "R_magn =0.34187222658118366   R_deg = 243.02148391752127\n",
      "R_indiv_magn = 0.48423158876509936\n",
      "LDir = 0.7060097575481095\n",
      "R_magn =0.3082134782184869   R_deg = 236.7010935597719\n",
      "R_indiv_magn = 0.4261539824399392\n",
      "LDir = 0.7232443926812898\n",
      "R_magn =0.27406685046181106   R_deg = 231.40905939298932\n",
      "R_indiv_magn = 0.3630099049091424\n",
      "LDir = 0.7549844969943923\n",
      "R_magn =0.2637818021077156   R_deg = 245.14842909692385\n",
      "R_indiv_magn = 0.3514259748809948\n",
      "LDir = 0.7506041697601932\n",
      "R_magn =0.17687501754756327   R_deg = 246.76718634000244\n",
      "R_indiv_magn = 0.24671725862825605\n",
      "LDir = 0.7169138410948042\n",
      "R_magn =0.2505658727453994   R_deg = 239.55983234947487\n",
      "R_indiv_magn = 0.3396082235639199\n",
      "LDir = 0.7378086140432896\n",
      "R_magn =0.2547683729904975   R_deg = 244.3304712672312\n",
      "R_indiv_magn = 0.32921882201195596\n",
      "LDir = 0.7738572522480056\n",
      "R_magn =0.3175891485265352   R_deg = 234.27369623003048\n",
      "R_indiv_magn = 0.4227446092161086\n",
      "LDir = 0.751255348034923\n",
      "R_magn =0.24520245832607906   R_deg = 250.10319073188776\n",
      "R_indiv_magn = 0.3221611956530443\n",
      "LDir = 0.7611172966658376\n",
      "R_magn =0.3164149437605095   R_deg = 231.94095305225719\n",
      "R_indiv_magn = 0.41969608907571054\n",
      "LDir = 0.7539144442764398\n",
      "R_magn =0.3095669047627052   R_deg = 214.32615115721504\n",
      "R_indiv_magn = 0.3873415962931251\n",
      "LDir = 0.7992090385470426\n",
      "R_magn =0.23704537502261175   R_deg = 244.67532607639313\n",
      "R_indiv_magn = 0.35108159028668356\n",
      "LDir = 0.6751860011487558\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": 11,
   "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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save Ftrace as numpy array\n",
    "\n",
    "np.save('R38G02_Ftrace_mean.npy', Ftrace_mean)\n",
    "np.save('R38G02_Ftrace_all.npy', Ftrace_all)\n",
    "np.save('R38G02_MOI.npy', MOI_flies)\n",
    "np.save('R38G02_LDir.npy', LDir_flies)\n",
    "np.save('R38G02_R_deg.npy', R_deg_flies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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