Raw File
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mousenums:  ['230', '355', '358', '362', '673', '674', '816', '825']\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.chdir('../')\n",
    "from helpers import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "timeO = []\n",
    "timeC = []\n",
    "for mouse in mousenums:\n",
    "    track, neur, behav = getdicts(foldnames, mouse, 'epm')\n",
    "    T = track['mouse_positionMS'].shape[0]\n",
    "    TO = behav['o1Ind'].size + behav['o2Ind'].size\n",
    "    TC = behav['c1Ind'].size + behav['c2Ind'].size\n",
    "    timeO.append(TO/T)\n",
    "    timeC.append(TC/T)\n",
    "timeO = np.array(timeO)*100\n",
    "timeC = np.array(timeC)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(0, np.mean(timeO))\n",
    "plt.bar(1, np.mean(timeC))\n",
    "\n",
    "plt.ylim(0, 100)\n",
    "plt.yticks([0, 40, 80])\n",
    "plt.xticks([])\n",
    "plt.legend(['open', 'closed'])\n",
    "plt.ylabel('% time on arms')\n",
    "plt.plot([0, 1], [80, 80], 'k')\n",
    "plt.text(0.5, 82, '***', ha='center')\n",
    "\n",
    "plt.errorbar(0, np.mean(timeO), sem(timeO), fmt='k_', ms=0, capsize=10)\n",
    "plt.errorbar(1, np.mean(timeC), sem(timeC), fmt='k_', ms=0, capsize=10)\n",
    "plt.title('1B-right')\n",
    "plt.savefig('./elife_figs/1B_right.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.92665917137156"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(timeO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.4784817849768563"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sem(timeO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66.93171973959502"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(timeC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.2805908995636415"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sem(timeC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16.62799691, 15.98451327, 12.10490921, 36.30304377, 36.78350515,\n",
       "       25.84294587, 16.87014428, 14.8962149 ])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timeO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([72.41188819, 76.25      , 78.42412015, 50.29993335, 46.65979381,\n",
       "       64.61845608, 72.79689234, 73.99267399])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timeC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RanksumsResult(statistic=-3.3606722016672235, pvalue=0.0007775304469403846)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranksums(timeO, timeC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
back to top