{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np, pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot') #im addicted to ggplot. so pretty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from random import choice\n",
    "\n",
    "def add_sing_trial(tar_pos,sing_pos):\n",
    "    counter = 0\n",
    "    item_vect = np.zeros((Items,1))\n",
    "    while True:\n",
    "        counter += 1\n",
    "        #holder = np.random.exponential(Rate,size=(7,1))\n",
    "        #holder[tar_pos] = np.random.exponential(tar_boost)\n",
    "        holder = np.random.normal(Rate,size=(7,1))*sigma\n",
    "        holder[tar_pos] += np.random.normal(tar_boost)#*sigma\n",
    "        if sing_pos and counter <= pd_time:\n",
    "            #holder[sing_pos] += np.random.exponential(sal_boost)\n",
    "            holder[sing_pos] += np.random.normal(sal_boost)#*sigma\n",
    "        elif sing_pos and counter > pd_time:\n",
    "            holder[sing_pos] += -np.random.normal(pd_boost)#*sigma\n",
    "            #holder[sing_pos] = 0\n",
    "        \n",
    "        holder += 1-holder[np.argmin(holder)]\n",
    "        item_vect += holder/np.sum(holder)\n",
    "        if np.sum(holder) < 1: print 'test'\n",
    "        if np.any(item_vect > Threshold): break\n",
    "    return {'RT':counter+np.random.normal(225),'Acc':np.argmax(item_vect) == tar_pos,'item_vect':item_vect,'tar_pos':tar_pos,'sing_pos':sing_pos}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "basic additional singleton paradigm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "698.470872308\n",
      "718.615041772\n",
      "1.0\n",
      "1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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IiMiF61eA/OQnPxm0Ao4ePUooFKKiooL09HSefPJJ/vnPf561nmEYF7ztcDhMOBxO3Pf5\nfDgcjouqV+yRmpqq904GlPrUhamsrEzc9nq9eL3ecwfIiRMnuOyyy/7rRvu73rnU19fjdrsZN24c\ncPJ66/v27cPpdNLa2pr4eerwYZfLRSQSSTw/Go3icrn63PapRp5Ol7AcnnT5URlo6lP953A48Pl8\nZy0/5xzIww8/zPPPP3/O3UP/+c9/eP7553n44YcvqrCsrCz279/P8ePHicfj1NfXk5eXx+zZs6mp\nqQGgpqaGgoICAAoKCqitraW7u5uWlhaam5vxeDwXVYOIiFw4I36OQ5g+++wzqqqq+Mc//sG4cePI\nyclhzJgxHDt2jE8++YSOjg7mzZvHV77yFcaPH39RRTz77LPU1tZimiaTJk1ixYoVdHZ2EggEiEQi\nZGdn4/f7GTt2LEBiMj8lJeWCD+Ntamq6qFrFHvq0KANNfar/cnNz+1x+zgA5pbu7m/379/Phhx9y\n9OhRxo0bx5VXXonH4yElpd9ng08aCpDhSX/sMtDUp/rvXAHyXxMgJSWFadOmMW3atAEvSkREhi+d\nXldERCxRgIiIiCUKEBERseS8AbJ58+ahqkNERIaZ8wZIX98IFxERAe3CEhERi857GO+JEyf461//\net4NLFu2bEALEhGR4eG8ARKPx4lGo0NVi4iIDCPnDZDU1FRd81xERPp03jkQXelPRETO5bwBotOX\niIjIuZw3QNasWXPOxz788EOefPLJAS9IRESGh/POgXR1dREMBjl48CA5OTnceeedtLe3s3XrVt55\n553ENctFRGTkOW+AbNmyhQMHDpCfn8+ePXv48MMPaWpqYt68eTz44IMXfR0QEREZvs4bIG+//TYb\nNmwgIyODL3/5y6xatYqf/vSnmhsREZHzz4F0dnYmrkWemZnJ6NGjFR4iIgL8lxFIT08P77777hnL\n/u/9a665ZuCrEhGRpHfeS9p+85vfPP+TDYOnnnpqwIsaTLqk7fCky4/KQFOf6j/L10QfbE1NTZSV\nlWEYBvF4nE8//ZRly5Zx9OhRtm/fntiFdvfddzNz5kwAgsEg1dXVmKZJcXEx+fn5F/R6Mvzoj10G\nmvpU/1m+Jvpgy83NZcOGDQD09vaycuVK5syZQ3V1NUVFRRQVFZ2xfmNjI3V1dQQCAaLRKGvXrqW8\nvBzDMOwoX0RkxEqq07nX19czYcIEsrKygL5PpRIKhSgsLMQ0TdxuNzk5OTQ0NAx1qSIiI57tI5DT\n1dbWcuONNybub9u2jddee40pU6Zw7733kp6eTiwWY+rUqYl1XC4XsVjMjnJFREa0pAmQ7u5uQqEQ\ny5cvB2DRokXccccdGIbBM888w9atW1mxYsUFbTMcDhMOhxP3fT4fDodjQOuWoZGamqr3TgaU+tSF\nqaysTNz2er14vd7kCZA9e/YwefLkxLfbT/+W+8KFCyktLQVOjjgikUjisWg0isvl6nObpxp5Ok2a\nDU+a8JSBpj7Vfw6HA5/Pd9bypJkD2bFjxxm7r1pbWxO3d+7cycSJEwEoKCigtraW7u5uWlpaaG5u\nxuPxDHm9IiIjXVKMQLq6uqivr+fBBx9MLPvjH//IwYMHMQyD7OxsHnjgAQDy8vKYO3cufr+flJQU\nSkpKdASWiIgNbP8eyFDT90CGJ+1ukIGmPtV/5/oeSNLswhIRkeFFASIiIpYoQERExBIFiIiIWKIA\nERERSxQgIiJiiQJEREQsUYCIiIglChAREbFEASIiIpYoQERExBIFiIiIWKIAERERS5LidO4iMnAi\nnb1EjnbbXUbSM1tb6enpsbuMC5I1NoWs0cnzuV8BInKJiRztZvW2D+wuQwZB6a2TyRqdancZCckT\nZSIiMqwoQERExBIFiIiIWKIAERERS2yfRG9qaqKsrAzDMIjH43z66acsW7aMm2++mbKyMg4fPozb\n7cbv95Oeng5AMBikuroa0zQpLi4mPz/f5laIiIw8tgdIbm4uGzZsAKC3t5eVK1cyZ84cqqqqmD59\nOosXL6aqqopgMMjy5ctpbGykrq6OQCBANBpl7dq1lJeXYxiGzS0RERlZkmoXVn19PRMmTCArK4tQ\nKMS8efMAmD9/Prt37wYgFApRWFiIaZq43W5ycnJoaGiws2wRkREpqQKktraWm266CYC2tjacTicA\nTqeTtrY2AGKxGFlZWYnnuFwuYrHY0BcrIjLC2b4L65Tu7m5CoRDLly/v83Eru6jC4TDhcDhx3+fz\n4XA4LNco9klNTdV7109ma6vdJcggMU3Ttr+DysrKxG2v14vX602eANmzZw+TJ09m/PjxwMlRR2tr\na+JnRkYGcHLEEYlEEs+LRqO4XK4+t3mqkadrb28fpBZYo9NO9I9pmjrtRD8Nt9+T9F9PT48t/4c5\nHA58Pt9Zy5MmQHbs2MGNN96YuD979mxqampYsmQJNTU1FBQUAFBQUEB5eTlFRUXEYjGam5vxeDx2\nlX3RdNqJS1eynXZCZKAlRYB0dXVRX1/Pgw8+mFi2ZMkSAoEA1dXVZGdn4/f7AcjLy2Pu3Ln4/X5S\nUlIoKSnREVgiIjZIigBJS0tjy5YtZywbN24cP/7xj/tcf+nSpSxdunQoShMRkXNIqqOwRERk+FCA\niIiIJQoQERGxRAEiIiKWKEBERMQSBYiIiFiiABEREUsUICIiYokCRERELFGAiIiIJQoQERGxRAEi\nIiKWKEBERMQSBYiIiFiiABEREUsUICIiYokCRERELFGAiIiIJUlxSduOjg42bdrERx99hGEYrFy5\nkj179rB9+3YyMjIAuPvuu5k5cyYAwWCQ6upqTNOkuLiY/Px8O8sXERmRkiJAfve73zFr1iy++93v\n0tPTQ1dXF3v27KGoqIiioqIz1m1sbKSuro5AIEA0GmXt2rWUl5djGIZN1YuIjEy278Lq6Ohg7969\nLFiwAADTNElPTwcgHo+ftX4oFKKwsBDTNHG73eTk5NDQ0DCkNYuISBKMQFpaWnA4HFRUVHDo0CEm\nT55McXExANu2beO1115jypQp3HvvvaSnpxOLxZg6dWri+S6Xi1gsZlP1IiIjl+0jkN7eXg4cOMCi\nRYsoLS0lLS2NqqoqFi1axFNPPcVjjz2G0+lk69atdpcqIiKnsX0E4nK5yMzMZMqUKQDccMMNVFVV\nMX78+MQ6CxcupLS0NLF+JBJJPBaNRnG5XH1uOxwOEw6HE/d9Ph8Oh2MwmmGZ2dpqdwkySEzTtKW/\nqU9duuzqUwCVlZWJ216vF6/Xa3+AOJ1OMjMzaWpqIjc3l/r6evLy8mhtbcXpdAKwc+dOJk6cCEBB\nQQHl5eUUFRURi8Vobm7G4/H0ue1TjTxde3v74DboAvX09NhdggySnp4eW/qb+tSly64+5XA48Pl8\nZy23PUAA7r//fn7xi1/Q3d3NhAkTWLVqFb/97W85ePAghmGQnZ3NAw88AEBeXh5z587F7/eTkpJC\nSUmJjsASEbFBUgTIpEmT+PnPf37Gsoceeuic6y9dupSlS5cOdlkiInIetk+ii4jI8KQAERERSxQg\nIiJiiQJEREQsUYCIiIglChAREbFEASIiIpYoQERExBIFiIiIWKIAERERSxQgIiJiiQJEREQsUYCI\niIglChAREbFEASIiIpYoQERExBIFiIiIWKIAERERSxQgIiJiSVJcE72jo4NNmzbx0UcfYRgGK1eu\nJCcnh7KyMg4fPozb7cbv95Oeng5AMBikuroa0zQpLi4mPz/f5haIiIw8SREgv/vd75g1axbf/e53\n6enpoauri7/97W9Mnz6dxYsXU1VVRTAYZPny5TQ2NlJXV0cgECAajbJ27VrKy8sxDMPuZoiIjCi2\n78Lq6Ohg7969LFiwAADTNElPTycUCjFv3jwA5s+fz+7duwEIhUIUFhZimiZut5ucnBwaGhpsq19E\nZKSyfQTS0tKCw+GgoqKCQ4cOMXnyZIqLi2lra8PpdALgdDppa2sDIBaLMXXq1MTzXS4XsVjMltpF\nREYy20cgvb29HDhwgEWLFlFaWkpaWhpVVVVnraddVCIiycX2EYjL5SIzM5MpU6YAcMMNN1BVVYXT\n6aS1tTXxMyMjI7F+JBJJPD8ajeJyufrcdjgcJhwOJ+77fD5yc3MHsTUXLjcXdk+fZHcZcglRn5LB\nUFlZmbjt9Xrxer32B4jT6SQzM5OmpiZyc3Opr68nLy+PvLw8ampqWLJkCTU1NRQUFABQUFBAeXk5\nRUVFxGIxmpub8Xg8fW77VCNl+KusrMTn89ldhlxC1KcuTF+/K9sDBOD+++/nF7/4Bd3d3UyYMIFV\nq1bR29tLIBCgurqa7Oxs/H4/AHl5ecydOxe/309KSgolJSXavSUiYgMjHo/H7S5C5L/Rp0UZaOpT\nF8/2SXSR/tCuSBlo6lMXTyMQERGxRCMQERGxRAEiIiKWKEBkyOzatYtly5bR1NQEwHvvvcf69esH\n7fUOHz7Mjh07Bm37Yo9ly5axevVqvve97xEIBDh+/PiQ19DR0cHLL7885K+bbBQgMmRqa2u5+uqr\nef311xPLBvMQ7JaWFgXIJWj06NGUlpbyxBNPYJomr7zyylnrDPbU7pEjR/j73/8+qK8xHCTF90Dk\n0tfZ2cm+ffv4yU9+wvr167nzzjuBk5/k1q9fT3NzM9dccw0lJSX09vayadMmPvjgAwzDYMGCBdx2\n2218+umnbNmyhfb2dlJTU3nwwQfJzc2loqKCMWPG8MEHH9Da2so999zD9ddfz5///GeamppYvXo1\n8+bN47bbbrP5tyADbdq0aXz44YccPnyYdevW4fF4OHDgAGvWrKGpqYnKysozvl+WlpbGn/70J958\n801GjRpFfn4+99x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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f00fca9bf10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Items = 7\n",
    "Threshold = 100\n",
    "Rate = 1\n",
    "pd_time = 250\n",
    "sigma = 1.75\n",
    "\n",
    "tar_boost = 2 #target uncertainty can decrease this score\n",
    "sal_boost = 3\n",
    "pd_boost = sal_boost+(sal_boost-tar_boost)/2.25\n",
    "\n",
    "RT = []\n",
    "Acc = []\n",
    "trial_type = []\n",
    "\n",
    "for trial in np.arange(1000):\n",
    "    trial_type.append(choice([0,1]))\n",
    "        \n",
    "    tar_pos = choice(np.arange(7))\n",
    "    \n",
    "    if trial_type[trial] == 0: \n",
    "        sing_pos = []\n",
    "    else:\n",
    "        sing_pos = tar_pos\n",
    "        while sing_pos == tar_pos:\n",
    "            sing_pos = choice(np.arange(7))\n",
    "            if sing_pos != tar_pos: break\n",
    "    \n",
    "    trial_dict = add_sing_trial(tar_pos,sing_pos)\n",
    "    RT.append(trial_dict['RT'])\n",
    "    Acc.append(trial_dict['Acc'])  \n",
    "    \n",
    "RT = np.array(RT)\n",
    "Acc = np.array(Acc)\n",
    "trial_type = np.array(trial_type)\n",
    "\n",
    "plt.bar([0, 1],[np.mean(RT[(trial_type==0) & Acc==1]), np.mean(RT[(trial_type==1) & Acc==1])]);\n",
    "plt.xticks([0.5, 1.5],['Absent', 'Present'])\n",
    "plt.xlim([-0.25, 2])\n",
    "plt.ylim([600, 1000])\n",
    "plt.ylabel('RT (ms)')\n",
    "\n",
    "print np.mean(RT[(trial_type==0) & Acc==1])\n",
    "print np.mean(RT[(trial_type==1) & Acc==1])\n",
    "\n",
    "print np.mean(Acc[trial_type==0])\n",
    "print np.mean(Acc[trial_type==1])    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "next i want to implement trials where the target can be a color singleton. both in the mixed and the non-mixed versions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "717.578806443\n",
      "698.752424483\n",
      "719.185837534\n",
      "1.0\n",
      "1.0\n",
      "1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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MmTOZOXMmra2toW9DdDgctLa2AudnLFOnTr3k9cFg8CvVICIiX16/AuTHP/7xoBVw+vRp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\nkv8HqvoDBRt8LNwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f00c30cc190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Items = 7\n",
    "Threshold = 100\n",
    "Rate = 1\n",
    "pd_time = 250\n",
    "sigma = 1.75\n",
    "\n",
    "tar_boost = 2 #target uncertainty can decrease this score\n",
    "sal_boost = 3\n",
    "pd_boost = sal_boost+(sal_boost-tar_boost)/2.25\n",
    "\n",
    "RT = []\n",
    "Acc = []\n",
    "trial_type = []\n",
    "\n",
    "for trial in np.arange(1000):\n",
    "    trial_type.append(choice([0,1]))\n",
    "        \n",
    "    tar_pos = choice(np.arange(7))\n",
    "    \n",
    "    if trial_type[trial] == 0: \n",
    "        sing_pos = []\n",
    "    else:\n",
    "        sing_pos = choice(np.arange(7))\n",
    "    \n",
    "    trial_dict = add_sing_trial(tar_pos,sing_pos)\n",
    "    if trial_dict['tar_pos'] == trial_dict['sing_pos']: trial_type[trial] = 2\n",
    "    RT.append(trial_dict['RT'])\n",
    "    Acc.append(trial_dict['Acc'])  \n",
    "    \n",
    "RT = np.array(RT)\n",
    "Acc = np.array(Acc)\n",
    "trial_type = np.array(trial_type)\n",
    "\n",
    "plt.bar(np.arange(3),[np.mean(RT[(trial_type==2) & Acc==1]), np.mean(RT[(trial_type==0) & Acc==1]), np.mean(RT[(trial_type==1) & Acc==1])]);\n",
    "plt.xticks([0.3, 1.3, 2.3],['Target','Absent', 'Present'])\n",
    "plt.xlim([-0.25, 3])\n",
    "plt.ylim([600, 1000])\n",
    "plt.ylabel('RT (ms)')\n",
    "\n",
    "print np.mean(RT[(trial_type==2) & Acc==1])\n",
    "print np.mean(RT[(trial_type==0) & Acc==1])\n",
    "print np.mean(RT[(trial_type==1) & Acc==1])\n",
    "\n",
    "print np.mean(Acc[trial_type==2]) \n",
    "print np.mean(Acc[trial_type==0])\n",
    "print np.mean(Acc[trial_type==1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "762.26996873\n",
      "822.313731635\n",
      "1.0\n",
      "1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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33HILv/71r2lra2PdunUsWrSIu+++2+bfggy1WbNm8dFHH3Hs2DE2btyIx+Ph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\nERGxRAEiIiKWKEBERMQSBYiIiFjyv1YHezZeHMx2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f00bf7415d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Items = 7\n",
    "Threshold = 100\n",
    "Rate = 1\n",
    "pd_time = 250\n",
    "sigma = 1.75\n",
    "\n",
    "tar_boost = 1.25 #target uncertainty can decrease this score\n",
    "sal_boost = 7\n",
    "pd_boost = sal_boost+(sal_boost-tar_boost)/2.25\n",
    "\n",
    "RT = []\n",
    "Acc = []\n",
    "trial_type = []\n",
    "\n",
    "for trial in np.arange(1000):\n",
    "    trial_type.append(choice([0,1]))\n",
    "        \n",
    "    tar_pos = choice(np.arange(7))\n",
    "    \n",
    "    if trial_type[trial] == 0: \n",
    "        sing_pos = []\n",
    "    else:\n",
    "        sing_pos = tar_pos\n",
    "        while sing_pos == tar_pos:\n",
    "            sing_pos = choice(np.arange(7))\n",
    "            if sing_pos != tar_pos: break\n",
    "    \n",
    "    trial_dict = add_sing_trial(tar_pos,sing_pos)\n",
    "    if trial_dict['tar_pos'] == trial_dict['sing_pos']: trial_type[trial] = 2\n",
    "    RT.append(trial_dict['RT'])\n",
    "    Acc.append(trial_dict['Acc'])  \n",
    "    \n",
    "RT = np.array(RT)\n",
    "Acc = np.array(Acc)\n",
    "trial_type = np.array(trial_type)\n",
    "\n",
    "plt.bar([0, 1],[np.mean(RT[(trial_type==0) & Acc==1]), np.mean(RT[(trial_type==1) & Acc==1])]);\n",
    "plt.xticks([0.5, 1.5],['Absent', 'Present'])\n",
    "plt.xlim([-0.25, 2])\n",
    "plt.ylim([600, 1000])\n",
    "plt.ylabel('RT (ms)')\n",
    "\n",
    "print np.mean(RT[(trial_type==0) & Acc==1])\n",
    "print np.mean(RT[(trial_type==1) & Acc==1])\n",
    "\n",
    "print np.mean(Acc[trial_type==0])\n",
    "print np.mean(Acc[trial_type==1])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "776.036717522\n",
      "763.503653522\n",
      "822.330381671\n",
      "0.988636363636\n",
      "1.0\n",
      "1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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Tk4PVasXpdJKWlkZDQ8OA1CEiIv3Xr0NY+/fvx+Fw8NFHH1303B133PG1CnA4HOTl5bFi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\nBTh4agAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f00bf796cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Items = 7\n",
    "Threshold = 100\n",
    "Rate = 1\n",
    "pd_time = 250\n",
    "sigma = 1.75\n",
    "\n",
    "tar_boost = 1.25 #target uncertainty can decrease this score\n",
    "sal_boost = 7\n",
    "pd_boost = sal_boost+(sal_boost-tar_boost)/2.25\n",
    "\n",
    "RT = []\n",
    "Acc = []\n",
    "trial_type = []\n",
    "\n",
    "for trial in np.arange(1000):\n",
    "    trial_type.append(choice([0,1]))\n",
    "        \n",
    "    tar_pos = choice(np.arange(7))\n",
    "    \n",
    "    if trial_type[trial] == 0: \n",
    "        sing_pos = []\n",
    "    else:\n",
    "        sing_pos = choice(np.arange(7))\n",
    "    \n",
    "    trial_dict = add_sing_trial(tar_pos,sing_pos)\n",
    "    if trial_dict['tar_pos'] == trial_dict['sing_pos']: trial_type[trial] = 2\n",
    "    RT.append(trial_dict['RT'])\n",
    "    Acc.append(trial_dict['Acc'])  \n",
    "    \n",
    "RT = np.array(RT)\n",
    "Acc = np.array(Acc)\n",
    "trial_type = np.array(trial_type)\n",
    "\n",
    "plt.bar(np.arange(3),[np.mean(RT[(trial_type==2) & Acc==1]), np.mean(RT[(trial_type==0) & Acc==1]), np.mean(RT[(trial_type==1) & Acc==1])]);\n",
    "plt.xticks([0.3, 1.3, 2.3],['Target','Absent', 'Present'])\n",
    "plt.xlim([-0.25, 3])\n",
    "plt.ylim([600, 1000])\n",
    "plt.ylabel('RT (ms)')\n",
    "\n",
    "print np.mean(RT[(trial_type==2) & Acc==1])\n",
    "print np.mean(RT[(trial_type==0) & Acc==1])\n",
    "print np.mean(RT[(trial_type==1) & Acc==1])\n",
    "\n",
    "print np.mean(Acc[trial_type==2]) \n",
    "print np.mean(Acc[trial_type==0])\n",
    "print np.mean(Acc[trial_type==1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}