{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import scipy.io\n",
    "import scipy.stats\n",
    "import random\n",
    "import numpy as np\n",
    "import os\n",
    "import itertools\n",
    "import pickle as pk\n",
    "\n",
    "import scipy\n",
    "from scipy.stats import norm\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('default')\n",
    "%matplotlib inline\n",
    "\n",
    "import sys, os\n",
    "sys.path.append(\"/Users/xiaoxuanj/Dropbox/jim_data/human_behavior/1600_rep/\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"/Users/xiaoxuanj/Dropbox/jim_unsupervised_learning_paper/human_behavior/1600_rep/learning_6face_overtime_combine_boot\", 'rb') as f:\n",
    "    u = pk._Unpickler(f)\n",
    "    u.encoding = 'latin1'\n",
    "    [dprime_mb_s, dprime_mb_b, percent_mb_s, percent_mb_b, dprime_ms_s, dprime_ms_b, percent_ms_s, percent_ms_b,dprime_nonmb_s, dprime_nonmb_b, percent_nonmb_s, percent_nonmb_b] = u.load()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6750041186324949"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent_mb_s.mean(1).mean(1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Medium big swap exposure')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sG2cd1o3SMuX77C1ebSWHRz/L4l+fZpEQF8PwXq5vZVSf1AYtnzH1qW/fvjz//PNcdtllZGZmcvnll1cEkq5du3LTTTcxZMgQunTpQr9+/UhMTGzQ8kXkmu2DBg1SW9jKn03bC/n7u4t587v1dEtqxe2n9Oeo/TuGu1h72bqzmC+ycl1gWZbDhq1uaodeqW0Y3acDo/qkMLRnMnGx0WEuqWlqFi9eTN++fcNdjGoVFBQQHx9PSUkJp512GhdffDGnnXZarY4Z6r2LyDxVHRSct0FrJCIyBngIiAaeUtVJQa8nAi8C3b2y3aeqz3qvrQK2A6VASfmbEZEk4H9AOrAKOEtVNzfA22nWSsuUF2et5r4Pl7C7pIw/HNWbK47s3Sg/jBNbx3LSgZ056cDOqCpZmwoq7lt5cfZqnvlyJS1iohiSkcToPq62ktkh3jrtTbNx2223MW3aNAoLCznuuOM49dRTG/T8DVYjEZFoYClwLJANzAHGq+qigDw3AYmqOlFEUoElQCdVLfICySBVzQ067j1AvqpOEpEbgPaqOrGqsliNpGrfrtnMX95cwML12xjRO4U7xvWnZ2p8uIu1T3YVlTJ7ZR4zluYyY1kOWZsKAOicGMfIzBRG9+nAiN4pJLb2N122iSxNpUZSHxprjWQwkKWqK7wCvQKMAxYF5FEgQdxXxXggHyip5rjjgCO8588DnwNVBhIT2padRdzz4RJe/mYNqfEteeScgznpgM5N+pt7qxbRHLFfB47YrwMA67bsquhbeX/Bz7w6N5sogYO6tauorRyU1i7kfTDGmNAaMpB0BdYGbGcDQ4LyPAJMBdYDCcCvVbXMe02Bj0REgSdUdbKX3lFVNwCo6gYR6RDq5CJyKXApQPfu3evg7TQfZWXK6/Oz+cf7P7F1VzEXD8/g6mMySYhrft/Su7ZrxfjB3Rk/uDslpWV8t9Z12k9flstDnyzjwWnLSGwVy4jMFEZ7U7h0SowLd7GNadQaMpCE+ooX3K52PPAdcBTQC/hYRGaq6jZguKqu9wLFxyLyk6rO8HtyL/BMBte0tU/voBn66edt3PLmAuas2swh3dtx16kH0K9L23AXq0HEREcxKD2JQelJXHvcfuTvKNrTab80h3d/2ABAn47xFbWVw9KTGmU/kTHh1JCBJBvoFrCdhqt5BLoImKSu4yZLRFYC+wPfqOp6AFXdJCJTcE1lM4CNItLZq410BjbV9xtpDgp2l/DQtKU88+Uq2sbFcM/pB3LGoWlERXCTTlKbFpxyUBdOOagLqspPP2+vGAn2/FereXLmSuJioxjaM7liwsleqW2adNOfMXWhIQPJHCBTRDKAdcDZwDlBedYARwMzRaQjsB+wQkTaAFGqut17fhxwh7fPVOACYJL38616fydNmKry/oKfuePtRfy8rZDxg7tx/fH7097uEN+LiNC3c1v6dm7LZaN7sbOohFkrvE77pTncscR17XVt18q7ITKFw3un0LYZNgea5ic+Pp6CgoJKp6ivqQYLJKpaIiJXAR/ihv8+o6oLRWSC9/rjwJ3AcyLyI64pbKKq5opIT2CK980vBvivqn7gHXoS8KqI/BYXiM5sqPfU1KzK3cFfpy5kxtIc+nZuy6PnHsKhPdqHu1hNQusWMRy1f8eKe2jW5u+smGzy7e/X8/I3a4iOEg7p3q6itnJA18SIruGZyNGg95Go6nvAe0Fpjwc8X4+rbQTvtwIIOZ2lqubhajGmEoXFpTz2+XIem76cFtFR3Dq2H78Z2sOmE6mFbkmtOW9oD84b2oPi0jLmr97MjGVuXrD7P17K/R8vJalNC0b0TmFUn1RGZabQoa112pua27FjB2eddRbZ2dmUlpZyyy23MHHiRM455xw+++wziouLmTx5MjfeeCNZWVlcd911TJgwgYKCAsaNG8fmzZspLi7mrrvuYty4cfVSRpsipZn7fMkmbp26kNV5Oxl7UBf+clJfOtoHWp2KjY5iSM9khvRM5rrjIbdgN18s23On/dTvXVdg385tGdXHjQY7NL09LWOs075Jef8G+PnHuj1mpwPghElVZvnggw/o0qUL7777LgBbt25l4sSJdOvWja+//pprrrmGCy+8kC+//JLCwkL69+/PhAkTiIuLY8qUKbRt25bc3FyGDh3KKaecUi99ehZImqkNW3dx5zuLeO/Hn+mZ0oYXfzuEEZkp4S5WREiJb8mpB3fl1IO7UlamLNqwzaut5PD0zJU8MX0FrVtEM6xncsWEk+kpbcJdbNNIHXDAAfz5z39m4sSJnHzyyYwcORKAU045peL1goICEhISSEhIIC4uji1bttCmTRtuuukmZsyYQVRUFOvWrWPjxo106tSpzstogaSZKS4t47kvV/HPaUspLVP+fFwffjeqp337DZOoKGFA10QGdE3kiiN6U7C7hK+X57l7V5bm8MlPbpBh96TWjOqTwrCeKXRKjCM1viUpCS1sUa/GpJqaQ33p06cP8+bN47333uPGG2/kuONc63/Llm7tn6ioqIrn5dslJSW89NJL5OTkMG/ePGJjY0lPT6ewsLBeymh/pc3InFX5/GXKApZs3M5R+3fg9lP60y3Jlq1tTOJbxnBsv44c28912q/K3VFRW3lj/jpenLVmr/ytYqNJSWhBcpuWpMS3JLXieQtSElqS3MalpcS3JLFVrA1FbobWr19PUlIS5513HvHx8Tz33HO+9tu6dSsdOnQgNjaWzz77jNWrV9dbGS2QNAN5Bbv5x/s/8dq8bLq2a8Xk3xzKsf062odKE5Ce0ob0lDacPyyd3SWlLNtYQE7BbnK37ya3oIi8gt3kFrjn2Zt38t3aLeTv2E1ZiFtqY6KE5HgXVJLjXbBJjW9ZkZbiPU+Nb0n7Ni2ItcEWTcKPP/7IddddR1RUFLGxsTz22GOcccYZ1e537rnnMnbsWAYNGsTAgQPZf//9662MNo18E1ZWprw8Zw33fLCEHbtL+N2onvz+qN7WHNLMlZYpW3YWkVtQ5AWZ3RXP8/Z6XkROwW6KSspCHqd969iK4FIeaFKCgo6rBbWM2Lv5bdLGxjdpo6lDC9Zt5eY3F/D92i0M7ZnEneMGkNkxIdzFMg0gOkpI9mod+1H171xV2b67hLzyoLN9N7k7irwaz+6K9AXrtpJXUMT23aHnSG3TIpqUBC/AtGlR8bw88ASmtY2LsdpwhLFA0sRs3VXMAx8t4T+zVpPUpgX//PVBnDqwq/3jmpBEhLZxsbSNiyXDx8iwwuLSvQJMYG2nvKltVd4O5q3eTP7OIkI1aLSIjgpoYqu6tpPUpoXNtNwMWCBpIlSVt75bz13vLiZvx25+M7QHfzpuPxJb2ZQcpu7ExUaT1r41ae2rH6RRUlpG/s4icrcXkbfDCzrbi8jd4f30AtFPG7aTt2M3xaW/jDoikNTaCzABgwpSElqQ0iYgLcHVesLRxKaqEfdFraZdHhZImoCsTdv5y5sLmLUin4PSEnn2wsM4IK1h12Q2JlhMdBQdEuLokFD9Da6qyrZdJW4gQUCNJ69gNzkBz7/L30JuwW52FpWGPE5CyxivCc0FmE6JcRzcvR1DMpLrZbr/uLg48vLySE5Ojphgoqrk5eURF+f/elpneyO2s6iEf32axVMzV9AqNprrx+zP+MHdrSnANHs7i0oqBgsE9u/k7SiqGNWWt6OI9Vt2VQSdHsmtGZKRxOCMZIZkJNXJ0Pfi4mKys7Pr7f6LxiouLo60tDRiY/du8bDO9ibmo4U/c/vbi1i3ZRenH5LGjSfuT0p8y+p3NKYZaN0ihtZJMdUGg5LSMhZv2M7slXnMWpHPhws38urcbMDNzDwkI4khPV1wSU9uXeNaRWxsLBkZGfv8PiKF1UgambX5O7n97YVMW7yJPh3juXPcAIb0TA53sYxpEsrKlCUbtzN7RR6zV+bzzcp88nYUAdAhoaWbEy0jiSEZSfTuEB8xzVV1pbIaiQWSRmJ3SSlPzVzJvz5dRpQIVx+TyUXDM+ymMWNqQVVZnlPArBX5zF6Zz+wVeWzavhuA5DYtGJyRxOCMJIZkJLN/pwSb9r8atWraEpF+QKmqLvG2j8UtIrUQuEdVQ/eMGV++zMrllrcWsCJnBycM6MQtJ/ejS7tW4S6WMU2eiNC7QwK9OyRw3tAeqCqr83Yye2Ues73g8v6CnwFIbBXLYelJDO3pgku/zm1tqQWf/PaRPA08BCwRkTTcKoSfA1cCbYEb66V0zdymbYXc9e5ipn6/nu5JrXn2osM4cr8O4S6WMc2WiFRMS/Prw7oDkL15pxdUXHPYtMUbATcv2qD09gzJSGZwRhIHpiVaC0El/AaSvsB87/mZwGxVPVFEjgSexQJJjZSUlvGfWat54KOl7C4p449HZ3L5Eb0idhoKY8IprX1r0g5tzemHpgHw89bCiqDyzcp8Pl/yE+Am0Dy0R3tvZFgSB3VrZ/+zHr+BJBoo8p4fzZ5VDpcDHeu6UM3Z/DWb+cuUBSzasI2RmSncMW6ArzuOjTENo1NiHOMGdmXcwK6AW6jsG69/ZfbKfO7/eCkALWKiOLhbu4oO/EO6t6dVi8gMLL4620Xka2AG8A7wETBYVX8UkWHAq6rarX6LWbfC0dm+eUcR93z4Ey9/s5ZObeP469h+nDCgk40aMaaJ2bKzyAUWr8aycP1WyhRio4UD09pV1FgGpScR37J53WFRq1FbIjIKeBNIBJ5X1Yu99H8AfVT19Doub71qyEBSVqa8Nj+bSe//xNZdxVw8PJ0/HtOn2f2BGROpthUWM2/VZmatzOOblfn8mL2VkjIlOkoY0KVtRY1lUHpSk5/SqNbDf0UkGmirqpsD0tKBnaq6qY7K2SAaKpAs3rCNW95cwNzVmxnUoz13nTaA/Tu1rffzGmPCZ8fuEuav2VzRgf/92q0UlZYhAn07tWVIz6SKDvykNi3CXdwaaRT3kYjIGNzor2jgKVWdFPR6IvAi0B3Xf3Ofqj4rIt2AF4BOQBkwWVUf8va5DfgdkOMd5iZVfY8q1HcgKdhdwoMfL+XZr1aR2CqWG0/Yn9MPSbMx6sZEoMLiUr5ds4XZXo1l/prNFBa7NWL6dIxnSEayd/d9kq95y8Kptk1bccAfcR3tHYC9xsCp6oE+jhENLAWOBbKBOcB4VV0UkOcmIFFVJ4pIKrAEFzySgc6qOl9EEoB5wKmqusgLJAWqel+1b8RTX4FEVXnvx5+5452FbNq+m7MP687EMfvRrnXT+tZhjKk/RSVl/JC9hdkr85m1Io95qzdXzBfWM6XNXjWWxnY/WW3n2vo3cBrwf8BXwL5UYwYDWaq6wivQK8A4YFFAHgUSxPVAxwP5QImqbgA2AKjqdhFZDHQN2jesVubu4K9vLWDmslz6d2nLY+cdyiHd24e7WMaYRqZFTBSD0l2fyZVH9qaktIwF67cxe4WrsbzzwwZe/mYtAN2SWrkaS0YSQ3smk9a+VaMcoOM3kJwKnKmq02pxrq7A2oDtbGBIUJ5HgKnAeiAB+LWq7rVOqNcvczAwOyD5KhE5H5gL/CmwHydgv0uBSwG6d+9ei7ext8LiUv79+XIe/3w5LWOiuG1sP84b2sPuiDXG+BITHcXAbu0Y2K0dl43uRWmZsnjDNm9UWB6fLN7Ia/PcRJSdE+O8iShdcMlIadMoAovfQLKTvYPAvgj1boNrNscD3wFHAb2Aj0VkpqpuAxCReOB14OryNOAx4E7vWHcC9wMX/+JEqpOByeCatmr5XgD4bMkmbn1rIWvydzJuYBduPrEvHdo27jZOY0zjFh0lDOiayICuifx2RAZlZcqyTQUV07p8kZXHm9+tByA1oSWDM5IY6gWX3qnxYemL9RtI7gGuFZHLg2sINZANBN5vkoareQS6CJikruMmS0RWAvsD34hILC6IvKSqb5TvoKoby5+LyJO4e13q1fotu7jj7UV8sPBneqa24b+XDOHw3in1fVpjTASKihL265TAfp0SOH9YOqrKitwdzF7haiyzV+bz7g8bAGjfOrZiEsohPZPYv1PbBlm/yG8gORYYCYwRkUVAceCLqnqKj2PMATJFJANYB5wNnBOUZw2uQ3+miHQE9gNWeH0mTwOLVfWBwB1EpLPXhwKuH2eBz/dUY8WlZYUbEq4AAB/OSURBVDz75UoenLaMMlWuO34/LhmZQcuYyLyb1RjT8ESEXqnx9EqN55wh3VFV1ubvYpZXY/lmVR4fLnTfr9vGxXBYelJFB37/LvUzEaXfQJILTKnNiVS1RESuAj7EDf99RlUXisgE7/XHcU1Tz4nIj7imsImqmisiI4DfAD+KyHfeIcuH+d4jIgNxTVurgMtqU86qTHztB974dh3H9O3ArWP718kKbMYYUxsiQvfk1nRPbs1Zg1yjz/otuyqGG89ekc8nP7lb/dq0iObRcw/hiDqeHNbWI6mBheu3sn5LIcf2s+nFjDFNx6ZthRVTulw6quc+fwmukxsSRaQn0A/37X9x+VDepqYxLmxljDGNXW0XtmqL66M4HXdnuZcsrwO/VdXtdVZSY4wxTYrfXpeHgAOBI4FW3uNoL+3B+imaMcaYpsBvIDkFuERVp6tqsff4HHeD36n1VjpjjDGNnt9A0grIC5GeD9gdeMYYE8H8BpIvgTtFpKKrX0TaALfj5t4yxhgTofzeR3IN8AGwTkR+wI3aOgjYgZvWxBhjTITyFUhUdYGIZALn4aYsEdy6IS+p6q56LJ8xxphGzvd6r17AeLIey2KMMaYJqjSQiMivgLdVtdh7XqnASRSNMcZElqpqJK/hVifc5D2vjOLmzjLGGBOBKg0kqhoV6rkxxhgTyAKEMcaYWvEVSETkLBE5LmD7ryKSLSIfikjn+iueMcaYxs5vjeS28icicghwE/AwEItb2tYYY0yE8jv8twewxHt+GvCmqt4jIh/hFqoyxhgTofzWSAqBBO/50cA07/nWgHRjjDERyG+NZCZwv4h8AQwCzvDS+wBr66Ngxhhjmga/NZKrgCJcAJmgquu99BOwpi1jjIlofufaygbGhki/us5LZIwxpknxO/w3VURSA7YPEJG7RGR8/RXNGGNMU+C3aetVvBqJiKQAM3Cjtx4XkT/5PZmIjBGRJSKSJSI3hHg9UUTeFpHvRWShiFxU3b4ikiQiH4vIMu9ne7/lMcYYU3t+A8mBwCzv+RlAlqr2B84HLvNzABGJBh7F9av0A8aLSL+gbFcCi1T1IOAIXAd/i2r2vQH4RFUzgU+8bWOMMQ2kJkvtFnjPjwGmes/nA918HmMwLgCtUNUi4BVgXFAeBRJERIB43FK+JdXsOw543nv+PLaGvDHGNCi/gWQZ8CsR6QYcB3zkpXcEtvg8Rlf2Hiqc7aUFegToC6wHfgT+qKpl1ezbUVU3AHg/O4Q6uYhcKiJzRWRuTk6OzyIbY4ypjt9AcjtwN7AKmKWqs73044FvfR5DQqRp0PbxwHdAF2Ag8IiItPW5b5VUdbKqDlLVQampqdXvYIwxxhe/w3/fEJHuuA/47wNemga87vNc2ezdDJaGq3kEugiYpKoKZInIStzSvlXtu1FEOqvqBm8CyU0+y2OMMaYO+J5GXlU3quq3QKqIRHlps1X1J5+HmANkikiGiLQAzmZPX0u5NbgpWBCRjsB+wIpq9p0KXOA9vwB4y+97MsYYU3t+7yOJFZF7RGQ7sA5I99LvFpEr/BxDVUtwd8h/CCwGXlXVhSIyQUQmeNnuBA4XkR9xI7AmqmpuZft6+0wCjhWRZcCx3rYxxpgGIq4VqZpMIncBp+OG1v4XOEBVV4jI6bgP+8H1W8y6NWjQIJ07d264i2GMMU2KiMxT1UHB6X4nbRwPXKyq00WkLCB9AW7iRmOMMRHKbx9JF2B1iPQY/AcjY4wxzZDfQLIQGBUi/SxgXt0VxxhjTFPjtzZxO/Cid0NiNHCmiOwPnAOcVF+FM8YY0/j5qpGo6tu42sdxQBlwK5AJjFXVaVXta4wxpnnz3b+hqh9ii1gZY4wJYuuRGGOMqZUGXY/EGGNM89Ng65EYY4xpnhpyPRJjjDHNUEOuR2KMMaYZasj1SIwxxjRDDbkeiTHGmGaoJveRbAQ2BqXNriS7McaYCOF7YStjjDEmFAskxhhjasUCiTHGmFqpNJCIyDMikuA9HyUitu6IMcaYX6iqRnIe0MZ7/hmQVP/FMcYY09RUVctYBfxeRD4CBBgmIptDZVTVGfVQNmOMMU1AVYHkOuBJ4EZAgSmV5FPcYlfVEpExwENe/qdUdVLQ69cB5waUrS+Q6j3+F5C1J/BXVX1QRG4DfgfkeK/dpKrv+SmPMcaY2qs0kKjqW8BbItIOyAf6A5v29UQiEg08ChwLZANzRGSqqi4KOOe9wL1e/rHANaqa751/YMBx1rF3YPunqt63r2Uzxhiz76rtQFfVLSJyJLBMVUtqca7BuFmDVwCIyCvAOGBRJfnHAy+HSD8aWK6qq2tRFmOMMXXE71K704FoEblYRO4TkXtF5CIRaVmDc3UF1gZsZ3tpvyAirYExhJ5+5Wx+GWCuEpEfvJFm7Ss55qUiMldE5ubk5ITKYowxZh/4XSGxH7AUeAAYAgwF/gksFZG+Ps8lIdK0krxjgS+9Zq3AcrQATgH+LyD5MaAXrulrA3B/qAOq6mRVHaSqg1JTU0NlMcYYsw/83pD4EPAd0F1VR6rqSKA7bgLHB30eI5u91y5JA9ZXkjdUrQPgBGC+N+8X4OYAU9VSVS3DDQ4Y7LM8xhhj6oDfQDIcNxpqW3mC9/xmYITPY8wBMkUkw6tZnM2eBbIqiEgiMBp4K8QxftFvIiKdAzZPAxb4LI8xxpg64Pdu9UKgXYj0RO+1aqlqiYhcBXyIG/77jKouFJEJ3uuPe1lPAz5S1R2B+3v9Jsfyy6V97xGRgbhmslUhXjfGGFOPRLWyboqATCLPA4fh7tcoX7t9GPAE8I2qXlRvJawHgwYN0rlz54a7GMYY06SIyDxVHRSc7rdp64+45XZn4moghcB0XAf81XVVSGOMMU2P3xUStwDjRKQ37m5zARapalZ9Fs4YY0zjV6MZfb3AYcHDGGNMBVuPxBhjTK1YIDHGGFMrFkiMMcbUSrWBRERiROQKEenSEAUyxhjTtFQbSLwZf+8FYuu/OMYYY5oav01bs4BD6rMgxhhjmia/w3+fBO4XkR7APGCv6UtUdX5dF8wYY0zT4DeQ/Nf7+UCI13wvtWuMMab58RtIMuq1FMYYY5osv1Ok2LK2xhhjQvJ9H4mInCAi74jIIhHp5qVdIiJH11/xjDHGNHZ+l9o9F3gVNwNwBnuGAkcD19dP0YwxxjQFfmsk1wO/U9VrgJKA9Fm4tdKNMcZEKL+BJBP4OkR6AdC27opjjDGmqfEbSNYDfUKkjwKW111xjDHGNDV+A8lk4GERGe5tdxORC4B7gMfqpWTGGGOaBL/Df+8RkUTgYyAO+AzYDdynqo/WY/mMMcY0cr5XSFTVm0Xkb0A/XE1mkaoW1FvJjDHGNAk1XY9EgUJgJ1Ba05OJyBgRWSIiWSJyQ4jXrxOR77zHAhEpFZEk77VVIvKj99rcgH2SRORjEVnm/Wxf03IZY4zZd37vI2kpIg8C+cD3wA9Avog8JCJxPo8RDTwKnICr1YwXkX6BeVT1XlUdqKoDgRuB6aqaH5DlSO/1QQFpNwCfqGom8Im3bYwxpoH4rZE8BpwBXIIbCtzbe34a8G+fxxgMZKnqClUtAl4BxlWRfzzwso/jjgOe954/D5zqszzGGGPqgN8+kjOBX6nqxwFpK0RkE/A6cLGPY3QF1gZsZwNDQmUUkdbAGOCqgGQFPhIRBZ5Q1cleekdV3QCgqhtEpEMlx7wUuBSge/fuPoprjDHGD781kh3AuhDp64BdPo8hIdK0krxjgS+DmrWGq+ohuKaxK0VklM/zuhOpTlbVQao6KDU1tSa7GmOMqYLfQPIv4FYRaVWe4D2/xXvNj2ygW8B2Gu5Gx1DOJqhZS1XXez83AVNwTWUAG0Wks1emzsAmn+UxxhhTBypt2hKRqUFJRwDrROQHb/sAb/82Ps81B8gUkQxcTeZs4JwQ500ERgPnBaS1AaJUdbv3/DjgDu/lqcAFwCTv51s+y2OMMaYOVNVHkhe0/XrQ9sqanEhVS0TkKuBD3KzBz6jqQhGZ4L3+uJf1NOAjVQ1czrcjMEVEysv8X1X9wHttEvCqiPwWWIPrzzHGGNNARLWyborma9CgQTp37tzqMxpjjKkgIvOCbr8Aan5DojHGGLMXX8N/vbvFbwOOBDoQFIBUNeSQW2OMMc2f3/tIXgD6427420jlw3aNMcZEGL+B5AhgtKrOr8eyGGOMaYL89pEsr0FeY4wxEcRvcPgj8A8ROcibfNEYY4wB/DdtZQGtgPkA3v0cFVTVgosxxkQov4HkZSAR+APW2W78KtwK+Suh80EgoaZaM8Y0B34DySBgsKouqM/CmGYkaxq8dRVs3wAdD4CR10K/cRBllVdjmhu/fSSLgLb1WRDTTBTtgHf/BC+eDnGJcPw/oKQQXrsIHjkM5v8HSorCXUpjTB3yG0j+AjwgIseISEdveduKR30W0DQha+fA4yNhztMw7Cq4dDoMuwKunA1nPg8t2sDUq+Dhg2H2E1C0M9wlNsbUAV9zbYlIWcBm4A4CaFPrbLe5tupYSRHMuAdm3g9tu8Kpj0HGyF/mU4WsT2DmfbDma2id4gLNYZe42osxplGrbK4tv30kR9ZxeUxzsWkxvHEp/PwDDDwXxvyj8qAgApnHuMfqr1zg+eQO+OIhGPw7GHo5tElp2PIbY2rNZv81+6asDGb92wWClvEw9iHoO7bmx1n/nQsoi9+GmDg49EI4/CpITKvzIhtjaqeyGonfpq1Dqnq9qU2dYoGklrasgSmXw+ovoM8JcMrDEF/LeTtzlsAXD8IP/wOJgoPOhhHXQHKvuimzMabWahtIynB9I4E3A1TsaH0kEUIVvvsvvD/RbZ8wyTVn1eU9IptXw1f/gvkvQFkx9D8NRlwLnQbU3TmMMfuktn0kGUHbscDBwM3AjbUsm2kKCnLgnavhp3egx3DXod6+R92fp30POOk+GHUdzHrUjQBb8Dr0GQMj/wTdBtf9OY0xtVKrPhIROQ64VVWH112R6p/VSGrop/fg7T+4O9WP/isMvRKiGmgOz12b4ZsnXX/Mrs2QPtIFlJ5H2N3yxjSw+lohcSUwsJbHMI1V4TZ460p4ZTwkdHL3hRz++4YLIgCt2sPo6+HqBXD83yEvC/5zKjx5FCx+x3X6G2PCyu8KicE3HQrQGbdq4pI6LpNpDFZ9CW9OgK3ZrgYw+gaIaRG+8rSMh2FXuntOvvsvfPkg/O9cSN3f9aEMOB2i/bbUGmPqUk072/dKBtYCv1bVWfVQtnpjTVtVKC6ET++Erx+F9ulw2hPQfUi4S/VLpSWwcIobOpyzGNr1gBFXw0HnQGxcuEtnTLNU21Fbo4OSyoAcIEtVS2pQiDHAQ0A08JSqTgp6/TrgXG8zBugLpAJtcMv9dvLOPVlVH/L2uQ34nVcegJtU9b2qymGBpBIbvoc3LnMfzIMuhmPvdDWBxqysDJZ+4O6WXzcP4ju5+1AOvajxl92Y+lZWBtvWQf5y1yyctwIGXwJJPffpcLUKJHXBWxBrKXAskA3MAcar6qJK8o8FrlHVo0SkM9BZVeeLSAIwDzhVVRd5gaRAVe/zWxYLJEFKS1xT0eeToHUyjHsEMo8Nd6lqRhVWTnc1lJUzXN/KkAkw+FJobdPBmWZMFQo2uUCRvxzyvKCRv8I9Sgr35I1pBb9+0c0usQ/2afiv3wkZVTXfR7bBuBrMCu/YrwDjcDMLhzIetw4KqroB2OA93y4ii4GuVexr/MpbDlMmQPY37p6Nkx5omh+8Im4kV88j3OSRXzwAn//D3ZMy6GLXv5LQKbxlNKY2dua7/9eK2kVAwCgq2JMvKhaSMiCpF/Q6yt3Um9QLkntDQud6GSxTXe9kLtUvYqU+jgPug39twHY2ELLxXURaA2OAq0K8lo67h2V2QPJVInI+MBf4k6puDrHfpcClAN27d/dR3GZOFeY+Ax/9BaJj4fSn4YAzwl2qutHtMBj/MmxcCF/8E75+xM02fPB5MPwPru/HmMZo9/a9A0TF8+Vu+Hs5iYJ23V1w6D7MBYvygJHYrcEHnlTZtBWibyTQGNxa7iWqWu1aJSJyJnC8ql7ibf8Gt1jW70Pk/TVwnqqODUqPB6YDf1PVN7y0juwJeHfimsAurqosEd+0tW2Dm849a5r7xjLuUWjbJdylqj95y+HLh9xoLy2DA850C22l7hfukplIVLxrT5AI7LvIy4Idm/bO2zYNknvuqVEkez/b9QjLKMp9atpS1ekhDnQIcDcwCngC9+HtRzbQLWA7DVhfSd6z8Zq1As4bC7wOvFQeRLwybgzI8yTwjs/yRKYFb8C717rRWSfe54bTNvcb+5J7ufnARk90o9HmPevm9Op7shva3OXgcJfQNDclRbBl9d41ijzvsS1777xtOri/0T7H7R0w2mdAi9bhKX8N+a7/iEgG8DfgTOANoJ+qLq/BueYAmd5x1uGCxTkhzpMIjAbOC0gT4Glgsao+EJS/s9eHAnAaYMsBh7JrM7z7Z1jwGnQ9FE6bDCm9w12qhpXYFcb83dVGZj8Osye7WYd7HQUj/ww9Dm/+QdXUnbJS2Lp2T40isO9iyxrQ0j1549q5AJE+3AsWAU1RcU1/8dlqR22JSDLwV2AC8CVwvaruU7uQiJwIPIgb/vuMqv5NRCYAqOrjXp4LgTGqenbAfiOAmcCPuOG/4A3zFZH/4O6uV2AVcFlAYAkp4pq2ln8Kb17pqs2jJ7ob+OzmPXfn/tynXS1lRw50G+pqKJnHWkAxjipsW//L0VB5WbB5FZQGLBvdIt4Nqy1vfgqsXTTFASwh7NPwXxG5Cbge9wF9g6p+UG8lbEARE0iKdsDHt8KcJyFlP/jVE9aME0rxLreW/JcPuWaHjge4Wku/cRDVpCa2NvtCFXbkhh4Nlb8CigOWhI5uGRAseu0dLOI7NvsvIPsaSMqAXcBn7KkJ/IKqnlIXhWwoERFIsue6lQvzl8PQK9xki7Gtwl2qxq2kCH78PzfSK2+Z+4AYcQ0ccFZ4p4cxdWPXloAO7qC+i93b9uSLinEj+yqCREBnd9uuDTvXXCOzr4HkOaof/ouqXlSr0jWwZh1ISoth+t3uxryELnDaY5AxKtylalrKSl3fycz73RLCbdPcsOGDf9NkOj8j1u4CryYRNBoqfznszAvIKNCu2y9HQyX1dCOirOk3pLDf2d6YNNtAsuknmHKpm+rkoHPcwlOVrZ9uqqcKWZ+46VfWfA2tU2DYFW6km13X8Ckpcv0TecsCmqK8msb2oO7RhM57AkR5wEjq5WocNidbjVkgCdDsAklZGcx+DKbdXrv1003lVn/laihZ06BlIgz+HQy9HNqkhLtkzZOqCwq55cEi4LF59d4jolqn7F2jCHzeok343kMzZIEkQLMKJFvWwpuXw6qZdbd+uqnc+u9cQFn8NsTEwaEXujVaEruGu2RNU+G2vYNEbkAto3jHnnwxrVxwSOnt1Sx6Q3Km679o1T585Y8wFkgCNItAogrfv+zWT9cyGDPJTQHSzEeNNBo5S+CLB92NjRIFA8fD8Kvdt2Gzt9Jirykq65c1jIKNe/IFTvuRnOmuZUqmN0dUl4ju5G4sLJAEaPKBZEcuvP1Ht35698Ndh7rNHxUem1e7iSHnvwBlxW7iyxHXQqcB4S5Zw1KF7T97AWKZq1GUB43Nq0I0RQXWLrxgkZQBMS3D9hZM9SyQBGjSgWTJ+zD192799KNucbPa2r0O4bd9o1tXfs7TULQd+oxxNzd2GxzuktWt3du9mkVgv4UXOAJnoI1ptedei/JAkeLVMqwpqsmyQBKgSQaSwm3w4U3w7X/cDXO/egI69g93qUywXZvhmyddUNm1GdJHuoDS84im0+xYWuxqWhVBIiBwFPwckFH2NEWVN0GVPyL8fovmygJJgCYXSALXTx9+NRxxgzUBNHa7C2D+867Za/sG6HKICyj7ndg4PmBVXf9EcAd33jLXFFUWsPBp6+TQ/RbtM2wIbYSxQBKgyQSS4kL47C746hFv/fTHofvQcJfK1ETJbjd9/ZcPug/o1L5u+pX+v2qYm952F/xy+GxueVPU9j35YuL2TCZYUbvIbFbzRJnas0ASoEkEkg0/wJTLYNMit/74cXfZGuRNWWkJLJzihg7nLHZ3T4+42t04Wttv9aUl3pTlIUZF7XWDnnc3d2AHd3mHd9u0xlFTMo2aBZIAjTqQlJW6b6+f/cN9Exz3aNNbP91UrqwMln7g7pZfNw/iO8HhV7kvC1V9UVB1MxRXBIqAkVGbV+7dFNUqae/O7fLAkZRh862ZWrFAEqDRBpK85e7mwrWzod+pcPI/rVmhuVKFldNdDWXlDDeSacjlcMhvXMD4xciorL0nFoxuWcmoqN72N2PqjQWSAI0ukKi6Vfs+/ItrNz/xfrd+elMZ5WNqZ+0c+OIBWPJe0Avi1t/eq9/CeySm2bBv0+D2aald0wC2/wxvXQVZH7shouP+bdNtRJpuh8H4l2HjQrcIWWI3FziSelpTlGkSLJCE08Ip8M41bnTWCfe6WWWtwzNydexv9waZJskCSTjs2gzvXQ8/vuruL/jVZPcN1BhjmiALJA2tfP30go1wxE3uJjVbRMcY04TZJ1hDKdoJ026FbyZDSh84+yXoeki4S2WMMbXWoA3yIjJGRJaISJaI3BDi9etE5DvvsUBESkUkqap9RSRJRD4WkWXez8Y3I1z2PHhipAsiQ6+Ay2ZYEDHGNBsNFkhEJBp4FDgB6AeMF5F+gXlU9V5VHaiqA4Ebgemqml/NvjcAn6hqJvCJt904lBbDZ3+Hp491HernT4Ux/7CROMaYZqUhaySDgSxVXaGqRcArwLgq8o8HXvax7zjgee/588CpdV7yfZGzBJ46BqbfDQeeBZd/CT1Hh7tUxhhT5xqyj6QrsDZgOxsYEiqjiLQGxgBX+di3o6puAFDVDSIS3nVmy8rgmydg2m0Q2xrOegH6VRUvjTGmaWvIQBLqNu3KbqsfC3ypqvn7sG/ok4tcClwK0L1795rs6t+WtfDWFW7Kiz5jYOzDkNCxfs5ljDGNREMGkmygW8B2GrC+krxns6dZq7p9N4pIZ6820hnYFOqAqjoZmAxuipSaF78Kqm7t7veuc+unj30YDjnfpjgxxkSEhuwjmQNkikiGiLTABYupwZlEJBEYDbzlc9+pwAXe8wuC9qt/O3Lh1d+4Kd879ocJX8ChF1gQMcZEjAarkahqiYhcBXwIRAPPqOpCEZngvf64l/U04CNV3VHdvt7Lk4BXReS3wBrgzIZ5R8CSD7z107fAsXfAsKtsIj1jTMSx2X/3xe7tbv30+S9AxwFw2hPQaUDdFdAYYxohm/23rqz+CqZMgK1rYcQ1cMSNtn66MSaiWSCpiRn3wqd/g/Y94KL3bf10Y4zBAknNtM9wHenH/c3WTzfGGI8Fkpo44Az3MMYYU8FWUTLGGFMrFkiMMcbUigUSY4wxtWKBxBhjTK1YIDHGGFMrFkiMMcbUigUSY4wxtWKBxBhjTK1E5KSNIpIDrN7H3VOA3DosTnNn16tm7HrVjF2vmqvNNeuhqqnBiREZSGpDROaGmv3ShGbXq2bsetWMXa+aq49rZk1bxhhjasUCiTHGmFqxQFJzk8NdgCbGrlfN2PWqGbteNVfn18z6SIwxxtSK1UiMMcbUigUSY4wxtWKBpAZEZIyILBGRLBG5IdzlaSxEJFpEvhWRd7ztJBH5WESWeT/bB+S90bt+S0Tk+PCVOjxE5BoRWSgiC0TkZRGJs+u1NxF5RkQ2iciCgLR7ReQnEflBRKaISLuA10JeIxE5VER+9F57WESkod9LQwh1vbz033vXZKGI3BOQXvfXS1Xt4eMBRAPLgZ5AC+B7oF+4y9UYHsC1wH+Bd7zte4AbvOc3AHd7z/t5160lkOFdz+hwl78Br1NXYCXQytt+FbjQrtcvrtMo4BBgQUDacUCM9/xuP9cI+AYYBgjwPnBCuN9bA16vI4FpQEtvu0N9Xi+rkfg3GMhS1RWqWgS8AowLc5nCTkTSgJOApwKSxwHPe8+fB04NSH9FVXer6kogC3ddI0kM0EpEYoDWwHrseu1FVWcA+UFpH6lqibc5C0jznoe8RiLSGWirql+r+5R8gT3XtVkJdb2Ay4FJqrrby7PJS6+X62WBxL+uwNqA7WwvLdI9CFwPlAWkdVTVDQDezw5eekRfQ1VdB9wHrAE2AFtV9SPsetXUxbhvzFD5NerqPQ9OjxR9gJEiMltEpovIYV56vVwvCyT+hWovjOix0yJyMrBJVef53SVEWsRcQ6/vYxyuSaEL0EZEzqtqlxBpEXO9QhGRm4ES4KXypBDZtIr0SBEDtAeGAtcBr3p9HvVyvWL2pYQRKhvoFrCdhmuWiGTDgVNE5EQgDmgrIi8CG0Wks6pu8KrM5dXqSL+GxwArVTUHQETeAA7HrpcvInIBcDJwtNf8ApVfo2z2NH8FpkeKbOAN7zp9IyJluMka6+V6WY3EvzlApohkiEgL4GxgapjLFFaqeqOqpqlqOu56fKqq5+GuywVetguAt7znU4GzRaSliGQAmbgOvkixBhgqIq29b4dHA4ux61UtERkDTAROUdWdAS+FvEZeE+F2ERnqXevz2XNdI8GbwFEAItIHN0Aol3q6XlYj8UlVS0TkKuBD3AiuZ1R1YZiL1VhNwlWlf4v78DwTQFUXisirwCJc88SVqloavmI2LFWdLSKvAfNx7/9b3HQV8dj1qiAiLwNHACkikg3cCtyIG2n0sTcqdZaqTqjmGl0OPAe0wvWpvE8zVMn1egZ4xhsSXARc4NVO6uV62RQpxhhjasWatowxxtSKBRJjjDG1YoHEGGNMrVggMcYYUysWSIwxxtSKBRJjjDG1YoHEGGNMrfw/4qaPgpG7HPsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(percent_mb_b.mean(1)[:,2], label='big')\n",
    "plt.plot(percent_mb_s.mean(1)[:,2], label='small')\n",
    "plt.ylabel('Percent correct', fontsize=14)\n",
    "plt.legend()\n",
    "plt.xticks(range(5), [0,400,800,1200,1600])\n",
    "plt.ylabel('Number of sessions', fontsize=14)\n",
    "plt.title('Medium big swap exposure', fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5, 100)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb58122b320>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x302.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure 2 plot\n",
    "fig = plt.figure(figsize = (12,4.2))\n",
    "ax = fig.add_subplot(121)\n",
    "temp = []\n",
    "for i in range(5):\n",
    "    temp.append(dprime_mb_b[i,:,0]-dprime_mb_b[0,:,0])\n",
    "print(np.array(temp).shape)\n",
    "ax.errorbar(np.linspace(1,5,5),np.ma.masked_invalid(temp).mean(1),np.ma.masked_invalid(temp).std(1),color = 'r', ecolor = 'r', label = 'Swapped',elinewidth=2)\n",
    "np.save('break_mb_b.npy', np.array(temp))\n",
    "\n",
    "temp = []\n",
    "for i in range(5):\n",
    "    temp.append(dprime_mb_s[i,:,0]-dprime_mb_s[0,:,0])\n",
    "ax.errorbar(np.linspace(1,5,5),np.ma.masked_invalid(temp).mean(1),np.ma.masked_invalid(temp).std(1),color = 'r', linestyle=\"dashed\", ecolor = 'r', label = 'Control size',elinewidth=2)\n",
    "np.save('break_mb_s.npy', np.array(temp))\n",
    "\n",
    "temp = []\n",
    "for i in range(5):\n",
    "    ttemp=[]\n",
    "    for p in [1,2]:\n",
    "        ttemp.append(dprime_mb_b[i,:,p]-dprime_mb_b[0,:,p])\n",
    "    temp.append(ttemp)\n",
    "temp=np.mean(temp,axis=1)\n",
    "ax.errorbar(np.linspace(1,5,5),np.ma.masked_invalid(temp).mean(1),np.ma.masked_invalid(temp).std(1),color = 'k', ecolor = 'k', label = 'Control pairs',elinewidth=2)\n",
    "np.save('break_mb_b_control.npy', np.array(temp))\n",
    "\n",
    "\n",
    "plt.plot([0.5,5.5],[0,0],':k')\n",
    "plt.xlabel('Number of sessions',fontsize = 16)\n",
    "plt.xlim(0.5,5.5)\n",
    "plt.ylim(-2,2)\n",
    "ax.set_yticks([-2,-1,0,1,2])\n",
    "ax.set_xticks([1,2,3,4,5])\n",
    "ax.set_xticklabels([0,400,800,1200,1600])\n",
    "plt.ylabel('Delta d-prime (Post-Pre)',fontsize = 16)\n",
    "ax.tick_params(axis='y', direction='out')\n",
    "ax.tick_params(axis='x', direction='out')\n",
    "plt.title('Nonswap MB to swap MB; n=102',fontsize = 16)\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.plot([3,3],[-2,2],':r')\n",
    "\n",
    "#plt.savefig('/mindhive/dicarlolab/u/jiaxx/figures/figure2_learning_overtime_switch_new.pdf', format='eps', dpi=1000)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb5810c1198>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x302.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure 2 plot\n",
    "fig = plt.figure(figsize = (12,4.2))\n",
    "ax = fig.add_subplot(121)\n",
    "mb = []\n",
    "for i in range(5):\n",
    "    mb.append(dprime_mb_b[i,:,0]-dprime_mb_b[0,:,0])\n",
    "\n",
    "ms = []\n",
    "for i in range(5):\n",
    "    ttemp=[]\n",
    "    for p in [1,2]:\n",
    "        ttemp.append(dprime_mb_b[i,:,p]-dprime_mb_b[0,:,p])\n",
    "    ms.append(ttemp)\n",
    "ms=np.mean(ms,axis=1)\n",
    "    \n",
    "effect = []\n",
    "for i in range(100):\n",
    "    #for j in range(100):\n",
    "    effect.append(np.array(mb)[:,i]-np.array(ms)[:,i])\n",
    "        \n",
    "ax.errorbar(np.linspace(1,5,5),np.ma.masked_invalid(effect).mean(0),np.ma.masked_invalid(effect).std(0),color = 'k', ecolor = 'k', label = 'MB swap',elinewidth=2)\n",
    "\n",
    "\n",
    "plt.plot([0.5,5.5],[0,0],':k')\n",
    "plt.xlabel('Number of sessions',fontsize = 16)\n",
    "plt.xlim(0.5,5.5)\n",
    "plt.ylim(-2,2)\n",
    "ax.set_yticks([-2,-1,0,1,2])\n",
    "ax.set_xticks([1,2,3,4,5])\n",
    "ax.set_xticklabels([0,400,800,1200,1600])\n",
    "plt.ylabel('Delta d-prime (Post-Pre)',fontsize = 16)\n",
    "ax.tick_params(axis='y', direction='out')\n",
    "ax.tick_params(axis='x', direction='out')\n",
    "#plt.title('Nonswap MB to swap MB; n=37',fontsize = 16)\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.plot([3,3],[-2,2],':r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_pval(tmp, val, m='left'):\n",
    "    # tmp is a normal distribution of bootstrapped samples\n",
    "    if len(tmp)>10:\n",
    "        if m=='left':\n",
    "            # distribution with mean smaller than 0\n",
    "            pval = len(np.where(tmp>=0)[0])/len(tmp)\n",
    "        else:\n",
    "            # distribution with mean larger than 0\n",
    "            pval = len(np.where(tmp<=0)[0])/len(tmp)\n",
    "        return pval\n",
    "    else:\n",
    "        print('empty entry')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.01\n",
      "0.0\n",
      "0.0\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "for i in np.arange(1,5):\n",
    "    tmp = np.ma.masked_invalid(effect)[:,i].data\n",
    "    pval = get_pval(tmp, 1, m='left')\n",
    "    print(pval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95% confidence interval  -0.1779417433917851 -1.2735306122975225\n"
     ]
    }
   ],
   "source": [
    "# confidence intervals\n",
    "alpha = 0.95\n",
    "tmp = np.ma.masked_invalid(effect)[:,1].data\n",
    "p = ((1.0-alpha)/2.0) * 100\n",
    "lower = np.percentile(tmp, p)\n",
    "p = (alpha+((1.0-alpha)/2.0)) * 100\n",
    "upper = np.percentile(tmp, p)\n",
    "print('95% confidence interval ', upper, lower, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.583717225033884"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = (alpha+((1.0-alpha)/2.0)) * 100\n",
    "np.percentile(tmp, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.010000000000000009"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "tmp = np.ma.masked_invalid(effect)[:,1].data\n",
    "1-stats.percentileofscore(tmp, 0)/100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2943004395178418"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ma.masked_invalid(effect)[:,1].data.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-24.49968103608336 8.139269848087883e-44\n",
      "-46.8221599851682 2.3559740153028575e-69\n",
      "-35.44439515127764 4.6479862534569314e-58\n",
      "-38.62947298933524 1.6397085870643543e-61\n"
     ]
    }
   ],
   "source": [
    "# t-test compared to 0\n",
    "from scipy.stats import ttest_ind, ttest_1samp\n",
    "for i in np.arange(1,5):\n",
    "    t, p = ttest_1samp(np.ma.masked_invalid(effect)[:,i].data,0)\n",
    "    print(t, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 5)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(effect).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('break_learning_effect.npy', np.array(effect))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict non-swap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb58147efd0>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x302.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure 2 plot\n",
    "fig = plt.figure(figsize = (12,4.2))\n",
    "ax = fig.add_subplot(121)\n",
    "temp = []\n",
    "for i in range(5):\n",
    "    temp.append(dprime_nonmb_b[i,:,0]-dprime_nonmb_b[0,:,0])\n",
    "ax.errorbar(np.linspace(1,5,5),np.ma.masked_invalid(temp).mean(1),np.ma.masked_invalid(temp).std(1),color = 'r', ecolor = 'r', label = 'Swapped',elinewidth=2)\n",
    "np.save('build_mb_b.npy', np.array(temp))\n",
    "\n",
    "temp = []\n",
    "for i in range(5):\n",
    "    temp.append(dprime_nonmb_s[i,:,0]-dprime_nonmb_s[0,:,0])\n",
    "ax.errorbar(np.linspace(1,5,5),np.ma.masked_invalid(temp).mean(1),np.ma.masked_invalid(temp).std(1),color = 'r', linestyle=\"dashed\", ecolor = 'r', label = 'Control size',elinewidth=2)\n",
    "np.save('build_mb_s.npy', np.array(temp))\n",
    "\n",
    "temp = []\n",
    "for i in range(5):\n",
    "    ttemp=[]\n",
    "    for p in [1,2]:\n",
    "        ttemp.append(dprime_nonmb_b[i,:,p]-dprime_nonmb_b[0,:,p])\n",
    "    temp.append(ttemp)\n",
    "temp=np.mean(temp,axis=1)\n",
    "ax.errorbar(np.linspace(1,5,5),np.ma.masked_invalid(temp).mean(1),np.ma.masked_invalid(temp).std(1),color = 'k', ecolor = 'k', label = 'Control pairs',elinewidth=2)\n",
    "np.save('build_mb_b_other.npy', np.array(temp))\n",
    "\n",
    "\n",
    "plt.plot([0.5,5.5],[0,0],':k')\n",
    "plt.xlabel('Number of sessions',fontsize = 16)\n",
    "plt.xlim(0.5,5.5)\n",
    "plt.ylim(-2,2)\n",
    "ax.set_yticks([-2,-1,0,1,2])\n",
    "ax.set_xticks([1,2,3,4,5])\n",
    "ax.set_xticklabels([0,400,800,1200,1600])\n",
    "plt.ylabel('Delta d-prime (Post-Pre)',fontsize = 16)\n",
    "ax.tick_params(axis='y', direction='out')\n",
    "ax.tick_params(axis='x', direction='out')\n",
    "plt.title('Nonswap MB; n=36',fontsize = 16)\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.plot([3,3],[-2,2],':r')\n",
    "\n",
    "#plt.savefig('/mindhive/dicarlolab/u/jiaxx/figures/figure2_learning_overtime_switch_new.pdf', format='eps', dpi=1000)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb5815cf978>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x302.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure 2 plot\n",
    "fig = plt.figure(figsize = (12,4.2))\n",
    "ax = fig.add_subplot(121)\n",
    "mb = []\n",
    "for i in range(5):\n",
    "    mb.append(dprime_nonmb_b[i,:,0]-dprime_nonmb_b[0,:,0])\n",
    "\n",
    "ms = []\n",
    "for i in range(5):\n",
    "    ttemp=[]\n",
    "    for p in [1,2]:\n",
    "        ttemp.append(dprime_nonmb_b[i,:,p]-dprime_nonmb_b[0,:,p])\n",
    "    ms.append(ttemp)\n",
    "ms=np.mean(ms,axis=1)\n",
    "    \n",
    "effect = []\n",
    "for i in range(100):\n",
    "    #for j in range(100):\n",
    "    effect.append(np.array(mb)[:,i]-np.array(ms)[:,i])\n",
    "        \n",
    "ax.errorbar(np.linspace(1,5,5),np.ma.masked_invalid(effect).mean(0),np.ma.masked_invalid(effect).std(0),color = 'k', ecolor = 'k', label = 'MB swap',elinewidth=2)\n",
    "\n",
    "\n",
    "plt.plot([0.5,5.5],[0,0],':k')\n",
    "plt.xlabel('Number of sessions',fontsize = 16)\n",
    "plt.xlim(0.5,5.5)\n",
    "plt.ylim(-2,2)\n",
    "ax.set_yticks([-2,-1,0,1,2])\n",
    "ax.set_xticks([1,2,3,4,5])\n",
    "ax.set_xticklabels([0,400,800,1200,1600])\n",
    "plt.ylabel('Delta d-prime (Post-Pre)',fontsize = 16)\n",
    "ax.tick_params(axis='y', direction='out')\n",
    "ax.tick_params(axis='x', direction='out')\n",
    "#plt.title('Nonswap MB to swap MB; n=37',fontsize = 16)\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.plot([3,3],[-2,2],':r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('build_learning effect.npy', np.array(effect))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.03\n",
      "0.02\n",
      "0.0\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "for i in np.arange(1,5):\n",
    "    tmp = np.ma.masked_invalid(effect)[:,i].data\n",
    "    pval = get_pval(tmp, 1, m='right')\n",
    "    print(pval)"
   ]
  },
  {
   "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}