add jupyter notebook, RTsum plot

3 files changed, 549 insertions, 0 deletions

diff --git a/experiment/analysis/RT.png b/experiment/analysis/RT.png
new file mode 100644
index 0000000..20cd0b8
--- /dev/null
+++ b/experiment/analysis/RT.png
diff --git a/experiment/analysis/analysis.ipynb b/experiment/analysis/analysis.ipynb
new file mode 100644
index 0000000..b140e96
--- /dev/null
+++ b/experiment/analysis/analysis.ipynb
@@ -0,0 +1,477 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d372d82b-0842-4c24-86d1-b75c6637d2a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "imported tools\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from pathlib import Path\n",
+    "from pprint import pprint\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "import tools\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3f7c451c-6afb-439d-8bfe-4e545c4f7992",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_path = Path(\"/home/niclas/repos/uni/thesis/experiment/data\")\n",
+    "\n",
+    "procedures = [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"overall\"]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "cffed6cb-ed44-4f8d-92dc-760b752d4302",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['random', 'blocked', 'fixed']"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "conditions = [x.stem for x in data_path.iterdir() if x.is_dir()]\n",
+    "conditions\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "511c7800-8ca9-457b-90b0-d3c3302f6ef0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = {}\n",
+    "for condition in conditions:\n",
+    "    data[condition] = {}\n",
+    "    for vp in (data_path / condition).iterdir():\n",
+    "        data[condition][vp.stem] = tools.fix_vp(tools.unpickle(vp / \"vp.pkl\"), procedures)\n",
+    "\n",
+    "data_train, data_test = tools.train_test_split(data)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "39503de8-fa01-4ce7-a0c9-90f337548945",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "condition = \"blocked\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8f79bdbb-ac99-4577-866a-d566eee527ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_blocked_fixed = tools.block_vps(data_train, \"fixed\")\n",
+    "train_blocked_random = tools.block_vps(data_train, \"random\")\n",
+    "train_blocked_blocked = tools.block_vps(data_train, \"blocked\")\n",
+    "\n",
+    "test_blocked_fixed = tools.block_vps(data_test, \"fixed\")\n",
+    "test_blocked_random = tools.block_vps(data_test, \"random\")\n",
+    "test_blocked_blocked = tools.block_vps(data_test, \"blocked\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3ab5e63b-9c5a-4c53-9d80-983a27f833e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#fig = tools.plot_vp(blocked_vps[list(blocked_vps.keys())[0]])\n",
+    "#plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e371b733-1b94-4ec9-8223-06b82dbac3df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#tools.plot_average_vps(test_blocked_fixed)\n",
+    "#plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "eb3f2e96-2246-4b08-a7d1-999161ab3fd3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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hQUVFRRo8eLAMw9BTTz2ln/3sZ7rjjjskSS+99JLsdrtWrVqlH//4x6qsrNQLL7ygFStWKCsrS5K0cuVKJScnq6CgQCNGjGijhwa0Lefh5pNgrekHAAD8IGWgZ3fIqnI1XSfM4jmfMtDfkeE02JgIwJkUlBVo/rb52l/z7Yxee7RdMzJmKCslK4CRwezOqUZYZWWlJCkuLk6SVFpaqoqKCg0fPtzbJzIyUjfeeKO2bvXUXSgqKtKxY8d8+iQlJSktLc3b51R1dXWqqqryuSAEuV1S6fvSrv/z/OvnbH9CTFSb9gMAAH4QZpWyF5w4ODWRcuI4ez6F8k0kv6Rcgxa8q+///kM9snqnvv/7DzVowbvUYgXgVVBWoLzCPJ8kmCQ5a5zKK8xTQVlBgCJDMGh1IswwDOXl5WnQoEFKS0uTJFVUVEiS7HbfqeV2u917rqKiQp06dVK3bt2a7XOqefPmyWazeS/JycmtDRvBavda6ak06aVR0usTPf8+leZp95OM3nFKtEXJIilMbl0ftls5YVt1fdhuhcktizy7R2b0jvNbTGbhchv64Muv9dbOr/TBl19TJw0AYC6pOdKYl6XYU3Z3jk3ytKfmBCYuNMLGRMGHpWnwN5fbpfnb5stoYpZvQ9uCbQv4W0SzWrQ08mSTJ0/WJ598oi1btjQ6Z7H4/tpmGEajtlOdrs/MmTOVl5fnPa6qqiIZFkp2r5Veu0+NljNUlXva/TSAtYZZNGt0qt5c9bz+K+JlJVm+rQW2z4jTL4/dp9tG/yTkpu3nl5RrzrrdPgPWRFuUZo1OVXZa4mmu2T5YSgEAaFJqjtRnpGd3yOr9nppgKQOZCWYiZ9qYyCLPxkTDUh18tpsES9MQCMXO4kYzwU5myFBFTYWKncXq7+jvx8gQLFqVCJsyZYrWrl2r9957Tz179vS2OxwOSZ5ZX4mJ334Bdjqd3lliDodD9fX1OnTokM+sMKfTqYEDm67NEBkZqcjIyNaEimDndkn509V0TY8TQ6L8GZ6BrR8Gstlh2zWi09ONfn1wWA5qaaenZQnrJyl0flVu+NX21Fen4Vfbpfem+zUZZrakHADAZMKsUu8bAh0FmtGSjYkGXBTvv8DQpIalaaeOixuWpi3KXEQyDO3iQM2BNu2H0NOipZGGYWjy5Ml644039O6776p3794+53v37i2Hw6GNGzd62+rr67V582Zvkqtfv36KiIjw6VNeXq6SkpJmE2EIYWVbpap9p+lgSFVfefq1txNJOYuMRv/jhOlElZH8GX6vXRYoZ/rVVvL8auuvZZIspQAAILixMVHwYGkaAqlHdI827YfQ06JE2KRJk7Ry5UqtWrVKMTExqqioUEVFhWprayV5lkTm5uZq7ty5WrNmjUpKSjR+/HhFR0frnnvukSTZbDZNnDhRU6dO1Z///Gf99a9/1b333qu+fft6d5EEvKqbn/Laqn7nwkxJORNoya+27c1sSTkAANBybEwUPFqyNA1oa+kJ6bJH22VptAGKh0UWOaIdSk9I93NkCBYtWhq5dOlSSVJmZqZP+7JlyzR+/HhJ0mOPPaba2lo99NBDOnTokK677jq98847iomJ8fZfvHixwsPDNWbMGNXW1mro0KFavny5rFZqNOAUXe1n7tOSfufCTEm5EwJZD8tMv9qylAIAgODXsDFRReXRJn/cskhyhOjGRGbD0jQEkjXMqhkZM5RXmCeLLD4zExuSY9MzpstKDUg0o0WJMMM482wKi8Wi2bNna/bs2c32iYqK0pIlS7RkyZKW3D1CUcpAz45OVeVquk6YxXM+xQ/Las2UlFPg62GZ6VdbMyXlAABA6zRsTPTgymJZ5Dvya/iZb9boVArlmwBL0xBoWSlZWpS5qMnNGqZnTKc+HU6r1btGAn4RZpWyF5zYNbKZIVH2fP/s+GSipJwZitSb6VdbMyXlAABA62WnJWrpvemNfuxzsPmNqTQsTXPWOJusE2aRRfZoO0vT0K6yUrI0JHmIip3FOlBzQD2ieyg9IZ2ZYDgjEmEwv9QcaczLnt0jT67RFZvkSYKl+mmXRpMk5cyytbiZfrU1U1IOAACcm+y0RA1LdQSs/APOjKVpMAtrmFX9Hf0DHQaCTIuK5QMBk5oj5ZZI4/4o3fmC59/cXf5Lgp0cx5iXpdhTfo2MTfK0+yEeMxWpb/jV1mHznWnlsEX5ZVZag4aknKRGJTNZSgEAQPCxhlk04KJ43Xr1+RpwUTyf4SbUsDQtITrBp90ebdeizEUsTQNgWswIQ/AIs0q9bwh0FJ5kV5+Rnt0hq/d7aoKlDPTP8kz51rkKk1sZYX9Tgr6RU+dpm7uP3Cfy2/6qh2WWX21ZSgEAAOBfLE0DEIxIhAGtEcCkXEOdqxFh2zQr4mUlWb6d+bXPiNOcY/dpgzvDr/WwGn61DTSzJOXMJJA7iwIA0Fout4vkSpBgaRqAYEMiDAgyGb3jdHfXnZp77KlG5xw6qKURT+nxiMeU0fsW/wdnAmZJyplBoHcWBQCgNQrKCprcCW5GxgyW2wEAzhk1woAgY5VbsyJeliSdOrGn4XhWxMuyyu3nyGAmDTuLnlpPrmFn0fyS8gBFBgBA8wrKCpRXmOeTBJMkZ41TeYV5KigrCFBkAICOgkQYEGzKtqpzbUWjJFiDMIvUubbCU8MMAeNyG/rgy6/11s6v9MGXX8vlbmo/y/a779PtLCp5dhb1Z0wAAJyJy+3S/G3zfXYgbNDQtmDbArncLn+HBgDoQFgaCQSb6v1n7tOSfmhzgV6S2JKdRVlGCgAwi2JncaOZYCczZKiipkLFzmJqUgEAWo0ZYUCw6Wpv235oU2ZYkni2O4b6a2dRswnkbD0AQPMO1Bxo034AADSFGWFAsEkZKMUmSVXlUpOL3yye8ykD/R1ZyDvTkkSLPEsSh6U62nXnxrPdMdSfO4uaRaBn6wEAmtcjukeb9gMAoCnMCAOCTZhVyl5w4uDUZMqJ4+z5nn7wq5YsSWxPGb3jlGiLavTX0cAiT/Ino3dcu8ZhNmaYrQcAaF56Qrrs0XZZmvkEs8giR7RD6Qnpfo4MANCRkAgDglFqjjTmZSn2lBkssUme9tScwMQV4syyJNEaZtGs0amSmk2Vatbo1HadlWY2bCAAAOZnDbNqRsYMSWqUDGs4np4xXVZ+7AMAnAMSYUCwSs2RckukcX+U7nzB82/uLpJgAWSmJYnZaYlaem+6HDbf+3LYorT03vSQWwZoltl6AIDTy0rJ0qLMRUqITvBpt0fbtShzkbJSsgIUGQCgo6BGmIm53C4VO4t1oOaAekT3UHpCOr+AwVeYVep9Q6CjwAkNSxIrKo82V71NDj8uScxOS9SwVIe2lR6U8/BRJcR47juUZoI1MMtsPQDAmWWlZGlI8hDGwQgYvocBHRuJMJMqKCvQ/G3zfbaQtkfbNSNjBr+EASbVsCTxwZXFssh3K4NALUm0hlk04KJ4v92fWZlpth5gZu+9955+/etfq6ioSOXl5VqzZo1uu+02nz6fffaZpk+frs2bN8vtduuKK67Qa6+9pl69ekmS6urqNG3aNP3hD39QbW2thg4dqueee049e/YMwCNCsLKGWdXf0T/QYSAE8T0M6PhYGmlCBWUFyivM83nzlSRnjVN5hXkqKCsIUGQAzoQliebEBgLA2Tly5IiuuuoqPfvss02e//LLLzVo0CD16dNHhYWF+vjjj/WLX/xCUVHfvufl5uZqzZo1Wr16tbZs2aLq6mqNGjVKLpfLXw8DAFqF72FAaLAYhhF0lYGrqqpks9lUWVmp2NjYQIfTplxul0a8PqLRm28DiyyyR9uVf2c+03MBE3O5DZYkmkzDrpFS07P1SFSGho48hmhrFoul0Yywu+++WxEREVqxYkWT16msrFSPHj20YsUK3XXXXZKkffv2KTk5WevXr9eIESPOeL+8RgACge9hQPA72zEEM8JMpthZ3OybryQZMlRRU6FiZ7EfowLQUg1LEm+9+nwNuCieJJgJmG22nstt6IMvv9ZbO7/SB19+zY6VMD232623335bl156qUaMGKGEhARdd911evPNN719ioqKdOzYMQ0fPtzblpSUpLS0NG3durXJ262rq1NVVZXPBQD8je9hQOigRpjJHKg50Kb9AIQ4t0sq2ypV75e62qWUgZ5NFkKUWTYQyC8p15x1u312sky0RWnW6FRmpcG0nE6nqqurNX/+fD3xxBNasGCB8vPzdccdd2jTpk268cYbVVFRoU6dOqlbt24+17Xb7aqoqGjydufNm6c5c+b44yEAQLP4HgaEDhJhJtMjukeb9gMQwnavlfKnS1X7vm2LTZKyF0ipOYGLK8ACvYFAwxLNU+d/VVQe1YMri1miCdNyu92SpFtvvVU//elPJUlXX321tm7dqueff1433nhjs9c1DEMWS9MJ55kzZyovL897XFVVpeTk5DaMHADOjO9hQOhgaaTJpCekyx5tl6WZks4WWeSIdig9Id3PkQFBwu2SSt+Xdv2f5193iBZn3r1Weu0+3ySYJFWVe9p3rw1MXCHO5TY0Z93uRkkw6du6ZXPW7fbrMkmWaOJsde/eXeHh4UpNTfVpv/zyy/Xvf/9bkuRwOFRfX69Dhw759HE6nbLb7U3ebmRkpGJjY30uAIJHR/kc4XsYEDqYEWYy1jCrZmTMUF5hniyyyDjp61LDm/L0jOkUaASaYqYZUIFckuh2eZ6HZtMtFil/htRnpN9iYvMAj22lB32WQ57KkFReeVTbSg/6ZdYaSzTREp06dVL//v31+eef+7R/8cUXSklJkST169dPERER2rhxo8aMGSNJKi8vV0lJiRYuXOj3mAGz6Wifhx3pc4TvYUDoIBFmQlkpWVqUuUjzt833Kdhoj7ZresZ0ZaVkBTA6oAlmqEPVMAPq1ORPwwyoMS/7LxkW6IRc2dbGM8F8GFLVV55+vW9o93A60iD5XDkPN58Ea02/c8ESTTSlurpa//jHP7zHpaWl2rlzp+Li4tSrVy89+uijuuuuuzR48GANGTJE+fn5WrdunQoLCyVJNptNEydO1NSpUxUfH6+4uDhNmzZNffv2VVYW4xeEto72edgRP0f4HgaEBothGEE3dzVUttV2uV0qdhbrQM0B9YjuofSEdH6BgPkEOukjeRJxT6WdJvlj8cSUu6v9E3TNJeQaptn7IyG36/+k1yeeud+dL0h9v9uuoTQ3SG747TsYB8nn4oMvv9b3f//hGfv94UfXt+uMMJfb0KAF7zY7O80iz26aW6bfFNQzFZoSKmOI1iosLNSQIUMatY8bN07Lly+XJL344ouaN2+e9u7dq8suu0xz5szRrbfe6u179OhRPfroo1q1apVqa2s1dOhQPffcc2dd94vXCB1RR/s87OifI3wPA4LT2Y4hmBFmYtYwq/o7+gc6DKB5ZpmFZZYZUGZZkti16To8re7XSmeqh2WRpx7WsFRHUA6SWyOjd5wSbVGqqDza5PPS8MUho3dcu8ZhtiWaMI/MzEyd6TfSCRMmaMKECc2ej4qK0pIlS7RkyZK2Dg8ISh3x87Cjf47wPQzo2CiWD6B1zpj0kSfp449i9dX7z9ynJf1aqyUJufaUMtAzA66ZYq+eGXLne/q1o5YMkkOFNcyiWaM9hcZPfXUajmeNTm33L0JmWqIJAB1dR/w85HMEQDAjEQagdcyS9JFMMwPKNAm5MKtnaaqkZtMt2fPbfZkog+SmZaclaum96XLYonzaHbYovy2NSYiJOnOnFvQDADSvI34e8jkCIJixNBJA65gl6SN9OwOqqlxNz1A7USOsnWdAmSYhJ3mWpI55uZn6bfP9smSVQXLzstMSNSzVEbCdw8yyRBMAQkFH/DzkcwRAMCMRBqB1zJT0aZgB9dp98gy9Th6S+W8GlGkScg1Sczz1yAK0oyeD5NOzhlkCVjelYYnmgyuLm/s/xi9LNAEgFHTEz0M+RwAEM5ZGAmgdk9Sh8mqYARV7yrKy2CT/Fe03yZLERjH1vsGzO2TvG/x632aph4WmmWGJJgA0y+2SSt/37IRc+r5/ao62k476ecjnCNA+XG6Xtlds1/p/rtf2iu1yBfH7n1lZjDNtDWRCbKsNmIR310ipyd8C/ZWAOpnbFbAZUF671zaxJPF8vy1JNJv8knLNWbfbp1Bwoi1Ks0anMkg2AZfbCNgSzUDEwhjC/HiN0PTnaJLnx6Yg/hztqJ+HZvocAYJdQVmB5m+br/0135aXsUfbNSNjhrJSsgIYWXA42zEEiTAA54akT9PMkJAzEVMNks3y2pglDpPw1xdExhDmx2vUCh3p/cT7I9upX1EC+CNbGzLV5yEAUykoK1BeYZ6MU97/LCfe/xZlLiIZdgYkwgD4T0cagKNjM8ssA7PEYRL5JeV6cGVxc19723SJDWMI8+M1aqGO9H7idklPpZ1mV+oTtTZzdzHOANChuNwujXh9hM9MsJNZZJE92q78O/Nl5f2vWWc7hqBGGIBzF8A6VAgSZqj10jDL4NQvWFXlnvbda0MrDpNwuQ3NWbe7yQLSDW1z1u2Wyx10v9sB7a+jvZ+UbT1NEkySDKnqK08/AOhAip3FzSbBJMmQoYqaChU7i/0YVcdFIgwA0L52r/X8wv/SKOn1iZ5/n0rz7xc0t8szY+J06Zb8Ge2foDNLHCayrfSgz3LIUxmSyiuPalvpQf8FBQSDjvh+Ut38l8BW9QOAIHGg5kCb9sPpkQgDALQfs8xWMMssA7PEYSLOw80nwVrTDwgZHfH9pKu9bfsBQJDoEd2jTfvh9EiEAQDah5lmK5hlloFZ4jCRhJioNu0HhIyO+H6SMtBTA0zNFY+3eDbkSRnoz6gAoN2lJ6TLHm33FsY/lUUWOaIdSk9I93NkHROJMABA+zDTbAWzzDIwSxwmktE7Tom2qNN97VWizbOzGoCTdMT3kzCrp8i/pMbJsBPH2fOpRQqgw7GGWTUjY4YkNUqGNRxPz5hOofw2QiIMANA+zDRbwSyzDMwSh4lYwyyaNTpVUrNfezVrdKqsYc09Z0CI6qjvJ6k50piXpdhTdoqNTfK0B9tOmABwlrJSsrQoc5ESohN82u3Rdi3KXKSslKwARdbxhAc6AABAB2Wm2QoNswxeu0+eL40nL9f04ywDs8RhMtlpiVp6b7rmrNvtUzjfYYvSrNGpyk5LPM21gRDVkd9PUnOkPiM9M4ar93s+J1IGBudjAUzE5Xap2FmsAzUH1CO6h9IT0plhZDJZKVkakjyE16mdWQzDCLr9yKuqqmSz2VRZWanY2NhAhwMAaIrb5dkdsqpcTdcJs3h+4c/d5b8vN7vXeuqWnbxkM/Z8z5dFf84yMEscJuNyG9pWelDOw0eVEONZDtnWM8EYQ5gfr1EL8X4C4CwUlBVo/rb52l/z7Ux8e7RdMzJmMNMIHcbZjiFIhAEA2k/DrpGSmpytEIhlLm6XOWYZmCWOEMMYwvx4jVqB95PgwWuFACgoK1BeYZ6MU36YbKg9xbI7dBRnO4ZocY2w9957T6NHj1ZSUpIsFovefPNNn/Pjx4+XxWLxuVx//fU+ferq6jRlyhR1795dXbp0UU5Ojvbu3dvSUAAAZmfGWi9hVqn3DVLf73r+DdQXELPEYSZul1T6vrTr/zz/+mNHUaAj4P0kOOxe65kp/dIo6fWJnn+fSvO0A+3E5XZp/rb5jZJgkrxtC7YtkIvPXLQ3E43zWlwj7MiRI7rqqqt0//33684772yyT3Z2tpYtW+Y97tSpk8/53NxcrVu3TqtXr1Z8fLymTp2qUaNGqaioSFYrH9wA0KFQ6wVno8nlXUmeGkgs7wIQ7LwzpE9JRlSVe9rZCADtpNhZ7LMc8lSGDFXUVKjYWaz+jv5+jAwhxWTjvBYnwm6++WbdfPPNp+0TGRkph8PR5LnKykq98MILWrFihbKyPNMvV65cqeTkZBUUFGjEiBEtDQkAYHYNsxWApvAFEUBH5nZ5vgA2WS/TkGSR8md4fjTiRyK0sQM1B9q0H9BiJ8Z5LhkqjorUAatVPVwupVeVyxqgcV6Ll0aejcLCQiUkJOjSSy/Vj370IzmdTu+5oqIiHTt2TMOHD/e2JSUlKS0tTVu3bm3y9urq6lRVVeVzgf+43C5tr9iu9f9cr+0V25k2CwBoO2f8gijPF0Q+ewAEq7KtvrMgGjGkqq88/YA21iO6R5v2A1rkxDivIDpKI5KTNCHRrukJ3TUh0a4RyYkqiO4ckHFei2eEncnNN9+s733ve0pJSVFpaal+8Ytf6KabblJRUZEiIyNVUVGhTp06qVu3bj7Xs9vtqqioaPI2582bpzlz5rR1qDgLZtpdhO1+AaADaskXRGYVAghG1c0vS2tVP6AF0hPSZY+2y1njbLJOmEUW2aPtSk9ID0B06PDKtqrg+CHlJXRv9NfntFqVlxCvRc7/KMvP47w2T4Tddddd3v9OS0vTtddeq5SUFL399tu64447mr2eYRiyWJreIn3mzJnKy8vzHldVVSk5ObntgkaTmttdxFnjVF5hnl93FzFTQg4A0Ib4ggigo+tqb9t+QAtYw6yakTFDeYV5ssji892uYdfI6RnTmWCAduE6XK758d08f3Wn5HsMi0UWw9CC+G4acrhc/vwLbJelkSdLTExUSkqK/v73v0uSHA6H6uvrdejQIZ9+TqdTdnvTb/6RkZGKjY31uaB9mWl3kYaE3KlFHhsScgVlBe0eAwCgnfAFEUBHlzLQUxRaTf/oL1mk2PM9/YB2kJWSpUWZi5QQneDTbo+2+3VyA0JPseuw9oeHN0qCNTAsFlWEh6vYddivcbV7Iuzrr7/Wnj17lJiYKEnq16+fIiIitHHjRm+f8vJylZSUaOBA3vzNoiW7i7QnMyXkAADtgC+IADq6MKtnZzRJjd/rThxnz6dQPtpVVkqWNty5QS+OeFELbligF0e8qPw780mCoV0diD27HzLPtl9bafHSyOrqav3jH//wHpeWlmrnzp2Ki4tTXFycZs+erTvvvFOJiYn617/+pccff1zdu3fX7bffLkmy2WyaOHGipk6dqvj4eMXFxWnatGnq27evdxdJBJ5Zdhdhu18A6OAaviC+dp88XwhP/uGDL4gAOojUHM/OaPnTfesixiZ53uPYGRd+YA2z8p0JftWjy9kluM62X1tpcSJsx44dGjJkiPe4oXbXuHHjtHTpUu3atUsvv/yyvvnmGyUmJmrIkCF69dVXFRMT473O4sWLFR4erjFjxqi2tlZDhw7V8uXLZbUyyDULs+wuYpaEHAB0WG6XpxB99X7P8sOUgf5POvEFEUAoSM2R+owM/HsuAPjJt5s17G9yf3CLJHu0w++bNbQ4EZaZmSnDaOoheGzYsOGMtxEVFaUlS5ZoyZIlLb17+IlZdhcxS0IOADqk3WubST4t8H/yiS+IAEJBmJUdcAGEDN/NGmSazRravUYYglPDH6z07R9oA3/+wTYk5E6N4eRYHAHIIANA0Nu91rMc8eQkmCRVlXvad6/1f0wNXxD7ftfzL0kwAACAoGbGzRpaPCMMoaPhD3b+tvk+dbrs0XZNz5julz9YtvsFgHbgdnlmgjU5Sd2QZJHyZ3hmaPH+CgAAgHOQlZKlIclDVOws1oGaA+oR3UPpCekB+x5PIgynZYY/WDMk5ACgQynb2ngmmA9DqvrK048lPAAAADhHZtqsgUQYzsgMf7BmSMgBQIdR3fxuvK3qBwAAAAQJEmEIGmZIyAFAh9D1LLeoPtt+AAAAQJCgWD4AAKEmZaBnd8hmNiKRLFLs+Z5+AAAAQAdCIgwAgFATZpWyF5w4ODUZduI4ez6F8gEAANDhkAgDACAUpeZIY16WYhN922OTPO2pOYGJCwAAAGhH1AgDACBUpeZIfUZ6does3u+pCZYykJlgAAAA6LBIhAEAEMrCrFLvGwIdBQAAAOAXLI0EAAAAAABASCARBgAAAAAAgJDA0kgAHYrL7VKxs1gHag6oR3QPpSeky0q9IwAAAACASIQB6EAKygo0f9t87a/Z722zR9s1I2OGslKyAhgZAAAAAMAMWBoJtILL7dL2iu1a/8/12l6xXS63K9AhBZQZno+CsgLlFeb5JMEkyVnjVF5hngrKCvweEwAAAADAXJgRBrQQs458meH5cLldmr9tvgwZjc4ZMmSRRQu2LdCQ5CEht0ySpaIAAAAA8C1mhAEtwKwjX2Z5PoqdxY1iOJkhQxU1FSp2FvslHrMoKCvQiNdHaMKGCZr+/nRN2DBBI14fEXJ/pwAAAADQgEQYcJbONOtIkhZsW+DXZYGBXJJopufjQM2BNu3XEZglSQkguLz33nsaPXq0kpKSZLFY9Oabbzbb98c//rEsFoueeuopn/a6ujpNmTJF3bt3V5cuXZSTk6O9e/e2b+AAAABniUQYcJbMNuso0LN9zPR89Iju0ab9gp2ZkpQAgsuRI0d01VVX6dlnnz1tvzfffFMfffSRkpKSGp3Lzc3VmjVrtHr1am3ZskXV1dUaNWqUXC7ecwAAbcztkkrfl3b9n+dfxrc4C9QIA86SmWYdNcz2OTXR0TDbZ1Hmonavz2Wm5yM9IV32aLucNc4mkz8WWWSPtis9Ib3dYzGDliQp+zv6+zEyAGZ388036+abbz5tn6+++kqTJ0/Whg0bNHLkSJ9zlZWVeuGFF7RixQplZXk+h1auXKnk5GQVFBRoxIgR7RY7ACDE7F4r5U+XqvZ92xabJGUvkFJzAhcXmuRyG9pWelDOw0eVEBOljN5xsoZZAhILiTDgLJll1pFZCsOb5fmQJGuYVTMyZiivME8WWXyeG4s8b67TM6aHTJF4MyUpAXQsbrdbY8eO1aOPPqorrrii0fmioiIdO3ZMw4cP97YlJSUpLS1NW7dubTIRVldXp7q6Ou9xVVVV+wQPAGfJdfy4/vbRBtUe+kqdu52vPteNkDWcr86msnut9Np90qnfiarKPe1jXiYZZiL5JeWas263yiuPetsSbVGaNTpV2WmJfo+HpZHAWWqYddSQWDmVRRY5oh3tPuvILEsSzfJ8NMhKydKizEVKiE7wabdH2/0yQ+5UgazfZqYkJYCOZcGCBQoPD9fDDz/c5PmKigp16tRJ3bp182m32+2qqKho8jrz5s2TzWbzXpKTk9s8bgA4W3/d8JL+88SlumLjPbp2x6O6YuM9+s8Tl+qvG14KdGho4HZ5ZoI1MTHA25Y/I3iXSXaw5Z75JeV6cGWxTxJMkioqj+rBlcXKLyn3e0yktYGzZJZZR2aZ7WOW5+NkWSlZGpI8RMXOYh2oOaAe0T2UnpDu95lgBWUFmr9tvk/C0h5t14yMGX5JyLFUFEB7KCoq0tNPP63i4mJZLC1bymAYRrPXmTlzpvLy8rzHVVVVJMMABMRfN7ykq7aeSPSf9JbVw/haPbY+rL9KumbEuIDEhpOUbfVdDtmIIVV95enX+wa/hdUmOthyT5fb0Jx1u5tNWVokzVm3W8NSHX5dJsmMMKAFzDDryEyzfczwfJzKGmZVf0d/3XLhLerv6B+QJFigd2tsSFJKajRjLxSXigJoG++//76cTqd69eql8PBwhYeHq6ysTFOnTtUFF1wgSXI4HKqvr9ehQ4d8rut0OmW325u83cjISMXGxvpcAMDfXMePK+mDOZKkU7+PNxwnfjBHruPH/RwZGqlufnVMq/qZRcNyz1OTfA3LPXevDUxc52Bb6cFGM8FOZkgqrzyqbaUH/ReUmBEGtFigZx2ZbbZPoJ8PMzFL/Tbp2yRlUzPTpmdMD8hSUf5GgOA2duxYbwH8BiNGjNDYsWN1//33S5L69euniIgIbdy4UWPGjJEklZeXq6SkRAsXLvR7zABwtv720QZdoa/VTNUPhVkkh77Wpx9t0BXfGdl0J/hH16Z/WGl1PzM443JPi2e5Z5+RUhCNoZ2Hm0+CtaZfWyERBrRCw6yjQN232ZYkBvL5MBOz7dZoliRloJeKAjh71dXV+sc//uE9Li0t1c6dOxUXF6devXopPj7ep39ERIQcDocuu+wySZLNZtPEiRM1depUxcfHKy4uTtOmTVPfvn0bJdEAdBBul2cJWvV+T+IhZWBQfVFvUHvoqzbth3aUMtCzXLCqXE0njiye8ykD/R1Z63XQ5Z4JMVFt2q+tkAgDgpDZZvvAwyz1204W6CRlw1LRU2fJNSwVDdRGBoFODpopDuBkO3bs0JAhQ7zHDbW7xo0bp+XLl5/VbSxevFjh4eEaM2aMamtrNXToUC1fvlxWK3/fQIfTgeoZde52fpv2QzsKs3r+xl67T54pfCePM09M6cueH1wJ2Q663DOjd5wSbVGqqDzaXMpSDluUMnrH+TUui2EYTcVjalVVVbLZbKqsrKSOBEIaX6TNZXvFdk3YMOGM/V4c8WJIzKBzuV0a8fqIZmfJNSzjzb8z329/t2aZnWaWOEIRYwjz4zUCgkRDPaNGX29PJCLGvBxUyTDX8eP6zxOXqofxdaMaYZLkNiSnJV49fv6FrOHMJzGFJhOx53uSYEH0tyfJszvkS6PO3G/cH4NqRpj07a6RUpMpSy29N13ZaYltcl9nO4agWD4QxAJdGB6+Guq3nVqgvoFFFjmiHSGzW2NLlor6gxk2MjBTHAAAtNoZ6xnJU8/I7fJnVOfEGh6ufQNmSfIkvU7WcFw+YFZQJ8FcbkMffPm13tr5lT748mu5Tn2gwSY1R8ot8SSH7nzB82/uruBLgknfLvdsrkidLJ4kXzAt9zwhOy1RS+9Nl8Pmu/zRYYtq0yRYSwTv/8UAYDJmrN8WSGZaKmqWjQzMEsepMTGzFADQIh20ntE1I8bpr5KSPpgju772tjst8SofMEvXjBgXuODOUX5Jueas2+2zg1+iLUqzRqcGJBHRZsKsQfU31qwTyz2N1+6TId8ZS2550mOWYFvueZLstEQNS3VoW+lBOQ8fVUKMZzmktanpl35AIgwA2hD1277VI7pHm/Y7F2bZyMAscTRgiSYAoFU6aD0jyZMMcw39gT79aINqD32lzt3OV5/rRsgRxDPBGpamnfozXEXlUT24sjhgs3LgK9/dX2/WP6L/inhZSZaD3vYKI16/PDZWt7n7KzuA8Z0ra5hFAy6KP3NHPwje/5sBwKTMsltjoDUsFXXWOJucAdVQI8wfS0XNMjvNLHFI5tzIAAAQJLra27afyVjDw3XFd0YGOow24XIbmrNud7OLWC2S5qzbrWGpjoDNzsG3r1O5O0Pv1F2rjLC/KUHfyKnztM3dR4bC9DGvU5uhRhgAtAPqt327VFRSo7pp/l4qapbZaWaJ40xLNCVpwbYFcgVRbRcAgB914HpGHc220oM+yyFPZUgqrzyqbaUHm+2D9nfy6+RWmD50p2qte6A+dKfKrTBepzZGIgwA0G4aloomRCf4tNuj7X6dcWSWjQzMEofZNjIAAASZE/WMPE79TDtxHMT1jDoS5+Hmk2Ct6Yf2wevkXyyNBAC0KzMsFTXLRgZmicNMSzRPRuF+AAgiqTnSmJc9u0eeXDg/NsmTBAvGnfs6oISYb3fqC5O70ZI794m5MSf3CyYut2GaAuzn4myf/2B9ncyGRBgAoN01LBUNJLNsZGCGOMyyRPNkFO4HgCCUmiP1GenZHbJ6v6cmWMpAZoKZSEbvOCXaonTV4fcaFWHfZ8Tpl8fu08cxg5XROy6AUbZOR9oJs+F1qqg82mQ9N4skhy0qKF8nM7IYhtHU82xqVVVVstlsqqysVGxsbKDDAQAEEbPMOgpkHC63SyNeH3HGjQzy78z3S0zNFe5vmCXXlstoGUOYH68RALStv254SVdtfViSdPJkKfeJj92PBz6ja0aMC0BkrdfcTpgNDy8Yd8JseEySfB5XMD8mfzvbMQQ1wgAAIcUsGxkEMg4zbWRA4X4AANqR26VrPp0vi8U3CSZ5ji0Wi675dIEURJ+zZ9oJU/LshOlyB9ecn+y0RC29N10Om+/yR4ctiiRYG2NpJAAAIcgMSzSllhXuD/TyWgAAgk7ZVqlq32n29zSkqq88/Xrf4NfQWqslO2EOuCjef4G1gey0RA1LdXSIumdmRiIMAIAQZYaNDMxauB8AgA6huvkfm1rVzwQ6+g6L1jBL0CXwgg2JMAAAQligNzIwY+F+AAA6jK72tu1nAuywiHNFjTAAABAw6QnpskfbG9Uqa2CRRY5oh9IT0v0cGQAAHUDKQCk2STrN4kjFnu/pFyQadlg8zSNSIjss4jRIhAEAgIAxU+F+AAA6nDCrlL3gxMGpqaMTx9nzPf2ChDXMolmjUyU1+4g0a3QqdbXQrBYnwt577z2NHj1aSUlJslgsevPNN33OG4ah2bNnKykpSZ07d1ZmZqY+/fRTnz51dXWaMmWKunfvri5duignJ0d79+49pwcCAACCU0Ph/oToBJ92e7RdizIX+a1wPwAAHVJqjjTmZSn2lF0HY5M87ak5gYnrHLDDIs5Fi2uEHTlyRFdddZXuv/9+3XnnnY3OL1y4UIsWLdLy5ct16aWX6oknntCwYcP0+eefKyYmRpKUm5urdevWafXq1YqPj9fUqVM1atQoFRUVyWoNnkw0AABoG2Yo3A8AQIeVmiP1GenZHbJ6v6cmWMrAoJoJdip2WERrWQzDMFp9ZYtFa9as0W233SbJMxssKSlJubm5mj59uiTP7C+73a4FCxboxz/+sSorK9WjRw+tWLFCd911lyRp3759Sk5O1vr16zVixIgz3m9VVZVsNpsqKysVGxvb2vABAECIYQxhfrxGAACgNc52DNGmNcJKS0tVUVGh4cOHe9siIyN14403auvWrZKkoqIiHTt2zKdPUlKS0tLSvH1OVVdXp6qqKp8LAAAAAAAA0BJtmgirqKiQJNntvluv2u1277mKigp16tRJ3bp1a7bPqebNmyebzea9JCcnt2XYAAAAAAAACAHtsmukxeK7JtcwjEZtpzpdn5kzZ6qystJ72bNnT5vFCgAAAAAAgNDQpokwh8MhSY1mdjmdTu8sMYfDofr6eh06dKjZPqeKjIxUbGyszwU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+      "text/plain": [
+       "<Figure size 1500x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.rcParams[\"figure.figsize\"] = [15,5]\n",
+    "fig, axes = plt.subplots(ncols=2, nrows=1)\n",
+    "tools.plot_average_vps(axes[0], \"fixed\", train_blocked_fixed)\n",
+    "tools.plot_average_vps(axes[0], \"random\", train_blocked_random)\n",
+    "tools.plot_average_vps(axes[0], \"blocked\", train_blocked_blocked)\n",
+    "\n",
+    "tools.plot_average_vps(axes[1], \"fixed\", test_blocked_fixed)\n",
+    "tools.plot_average_vps(axes[1], \"random\", test_blocked_random)\n",
+    "tools.plot_average_vps(axes[1], \"blocked\", test_blocked_blocked)\n",
+    "\n",
+    "axes[0].set_title(\"Train\")\n",
+    "axes[1].set_title(\"Test\")\n",
+    "plt.xlabel(\"Block\")\n",
+    "plt.ylabel(\"RTsum\")\n",
+    "plt.legend()\n",
+    "plt.savefig(\"RT.png\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "497bd4dc-943a-41f3-a694-3f4b8f049dee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>train</th>\n",
+       "      <th>test</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>vp05</th>\n",
+       "      <td>0.755556</td>\n",
+       "      <td>0.836667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vp02</th>\n",
+       "      <td>0.842222</td>\n",
+       "      <td>0.983333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vp09</th>\n",
+       "      <td>0.806667</td>\n",
+       "      <td>0.923333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vp11</th>\n",
+       "      <td>0.842222</td>\n",
+       "      <td>0.870000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vp07</th>\n",
+       "      <td>0.733333</td>\n",
+       "      <td>0.956667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vp08</th>\n",
+       "      <td>0.711111</td>\n",
+       "      <td>0.830000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vp21</th>\n",
+       "      <td>0.871111</td>\n",
+       "      <td>0.470000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vp06</th>\n",
+       "      <td>0.726667</td>\n",
+       "      <td>0.950000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vp03</th>\n",
+       "      <td>0.813333</td>\n",
+       "      <td>0.923333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vp04</th>\n",
+       "      <td>0.808889</td>\n",
+       "      <td>0.983333</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         train      test\n",
+       "vp05  0.755556  0.836667\n",
+       "vp02  0.842222  0.983333\n",
+       "vp09  0.806667  0.923333\n",
+       "vp11  0.842222  0.870000\n",
+       "vp07  0.733333  0.956667\n",
+       "vp08  0.711111  0.830000\n",
+       "vp21  0.871111  0.470000\n",
+       "vp06  0.726667  0.950000\n",
+       "vp03  0.813333  0.923333\n",
+       "vp04  0.808889  0.983333"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "condition = \"fixed\"\n",
+    "df = pd.DataFrame([tools.total_accuracy(data[condition][vp], procedures) for vp in data[condition].keys()], index=data[condition].keys(), columns=[\"train\", \"test\"])\n",
+    "df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "143f7497-2c6c-492c-85ab-da3d2cf2a828",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>overall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>vp14</th>\n",
+       "      <td>0.992</td>\n",
+       "      <td>0.976</td>\n",
+       "      <td>0.992</td>\n",
+       "      <td>0.976</td>\n",
+       "      <td>0.400</td>\n",
+       "      <td>0.600</td>\n",
+       "      <td>0.968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vp18</th>\n",
+       "      <td>0.976</td>\n",
+       "      <td>0.976</td>\n",
+       "      <td>0.960</td>\n",
+       "      <td>0.392</td>\n",
+       "      <td>0.600</td>\n",
+       "      <td>0.984</td>\n",
+       "      <td>0.904</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vp15</th>\n",
+       "      <td>0.992</td>\n",
+       "      <td>0.992</td>\n",
+       "      <td>0.960</td>\n",
+       "      <td>0.392</td>\n",
+       "      <td>0.592</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.928</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vp20</th>\n",
+       "      <td>0.992</td>\n",
+       "      <td>0.376</td>\n",
+       "      <td>0.952</td>\n",
+       "      <td>0.976</td>\n",
+       "      <td>0.976</td>\n",
+       "      <td>0.560</td>\n",
+       "      <td>0.784</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vp10</th>\n",
+       "      <td>0.968</td>\n",
+       "      <td>0.360</td>\n",
+       "      <td>0.592</td>\n",
+       "      <td>0.984</td>\n",
+       "      <td>0.984</td>\n",
+       "      <td>0.992</td>\n",
+       "      <td>0.712</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vp13</th>\n",
+       "      <td>0.384</td>\n",
+       "      <td>0.960</td>\n",
+       "      <td>0.928</td>\n",
+       "      <td>0.560</td>\n",
+       "      <td>0.992</td>\n",
+       "      <td>0.968</td>\n",
+       "      <td>0.568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vp17</th>\n",
+       "      <td>0.392</td>\n",
+       "      <td>0.968</td>\n",
+       "      <td>0.584</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.992</td>\n",
+       "      <td>0.648</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vp12</th>\n",
+       "      <td>0.992</td>\n",
+       "      <td>0.592</td>\n",
+       "      <td>0.392</td>\n",
+       "      <td>0.976</td>\n",
+       "      <td>0.960</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.016</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vp19</th>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.992</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.576</td>\n",
+       "      <td>0.992</td>\n",
+       "      <td>0.992</td>\n",
+       "      <td>0.848</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vp16</th>\n",
+       "      <td>0.976</td>\n",
+       "      <td>0.600</td>\n",
+       "      <td>0.376</td>\n",
+       "      <td>0.976</td>\n",
+       "      <td>0.992</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.752</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          1      2      3      4      5      6  overall\n",
+       "vp14  0.992  0.976  0.992  0.976  0.400  0.600    0.968\n",
+       "vp18  0.976  0.976  0.960  0.392  0.600  0.984    0.904\n",
+       "vp15  0.992  0.992  0.960  0.392  0.592  1.000    0.928\n",
+       "vp20  0.992  0.376  0.952  0.976  0.976  0.560    0.784\n",
+       "vp10  0.968  0.360  0.592  0.984  0.984  0.992    0.712\n",
+       "vp13  0.384  0.960  0.928  0.560  0.992  0.968    0.568\n",
+       "vp17  0.392  0.968  0.584  1.000  1.000  0.992    0.648\n",
+       "vp12  0.992  0.592  0.392  0.976  0.960  1.000    0.016\n",
+       "vp19  1.000  0.992  0.000  0.576  0.992  0.992    0.848\n",
+       "vp16  0.976  0.600  0.376  0.976  0.992  1.000    0.752"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "condition = \"random\"\n",
+    "proc_accs = [\n",
+    "    tools.count_correct(data[condition][vp], data[condition][vp].keys(), procedures)\n",
+    "    for vp in data[condition].keys()\n",
+    "]\n",
+    "for vp in proc_accs:\n",
+    "    for proc in vp.keys():\n",
+    "        vp[proc] /= len(next(iter(data[condition].values())).keys())\n",
+    "df = pd.DataFrame(proc_accs, index=data[condition].keys())\n",
+    "df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52d6e2e6-999d-47a2-a829-cee5042d5c68",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/experiment/analysis/tools.py b/experiment/analysis/tools.py
index d32ccd3..1dffc9a 100644
--- a/experiment/analysis/tools.py
+++ b/experiment/analysis/tools.py
@@ -2,6 +2,9 @@
 
 import pickle
 from copy import deepcopy
+import matplotlib.pyplot as plt
+import numpy as np
+
 
 def unpickle(pkl):
     with open(pkl, "rb") as f:
@@ -9,6 +12,73 @@ def unpickle(pkl):
     return data
 
 
+def fix_vp(data, procedures):
+    procs = deepcopy(procedures)
+    if data["train_0"]["procedure_order"] == data["test_0"]["procedure_order"]:
+        keys = list(data["train_0"].keys())
+        keys.remove("procedure_order")
+        keys.remove("water_sample")
+        for key in keys:
+            procs.remove(key)
+        proc_from = keys[2]
+        proc_to = procs[0]
+        for train in [x for x in data.keys() if x.startswith("train")]:
+            vp = deepcopy(data[train])
+            vp[proc_to] = vp.pop(proc_from)
+            data[train] = vp
+    return data
+
+def block_vps(data, condition):
+    blocked_vps = {}
+    for vp in data[condition].keys():
+        blocked_vps[vp] = blocked_time(data[condition][vp])
+    return blocked_vps
+
+
+def blocked_time(vp):
+    key_stem = list(vp.keys())[0].split("_")[0]
+
+    trial_count = len(vp.keys())
+    block_size = 5
+    block_count = trial_count / block_size
+
+    result = {}
+    sum_time = 0
+    block_i = 0
+    for trial in range(trial_count):
+        if trial % 5 == 0:
+            sum_time = 0
+            block_i += 1
+        sum_time += sum_time_over_trial(vp[f"{key_stem}_{trial}"])
+        result[block_i] = sum_time
+    return result
+
+
+def sum_time_over_trial(trial):
+    total_time = 0
+    for proc in trial.keys():
+        if proc != "procedure_order" and proc != "water_sample":
+            total_time += trial[proc]["time"]
+    return total_time
+
+
+def plot_vp(ax, data_dict):
+    x = data_dict.keys()
+    y = data_dict.values()
+
+    ax.scatter(x, y)
+
+
+def plot_average_vps(ax, label, blocked_vps):
+    xlist = [list(blocked_vps[x].keys()) for x in blocked_vps]
+    ylist = [list(blocked_vps[x].values()) for x in blocked_vps]
+    x = xlist[0]
+    yarray = np.array(ylist)
+    y = np.average(yarray, axis=0)
+
+    ax.scatter(x, y, label=label)
+
+
 def count_correct(vp, trials, procedures):
     trials_correct = {}
     for proc in procedures:
@@ -52,9 +122,11 @@ def train_test_split(data):
                     if string in trial:
                         new_dict[cond][vp][trial] = data[cond][vp][trial]
         return new_dict
+
     data_train = delete_trials(data, "train")
     data_test = delete_trials(data, "test")
 
     return data_train, data_test
 
+
 print("imported tools")