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-rw-r--r--experiment/analysis/RT.pngbin35267 -> 35757 bytes
-rw-r--r--experiment/analysis/analysis.ipynb203
-rw-r--r--experiment/analysis/analysis.py33
-rw-r--r--experiment/analysis/tools.py6
4 files changed, 139 insertions, 103 deletions
diff --git a/experiment/analysis/RT.png b/experiment/analysis/RT.png
index 20cd0b8..3d36478 100644
--- a/experiment/analysis/RT.png
+++ b/experiment/analysis/RT.png
Binary files differ
diff --git a/experiment/analysis/analysis.ipynb b/experiment/analysis/analysis.ipynb
index b140e96..3da688b 100644
--- a/experiment/analysis/analysis.ipynb
+++ b/experiment/analysis/analysis.ipynb
@@ -21,7 +21,9 @@
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
- "import tools\n"
+ "import tools\n",
+ "\n",
+ "plt.rcParams[\"axes.prop_cycle\"] = plt.cycler(\"color\", plt.cm.tab10.colors)"
]
},
{
@@ -31,7 +33,7 @@
"metadata": {},
"outputs": [],
"source": [
- "data_path = Path(\"/home/niclas/repos/uni/thesis/experiment/data\")\n",
+ "data_path = Path(\"/home/niclas/repos/uni/master_thesis/experiment/data\")\n",
"\n",
"procedures = [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"overall\"]\n"
]
@@ -45,7 +47,7 @@
{
"data": {
"text/plain": [
- "['random', 'blocked', 'fixed']"
+ "['random', 'fixed', 'blocked']"
]
},
"execution_count": 3,
@@ -81,7 +83,8 @@
"metadata": {},
"outputs": [],
"source": [
- "condition = \"blocked\""
+ "condition = \"blocked\"\n",
+ "#print(data_train[\"fixed\"])"
]
},
{
@@ -125,13 +128,13 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 9,
"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>"
]
@@ -162,7 +165,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 13,
"id": "497bd4dc-943a-41f3-a694-3f4b8f049dee",
"metadata": {},
"outputs": [
@@ -193,54 +196,54 @@
" </thead>\n",
" <tbody>\n",
" <tr>\n",
- " <th>vp05</th>\n",
- " <td>0.755556</td>\n",
- " <td>0.836667</td>\n",
+ " <th>vp12</th>\n",
+ " <td>0.822222</td>\n",
+ " <td>0.820000</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>vp02</th>\n",
- " <td>0.842222</td>\n",
- " <td>0.983333</td>\n",
+ " <th>vp19</th>\n",
+ " <td>0.966667</td>\n",
+ " <td>0.800000</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>vp09</th>\n",
- " <td>0.806667</td>\n",
- " <td>0.923333</td>\n",
+ " <th>vp15</th>\n",
+ " <td>0.973333</td>\n",
+ " <td>0.980000</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>vp11</th>\n",
- " <td>0.842222</td>\n",
- " <td>0.870000</td>\n",
+ " <th>vp17</th>\n",
+ " <td>0.911111</td>\n",
+ " <td>0.960000</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>vp07</th>\n",
- " <td>0.733333</td>\n",
- " <td>0.956667</td>\n",
+ " <th>vp20</th>\n",
+ " <td>0.906667</td>\n",
+ " <td>0.980000</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>vp08</th>\n",
- " <td>0.711111</td>\n",
- " <td>0.830000</td>\n",
+ " <th>vp10</th>\n",
+ " <td>0.924444</td>\n",
+ " <td>0.943333</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>vp21</th>\n",
- " <td>0.871111</td>\n",
- " <td>0.470000</td>\n",
+ " <th>vp16</th>\n",
+ " <td>0.957778</td>\n",
+ " <td>0.926667</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>vp06</th>\n",
- " <td>0.726667</td>\n",
- " <td>0.950000</td>\n",
+ " <th>vp13</th>\n",
+ " <td>0.857778</td>\n",
+ " <td>0.946667</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>vp03</th>\n",
- " <td>0.813333</td>\n",
- " <td>0.923333</td>\n",
+ " <th>vp18</th>\n",
+ " <td>0.962222</td>\n",
+ " <td>0.970000</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>vp04</th>\n",
- " <td>0.808889</td>\n",
- " <td>0.983333</td>\n",
+ " <th>vp14</th>\n",
+ " <td>0.982222</td>\n",
+ " <td>0.986667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -248,25 +251,25 @@
],
"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"
+ "vp12 0.822222 0.820000\n",
+ "vp19 0.966667 0.800000\n",
+ "vp15 0.973333 0.980000\n",
+ "vp17 0.911111 0.960000\n",
+ "vp20 0.906667 0.980000\n",
+ "vp10 0.924444 0.943333\n",
+ "vp16 0.957778 0.926667\n",
+ "vp13 0.857778 0.946667\n",
+ "vp18 0.962222 0.970000\n",
+ "vp14 0.982222 0.986667"
]
},
- "execution_count": 10,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "condition = \"fixed\"\n",
+ "condition = \"random\"\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"
]
@@ -309,24 +312,24 @@
" </thead>\n",
" <tbody>\n",
" <tr>\n",
- " <th>vp14</th>\n",
- " <td>0.992</td>\n",
- " <td>0.976</td>\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>0.400</td>\n",
- " <td>0.600</td>\n",
- " <td>0.968</td>\n",
+ " <td>0.960</td>\n",
+ " <td>1.000</td>\n",
+ " <td>0.016</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",
+ " <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>vp15</th>\n",
@@ -339,6 +342,16 @@
" <td>0.928</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>vp20</th>\n",
" <td>0.992</td>\n",
" <td>0.376</td>\n",
@@ -359,6 +372,16 @@
" <td>0.712</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",
+ " <tr>\n",
" <th>vp13</th>\n",
" <td>0.384</td>\n",
" <td>0.960</td>\n",
@@ -369,44 +392,24 @@
" <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",
+ " <th>vp18</th>\n",
+ " <td>0.976</td>\n",
" <td>0.976</td>\n",
" <td>0.960</td>\n",
- " <td>1.000</td>\n",
- " <td>0.016</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>vp19</th>\n",
- " <td>1.000</td>\n",
- " <td>0.992</td>\n",
- " <td>0.000</td>\n",
- " <td>0.576</td>\n",
+ " <th>vp14</th>\n",
" <td>0.992</td>\n",
+ " <td>0.976</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>0.400</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",
+ " <td>0.968</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -414,16 +417,16 @@
],
"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",
+ "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",
"vp15 0.992 0.992 0.960 0.392 0.592 1.000 0.928\n",
+ "vp17 0.392 0.968 0.584 1.000 1.000 0.992 0.648\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",
+ "vp16 0.976 0.600 0.376 0.976 0.992 1.000 0.752\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"
+ "vp18 0.976 0.976 0.960 0.392 0.600 0.984 0.904\n",
+ "vp14 0.992 0.976 0.992 0.976 0.400 0.600 0.968"
]
},
"execution_count": 11,
@@ -469,7 +472,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.5"
+ "version": "3.12.2"
}
},
"nbformat": 4,
diff --git a/experiment/analysis/analysis.py b/experiment/analysis/analysis.py
new file mode 100644
index 0000000..bd2d2b9
--- /dev/null
+++ b/experiment/analysis/analysis.py
@@ -0,0 +1,33 @@
+import pandas as pd
+from pathlib import Path
+from pprint import pprint
+
+import tools
+
+data_path = Path("/home/niclas/repos/uni/master_thesis/experiment/data")
+
+procedures = ["1", "2", "3", "4", "5", "6", "overall"]
+
+conditions = [x.stem for x in data_path.iterdir() if x.is_dir()]
+
+data = {}
+for condition in conditions:
+ data[condition] = {}
+ for vp in (data_path / condition).iterdir():
+ data[condition][vp.stem] = tools.unpickle(vp / "vp.pkl")
+
+data_train, data_test = tools.train_test_split(data)
+print(data_train["random"]["vp12"])
+
+condition = "random"
+df = pd.DataFrame([tools.total_accuracy(data[condition][vp], procedures) for vp in data[condition].keys()], index=data[condition].keys(), columns=["train", "test"])
+
+condition = "random"
+proc_accs = [
+ tools.count_correct(data[condition][vp], data[condition][vp].keys(), procedures)
+ for vp in data[condition].keys()
+]
+for vp in proc_accs:
+ for proc in vp.keys():
+ vp[proc] /= len(next(iter(data[condition].values())).keys())
+df = pd.DataFrame(proc_accs, index=data[condition].keys())
diff --git a/experiment/analysis/tools.py b/experiment/analysis/tools.py
index 1dffc9a..cde322f 100644
--- a/experiment/analysis/tools.py
+++ b/experiment/analysis/tools.py
@@ -44,8 +44,8 @@ def blocked_time(vp):
result = {}
sum_time = 0
- block_i = 0
- for trial in range(trial_count):
+ block_i = 1
+ for trial in range(1, trial_count):
if trial % 5 == 0:
sum_time = 0
block_i += 1
@@ -119,7 +119,7 @@ def train_test_split(data):
for vp in data[cond].keys():
new_dict[cond][vp] = {}
for trial in data[cond][vp].keys():
- if string in trial:
+ if string in trial and trial != "train_0":
new_dict[cond][vp][trial] = data[cond][vp][trial]
return new_dict
generated by cgit v1.2.3 (git 2.25.1) at 2024-12-23 15:34:27 +0000