kurzti88066/Fahrsimulator_MSY2526_AI
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Michael Weig 2025-11-10 11:31:42 +01:00
parent fb29a1333e
commit 7c4fadb951
1 changed files with 57 additions and 9 deletions
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60
EDA/researchOnSubjectPerformance.ipynb
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@ -45,7 +45,8 @@
"# Connect once\n",
"oc = owncloud.Client.from_public_link(url, folder_password=password)\n",
"# File pattern\n",
"base = \"adabase-public-{num:04d}-v_0_0_2.h5py\""
"# base = \"adabase-public-{num:04d}-v_0_0_2.h5py\"\n",
"base = \"{num:04d}-*.h5py\""
]
},
{
@ -59,11 +60,25 @@
"performance_data = []\n",
"\n",
"for i in range(num_files):\n",
" file_name = base.format(num=i)\n",
" file_pattern = f\"{i:04d}-*\"\n",
" \n",
" # Get list of files matching the pattern\n",
" files = oc.list('.')\n",
" matching_files = [f.get_name() for f in files if f.get_name().startswith(f\"{i:04d}-\")]\n",
" \n",
" if matching_files:\n",
" file_name = matching_files[0] # Take the first matching file\n",
" local_tmp = f\"tmp_{i:04d}.h5\"\n",
" \n",
" oc.get_file(file_name, local_tmp)\n",
" print(f\"{file_name} geöffnet\")\n",
" else:\n",
" print(f\"Keine Datei gefunden für Muster: {file_pattern}\")\n",
" # file_name = base.format(num=i)\n",
" # local_tmp = f\"tmp_{i:04d}.h5\"\n",
"\n",
" # oc.get_file(file_name, local_tmp)\n",
" # print(f\"{file_name} geöffnet\")\n",
"\n",
" # check SIGNALS table for AUs\n",
" with pd.HDFStore(local_tmp, mode=\"r\") as store:\n",
@ -111,7 +126,7 @@
" combination_cols = sorted([c for c in performance_df.columns if c.startswith(\"STUDY_\")])\n",
" final_cols = [\"subjectID\", \"overall_score\"] + combination_cols\n",
" performance_df = performance_df[final_cols]\n",
" performance_df.to_csv(\"au_performance.csv\", index=False)\n",
" performance_df.to_csv(\"n_au_performance.csv\", index=False)\n",
"\n",
" print(f\"\\nGesamt Subjects mit Action Units: {len(performance_df)}\")\n",
"else:\n",
@ -135,9 +150,42 @@
"metadata": {},
"outputs": [],
"source": [
"with pd.HDFStore(local_tmp, mode=\"r\") as store:\n",
" performance = store.select(\"PERFORMANCE\")\n",
"performance"
"with pd.HDFStore(\"tmp_0000.h5\", mode=\"r\") as store:\n",
" md = store.select(\"META\")\n",
"print(\"File 0:\")\n",
"print(md)\n",
"with pd.HDFStore(\"tmp_0001.h5\", mode=\"r\") as store:\n",
" md = store.select(\"META\")\n",
"print(\"File 1\")\n",
"print(md)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8067036b",
"metadata": {},
"outputs": [],
"source": [
"pd.set_option('display.max_columns', None)\n",
"pd.set_option('display.max_rows', None)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f18e7385",
"metadata": {},
"outputs": [],
"source": [
"with pd.HDFStore(\"tmp_0000.h5\", mode=\"r\") as store:\n",
" md = store.select(\"SIGNALS\", start=0, stop=1)\n",
"print(\"File 0:\")\n",
"md.head()\n",
"# with pd.HDFStore(\"tmp_0001.h5\", mode=\"r\",start=0, stop=1) as store:\n",
"# md = store.select(\"SIGNALS\")\n",
"# print(\"File 1\")\n",
"# print(md.columns)"
]
}
],