214 lines
6.3 KiB
Plaintext
214 lines
6.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "8fb02733",
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"metadata": {},
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"source": [
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"Imports"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "96f3b128",
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"metadata": {},
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"outputs": [],
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"source": [
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"import yaml\n",
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"import owncloud\n",
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"import pandas as pd\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c20cee7c",
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"metadata": {},
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"source": [
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"Connection to Owncloud"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c4c94558",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load credentials\n",
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"with open(\"../login.yaml\") as f:\n",
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" cfg = yaml.safe_load(f)\n",
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" \n",
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"url, password = cfg[0][\"url\"], cfg[1][\"password\"]\n",
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"\n",
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"# Connect once\n",
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"oc = owncloud.Client.from_public_link(url, folder_password=password)\n",
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"# File pattern\n",
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"# base = \"adabase-public-{num:04d}-v_0_0_2.h5py\"\n",
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"base = \"{num:04d}-*.h5py\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "07c03d07",
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"metadata": {},
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"outputs": [],
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"source": [
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"num_files = 2 # number of files to process (min: 1, max: 30)\n",
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"performance_data = []\n",
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"\n",
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"for i in range(num_files):\n",
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" file_pattern = f\"{i:04d}-*\"\n",
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" \n",
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" # Get list of files matching the pattern\n",
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" files = oc.list('.')\n",
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" matching_files = [f.get_name() for f in files if f.get_name().startswith(f\"{i:04d}-\")]\n",
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" \n",
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" if matching_files:\n",
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" file_name = matching_files[0] # Take the first matching file\n",
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" local_tmp = f\"tmp_{i:04d}.h5\"\n",
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" \n",
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" oc.get_file(file_name, local_tmp)\n",
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" print(f\"{file_name} geöffnet\")\n",
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" else:\n",
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" print(f\"Keine Datei gefunden für Muster: {file_pattern}\")\n",
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" # file_name = base.format(num=i)\n",
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" # local_tmp = f\"tmp_{i:04d}.h5\"\n",
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"\n",
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" # oc.get_file(file_name, local_tmp)\n",
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" # print(f\"{file_name} geöffnet\")\n",
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"\n",
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" # check SIGNALS table for AUs\n",
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" with pd.HDFStore(local_tmp, mode=\"r\") as store:\n",
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" cols = store.select(\"SIGNALS\", start=0, stop=1).columns\n",
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" au_cols = [c for c in cols if c.startswith(\"AU\")]\n",
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" if not au_cols:\n",
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" print(f\"Subject {i} enthält keine AUs\")\n",
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" continue\n",
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"\n",
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" # load performance table\n",
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" with pd.HDFStore(local_tmp, mode=\"r\") as store:\n",
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" perf_df = store.select(\"PERFORMANCE\")\n",
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"\n",
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" f1_cols = [c for c in [\"AUDITIVE F1\", \"VISUAL F1\", \"F1\"] if c in perf_df.columns]\n",
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" if not f1_cols:\n",
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" print(f\"Subject {i}: keine F1-Spalten gefunden\")\n",
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" continue\n",
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"\n",
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" subject_entry = {\"subjectID\": i}\n",
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" valid_scores = []\n",
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"\n",
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" # iterate rows: each (study, level, phase)\n",
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" for _, row in perf_df.iterrows():\n",
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" study, level, phase = row[\"STUDY\"], row[\"LEVEL\"], row[\"PHASE\"]\n",
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" col_name = f\"STUDY_{study}_LEVEL_{level}_PHASE_{phase}\"\n",
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"\n",
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" # collect valid F1 values among the three columns\n",
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" scores = [row[c] for c in f1_cols if pd.notna(row[c])]\n",
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" if scores:\n",
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" mean_score = float(np.mean(scores))\n",
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" subject_entry[col_name] = mean_score\n",
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" valid_scores.extend(scores)\n",
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"\n",
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" # compute overall average across all valid combinations\n",
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" if valid_scores:\n",
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" subject_entry[\"overall_score\"] = float(np.mean(valid_scores))\n",
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" performance_data.append(subject_entry)\n",
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" print(f\"Subject {i}: {len(valid_scores)} gültige Scores, Overall = {subject_entry['overall_score']:.3f}\")\n",
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" else:\n",
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" print(f\"Subject {i}: keine gültigen F1-Scores\")\n",
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"\n",
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"# build dataframe\n",
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"if performance_data:\n",
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" performance_df = pd.DataFrame(performance_data)\n",
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" combination_cols = sorted([c for c in performance_df.columns if c.startswith(\"STUDY_\")])\n",
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" final_cols = [\"subjectID\", \"overall_score\"] + combination_cols\n",
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" performance_df = performance_df[final_cols]\n",
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" performance_df.to_csv(\"n_au_performance.csv\", index=False)\n",
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"\n",
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" print(f\"\\nGesamt Subjects mit Action Units: {len(performance_df)}\")\n",
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"else:\n",
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" print(\"Keine gültigen Daten gefunden.\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0bcaf065",
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"metadata": {},
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"outputs": [],
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"source": [
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"performance_df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "db95eea7",
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"metadata": {},
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"outputs": [],
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"source": [
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"with pd.HDFStore(\"tmp_0000.h5\", mode=\"r\") as store:\n",
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" md = store.select(\"META\")\n",
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"print(\"File 0:\")\n",
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"print(md)\n",
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"with pd.HDFStore(\"tmp_0001.h5\", mode=\"r\") as store:\n",
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" md = store.select(\"META\")\n",
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"print(\"File 1\")\n",
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"print(md)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8067036b",
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"metadata": {},
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"outputs": [],
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"source": [
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"pd.set_option('display.max_columns', None)\n",
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"pd.set_option('display.max_rows', None)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f18e7385",
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"metadata": {},
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"outputs": [],
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"source": [
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"with pd.HDFStore(\"tmp_0000.h5\", mode=\"r\") as store:\n",
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" md = store.select(\"SIGNALS\", start=0, stop=1)\n",
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"print(\"File 0:\")\n",
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"md.head()\n",
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"# with pd.HDFStore(\"tmp_0001.h5\", mode=\"r\",start=0, stop=1) as store:\n",
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"# md = store.select(\"SIGNALS\")\n",
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"# print(\"File 1\")\n",
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"# print(md.columns)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "base",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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