{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8fb02733",
   "metadata": {},
   "source": [
    "Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebd32616-1e11-4b15-805e-481e010e03fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install pyocclient\n",
    "import yaml\n",
    "import owncloud\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import hashlib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c20cee7c",
   "metadata": {},
   "source": [
    "Connection to Owncloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4c94558",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load credentials\n",
    "with open(\"../login.yaml\") as f:\n",
    "    cfg = yaml.safe_load(f)\n",
    "   \n",
    "url, password = cfg[0][\"url\"], cfg[1][\"password\"]\n",
    "\n",
    "# Connect once\n",
    "oc = owncloud.Client.from_public_link(url, folder_password=password)\n",
    "# File pattern\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07c03d07",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_files = 30\n",
    "performance_data = []\n",
    "base = \"adabase-public-{num:04d}-v_0_0_2.h5py\"  # remote name pattern\n",
    "for i in range(num_files):\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",
    "\n",
    "    # quick checksum to detect identical downloads\n",
    "    with open(local_tmp, \"rb\") as fh:\n",
    "        file_hash = hashlib.sha1(fh.read()).hexdigest()\n",
    "    print(f\"File {i}: {file_name}  checksum={file_hash}\")\n",
    "\n",
    "    # check SIGNALS table for AUs\n",
    "    with pd.HDFStore(local_tmp, mode=\"r\") as store:\n",
    "        cols = store.select(\"SIGNALS\", start=0, stop=1).columns\n",
    "    au_cols = [c for c in cols if c.startswith(\"AU\")]\n",
    "    if not au_cols:\n",
    "        print(f\"Subject {i} enthält keine AUs\")\n",
    "        continue\n",
    "\n",
    "    # load performance table (make a copy)\n",
    "    with pd.HDFStore(local_tmp, mode=\"r\") as store:\n",
    "        perf_df = store.select(\"PERFORMANCE\").copy()\n",
    "\n",
    "    # print(f\"Subject {i}: PERFORMANCE rows={len(perf_df)}\")\n",
    "    # print(perf_df.head(3).to_string(index=False))\n",
    "\n",
    "    f1_cols = [c for c in [\"AUDITIVE F1\", \"VISUAL F1\", \"F1\"] if c in perf_df.columns]\n",
    "    if not f1_cols:\n",
    "        print(f\"Subject {i}: keine F1-Spalten gefunden\")\n",
    "        continue\n",
    "\n",
    "    # drop rows that have all F1s NaN (no valid score for that combo)\n",
    "    perf_df = perf_df.dropna(subset=f1_cols, how=\"all\")\n",
    "    if perf_df.empty:\n",
    "        print(f\"Subject {i}: keine gültigen F1-Daten nach Filter\")\n",
    "        continue\n",
    "\n",
    "    subject_entry = {\"subjectID\": i}\n",
    "    combo_means = []\n",
    "\n",
    "    for _, row in perf_df.iterrows():\n",
    "        study, level, phase = row[\"STUDY\"], row[\"LEVEL\"], row[\"PHASE\"]\n",
    "        col_name = f\"STUDY_{study}_LEVEL_{level}_PHASE_{phase}\"\n",
    "\n",
    "        # mean of available F1 cols for this single combination\n",
    "        vals = [float(row[c]) for c in f1_cols if pd.notna(row[c])]\n",
    "        if not vals:\n",
    "            continue\n",
    "        mean_for_combo = float(np.mean(vals))\n",
    "        subject_entry[col_name] = mean_for_combo\n",
    "        combo_means.append(mean_for_combo)\n",
    "\n",
    "    # overall: mean of per-combination means (not flattened raw F1s)\n",
    "    if combo_means:\n",
    "        subject_entry[\"overall_score\"] = float(np.mean(combo_means))\n",
    "        performance_data.append(subject_entry)\n",
    "        print(f\"Subject {i}: combos={len(combo_means)} overall={subject_entry['overall_score']:.4f}\")\n",
    "    else:\n",
    "        print(f\"Subject {i}: keine gültigen Kombinationen\")\n",
    "\n",
    "# build dataframe\n",
    "if performance_data:\n",
    "    performance_df = pd.DataFrame(performance_data)\n",
    "    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.reindex(columns=final_cols)  # keeps missing combo cols as NaN\n",
    "    performance_df.to_csv(\"au_performance.csv\", index=False)\n",
    "    print(f\"\\nGesamt Subjects mit Action Units: {len(performance_df)}\")\n",
    "else:\n",
    "    print(\"Keine gültigen Daten gefunden.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0bcaf065",
   "metadata": {},
   "outputs": [],
   "source": [
    "performance_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db95eea7",
   "metadata": {},
   "outputs": [],
   "source": [
    "with pd.HDFStore(local_tmp, mode=\"r\") as store:\n",
    "    performance = store.select(\"PERFORMANCE\")\n",
    "performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e17fd7eb-8600-4c31-9212-d1eeb9e74736",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def hash_perf(local_tmp):\n",
    "    with pd.HDFStore(local_tmp, mode=\"r\") as s:\n",
    "        df = s.select(\"PERFORMANCE\")\n",
    "    # hash based on data values only\n",
    "    return hashlib.sha1(pd.util.hash_pandas_object(df, index=True).values).hexdigest()\n",
    "\n",
    "hashes = []\n",
    "for i in range(5):\n",
    "    local_tmp = f\"tmp_{i:04d}.h5\"\n",
    "    try:\n",
    "        hashes.append((i, hash_perf(local_tmp)))\n",
    "    except Exception as e:\n",
    "        hashes.append((i, str(e)))\n",
    "\n",
    "print(hashes)\n"
   ]
  }
 ],
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