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notebooks/01_data_exploration.ipynb

@@ -350,28 +350,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# Select one representative station per major class\n",
-    "# We pick stations with good data coverage (high nb_mois_total)\n",
-    "TARGET_CLASSES = [\"Poreux\", \"Fissure\", \"Karstique\", \"Double porosite F+P\",\n",
-    "                  \"Double porosite K+P\", \"Composite\"]\n",
-    "\n",
-    "example_stations = []\n",
-    "for cls in TARGET_CLASSES:\n",
-    "    candidates = meta_labeled[\n",
-    "        (meta_labeled[\"milieu_label\"] == cls) &\n",
-    "        (meta_labeled[\"nb_mois_total\"] > meta_labeled[\"nb_mois_total\"].quantile(0.7))\n",
-    "    ].sort_values(\"nb_mois_total\", ascending=False)\n",
-    "    if len(candidates) > 0:\n",
-    "        # Pick the station closest to the median coverage in top 30%\n",
-    "        mid = len(candidates) // 2\n",
-    "        example_stations.append(candidates.iloc[mid])\n",
-    "\n",
-    "example_ids = [s[\"code_bss\"] for s in example_stations]\n",
-    "print(f\"Selected {len(example_ids)} stations:\")\n",
-    "for s in example_stations:\n",
-    "    print(f\"  {s['code_bss']:20s}  {s['milieu_label']:30s}  ({s['nb_mois_total']} months)\")"
-   ]
+   "source": "# Select one representative station per major class\n# We pick stations with good data coverage (high nb_mois_total)\n# AND that exist in the time-series datasets with sufficient daily coverage\n# (metadata has ~4200 stations, but uni/multi only ~2000, some with sparse data)\nTARGET_CLASSES = [\"Poreux\", \"Fissure\", \"Karstique\", \"Double porosite F+P\",\n                  \"Double porosite K+P\", \"Composite\"]\n\n# Compute daily coverage per station to ensure windowing works downstream\n_station_stats = uni.dropna(subset=[\"niveau_nappe_eau\"]).groupby(\"code_bss\").agg(\n    _n_valid=(\"date\", \"count\"),\n    _date_min=(\"date\", \"min\"),\n    _date_max=(\"date\", \"max\"),\n)\n_station_stats[\"_span_days\"] = (_station_stats[\"_date_max\"] - _station_stats[\"_date_min\"]).dt.days\n_station_stats[\"_coverage\"] = _station_stats[\"_n_valid\"] / _station_stats[\"_span_days\"].clip(lower=1)\n\n# Require 4+ years of data and >80% daily coverage\n_good_stations = set(_station_stats[\n    (_station_stats[\"_span_days\"] >= 365 * 4) &\n    (_station_stats[\"_coverage\"] > 0.8)\n].index)\n\nexample_stations = []\nfor cls in TARGET_CLASSES:\n    candidates = meta_labeled[\n        (meta_labeled[\"milieu_label\"] == cls) &\n        (meta_labeled[\"code_bss\"].isin(_good_stations)) &\n        (meta_labeled[\"nb_mois_total\"] > meta_labeled[\"nb_mois_total\"].quantile(0.7))\n    ].sort_values(\"nb_mois_total\", ascending=False)\n    if len(candidates) > 0:\n        # Pick the station closest to the median coverage in top 30%\n        mid = len(candidates) // 2\n        example_stations.append(candidates.iloc[mid])\n\nexample_ids = [s[\"code_bss\"] for s in example_stations]\nprint(f\"Selected {len(example_ids)} stations:\")\nfor s in example_stations:\n    print(f\"  {s['code_bss']:20s}  {s['milieu_label']:30s}  ({s['nb_mois_total']} months)\")"
   },
   {
    "cell_type": "code",