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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Embedding Benchmark Analysis\n",
+    "\n",
+    "Comparative evaluation of time series embedding methods for piezometric groundwater stations.  \n",
+    "**Dataset**: ~2000 French groundwater stations, daily frequency, 2010--2024.  \n",
+    "**Encoders**: MiniRocket, TS2Vec, SoftCLT, Catch22, PCA brut, Random baseline.  \n",
+    "**Input spaces**: univariate (groundwater level only) and multivariate (+ temperature, precipitation, evapotranspiration).  \n",
+    "\n",
+    "This notebook reproduces and extends the analysis from `reports/benchmark_20260318/comparison_report.md`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import pathlib\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import plotly.express as px\n",
+    "import plotly.graph_objects as go\n",
+    "from plotly.subplots import make_subplots\n",
+    "\n",
+    "pd.set_option(\"display.max_columns\", 30)\n",
+    "pd.set_option(\"display.precision\", 4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 1. Load Results\n",
+    "\n",
+    "Parse `metrics.json` and build a flat DataFrame with one row per method, containing all metrics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "METRICS_PATH = pathlib.Path(\"../reports/benchmark_20260318/data/metrics.json\")\n",
+    "\n",
+    "with open(METRICS_PATH) as f:\n",
+    "    raw = json.load(f)\n",
+    "\n",
+    "print(f\"Loaded {len(raw)} method entries.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rows = []\n",
+    "for entry in raw:\n",
+    "    row = {\n",
+    "        \"method\": entry[\"space\"],\n",
+    "        \"input_space\": entry.get(\"input_space\", \"unknown\"),\n",
+    "        \"dim\": entry[\"embedding_dim\"],\n",
+    "        \"n_stations\": entry[\"n_stations\"],\n",
+    "        # Classification — milieu_eh\n",
+    "        \"lp_balacc_milieu\": entry[\"linear_milieu\"][\"balanced_accuracy\"],\n",
+    "        \"lp_f1_milieu\": entry[\"linear_milieu\"][\"macro_f1\"],\n",
+    "        \"fisher_milieu\": entry[\"fisher_milieu\"][\"ratio\"],\n",
+    "        # Retrieval — milieu_eh\n",
+    "        \"p1_milieu\": entry[\"knn_retrieval\"][\"precision@1\"],\n",
+    "        \"p5_milieu\": entry[\"knn_retrieval\"][\"precision@5\"],\n",
+    "        \"p10_milieu\": entry[\"knn_retrieval\"][\"precision@10\"],\n",
+    "        # Clustering\n",
+    "        \"ami_milieu\": entry[\"clustering_milieu\"].get(\"ami\"),\n",
+    "        # Dynamic typology\n",
+    "        \"lp_balacc_typo\": entry[\"linear_typology\"][\"balanced_accuracy\"],\n",
+    "        \"lp_f1_typo\": entry[\"linear_typology\"][\"macro_f1\"],\n",
+    "        \"fisher_typo\": entry[\"fisher_typology\"][\"ratio\"],\n",
+    "        \"p5_typo\": entry[\"knn_typology\"][\"precision@5\"],\n",
+    "        # Spatial\n",
+    "        \"mantel_r\": entry[\"mantel_geo\"][\"r\"],\n",
+    "        \"mantel_p\": entry[\"mantel_geo\"][\"p\"],\n",
+    "        # Regression\n",
+    "        \"altitude_r2\": entry[\"regression\"][\"altitude\"][\"r2\"],\n",
+    "        \"altitude_rho\": entry[\"regression\"][\"altitude\"][\"spearman\"],\n",
+    "        # Intrinsic quality\n",
+    "        \"participation_ratio\": entry[\"participation_ratio\"],\n",
+    "        \"uniformity\": entry[\"uniformity\"],\n",
+    "    }\n",
+    "    rows.append(row)\n",
+    "\n",
+    "df = pd.DataFrame(rows)\n",
+    "\n",
+    "# Derive helper columns\n",
+    "df[\"encoder\"] = df[\"method\"].str.replace(r\" \\((uni|multi)\\)\", \"\", regex=True).str.replace(\" +W\", \"\", regex=False)\n",
+    "df[\"whitened\"] = df[\"method\"].str.contains(\"+W\", regex=False)\n",
+    "df[\"is_baseline\"] = df[\"encoder\"].isin([\"Random\", \"PCA brut\"])\n",
+    "\n",
+    "# Remove duplicate Random entries (one per space)\n",
+    "df = df.drop_duplicates(subset=[\"method\", \"input_space\"])\n",
+    "\n",
+    "print(f\"DataFrame shape: {df.shape}\")\n",
+    "df[[\"method\", \"input_space\", \"dim\", \"whitened\", \"lp_balacc_milieu\", \"p5_milieu\", \"mantel_r\", \"participation_ratio\"]].sort_values(\"lp_balacc_milieu\", ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## Color Scheme\n",
+    "\n",
+    "Consistent color mapping: one hue per encoder, lighter shade for whitened (+W) variants."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Base colors per encoder\n",
+    "ENCODER_COLORS = {\n",
+    "    \"MiniRocket\": \"#1f77b4\",     # blue\n",
+    "    \"TS2Vec\": \"#ff7f0e\",         # orange\n",
+    "    \"SoftCLT\": \"#2ca02c\",        # green\n",
+    "    \"Catch22\": \"#9467bd\",        # purple\n",
+    "    \"PCA brut\": \"#8c564b\",       # brown\n",
+    "    \"Random\": \"#7f7f7f\",         # grey\n",
+    "}\n",
+    "\n",
+    "# Lighter shades for +W variants\n",
+    "ENCODER_COLORS_W = {\n",
+    "    \"MiniRocket\": \"#aec7e8\",\n",
+    "    \"TS2Vec\": \"#ffbb78\",\n",
+    "    \"SoftCLT\": \"#98df8a\",\n",
+    "    \"Catch22\": \"#c5b0d5\",\n",
+    "    \"PCA brut\": \"#c49c94\",\n",
+    "    \"Random\": \"#c7c7c7\",\n",
+    "}\n",
+    "\n",
+    "\n",
+    "def method_color(method_name: str) -> str:\n",
+    "    \"\"\"Return color for a method name.\"\"\"\n",
+    "    encoder = method_name.replace(\" (uni)\", \"\").replace(\" (multi)\", \"\").replace(\" +W\", \"\")\n",
+    "    if \"+W\" in method_name:\n",
+    "        return ENCODER_COLORS_W.get(encoder, \"#c7c7c7\")\n",
+    "    return ENCODER_COLORS.get(encoder, \"#7f7f7f\")\n",
+    "\n",
+    "\n",
+    "def method_colors(methods: list[str]) -> list[str]:\n",
+    "    \"\"\"Return list of colors matching a list of method names.\"\"\"\n",
+    "    return [method_color(m) for m in methods]\n",
+    "\n",
+    "\n",
+    "# Common layout for publication-quality figures\n",
+    "LAYOUT_DEFAULTS = dict(\n",
+    "    template=\"plotly_white\",\n",
+    "    font=dict(family=\"Serif\", size=13),\n",
+    "    width=900,\n",
+    "    height=500,\n",
+    "    margin=dict(l=60, r=30, t=50, b=80),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 2. Classification Performance (milieu_eh)\n",
+    "\n",
+    "Linear probe balanced accuracy and macro F1 for hydrogeological environment classification.  \n",
+    "Grouped by input space (univariate vs. multivariate). The random baseline (BalAcc = 0.1147) is shown as a horizontal line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for space_label, space_val in [(\"Univariate\", \"uni\"), (\"Multivariate\", \"multi\")]:\n",
+    "    sub = df[df[\"input_space\"] == space_val].sort_values(\"lp_balacc_milieu\", ascending=False).copy()\n",
+    "    if sub.empty:\n",
+    "        continue\n",
+    "\n",
+    "    methods = sub[\"method\"].tolist()\n",
+    "    colors = method_colors(methods)\n",
+    "\n",
+    "    fig = go.Figure()\n",
+    "\n",
+    "    fig.add_trace(go.Bar(\n",
+    "        x=methods, y=sub[\"lp_balacc_milieu\"],\n",
+    "        name=\"LP Balanced Accuracy\",\n",
+    "        marker_color=colors,\n",
+    "        text=sub[\"lp_balacc_milieu\"].round(3).astype(str),\n",
+    "        textposition=\"outside\",\n",
+    "    ))\n",
+    "\n",
+    "    fig.add_trace(go.Bar(\n",
+    "        x=methods, y=sub[\"lp_f1_milieu\"],\n",
+    "        name=\"LP Macro F1\",\n",
+    "        marker_color=[c + \"99\" for c in colors],  # semi-transparent\n",
+    "        text=sub[\"lp_f1_milieu\"].round(3).astype(str),\n",
+    "        textposition=\"outside\",\n",
+    "    ))\n",
+    "\n",
+    "    # Random baseline\n",
+    "    random_balacc = df[df[\"method\"] == \"Random\"][\"lp_balacc_milieu\"].iloc[0]\n",
+    "    fig.add_hline(y=random_balacc, line_dash=\"dash\", line_color=\"#7f7f7f\",\n",
+    "                  annotation_text=f\"Random baseline ({random_balacc:.3f})\",\n",
+    "                  annotation_position=\"top left\")\n",
+    "\n",
+    "    # Highlight best method\n",
+    "    best_idx = sub[\"lp_balacc_milieu\"].idxmax()\n",
+    "    best_method = sub.loc[best_idx, \"method\"]\n",
+    "    best_val = sub.loc[best_idx, \"lp_balacc_milieu\"]\n",
+    "\n",
+    "    fig.update_layout(\n",
+    "        **LAYOUT_DEFAULTS,\n",
+    "        title=f\"Classification Performance (milieu_eh) -- {space_label} Input\",\n",
+    "        yaxis_title=\"Score\",\n",
+    "        barmode=\"group\",\n",
+    "        yaxis_range=[0, max(sub[\"lp_balacc_milieu\"].max(), sub[\"lp_f1_milieu\"].max()) * 1.2],\n",
+    "        legend=dict(x=0.75, y=0.95),\n",
+    "        annotations=[\n",
+    "            dict(\n",
+    "                x=best_method, y=best_val + 0.02,\n",
+    "                text=f\"Best: {best_method}\", showarrow=False,\n",
+    "                font=dict(size=11, color=\"#d62728\"),\n",
+    "            )\n",
+    "        ] + (fig.layout.annotations or []),\n",
+    "    )\n",
+    "\n",
+    "    fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observations**:\n",
+    "- In multivariate mode, TS2Vec achieves the highest balanced accuracy (0.489), followed by SoftCLT (0.473). Both contrastive methods benefit from the additional climate covariates.\n",
+    "- In univariate mode, all methods cluster between 0.26--0.30, with MiniRocket +W slightly ahead (0.302).\n",
+    "- F1 tracks BalAcc closely. The large gap between BalAcc and F1 reflects the class imbalance (8 classes, Porous = 48%).\n",
+    "- All learned methods substantially outperform the random baseline."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 3. Retrieval Quality (k-NN Precision@5)\n",
+    "\n",
+    "Precision@5 measures whether the 5 nearest neighbors in embedding space share the same hydrogeological label.  \n",
+    "The random baseline is the expected precision under class frequency distribution: $\\sum p_i^2 = 0.2944$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RANDOM_BASELINE_P5 = 0.2944\n",
+    "\n",
+    "for space_label, space_val in [(\"Univariate\", \"uni\"), (\"Multivariate\", \"multi\")]:\n",
+    "    sub = df[df[\"input_space\"] == space_val].sort_values(\"p5_milieu\", ascending=False).copy()\n",
+    "    if sub.empty:\n",
+    "        continue\n",
+    "\n",
+    "    methods = sub[\"method\"].tolist()\n",
+    "    colors = method_colors(methods)\n",
+    "\n",
+    "    fig = go.Figure()\n",
+    "    fig.add_trace(go.Bar(\n",
+    "        x=methods, y=sub[\"p5_milieu\"],\n",
+    "        marker_color=colors,\n",
+    "        text=sub[\"p5_milieu\"].round(3).astype(str),\n",
+    "        textposition=\"outside\",\n",
+    "    ))\n",
+    "\n",
+    "    fig.add_hline(y=RANDOM_BASELINE_P5, line_dash=\"dash\", line_color=\"#7f7f7f\",\n",
+    "                  annotation_text=f\"Random baseline ({RANDOM_BASELINE_P5})\",\n",
+    "                  annotation_position=\"top left\")\n",
+    "\n",
+    "    fig.update_layout(\n",
+    "        **LAYOUT_DEFAULTS,\n",
+    "        title=f\"Retrieval Quality: Precision@5 (milieu_eh) -- {space_label}\",\n",
+    "        yaxis_title=\"Precision@5\",\n",
+    "        yaxis_range=[0, sub[\"p5_milieu\"].max() * 1.2],\n",
+    "        showlegend=False,\n",
+    "    )\n",
+    "    fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observations**:\n",
+    "- Whitened variants (+W) consistently improve retrieval. In multivariate space, SoftCLT +W and TS2Vec +W reach P@5 > 0.52, a +14% relative gain over their raw counterparts.\n",
+    "- Whitening isotropizes the embedding, reducing hubness and making k-NN distances more meaningful.\n",
+    "- Even the raw MiniRocket (multi) embeddings already reach P@5 = 0.44, well above the random baseline of 0.29."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 4. Dynamic Typology (inertial / annual / reactive)\n",
+    "\n",
+    "Data-driven labels from lag-365 autocorrelation. Tests whether embeddings capture temporal dynamics (not geological structure).  \n",
+    "Note: labels are derived from the same time series, so this is a **consistency check**, not an independent evaluation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for space_label, space_val in [(\"Univariate\", \"uni\"), (\"Multivariate\", \"multi\")]:\n",
+    "    sub = df[df[\"input_space\"] == space_val].sort_values(\"lp_balacc_typo\", ascending=False).copy()\n",
+    "    if sub.empty:\n",
+    "        continue\n",
+    "\n",
+    "    methods = sub[\"method\"].tolist()\n",
+    "    colors = method_colors(methods)\n",
+    "\n",
+    "    fig = go.Figure()\n",
+    "\n",
+    "    fig.add_trace(go.Bar(\n",
+    "        x=methods, y=sub[\"lp_balacc_typo\"],\n",
+    "        name=\"LP Balanced Accuracy\",\n",
+    "        marker_color=colors,\n",
+    "        text=sub[\"lp_balacc_typo\"].round(3).astype(str),\n",
+    "        textposition=\"outside\",\n",
+    "    ))\n",
+    "\n",
+    "    fig.add_trace(go.Bar(\n",
+    "        x=methods, y=sub[\"lp_f1_typo\"],\n",
+    "        name=\"LP Macro F1\",\n",
+    "        marker_color=[c + \"99\" for c in colors],\n",
+    "        text=sub[\"lp_f1_typo\"].round(3).astype(str),\n",
+    "        textposition=\"outside\",\n",
+    "    ))\n",
+    "\n",
+    "    # Random baseline for typology\n",
+    "    random_typo = df[df[\"method\"] == \"Random\"][\"lp_balacc_typo\"].iloc[0]\n",
+    "    fig.add_hline(y=random_typo, line_dash=\"dash\", line_color=\"#7f7f7f\",\n",
+    "                  annotation_text=f\"Random baseline ({random_typo:.3f})\",\n",
+    "                  annotation_position=\"top left\")\n",
+    "\n",
+    "    fig.update_layout(\n",
+    "        **LAYOUT_DEFAULTS,\n",
+    "        title=f\"Dynamic Typology Performance -- {space_label} Input\",\n",
+    "        yaxis_title=\"Score\",\n",
+    "        barmode=\"group\",\n",
+    "        yaxis_range=[0, sub[\"lp_balacc_typo\"].max() * 1.15],\n",
+    "        legend=dict(x=0.75, y=0.95),\n",
+    "    )\n",
+    "    fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observations**:\n",
+    "- MiniRocket dominates the dynamic typology task in both spaces. MiniRocket +W (uni) reaches BalAcc = 0.762, the highest score in the entire benchmark.\n",
+    "- This makes sense: MiniRocket extracts random convolutional features that directly capture temporal patterns (frequency content, trend, seasonality).\n",
+    "- Contrastive methods (TS2Vec, SoftCLT) are optimized for instance discrimination, not temporal pattern classification, which explains their lower performance here.\n",
+    "- The gap between MiniRocket and contrastive methods is larger in univariate mode, where covariates cannot compensate."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 5. Spatial Coherence (Mantel Test)\n",
+    "\n",
+    "The Mantel statistic *r* measures the correlation between pairwise embedding distances and pairwise geographic distances.  \n",
+    "A positive *r* means that stations close in embedding space tend to be geographically close."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for space_label, space_val in [(\"Univariate\", \"uni\"), (\"Multivariate\", \"multi\")]:\n",
+    "    sub = df[df[\"input_space\"] == space_val].sort_values(\"mantel_r\", ascending=False).copy()\n",
+    "    if sub.empty:\n",
+    "        continue\n",
+    "\n",
+    "    methods = sub[\"method\"].tolist()\n",
+    "    colors = method_colors(methods)\n",
+    "\n",
+    "    fig = go.Figure()\n",
+    "    fig.add_trace(go.Bar(\n",
+    "        x=methods, y=sub[\"mantel_r\"],\n",
+    "        marker_color=colors,\n",
+    "        text=sub[\"mantel_r\"].round(4).astype(str),\n",
+    "        textposition=\"outside\",\n",
+    "    ))\n",
+    "\n",
+    "    fig.add_hline(y=0, line_dash=\"dot\", line_color=\"#7f7f7f\")\n",
+    "\n",
+    "    fig.update_layout(\n",
+    "        **LAYOUT_DEFAULTS,\n",
+    "        title=f\"Spatial Coherence: Mantel r -- {space_label}\",\n",
+    "        yaxis_title=\"Mantel r (embedding vs. geographic distance)\",\n",
+    "        showlegend=False,\n",
+    "    )\n",
+    "    fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observations**:\n",
+    "- Whitening dramatically improves spatial coherence. SoftCLT +W (multi) reaches r = 0.214, the highest overall.\n",
+    "- In multivariate mode, raw embeddings already show positive Mantel r (0.09--0.12), indicating that climate covariates encode spatial proximity.\n",
+    "- In univariate mode, some raw methods have near-zero or negative Mantel r, which means their embedding geometry has no geographic structure.\n",
+    "- The whitened multivariate embeddings (r = 0.17--0.21) confirm that post-hoc isotropization reveals latent spatial structure that was masked by dimension collapse."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 6. Intrinsic Quality\n",
+    "\n",
+    "### 6a. Participation Ratio\n",
+    "\n",
+    "The Participation Ratio (PR) measures the effective dimensionality of the embedding. A PR of *d* means the embedding variance is spread across *d* effective dimensions.  \n",
+    "Higher PR = more dimensions are used (less information waste)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for space_label, space_val in [(\"Univariate\", \"uni\"), (\"Multivariate\", \"multi\")]:\n",
+    "    sub = df[df[\"input_space\"] == space_val].sort_values(\"participation_ratio\", ascending=False).copy()\n",
+    "    if sub.empty:\n",
+    "        continue\n",
+    "\n",
+    "    methods = sub[\"method\"].tolist()\n",
+    "    colors = method_colors(methods)\n",
+    "\n",
+    "    fig = go.Figure()\n",
+    "    fig.add_trace(go.Bar(\n",
+    "        x=methods, y=sub[\"participation_ratio\"],\n",
+    "        marker_color=colors,\n",
+    "        text=sub[\"participation_ratio\"].round(1).astype(str),\n",
+    "        textposition=\"outside\",\n",
+    "    ))\n",
+    "\n",
+    "    fig.update_layout(\n",
+    "        **LAYOUT_DEFAULTS,\n",
+    "        title=f\"Participation Ratio (effective dimensionality) -- {space_label}\",\n",
+    "        yaxis_title=\"Participation Ratio\",\n",
+    "        showlegend=False,\n",
+    "    )\n",
+    "    fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6b. Uniformity\n",
+    "\n",
+    "Uniformity measures how well embeddings are spread on the unit hypersphere.  \n",
+    "More negative = better spread (ideal uniform distribution on the sphere).  \n",
+    "Values near 0 indicate **dimensional collapse**: all embeddings cluster in a low-dimensional subspace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for space_label, space_val in [(\"Univariate\", \"uni\"), (\"Multivariate\", \"multi\")]:\n",
+    "    sub = df[df[\"input_space\"] == space_val].sort_values(\"uniformity\").copy()  # more negative = better\n",
+    "    if sub.empty:\n",
+    "        continue\n",
+    "\n",
+    "    methods = sub[\"method\"].tolist()\n",
+    "    colors = method_colors(methods)\n",
+    "\n",
+    "    fig = go.Figure()\n",
+    "    fig.add_trace(go.Bar(\n",
+    "        x=methods, y=sub[\"uniformity\"],\n",
+    "        marker_color=colors,\n",
+    "        text=sub[\"uniformity\"].round(2).astype(str),\n",
+    "        textposition=\"outside\",\n",
+    "    ))\n",
+    "\n",
+    "    # Highlight collapse zone\n",
+    "    fig.add_hrect(y0=-0.5, y1=0, fillcolor=\"#ff000015\", line_width=0,\n",
+    "                  annotation_text=\"Collapse zone\", annotation_position=\"top right\")\n",
+    "\n",
+    "    fig.update_layout(\n",
+    "        **LAYOUT_DEFAULTS,\n",
+    "        title=f\"Uniformity (embedding spread) -- {space_label}\",\n",
+    "        yaxis_title=\"Uniformity (more negative = better)\",\n",
+    "        showlegend=False,\n",
+    "    )\n",
+    "    fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observations**:\n",
+    "- **Dimensional collapse is severe in contrastive methods.** TS2Vec and SoftCLT (raw) have uniformity near 0 (-0.12 multi, -0.34/-0.50 uni) and PR of only 1.7--3.0. This means they collapse onto 2--3 effective dimensions out of 320.\n",
+    "- MiniRocket (raw) fares better (uniformity -2.15 to -2.39, PR ~2.3--3.0) but still underuses its 320 dimensions.\n",
+    "- **Whitening fully resolves the collapse.** All +W variants achieve PR = 64 (the target PCA dimension) and uniformity near -3.8, matching the ideal Random baseline.\n",
+    "- This explains why whitened methods improve so much on retrieval (P@5) and Mantel r: isotropic embeddings make distance-based metrics meaningful."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 7. Whitening Effect\n",
+    "\n",
+    "For each encoder, show the delta (whitened - raw) on key metrics.  \n",
+    "Positive delta means whitening helped; negative means it hurt."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Build pairs: raw vs whitened\n",
+    "deltas = []\n",
+    "for space_val in [\"uni\", \"multi\"]:\n",
+    "    sub = df[df[\"input_space\"] == space_val]\n",
+    "    raw_methods = sub[~sub[\"whitened\"] & ~sub[\"is_baseline\"]]\n",
+    "    for _, raw_row in raw_methods.iterrows():\n",
+    "        w_name = raw_row[\"method\"] + \" +W\"\n",
+    "        w_row = sub[sub[\"method\"] == w_name]\n",
+    "        if w_row.empty:\n",
+    "            continue\n",
+    "        w_row = w_row.iloc[0]\n",
+    "        deltas.append({\n",
+    "            \"encoder\": raw_row[\"method\"],\n",
+    "            \"space\": space_val,\n",
+    "            \"delta_balacc_milieu\": w_row[\"lp_balacc_milieu\"] - raw_row[\"lp_balacc_milieu\"],\n",
+    "            \"delta_f1_milieu\": w_row[\"lp_f1_milieu\"] - raw_row[\"lp_f1_milieu\"],\n",
+    "            \"delta_p5_milieu\": w_row[\"p5_milieu\"] - raw_row[\"p5_milieu\"],\n",
+    "            \"delta_mantel\": w_row[\"mantel_r\"] - raw_row[\"mantel_r\"],\n",
+    "            \"delta_balacc_typo\": w_row[\"lp_balacc_typo\"] - raw_row[\"lp_balacc_typo\"],\n",
+    "            \"delta_pr\": w_row[\"participation_ratio\"] - raw_row[\"participation_ratio\"],\n",
+    "            \"delta_uniformity\": w_row[\"uniformity\"] - raw_row[\"uniformity\"],\n",
+    "        })\n",
+    "\n",
+    "df_delta = pd.DataFrame(deltas)\n",
+    "df_delta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scatter: method vs delta on key metrics\n",
+    "metrics_to_plot = [\n",
+    "    (\"delta_balacc_milieu\", \"Delta LP BalAcc (milieu_eh)\"),\n",
+    "    (\"delta_p5_milieu\", \"Delta Precision@5 (milieu_eh)\"),\n",
+    "    (\"delta_mantel\", \"Delta Mantel r\"),\n",
+    "    (\"delta_balacc_typo\", \"Delta LP BalAcc (dynamic typology)\"),\n",
+    "]\n",
+    "\n",
+    "fig = make_subplots(\n",
+    "    rows=2, cols=2,\n",
+    "    subplot_titles=[t for _, t in metrics_to_plot],\n",
+    "    vertical_spacing=0.15,\n",
+    "    horizontal_spacing=0.12,\n",
+    ")\n",
+    "\n",
+    "for idx, (col, title) in enumerate(metrics_to_plot):\n",
+    "    row, c = divmod(idx, 2)\n",
+    "    row += 1\n",
+    "    c += 1\n",
+    "\n",
+    "    labels = df_delta[\"encoder\"] + \" (\" + df_delta[\"space\"] + \")\"\n",
+    "    colors_list = [method_color(m) for m in df_delta[\"encoder\"]]\n",
+    "\n",
+    "    fig.add_trace(\n",
+    "        go.Bar(\n",
+    "            x=labels, y=df_delta[col],\n",
+    "            marker_color=colors_list,\n",
+    "            text=df_delta[col].round(3).astype(str),\n",
+    "            textposition=\"outside\",\n",
+    "            showlegend=False,\n",
+    "        ),\n",
+    "        row=row, col=c,\n",
+    "    )\n",
+    "    fig.add_hline(y=0, line_dash=\"dot\", line_color=\"#7f7f7f\", row=row, col=c)\n",
+    "\n",
+    "fig.update_layout(\n",
+    "    template=\"plotly_white\",\n",
+    "    font=dict(family=\"Serif\", size=11),\n",
+    "    width=1000,\n",
+    "    height=700,\n",
+    "    title=\"Effect of Whitening (+W) on Key Metrics\",\n",
+    "    margin=dict(l=50, r=30, t=80, b=80),\n",
+    ")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observations**:\n",
+    "- Whitening **always improves** Precision@5 and Mantel r. The effect is strongest for contrastive methods that suffer from dimensional collapse.\n",
+    "- On classification (LP BalAcc), whitening has mixed effects: it slightly helps MiniRocket but can slightly hurt TS2Vec and SoftCLT. The linear probe can compensate for anisotropy, so whitening is less critical here.\n",
+    "- On dynamic typology, whitening has a small positive effect for MiniRocket but a small negative effect for contrastive methods.\n",
+    "- The main benefit of whitening is for **distance-based evaluation** (k-NN, Mantel), not for **linear evaluation** (probes)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 8. Uni vs Multi Comparison\n",
+    "\n",
+    "For each encoder (MiniRocket, TS2Vec, SoftCLT), compare univariate vs. multivariate performance side by side."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Filter to encoders available in both spaces (exclude Catch22, PCA brut, Random)\n",
+    "encoders_both = [\"MiniRocket\", \"TS2Vec\", \"SoftCLT\"]\n",
+    "metrics_compare = [\n",
+    "    (\"lp_balacc_milieu\", \"LP BalAcc (milieu_eh)\"),\n",
+    "    (\"p5_milieu\", \"P@5 (milieu_eh)\"),\n",
+    "    (\"lp_balacc_typo\", \"LP BalAcc (dynamic typology)\"),\n",
+    "    (\"mantel_r\", \"Mantel r\"),\n",
+    "]\n",
+    "\n",
+    "fig = make_subplots(\n",
+    "    rows=2, cols=2,\n",
+    "    subplot_titles=[t for _, t in metrics_compare],\n",
+    "    vertical_spacing=0.18,\n",
+    "    horizontal_spacing=0.12,\n",
+    ")\n",
+    "\n",
+    "for idx, (metric_col, metric_label) in enumerate(metrics_compare):\n",
+    "    row, c = divmod(idx, 2)\n",
+    "    row += 1\n",
+    "    c += 1\n",
+    "\n",
+    "    for space_val, pattern_suffix in [(\"uni\", \"(uni)\"), (\"multi\", \"(multi)\")]:\n",
+    "        vals = []\n",
+    "        for enc in encoders_both:\n",
+    "            method_name = f\"{enc} {pattern_suffix}\"\n",
+    "            match = df[(df[\"method\"] == method_name) & (df[\"input_space\"] == space_val)]\n",
+    "            if not match.empty:\n",
+    "                vals.append(match.iloc[0][metric_col])\n",
+    "            else:\n",
+    "                vals.append(None)\n",
+    "\n",
+    "        fig.add_trace(\n",
+    "            go.Bar(\n",
+    "                x=encoders_both, y=vals,\n",
+    "                name=space_val.capitalize(),\n",
+    "                marker_color=\"#1f77b4\" if space_val == \"uni\" else \"#ff7f0e\",\n",
+    "                marker_opacity=0.8,\n",
+    "                text=[f\"{v:.3f}\" if v is not None else \"\" for v in vals],\n",
+    "                textposition=\"outside\",\n",
+    "                showlegend=(idx == 0),  # only show legend once\n",
+    "            ),\n",
+    "            row=row, col=c,\n",
+    "        )\n",
+    "\n",
+    "fig.update_layout(\n",
+    "    template=\"plotly_white\",\n",
+    "    font=dict(family=\"Serif\", size=11),\n",
+    "    width=1000,\n",
+    "    height=700,\n",
+    "    title=\"Univariate vs. Multivariate: Side-by-Side Comparison\",\n",
+    "    barmode=\"group\",\n",
+    "    margin=dict(l=50, r=30, t=80, b=60),\n",
+    "    legend=dict(x=0.85, y=1.0),\n",
+    ")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Quantify the multivariate gain\n",
+    "print(\"Multivariate gain (multi - uni) for raw encoders:\")\n",
+    "print(\"=\" * 65)\n",
+    "for enc in encoders_both:\n",
+    "    uni = df[(df[\"method\"] == f\"{enc} (uni)\") & (df[\"input_space\"] == \"uni\")]\n",
+    "    multi = df[(df[\"method\"] == f\"{enc} (multi)\") & (df[\"input_space\"] == \"multi\")]\n",
+    "    if uni.empty or multi.empty:\n",
+    "        continue\n",
+    "    uni, multi = uni.iloc[0], multi.iloc[0]\n",
+    "    print(f\"\\n{enc}:\")\n",
+    "    for col, label in [(\"lp_balacc_milieu\", \"LP BalAcc (milieu)\"), (\"p5_milieu\", \"P@5\"),\n",
+    "                       (\"lp_balacc_typo\", \"LP BalAcc (typo)\"), (\"mantel_r\", \"Mantel r\")]:\n",
+    "        delta = multi[col] - uni[col]\n",
+    "        pct = delta / max(abs(uni[col]), 1e-6) * 100\n",
+    "        print(f\"  {label:30s}: {delta:+.4f} ({pct:+.1f}%)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observations**:\n",
+    "- Multivariate input provides a **massive boost for geological classification** (milieu_eh): TS2Vec goes from 0.267 (uni) to 0.489 (multi), an 83% relative improvement.\n",
+    "- For dynamic typology, multivariate input is **neutral or slightly negative**: temporal dynamics are primarily captured by the groundwater level itself, not by climate covariates.\n",
+    "- Spatial coherence (Mantel r) improves with multivariate input because ERA5 covariates (temperature, precipitation) carry geographic information.\n",
+    "- The climate covariates provide complementary geological and spatial signal that the univariate series alone cannot capture."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 9. Summary Ranking Table\n",
+    "\n",
+    "Rank all methods across key metrics. Lower mean rank = better overall performance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rank_metrics = {\n",
+    "    \"LP BalAcc (milieu)\": (\"lp_balacc_milieu\", True),\n",
+    "    \"LP F1 (milieu)\": (\"lp_f1_milieu\", True),\n",
+    "    \"P@5 (milieu)\": (\"p5_milieu\", True),\n",
+    "    \"LP BalAcc (typo)\": (\"lp_balacc_typo\", True),\n",
+    "    \"Mantel r\": (\"mantel_r\", True),\n",
+    "    \"Participation Ratio\": (\"participation_ratio\", True),\n",
+    "}\n",
+    "\n",
+    "ranking_tables = {}\n",
+    "for space_label, space_val in [(\"Univariate\", \"uni\"), (\"Multivariate\", \"multi\")]:\n",
+    "    sub = df[df[\"input_space\"] == space_val].copy()\n",
+    "    if sub.empty:\n",
+    "        continue\n",
+    "\n",
+    "    rank_df = pd.DataFrame(index=sub[\"method\"])\n",
+    "    for metric_label, (col, ascending) in rank_metrics.items():\n",
+    "        rank_df[metric_label] = sub[col].rank(ascending=not ascending, method=\"min\").values\n",
+    "\n",
+    "    rank_df[\"Mean Rank\"] = rank_df.mean(axis=1)\n",
+    "    rank_df = rank_df.sort_values(\"Mean Rank\")\n",
+    "    ranking_tables[space_label] = rank_df\n",
+    "\n",
+    "    print(f\"\\n{'=' * 80}\")\n",
+    "    print(f\"Overall Ranking -- {space_label} Input Space\")\n",
+    "    print(f\"{'=' * 80}\")\n",
+    "    print(rank_df.round(2).to_string())\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Color-coded heatmap of ranks\n",
+    "for space_label, rank_df in ranking_tables.items():\n",
+    "    # Exclude \"Mean Rank\" column for the heatmap body\n",
+    "    display_df = rank_df.drop(columns=[\"Mean Rank\"])\n",
+    "\n",
+    "    fig = go.Figure(data=go.Heatmap(\n",
+    "        z=display_df.values,\n",
+    "        x=display_df.columns.tolist(),\n",
+    "        y=display_df.index.tolist(),\n",
+    "        colorscale=[\n",
+    "            [0.0, \"#2ca02c\"],   # rank 1 = green\n",
+    "            [0.5, \"#ffffbf\"],   # middle = yellow\n",
+    "            [1.0, \"#d62728\"],   # worst = red\n",
+    "        ],\n",
+    "        text=display_df.values.round(0).astype(int).astype(str),\n",
+    "        texttemplate=\"%{text}\",\n",
+    "        textfont=dict(size=12),\n",
+    "        hovertemplate=\"Method: %{y}<br>Metric: %{x}<br>Rank: %{z}<extra></extra>\",\n",
+    "        colorbar=dict(title=\"Rank\"),\n",
+    "    ))\n",
+    "\n",
+    "    # Add Mean Rank as annotation on the right\n",
+    "    for i, (method, mean_rank) in enumerate(rank_df[\"Mean Rank\"].items()):\n",
+    "        fig.add_annotation(\n",
+    "            x=len(display_df.columns) - 0.3,\n",
+    "            y=method,\n",
+    "            text=f\"  Avg: {mean_rank:.2f}\",\n",
+    "            showarrow=False,\n",
+    "            xanchor=\"left\",\n",
+    "            font=dict(size=11, color=\"black\"),\n",
+    "        )\n",
+    "\n",
+    "    fig.update_layout(\n",
+    "        template=\"plotly_white\",\n",
+    "        font=dict(family=\"Serif\", size=12),\n",
+    "        width=900,\n",
+    "        height=50 + 40 * len(display_df),\n",
+    "        title=f\"Method Ranking Heatmap -- {space_label} Input\",\n",
+    "        margin=dict(l=200, r=120, t=50, b=60),\n",
+    "        xaxis=dict(side=\"top\"),\n",
+    "        yaxis=dict(autorange=\"reversed\"),\n",
+    "    )\n",
+    "    fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 10. Key Takeaways\n",
+    "\n",
+    "1. **Contrastive methods (TS2Vec, SoftCLT) excel at geological classification in multivariate mode.** TS2Vec multi achieves the highest LP BalAcc (0.489) for milieu_eh, a 70% relative improvement over the univariate case. The climate covariates provide geological signal that the groundwater level alone cannot capture.\n",
+    "\n",
+    "2. **MiniRocket dominates temporal dynamics.** For the data-driven dynamic typology (inertial/annual/reactive), MiniRocket +W (uni) reaches BalAcc = 0.762, outperforming all contrastive methods by 15+ points. Random convolutional features are well suited to capturing frequency content and autocorrelation structure.\n",
+    "\n",
+    "3. **Dimensional collapse is the main failure mode of contrastive learning.** Raw TS2Vec and SoftCLT embeddings collapse onto 2--3 effective dimensions (out of 320), with uniformity near 0. This makes k-NN retrieval and distance-based metrics unreliable.\n",
+    "\n",
+    "4. **Post-hoc whitening (ZCA + PCA to 64d) consistently improves retrieval and spatial metrics.** P@5 gains of +6 to +8 points; Mantel r doubles or triples. Whitening resolves the dimensional collapse without requiring retraining.\n",
+    "\n",
+    "5. **No single encoder wins everywhere.** The best choice depends on the downstream task: TS2Vec (multi) for geology-aware embeddings, MiniRocket (uni) +W for temporal pattern classification, SoftCLT (multi) +W for the best overall retrieval and spatial coherence.\n",
+    "\n",
+    "6. **All learned methods substantially outperform the random baseline** (BalAcc 0.115), confirming that the embeddings capture meaningful structure. Even the simplest approach (PCA brut) outperforms random on most metrics."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}