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backend/app/analysis/charts.py

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+"""Matplotlib-based chart generation for paper figures."""
+
+import base64
+from io import BytesIO
+from typing import Dict, List
+
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+
+matplotlib.rcParams["font.sans-serif"] = ["DejaVu Sans"]
+matplotlib.rcParams["axes.unicode_minus"] = False
+
+
+def _to_base64(fig: matplotlib.figure.Figure) -> str:
+    buf = BytesIO()
+    fig.savefig(buf, format="png", dpi=150, bbox_inches="tight")
+    buf.seek(0)
+    return base64.b64encode(buf.read()).decode("utf-8")
+
+
+def heatmap(data: Dict[str, Dict[str, float]], title: str = "Heatmap") -> str:
+    """Generate a heatmap from a nested dict (rows × columns)."""
+    rows = list(data.keys())
+    cols = sorted({c for row in data.values() for c in row.keys()})
+    matrix = np.array([[data[row].get(col, 0.0) for col in cols] for row in rows])
+
+    fig, ax = plt.subplots(figsize=(8, 6))
+    im = ax.imshow(matrix, cmap="YlOrRd", aspect="auto")
+    ax.set_xticks(np.arange(len(cols)))
+    ax.set_yticks(np.arange(len(rows)))
+    ax.set_xticklabels(cols)
+    ax.set_yticklabels(rows)
+    ax.set_title(title)
+
+    for i in range(len(rows)):
+        for j in range(len(cols)):
+            text = ax.text(j, i, f"{matrix[i, j]:.2f}", ha="center", va="center", color="black")
+
+    fig.colorbar(im, ax=ax)
+    encoded = _to_base64(fig)
+    plt.close(fig)
+    return encoded
+
+
+def boxplot(groups: Dict[str, List[float]], title: str = "Boxplot") -> str:
+    fig, ax = plt.subplots(figsize=(8, 6))
+    labels = list(groups.keys())
+    values = [groups[label] for label in labels]
+    ax.boxplot(values)
+    ax.set_xticklabels(labels)
+    ax.set_title(title)
+    ax.set_ylabel("Score")
+    encoded = _to_base64(fig)
+    plt.close(fig)
+    return encoded
+
+
+def grouped_bar(
+    data: Dict[str, Dict[str, float]],
+    title: str = "Grouped Bar Chart",
+) -> str:
+    fig, ax = plt.subplots(figsize=(10, 6))
+    categories = list(data.keys())
+    subcategories = sorted({sc for row in data.values() for sc in row.keys()})
+    x = np.arange(len(categories))
+    width = 0.8 / len(subcategories)
+
+    for idx, subcat in enumerate(subcategories):
+        values = [data[cat].get(subcat, 0.0) for cat in categories]
+        ax.bar(x + idx * width, values, width, label=subcat)
+
+    ax.set_xticks(x + width * (len(subcategories) - 1) / 2)
+    ax.set_xticklabels(categories)
+    ax.set_ylabel("Score")
+    ax.set_title(title)
+    ax.legend()
+    encoded = _to_base64(fig)
+    plt.close(fig)
+    return encoded