feat(eda): motor AutomaticEDA fase 4a — render fixes + keep-together + glosario clicable

Mejoras transversales del motor de render (no del contenido de capítulos):

1. Fix negrita pisa texto (PDF): _place_rich_lines mide el ancho REAL de cada
   span con las métricas de fuente del renderer (peso correcto) en vez del
   grid de ancho medio; negrita y normal en la misma línea ya no se solapan.
2. Zebra striping: filas pares sombreadas (#f6f8fa) en DataTable (PDF + PPTX),
   coherente al partir tablas largas (índice de fila lógico, no por página).
3. Keep-together: bloque Group nuevo; el renderer mide el grupo entero y lo
   mueve completo a la página/slide siguiente si no cabe, y encoge la figura
   (height_in) para dejar sitio a su título y texto. num_distr lo usa.
4. Caption siempre visible en toda figura PPTX (fallback al heading); la figura
   reserva el alto de su caption para que ambos quepan en el mismo slide.
5. Portada construida al final (con resumen agregado del análisis vía
   ctx['document_summary']) pero colocada primera por build_document.
6. Glosario: capítulo nuevo (último) + GlossaryCollector en ctx; los capítulos
   registran términos y marcan apariciones con [[term:key]]...[[/term]]. Links
   clicables reales: PDF (PyMuPDF, link GOTO) y PPTX (slide-jump nativo).
   Enganchado "entropía" en cat_distr como ejemplo end-to-end.

Funciones reutilizables delegadas a fn-constructor (tag eda):
- add_pdf_internal_links_py_datascience (PyMuPDF)
- pptx_link_run_to_slide_py_datascience (slide-jump)

Contrato docs/automatic_eda_contract.md actualizado (§1/§3/§5 + §11 nueva) con
la API de glosario, keep-together y zebra para la siguiente fase. PyMuPDF
declarado en pyproject. Suite verde (90 tests); golden titanic verificado.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

python/functions/datascience/automatic_eda/chapters/cat_distr.py

@@ -33,10 +33,23 @@ import math
 
 from .. import model
 
-CHAPTER_VERSION = "1.0.0"
+CHAPTER_VERSION = "1.1.0"
 CHAPTER_ID = "cat_distr"
 CHAPTER_TITLE = "Distribuciones categóricas"
 
+# Glossary term this chapter explains. Registered in the shared collector and
+# marked clickable on its first appearance (end-to-end glossary example —
+# mejora 6). Other chapters hook their own terms the same way (see the contract).
+_TERM_ENTROPIA_KEY = "entropia"
+_TERM_ENTROPIA_LABEL = "Entropía (de Shannon)"
+_TERM_ENTROPIA_DEF = (
+    "Medida, en bits, de cómo de repartidos están los valores de una columna "
+    "categórica. Vale 0 cuando una sola categoría concentra todas las filas "
+    "(máxima previsibilidad) y alcanza su máximo, log2(k) para k categorías "
+    "distintas, cuando todas aparecen por igual (máxima diversidad). La entropía "
+    "normalizada (entropía dividida por su máximo) la lleva al rango 0–1 para "
+    "comparar columnas con distinto número de categorías.")
+
 # Cap the number of categorical columns rendered to keep the document bounded;
 # the rest are summarized in a closing note (no silent truncation).
 MAX_COLS = 40
@@ -337,10 +350,14 @@ def _topk_table(cat: dict):
                            note=note)
 
 
-def _intro_blocks(n_rows):
+def _intro_blocks(n_rows, mark_term: bool = False):
     total = _fmt_int(n_rows)
+    # Mark the first appearance of the term as a clickable glossary jump when the
+    # term was registered (mark_term). The visible text is identical either way.
+    entropia = ("[[term:entropia]]**entropía de Shannon**[[/term]]" if mark_term
+                else "**entropía de Shannon**")
     text = (
-        "La **entropía de Shannon** mide cómo de repartidos están los valores de "
+        f"La {entropia} mide cómo de repartidos están los valores de "
         "una columna categórica, en bits. Vale 0 cuando una sola categoría "
         "concentra todas las filas (máxima previsibilidad) y alcanza su máximo, "
         "log2(k) para k categorías distintas, cuando todas aparecen por igual "
@@ -370,7 +387,15 @@ def build_cat_distr(profile: dict, ctx: dict):
         return None
 
     n_rows = profile.get("n_rows")
-    blocks = list(_intro_blocks(n_rows))
+    # Register "entropía" in the shared glossary collector (if present) and mark
+    # its first appearance clickable. End-to-end glossary example (mejora 6).
+    glossary = ctx.get("glossary")
+    mark_term = False
+    if isinstance(glossary, model.GlossaryCollector):
+        glossary.add(_TERM_ENTROPIA_KEY, _TERM_ENTROPIA_LABEL,
+                     _TERM_ENTROPIA_DEF)
+        mark_term = True
+    blocks = list(_intro_blocks(n_rows, mark_term=mark_term))
 
     rendered = cat_cols[:MAX_COLS]
     for col in rendered:

python/functions/datascience/automatic_eda/chapters/glosario.py

@@ -0,0 +1,47 @@
+"""Glossary chapter (GLOSARIO) — always the last chapter, clickable terms.
+
+Renders one entry per glossary term that the other chapters registered during
+the document build through ``ctx['glossary'].add(key, label, definition)`` (see
+``GlossaryCollector`` in ``model.py``). Each entry is a clickable destination:
+every in-text appearance a chapter marked with ``[[term:key]]texto[[/term]]``
+becomes a real jump to its entry here — PDF link annotations (PyMuPDF) and PPTX
+native slide jumps, both wired by the renderers.
+
+Returns ``None`` when no term was registered (there is nothing to show), so the
+chapter simply disappears from documents that did not mark any term.
+
+Contract: build_<id>(profile, ctx) -> Chapter | None ; CHAPTER_VERSION = "x.y.z".
+"""
+
+from __future__ import annotations
+
+from .. import model
+
+CHAPTER_VERSION = "1.0.0"
+CHAPTER_ID = "glosario"
+CHAPTER_TITLE = "Glosario"
+
+
+def build_glosario(profile: dict, ctx: dict):
+    """Build the glossary Chapter from the shared collector, or None if empty."""
+    ctx = ctx or {}
+    glossary = ctx.get("glossary")
+    if not isinstance(glossary, model.GlossaryCollector) or not glossary:
+        return None
+
+    blocks = [
+        model.Heading(text="Glosario de términos", level=1),
+        model.Markdown(text=(
+            "Definición de los términos técnicos que aparecen en el informe. "
+            "Cada término va resaltado en el texto y, al pulsarlo, salta a su "
+            "definición en esta sección.")),
+    ]
+    # One clickable destination per term, alphabetically by visible label.
+    for term in glossary.terms(by="label"):
+        blocks.append(model.GlossaryEntry(
+            key=model._safe_str(term.get("key")),
+            label=model._safe_str(term.get("label")),
+            definition=model._safe_str(term.get("definition"))))
+
+    return model.Chapter(id=CHAPTER_ID, title=CHAPTER_TITLE,
+                         version=CHAPTER_VERSION, blocks=blocks)

python/functions/datascience/automatic_eda/chapters/num_distr.py

@@ -34,7 +34,7 @@ try:
 except Exception:  # noqa: BLE001 — keep the chapter importable no matter what.
     build_boxplot_stats = None  # type: ignore[assignment]
 
-CHAPTER_VERSION = "1.0.0"
+CHAPTER_VERSION = "1.1.0"
 CHAPTER_ID = "num_distr"
 CHAPTER_TITLE = "Distribuciones numéricas"
 
@@ -278,12 +278,17 @@ def build_num_distr(profile: dict, ctx: dict):
                 box = build_boxplot_stats(numeric) or {}
             except Exception:  # noqa: BLE001 — degrade, never raise.
                 box = {}
-        blocks.append(model.Heading(text=str(name), level=2))
-        blocks.append(model.Figure(
-            make=_figure_maker(name, numeric, box),
-            caption=f"Distribución de «{name}» — histograma (media/mediana/±σ) "
-                    f"y boxplot."))
-        blocks.append(model.Markdown(text=_stats_note(name, numeric, box)))
+        # Keep the column heading, its figure and its stats note together on the
+        # same page/slide (mejora 3 — keep-together): the renderers measure the
+        # whole Group and move it whole when it would not fit.
+        blocks.append(model.Group(blocks=[
+            model.Heading(text=str(name), level=2),
+            model.Figure(
+                make=_figure_maker(name, numeric, box),
+                caption=f"Distribución de «{name}» — histograma "
+                        f"(media/mediana/±σ) y boxplot."),
+            model.Markdown(text=_stats_note(name, numeric, box)),
+        ]))
 
     return model.Chapter(id=CHAPTER_ID, title=CHAPTER_TITLE,
                          version=CHAPTER_VERSION, blocks=blocks)

python/functions/datascience/automatic_eda/chapters/num_distr_test.py

@@ -65,19 +65,33 @@ def _pdf_text(path: str) -> str:
     return re.sub(r"\s+", " ", txt)
 
 
+def _flatten(blocks):
+    """Expand keep-together Groups so the per-column heading/figure/markdown are
+    inspectable as a flat block list (the chapter wraps each column in a Group)."""
+    out = []
+    for b in blocks:
+        if getattr(b, "kind", "") == "group":
+            out.extend(_flatten(getattr(b, "blocks", []) or []))
+        else:
+            out.append(b)
+    return out
+
+
 def test_golden_chapter_estructura_y_bloques():
     ch = build_num_distr(_profile(n_numeric=2), {})
     assert ch is not None
     assert ch.id == "num_distr"
     assert ch.version == CHAPTER_VERSION
-    kinds = [b.kind for b in ch.blocks]
+    # Per-column blocks are wrapped in keep-together Groups: flatten to inspect.
+    flat = _flatten(ch.blocks)
+    kinds = [b.kind for b in flat]
     # Heading + intro Markdown, then per column: Heading + Figure + Markdown.
     assert kinds[0] == "heading"
     assert kinds[1] == "markdown"
     assert kinds.count("figure") == 2          # one figure per numeric column.
     assert kinds.count("heading") == 1 + 2     # chapter title + one per column.
     # Each figure has a lazy maker that produces a real matplotlib figure.
-    figs = [b for b in ch.blocks if b.kind == "figure"]
+    figs = [b for b in flat if b.kind == "figure"]
     fig = figs[0].make()
     assert fig is not None
     # Two stacked axes: histogram + boxplot share the figure.
@@ -90,7 +104,8 @@ def test_golden_media_mediana_sigma_y_boxplot_presentes():
     # The intro documents the three reference lines and the Tukey boxplot; the
     # per-column note carries the actual mean/median/σ numbers and the shape.
     ch = build_num_distr(_profile(n_numeric=1, extra_categorical=False), {})
-    md_texts = " ".join(b.text for b in ch.blocks if b.kind == "markdown")
+    md_texts = " ".join(b.text for b in _flatten(ch.blocks)
+                        if b.kind == "markdown")
     assert "media" in md_texts and "mediana" in md_texts
     assert "±1σ" in md_texts or "σ" in md_texts
     assert "boxplot" in md_texts.lower()
@@ -126,7 +141,8 @@ def test_anti_corte_muchas_columnas_pdf_y_pptx():
     # 8 numeric columns + long note text: nothing may be cut. Every column
     # heading must survive in both the PDF text and the PPTX deck.
     ch = build_num_distr(_profile(n_numeric=8), {})
-    names = [b.text for b in ch.blocks if b.kind == "heading" and b.level == 2]
+    names = [b.text for b in _flatten(ch.blocks)
+             if b.kind == "heading" and b.level == 2]
     assert len(names) == 8
     with tempfile.TemporaryDirectory() as d:
         pdf = os.path.join(d, "num.pdf")

python/functions/datascience/automatic_eda/chapters/portada.py

@@ -17,7 +17,7 @@ from datetime import datetime, timezone
 
 from .. import model
 
-CHAPTER_VERSION = "1.0.0"
+CHAPTER_VERSION = "1.1.0"
 CHAPTER_ID = "portada"
 CHAPTER_TITLE = "Portada"
 
@@ -67,6 +67,53 @@ def _fmt_int(v) -> str:
         return str(v)
 
 
+def _fmt_pct(value) -> str:
+    """Format a percentage that may arrive as a 0–1 fraction or a 0–100 number."""
+    if value is None:
+        return "—"
+    try:
+        v = float(value)
+    except (TypeError, ValueError):
+        return str(value)
+    if 0 < v <= 1.0:
+        v *= 100.0
+    return f"{v:.1f}%"
+
+
+def _summary_blocks(summary) -> list:
+    """Mini-summary of the rest of the analysis, shown on the cover (mejora 5).
+
+    The cover is built AFTER the body (``build_document`` passes the aggregated
+    ``ctx['document_summary']``), so it can reflect what the analysis found:
+    shape, column types, quality flags and which chapters were included. Returns
+    an empty list when there is no summary (the cover degrades to its metadata
+    table only)."""
+    if not isinstance(summary, dict) or not summary:
+        return []
+    rows = []
+    n_num = summary.get("n_numeric")
+    n_cat = summary.get("n_categorical")
+    if n_num is not None or n_cat is not None:
+        rows.append(("Columnas numéricas / categóricas",
+                     f"{_fmt_int(n_num)} / {_fmt_int(n_cat)}"))
+    if summary.get("duplicate_pct") is not None:
+        rows.append(("Filas duplicadas", _fmt_pct(summary.get("duplicate_pct"))))
+    if summary.get("null_cell_pct") is not None:
+        rows.append(("Celdas nulas", _fmt_pct(summary.get("null_cell_pct"))))
+    titles = summary.get("chapter_titles") or []
+    if titles:
+        rows.append(("Capítulos del informe", _fmt_int(len(titles))))
+
+    blocks = [model.Heading(text="Resumen del análisis", level=2)]
+    if rows:
+        blocks.append(model.KVTable(rows=rows))
+    if titles:
+        bullets = "\n".join(f"- {model._safe_str(t)}" for t in titles)
+        blocks.append(model.Markdown(
+            text="Este informe incluye los siguientes capítulos:\n" + bullets))
+    return blocks
+
+
 def _fmt_date_eu(value) -> str:
     """Format a date/ISO string as European DD/MM/AAAA HH:mm (UI convention).
 
@@ -152,5 +199,8 @@ def build_portada(profile: dict, ctx: dict):
         model.Markdown(text=str(granularity)),
     ]
 
+    # Mini-summary of the rest of the analysis (built last, shown on the cover).
+    blocks.extend(_summary_blocks(ctx.get("document_summary")))
+
     return model.Chapter(id=CHAPTER_ID, title=CHAPTER_TITLE,
                          version=CHAPTER_VERSION, blocks=blocks)