diff --git a/doc/_build/html/.doctrees/api.doctree b/doc/_build/html/.doctrees/api.doctree index 3ccb919..64d997a 100644 Binary files a/doc/_build/html/.doctrees/api.doctree and b/doc/_build/html/.doctrees/api.doctree differ diff --git a/doc/_build/html/.doctrees/environment.pickle b/doc/_build/html/.doctrees/environment.pickle index 06556a5..981c093 100644 Binary files a/doc/_build/html/.doctrees/environment.pickle and b/doc/_build/html/.doctrees/environment.pickle differ diff --git a/doc/_build/html/_modules/moderndive/infer/core.html b/doc/_build/html/_modules/moderndive/infer/core.html index 8170b7c..f1820d0 100644 --- a/doc/_build/html/_modules/moderndive/infer/core.html +++ b/doc/_build/html/_modules/moderndive/infer/core.html @@ -684,10 +684,14 @@

Source code for moderndive.infer.core

 
         return get_fit_p_value(self, obs_stat=obs_stat, direction=direction)
 
-    def visualize(self, bins: int = 20, *, engine: str = "plotly"):
+    def visualize(
+        self, bins: int = 20, *, engine: str = "plotly", shade_pvalue=None, shade_ci=None
+    ):
         from .viz import visualize_fit
 
-        return visualize_fit(self, bins=bins, engine=engine)
+ return visualize_fit( + self, bins=bins, engine=engine, shade_pvalue=shade_pvalue, shade_ci=shade_ci + ) diff --git a/doc/_build/html/_modules/moderndive/infer/viz.html b/doc/_build/html/_modules/moderndive/infer/viz.html index a4e985f..b1b4f76 100644 --- a/doc/_build/html/_modules/moderndive/infer/viz.html +++ b/doc/_build/html/_modules/moderndive/infer/viz.html @@ -273,7 +273,14 @@

Source code for moderndive.infer.viz

 
 @dataclass(frozen=True)
 class ShadeSpec:
-    """Engine-neutral description of a shading layer (p-value tail or CI)."""
+    """Engine-neutral description of a shading layer (p-value tail or CI).
+
+    For a single-panel plot the scalar fields are used. For a faceted
+    regression-fit plot (:func:`visualize_fit`), ``per_term`` holds one value per
+    term so each facet is shaded independently — a tuple of ``(term, payload)``
+    pairs where ``payload`` is the observed statistic (p-value) or a
+    ``(lower, upper)`` pair (confidence interval).
+    """
 
     kind: str  # "p_value" | "confidence_interval"
     obs_stat: float | None = None
@@ -281,6 +288,7 @@ 
Source code for moderndive.infer.viz
     lower: float | None = None
     upper: float | None = None
     color: str | None = None
+    per_term: tuple | None = None
 
 
 class InferPlot:
@@ -291,11 +299,15 @@ 
Source code for moderndive.infer.viz
     raw ``ggplot`` is also available via :attr:`gg`.
     """
 
-    def __init__(self, figure, engine: str):
+    def __init__(self, figure, engine: str, terms: list[str] | None = None):
         self.figure = figure
         self.engine = engine
+        # Facet terms for a regression-fit plot (enables per-facet shading).
+        self.terms = terms
 
     def __add__(self, other):
+        if isinstance(other, ShadeSpec) and other.per_term is not None:
+            return self._add_per_facet(other)
         if self.engine == "plotnine":
             from . import _plotnine as P
 
@@ -305,19 +317,36 @@ 
Source code for moderndive.infer.viz
                     if other.kind == "p_value"
                     else P.shade_ci_layers(other)
                 )
-                return InferPlot(self.figure + layers, "plotnine")
+                return InferPlot(self.figure + layers, "plotnine", self.terms)
             # Any other plotnine object/layer/list.
-            return InferPlot(self.figure + other, "plotnine")
+            return InferPlot(self.figure + other, "plotnine", self.terms)
 
         from . import _plotly as PX
 
         if isinstance(other, ShadeSpec):
-            return InferPlot(PX.apply_shade_px(self.figure, other), "plotly")
+            return InferPlot(PX.apply_shade_px(self.figure, other), "plotly", self.terms)
         raise TypeError(
             "Only a ShadeSpec (from shade_p_value/shade_confidence_interval) can be "
             "added to a plotly InferPlot."
         )
 
+    def _add_per_facet(self, spec: ShadeSpec):
+        """Apply per-term shading to a faceted regression-fit plot (both engines)."""
+        if self.terms is None:
+            raise TypeError(
+                "Per-term shading requires a faceted fit plot from visualize_fit(); "
+                "pass a per-term obs_stat/endpoints (a FitResult or a term-keyed table)."
+            )
+        if self.engine == "plotnine":
+            from . import _plotnine as P
+
+            return InferPlot(
+                P.apply_fit_shade_gg(self.figure, spec, self.terms), "plotnine", self.terms
+            )
+        from . import _plotly as PX
+
+        return InferPlot(PX.apply_fit_shade_px(self.figure, spec, self.terms), "plotly", self.terms)
+
     @property
     def gg(self):
         """The underlying plotnine ``ggplot`` (only when ``engine='plotnine'``)."""
@@ -341,6 +370,23 @@ 
Source code for moderndive.infer.viz
             return self.figure.write_html(path_str)
         return self.figure.write_image(path_str)
 
+    def _repr_mimebundle_(self, include=None, exclude=None):
+        """Rich display for Jupyter/Quarto across both engines.
+
+        Both plotnine ``ggplot`` and plotly ``Figure`` expose
+        ``_repr_mimebundle_``, so we delegate to it and the wrapped figure renders
+        exactly as a bare figure would. We fall back to ``text/html`` and finally
+        to no output. This is required because ``_repr_html_`` alone returns
+        ``None`` for a ``ggplot`` — without this method, plotnine-engine
+        ``InferPlot``s render blank in notebooks and Quarto.
+        """
+        fig = self.figure
+        if hasattr(fig, "_repr_mimebundle_"):
+            return fig._repr_mimebundle_(include=include, exclude=exclude)
+        if hasattr(fig, "_repr_html_"):
+            return {"text/html": fig._repr_html_()}
+        return None
+
     def _repr_html_(self):
         fig = self.figure
         return fig._repr_html_() if hasattr(fig, "_repr_html_") else None
@@ -402,16 +448,32 @@ 
Source code for moderndive.infer.viz
 
 
 
-def visualize_fit(fit, bins: int = 20, *, engine: str = "plotly") -> InferPlot:
-    """Faceted histogram of a regression fit distribution, one panel per term."""
+def visualize_fit(
+    fit, bins: int = 20, *, engine: str = "plotly", shade_pvalue=None, shade_ci=None
+) -> InferPlot:
+    """Faceted histogram of a regression fit distribution, one panel per term.
+
+    Pass ``shade_pvalue=``/``shade_ci=`` to shade each facet from per-term values
+    (a ``FitResult`` of observed estimates, or a term-keyed CI/p-value table), or
+    compose the same per-term :class:`ShadeSpec` with ``+``.
+    """
     engine = C.resolve_engine(engine)
+    terms = fit.data["term"].unique(maintain_order=True).to_list()
     if engine == "plotnine":
         from . import _plotnine as P
 
-        return InferPlot(P.visualize_fit_gg(fit, bins), engine)
-    from . import _plotly as PX
+        fig = P.visualize_fit_gg(fit, bins)
+    else:
+        from . import _plotly as PX
+
+        fig = PX.visualize_fit_px(fit, bins)
 
-    return InferPlot(PX.visualize_fit_px(fit, bins), engine)
+    plot = InferPlot(fig, engine, terms)
+    if shade_pvalue is not None:
+        plot = plot + _coerce_pvalue_spec(shade_pvalue)
+    if shade_ci is not None:
+        plot = plot + _coerce_ci_spec(shade_ci)
+    return plot
 
 
 def visualize_theoretical(theoretical, bins: int = 100, *, engine: str = "plotly") -> InferPlot:
@@ -431,16 +493,52 @@ 
Source code for moderndive.infer.viz
     return InferPlot(PX.density_curve_px(x, density, title), engine)
 
 
+def _per_term_obs(obs_stat) -> dict | None:
+    """Extract a ``{term: observed}`` mapping for per-facet p-value shading.
+
+    Accepts an observed ``FitResult`` (term/estimate), a term-keyed polars frame
+    (``term`` + ``estimate`` or ``stat``), or a dict. Returns ``None`` for a scalar.
+    """
+    import polars as pl
+
+    if isinstance(obs_stat, pl.DataFrame) and "term" in obs_stat.columns:
+        valcol = "estimate" if "estimate" in obs_stat.columns else "stat"
+        return {r["term"]: float(r[valcol]) for r in obs_stat.iter_rows(named=True)}
+    data = getattr(obs_stat, "data", None)
+    if isinstance(data, pl.DataFrame) and {"term", "estimate"} <= set(data.columns):
+        return {r["term"]: float(r["estimate"]) for r in data.iter_rows(named=True)}
+    if isinstance(obs_stat, dict):
+        return {k: float(v) for k, v in obs_stat.items()}
+    return None
+
+
+def _per_term_ci(endpoints) -> dict | None:
+    """Extract a ``{term: (lower, upper)}`` mapping for per-facet CI shading."""
+    import polars as pl
+
+    if isinstance(endpoints, pl.DataFrame) and "term" in endpoints.columns:
+        return {
+            r["term"]: (float(r["lower_ci"]), float(r["upper_ci"]))
+            for r in endpoints.iter_rows(named=True)
+        }
+    return None
+
+
 

 [docs]
 def shade_p_value(obs_stat, direction: str, *, color: str | None = None) -> ShadeSpec:
     """A p-value shading spec; add it to a ``visualize()`` plot with ``+``.
 
-    ``direction`` ∈ {right/greater, left/less, two-sided}.
+    ``direction`` ∈ {right/greater, left/less, two-sided}. For a faceted
+    :func:`visualize_fit` plot, pass a per-term ``obs_stat`` — an observed
+    ``FitResult``, a ``term``-keyed frame, or a dict — to shade each facet.
     """
-    return ShadeSpec(
-        kind="p_value", obs_stat=float(obs_stat), direction=direction, color=color
-    )

+    per = _per_term_obs(obs_stat)
+    if per is not None:
+        return ShadeSpec(
+            kind="p_value", direction=direction, color=color, per_term=tuple(sorted(per.items()))
+        )
+    return ShadeSpec(kind="p_value", obs_stat=float(obs_stat), direction=direction, color=color)

 
 
 
@@ -449,8 +547,15 @@ 
Source code for moderndive.infer.viz
 def shade_confidence_interval(endpoints, color: str | None = None) -> ShadeSpec:
     """A confidence-interval shading spec; add it to a ``visualize()`` plot with ``+``.
 
-    ``endpoints`` is a CI DataFrame (``lower_ci``/``upper_ci``) or a ``(lower, upper)`` tuple.
+    ``endpoints`` is a CI DataFrame (``lower_ci``/``upper_ci``) or a ``(lower, upper)``
+    tuple. For a faceted :func:`visualize_fit` plot, pass a per-term CI table (with a
+    ``term`` column) to shade each facet from its own interval.
     """
+    per = _per_term_ci(endpoints)
+    if per is not None:
+        return ShadeSpec(
+            kind="confidence_interval", color=color, per_term=tuple(sorted(per.items()))
+        )
     lower, upper = C.ci_endpoints(endpoints)
     return ShadeSpec(kind="confidence_interval", lower=lower, upper=upper, color=color)

 
diff --git a/doc/_build/html/_static/moderndive-logo.png b/doc/_build/html/_static/moderndive-logo.png
index 4c9ae70..1c6c251 100644
Binary files a/doc/_build/html/_static/moderndive-logo.png and b/doc/_build/html/_static/moderndive-logo.png differ
diff --git a/doc/_build/html/api.html b/doc/_build/html/api.html
index ceb0340..753f6b6 100644
--- a/doc/_build/html/api.html
+++ b/doc/_build/html/api.html
@@ -587,7 +587,9 @@ 
Getters and visualization
 moderndive.shade_p_value(obs_stat, direction, *, color=None)[source]
 
A p-value shading spec; add it to a visualize() plot with +.

-
direction ∈ {right/greater, left/less, two-sided}.

+
direction ∈ {right/greater, left/less, two-sided}. For a faceted
+visualize_fit() plot, pass a per-term obs_stat — an observed
+FitResult, a term-keyed frame, or a dict — to shade each facet.

 

 
Return type:

 
ShadeSpec

@@ -605,7 +607,9 @@ 
Getters and visualization
 moderndive.shade_confidence_interval(endpoints, color=None)[source]
 
A confidence-interval shading spec; add it to a visualize() plot with +.

-
endpoints is a CI DataFrame (lower_ci/upper_ci) or a (lower, upper) tuple.

+
endpoints is a CI DataFrame (lower_ci/upper_ci) or a (lower, upper)
+tuple. For a faceted visualize_fit() plot, pass a per-term CI table (with a
+term column) to shade each facet from its own interval.

 

 
Return type:

 
ShadeSpec

diff --git a/doc/_build/html/searchindex.js b/doc/_build/html/searchindex.js
index b0f25d3..3ba6519 100644
--- a/doc/_build/html/searchindex.js
+++ b/doc/_build/html/searchindex.js
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--- a/pyproject.toml
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 [project]
 name = "moderndive"
 version = "0.1.0"
-description = "Python companion for ModernDive: a tidy simulation-inference grammar, regression helpers, and datasets"
+description = "Python companion for ModernDive: a tidy simulation-inference grammar, regression helpers, datasets, and dual-engine plotly/plotnine plots"
 readme = "README.md"
 requires-python = ">=3.11"
 license = { text = "MIT" }