fix(matplotlib): corriger le rendu des histogrammes adaptatifs

.github/workflows/ci.yaml

@@ -30,6 +30,35 @@ jobs:
       - name: Run pre-commit hooks
         run: uv run pre-commit run --all-files
 
+  test-base-install:
+    name: Test base install without Matplotlib
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: astral-sh/setup-uv@v9.0.0
+        with:
+          enable-cache: true
+          version: "0.11.32"
+
+      - name: Test base API without optional dependencies
+        run: |
+          uv run --isolated --no-project --with . python - <<'PY'
+          import importlib.util
+
+          import numpy as np
+
+          import khisto
+
+          assert importlib.util.find_spec("matplotlib") is None
+          counts, edges = khisto.histogram(
+              np.array([1.0, 2.0, 3.0]),
+              density=False,
+          )
+          assert counts.sum() == 3
+          assert len(edges) == len(counts) + 1
+          PY
+
   get-python-versions:
     name: Get Python versions
     runs-on: ubuntu-latest

CHANGELOG.md

@@ -2,6 +2,19 @@
 
 All notable changes to Khisto are documented in this file.
 
+## Unreleased
+
+### Changed
+
+- Draw bar edges in their face color by default so that very narrow adaptive bins
+  remain visible.
+- Reject ``histtype="barstacked"` because Khisto only accepts a single dataset.
+
+### Fixed
+
+- Reuse Khisto frequencies when plotting so values remain assigned to the bins
+  selected by Khisto, including for extreme finite values.
+
 ## [1.0.2] - 2026-09-15
 
 ### Added

docs/api_comparison.md

@@ -139,7 +139,7 @@ khisto.matplotlib.hist(
 | **Reverse cumulative** | Supported with negative `cumulative` | Supported with negative `cumulative` |
 | **Stacked** | Supported | Not supported |
 | **Weights** | Supported | Not supported |
-| **Unsupported histogram args** | None | `bins`, `stacked`, and `weights` raise a `TypeError` |
+| **Unsupported histogram args** | None | `bins`, `stacked`, and `weights` raise a `TypeError`; `histtype="barstacked"` raises a `ValueError` |
 | **Multiple datasets** | Supported | Not supported; only 1-D arrays are accepted |
 
 #### Usage Comparison

docs/conf.py

@@ -8,9 +8,12 @@
 import os
 import re
 import sys
+from functools import wraps
 from pathlib import Path
 from importlib import metadata
 
+import khisto.matplotlib
+
 DOCS_DIR = Path(__file__).resolve().parent
 ROOT_DIR = DOCS_DIR.parent
 
@@ -55,6 +58,18 @@
     "special-members": False,
 }
 
+_runtime_hist = khisto.matplotlib.hist
+
+
+@wraps(_runtime_hist)
+def _documented_hist(*args, **kwargs):
+    return _runtime_hist(*args, **kwargs)
+
+
+_documented_hist.__module__ = khisto.matplotlib.__name__
+_documented_hist.__qualname__ = "hist"
+khisto.matplotlib.hist = _documented_hist
+
 ## Intersphinx extension config
 intersphinx_mapping = {
     "python": ("https://docs.python.org/3", None),

docs/index.rst

@@ -44,10 +44,10 @@ Get started
 .. code-block:: python
 
    import numpy as np
-   from khisto import histogram
+   import khisto
 
    data = np.random.normal(0, 1, 10_000)
-   hist, bin_edges = histogram(data)          # optimal bins, no guessing
+   hist, bin_edges = khisto.histogram(data)          # optimal bins, no guessing
 
 .. grid:: 1 1 2 2
    :gutter: 3

pyproject.toml

@@ -59,6 +59,7 @@ test = [
     "pytest-xdist>=3.6",
     "pytest-cov>=6",
     "pytest-sugar>=1.0",
+    "typing-extensions>=4.0; python_version < '3.11'",
 ]
 lint = [
     "pre-commit>=4.1",

sandbox/khisto_demo.ipynb

@@ -655,7 +655,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "khisto-python",
+   "display_name": "khisto-python (3.12.3)",
    "language": "python",
    "name": "python3"
   },

src/khisto/__init__.py

@@ -31,8 +31,10 @@
 
 from .core import HistogramResult
 from .histogram import histogram
+from .matplotlib import hist
 
 __all__ = [
     "HistogramResult",
+    "hist",
     "histogram",
 ]

src/khisto/matplotlib/hist.py

@@ -6,26 +6,75 @@
 
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, Literal, overload
 
 import numpy as np
-from matplotlib.axes import Axes
 
 from khisto.histogram import histogram as khisto_histogram
 
 if TYPE_CHECKING:
-    from numpy.typing import ArrayLike
+    from matplotlib.axes import Axes
+    from matplotlib.container import BarContainer
+    from matplotlib.patches import Polygon
+    from numpy.typing import ArrayLike, NDArray
 
 
+@overload
 def hist(
     x: ArrayLike,
     range: tuple[float, float] | None = None,
     max_bins: int | None = None,
     density: bool = True,
     *,
     ax: Axes | None = None,
+    histtype: Literal["bar"] = "bar",
     **kwargs: Any,
-) -> tuple[np.ndarray, np.ndarray, Any]:
+) -> tuple[NDArray[np.float64], NDArray[np.float64], BarContainer]: ...
+
+
+@overload
+def hist(
+    x: ArrayLike,
+    range: tuple[float, float] | None = None,
+    max_bins: int | None = None,
+    density: bool = True,
+    *,
+    ax: Axes | None = None,
+    histtype: Literal["step", "stepfilled"],
+    **kwargs: Any,
+) -> tuple[NDArray[np.float64], NDArray[np.float64], list[Polygon]]: ...
+
+
+@overload
+def hist(
+    x: ArrayLike,
+    range: tuple[float, float] | None = None,
+    max_bins: int | None = None,
+    density: bool = True,
+    *,
+    ax: Axes | None = None,
+    histtype: str,
+    **kwargs: Any,
+) -> tuple[
+    NDArray[np.float64],
+    NDArray[np.float64],
+    BarContainer | list[Polygon],
+]: ...
+
+
+def hist(
+    x: ArrayLike,
+    range: tuple[float, float] | None = None,
+    max_bins: int | None = None,
+    density: bool = True,
+    *,
+    ax: Axes | None = None,
+    **kwargs: Any,
+) -> tuple[
+    NDArray[np.float64],
+    NDArray[np.float64],
+    BarContainer | list[Polygon],
+]:
     """Compute and plot an optimal histogram.
 
     Parameters
@@ -49,7 +98,8 @@ def hist(
         Axes object to plot on. If not provided, the current axes will be used.
     **kwargs :
         other keyword arguments are described in ``matplotlib.pyplot.hist``. The ``bins``,
-        ``weights``, and stacked/multiple dataset features are not supported.
+        ``weights``, ``stacked``, ``histtype="barstacked"``, and multiple dataset
+        features are not supported.
 
     Returns
     -------
@@ -58,7 +108,7 @@ def hist(
     bins : ndarray
         Bin edges.
     patches
-        Container with the bar patches.
+        Container with the bar patches, or a list containing the step polygon.
 
     .. note::
         Khiops bins are left-open and right-closed, ``(lower, upper]``, unlike
@@ -70,6 +120,11 @@ def hist(
     matplotlib.pyplot.hist : Matplotlib's histogram function.
     khisto.histogram : Underlying histogram computation.
     """
+    # optional dependency; only import if strictly needed.
+    import matplotlib.pyplot as plt
+    from matplotlib.container import BarContainer
+    from matplotlib.patches import Polygon
+
     unsupported_kwargs = {
         "bins": "Use max_bins to limit the number of bins.",
         "stacked": "Stacked histograms are not supported.",
@@ -79,16 +134,51 @@ def hist(
         if name in kwargs:
             raise TypeError(f"{name} is not supported. {hint}")
 
-    # Compute histogram using khisto
-    _, bin_edges = khisto_histogram(x, range=range, max_bins=max_bins, density=density)
+    histtype = kwargs.get("histtype", "bar")
+    if histtype == "barstacked":
+        raise ValueError(
+            "histtype='barstacked' is not supported. Khisto only accepts a single dataset."
+        )
 
-    if ax is None:
-        # optional dependency; only import if strictly needed.
-        import matplotlib.pyplot as plt
+    # Use frequencies so Matplotlib applies density and cumulative only once.
+    frequencies, bin_edges = khisto_histogram(
+        x,
+        range=range,
+        max_bins=max_bins,
+        density=False,
+    )
 
+    if ax is None:
         ax = plt.gca()
 
-    # Khiops bins are right-closed, whereas Matplotlib bins are left-closed.
-    # Moving each value down one ULP preserves Khiops assignments at shared edges.
-    plot_values = np.nextafter(np.asarray(x, dtype=np.float64), -np.inf)
-    return ax.hist(plot_values, bin_edges, density=density, range=range, **kwargs)
+    # Weighted left edges preserve Khiops' right-closed bins and [-1e100, 1e100]
+    # clamping when Matplotlib renders its left-closed bins.
+    cumulative = kwargs.get("cumulative", False)
+    plot_weights = (
+        frequencies / frequencies.sum() if density and cumulative else frequencies
+    )
+    values, edges, patches = ax.hist(
+        x=bin_edges[:-1],
+        bins=bin_edges.tolist(),
+        weights=plot_weights,
+        density=density and not cumulative,
+        **kwargs,
+    )
+    if isinstance(values, list):
+        raise TypeError("Matplotlib unexpectedly returned multiple histograms.")
+    if isinstance(patches, BarContainer):
+        histogram_patches: BarContainer | list[Polygon] = patches
+    elif isinstance(patches, list):
+        histogram_patches = [patch for patch in patches if isinstance(patch, Polygon)]
+        if len(histogram_patches) != len(patches):
+            raise TypeError("Matplotlib returned unexpected histogram patches.")
+    else:
+        raise TypeError("Matplotlib returned unexpected histogram patches.")
+
+    if histtype == "bar" and not {"edgecolor", "ec"} & kwargs.keys():
+        if not isinstance(histogram_patches, BarContainer):
+            raise TypeError("Matplotlib unexpectedly returned non-bar patches.")
+        for patch in histogram_patches.patches:
+            patch.set_edgecolor(patch.get_facecolor())
+
+    return values, edges, histogram_patches