hist.basehist module

hist.basehist module#

class hist.basehist.BaseHist(arg: dict[str, Any], /, *, data: ndarray[tuple[Any, ...], dtype[Any]] | None = ..., metadata: Any = ..., label: str | None = ..., name: str | None = ...)#
class hist.basehist.BaseHist(arg: Self | Histogram[S], /, *, data: ndarray[tuple[Any, ...], dtype[Any]] | None = ..., metadata: Any = ..., label: str | None = ..., name: str | None = ...)
class hist.basehist.BaseHist(*axes: AxisProtocol | tuple[int, float, float] | Storage | str, storage: S = ..., metadata: Any = ..., data: ndarray[tuple[Any, ...], dtype[Any]] | None = ..., label: str | None = ..., name: str | None = ...)

Bases: Histogram[S], Generic[S]

property T: Self#
density() → ndarray[tuple[Any, ...], dtype[Any]]#

Density NumPy array.

fill(*args: Any, weight: Any | None = None, sample: Any | None = None, threads: int | None = None, **kwargs: Any) → Self#

Insert data into the histogram using names and indices, return a Hist object.

fill_flattened(*args: Any, weight: Any | None = None, sample: Any | None = None, threads: int | None = None, **kwargs: Any) → Self#
classmethod from_columns(data: Mapping[str, Any], axes: Sequence[str | AxisProtocol], *, weight: str | None = None, storage: Storage = Double()) → Self#
integrate(name: int | str, i_or_list: list[str | int], j: InnerIndexing | None = None) → Self#
integrate(name: int | str, i_or_list: InnerIndexing | None = None, j: InnerIndexing | None = None) → IntHists | int
integrate(name: int | str, i_or_list: InnerIndexing | None = None, j: InnerIndexing | None = None) → FloatHists | float
integrate(name: int | str, i_or_list: InnerIndexing | None = None, j: InnerIndexing | None = None) → ListHists | list[float]
integrate(name: int | str, i_or_list: InnerIndexing | None = None, j: InnerIndexing | None = None) → WeightHists | WeightedSum
integrate(name: int | str, i_or_list: InnerIndexing | None = None, j: InnerIndexing | None = None) → MeanHists | Mean
integrate(name: int | str, i_or_list: InnerIndexing | None = None, j: InnerIndexing | None = None) → WeightedMeanHists | WeightedMean
integrate(name: int | str, i_or_list: InnerIndexing | None = None, j: InnerIndexing | None = None) → Self | int | float | list[float] | Any
plot(*args: Any, overlay: str | None = None, **kwargs: Any) → Hist1DArtists | Hist2DArtists#

Plot method for BaseHist object.

plot1d(*, ax: matplotlib.axes.Axes | None = None, overlay: str | int | None = None, legend: bool = True, **kwargs: Any) → Hist1DArtists#

Plot1d method for BaseHist object.

Parameters:

  • ax (matplotlib.axes.Axes, optional) – Axes to plot on. If None, uses current axes or creates new ones.

  • overlay (str or int, optional) – Name or index of the axis to overlay. If None, automatically selects the first discrete axis for multi-dimensional histograms.

  • legend (bool, default True) – Whether to automatically add a legend when plotting stacked categories. The legend title is set from the axis label if available.

  • **kwargs (Any) – Additional keyword arguments passed to the underlying plot functions.

Returns:

The matplotlib artists created by the plot.

Return type:

Hist1DArtists

plot2d(*, ax: matplotlib.axes.Axes | None = None, **kwargs: Any) → Hist2DArtists#

Plot2d method for BaseHist object.

plot2d_full(*, ax_dict: dict[str, matplotlib.axes.Axes] | None = None, **kwargs: Any) → tuple[Hist2DArtists, Hist1DArtists, Hist1DArtists]#

Plot2d_full method for BaseHist object.

Pass a dict of axes to ax_dict, otherwise, the current figure will be used.

plot_pie(*, ax: matplotlib.axes.Axes | None = None, **kwargs: Any) → Any#
plot_pull(func: Callable[[np.typing.NDArray[Any]], np.typing.NDArray[Any]] | str, *, ax_dict: dict[str, matplotlib.axes.Axes] | None = None, **kwargs: Any) → tuple[FitResultArtists, RatiolikeArtists]#

plot_pull method for BaseHist object.

Return a tuple of artists following a structure of (main_ax_artists, subplot_ax_artists)

plot_ratio(other: hist.BaseHist[Any] | Callable[[np.typing.NDArray[Any]], np.typing.NDArray[Any]] | str, *, ax_dict: dict[str, matplotlib.axes.Axes] | None = None, **kwargs: Any) → tuple[MainAxisArtists, RatiolikeArtists]#

plot_ratio method for BaseHist object.

Return a tuple of artists following a structure of (main_ax_artists, subplot_ax_artists)

profile(axis: int | str) → Self#

Returns a profile (Mean/WeightedMean) histogram from a normal histogram with N-1 axes. The axis given is profiled over and removed from the final histogram.

project(*args: int | str) → Self#

Projection of axis idx.

show(**kwargs: Any) → Any#

Pretty print histograms to the console.

sort(axis: int | str, key: Callable[[int], SupportsLessThan] | Callable[[str], SupportsLessThan] | None = None, reverse: bool = False) → Self#

Sort a categorical axis.

stack(axis: int | str) → Stack[S]#

Returns a stack from a normal histogram axes.

class hist.basehist.SupportsLessThan(*args, **kwargs)#

Bases: Protocol

hist.basehist.process_mistaken_quick_construct(axes: Sequence[AxisProtocol | tuple[int, float, float] | ConstructProxy]) → Generator[AxisProtocol | tuple[int, float, float], None, None]#
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