hist package

hist package#

class hist.BaseHist(arg: dict[str, Any], /, *, data: NDArray[Any] | None = ..., metadata: Any = ..., label: str | None = ..., name: str | None = ...)#

class hist.BaseHist(arg: Self | bh.Histogram[S], /, *, data: NDArray[Any] | None = ..., metadata: Any = ..., label: str | None = ..., name: str | None = ...)

Bases: Histogram[S], Generic[S]

property T: Self#

density() → NDArray[Any]#: Density NumPy array.

expand_cats(*, name: Callable[..., str] | None = None) → dict[str, Self]#

Expand all categorical axes into a dict of histograms.

One histogram is produced for every combination of categories. The name callable receives the category values, one per categorical axis, and returns the key; the default joins them with _.

fill(*args: ArrayLike, weight: ArrayLike | None = None, sample: ArrayLike | None = None, threads: int | None = None, **kwargs: ArrayLike) → 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, ArrayLike], axes: Sequence[str | AxisProtocol], *, weight: str | None = None, storage: hist.storage.Storage | None = None) → 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] | bh.accumulators.Accumulator

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, flow: bool = True) → 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.Hist(arg: dict[str, Any], /, *, data: NDArray[Any] | None = ..., metadata: Any = ..., label: str | None = ..., name: str | None = ...)#
class hist.Hist(arg: Self | bh.Histogram[S], /, *, data: NDArray[Any] | None = ..., metadata: Any = ..., label: str | None = ..., name: str | None = ...)
class hist.Hist(*axes: AxisProtocol | tuple[int, float, float] | Storage | str, storage: S = ..., metadata: Any = ..., data: NDArray[Any] | None = ..., label: str | None = ..., name: str | None = ...): Bases: BaseHist[S], Generic[S]

class hist.NamedHist(*args: Any, **kwargs: Any)#

Bases: BaseHist[S], Generic[S]

fill(weight: ArrayLike | None = None, sample: ArrayLike | None = None, threads: int | None = None, **kwargs: ArrayLike) → Self#: Insert data into the histogram using names and return a NamedHist object. NamedHist could only be filled by names.

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

project(*args: int | str, flow: bool = True) → Self#: Projection of axis idx.

class hist.Stack(*args: BaseHist[S])#

Bases: Generic[S]

property axes: NamedAxesTuple#

classmethod from_dict(d: Mapping[str, BaseHist[S]]) → Self#: Create a Stack from a dictionary of histograms. The keys of the dictionary are used as names.

classmethod from_iter(iterable: Iterable[BaseHist[S]]) → Self#: Create a Stack from an iterable of histograms.

plot(*, ax: mpl.axes.Axes | None = None, **kwargs: Any) → Any#: Plot method for Stack object.

project(*args: int | str) → Self#: Project the Stack onto a new axes.

show(**kwargs: object) → Any#: Pretty print the stacked histograms to the console.

class hist.at(value: int)#

Bases: object

value#

class hist.loc(value: str | float, offset: int = 0)#

Bases: Locator

value#

Bases: object

axis#

axis_mapping(axis: PlottableAxis) → tuple[Sequence[int], PlottableAxis | None]#

edges#

factor#

group_mapping(axis: PlottableAxis) → Sequence[int]#: Return the list of group sizes (numbers of adjacent bins to merge) for axis. For explicit groups, the sum of the group sizes must equal the number of bins in the axis. For a factor, this returns len(axis) // factor groups of size factor; if the factor does not divide the number of bins evenly, the leftover bins are not part of any group (a consumer should merge them into the overflow bin, matching the C++ factor-based rebinning).

groups#

hist.sum(iterable, /, start=0)#

Return the sum of a ‘start’ value (default: 0) plus an iterable of numbers

When the iterable is empty, return the start value. This function is intended specifically for use with numeric values and may reject non-numeric types.

hist package

Contents

hist package#

Subpackages#

Submodules#