Constant memory histogram data structure.

Parameters:¶

num_bins : int

Number of bins to store multiplicities within.

window : tuple[float, float]

Range of x-axis to use for binning.

align : {"left", "center"}

Alignment of x-axis. Valid options are “left” or “center”. If "left", window will be relative to x=0. If "center", window will be centered relative to x=expected. Default is "left".

expected : float, optional

Expected value for distribution’s measure of location.

num_bins¶

Number of bins to store multiplicities within.

Type:¶

int

window¶

Range of x-axis values over which the histogram should track.

Type:¶

tuple[float, float]

align¶

Alignment of x-axis. Valid options are “left” or “center”. If "left", window will be relative to x=0. If "center", window will be centered relative to x=expected. Default is "left".

Type:¶

{“left”, “center”}

expected¶

Expected value for distribution’s measure of location.

Type:¶

float, optional

x_range¶

Low and high boundaries of the binned range.

Type:¶

tuple[float, float]

counts¶

Integer counts of values which entered each bin.

Type:¶

ndarray[int32]

bin_edges¶

Sequence of values defining the boundaries of the bins.

Type:¶

ndarray[float64]

Public members¶

align : 'left' | 'center' = 'left'

expected : float | None = None

__eq__(other: Self) → bool

Determines if two Histogram instances contain the same bin_edges, counts, and normalisation for pdf.

__add__(other: Histogram) → Histogram

Merges two Histogram instances, by adding their counts and _total normalisation variables. Must have the same bins.

property total : int

update(val) → None

Records a new value to the binned counts.

to_json(fname: str | Path | IOBase, encoding: str = 'utf-8') → None

Serialise and store Histogram object as a JSON file.

classmethod from_json(fname: str | Path | IOBase, ...) → Histogram

Instantiate a histogram from JSON file, encoded using the Histogram.to_json() method.

property bin_width : float

The width of all bins along the x-axis.

property pdf : tuple[ndarray[Any, dtype[float64]], ndarray[Any, dtype[float64]]]

Bin centers and count density. May be used for bar chart plots.

property missed : int

Number of update values which fell out-of-bounds of the histogram’s domain.

density() → ndarray[Any, dtype[float64]]

Probability density of the histogram, normalised by the total count of the histogram.

midpoints() → ndarray[Any, dtype[float64]]

Midpoints of the bins along the x and y axes, respectively.

serialize() → SerializedHistogram

Converts Histogram into serialized representation.

classmethod from_interface(hist: HistogramLike, ...) → Self

Instantiates Histogram from a generic HistogramLike interface.

classmethod from_serialized(hist_dict: SerializedHistogram) → Self

Instantiates Histogram from serialized data.

copy() → Self

Returns a copy of the Histogram instance.

Histogram(num_bins: int, window: tuple[float, float], ...)

Initialize self. See help(type(self)) for accurate signature.

__repr__()

Return repr(self).

num_bins : int

window : tuple[float, float]

counts : ndarray[Any, dtype[int32]]

bin_edges : ndarray[Any, dtype[float64]]