khisto.core.HistogramResult

class khisto.core.HistogramResult(lower_bounds: NDArray[np.float64], upper_bounds: NDArray[np.float64], frequencies: NDArray[np.int64], probabilities: NDArray[np.float64], densities: NDArray[np.float64], is_best: bool = False, granularity: int = 0, level: float = 0.0, information_rate: float = 0.0, peak_interval_number: int = 0, spike_interval_number: int = 0, empty_interval_number: int = 0)

Bases: object

Result of optimal histogram computation.

Attributes:

lower_boundsNDArray[np.float64]

Lower bounds of each bin.

upper_boundsNDArray[np.float64]

Upper bounds of each bin.

frequenciesNDArray[np.int64]

Count of values in each bin.

probabilitiesNDArray[np.float64]

Probability of each bin (frequency / total).

densitiesNDArray[np.float64]

Density of each bin (probability / bin_width).

is_bestbool

Whether this histogram is the optimal one.

granularityint

The granularity level of this histogram.

levelfloat

The information level of this histogram.

information_ratefloat

The information rate of this histogram (percentage of the finest level).

peak_interval_numberint

Number of peak intervals in this histogram.

spike_interval_numberint

Number of spike intervals in this histogram.

empty_interval_numberint

Number of empty intervals in this histogram.

__init__(lower_bounds: NDArray[np.float64], upper_bounds: NDArray[np.float64], frequencies: NDArray[np.int64], probabilities: NDArray[np.float64], densities: NDArray[np.float64], is_best: bool = False, granularity: int = 0, level: float = 0.0, information_rate: float = 0.0, peak_interval_number: int = 0, spike_interval_number: int = 0, empty_interval_number: int = 0) → None

Methods

__init__(lower_bounds, upper_bounds, ...[, ...])

property bin_centers: NDArray[np.float64]

Return center of each bin.

property bin_edges: NDArray[np.float64]

Return bin edges array (n_bins + 1 values). lower_bounds and upper_bounds are equal for adjacent bins.

property bin_widths: NDArray[np.float64]

Return width of each bin.