PartitionedHistogram1D#

class libcasm.monte.sampling.PartitionedHistogram1D(
self: PartitionedHistogram1D,
partition_names: list[str],
initial_begin: float = 0.0,
bin_width: float = 1.0,
is_log: bool = False,
max_size: int = 10000,
)#

Bases: pybind11_object

Data structure for holding 1 or more 1D histograms of related data (i.e. event rates by event type)

Constructor

Parameters:

  • partition_names (list[str]) – Names of the partitions. A separate histogram is created for each partition (i.e. activation energies for A-Va hops in one partition and activation energies for B-Va hops in another partition).

  • initial_begin (float = 0.0) – Initial begin coordinate, specifying the beginning of the range for the first bin. The bin number for a particular value is calculated as (value - begin) / bin_width, so the range for bin i is [begin, begin + i*bin_width). Coordinates are adjusted to fit the data encountered by starting begin at initial_begin and adjusting it as necessary by multiples of bin_width.

  • bin_width (float = 1.0) – Bin width.

  • is_log (bool = False) – True if bin coordinate spacing is log-scaled; False otherwise.

  • max_size (int = 10000) – Maximum number of bins to create. If adding an additional data point would cause the number of bins to exceed max_size, the count / weight is instead added to the out_of_range_count of the PartitionedHistogram1D.

Methods

combined_histogram

Histogram1D : The histogram constructed by merging the individual histograms for each partition

histograms

Histogram1DVector : A list-like container of Histogram1D, containing one for each partition

insert

Insert a value into the histogram for a partition, with an optional weight

partition_names

list[str] : The names of the partitions

to_dict

Represent the monte::PartitionedHistogram1D as a Python dict.