dask_histogram.boost.Histogram

class dask_histogram.boost.Histogram(*axes, storage=Double(), metadata=None, split_every=None)

Histogram object capable of lazy computation.

Parameters:

• *axes (boost_histogram.axis.Axis) – Provide one or more Axis objects.

• storage (boost_histogram.storage.Storage, optional) – Select a storage to use in the histogram. The default storage type is boost_histogram.storage.Double.

• metadata (Any) – Data that is passed along if a new histogram is created.

See also

dask_histogram.histogram, dask_histogram.histogram2d, dask_histogram.histogramdd

Examples

A two dimensional histogram with one fixed bin width axis and another variable bin width axis:

Note that (for convenience) the boost_histogram.axis namespace is mirrored as dask_histogram.axis and the boost_histogram.storage namespace is mirrored as dask_histogram.storage.

>>> import dask.array as da
>>> import dask_histogram.boost as dhb
>>> x = da.random.standard_normal(size=(1000,), chunks=200)
>>> y = da.random.standard_normal(size=(1000,), chunks=200)
>>> w = da.random.uniform(0.2, 0.8, size=(1000,), chunks=200)
>>> h = dhb.Histogram(
...     dhb.axis.Regular(10, -3, 3),
...     dhb.axis.Variable([-3, -2, -1, 0, 1.1, 2.2, 3.3]),
...     storage=dhb.storage.Weight()
... ).fill(x, y, weight=w).compute()

__init__(*axes, storage=Double(), metadata=None, split_every=None)

Construct a Histogram object.

Methods

__init__(*axes[, storage, metadata, split_every])	Construct a Histogram object.
agg_histogram()
compute(**kwargs)	Compute this dask collection
copy(*[, deep])	Make a copy of the histogram.
counts([flow])	Returns the number of entries in each bin for an unweighted histogram or profile and an effective number of entries (defined below) for a weighted histogram or profile.
empty([flow])	Check to see if the histogram has any non-default values.
fill(*args[, weight, sample, threads])	Stage a fill call using a Dask collection as input.
persist(**kwargs)	Persist this dask collection into memory
project(*args)	Project to a single axis or several axes on a multidimensional histogram.
reset()	Clear the bin counters.
staged_fills()	Check if histogram has staged fills.
sum([flow])	Compute the sum over the histogram bins (optionally including the flow bins).
to_dask_array([flow, dd])	Convert to dask.array style of return arrays.
to_delayed()	Histogram as a delayed object.
to_numpy([flow, dd, view])	Convert to a NumPy style tuple of return arrays.
values([flow])	Returns the accumulated values.
variances([flow])	Returns the estimated variance of the accumulated values.
view([flow])	Return a view into the data, optionally with overflow turned on.
visualize([filename, format, optimize_graph])	Render the computation of this object's task graph using graphviz.

Attributes

axes
dask
dask_name
kind	Returns Kind.COUNT if this is a normal summing histogram, and Kind.MEAN if this is a mean histogram.
ndim	Number of axes (dimensions) of the histogram.
shape	Tuple of axis sizes (not including underflow/overflow).
size	Total number of bins in the histogram (including underflow/overflow).
storage_type