boost_histogram¶
boost_histogram.axis¶

class
boost_histogram.axis.
ArrayTuple
(iterable=(), /)¶ Bases:
tuple

broadcast
()¶ The arrays in this tuple will be compressed if possible to save memory. Use this method to broadcast them out into their full memory representation.


class
boost_histogram.axis.
AxesTuple
(iterable=(), /)¶ Bases:
tuple

bin
(*indexes)¶ Return the edges of the bins as a tuple for a continuous axis or the bin value for a noncontinuous axis, when given an index.

property
centers
¶

property
edges
¶

property
extent
¶

index
(*values)¶ Return the fractional index(es) given a value (or values) on the axis.

property
size
¶

value
(*indexes)¶ Return the value(s) given an (fractional) index (or indices).

property
widths
¶


class
boost_histogram.axis.
Axis
¶ Bases:
object

bin
(index)¶ Return the edges of the bins as a tuple for a continuous axis or the bin value for a noncontinuous axis, when given an index.

property
centers
¶ An array of bin centers.

property
edges
¶

property
extent
¶ Return number of bins including under and overflow.

index
(value)¶ Return the fractional index(es) given a value (or values) on the axis.

property
options
¶ Return the options. Fields: .underflow  True if axes captures values that are too small .overflow  True if axes captures values that are too large
(or nonvalid for category axes)
.growth  True if axis can grow .circular  True if axis wraps around

property
size
¶ Return number of bins excluding under and overflow.

value
(index)¶ Return the value(s) given an (fractional) index (or indices).

property
widths
¶ An array of bin widths.


class
boost_histogram.axis.
Boolean
(*, metadata=None)¶ Bases:
boost_histogram.axis.Axis

class
boost_histogram.axis.
IntCategory
(categories, *, metadata=None, growth=False)¶ Bases:
boost_histogram._internal.axis.BaseCategory

class
boost_histogram.axis.
Integer
(start, stop, *, metadata=None, underflow=True, overflow=True, growth=False)¶ Bases:
boost_histogram.axis.Axis

class
boost_histogram.axis.
Regular
(bins, start, stop, *, metadata=None, underflow=True, overflow=True, growth=False, circular=False, transform=None)¶ Bases:
boost_histogram.axis.Axis

property
transform
¶

property

class
boost_histogram.axis.
StrCategory
(categories, *, metadata=None, growth=False)¶ Bases:
boost_histogram._internal.axis.BaseCategory

index
(value)¶ Return the fractional index(es) given a value (or values) on the axis.


class
boost_histogram.axis.
Variable
(edges, *, metadata=None, underflow=True, overflow=True, growth=False)¶ Bases:
boost_histogram.axis.Axis
boost_histogram.axis.transform¶

class
boost_histogram.axis.transform.
AxisTransform
¶ Bases:
object

forward
(value)¶ Compute the forward transform

inverse
(value)¶ Compute the inverse transform


class
boost_histogram.axis.transform.
Function
(forward, inverse, *, convert=None, name='')¶

class
boost_histogram.axis.transform.
Pow
(power)¶ Bases:
boost_histogram.axis.transform.AxisTransform

property
power
¶ The power of the transform

property
boost_histogram.accumulators¶
boost_histogram.numpy¶

boost_histogram.numpy.
histogram
(a, bins=10, range=None, normed=None, weights=None, density=None, *, histogram=None, storage=None, threads=None)¶ Return a boosthistogram object using the same arguments as numpy’s histogram. This does not support the deprecated normed=True argument. Three extra arguments are added: histogram=bh.Histogram will enable object based output, storage=bh.storage.* lets you set the storage used, and threads=int lets you set the number of threads to fill with (0 for auto, None for 1).
Compute the histogram of a set of data.
 Parameters
a (array_like) – Input data. The histogram is computed over the flattened array.
bins (int or sequence of scalars or str, optional) –
If bins is an int, it defines the number of equalwidth bins in the given range (10, by default). If bins is a sequence, it defines a monotonically increasing array of bin edges, including the rightmost edge, allowing for nonuniform bin widths.
New in version 1.11.0.
If bins is a string, it defines the method used to calculate the optimal bin width, as defined by histogram_bin_edges.
range ((float, float), optional) – The lower and upper range of the bins. If not provided, range is simply
(a.min(), a.max())
. Values outside the range are ignored. The first element of the range must be less than or equal to the second. range affects the automatic bin computation as well. While bin width is computed to be optimal based on the actual data within range, the bin count will fill the entire range including portions containing no data.normed (bool, optional) –
Deprecated since version 1.6.0.
This is equivalent to the density argument, but produces incorrect results for unequal bin widths. It should not be used.
Changed in version 1.15.0: DeprecationWarnings are actually emitted.
weights (array_like, optional) – An array of weights, of the same shape as a. Each value in a only contributes its associated weight towards the bin count (instead of 1). If density is True, the weights are normalized, so that the integral of the density over the range remains 1.
density (bool, optional) –
If
False
, the result will contain the number of samples in each bin. IfTrue
, the result is the value of the probability density function at the bin, normalized such that the integral over the range is 1. Note that the sum of the histogram values will not be equal to 1 unless bins of unity width are chosen; it is not a probability mass function.Overrides the
normed
keyword if given.
 Returns
hist (array) – The values of the histogram. See density and weights for a description of the possible semantics.
bin_edges (array of dtype float) – Return the bin edges
(length(hist)+1)
.
See also
histogramdd()
,bincount()
,searchsorted()
,digitize()
,histogram_bin_edges()
Notes
All but the last (righthandmost) bin is halfopen. In other words, if bins is:
[1, 2, 3, 4]
then the first bin is
[1, 2)
(including 1, but excluding 2) and the second[2, 3)
. The last bin, however, is[3, 4]
, which includes 4.Examples
>>> np.histogram([1, 2, 1], bins=[0, 1, 2, 3]) (array([0, 2, 1]), array([0, 1, 2, 3])) >>> np.histogram(np.arange(4), bins=np.arange(5), density=True) (array([0.25, 0.25, 0.25, 0.25]), array([0, 1, 2, 3, 4])) >>> np.histogram([[1, 2, 1], [1, 0, 1]], bins=[0,1,2,3]) (array([1, 4, 1]), array([0, 1, 2, 3]))
>>> a = np.arange(5) >>> hist, bin_edges = np.histogram(a, density=True) >>> hist array([0.5, 0. , 0.5, 0. , 0. , 0.5, 0. , 0.5, 0. , 0.5]) >>> hist.sum() 2.4999999999999996 >>> np.sum(hist * np.diff(bin_edges)) 1.0
New in version 1.11.0.
Automated Bin Selection Methods example, using 2 peak random data with 2000 points:
>>> import matplotlib.pyplot as plt >>> rng = np.random.RandomState(10) # deterministic random data >>> a = np.hstack((rng.normal(size=1000), ... rng.normal(loc=5, scale=2, size=1000))) >>> _ = plt.hist(a, bins='auto') # arguments are passed to np.histogram >>> plt.title("Histogram with 'auto' bins") Text(0.5, 1.0, "Histogram with 'auto' bins") >>> plt.show()

boost_histogram.numpy.
histogram2d
(x, y, bins=10, range=None, normed=None, weights=None, density=None, *, histogram=None, storage=None, threads=None)¶ Return a boosthistogram object using the same arguments as numpy’s histogram2d. This does not support the deprecated normed=True argument. Three extra arguments are added: histogram=bh.Histogram will enable object based output, storage=bh.storage.* lets you set the storage used, and threads=int lets you set the number of threads to fill with (0 for auto, None for 1).
Compute the bidimensional histogram of two data samples.
 Parameters
x (array_like, shape (N,)) – An array containing the x coordinates of the points to be histogrammed.
y (array_like, shape (N,)) – An array containing the y coordinates of the points to be histogrammed.
bins (int or array_like or [int, int] or [array, array], optional) –
The bin specification:
If int, the number of bins for the two dimensions (nx=ny=bins).
If array_like, the bin edges for the two dimensions (x_edges=y_edges=bins).
If [int, int], the number of bins in each dimension (nx, ny = bins).
If [array, array], the bin edges in each dimension (x_edges, y_edges = bins).
A combination [int, array] or [array, int], where int is the number of bins and array is the bin edges.
range (array_like, shape(2,2), optional) – The leftmost and rightmost edges of the bins along each dimension (if not specified explicitly in the bins parameters):
[[xmin, xmax], [ymin, ymax]]
. All values outside of this range will be considered outliers and not tallied in the histogram.density (bool, optional) – If False, the default, returns the number of samples in each bin. If True, returns the probability density function at the bin,
bin_count / sample_count / bin_area
.normed (bool, optional) – An alias for the density argument that behaves identically. To avoid confusion with the broken normed argument to histogram, density should be preferred.
weights (array_like, shape(N,), optional) – An array of values
w_i
weighing each sample(x_i, y_i)
. Weights are normalized to 1 if normed is True. If normed is False, the values of the returned histogram are equal to the sum of the weights belonging to the samples falling into each bin.
 Returns
H (ndarray, shape(nx, ny)) – The bidimensional histogram of samples x and y. Values in x are histogrammed along the first dimension and values in y are histogrammed along the second dimension.
xedges (ndarray, shape(nx+1,)) – The bin edges along the first dimension.
yedges (ndarray, shape(ny+1,)) – The bin edges along the second dimension.
See also
histogram()
1D histogram
histogramdd()
Multidimensional histogram
Notes
When normed is True, then the returned histogram is the sample density, defined such that the sum over bins of the product
bin_value * bin_area
is 1.Please note that the histogram does not follow the Cartesian convention where x values are on the abscissa and y values on the ordinate axis. Rather, x is histogrammed along the first dimension of the array (vertical), and y along the second dimension of the array (horizontal). This ensures compatibility with histogramdd.
Examples
>>> from matplotlib.image import NonUniformImage >>> import matplotlib.pyplot as plt
Construct a 2D histogram with variable bin width. First define the bin edges:
>>> xedges = [0, 1, 3, 5] >>> yedges = [0, 2, 3, 4, 6]
Next we create a histogram H with random bin content:
>>> x = np.random.normal(2, 1, 100) >>> y = np.random.normal(1, 1, 100) >>> H, xedges, yedges = np.histogram2d(x, y, bins=(xedges, yedges)) >>> H = H.T # Let each row list bins with common y range.
imshow
can only display square bins:>>> fig = plt.figure(figsize=(7, 3)) >>> ax = fig.add_subplot(131, title='imshow: square bins') >>> plt.imshow(H, interpolation='nearest', origin='lower', ... extent=[xedges[0], xedges[1], yedges[0], yedges[1]]) <matplotlib.image.AxesImage object at 0x...>
pcolormesh
can display actual edges:>>> ax = fig.add_subplot(132, title='pcolormesh: actual edges', ... aspect='equal') >>> X, Y = np.meshgrid(xedges, yedges) >>> ax.pcolormesh(X, Y, H) <matplotlib.collections.QuadMesh object at 0x...>
NonUniformImage
can be used to display actual bin edges with interpolation:>>> ax = fig.add_subplot(133, title='NonUniformImage: interpolated', ... aspect='equal', xlim=xedges[[0, 1]], ylim=yedges[[0, 1]]) >>> im = NonUniformImage(ax, interpolation='bilinear') >>> xcenters = (xedges[:1] + xedges[1:]) / 2 >>> ycenters = (yedges[:1] + yedges[1:]) / 2 >>> im.set_data(xcenters, ycenters, H) >>> ax.images.append(im) >>> plt.show()

boost_histogram.numpy.
histogramdd
(a, bins=10, range=None, normed=None, weights=None, density=None, *, histogram=None, storage=None, threads=None)¶ Return a boosthistogram object using the same arguments as numpy’s histogramdd. This does not support the deprecated normed=True argument. Three extra arguments are added: histogram=bh.Histogram will enable object based output, storage=bh.storage.* lets you set the storage used, and threads=int lets you set the number of threads to fill with (0 for auto, None for 1).
Compute the multidimensional histogram of some data.
 Parameters
sample ((N, D) array, or (D, N) array_like) –
The data to be histogrammed.
Note the unusual interpretation of sample when an array_like:
When an array, each row is a coordinate in a Ddimensional space  such as
histogramdd(np.array([p1, p2, p3]))
.When an array_like, each element is the list of values for single coordinate  such as
histogramdd((X, Y, Z))
.
The first form should be preferred.
bins (sequence or int, optional) –
The bin specification:
A sequence of arrays describing the monotonically increasing bin edges along each dimension.
The number of bins for each dimension (nx, ny, … =bins)
The number of bins for all dimensions (nx=ny=…=bins).
range (sequence, optional) – A sequence of length D, each an optional (lower, upper) tuple giving the outer bin edges to be used if the edges are not given explicitly in bins. An entry of None in the sequence results in the minimum and maximum values being used for the corresponding dimension. The default, None, is equivalent to passing a tuple of D None values.
density (bool, optional) – If False, the default, returns the number of samples in each bin. If True, returns the probability density function at the bin,
bin_count / sample_count / bin_volume
.normed (bool, optional) – An alias for the density argument that behaves identically. To avoid confusion with the broken normed argument to histogram, density should be preferred.
weights ((N,) array_like, optional) – An array of values w_i weighing each sample (x_i, y_i, z_i, …). Weights are normalized to 1 if normed is True. If normed is False, the values of the returned histogram are equal to the sum of the weights belonging to the samples falling into each bin.
 Returns
H (ndarray) – The multidimensional histogram of sample x. See normed and weights for the different possible semantics.
edges (list) – A list of D arrays describing the bin edges for each dimension.
See also
histogram()
1D histogram
histogram2d()
2D histogram
Examples
>>> r = np.random.randn(100,3) >>> H, edges = np.histogramdd(r, bins = (5, 8, 4)) >>> H.shape, edges[0].size, edges[1].size, edges[2].size ((5, 8, 4), 6, 9, 5)
boost_histogram.tag¶

class
boost_histogram.tag.
Slicer
¶ Bases:
object
This is a simple class to make slicing inside dictionaries simpler. This is how it should be used:
s = bh.tag.Slicer()
h[{0: s[::bh.rebin(2)]}] # rebin axis 0 by two

class
boost_histogram.tag.
loc
(value, offset=0)¶ Bases:
boost_histogram.tag.Locator

value
¶


boost_histogram.tag.
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 nonnumeric types.
boost_histogram.utils¶

boost_histogram.utils.
set_family
(family)¶ Decorator to set the family of a class. When an object is produced from the C++ bindings, it will look through all subclasses of the base class (Axis, Transform, Storage, etc.) to find a match. If possible, it will match the family of the object that is producing it (Histogram, Axis, etc.). If a family is not found, it will return the main family as a fallback.