# Histogramming: NumPy-like¶

pygram11.histogram(x, bins=10, range=None, weights=None, density=False, flow=False, omp='auto')[source]

Compute the histogram for the data x.

This function provides an API very simiar to numpy.histogram(). Keep in mind that the returns are different.

Parameters: x (array_like) – Data to histogram. bins (int or sequence of scalars, optional) – If bins is an int, that many equal-width bins will be used to construct the histogram in the given range. If bins is a sequence, it must define a monotonically increasing array of bin edges. This allows for nonuniform bin widths. range ((float, float), optional) – The range over which the histogram is constructed. If a range is not provided then the default is (x.min(), x.max()). Values outside of the range are ignored. If bins is a sequence, this options is ignored. weights (array_like, optional) – An array of weights associated to each element of x. Each value of x will contribute its associated weight to the bin count. If argument is two-dimensional then the function will return histogram data for all weight variations. The first dimension of the shape of the weights (if multiple variations) must be the shape of x. density (bool) – normalize histogram bins as value of PDF such that the integral over the range is 1. flow (bool) – if True the under and overflow bin contents are added to the first and last bins, respectively omp (bool or str) – if True, use OpenMP if available; if “auto” (and OpenMP is available), enables OpenMP if len(x) > 10^4 for fixed width and > 10^3 for variaxsble width bins. numpy.ndarray – bin counts (heights) numpy.ndarray, – Poisson uncertainty on each bin count (None if weights are absent)

Examples

A histogram of x with 20 bins between 0 and 100.

>>> h, __ = fix1d(x, bins=20, range=(0, 100))


The same data, now histogrammed weighted & accelerated with OpenMP.

>>> h, err = histogram(x, bins=20, range=(0, 100), omp=True)


Multiple histograms of x with 50 bins between 0 and 100; using 20 different weight variations:

>>> x = np.random.randn(10000)
>>> twenty_weights = np.random.rand(x.shape, 20)
>>> h, err = histogram(x, bins=50, range=(-3, 3), weights=twenty_weights, omp=True)


h and err are now shape (50, 20). Each column represents the histogram of the data with the respective weight.

A histogram of x where the edges are defined by the list [1, 5, 10, 12]:

>>> h, __ = var1d(x, bins=[1, 5, 10, 12])


The same data, now weighted and accelerated with OpenMP:

>>> h, err = histogram(x, bin=[1, 5, 10, 12], weights=w, omp=True)


Multiple histograms of x (with 9 variable width bins); using 20 different weight variations (also shifting under and overflow contents):

>>> x = np.random.randn(10000)
>>> bins = [-2, -1.5, -1.2, -1, -0.8, 0, 1.2, 1.5, 3.0]
>>> twenty_weights = np.random.rand(x.shape, 20)
>>> h, err = histogram(x, bins=bins, weights=twenty_weights, flow=True, omp=True)


h and err are now shape (9, 20). Each column represents the histogram of the data with the respective weight.

pygram11.histogram2d(x, y, bins=10, range=None, weights=None, omp=False)[source]

Compute the two-dimensional histogram for the data (x, y).

This function provides an API very simiar to numpy.histogram2d(). Keep in mind that the returns are different.

Parameters: x (array_like) – Array representing the x coordinate of the data to histogram. y (array_like) – Array representing the y coordinate of the data to histogram. 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_like, array_like], the bin edges in each dimension (x_edges, y_edges = bins). range (array_like, shape(2,2), optional) – The edges of this histogram along each dimension. If bins is not integral, then this parameter is ignored. If None, the default is [[x.min(), x.max()], [y.min(), y.max()]]. weights (array_like) – An array of weights associated to each element $$(x_i, y_i)$$ pair. Each pair of the the data will contribute its associated weight to the bin count. omp (bool) – Use OpenMP if available numpy.ndarray – bin counts (heights) numpy.ndarray – Poisson uncertainty on each bin count (None if weights are absent)