# Histogramming: NumPy-like¶

pygram11.histogram(x, bins=10, range=None, weights=None, density=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 the x will contribute its associated weight to the bin count. density (bool) – normalize histogram bins as value of PDF such that the integral over the range is 1. 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 variable width bins. numpy.ndarray – bin counts (heights) numpy.ndarray – sum of weights squared (only if weights is not None)
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 – sum of weights squared (only if weights is not None)