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.
Returns:

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
Returns: