var1dmw(x, weights, bins, flow=False)¶
Histogram data with multiple weight variations and variable width bins.
The weights array must have a total number of rows equal to the length of the input data. The number of columns in the weights array is equal to the number of weight variations. (The weights array must be an M x N matrix where M is the length of x and N is the number of weight variations).
Using three different weight variations:
>>> rng = np.random.default_rng(123) >>> x = rng.standard_normal(10000) >>> weights = nb.abs(rng.standard_normal((x.shape, 3))) >>> edges = np.array([-3.0, -2.5, -1.5, -0.25, 0.25, 2.0, 3.0]) >>> h, err = var1dmw(x, weights, edges) >>> h.shape (6, 3) >>> err.shape (6, 3)