dpnp.histogram2d

dpnp.histogram2d(x, y, bins=10, range=None, density=None, weights=None)

Compute the bi-dimensional histogram of two data samples.

For full documentation refer to numpy.histogram2d.

Warning

This function may synchronize in order to check a monotonically increasing array of bin edges. This may harm performance in some applications.

Parameters:

• x ({dpnp.ndarray, usm_ndarray} of shape (N,)) -- An array containing the x coordinates of the points to be histogrammed.

• y ({dpnp.ndarray, usm_ndarray} of shape (N,)) -- An array containing the y coordinates of the points to be histogrammed.

• bins ({int, dpnp.ndarray, usm_ndarray, [int, int], [array, array], [int, array], [array, int]}, optional) --

  The bins specification:

  • If int, the number of bins for the two dimensions (nx=ny=bins).

  • If array, 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.

  Default: 10.

• range ({None, dpnp.ndarray, usm_ndarray} of shape (2, 2), optional) --

  The leftmost and rightmost edges of the bins along each dimension If None the ranges are [[x.min(), x.max()], [y.min(), y.max()]]. All values outside of this range will be considered outliers and not tallied in the histogram.

  Default: None.

• density ({None, bool}, optional) --

  If False or None, 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.

  Default: None.

• weights ({None, dpnp.ndarray, usm_ndarray} of shape (N,), optional) --

  An array of values w_i weighing each sample (x_i, y_i). Weights are normalized to 1 if density is True. If density is False, the values of the returned histogram are equal to the sum of the weights belonging to the samples falling into each bin. If None all samples are assigned a weight of 1.

  Default: None.

Returns:

• H (dpnp.ndarray of shape (nx, ny)) -- The bi-dimensional 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 (dpnp.ndarray of shape (nx+1,)) -- The bin edges along the first dimension.

• yedges (dpnp.ndarray of shape (ny+1,)) -- The bin edges along the second dimension.

See also

dpnp.histogram

1D histogram

dpnp.histogramdd

Multidimensional histogram

Notes

When density 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

>>> import dpnp as np
>>> x = np.random.randn(20).astype("float32")
>>> y = np.random.randn(20).astype("float32")
>>> hist, edges_x, edges_y = np.histogram2d(x, y, bins=(4, 3))
>>> hist.shape
(4, 3)
>>> hist
array([[1., 2., 0.],
       [0., 3., 1.],
       [1., 4., 1.],
       [1., 3., 3.]], dtype=float32)
>>> edges_x.shape
(5,)
>>> edges_x
array([-1.7516936 , -0.96109843, -0.17050326,  0.62009203,  1.4106871 ],
      dtype=float32)
>>> edges_y.shape
(4,)
>>> edges_y
array([-2.6604428 , -0.94615364,  0.76813555,  2.4824247 ], dtype=float32)

Please note, that resulting values of histogram and edges may vary.