- cupy.histogram2d(x, y, bins=10, range=None, weights=None, density=None)#
Compute the bi-dimensional histogram of two data samples.
x (cupy.ndarray) – The first array of samples to be histogrammed.
y (cupy.ndarray) – The second array of samples to be histogrammed.
The bin specification:
A sequence of arrays describing the monotonically increasing bin edges along each dimension.
The number of bins for each dimension (nx, ny)
The number of bins for all dimensions (nx=ny=bins).
range (sequence, optional) – A sequence of length two, each an optional (lower, upper) tuple giving the outer bin edges to be used if the edges are not given explicitly in bins. An entry of None in the sequence results in the minimum and maximum values being used for the corresponding dimension. The default, None, is equivalent to passing a tuple of two None values.
weights (cupy.ndarray) – An array of values w_i weighing each sample (x_i, y_i). The values of the returned histogram are equal to the sum of the weights belonging to the samples falling into each bin.
density (bool, optional) – If False, 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.
- H (cupy.ndarray):
The multidimensional histogram of sample x. See normed and weights for the different possible semantics.
- edges0 (tuple of cupy.ndarray):
A list of D arrays describing the bin edges for the first dimension.
- edges1 (tuple of cupy.ndarray):
A list of D arrays describing the bin edges for the second dimension.
- Return type
This function may synchronize the device.