+
+ +
+

dpnp.histogram2d

+
+
+dpnp.histogram2d(x, y, bins=10, range=None, density=None, weights=None)[source]
+

Compute the bi-dimensional histogram of two data samples.

+
+
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.

+
+ +
+ + +
+