dpnp.atan2¶

dpnp.atan2 = <DPNPBinaryFunc 'atan2'>¶

Calculates the inverse tangent of the quotient \(\frac{x1_i}{x2_i}\) for each element \(x1_i\) of the input array x1 with the respective element \(x2_i\) of the input array x2.

Note that dpnp.arctan2 is an alias of dpnp.atan2. This function is not defined for complex-valued arguments; for the so-called argument of complex values, use dpnp.angle.

For full documentation refer to numpy.atan2.

Parameters:

x1{dpnp.ndarray, usm_ndarray, scalar}

First input array, expected to have a real-valued floating-point data type.

x2{dpnp.ndarray, usm_ndarray, scalar}

Second input array, also expected to have a real-valued floating-point data type.

out{None, dpnp.ndarray, usm_ndarray, tuple of ndarray}, optional

Output array to populate. Array must have the correct shape and the expected data type. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.

Default: None.

order{None, "C", "F", "A", "K"}, optional

Memory layout of the newly output array, if parameter out is None.

Default: "K".

Returns:

outdpnp.ndarray: An array containing the inverse tangent of the quotient \(\frac{x1}{x2}\), in radians. The returned array must have a real-valued floating-point data type determined by Type Promotion Rules.

Limitations

Parameters where and subok are supported with their default values. Keyword argument kwargs is currently unsupported. Otherwise NotImplementedError exception will be raised.

See also

dpnp.atan: Trigonometric inverse tangent, element-wise.
dpnp.tan: Compute tangent element-wise.
dpnp.angle: Return the angle of the complex argument.
dpnp.asin: Trigonometric inverse sine, element-wise.
dpnp.acos: Trigonometric inverse cosine, element-wise.
dpnp.atanh: Inverse hyperbolic tangent, element-wise.

Notes

At least one of x1 or x2 must be an array.

If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).

Examples

>>> import dpnp as np
>>> x1 = np.array([1., -1.])
>>> x2 = np.array([0., 0.])
>>> np.atan2(x1, x2)
array([1.57079633, -1.57079633])

>>> x1 = np.array([0., 0., np.inf])
>>> x2 = np.array([+0., -0., np.inf])
>>> np.atan2(x1, x2)
array([0.0 , 3.14159265, 0.78539816])

>>> x1 = np.array([-1, +1, +1, -1])
>>> x2 = np.array([-1, -1, +1, +1])
>>> np.atan2(x1, x2) * 180 / np.pi
array([-135.,  -45.,   45.,  135.])