Constants

DPNP includes several constants:

dpnp.DLDeviceType = <enum 'DLDeviceType'>

An enum.IntEnum for the types of DLDevices supported by the DLPack protocol.

kDLCPU:
CPU (host) device

kDLCUDA:
CUDA GPU device

kDLCUDAHost:
Pinned CUDA CPU memory by cudaMallocHost

kDLOpenCL:
OpenCL device

kDLVulkan:
Vulkan buffer

kDLMetal:
Metal for Apple GPU

kDLVPI:
Verilog simulator buffer

kDLROCM:
ROCm GPU device

kDLROCMHost:
Pinned ROCm CPU memory allocated by hipMallocHost

kDLExtDev:
Reserved extension device type used to test new devices

kDLCUDAManaged:
CUDA managed/unified memory allocated by cudaMallocManaged

kDLOneAPI:
Unified shared memory allocated on a oneAPI non-partitioned device

kDLWebGPU:
Device support for WebGPU standard

kDLHexagon:
Qualcomm Hexagon DSP

kDLMAIA:
Microsoft MAIA device

dpnp.e

Euler's constant, base of natural logarithms, Napier's constant.

e = 2.71828182845904523536028747135266249775724709369995...

See Also

isinf() : Shows which elements are positive or negative infinity

isposinf() : Shows which elements are positive infinity

isneginf() : Shows which elements are negative infinity

isnan() : Shows which elements are Not a Number

isfinite() : Shows which elements are finite (not one of Not a Number, positive infinity and negative infinity)

Notes

DPNP uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Also that positive infinity is not equivalent to negative infinity. But infinity is equivalent to positive infinity.

Examples

>>> import dpnp as np
>>> np.inf
inf
>>> np.array([1]) / 0.0
array([inf])

dpnp.nan

IEEE 754 floating point representation of Not a Number (NaN).

Returns

y : A floating point representation of Not a Number.

See Also

isnan() : Shows which elements are Not a Number

isfinite() : Shows which elements are finite (not one of Not a Number, positive infinity and negative infinity)

Notes

DPNP uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity.

Examples

>>> import dpnp as np
>>> np.nan
nan
>>> np.log(np.array(-1))
array(nan)
>>> np.log(np.array([-1, 1, 2]))
array([       nan, 0.        , 0.69314718])

dpnp.newaxis

A convenient alias for None, useful for indexing arrays.

Examples

>>> import dpnp as np
>>> np.newaxis is None
True
>>> x = np.arange(3)
>>> x
array([0, 1, 2])
>>> x[:, np.newaxis]
array([[0],
       [1],
       [2]])
>>> x[:, np.newaxis, np.newaxis]
array([[[0]],
       [[1]],
       [[2]]])
>>> x[:, np.newaxis] * x
array([[0, 0, 0],
       [0, 1, 2],
       [0, 2, 4]])

Outer product, same as ``outer(x, y)``:

>>> y = np.arange(3, 6)
>>> x[:, np.newaxis] * y
array([[ 0,  0,  0],
    [ 3,  4,  5],
    [ 6,  8, 10]])

``x[np.newaxis, :]`` is equivalent to ``x[np.newaxis]`` and ``x[None]``:

>>> x[np.newaxis, :].shape
(1, 3)
>>> x[np.newaxis].shape
(1, 3)
>>> x[None].shape
(1, 3)
>>> x[:, np.newaxis].shape
(3, 1)

dpnp.pi

pi = 3.1415926535897932384626433...

References

https://en.wikipedia.org/wiki/Pi