numpy_ipps.trigonometric module

Trigonometric and Hyperbolic Functions.

class numpy_ipps.trigonometric.Acos

Acos Function.

dst[n]  <-  arccos( src[n] )

Numpy dtype candidates: float32, float64, complex64, complex128

Intel IPP Signal accuracy candidates: LEVEL_1, LEVEL_3, LEVEL_2 (default)

Constructor signature: (size, dtype, accuracy=None)

Call signature: (dtype, accuracy)

dtype_candidates = (<class 'numpy.float32'>, <class 'numpy.float64'>, <class 'numpy.complex64'>, <class 'numpy.complex128'>)

ipps_accuracies = (<Accuracy.LEVEL_1: 1>, <Accuracy.LEVEL_3: 3>, <Accuracy.LEVEL_2: 2>)

class numpy_ipps.trigonometric.Acosh

Acosh Function.

dst[n]  <-  arcosh( src[n] )

Numpy dtype candidates: float32, float64, complex64, complex128

Intel IPP Signal accuracy candidates: LEVEL_1, LEVEL_3, LEVEL_2 (default)

Constructor signature: (size, dtype, accuracy=None)

Call signature: (dtype, accuracy)

dtype_candidates = (<class 'numpy.float32'>, <class 'numpy.float64'>, <class 'numpy.complex64'>, <class 'numpy.complex128'>)

ipps_accuracies = (<Accuracy.LEVEL_1: 1>, <Accuracy.LEVEL_3: 3>, <Accuracy.LEVEL_2: 2>)

class numpy_ipps.trigonometric.Asin

Asin Function.

dst[n]  <-  arcsin( src[n] )

Numpy dtype candidates: float32, float64, complex64, complex128

Intel IPP Signal accuracy candidates: LEVEL_1, LEVEL_3, LEVEL_2 (default)

Constructor signature: (size, dtype, accuracy=None)

Call signature: (dtype, accuracy)

dtype_candidates = (<class 'numpy.float32'>, <class 'numpy.float64'>, <class 'numpy.complex64'>, <class 'numpy.complex128'>)

ipps_accuracies = (<Accuracy.LEVEL_1: 1>, <Accuracy.LEVEL_3: 3>, <Accuracy.LEVEL_2: 2>)

class numpy_ipps.trigonometric.Asinh

Asinh Function.

dst[n]  <-  arsinh( src[n] )

Numpy dtype candidates: float32, float64, complex64, complex128

Intel IPP Signal accuracy candidates: LEVEL_1, LEVEL_3, LEVEL_2 (default)

Constructor signature: (size, dtype, accuracy=None)

Call signature: (dtype, accuracy)

dtype_candidates = (<class 'numpy.float32'>, <class 'numpy.float64'>, <class 'numpy.complex64'>, <class 'numpy.complex128'>)

ipps_accuracies = (<Accuracy.LEVEL_1: 1>, <Accuracy.LEVEL_3: 3>, <Accuracy.LEVEL_2: 2>)

class numpy_ipps.trigonometric.Atan

Atan Function.

dst[n]  <-  arctan( src[n] )

Numpy dtype candidates: float32, float64, complex64, complex128

Intel IPP Signal accuracy candidates: LEVEL_1, LEVEL_3, LEVEL_2 (default)

Constructor signature: (size, dtype, accuracy=None)

Call signature: (dtype, accuracy)

dtype_candidates = (<class 'numpy.float32'>, <class 'numpy.float64'>, <class 'numpy.complex64'>, <class 'numpy.complex128'>)

ipps_accuracies = (<Accuracy.LEVEL_1: 1>, <Accuracy.LEVEL_3: 3>, <Accuracy.LEVEL_2: 2>)

class numpy_ipps.trigonometric.Atan2

Atan2 Function.

dst[n]  <-  arctan( src2[n] / src1[n] )

Numpy dtype candidates: float32, float64

Intel IPP Signal accuracy candidates: LEVEL_1, LEVEL_3, LEVEL_2 (default)

Constructor signature: (dtype, accuracy=None, size=None)

Call signature: (src1, src2, dst)

dtype_candidates = (<class 'numpy.float32'>, <class 'numpy.float64'>)

ipps_accuracies = (<Accuracy.LEVEL_1: 1>, <Accuracy.LEVEL_3: 3>, <Accuracy.LEVEL_2: 2>)

class numpy_ipps.trigonometric.Atanh

Atanh Function.

dst[n]  <-  artanh( src[n] )

Numpy dtype candidates: float32, float64, complex64, complex128

Intel IPP Signal accuracy candidates: LEVEL_1, LEVEL_3, LEVEL_2 (default)

Constructor signature: (dtype, accuracy=None, size=None)

Call signature: (src, dst)

dtype_candidates = (<class 'numpy.float32'>, <class 'numpy.float64'>, <class 'numpy.complex64'>, <class 'numpy.complex128'>)

ipps_accuracies = (<Accuracy.LEVEL_1: 1>, <Accuracy.LEVEL_3: 3>, <Accuracy.LEVEL_2: 2>)

class numpy_ipps.trigonometric.Cos

Cos Function.

dst[n]  <-  cos( src[n] )

Numpy dtype candidates: float32, float64, complex64, complex128

Intel IPP Signal accuracy candidates: LEVEL_1, LEVEL_3, LEVEL_2 (default)

Constructor signature: (size, dtype, accuracy=None)

Call signature: (dtype, accuracy)

dtype_candidates = (<class 'numpy.float32'>, <class 'numpy.float64'>, <class 'numpy.complex64'>, <class 'numpy.complex128'>)

ipps_accuracies = (<Accuracy.LEVEL_1: 1>, <Accuracy.LEVEL_3: 3>, <Accuracy.LEVEL_2: 2>)

class numpy_ipps.trigonometric.Cosh

Cosh Function.

dst[n]  <-  cosh( src[n] )

Numpy dtype candidates: float32, float64, complex64, complex128

Intel IPP Signal accuracy candidates: LEVEL_1, LEVEL_3, LEVEL_2 (default)

Constructor signature: (dtype, accuracy=None, size=None)

Call signature: (src, dst)

dtype_candidates = (<class 'numpy.float32'>, <class 'numpy.float64'>, <class 'numpy.complex64'>, <class 'numpy.complex128'>)

ipps_accuracies = (<Accuracy.LEVEL_1: 1>, <Accuracy.LEVEL_3: 3>, <Accuracy.LEVEL_2: 2>)

class numpy_ipps.trigonometric.Hypot

Hypot Function.

dst[n]  <-  sqrt(   src1[n] * src1[n]  +  src2[n] * src2[n]   )

Numpy dtype candidates: float32, float64

Intel IPP Signal accuracy candidates: LEVEL_1, LEVEL_3, LEVEL_2 (default)

Constructor signature: (dtype, accuracy=None, size=None)

Call signature: (src1, src2, dst)

dtype_candidates = (<class 'numpy.float32'>, <class 'numpy.float64'>)

ipps_accuracies = (<Accuracy.LEVEL_1: 1>, <Accuracy.LEVEL_3: 3>, <Accuracy.LEVEL_2: 2>)

class numpy_ipps.trigonometric.Sin

Sin Function.

dst[n]  <-  sin( src[n] )

Numpy dtype candidates: float32, float64, complex64, complex128

Intel IPP Signal accuracy candidates: LEVEL_1, LEVEL_3, LEVEL_2 (default)

Constructor signature: (size, dtype, accuracy=None)

Call signature: (dtype, accuracy)

dtype_candidates = (<class 'numpy.float32'>, <class 'numpy.float64'>, <class 'numpy.complex64'>, <class 'numpy.complex128'>)

ipps_accuracies = (<Accuracy.LEVEL_1: 1>, <Accuracy.LEVEL_3: 3>, <Accuracy.LEVEL_2: 2>)

class numpy_ipps.trigonometric.Sinc

Sinc Function.

Numpy dtype candidates: float32, float64

Intel IPP Signal accuracy candidates: LEVEL_1, LEVEL_3, LEVEL_2 (default)

Constructor signature: (size, dtype, level=None, accuracy=None)

Call signature: (src, dst)

dtype_candidates = (<class 'numpy.float32'>, <class 'numpy.float64'>)

ipps_accuracies = (<Accuracy.LEVEL_1: 1>, <Accuracy.LEVEL_3: 3>, <Accuracy.LEVEL_2: 2>)

class numpy_ipps.trigonometric.Sinh

Sinh Function.

dst[n]  <-  sinh( src[n] )

Numpy dtype candidates: float32, float64, complex64, complex128

Intel IPP Signal accuracy candidates: LEVEL_1, LEVEL_3, LEVEL_2 (default)

Constructor signature: (dtype, accuracy=None, size=None)

Call signature: (src, dst)

dtype_candidates = (<class 'numpy.float32'>, <class 'numpy.float64'>, <class 'numpy.complex64'>, <class 'numpy.complex128'>)

ipps_accuracies = (<Accuracy.LEVEL_1: 1>, <Accuracy.LEVEL_3: 3>, <Accuracy.LEVEL_2: 2>)

class numpy_ipps.trigonometric.Sinhc

Sinhc Function.

Numpy dtype candidates: float32, float64

Intel IPP Signal accuracy candidates: LEVEL_1, LEVEL_3, LEVEL_2 (default)

Constructor signature: (size, dtype, level=None, accuracy=None)

Call signature: (src, dst)

dtype_candidates = (<class 'numpy.float32'>, <class 'numpy.float64'>)

ipps_accuracies = (<Accuracy.LEVEL_1: 1>, <Accuracy.LEVEL_3: 3>, <Accuracy.LEVEL_2: 2>)

class numpy_ipps.trigonometric.Tan

Tan Function.

dst[n]  <-  tan( src[n] )

Numpy dtype candidates: float32, float64, complex64, complex128

Intel IPP Signal accuracy candidates: LEVEL_1, LEVEL_3, LEVEL_2 (default)

Constructor signature: (size, dtype, accuracy=None)

Call signature: (dtype, accuracy)

dtype_candidates = (<class 'numpy.float32'>, <class 'numpy.float64'>, <class 'numpy.complex64'>, <class 'numpy.complex128'>)

ipps_accuracies = (<Accuracy.LEVEL_1: 1>, <Accuracy.LEVEL_3: 3>, <Accuracy.LEVEL_2: 2>)

class numpy_ipps.trigonometric.Tanh

Tanh Function.

dst[n]  <-  tanh( src[n] )

Numpy dtype candidates: float32, float64, complex64, complex128

Intel IPP Signal accuracy candidates: LEVEL_1, LEVEL_3, LEVEL_2 (default)

Constructor signature: (size, dtype, accuracy=None)

Call signature: (dtype, accuracy)

dtype_candidates = (<class 'numpy.float32'>, <class 'numpy.float64'>, <class 'numpy.complex64'>, <class 'numpy.complex128'>)

ipps_accuracies = (<Accuracy.LEVEL_1: 1>, <Accuracy.LEVEL_3: 3>, <Accuracy.LEVEL_2: 2>)

class numpy_ipps.trigonometric.Tanhc

Tanhc Function.

Numpy dtype candidates: float32, float64

Intel IPP Signal accuracy candidates: LEVEL_1, LEVEL_3, LEVEL_2 (default)

Constructor signature: (size, dtype, level=None, accuracy=None)

Call signature: (src, dst)

dtype_candidates = (<class 'numpy.float32'>, <class 'numpy.float64'>)

ipps_accuracies = (<Accuracy.LEVEL_1: 1>, <Accuracy.LEVEL_3: 3>, <Accuracy.LEVEL_2: 2>)