Source code for mindspore.ops.function.spectral_func

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"""Defines spectral operators with functional form."""

from mindspore.ops import operations as P
from mindspore.common import dtype as mstype
from .._primitive_cache import _get_cache_prim


[docs]def blackman_window(window_length, periodic=True, *, dtype=None): r""" Blackman window function, usually used to extract finite signal segment for FFT. The `window_length` is a input tensor which determines the returned window size, and its data should be an integer. In particular, if `window_length` is equal to `1`, only a single value `1` exists in the returned window. Attr `periodic` determines whether the returned window removes the last duplicate value from the symmetric window and prepares to be a periodic window with functions. Therefore, if attr `periodic` is true, the :math:`N` in formula is :math:`window\_length + 1`. .. math:: w[n] = 0.42 - 0.5 cos(\frac{2\pi n}{N - 1}) + 0.08 cos(\frac{4\pi n}{N - 1}) where :math:`N` is the full window size, and n is natural number less than :math:`N` :[0, 1, ..., N-1]. Args: window_length (Tensor): The size of returned window, with data type int32, int64. The input data should be an integer with a value of [0, 1000000]. periodic (bool, optional): Indicates whether to returns a window to be used as periodic function or a symmetric window. Default: ``True`` . Keyword Args: dtype (mindspore.dtype, optional): The data type of returned tensor. Only float16, float32 and float64 is allowed. Default: ``None`` . Returns: A 1-D tensor of size `window_length` containing the window. Its datatype is set by the attr `dtype`. If 'dtype' is None, output datatype is float32. Raises: TypeError: If `window_length` is not a Tensor. TypeError: If `periodic` is not a bool. TypeError: If `dtype` is not one of: float16, float32, float64. TypeError: If the type of `window_length` is not one of: int32, int64. ValueError: If the value range of `window_length` is not [0, 1000000]. ValueError: If the dimension of `window_length` is not 0. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import mindspore >>> from mindspore import Tensor, ops >>> window_length = Tensor(10, mindspore.int32) >>> output = ops.blackman_window(window_length, periodic=True, dtype=mindspore.float32) >>> print(output) [-2.9802322e-08 4.0212840e-02 2.0077014e-01 5.0978714e-01 8.4922993e-01 1.0000000e+00 8.4922981e-01 5.0978690e-01 2.0077008e-01 4.0212870e-02] """ if dtype is None: dtype = mstype.float32 blackman_window_op = _get_cache_prim(P.BlackmanWindow)(periodic, dtype) return blackman_window_op(window_length)
[docs]def bartlett_window(window_length, periodic=True, *, dtype=None): r""" Bartlett window function is a triangular-shaped weighting function used for smoothing or frequency analysis of signals in digital signal processing. The `window_length` is a input tensor which determines the returned window size, and its data should be an integer. In particular, if `window_length` is equal to `1`, only a single value 1 exists in the returned window. Attr `periodic` determines whether the returned window removes the last duplicate value from the symmetric window and prepares to be a periodic window with functions. Therefore, if attr `periodic` is true, the :math:`N` in formula is :math:`window\_length + 1`. .. math:: w[n] = 1 - \left| \frac{2n}{N-1} - 1 \right| = \begin{cases} \frac{2n}{N - 1} & \text{if } 0 \leq n \leq \frac{N - 1}{2} \\ 2 - \frac{2n}{N - 1} & \text{if } \frac{N - 1}{2} < n < N \\ \end{cases}, where N is the full window size. Args: window_length (Tensor): The size of returned window, with data type int32, int64. The input data should be an integer with a value of [0, 1000000]. periodic (bool, optional): Indicates whether to returns a window to be used as periodic function or a symmetric window. Default: ``True`` , indicating that the returned window is a periodic function. Keyword Args: dtype (mindspore.dtype, optional): The datatype of returned tensor. Only float16, float32 and float64 are allowed. Default: ``None`` . Returns: A 1-D tensor of size `window_length` containing the window. Its datatype is set by the attr `dtype`. If `dtype` is None, output datatype is float32. Raises: TypeError: If `window_length` is not a Tensor. TypeError: If the type of `window_length` is not one of: int32, int64. TypeError: If `periodic` is not a bool. TypeError: If `dtype` is not one of: float16, float32, float64. ValueError: If the value range of `window_length` is not [0, 1000000]. ValueError: If the dimension of `window_length` is not 0. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> from mindspore import Tensor, ops >>> from mindspore import dtype as mstype >>> window_length = Tensor(5, mstype.int32) >>> output = ops.bartlett_window(window_length, periodic=True, dtype=mstype.float32) >>> print(output) [0. 0.4 0.8 0.8 0.4] """ if dtype is None: dtype = mstype.float32 bartlett_window_op = _get_cache_prim(P.BartlettWindow)(periodic, dtype) return bartlett_window_op(window_length)
__all__ = [ 'blackman_window', 'bartlett_window', ] __all__.sort()