# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""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
[文档]def blackman_window(window_length, periodic=True, *, dtype=None):
r"""
Blackman window function.
Usually used to extract finite signal segment for FFT.
.. 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 window.
periodic (bool, optional): If ``True`` , return a periodic window. If ``False``, return a symmetric window.
Default ``True`` .
Keyword Args:
dtype (mindspore.dtype, optional): The data type specified. Default ``None`` .
Returns:
A 1-D tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> window_length = mindspore.tensor(10)
>>> output = mindspore.ops.blackman_window(window_length)
>>> 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)
[文档]def bartlett_window(window_length, periodic=True, *, dtype=None):
r"""
Bartlett window function.
A triangular-shaped weighting function used for smoothing or frequency analysis of signals in digital signal
processing.
.. 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 :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 window.
periodic (bool, optional): If ``True`` , return a periodic window. If ``False``, return a symmetric window.
Default ``True`` .
Keyword Args:
dtype (mindspore.dtype, optional): The data type specified. Default ``None`` .
Returns:
A 1-D tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> window_length = mindspore.tensor(5)
>>> output = mindspore.ops.bartlett_window(window_length)
>>> print(output)
[0. 0.4 0.8 0.8 0.4]
>>> output = mindspore.ops.bartlett_window(window_length, periodic=False)
>>> print(output)
[0. 0.5 1. 0.5 0. ]
"""
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()