# Copyright 2022-2023 XProbe Inc.
# derived from copyright 1999-2021 Alibaba Group Holding 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.
import numpy as np
from ... import opcodes as OperandDef
from ...core import NotSupportTile, recursive_tile
from ...serialization.serializables import Float64Field, Int32Field, KeyField
from ..core import TensorOrder
from ..datasource import arange
from ..operands import TensorHasInput, TensorOperand, TensorOperandMixin
class TensorFFTFreq(TensorOperand, TensorOperandMixin):
_op_type_ = OperandDef.FFTFREQ
_n = Int32Field("n")
_d = Float64Field("d")
def __init__(self, n=None, d=None, **kw):
super().__init__(_n=n, _d=d, **kw)
@property
def n(self):
return self._n
@property
def d(self):
return self._d
def __call__(self, chunk_size=None):
shape = (self.n,)
return self.new_tensor(
None, shape, raw_chunk_size=chunk_size, order=TensorOrder.C_ORDER
)
@classmethod
def tile(cls, op):
tensor = op.outputs[0]
in_tensor = yield from recursive_tile(
arange(
op.n,
gpu=op.gpu,
dtype=op.dtype,
chunks=tensor.extra_params.raw_chunk_size,
)
)
out_chunks = []
for c in in_tensor.chunks:
chunk_op = TensorFFTFreqChunk(n=op.n, d=op.d, dtype=op.dtype)
out_chunk = chunk_op.new_chunk(
[c], shape=c.shape, index=c.index, order=tensor.order
)
out_chunks.append(out_chunk)
new_op = op.copy()
return new_op.new_tensors(
op.inputs,
tensor.shape,
order=tensor.order,
chunks=out_chunks,
nsplits=in_tensor.nsplits,
**tensor.extra_params
)
class TensorFFTFreqChunk(TensorHasInput, TensorOperandMixin):
_op_type_ = OperandDef.FFTFREQ_CHUNK
_input = KeyField("input")
_n = Int32Field("n")
_d = Float64Field("d")
def __init__(self, n=None, d=None, dtype=None, **kw):
super().__init__(_n=n, _d=d, dtype=dtype, **kw)
@property
def n(self):
return self._n
@property
def d(self):
return self._d
def _set_inputs(self, inputs):
super()._set_inputs(inputs)
self._input = self._inputs[0]
@classmethod
def tile(cls, op):
raise NotSupportTile(
"FFTFreqChunk is a chunk operand which does not support tile"
)
@classmethod
def execute(cls, ctx, op):
n, d = op.n, op.d
x = ctx[op.inputs[0].key].copy()
x[x >= (n + 1) // 2] -= n
x /= n * d
ctx[op.outputs[0].key] = x
[docs]def fftfreq(n, d=1.0, gpu=None, chunk_size=None):
"""
Return the Discrete Fourier Transform sample frequencies.
The returned float tensor `f` contains the frequency bin centers in cycles
per unit of the sample spacing (with zero at the start). For instance, if
the sample spacing is in seconds, then the frequency unit is cycles/second.
Given a window length `n` and a sample spacing `d`::
f = [0, 1, ..., n/2-1, -n/2, ..., -1] / (d*n) if n is even
f = [0, 1, ..., (n-1)/2, -(n-1)/2, ..., -1] / (d*n) if n is odd
Parameters
----------
n : int
Window length.
d : scalar, optional
Sample spacing (inverse of the sampling rate). Defaults to 1.
gpu : bool, optional
Allocate the tensor on GPU if True, False as default
chunk_size : int or tuple of int or tuple of ints, optional
Desired chunk size on each dimension
Returns
-------
f : Tensor
Array of length `n` containing the sample frequencies.
Examples
--------
>>> import mars.tensor as mt
>>> signal = mt.array([-2, 8, 6, 4, 1, 0, 3, 5], dtype=float)
>>> fourier = mt.fft.fft(signal)
>>> n = signal.size
>>> timestep = 0.1
>>> freq = mt.fft.fftfreq(n, d=timestep)
>>> freq.execute()
array([ 0. , 1.25, 2.5 , 3.75, -5. , -3.75, -2.5 , -1.25])
"""
n, d = int(n), float(d)
op = TensorFFTFreq(n=n, d=d, dtype=np.dtype(float), gpu=gpu)
return op(chunk_size)