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#
# 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
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import numpy as np
from maxframe import opcodes
from maxframe.serialization.serializables import Float64Field, Int32Field
from maxframe.tensor.core import TensorOrder
from maxframe.tensor.operators import TensorOperator, TensorOperatorMixin
class TensorRFFTFreq(TensorOperator, TensorOperatorMixin):
_op_type_ = opcodes.RFFTFREQ
n = Int32Field("n")
d = Float64Field("d")
def __call__(self, chunk_size=None):
shape = (self.n // 2 + 1,)
return self.new_tensor(
None, shape, raw_chunk_size=chunk_size, order=TensorOrder.C_ORDER
)
[docs]
def rfftfreq(n, d=1.0, gpu=None, chunk_size=None):
"""
Return the Discrete Fourier Transform sample frequencies
(for usage with rfft, irfft).
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] / (d*n) if n is even
f = [0, 1, ..., (n-1)/2-1, (n-1)/2] / (d*n) if n is odd
Unlike `fftfreq` (but like `scipy.fftpack.rfftfreq`)
the Nyquist frequency component is considered to be positive.
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
Tensor of length ``n//2 + 1`` containing the sample frequencies.
Examples
--------
>>> import maxframe.tensor as mt
>>> signal = mt.array([-2, 8, 6, 4, 1, 0, 3, 5, -3, 4], dtype=float)
>>> fourier = mt.fft.rfft(signal)
>>> n = signal.size
>>> sample_rate = 100
>>> freq = mt.fft.fftfreq(n, d=1./sample_rate)
>>> freq.execute()
array([ 0., 10., 20., 30., 40., -50., -40., -30., -20., -10.])
>>> freq = mt.fft.rfftfreq(n, d=1./sample_rate)
>>> freq.execute()
array([ 0., 10., 20., 30., 40., 50.])
"""
n, d = int(n), float(d)
op = TensorRFFTFreq(n=n, d=d, dtype=np.dtype(float), gpu=gpu)
return op(chunk_size=chunk_size)