STFT
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The STFT node is based on a numpy or scipy function. The description of that function is as follows:
Compute the Short Time Fourier Transform (STFT).
STFTs can be used as a way of quantifying the change of a nonstationary signal's frequency and phase content over time. Params: select_return : 'f', 't', 'Zxx' Select the desired object to return.
See the respective function docs for descriptors. x : array_like Time series of measurement values. fs : float Sampling frequency of the 'x' time series.
Defaults to 1.0. window : str or tuple or array_like Desired window to use.
If 'window' is a string or tuple, it is passed to 'get_window'
to generate the window values, which are DFT-even by default.
See 'get_window' for a list of windows and required parameters.
If 'window' is array_like it will be used directly as the window
and its length must be nperseg.
Defaults to a Hann window. nperseg : int Length of each segment.
Defaults to 256. noverlap : int Number of points to overlap between segments.
If 'None', noverlap = nperseg // 2.
Defaults to 'None'.
When specified, the COLA constraint must be met (see Notes below). nfft : int Length of the FFT used, if a zero padded FFT is desired.
If 'None', the FFT length is 'nperseg'.
Defaults to 'None'. detrend : str or function or 'False' Specifies how to detrend each segment.
If 'detrend' is a string, it is passed as the 'type' argument to the 'detrend' function.
If it is a function, it takes a segment and returns a detrended segment.
If 'detrend' is 'False', no detrending is done.
Defaults to 'False'. return_onesided : bool If 'True', return a one-sided spectrum for real data.
If 'False' return a two-sided spectrum.
Defaults to 'True', but for complex data, a two-sided spectrum is always returned. boundary : str or None Specifies whether the input signal is extended at both ends, and
how to generate the new values, in order to center the first
windowed segment on the first input point.
This has the benefit of enabling reconstruction of the first input point
when the employed window function starts at zero.
Valid options are ['even', 'odd', 'constant', 'zeros', None].
Defaults to 'zeros', for zero padding extension.
I.e. [1, 2, 3, 4] is extended to [0, 1, 2, 3, 4, 0] for nperseg=3. padded : bool Specifies whether the input signal is zero-padded at the end to
make the signal fit exactly into an integer number of window
segments, so that all of the signal is included in the output.
Defaults to 'True'.
Padding occurs after boundary extension, if 'boundary' is not 'None',
and 'padded' is 'True', as is the default. axis : int Axis along which the STFT is computed.
The default is over the last axis (i.e. axis=-1). scaling : {'spectrum', 'psd'} The default 'spectrum' scaling allows each frequency line of 'Zxx' to
be interpreted as a magnitude spectrum.
The 'psd' option scales each line to a power spectral density.
It allows to calculate the signal's energy by numerically integrating over abs(Zxx)**2. .. versionadded : : 1.9.0 Returns: out : DataContainer type 'ordered pair', 'scalar', or 'matrix'
Python Code
from flojoy import OrderedPair, flojoy, Matrix, Scalar
import numpy as np
from typing import Literal
import scipy.signal
@flojoy
def STFT(
default: OrderedPair | Matrix,
fs: float = 1.0,
window: str = "hann",
nperseg: int = 2,
noverlap: int = 1,
nfft: int = 2,
detrend: bool = False,
return_onesided: bool = True,
boundary: str = "zeros",
padded: bool = True,
axis: int = -1,
scaling: str = "spectrum",
select_return: Literal["f", "t", "Zxx"] = "f",
) -> OrderedPair | Matrix | Scalar:
"""The STFT node is based on a numpy or scipy function.
The description of that function is as follows:
Compute the Short Time Fourier Transform (STFT).
STFTs can be used as a way of quantifying the change of a nonstationary signal's frequency and phase content over time.
Parameters
----------
select_return : 'f', 't', 'Zxx'
Select the desired object to return.
See the respective function docs for descriptors.
x : array_like
Time series of measurement values.
fs : float, optional
Sampling frequency of the 'x' time series.
Defaults to 1.0.
window : str or tuple or array_like, optional
Desired window to use.
If 'window' is a string or tuple, it is passed to 'get_window'
to generate the window values, which are DFT-even by default.
See 'get_window' for a list of windows and required parameters.
If 'window' is array_like it will be used directly as the window
and its length must be nperseg.
Defaults to a Hann window.
nperseg : int, optional
Length of each segment.
Defaults to 256.
noverlap : int, optional
Number of points to overlap between segments.
If 'None', noverlap = nperseg // 2.
Defaults to 'None'.
When specified, the COLA constraint must be met (see Notes below).
nfft : int, optional
Length of the FFT used, if a zero padded FFT is desired.
If 'None', the FFT length is 'nperseg'.
Defaults to 'None'.
detrend : str or function or 'False', optional
Specifies how to detrend each segment.
If 'detrend' is a string, it is passed as the 'type' argument to the 'detrend' function.
If it is a function, it takes a segment and returns a detrended segment.
If 'detrend' is 'False', no detrending is done.
Defaults to 'False'.
return_onesided : bool, optional
If 'True', return a one-sided spectrum for real data.
If 'False' return a two-sided spectrum.
Defaults to 'True', but for complex data, a two-sided spectrum is always returned.
boundary : str or None, optional
Specifies whether the input signal is extended at both ends, and
how to generate the new values, in order to center the first
windowed segment on the first input point.
This has the benefit of enabling reconstruction of the first input point
when the employed window function starts at zero.
Valid options are ['even', 'odd', 'constant', 'zeros', None].
Defaults to 'zeros', for zero padding extension.
I.e. [1, 2, 3, 4] is extended to [0, 1, 2, 3, 4, 0] for nperseg=3.
padded : bool, optional
Specifies whether the input signal is zero-padded at the end to
make the signal fit exactly into an integer number of window
segments, so that all of the signal is included in the output.
Defaults to 'True'.
Padding occurs after boundary extension, if 'boundary' is not 'None',
and 'padded' is 'True', as is the default.
axis : int, optional
Axis along which the STFT is computed.
The default is over the last axis (i.e. axis=-1).
scaling: {'spectrum', 'psd'}
The default 'spectrum' scaling allows each frequency line of 'Zxx' to
be interpreted as a magnitude spectrum.
The 'psd' option scales each line to a power spectral density.
It allows to calculate the signal's energy by numerically integrating over abs(Zxx)**2.
.. versionadded:: 1.9.0
Returns
-------
DataContainer
type 'ordered pair', 'scalar', or 'matrix'
"""
result = scipy.signal.stft(
x=default.y,
fs=fs,
window=window,
nperseg=nperseg,
noverlap=noverlap,
nfft=nfft,
detrend=detrend,
return_onesided=return_onesided,
boundary=boundary,
padded=padded,
axis=axis,
scaling=scaling,
)
return_list = ["f", "t", "Zxx"]
if isinstance(result, tuple):
res_dict = {}
num = min(len(result), len(return_list))
for i in range(num):
res_dict[return_list[i]] = result[i]
result = res_dict[select_return]
else:
result = result._asdict()
result = result[select_return]
if isinstance(result, np.ndarray):
result = OrderedPair(x=default.x, y=result)
else:
assert isinstance(
result, np.number | float | int
), f"Expected np.number, float or int for result, got {type(result)}"
result = Scalar(c=float(result))
return result