PERIODOGRAM

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The PERIODOGRAM node is based on a numpy or scipy function. The description of that function is as follows: Estimate power spectral density using a periodogram. Params: select_return : 'f', 'Pxx'. 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 'boxcar'. nfft : int Length of the FFT used. If 'None', the length of 'x' will be used. 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 'constant'. 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. scaling : { 'density', 'spectrum' } Selects between computing the power spectral density ('density') where 'Pxx' has units of V**2/Hz and computing the power spectrum ('spectrum') where 'Pxx' has units of V**2, if 'x' is measured in V and 'fs' is measured in Hz. Defaults to 'density'. axis : int Axis along which the periodogram is computed; the default is over the last axis (i.e. axis=-1). 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 PERIODOGRAM(
    default: OrderedPair | Matrix,
    fs: float = 1.0,
    window: str = "boxcar",
    nfft: int = 2,
    detrend: str = "constant",
    return_onesided: bool = True,
    scaling: str = "density",
    axis: int = -1,
    select_return: Literal["f", "Pxx"] = "f",
) -> OrderedPair | Matrix | Scalar:
    """The PERIODOGRAM node is based on a numpy or scipy function.

    The description of that function is as follows:

        Estimate power spectral density using a periodogram.

    Parameters
    ----------
    select_return : 'f', 'Pxx'.
        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 'boxcar'.
    nfft : int, optional
        Length of the FFT used.
        If 'None', the length of 'x' will be used.
    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 'constant'.
    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.
    scaling : { 'density', 'spectrum' }, optional
        Selects between computing the power spectral density ('density')
        where 'Pxx' has units of V**2/Hz and computing the power
        spectrum ('spectrum') where 'Pxx' has units of V**2, if 'x'
        is measured in V and 'fs' is measured in Hz.
        Defaults to 'density'.
    axis : int, optional
        Axis along which the periodogram is computed;
        the default is over the last axis (i.e. axis=-1).

    Returns
    -------
    DataContainer
        type 'ordered pair', 'scalar', or 'matrix'
    """

    result = scipy.signal.periodogram(
        x=default.y,
        fs=fs,
        window=window,
        nfft=nfft,
        detrend=detrend,
        return_onesided=return_onesided,
        scaling=scaling,
        axis=axis,
    )

    return_list = ["f", "Pxx"]
    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

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