umbmid/build.py
def convert_to_idft_dataset(fd_dataset):
    """Convert the freq-domain data to the time-domain via the IDFT

    Converts each sample in the fd_dataset from the frequency-domain
    to the time-domain via the IDFT.

    Parameters
    ----------
    fd_dataset : array_like
        The measured S-parameters in the frequency domain for each
        sample in the dataset

    Returns
    -------
    idft_dataset : array_like
        The time-domain representation of the data for each sample in
        the dataset, obtained via the IDFT
    """

    idft_dataset = np.zeros_like(fd_dataset)  # Init array to return

    # For each sample in the dataset
    for expt_idx in range(fd_dataset.shape[0]):

        print('\t\tWorking on expt [%4d / %4d]' % (expt_idx + 1,
                                                   fd_dataset.shape[0]))

        # Convert to the time-domain via the IDFT
        idft_dataset[expt_idx, :, :] = np.fft.ifft(fd_dataset[expt_idx, :, :],
                                                   axis=0)

    return idft_dataset
Convert the freq-domain data to the time-domain via the IDFT

    Converts each sample in the fd_dataset from the frequency-domain
    to the time-domain via the IDFT.

    Parameters
    ----------
    fd_dataset : array_like
        The measured S-parameters in the frequency domain for each
        sample in the dataset

    Returns
    -------
    idft_dataset : array_like
        The time-domain representation of the data for each sample in
        the dataset, obtained via the IDFT
    
Convert the freq-domain data to the time-domain via the IDFT
Converts each sample in the fd_dataset from the frequency-domain
to the time-domain via the IDFT.
Convert the freq - domain data to the time - domain via the IDFT Converts each sample in the fd_dataset from the frequency - domain to the time - domain via the IDFT .
def convert_to_idft_dataset(fd_dataset):
    idft_dataset = np.zeros_like(fd_dataset)  
    for expt_idx in range(fd_dataset.shape[0]):
        print('\t\tWorking on expt [%4d / %4d]' % (expt_idx + 1,
                                                   fd_dataset.shape[0]))
        idft_dataset[expt_idx, :, :] = np.fft.ifft(fd_dataset[expt_idx, :, :],
                                                   axis=0)
    return idft_dataset
def convert_to_idft_dataset ( fd_dataset ) : idft_dataset = np . zeros_like ( fd_dataset ) for expt_idx in range ( fd_dataset . shape [ 0 ] ) : print ( '\t\tWorking on expt [%4d / %4d]' % ( expt_idx + 1 , fd_dataset . shape [ 0 ] ) ) idft_dataset [ expt_idx , : , : ] = np . fft . ifft ( fd_dataset [ expt_idx , : , : ] , axis = 0 ) return idft_dataset
Convert the freq-domain data to the time-domain via the IDFT
Converts each sample in the fd_dataset from the frequency-domain
to the time-domain via the IDFT.
Convert the freq - domain data to the time - domain via the IDFT Converts each sample in the fd_dataset from the frequency - domain to the time - domain via the IDFT .
"""Convert the freq-domain data to the time-domain via the IDFT

    Converts each sample in the fd_dataset from the frequency-domain
    to the time-domain via the IDFT.

    Parameters
    ----------
    fd_dataset : array_like
        The measured S-parameters in the frequency domain for each
        sample in the dataset

    Returns
    -------
    idft_dataset : array_like
        The time-domain representation of the data for each sample in
        the dataset, obtained via the IDFT
    """ # Init array to return
 # For each sample in the dataset
 # Convert to the time-domain via the IDFT

The time-domain representation of the data for each sample in
the dataset, obtained via the IDFT
The time - domain representation of the data for each sample in the dataset obtained via the IDFT
array_like

fd_dataset
The measured S-parameters in the frequency domain for each
sample in the dataset
The measured S - parameters in the frequency domain for each sample in the dataset