tna.classes.TransientNutations
- class tna.classes.TransientNutations[source]
Transient nutation data class.
Transient nutation data can be loaded and processed.
- The __init__ functions creates the following attributes but sets them to
- None. Attributes are filled with values using the methods.
- field
Magnetic field array (for 2D data sets).
- Type:
np.ndarray
- time
Time point array.
- Type:
np.ndarray
- spc
Intensities of the experimental data. May be 1D or 2D.
- Type:
np.ndarray
- chosen_field
Chosen magnetic field point to get the time trace of two-dimensional spectra.
- Type:
float
- t_signal
Signal intensities along the time axis, (processed) experimental data.
- Type:
np.ndarray
- freq_signal
Fourier transform of the t_signal.
- Type:
np.ndarray
- t
(Processed) time axis.
- Type:
np.ndarray
- freq
Frequency axis for the freq_signal.
- Type:
np.ndarray
Methods
__init__()baseline_correction([deg])Baseline correction of the signal by using a polynomial fit function.
choose_field(field)Get the time signal at the given field point out of a 2dimensional spectrum (spc).
fourier_transformation([zero_filling, ...])Do a discrete fast fourier transformation of the time signal using the scipy function fft.fft.
load_1d(filename[, prodel])Load one dimensional transient nutation data in .DSC/.DTA format.
load_2d(filename[, prodel])Load two-dimensional transient nutation data in .DSC/.DTA format.
mean_subtraction()Subtraction of the mean of the signal from the signal.
reconstruction()Reconstruction of a time signal use the Yule-Walker algorithm.
wdw_chebwin([at])Convolve a time signal with the Dolph-Chebyshev window.
wdw_hamming([alpha])Convolve a time signal with the Hamming window.
wdw_kaiser([beta])Convolve the signal with the Kaiser window for ripple suppression and causes signal broadening of the fourier transformed signal.
wdw_lorentz_gauss(tau, sigma)Convolve the time signal by the Lorentz-Gauss window.
wdw_sinebell([phi])Convolve the time signal by the phase-shifted Sinebell window.