aiida_vibroscopy.utils.broadenings

Broadening functions for plottin spectra.

Module Contents

Functions

lorentz(x_range, peak, intensity, gamma)	Compute a Lorentzian function.
multilorentz(x_range, peaks, intensities, gammas)	Compute Lorentzian function for multiple peaks, and sum it.
voigt_profile(x_range, peak, intensity, gamma_lorentz, ...)	Compute a Voigt profile convolution.
multilvoigt(x_range, peaks, intensities, ...)	Compute Lorentzian function for multiple peaks, and sum it.
gaussian(x_range, peak, intensity, sigma)	Compute a Gaussian function.

aiida_vibroscopy.utils.broadenings.lorentz(x_range: numpy.ndarray, peak: float, intensity: float, gamma: float)

Compute a Lorentzian function.

Note

assuming all quantities in cm-1, but intensity at arb. units

Parameters:

• x_range – frequency range

• peak – peak position

• intensity – intensity of the peak

• gamma – broadening, full width at half maximum (FWHM)

Returns:

numpy.ndarray

aiida_vibroscopy.utils.broadenings.multilorentz(x_range: numpy.ndarray, peaks: list[float], intensities: list[float], gammas: float | list[float])

Compute Lorentzian function for multiple peaks, and sum it.

Note

assuming all quantities in cm-1, but intensities at arb. units

Parameters:

• x_range – frequency range

• peaks – list of peak positions

• intensities – list of intensities for each position

• gammas – list of broadenings, i.e. full width at half maximum (FWHM)

Returns:

numpy.ndarray

aiida_vibroscopy.utils.broadenings.voigt_profile(x_range: numpy.ndarray, peak: float, intensity: float, gamma_lorentz: float, sigma_gaussian: float)

Compute a Voigt profile convolution.

Note

assuming all quantities in cm-1, but intensities at arb. units; implementation as from Ida et al. J. Appl. Cryst. (2000). 33, 1311

Important

FWHM_Gaussian = 2 * sqrt(2 * ln(2)) * sigma_gaussian; FWHM_Gaussian ~ 2.355 * sigma_gaussian

Parameters:

• x_range – frequency range

• peak – peak position

• intensity – intensity of the peak

• gamma_lorentz – Lorentzian broadening, full width at half maximum (FWHM)

• sigma_gaussian – Guassian broadening, corresponding to the deviation standard (not FWHM)

Returns:

numpy.ndarray

aiida_vibroscopy.utils.broadenings.multilvoigt(x_range: numpy.ndarray, peaks: list[float], intensities: list[float], gammas_lorentz: float | list[float], sigma_gaussian: float)

Compute Lorentzian function for multiple peaks, and sum it.

Note

assuming all quantities in cm-1, but intensities at arb. units

Important

FWHM_Gaussian = 2 * sqrt(2 * ln(2)) * sigma_gaussian; FWHM_Gaussian ~ 2.355 * sigma_gaussian

Parameters:

• x_range – frequency range

• peaks – list of peak positions

• intensities – list of intensities for each position

• gammas_lorentz – list (or single value) of Lorentzian broadenings, i.e. full width at half maximum (FWHM)

• sigma_gaussian – Guassian broadening, corresponding to sigma

Returns:

numpy.ndarray

aiida_vibroscopy.utils.broadenings.gaussian(x_range: numpy.ndarray, peak: float, intensity: float, sigma: float)

Compute a Gaussian function.

Note

assuming all quantities in cm-1, but intensity at arb. units

Important

FWHM_Gaussian = 2 * sqrt(2 * ln(2)) * sigma_gaussian; FWHM_Gaussian ~ 2.355 * sigma_gaussian

Parameters:

• x_range – frequency range

• peak – peak position

• intensity – intensity of the peak

• sigma – standard deviation of the Gaussian

Returns:

numpy.ndarray