VesuvioPeakPrediction v1

Summary

Predicts parameters for Vesuvio peak widths using Debye or Einstein methods

Properties

Name	Direction	Type	Default	Description
Model	Input	string	Einstein	Model used to make predictions. Allowed values: [‘Debye’, ‘Einstein’, ‘Classical’]
Temperature	Input	dbl list		Temperature (K)
AtomicMass	Input	number	1	Atomic Mass (AMU)
Frequency	Input	number	1	Fundamental frequency of oscillator (mEV)
DebyeTemperature	Input	number	1	Debye Temperature (K)
OutputTable	Output	TableWorkspace	vesuvio_params	The name of the output table

Description

This algorithm uses either the Debye or Einstein method to calculate kinetic energy and root mean squared momentum and in the Debye case, root mean squared displacement, from a given temperature and atomic mass. The outputs from this can be used to help predict the nature of peaks in Vesuvio data.

Usage

Example - VesuvioPeakPrediction

vesuvio_debye_params = VesuvioPeakPrediction(Model='Debye', Temperature=[300], AtomicMass=63.5, Frequency=20, DebyeTemperature=347)

vesuvio_einstein_params= VesuvioPeakPrediction(Model='Einstein', Temperature=[300], AtomicMass=63.5, Frequency=20, DebyeTemperature=347)

vp = vesuvio_debye_params
print('--------Debye--------')
for c in vp.keys():
    print('%s: %.4f' %(c, vp.column(c)[0]))
vp = vesuvio_einstein_params
print('\n--------Einstein--------')
for c in vp.keys():
    print('%s: %.4f' %(c, vp.column(c)[0]))

Output:

--------Debye--------
Temperature(K): 300.0000
Atomic Mass(AMU): 63.5000
Debye Temp(K): 347.0000
Kinetic Energy(mEV): 41.2028
RMS Momentum(A-1): 20.4267
RMS Displacement(A): 0.0769

--------Einstein--------
Temperature(K): 300.0000
Atomic Mass(AMU): 63.5000
Frequency(mEV): 20.0000
Kinetic Energy(mEV): 13.5644
Effective Temp(K): 314.8156
RMS Momentum(A): 20.3903

Categories: AlgorithmIndex | Inelastic\Indirect\Vesuvio

Source

Python: VesuvioPeakPrediction.py