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VesuvioPeakPrediction v1

../_images/VesuvioPeakPrediction-v1_dlg.png

VesuvioPeakPrediction dialog.

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’]

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