VesuvioPeakPrediction dialog.
Table of Contents
| 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 |
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.
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
Python: VesuvioPeakPrediction.py (last modified: 2019-03-11)