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CalculatePlaczekSelfScattering dialog.
Table of Contents
| Name | Direction | Type | Default | Description |
|---|---|---|---|---|
| InputWorkspace | Input | MatrixWorkspace | Mandatory | Raw diffraction data workspace for associated correction to be calculated for. Workspace must have instrument and sample data. |
| IncidentSpecta | Input | MatrixWorkspace | Mandatory | Workspace of fitted incident spectrum with it’s first derivative. Must be in units of Wavelength. |
| OutputWorkspace | Output | MatrixWorkspace | Mandatory | Workspace with the Self scattering correction |
| CrystalDensity | Input | number | Optional | The crystalographic density of the sample material. |
This algorithm takes an incident spectrum function and it’s derivative, along with the workspace containing spectrum info and calculates the time-of-flight Placzek scattering correction from the workspaces material sample and detector info. [1] [2] [3] For obtaining the incident spectrum from a measurement (ie beam monitors or calibrant sample), the FitIncidentSpectrum can provide the necessary inputs.
Note: Version 2 is simply a passthrough to CalculatePlaczek so that scripts still using this algorithm may benefit from the updated calculations.
Example: calculate Placzek self scattering correction using a sample detector setup [4]
from mantid.simpleapi import *
import matplotlib.pyplot as plt
import numpy as np
# Create the workspace to hold the already corrected incident spectrum
incident_wksp_name = 'incident_spectrum_wksp'
binning_incident = "0.1,0.02,5.0"
binning_for_calc = "0.2,0.02,4.0"
binning_for_fit = "0.15,0.02,4.5"
incident_wksp = CreateSampleWorkspace(
OutputWorkspace=incident_wksp_name,
NumBanks=10,
XMin=0.1,
XMax=5.0,
BinWidth=0.002,
Xunit='Wavelength')
incident_wksp = ConvertToPointData(InputWorkspace=incident_wksp)
# Spectrum function given in Milder et al. Eq (5)
def incident_spectrum(wavelengths, phi_max, phi_epi, alpha, lambda_1, lambda_2,
lamda_t):
delta_term = 1. / (1. + np.exp((wavelengths - lambda_1) / lambda_2))
term_1 = phi_max * (
lambda_t**4. / wavelengths**5.) * np.exp(-(lambda_t / wavelengths)**2.)
term_2 = phi_epi * delta_term / (wavelengths**(1 + 2 * alpha))
return term_1 + term_2
# Variables for polyethlyene moderator at 300K
phi_max = 6324
phi_epi = 786
alpha = 0.099
lambda_1 = 0.67143
lambda_2 = 0.06075
lambda_t = 1.58
# Add the incident spectrum to the workspace
corrected_spectrum = incidentSpectrum(
incident_wksp.readX(0), phi_max, phi_epi, alpha, lambda_1, lambda_2, lambda_t)
incident_wksp.setY(0, corrected_spectrum)
incident_spectrum = FitIncidentSpectrum(
InputWorkspace='incident_wksp',
OutputWorkspace='fit_wksp',
BinningForCalc=binning_for_calc,
BinningForFit=binning_for_fit)
SetSampleMaterial(
InputWorkspace='fit_wksp',
ChemicalFormula='Co')
CalculatePlaczekSelfScattering(
InputWorkspace='incident_wksp',
InputSpectra='fit_wksp',
OutputWorkspace='placzek_scattering')
| [1] |
|
| [2] | J.G. Powles, (1973), The analysis of a time-of-flight neutron diffractometer for amorphous materials: the structure of a molecule in a liquid, Molecular Physics, Volume 26, Issue 6, Page 1325-1350, doi: 10.1080/00268977300102521 |
| [3] | Howe, McGreevy, and Howells, J., (1989), The analysis of liquid structure data from time-of-flight neutron diffractometry,Journal of Physics: Condensed Matter, Volume 1, Issue 22, pp. 3433-3451, doi: 10.1088/0953-8984/1/22/005 |
| [4] |
|
Categories: AlgorithmIndex | CorrectionFunctions
C++ header: CalculatePlaczekSelfScattering2.h
C++ source: CalculatePlaczekSelfScattering2.cpp