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NMoldyn4Interpolation v1

NMoldyn4Interpolation dialog.

Summary

Maps NMoldyn simulated s(q,e) data onto OSIRIS’ Q and E values

Properties

Name	Direction	Type	Default	Description
InputWorkspace	Input	Workspace	Mandatory	Simulated workspace
ReferenceWorkspace	Input	Workspace	Mandatory	Reference OSIRIS workspace to provide values
EFixed	Input	number	1.845	EFixed value of OSIRIS data (should be default in almost all circumstances)
OutputWorkspace	Output	Workspace	Mandatory	Output Workspace of remapped simulation data

Description

Given simulated s(q,e) data workspace and a reference OSIRIS s(q,e) workspace, interpolates the simulated data onto the same (q, e) grid as the reference workspace. This allows direct comparison between simulated and experimental data upon the same axes. Currently only supports OSIRIS experimental data.

Usage

Example 1 - Interpolate a simulated data set onto the axes of an experimental set

#create a simulated S(Q,E) workspace of a sin function
x_data = np.arange(-2., 2., 0.05)
q_data = np.arange(0.5, 1.3, 0.1)
y_data = np.asarray([val*(np.cos(5*x_data)+1) for val in q_data])
y_data = y_data.flatten()
x_data = np.tile(x_data, len(q_data))
sim_ws= CreateWorkspace(DataX=x_data, DataY=y_data, NSpec = len(q_data),
                        VerticalAxisUnit='MomentumTransfer',
                        VerticalAxisValues=q_data)
#create an empty OSIRIS workspace (this would be your experimental OSIRIS resolution function)
idf_dir = config['instrumentDefinition.directory']
osiris = LoadEmptyInstrument(idf_dir + 'OSIRIS_Definition.xml')
osiris = CropWorkspace(osiris,  StartWorkspaceIndex=970, EndWorkspaceIndex=980)
osiris = Rebin(osiris, [-0.6, 0.02, 0.6])
#interpolate the two workspaces
interpolated_ws = NMoldyn4Interpolation(sim_ws, osiris)
print('No. of Q-values in simulation = {}'.format(sim_ws.getNumberHistograms()))
print('No. of Q-values in reference = {}'.format(osiris.getNumberHistograms()))
print('No. of Q-values in interpolated set = {}'.format(interpolated_ws.getNumberHistograms()))

Output:

No. of Q-values in simulation = 8
No. of Q-values in reference = 11
No. of Q-values in interpolated set = 11

Categories: AlgorithmIndex | Simulation | Inelastic\DataHandling

Source

Python: NMoldyn4Interpolation.py