GroupToXResolution v1#

Summary#

Groups points within intervals given by the Dx into single points

Properties#

Name	Direction	Type	Default	Description
InputWorkspace	Input	MatrixWorkspace	Mandatory	An input workspace with Dx values.
OutputWorkspace	Output	MatrixWorkspace	Mandatory	The grouped workspace.
FractionOfDx	Input	number	0.2	A fraction of Dx to group the points to.

Description#

This algorithm groups the points of a single histogram workspace according to the X resolution stored in the DX array.

The figure below shows schematically how the grouping procedure proceeds.

Schematic image of the point grouping algorithm.

Select first ungrouped point \(i\). The X resolution (DX) for this point is \(D_{i}\).
Calculate grouping width \(w_{i} =\) FractionOfDx * \(D_{i}\).
Select ungrouped points within \(w_{i}\) from point \(i\).
Calculate the average X, average Y, and the square root of the averaged squared sums of E of the selected points.
Calculate new resolution \(D^{\circ} = \sqrt{D_{i}^{2} + (0.68 \Delta X)^{2}}\) where \(\Delta X\) is span of the X values of the selected points.
Replace the selected points with a single grouped point.
Return to 1.

Usage#

Example - Grouping points to X resolution

from mantid.simpleapi import CreateWorkspace, DeleteWorkspaces, GroupToXResolution
import matplotlib.pyplot as plt
import numpy as np
# Create a workspace with exponential decay.
Xs = np.arange(0.01, 5., 0.01)
Ys = np.exp(-Xs)
# A clumsy way for filling a numpy array.
# Numpy version > 1.7 would support 'DXs = full_like(Ys, 1.)'
DXs = np.empty_like(Ys)
DXs.fill(1.)
original = CreateWorkspace(Xs, Ys, Dx=DXs, NSpec=1)
grouped = GroupToXResolution(original)
# Plot side-by-side comparison.
fig, (left, right) = plt.subplots(ncols=2, subplot_kw={'projection':'mantid'})
left.errorbar(original, linestyle='-')
left.set_title('Original')
right.errorbar(grouped, linestyle='-')
right.set_title('Grouped')
# Uncomment the next line to show the plot window.
#fig.show()
DeleteWorkspaces(['original', 'grouped'])