Time-of-Flight Powder Diffraction Calibration¶

Relative to a specific spectrum¶

This technique of calibration uses a reference spectrum to calibrate the rest of the instrument to. The main algorithm that does this is GetDetectorOffsets whose InputWorkspace is the OutputWorkspace of CrossCorrelate. Generically the workflow is

1. Load the calibration data
2. Convert the X-Units to d-spacing using ConvertUnits. The “offsets” calculated are relative reference spectrum’s geometry so using AlignDetectors will violate the assumptions for other algorithms used with time-of-flight powder diffraction and give the wrong results for focused data.
3. Run Rebin to set a common d-spacing bin structure across all of the spectra, you will need fine enough bins to allow fitting of your peak. Whatever you choose, make a note of it you may need it later.
4. Take a look at the data using the SpectrumViewer or a color map plot, find the workspace index of a spectrum that has the peak close to the known reference position. This will be the reference spectra and will be used in the next step.
5. Cross correlate the spectrum with CrossCorrelate, enter the workspace index as the ReferenceSpectra you found in the last step.
6. Run GetDetectorOffsets, the InputWorkspace if the output from CrossCorrelate. Use the rebinning step you made a note of in step 3 as the step parameter, and DReference as the expected value of the reference peak that you are fitting to. XMax and XMin define the window around the reference peak to search for the peak in each spectra, if you find that some spectra do not find the peak try increasing those values.
7. The output is an OffsetsWorspace. See below for how to save, load, and use the workspace.

Using known peak positions¶

These techniques require knowing the precise location, in d-space, of diffraction peaks and benefit from knowing more. GetDetOffsetsMultiPeaks and PDCalibration are the main choices for this. Both algorithms fit individual peak positions and use those fits to generate the calibration information.

The workflow for GetDetOffsetsMultiPeaks is identical to that of GetDetectorOffsets without the cross-correlation step (5). The main difference in the operation of the algorithm is that it essentially calculates an offset from each peak then calculates a weighted average of those offsets for the individual spectrum.

The workflow for PDCalibration differs significantly from that of the other calibration techniques. It requires the data to be in time-of-flight, then uses either the instrument geometry, or a previous calibration, to convert the peak positions to time-of-flight. The individual peaks fits are then used to calculate \(DIFC\) values directly. The benefit of this method, is that it allows for calibrating starting from a “good” calibration, rather than returning back to the instrument geometry. The steps for using this are

1. Load the calibration data
2. Run PDCalibration with appropriate properties
3. The OutputCalibrationTable is a TableWorkspace. See below for how to save, load, and use the workspace.

Workflow algorithms¶

CalibrateRectangularDetectors will do most of the workflow for you, including applying the calibration to the data. While its name suggests it is only for a particular subset of detector types, it is not. It has many options for selecting between GetDetectorOffsets and GetDetOffsetsMultiPeaks.

Group Calibration¶

Some script have been created that provided a workflow for calibrating the instrument in groups using a combination of CrossCorrelate, GetDetectorOffsets and PDCalibration.

It works by performing the cross-correlations on only the detectors within a group, after which the grouped detectors are merge together to use with PDCalibration. The difc from the PDCalibration and cross-correlation are combined using CombineDiffCal

The workflow follows these step:

1. Load data, usually diamond
2. Convert to d-spacing
3. CrossCorrelate a portion of the instrument according to the group information
4. GetDetectorOffsets to calculate offsets for individual pixels with a group
5. ConvertDiffCal to convert these constants to \(DIFC_{CC}\)
6. Use \(DIFC_{CC}\) to convert the origonal data to d-spacing. DiffractionFocus allows for combining a portion of the instrument into a single spectrum for improved statistics
7. Pick an arbitrary constant, \(DIFC_{arb}\) to convert this combined spectrum back to time-of-flight
8. PDCalibration the combined spectrum to determine a conversion constant \(DIFC_{PD}\)
9. Use CombineDiffCal to combine \(DIFC_{CC}\), \(DIFC_{arb}\), and \(DIFC_{PD}\) into a new calibration constant, \(DIFC_{eff}\)

# create a fake starting workspace in d-spacing then convert to TOF for calibration
myFunc = "name=Gaussian, PeakCentre=1, Height=100, Sigma=0.01;name=Gaussian, PeakCentre=2, Height=100, Sigma=0.01;name=Gaussian, PeakCentre=3, Height=100, Sigma=0.01"
ws_d = CreateSampleWorkspace("Event","User Defined", myFunc, BankPixelWidth=1, XUnit='dSpacing', XMax=5, BinWidth=0.001, NumEvents=10000, NumBanks=6)
for n in range(1,7):
    MoveInstrumentComponent(ws_d, ComponentName=f'bank{n}', X=1, Y=0, Z=1, RelativePosition=False)

# Offset the different spectra
ws_d = ScaleX(ws_d, Factor=1.05, IndexMin=1, IndexMax=1)
ws_d = ScaleX(ws_d, Factor=0.95, IndexMin=2, IndexMax=2)
ws_d = ScaleX(ws_d, Factor=1.05, IndexMin=3, IndexMax=5)
ws_d = ScaleX(ws_d, Factor=1.02, IndexMin=3, IndexMax=4)
ws_d = ScaleX(ws_d, Factor=0.98, IndexMin=4, IndexMax=5)
ws_d = Rebin(ws_d, '0,0.001,5')
ws = ConvertUnits(ws_d, Target='TOF')

# Make 2 groups of 3 detectors each
groups, _, _, = CreateGroupingWorkspace(InputWorkspace=ws, ComponentName='basic_rect', CustomGroupingString='1-3,4-6')

# starting DIFC are all the same
detectorTable = CreateDetectorTable(ws)

print("DetID DIFC")
for detid, difc in zip(detectorTable.column('Detector ID(s)'), detectorTable.column('DIFC')):
    print(f'{detid:>5} {difc:.1f}')

DetID DIFC
    1 2208.3
    2 2208.3
    3 2208.3
    4 2208.3
    5 2208.3
    6 2208.3

from Calibration.tofpd.group_calibration import cc_calibrate_groups

cc_diffcal, to_skip = cc_calibrate_groups(ws,
                                          groups,
                                          DReference=2.0,
                                          Xmin=1.75,
                                          Xmax=2.25,
                                          OffsetThreshold=1.0)

print("DetID DIFC")
for detid, difc in zip(cc_diffcal.column('detid'), cc_diffcal.column('difc')):
    print(f'{detid:>5} {difc:.1f}')

DetID DIFC
    1 2208.3
    2 2318.6
    3 2098.0
    4 2255.4
    5 2208.3
    6 2163.0

# In this case, cycling through cross correlation until offset converges.
cc_diffcal, to_skip = cc_calibrate_groups(ws,
                                          groups,
                                          DReference=2.0,
                                          Xmin=1.75,
                                          Xmax=2.25,
                                          OffsetThreshold=1E-4)

print("DetID DIFC")
for detid, difc in zip(cc_diffcal.column('detid'), cc_diffcal.column('difc')):
    print(f'{detid:>5} {difc:.1f}')

DetID DIFC
    1 2208.3
    2 2318.7
    3 2097.9
    4 2253.3
    5 2208.3
    6 2165.0

# Turn on cross correlation cycling but skip cross correlation for group-1.
cc_diffcal, to_skip = cc_calibrate_groups(ws,
                                          groups,
                                          DReference=2.0,
                                          Xmin=1.75,
                                          Xmax=2.25,
                                          OffsetThreshold=1E-4,
                                          SkipCrossCorrelation=[1])

print("DetID DIFC")
for detid, difc in zip(cc_diffcal.column('detid'), cc_diffcal.column('difc')):
    print(f'{detid:>5} {difc:.1f}')

DetID DIFC
    1 2208.3
    2 2208.3
    3 2208.3
    4 2253.3
    5 2208.3
    6 2165.0

from Calibration.tofpd.group_calibration import pdcalibration_groups

diffcal = pdcalibration_groups(ws,
                               groups,
                               cc_diffcal,
                               to_skip,
                               PeakPositions = [1.0, 2.0, 3.0],
                               PeakFunction='Gaussian',
                               PeakWindow=0.4)

print("DetID DIFC")
for detid, difc in zip(diffcal.column('detid'), diffcal.column('difc')):
    print(f'{detid:>5} {difc:.1f}')

DetID DIFC
    1 2208.7
    2 2319.1
    3 2098.3
    4 2365.5
    5 2318.2
    6 2272.7

The evolution in the calibration can be seen with

import matplotlib.pyplot as plt
from mantid import plots

ws_d = Rebin(ws_d, '0.75,0.01,3.5')

ApplyDiffCal(ws, CalibrationWorkspace=cc_diffcal)
ws_d_after_cc = ConvertUnits(ws, Target='dSpacing')
ws_d_after_cc = Rebin(ws_d_after_cc, '0.75,0.01,3.5')

ApplyDiffCal(ws, CalibrationWorkspace=diffcal)
ws_d_after_cc_and_pd = ConvertUnits(ws, Target='dSpacing')
ws_d_after_cc_and_pd = Rebin(ws_d_after_cc_and_pd, '0.75,0.01,3.5')

fig = plt.figure(figsize=(6.4,9.6))
ax1 = fig.add_subplot(311, projection = 'mantid')
ax2 = fig.add_subplot(312, projection = 'mantid')
ax3 = fig.add_subplot(313, projection = 'mantid')

for n in range(1,7):
    ax1.plot(ws_d, specNum=n)
    ax2.plot(ws_d_after_cc, specNum=n)
    ax3.plot(ws_d_after_cc_and_pd, specNum=n)

ax1.set_title('Starting peaks')
ax2.set_title('After cross-correlation, spectra in two groups')
ax3.set_title('After all calibration')
fig.tight_layout()
#fig.savefig('tofpd_group_calibration.png')
fig.show()

The same complete calibration can just be run with just group_calibration.do_group_calibration.

from Calibration.tofpd.group_calibration import do_group_calibration

diffcal = do_group_calibration(ws,
                               groups,
                               cc_kwargs={
                                   "DReference": 2.0,
                                   "Xmin": 1.75,
                                   "Xmax": 2.25,
                                   "OffsetThreshold": 1.0},
                               pdcal_kwargs={
                                   "PeakPositions": [1.0, 2.0, 3.0],
                                   "PeakFunction": 'Gaussian',
                                   "PeakWindow": 0.4})

print("DetID DIFC")
for detid, difc in zip(diffcal.column('detid'), diffcal.column('difc')):
    print(f'{detid:>5} {difc:.1f}')

DetID DIFC
    1 2208.7
    2 2319.0
    3 2098.4
    4 2367.7
    5 2318.2
    6 2270.7

The resulting diffcal can be saved with SaveDiffCal.

SaveDiffCal(CalibrationWorkspace=diffcal,
            MaskWorkspace=mask,
            Filename='calibration.h5')

Group calibration how-to’s¶

Generate grouping file

The first stage of the group calibration is to generate suitable grouping scheme for all spectra involved. The principle is to group similar spectra together. A natural choice for generating grouping file is to use CreateGroupingWorkspace algorithm which embodies several choices of grouping detectors according to physical geometry. A generic approach has also been implemented into the framework of mantidtotalscattering, which automatically groups input spectra according to the similarity among each other, based on a unsupervised clustering algorithm. mantidtotalscattering has been deployed on SNS analysis cluster and therefore the generic grouping routine can be accessed easily from analysis. To activate the mantidtotalscattering conda environment, one needs to first log into analysis cluster and the following commands could be executed from terminal,

. /opt/anaconda/etc/profile.d/conda.sh
conda activate mantidtotalscattering

With the mantidtotalscattering conda environment active, here follows is provided a simple Python script for calling the generic grouping routine on analysis,

#!/usr/bin/env python
import sys
import json
from total_scattering.autogrouping.autogrouping import main

jsonfile = "/SNS/users/y8z/Temp/autogrouping_config.json"
with open(jsonfile, 'r') as jf:
    config = json.load(jf)
# execute
main(config)

An example json file is presented below to control the grouping behavior,

{
    "DiamondFile": "/SNS/NOM/IPTS-24637/nexus/NOM_144974.nxs.h5",
    "MaskFile": "/SNS/users/y8z/Temp/mask144974.out",

    "GroupingMethod": "KMEANS_ED",
    "NumberOutputGroups": "4",
    "StandardScaling": false,

    "FittingFunctionParameters": "Mixing,Intensity,PeakCentre,FWHM",
    "FitPeaksArgs": { "PeakFunction": "PseudoVoigt",
                    "PeakParameterNames": "Mixing",
                    "PeakParameterValues": "0.6",
                    "HighBackground": false,
                    "MinimumPeakHeight": 3,
                    "ConstrainPeakPositions": false
                    },
    "DiamondPeaks": "0.8920,1.0758,1.2615",
    "ParameterThresholds": { "PeakCentre": "(0.01,10.0)",
                            "Height": "(0.0,10000.0)"
                        },

    "FilterByChi2": { "Enable": true,
                    "Value": 1e4
                    },


    "OutputGroupingFile": "./outputgrouping.xml",
    "OutputMaskFile": "./outputmask.txt",

    "OutputFitParamFile": "./outputfitparamtable.nxs",

    "CacheDir": "./tmp/",

    "Plots": { "Grouping": true,
            "ED_Features": true,
            "PCA": true,
            "KMeans_Elbow": true,
            "KMeans_Silhouette": true}
}

and description for entries in the input json file is summarized in the following table,

Name	Description
DiamondFile	Full name of the input nexus file. For calibration purpose, usually a diamond measurement will be used.
MaskFile	Full name of the input mask file. The file should contain a whole bunch of lines with a single integter in each line specifying the detector ID to be masked (index starting from 0).
GroupingMethod	The method to be used for grouping. Valid input could be KMEANS_CC, KMEANS_DG, KMEANS_ED, DBSCAN_CC, DBSCAN_DG and DBSCAN_ED. KMEANS and DBSCAN refers to the two clustering methods. The second part of those values refers to the method for calculating similarity between spectra. CC for cross-correlation, DG for De Gelder similarity and ED for Euclidean distance in parameter space.
NumberOutputGroups	The number of groups to cluster all input spectra into. If using DBSCAN method, there is no need to specify this parameter.
StandardScaling	Whether or not to scale the input spectra by removing the mean and scaling to unit variance before clustering.
WorkspaceIndexRange	Range of workspace indeces to include in automatic grouping process.
FittingFunctionParameters	If ED method is to be used for calculating similarity between spectra, this specifies the peak parameters to fit and to be used as the coordinate components in parameter space.
FitPeaksArgs	Refer to the input parameters for FitPeaks algorithm.
DiamondPeaks	If ED method is to be used for calculating similarity between spectra, this specifies the diamond peaks, as specified by the nominal peak positions, to be used for peak fitting and clustering.
ParameterThresholds	If ED method is to be used for calculating similarity between spectra, this specifies the threshold for relevant peak parameters. The threshold for each relevant peak parameter will be given as sub-entries.
FilterByChi2	If ED method is to be used for calculating similarity between spectra, this specifies whether or not to mask out pixels based on chi square of peak fitting. Among the two sub-entries, Enable is a boolean trigger and Value is the threshold of chi square.
OutputGroupingFile	Full name of the output grouping file.
OutputMaskFile	Full name of the output masking file.
OutputFitParamFile	If ED method is to be used for calculating similarity between spectra, this specifies the full name of the output fit parameters file.
CacheDir	Cache directory.
Plots	A series of boolean variables control the plotting options. Grouping for plotting the grouping of detectors. ED_Features for plotting parameters correlation features. KMeans_Elbow for plotting the elbow analysis result. KMeans_Silhouette for plotting the Silhouette score.

Here, it is worth noting that detectors may be masked out as belonging to none of the generated groups. For example, when using the ED method for defining the similarity between spectra, detectors will be masked out at the fitting stage if the corresponding spectra cannot be fitted successfully.

Following is presented the clustering result for a NOMAD diamond measurement data,

Note

For certain instruments (e.g., POWGEN), the automatic grouping routine may not work due to special d-space coverage for detectors. In this case, one may need to treat various ranges of detectors individually (using the input entry WorkspaceIndexRange in the input json file) and also some of the groups may need to be manually specified.

Group calibration

Having the grouping file (and potentially the masking file) ready, one can then open Mantid workbench interface and trigger the group calibration routine, using a simple Python script, as presented below,

# import mantid algorithms, numpy and matplotlib
from mantid.simpleapi import *
import matplotlib.pyplot as plt
import numpy as np
from Calibration.tofpd import group_calibration

infile = "/SNS/NOM/shared/User_story_test/NOM_US-231_240/group_calib.json"

group_calibration.process_json(infile)

Here follows is presented a demo input json file,

{
    "Calibrant": "161450",
    "Groups": "/SNS/NOM/shared/User_story_test/NOM_US-231_240/outputgrouping.xml",
    "Mask": "/SNS/NOM/shared/User_story_test/NOM_US-231_240/outputmask.xml",
    "Instrument": "NOMAD",
    "Date" : "2021_07_21",
    "SampleEnvironment": "shifter",
    "CalDirectory": "/SNS/NOM/shared/User_story_test/NOM_US-231_240/",
    "CrossCorrelate": {"Step": 0.001,
                    "DReference": 1.2615,
                    "Xmin": 1.0,
                    "Xmax": 3.0,
                    "MaxDSpaceShift": 0.25,
                    "OffsetThreshold": 1E-4,
                    "SkipCrossCorrelation": [1,2,3]},
    "PDCalibration": {"TofBinning": [300,0.01,16666],
                    "PeakFunction": "Gaussian",
                    "PeakWindow": 0.1,
                    "PeakWidthPercent": 0.001}
}

Parameters in the input json file should be self-explaining. Here only the Calibrant and Groups entries are mandatory. For CrossCorrelate entries, one can refer to the parameters for CrossCorrelate and GetDetectorOffsets. For PDCalibration entries, one can refer to the parameters for PDCalibration. In the group calibration workflow, one of the crucial steps is to cross correlate spectra in a single group. A cycling cross correlation scheme is introduced at this point to continue cross correlate spectra until the median value of the offset of all spectra in a single group is below the preset threshold (specified by the OffsetThreshol parameter). If the OffsetThreshold is set to 1.0 or larger, that means no cycling of cross correlation will be conducted. The SkipCrossCorrelation parameter is to control the skipping of cross correlation for specified groups of spectra. For Xmin, Xmax, MaxDSpaceShift and OffsetThreshold parameters, they can be either provided with a single number or a list. When a single number is given, the value will apply to all groups, whereas if a list is given, each entry in the list will apply to each single group respectively.

After the group calibration is complete, one can then inspect the quality of calibration by generating various diagnostics plots as documented in Calibration Diagnostics.

Saving and Loading Calibration¶

There are two basic formats for the calibration information. The legacy ascii format is described in CalFile. The newer HDF5 version is described alongside the description of calibration table.

Saving and loading the HDF5 format is done with SaveDiffCal and LoadDiffCal.

Saving and loading the legacy format is done with SaveCalFile and LoadCalFile. This can be converted from an OffsetsWorkspace to a calibration table using ConvertDiffCal.

Pixel-by-pixel results¶

There are some common ways of diagnosing the calibration results. One of the more common is to plot the aligned data in d-spacing. While this can be done via the “colorfill” plot or sliceviewer, a function has been created to annotate the plot with additional information. This can be done using the following code

from mantid.simpleapi import (AlignDetectors, LoadDiffCal, LoadEventNexus, LoadInstrument, Rebin)
from Calibration.tofpd import diagnostics

LoadEventNexus(Filename='VULCAN_192227.nxs.h5', OutputWorkspace='ws')
Rebin(InputWorkspace='ws', OutputWorkspace='ws', Params=(5000,-.002,70000))
LoadDiffCal(Filename='VULCAN_Calibration_CC_4runs_hybrid.h5', InputWorkspace='ws', WorkspaceName='VULCAN')
AlignDetectors(InputWorkspace='ws', OutputWorkspace='ws', CalibrationWorkspace='VULCAN_cal')
diagnostics.plot2d(mtd['ws'], horiz_markers=[8*512*20, 2*8*512*20], xmax=1.3)

Here the expected peak positions are vertical lines, the horizontal lines are boundaries between banks. When run interactively, the zoom/pan tools are available.

DIFC of unwrapped instrument¶

To check the consistency of pixel-level calibration, the DIFC value of each pixel can be compared between two different instrument calibrations. The percent change in DIFC value is plotted over a view of the unwrapped instrument where the horizontal and vertical axis corresponds to the polar and azimuthal angle, respectively. The azimuthal angle of 0 corresponds to the direction parallel of the positive Y-axis in 3D space.

Below is an example of the change in DIFC between two different calibrations of the NOMAD instrument.

This plot can be generated several different ways: by using calibration files, calibration workspaces, or resulting workspaces from CalculateDIFC. The first input parameter is always required and represents the new calibration. The second parameter is optional and represents the old calibration. When it is not specified, the default instrument geometry is used for comparison. Masks can be included by providing a mask using the mask parameter. To control the scale of the plot, a tuple of the minimum and maximum percentage can be specified for the vrange parameter.

from Calibration.tofpd import diagnostics

# Use filenames to generate the plot
fig, ax = diagnostics.difc_plot2d("NOM_calibrate_d135279_2019_11_28.h5", "NOM_calibrate_d131573_2019_08_18.h5")

When calibration tables are used as inputs, an additional workspace parameter is needed (instr_ws) to hold the instrument definition. This can be the GroupingWorkspace generated with the calibration tables from LoadDiffCal as seen below.

from mantid.simpleapi import LoadDiffCal
from Calibration.tofpd import diagnostics

# Use calibration tables to generate the plot
LoadDiffCal(Filename="NOM_calibrate_d135279_2019_11_28.h5", WorkspaceName="new")
LoadDiffCal(Filename="NOM_calibrate_d131573_2019_08_18.h5", WorkspaceName="old")
fig, ax = diagnostics.difc_plot2d("new_cal", "old_cal", instr_ws="new_group")

Finally, workspaces with DIFC values can be used directly:

from mantid.simpleapi import CalculateDIFC, LoadDiffCal
from Calibration.tofpd import diagnostics

# Use the results from CalculateDIFC directly
LoadDiffCal(Filename="NOM_calibrate_d135279_2019_11_28.h5", WorkspaceName="new")
LoadDiffCal(Filename="NOM_calibrate_d131573_2019_08_18.h5", WorkspaceName="old")
difc_new = CalculateDIFC(InputWorkspace="new_group", CalibrationWorkspace="new_cal")
difc_old = CalculateDIFC(InputWorkspace="old_group", CalibrationWorkspace="old_cal")
fig, ax = diagnostics.difc_plot2d(difc_new, difc_old)

A mask can also be applied with a MaskWorkspace to hide pixels from the plot:

from mantid.simpleapi import LoadDiffCal
from Calibration.tofpd import diagnostics

# Use calibration tables to generate the plot
LoadDiffCal(Filename="NOM_calibrate_d135279_2019_11_28.h5", WorkspaceName="new")
LoadDiffCal(Filename="NOM_calibrate_d131573_2019_08_18.h5", WorkspaceName="old")
fig, ax = diagnostics.difc_plot2d("new_cal", "old_cal", instr_ws="new_group", mask="new_mask")

Relative Strain¶

Plotting the relative strain of the d-spacing for a peak to the nominal d value (\(\frac{observed}{expected}\)) can be used as another method to check the calibration consistency at the pixel level. The relative strain is plotted along the Y-axis for each detector pixel, with the mean and standard deviation reported on the plot. A solid black line is drawn at the mean, and two dashed lines are drawn above and below the mean by a threshold percentage (one percent of the mean by default). This can be used to determine which pixels are bad up to a specific threshold.

Below is an example of the relative strain plot for VULCAN at peak position 1.2615:

The plot shown above can be generated from the following script:

import numpy as np
from mantid.simpleapi import (LoadEventAndCompress, LoadInstrument, PDCalibration, Rebin)
from Calibration.tofpd import diagnostics

FILENAME = 'VULCAN_192227.nxs.h5'
CALFILE = 'VULCAN_Calibration_CC_4runs_hybrid.h5'

peakpositions = np.asarray(
  (0.3117, 0.3257, 0.3499, 0.3916, 0.4205, 0.4645, 0.4768, 0.4996, 0.515, 0.5441, 0.5642, 0.6307, 0.6867,
   0.7283, 0.8186, 0.892, 1.0758, 1.2615, 2.06))

LoadEventAndCompress(Filename=FILENAME, OutputWorkspace='ws', FilterBadPulses=0)
LoadInstrument(Workspace='ws', InstrumentName="VULCAN", RewriteSpectraMap='True')
Rebin(InputWorkspace='ws', OutputWorkspace='ws', Params=(5000, -.002, 70000))

PDCalibration(InputWorkspace='ws', TofBinning=(5000,-.002,70000),
              PeakPositions=peakpositions,
              MinimumPeakHeight=5,
              OutputCalibrationTable='calib',
              DiagnosticWorkspaces='diag')

dspacing = diagnostics.collect_peaks('diag_dspacing', 'dspacing', donor='diag_fitted',
                                     infotype='dspacing')
strain = diagnostics.collect_peaks('diag_dspacing', 'strain', donor='diag_fitted')

fig, ax = diagnostics.plot_peakd('strain', 1.2615, drange=(0, 200000), plot_regions=True, show_bad_cnt=True)

To plot the relative strain for multiple peaks, an array of positions can be passed instead of a single value. For example, using peakpositions in place of 1.2615 in the above example results in the relative strain for all peaks being plotted as shown below.

The vertical lines shown in the plot are drawn between detector regions and can be used to report the count of bad pixels found in each region. The solid vertical line indicates the start of a region, while the dashed vertical line indicates the end of a region. The vertical lines can be turned off with plot_regions=False and displaying the number of bad counts for each region can also be disabled with show_bad_cnt=False. When plot_regions=False but show_bad_cnt=True, a single count of bad pixels over the entire range is shown at the bottom center of the plot.

As seen in the above example, the x-range of the plot can be narrowed down using the drange option, which accepts a tuple of the starting detector ID and ending detector ID to plot.

To adjust the horizontal bars above and below the mean, a percent can be passed to the threshold option.

Pearson Correlation Coefficient¶

It can be useful to compare the linearity of the relationship between time of flight and d-spacing for each peak involved in calibration. In theory, the relationship between (TOF, d-spacing) will always be perfectly linear, but in practice, that is not always the case. This diagnostic plot primarily serves as a tool to ensure that the calibration makes sense, i.e., that a single DIFC parameter is enough to do the transformation. In the ideal case, all Pearson correlation coefficients will be close to 1. For more on Pearson correlation coefficients please see this wikipedia article. Below is an example plot for the Pearson correlation coefficient of (TOF, d-spacing).

The following script can be used to generate the above plot.

# import mantid algorithms, numpy and matplotlib
from mantid.simpleapi import *
import matplotlib.pyplot as plt
import numpy as npfrom Calibration.tofpd import diagnosticsFILENAME = 'VULCAN_192226.nxs.h5'  # 88 sec

FILENAME = 'VULCAN_192227.nxs.h5'  # 2.8 hour
CALFILE = 'VULCAN_Calibration_CC_4runs_hybrid.h5'peakpositions = np.asarray(
  (0.3117, 0.3257, 0.3499, 0.3916, 0.4205, 0.4645, 0.4768, 0.4996, 0.515, 0.5441, 0.5642, 0.6307, 0.6867,
   0.7283, 0.8186, 0.892, 1.0758, 1.2615, 2.06))

peakpositions = peakpositions[peakpositions > 0.4]
peakpositions = peakpositions[peakpositions < 1.5]
peakpositions.sort()LoadEventAndCompress(Filename=FILENAME, OutputWorkspace='ws', FilterBadPulses=0)

LoadInstrument(Workspace='ws', Filename="mantid/instrument/VULCAN_Definition.xml", RewriteSpectraMap='True')
Rebin(InputWorkspace='ws', OutputWorkspace='ws', Params=(5000, -.002, 70000))
PDCalibration(InputWorkspace='ws', TofBinning=(5000,-.002,70000),
           PeakPositions=peakpositions,
           MinimumPeakHeight=5,
           OutputCalibrationTable='calib',
           DiagnosticWorkspaces='diag')
center_tof = diagnostics.collect_fit_result('diag_fitparam', 'center_tof', peakpositions, donor='ws', infotype='centre')
fig, ax = diagnostics.plot_corr('center_tof')

Peak Information¶

Plotting the fitted peak parameters for different instrument banks can also provide useful information for calibration diagnostics. The fitted peak parameters from FitPeaks (center, width, height, and intensity) are plotted for each bank at different peak positions. This can be used to help calibrate each group rather than individual detector pixels.

The above figure can be generated using the following script:

import numpy as np
from mantid.simpleapi import (AlignAndFocusPowder, ConvertUnits, FitPeaks, LoadEventAndCompress,
                              LoadDiffCal, LoadInstrument)
from Calibration.tofpd import diagnostics

FILENAME = 'VULCAN_192227.nxs.h5'  # 2.8 hour
CALFILE = 'VULCAN_Calibration_CC_4runs_hybrid.h5'

peakpositions = np.asarray(
    (0.3117, 0.3257, 0.3499, 0.3916, 0.4205, 0.4645, 0.4768, 0.4996, 0.515, 0.5441, 0.5642, 0.6307, 0.6867,
     0.7283, 0.8186, 0.892, 1.0758, 1.2615, 2.06))
peakpositions = peakpositions[peakpositions > 0.4]
peakpositions = peakpositions[peakpositions < 1.5]
peakpositions.sort()
peakwindows = diagnostics.get_peakwindows(peakpositions)

LoadEventAndCompress(Filename=FILENAME, OutputWorkspace='ws', FilterBadPulses=0)
LoadInstrument(Workspace='ws', InstrumentName="VULCAN", RewriteSpectraMap='True')

LoadDiffCal(Filename=CALFILE, InputWorkspace='ws', WorkspaceName='VULCAN')
AlignAndFocusPowder(InputWorkspace='ws',
                    OutputWorkspace='focus',
                    GroupingWorkspace="VULCAN_group",
                    CalibrationWorkspace="VULCAN_cal",
                    MaskWorkspace="VULCAN_mask",
                    Dspacing=True,
                    Params="0.3,3e-4,1.5")

ConvertUnits(InputWorkspace='focus', OutputWorkspace='focus', Target='dSpacing', EMode='Elastic')
FitPeaks(InputWorkspace='focus',
        OutputWorkspace='output',
        PeakFunction='Gaussian',
        RawPeakParameters=False,
        HighBackground=False,  # observe background
        ConstrainPeakPositions=False,
        MinimumPeakHeight=3,
        PeakCenters=peakpositions,
        FitWindowBoundaryList=peakwindows,
        FittedPeaksWorkspace='fitted',
        OutputPeakParametersWorkspace='parameters')

fig, ax = diagnostics.plot_peak_info('parameters', peakpositions)

Category: Calibration