diff --git a/docs/source/concepts/calibration/PowderDiffractionCalibration.rst b/docs/source/concepts/calibration/PowderDiffractionCalibration.rst index 53a44808f0fd7085789d10a15151ed74e8807022..162adbd741889e2db150f59685620c740bf0a9d5 100644 --- a/docs/source/concepts/calibration/PowderDiffractionCalibration.rst +++ b/docs/source/concepts/calibration/PowderDiffractionCalibration.rst @@ -364,7 +364,7 @@ in calibration. In theory, the relationship between (TOF, d-spacing) will always 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 <https://en.wikipedia.org/wiki/Pearson_correlation_coefficient>`_. Below is an example plot for the Pearson correlation +`this wikipedia article <https://en.wikipedia.org/wiki/Pearson_correlation_coefficient>`_. Below is an example plot for the Pearson correlation coefficient of (TOF, d-spacing). .. figure:: /images/VULCAN_pearsoncorr.png @@ -377,16 +377,16 @@ The following script can be used to generate the above plot. 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),