diff --git a/docs/source/concepts/calibration/PowderDiffractionCalibration.rst b/docs/source/concepts/calibration/PowderDiffractionCalibration.rst
index dbac0a2b927a0759abb455fee4b256a326ff6a5e..623a2e7fc7d0d4a4b1b38aefd79fe5853b35d777 100644
--- a/docs/source/concepts/calibration/PowderDiffractionCalibration.rst
+++ b/docs/source/concepts/calibration/PowderDiffractionCalibration.rst
@@ -295,8 +295,8 @@ A mask can also be applied with a ``MaskWorkspace`` to hide pixels from the plot
Relative Strain
###############
-Plotting the relative strain of the d-spacing for a peak to the nominal d value can be used as
-another method to check the calibration consistency at the pixel level. The relative strain
+Plotting the relative strain of the d-spacing for a peak to the nominal d value (:math:`\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
diff --git a/scripts/Calibration/tofpd/diagnostics.py b/scripts/Calibration/tofpd/diagnostics.py
index f25bc1745bebdee7127cfd8df3d4d4e48a5f69d1..18e5e8ec7f2f1479e6ab6b5ad11d048980477c07 100644
--- a/scripts/Calibration/tofpd/diagnostics.py
+++ b/scripts/Calibration/tofpd/diagnostics.py
@@ -4,18 +4,20 @@
# NScD Oak Ridge National Laboratory, European Spallation Source,
# Institut Laue - Langevin & CSNS, Institute of High Energy Physics, CAS
# SPDX - License - Identifier: GPL - 3.0 +
-
-from mantid.plots.resampling_image.samplingimage import imshow_sampling
-from mantid.plots.datafunctions import get_axes_labels
-from mantid.simpleapi import CalculateDIFC, LoadDiffCal, mtd
-from mantid.plots.utility import colormap_as_plot_color
-from mantid.api import WorkspaceFactory
+from typing import Union
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
from matplotlib.collections import PatchCollection
import matplotlib.offsetbox as pltbox
import numpy as np
+from mantid.api import WorkspaceFactory
+from mantid.dataobjects import TableWorkspace, Workspace2D
+from mantid.plots.datafunctions import get_axes_labels
+from mantid.plots.resampling_image.samplingimage import imshow_sampling
+from mantid.plots.utility import colormap_as_plot_color
+from mantid.simpleapi import CalculateDIFC, LoadDiffCal, mtd
+
# Diamond peak positions in d-space which may differ from actual sample
DIAMOND = np.asarray((2.06,1.2615,1.0758,0.892,0.8186,0.7283,0.6867,0.6307,0.5642,0.5441,0.515,0.4996,0.4768,
@@ -84,24 +86,33 @@ def _get_difc_ws(wksp, instr_ws=None):
return difc_ws
-def __get_regions(x):
- # Returns a list of tuples with start,stop indices
- # indicating a detector region
+def __get_regions(x: np.ndarray):
+ """
+ Find the beginning and end of all sequences in x
+ Ex: [1, 2, 3, 5, 6, 8, 9] will return [(1, 3), (5, 6), (8, 9)]
+ :param x: Array of detector IDs (in ascending order)
+ :return: List of tuples with start,stop indices to mark detector regions
+ """
regions = []
- lower = np.asarray((x+1)[:-1], dtype=int)
- upper = np.asarray((x-1)[1:], dtype=int)
- gaps = lower<=upper
+ # Compute a +/- 1 shift in indices and compare them to find gaps in the sequence
+ lower = np.asarray((x + 1)[:-1], dtype=int)
+ upper = np.asarray((x - 1)[1:], dtype=int)
+ gaps = lower <= upper
lower, upper = lower[gaps], upper[gaps]
i = 0
+ # Use the start and stop of each gap to find sequential regions instead
for start, stop in zip(lower, upper):
if i == 0:
- regions.append((int(np.min(x)), int(start-1)))
+ # Starting edge case - the start of x until the start of the first gap
+ regions.append((int(np.min(x)), int(start - 1)))
else:
- regions.append((int(upper[i-1]+1), int(start-1)))
+ # Mark the region between the end of the previous gap and the start of the next gap
+ regions.append((int(upper[i - 1] + 1), int(start - 1)))
i = i + 1
- # Add ending region if there was atleast one region found:
+ # Add ending region if there was at least one region found:
if i > 0:
- regions.append((int(upper[i-1]+1), int(np.max(x))))
+ # Ending edge case: the end of the last gap until the end of the input
+ regions.append((int(upper[i - 1] + 1), int(np.max(x))))
return regions
@@ -265,20 +276,23 @@ def difc_plot2d(calib_new, calib_old=None, instr_ws=None, mask=None, vrange=(0,1
return fig, ax
-def __calculate_strain(obs, exp: np.ndarray):
- return np.asarray(list(obs.values())[1:-2]) / exp
+def __calculate_strain(obs: dict, exp: np.ndarray):
+ """Computes the fraction of observed d-spacing values to expected (diamond) peak positions"""
+ return __calculate_dspacing(obs) / exp
-def __calculate_difference(obs, exp: np.ndarray):
- obs_ndarray = np.asarray(list(obs.values())[1:-2])
- return np.abs(obs_ndarray - exp) / exp
+def __calculate_difference(obs: dict, exp: np.ndarray):
+ """Calculate the difference between d-spacing values and expected d-spacing values"""
+ return np.abs(__calculate_dspacing(obs) - exp) / exp
-def __calculate_dspacing(obs):
+def __calculate_dspacing(obs: dict):
+ """Extract the dspacing values from a row in output dspace table workspace from PDCalibration"""
+ # Trim the table row since this is from the output of PDCalibration, so remove detid, chisq and normchisq columns
return np.asarray(list(obs.values())[1:-2])
-def __create_outputws(donor, numSpec, numPeaks):
+def __create_outputws(donor: Union[str, Workspace2D], numSpec, numPeaks):
'''The resulting workspace needs to be added to the ADS'''
# convert the d-space table to a Workspace2d
if donor:
@@ -291,7 +305,17 @@ def __create_outputws(donor, numSpec, numPeaks):
return output
-def collect_peaks(wksp, outputname: str, donor=None, infotype: str = 'strain'):
+def collect_peaks(wksp: Union[str, TableWorkspace], outputname: str, donor: Union[str, Workspace2D] = None,
+ infotype: str = 'strain'):
+ """
+ Calculate different types of information for each peak position in wksp and create a new Workspace2D
+ where each column is a peak position and each row is the info for each workspace index in donor
+ :param wksp: Input TableWorkspace (i.e, dspacing table from PDCalibration)
+ :param outputname: Name of the Workspace2D to create with the information
+ :param donor: Name of the donor Workspace2D, used to create the shape of the output
+ :param infotype: 'strain', 'difference', or 'dpsacing' to specify which kind of data to output
+ :return: Created Workspace2D with fractional difference, relative difference, or dspacing values
+ """
if infotype not in ['strain', 'difference', 'dspacing']:
raise ValueError('Do not know how to calculate "{}"'.format(infotype))
@@ -321,8 +345,19 @@ def collect_peaks(wksp, outputname: str, donor=None, infotype: str = 'strain'):
return mtd[outputname]
-def collect_fit_result(wksp, outputname: str, peaks, donor=None, infotype: str = 'centre', chisq_max: float = 1.e4):
- '''This assumes that the input is sorted by wsindex then peakindex'''
+def collect_fit_result(wksp: Union[str, TableWorkspace], outputname: str, peaks, donor: Union[str, Workspace2D] = None,
+ infotype: str = 'centre', chisq_max: float = 1.e4):
+ """
+ Extracts different information about fit results from a TableWorkspace and places into a Workspace2D
+ This assumes that the input is sorted by wsindex then peakindex
+ :param wksp: Input TableWorkspace from PDCalibration or FitPeaks (should have infotype as a column)
+ :param outputname: Name of output Workspace2D created
+ :param peaks: Array of peak positions
+ :param donor: Optional Workspace2D to use to determine output size
+ :param infotype: Type of fit information to extract ("centre", "width", "height", or "intensity")
+ :param chisq_max: Max chisq value that should be included in output, data gets set to nan if above this
+ :return: Created Workspace2D with infotype extracted from wksp
+ """
KNOWN_COLUMNS = ['centre', 'width', 'height', 'intensity']
if infotype not in KNOWN_COLUMNS:
raise ValueError(f'Do not know how to extract "{infotype}"')
@@ -353,14 +388,14 @@ def collect_fit_result(wksp, outputname: str, peaks, donor=None, infotype: str =
mtd.addOrReplace(outputname, output)
-def extract_peak_info(wksp, outputname: str, peak_position: float):
- '''
+def extract_peak_info(wksp: Union[str, Workspace2D], outputname: str, peak_position: float):
+ """
Extract information about a single peak from a Workspace2D. The input workspace is expected to have
common x-axis of observed d-spacing. The y-values and errors are extracted.
The output workspace will be a single spectra with the x-axis being the detector-id. The y-values
and errors are extracted from the input workspace.
- '''
+ """
# confirm that the input is a workspace pointer
wksp = mtd[str(wksp)]
numSpec = wksp.getNumberHistograms()
@@ -396,7 +431,8 @@ def extract_peak_info(wksp, outputname: str, peak_position: float):
return mtd[outputname]
-def plot_peakd(wksp, peak_positions, plot_regions=True, show_bad_cnt=True, drange=(0,0), threshold=0.01):
+def plot_peakd(wksp: Union[str, Workspace2D], peak_positions, plot_regions=True, show_bad_cnt=True, drange=(0, 0),
+ threshold=0.01):
"""
Plots peak d spacing value for each peak position in peaks
:param wksp: Workspace returned from collect_peaks