def testDoNotAdjustDetector(self):
# Ensure detector position does not change when no offsets are given
orig = LoadMD("HB3A_data.nxs", LoadHistory=False)
orig_pos = orig.getExperimentInfo(0).getInstrument().getDetector(1).getPos()
result = HB3AAdjustSampleNorm(InputWorkspaces=orig,
DetectorHeightOffset=0.0,
DetectorDistanceOffset=0.0)
new_pos = result.getExperimentInfo(0).getInstrument().getDetector(1).getPos()
# Verify detector adjustment
self.__checkAdjustments(orig_pos, new_pos, 0.0, 0.0)
DeleteWorkspace(orig, result)
def runTest(self):
# Load raw data (bank 1)
wsMD = LoadMD(
"WISH38237_MD.nxs") # default so doesn't get overwrite van
# For each mod vec, predict and integrate peaks and combine
qs = [(0.15, 0, 0.3), (-0.15, 0, 0.3)]
all_pks = CreatePeaksWorkspace(InstrumentWorkspace=wsMD,
NumberOfPeaks=0,
OutputWorkspace="all_pks")
LoadIsawUB(InputWorkspace=all_pks,
Filename='Wish_Diffuse_Scattering_ISAW_UB.mat')
# PredictPeaks
parent = PredictPeaks(InputWorkspace=all_pks,
WavelengthMin=0.8,
WavelengthMax=9.3,
MinDSpacing=0.5,
ReflectionCondition="Primitive")
self._pfps = []
self._saved_files = []
for iq, q in enumerate(qs):
wsname = f'pfp_{iq}'
PredictFractionalPeaks(Peaks=parent,
IncludeAllPeaksInRange=True,
Hmin=0,
Hmax=0,
Kmin=1,
Kmax=1,
Lmin=0,
Lmax=1,
ReflectionCondition='Primitive',
MaxOrder=1,
ModVector1=",".join([str(qi) for qi in q]),
FracPeaks=wsname)
FilterPeaks(InputWorkspace=wsname,
OutputWorkspace=wsname,
FilterVariable='Wavelength',
FilterValue=9.3,
Operator='<') # should get rid of one peak in q1 table
FilterPeaks(InputWorkspace=wsname,
OutputWorkspace=wsname,
FilterVariable='Wavelength',
FilterValue=0.8,
Operator='>')
IntegratePeaksMD(InputWorkspace=wsMD,
PeakRadius='0.1',
BackgroundInnerRadius='0.1',
BackgroundOuterRadius='0.15',
PeaksWorkspace=wsname,
OutputWorkspace=wsname,
IntegrateIfOnEdge=False,
UseOnePercentBackgroundCorrection=False)
all_pks = CombinePeaksWorkspaces(LHSWorkspace=all_pks,
RHSWorkspace=wsname)
self._pfps.append(ADS.retrieve(wsname))
self._filepath = os.path.join(config['defaultsave.directory'],
'WISH_IntegratedSatellite.int')
SaveReflections(InputWorkspace=all_pks,
Filename=self._filepath,
Format='Jana')
self._all_pks = all_pks
def testAdjustDetector(self):
# Test a slight adjustment of the detector position
height_adj = 0.75
dist_adj = 0.25
orig = LoadMD("HB3A_data.nxs", LoadHistory=False)
# Get the original detector position before adjustment
orig_pos = orig.getExperimentInfo(0).getInstrument().getDetector(1).getPos()
result = HB3AAdjustSampleNorm(InputWorkspaces=orig,
DetectorHeightOffset=height_adj,
DetectorDistanceOffset=dist_adj)
# Get the updated detector position
new_pos = result.getExperimentInfo(0).getInstrument().getDetector(1).getPos()
# Verify detector adjustment
self.__checkAdjustments(orig_pos, new_pos, height_adj, dist_adj)
DeleteWorkspace(orig, result)
from mantid.simpleapi import LoadMD, CreateMDHistoWorkspace
from mantid.geometry import SpaceGroupFactory
from astropy.convolution import convolve, convolve_fft, Gaussian1DKernel
import numpy as np
import matplotlib.pyplot as plt
filename = '/SNS/CORELLI/IPTS-16344/shared/symm_007K_long_all_ub_13feb.nxs'
filename = '/home/rwp/symm_007K_long_all_ub_13feb_small.nxs'
space_group = 'I 2 3'
ws = LoadMD(filename, LoadHistory=False)
# Check alignment
if not np.all(
ws.getExperimentInfo(0).run().getLogData('W_MATRIX').value ==
[1., 0., 0., 0., 1., 0., 0., 0., 1.]):
print('Not alligned with HKL')
exit()
# Check dimensions
# Check centered on zero
# Should also check center bin is 0
dim_list = []
dim_names = ['[H,0,0]', '[0,K,0]', '[0,0,L]']
for d in range(ws.getNumDims()):
dim = ws.getDimension(d)
if dim.name not in dim_names:
print("Dimensions must be one of ", dim_names)
exit()
if dim.getMaximum() != -dim.getMinimum():
print('Workspace dimensions must be centered on zero')
from mantid.simpleapi import LoadMD import numpy as np import matplotlib.pyplot as plt from skimage.morphology import reconstruction, ball, disk, erosion, closing, opening from skimage.feature import peak_local_max from skimage.restoration import inpaint from scipy import ndimage as ndi filename = '/SNS/CORELLI/IPTS-16344/shared/symm_007K_long_all_ub_13feb.nxs' ws = LoadMD(filename, LoadHistory=False) signal = ws.getSignalArray().copy() # Do something with nan's and inf's signal[np.isnan(signal)] = 0 signal[np.isinf(signal)] = 0 image = signal[251] seed = np.copy(image) seed[1:-1, 1:-1] = image.min() mask = image dilated = reconstruction(seed, mask, method='dilation') dilated2 = reconstruction(seed, mask, method='dilation', selem=disk(3)) plt.imshow(dilated, vmax=1e-4) plt.show() eros = erosion(image) eros = erosion(image, selem=disk(2))
from mantid.simpleapi import LoadMD
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.transforms as mtransforms
import numpy as np
benzil = LoadMD('/SNS/users/rwp/benzil/benzil_300K_bkg_subtract_sym_All_noCC_flat2_fft.nxs')
signal = benzil.getSignalArray()
x, y = np.meshgrid(np.linspace(-10,10,501),np.linspace(-10,10,501))
plt.pcolormesh(x, y, signal[:,:,252], norm=colors.SymLogNorm(linthresh=0.2, linscale=0, vmin=-1.0, vmax=1.0), cmap='coolwarm')
plt.colorbar()
plt.show()
plt.pcolormesh(x, y, signal[:,260,:], norm=colors.SymLogNorm(linthresh=0.05, linscale=1, vmin=-1.0, vmax=1.0), cmap='coolwarm')
plt.colorbar()
plt.show()
fig = plt.pcolormesh(x, y, signal[:,:,252].transpose(), vmax=1,vmin=0,cmap='viridis')
trans_data = mtransforms.Affine2D().skew(np.arctan(np.sin(np.deg2rad(-30))), 0) + fig.get_transform()
fig.set_transform(trans_data)
plt.show()
fig = plt.pcolormesh(x, y, signal[:,:,252], norm=colors.SymLogNorm(linthresh=0.1, linscale=1, vmin=-1.0, vmax=1.0), cmap='coolwarm')
trans_data = mtransforms.Affine2D().skew(np.arctan(np.sin(np.deg2rad(-30))), 0) + fig.get_transform()
fig.set_transform(trans_data)
plt.colorbar()
plt.show()
from mantid.simpleapi import LoadMD
from mantid.kernel import version_str
import json
import sys
import getpass
import datetime
output_file = sys.argv[1]
md = LoadMD(output_file)
if not md.isMDHistoWorkspace():
raise ValueError('Needs to be MDHistoWorkspace')
output = {}
output['output_files'] = [{'location': output_file,
'type': 'processed',
'purpose': 'reduced-data',
'fields':{'mdhistoworkspace.experiment0.sample.material.temperature':'temperature',
'mdhistoworkspace.data.signal@axes':'axes',
'mdhistoworkspace.experiment0.sample.oriented_lattice.orientation_matrix':'orientation_matrix',
'mdhistoworkspace.experiment0.sample.oriented_lattice.unit_cell_a':'unit_cell_a',
'mdhistoworkspace.experiment0.sample.oriented_lattice.unit_cell_b':'unit_cell_b',
'mdhistoworkspace.experiment0.sample.oriented_lattice.unit_cell_c':'unit_cell_c',
'mdhistoworkspace.experiment0.sample.oriented_lattice.unit_cell_alpha':'unit_cell_alpha',
'mdhistoworkspace.experiment0.sample.oriented_lattice.unit_cell_beta':'unit_cell_beta',
'mdhistoworkspace.experiment0.sample.oriented_lattice.unit_cell_gamma':'unit_cell_gamma'
}}]
output['user'] = getpass.getuser()
output['created'] = datetime.datetime.now().replace(microsecond=0).isoformat()+'.00-05:00'
def _load_MDWorkspace_and_test_it(self, save_name):
filename = self.working_directory + '/' + save_name + ".nxs"
ws = LoadMD(Filename=filename)
ws_is_a_mdworkspace = isinstance(ws, IMDEventWorkspace) or isinstance(
ws, MDHistoWorkspace)
self.assertEqual(ws_is_a_mdworkspace, True)
def PyExec(self):
load_van = not self.getProperty("VanadiumFile").isDefault
load_files = not self.getProperty("Filename").isDefault
output = self.getProperty("OutputType").value
if load_files:
datafiles = self.getProperty("Filename").value
else:
datafiles = list(
map(str.strip,
self.getProperty("InputWorkspaces").value.split(",")))
prog = Progress(self, 0.0, 1.0, len(datafiles) + 1)
vanadiumfile = self.getProperty("VanadiumFile").value
vanws = self.getProperty("VanadiumWorkspace").value
height = self.getProperty("DetectorHeightOffset").value
distance = self.getProperty("DetectorDistanceOffset").value
wslist = []
out_ws = self.getPropertyValue("OutputWorkspace")
out_ws_name = out_ws
if load_van:
vanws = LoadMD(vanadiumfile, StoreInADS=False)
has_multiple = len(datafiles) > 1
for in_file in datafiles:
if load_files:
scan = LoadMD(in_file,
LoadHistory=False,
OutputWorkspace="__scan")
else:
scan = mtd[in_file]
# Make sure the workspace has experiment info, otherwise SetGoniometer will add some, causing issues.
if scan.getNumExperimentInfo() == 0:
raise RuntimeError(
"No experiment info was found in '{}'".format(in_file))
prog.report()
self.log().information("Processing '{}'".format(in_file))
SetGoniometer(Workspace=scan,
Axis0='omega,0,1,0,-1',
Axis1='chi,0,0,1,-1',
Axis2='phi,0,1,0,-1',
Average=False)
# If processing multiple files, append the base name to the given output name
if has_multiple:
if load_files:
out_ws_name = out_ws + "_" + os.path.basename(
in_file).strip(',.nxs')
else:
out_ws_name = out_ws + "_" + in_file
wslist.append(out_ws_name)
exp_info = scan.getExperimentInfo(0)
self.__move_components(exp_info, height, distance)
# Get the wavelength from experiment info if it exists, or fallback on property value
wavelength = self.__get_wavelength(exp_info)
# set the run number to be the same as scan number, this will be used for peaks
if not exp_info.run().hasProperty('run_number') and exp_info.run(
).hasProperty('scan'):
try:
exp_info.mutableRun().addProperty(
'run_number',
int(exp_info.run().getProperty('scan').value), True)
except ValueError:
# scan must be a int
pass
# Use ConvertHFIRSCDtoQ (and normalize van), or use ConvertWANDSCtoQ which handles normalization itself
if output == "Q-sample events":
norm_data = self.__normalization(scan, vanws, load_files)
minvals = self.getProperty("MinValues").value
maxvals = self.getProperty("MaxValues").value
merge = self.getProperty("MergeInputs").value
ConvertHFIRSCDtoMDE(InputWorkspace=norm_data,
Wavelength=wavelength,
MinValues=minvals,
MaxValues=maxvals,
OutputWorkspace=out_ws_name)
DeleteWorkspace(norm_data)
elif output == 'Q-sample histogram':
bin0 = self.getProperty("BinningDim0").value
bin1 = self.getProperty("BinningDim1").value
bin2 = self.getProperty("BinningDim2").value
# Convert to Q space and normalize with from the vanadium
ConvertWANDSCDtoQ(
InputWorkspace=scan,
NormalisationWorkspace=vanws,
Frame='Q_sample',
Wavelength=wavelength,
NormaliseBy=self.getProperty("NormaliseBy").value,
BinningDim0=bin0,
BinningDim1=bin1,
BinningDim2=bin2,
OutputWorkspace=out_ws_name)
if load_files:
DeleteWorkspace(scan)
else:
norm_data = self.__normalization(scan, vanws, load_files)
RenameWorkspace(norm_data, OutputWorkspace=out_ws_name)
if has_multiple:
out_ws_name = out_ws
if output == "Q-sample events" and merge:
MergeMD(InputWorkspaces=wslist, OutputWorkspace=out_ws_name)
DeleteWorkspaces(wslist)
else:
GroupWorkspaces(InputWorkspaces=wslist,
OutputWorkspace=out_ws_name)
# Don't delete workspaces if they were passed in
if load_van:
DeleteWorkspace(vanws)
self.setProperty("OutputWorkspace", out_ws_name)
from mantid.simpleapi import LoadNexus, LoadMD
for _ in range(5):
ws1 = LoadNexus('/HFIR/HB2C/IPTS-7776/shared/autoreduce/HB2C_3000.nxs')
ws2 = LoadNexus(
'/HFIR/HB2C/IPTS-7776/shared/autoreduce/HB2C_3000_group_4x4.nxs')
md1 = LoadMD('/HFIR/HB2C/IPTS-7776/shared/autoreduce/HB2C_3000_MDE.nxs')
md2 = LoadMD(
'/HFIR/HB2C/IPTS-7776/shared/autoreduce/HB2C_3000_group_4x4_MDE.nxs')
print()
# Load data and accumulate to Q sample MD event workspace
iptsdir = '/HFIR/HB2C/IPTS-{}/'.format(IPTS)
name_MDE = name + '_MDE'
if not append and name_MDE in mtd:
DeleteWorkspace(name_MDE)
for run in range(first_run, last_run + 1, load_every):
if use_autoreduced:
filename = iptsdir + 'shared/autoreduce/HB2C_{}_MDE.nxs'.format(run)
try:
LoadMD(Filename=filename,
LoadHistory=False,
OutputWorkspace='__md')
except ValueError:
filename = iptsdir + 'nexus/HB2C_{}.nxs.h5'.format(run)
LoadWAND(filename, OutputWorkspace='__ws')
ConvertToMD('__ws',
QDimensions='Q3D',
dEAnalysisMode='Elastic',
Q3DFrames='Q_sample',
OutputWorkspace='__md',
MinValues='-10,-1,-10',
MaxValues='10,1,10')
accumulateMD('__md', OutputWorkspace=name_MDE)
SaveMD(
name_MDE, iptsdir + 'shared/' + name +
