def get_ws_MDE(self, slicepoint, bin_params):
params = {}
for n in range(self._get_ws().getNumDims()):
if slicepoint[n] is None:
params['AlignedDim{}'.format(n)] = '{},{},{},{}'.format(self._get_ws().getDimension(n).name,
self._get_ws().getDimension(n).getMinimum(),
self._get_ws().getDimension(n).getMaximum(),
bin_params[n])
else:
params['AlignedDim{}'.format(n)] = '{},{},{},{}'.format(self._get_ws().getDimension(n).name,
slicepoint[n]-bin_params[n]/2,
slicepoint[n]+bin_params[n]/2,
1)
return BinMD(InputWorkspace=self._get_ws(),
OutputWorkspace=self._get_ws().name()+'_rebinned', **params)
def test_qtohkl_corelli(self):
Load(Filename='CORELLI_29782.nxs', OutputWorkspace='data')
SetGoniometer(Workspace='data', Axis0='BL9:Mot:Sample:Axis1,0,1,0,1')
LoadIsawUB(InputWorkspace='data',
Filename='SingleCrystalDiffuseReduction_UB.mat')
ConvertToMD(InputWorkspace='data',
QDimensions='Q3D',
dEAnalysisMode='Elastic',
Q3DFrames='HKL',
QConversionScales='HKL',
OutputWorkspace='HKL',
Uproj='1,1,0',
Vproj='1,-1,0',
Wproj='0,0,1',
MinValues='-20,-20,-20',
MaxValues='20,20,20')
hkl_binned = BinMD('HKL',
AlignedDim0='[H,H,0],-10.05,10.05,201',
AlignedDim1='[H,-H,0],-10.05,10.05,201',
AlignedDim2='[0,0,L],-1.05,1.05,21')
ConvertToMD(InputWorkspace='data',
QDimensions='Q3D',
dEAnalysisMode='Elastic',
Q3DFrames='Q_sample',
OutputWorkspace='q_sample',
MinValues='-20,-20,-20',
MaxValues='20,20,20')
hkl = ConvertQtoHKLMDHisto(
InputWorkspace=mtd["q_sample"],
Uproj="1,1,0",
Vproj="1,-1,0",
Wproj="0,0,1",
Extents="-10.05,10.05,-10.05,10.05,-1.05,1.05",
Bins="201,201,21")
for i in range(hkl.getNumDims()):
self.assertEqual(
hkl_binned.getDimension(i).name,
hkl.getDimension(i).name)
orig_sig = hkl_binned.getSignalArray()
new_sig = hkl.getSignalArray()
np.testing.assert_allclose(np.asarray(new_sig),
np.asarray(orig_sig),
rtol=1.0e-5,
atol=3)
def test_mdhisto_workspace_q(self):
from mantid.simpleapi import (CreateMDWorkspace, FakeMDEventData,
BinMD)
md_event = CreateMDWorkspace(Dimensions=3,
Extents=[-10, 10, -10, 10, -10, 10],
Names='Q_x,Q_y,Q_z',
Units='U,U,U',
Frames='QLab,QLab,QLab',
StoreInADS=False)
FakeMDEventData(InputWorkspace=md_event,
PeakParams=[100000, 0, 0, 0, 1],
StoreInADS=False) # Add Peak
md_histo = BinMD(InputWorkspace=md_event,
AlignedDim0='Q_y,-10,10,3',
AlignedDim1='Q_x,-10,10,4',
AlignedDim2='Q_z,-10,10,5',
StoreInADS=False)
histo_data_array = mantidcompat.convert_MDHistoWorkspace_to_data_array(
md_histo)
self.assertEqual(histo_data_array.coords[sc.Dim.Qx].values.shape,
(4, ))
self.assertEqual(histo_data_array.coords[sc.Dim.Qy].values.shape,
(3, ))
self.assertEqual(histo_data_array.coords[sc.Dim.Qz].values.shape,
(5, ))
self.assertEqual(histo_data_array.coords[sc.Dim.Qx].unit,
sc.units.dimensionless / sc.units.angstrom)
self.assertEqual(histo_data_array.coords[sc.Dim.Qy].unit,
sc.units.dimensionless / sc.units.angstrom)
self.assertEqual(histo_data_array.coords[sc.Dim.Qz].unit,
sc.units.dimensionless / sc.units.angstrom)
self.assertEquals(histo_data_array.values.shape, (3, 4, 5))
# Sum over 2 dimensions to simplify finding max.
max_1d = sc.sum(sc.sum(histo_data_array, dim=sc.Dim.Qy),
dim=sc.Dim.Qx).values
max_index = np.argmax(max_1d)
# Check position of max 'peak'
self.assertEqual(np.floor(len(max_1d) / 2), max_index)
# All events in central 'peak'
self.assertEqual(100000, max_1d[max_index])
self.assertTrue('nevents' in histo_data_array.attrs)
def _compute_slice_PSD(self, workspace, x_axis, y_axis, norm_to_one):
assert isinstance(workspace, IMDEventWorkspace)
n_x_bins = get_number_of_steps(x_axis)
n_y_bins = get_number_of_steps(y_axis)
x_dim_id = self.dimension_index(workspace, x_axis)
y_dim_id = self.dimension_index(workspace, y_axis)
x_dim = workspace.getDimension(x_dim_id)
y_dim = workspace.getDimension(y_dim_id)
xbinning = x_dim.getName() + "," + str(x_axis.start) + "," + str(
x_axis.end) + "," + str(n_x_bins)
ybinning = y_dim.getName() + "," + str(y_axis.start) + "," + str(
y_axis.end) + "," + str(n_y_bins)
return BinMD(InputWorkspace=workspace,
AxisAligned="1",
AlignedDim0=xbinning,
AlignedDim1=ybinning,
StoreInADS=False)
def perform_non_axis_aligned_cut_to_workspace(self, vectors, extents,
nbins):
projection_params = {}
# construct projection string for x-axis label
cens = np.mean(extents.reshape(2, len(vectors), order='F'),
axis=0) # in u{1..3} basis
ix = np.where(nbins > 1)[0][0] # index of x-axis
ivecs = list(range(len(vectors)))
ivecs.pop(ix)
cen_vec = np.zeros(vectors[0].shape) # position at x = 0
for ivec in ivecs:
cen_vec = cen_vec + cens[ivec] * vectors[ivec]
proj_str = '(' + ' '.join([
f'{np.round(c,2)}+{np.round(x,2)}x'
if abs(x) > 0 else f'{np.round(c,2)}'
for c, x in zip(cen_vec, vectors[ix])
]) + ')'
proj_str = proj_str.replace('+-', '-')
for ivec, vec in enumerate(vectors):
# calc length
length = None
if self.get_frame() == SpecialCoordinateSystem.HKL:
try:
lattice = self._get_ws().getExperimentInfo(
0).sample().getOrientedLattice()
length = 2 * np.pi / lattice.d(*vec)
except:
pass
else:
length = np.sqrt(np.sum(vec**2))
unit_str = f'in {np.round(length,2)} Ang^-1' if length is not None else 'r.l.u.'
xlab = proj_str if ivec == ix else f'u{ivec+1}'
vec_str = ','.join(str(v) for v in vec)
projection_params[f'BasisVector{ivec}'] = ', '.join(
[xlab, unit_str, vec_str])
BinMD(InputWorkspace=self._get_ws(),
AxisAligned=False,
OutputExtents=extents,
OutputBins=nbins,
NormalizeBasisVectors=False,
OutputWorkspace=self._1Dcut_name,
**projection_params)
return self._1Dcut_name
def get_ws_MDE(self,
slicepoint: Sequence[Optional[float]],
bin_params: Optional[Sequence[float]],
limits: Optional[tuple] = None,
dimension_indices: Optional[tuple] = None):
"""
:param slicepoint: ND sequence of either None or float. A float defines the point
in that dimension for the slice.
:param bin_params: ND sequence containing the number of bins for each dimension or None to use
the existing data
:param limits: An optional 2-tuple sequence containing limits for plotting dimensions. If
not provided the full extent of each dimension is used
"""
workspace = self._get_ws()
params, _, __ = _roi_binmd_parameters(workspace, slicepoint, bin_params, limits, dimension_indices)
params['EnableLogging'] = LOG_GET_WS_MDE_ALGORITHM_CALLS
binned = BinMD(InputWorkspace=workspace, OutputWorkspace=self._rebinned_name, **params)
return binned
def convertQSampleToHKL(ws,
OutputWorkspace='__md_hkl',
norm=None,
UB=None,
Extents=[-10, 10, -10, 10, -10, 10],
Bins=[101, 101, 101],
Append=False,
Uproj=(1, 0, 0),
Vproj=(0, 1, 0),
Wproj=(0, 0, 1)):
ol = OrientedLattice()
ol.setUB(UB)
q1 = ol.qFromHKL(Uproj)
q2 = ol.qFromHKL(Vproj)
q3 = ol.qFromHKL(Wproj)
BinMD(InputWorkspace=ws,
AxisAligned=False,
NormalizeBasisVectors=False,
BasisVector0='Q1,A^-1,{},{},{}'.format(q1.X(), q1.Y(), q1.Z()),
BasisVector1='Q2,A^-1,{},{},{}'.format(q2.X(), q2.Y(), q2.Z()),
BasisVector2='Q3,A^-1,{},{},{}'.format(q3.X(), q3.Y(), q3.Z()),
OutputExtents=Extents,
OutputBins=Bins,
TemporaryDataWorkspace=OutputWorkspace
if Append and mtd.doesExist(OutputWorkspace) else None,
OutputWorkspace=OutputWorkspace)
if norm is not None:
mtd[str(norm)].run().getGoniometer().setR(
mtd[str(ws)].getExperimentInfo(0).run().getGoniometer().getR())
convertToHKL(
norm,
OutputWorkspace=str(OutputWorkspace) + '_norm',
UB=UB,
Extents=Extents,
Bins=Bins,
Append=Append,
Scale=mtd[ws].getExperimentInfo(0).run().getProtonCharge(),
Uproj=Uproj,
Vproj=Vproj,
Wproj=Wproj)
return OutputWorkspace
def PyExec(self):
input_ws = self.getProperty("InputWorkspace").value
extents = self.getProperty("Extents").value
bins = self.getProperty("Bins").value
# Get the UB from either the PeaksWS if provided, or from the input workspace
if not self.getProperty("PeaksWorkspace").isDefault:
peak_ws = self.getProperty("PeaksWorkspace").value
self._lattice = peak_ws.sample().getOrientedLattice()
else:
self._lattice = input_ws.getExperimentInfo(0).sample().getOrientedLattice()
# Get axis names and units from u,v,w projections, as done in ConvertWANDSCDtoQ:
w = np.eye(3)
w[:, 0] = self.getProperty("Uproj").value
w[:, 1] = self.getProperty("Vproj").value
w[:, 2] = self.getProperty("Wproj").value
char_dict = {0: '0', 1: '{1}', -1: '-{1}'}
chars = ['H', 'K', 'L']
names = ['[' + ','.join(char_dict.get(j, '{0}{1}')
.format(j, chars[np.argmax(np.abs(w[:, i]))]) for j in w[:, i]) + ']' for i in range(3)]
q = [self._lattice.qFromHKL(w[i]) for i in range(3)]
units = ['in {:.3f} A^-1'.format(q[i].norm()) for i in range(3)]
mdhist = BinMD(InputWorkspace=input_ws, AxisAligned=False, NormalizeBasisVectors=False,
BasisVector0='{},{},{},{},{}'.format(names[0], units[0], q[0].X(), q[0].Y(), q[0].Z()),
BasisVector1='{},{},{},{},{}'.format(names[1], units[1], q[1].X(), q[1].Y(), q[1].Z()),
BasisVector2='{},{},{},{},{}'.format(names[2], units[2], q[2].X(), q[2].Y(), q[2].Z()),
OutputExtents=extents,
OutputBins=bins)
SetMDFrame(mdhist, MDFrame='HKL', Axes='0, 1, 2')
self.setProperty("OutputWorkspace", mdhist)
DeleteWorkspace(mdhist)
def convertToHKL(ws,
OutputWorkspace='__md_hkl',
UB=None,
Append=False,
scale=None,
BinningDim0='-10.05,10.05,201',
BinningDim1='-10.05,10.05,201',
BinningDim2='-10.05,10.05,201',
Uproj=(1, 0, 0),
Vproj=(0, 1, 0),
Wproj=(0, 0, 1)):
"""Output MDHistoWorkspace in HKL
"""
SetUB(ws, UB=UB)
ConvertToMD(ws,
QDimensions='Q3D',
QConversionScales='HKL',
dEAnalysisMode='Elastic',
Q3DFrames='HKL',
OutputWorkspace='__temp',
Uproj=Uproj,
Vproj=Vproj,
Wproj=Wproj)
if scale is not None:
mtd['__temp'] *= scale
BinMD(InputWorkspace='__temp',
TemporaryDataWorkspace=OutputWorkspace
if Append and mtd.doesExist(OutputWorkspace) else None,
OutputWorkspace=OutputWorkspace,
AlignedDim0=mtd['__temp'].getDimension(0).name + ',' + BinningDim0,
AlignedDim1=mtd['__temp'].getDimension(1).name + ',' + BinningDim1,
AlignedDim2=mtd['__temp'].getDimension(2).name + ',' + BinningDim2)
DeleteWorkspace('__temp')
return OutputWorkspace
def rebin_MDH(self, slicepoint: Sequence[Optional[float]],
limits: Sequence[tuple]):
"""Rebin an MDHistoWorkspace by going back to the original MDEventWorkspace but setting
new limits on the output extents. It replaces the internal workspace object with the new
MDHistoWorkspace generated.
:param slicepoint: ND sequence of either None or float. A float defines the point
in that dimension for the slice.
:param bin_params: ND sequence containing the number of bins for each dimension
:param limits: An optional 2-tuple sequence containing limits for plotting dimensions. If
not provided the full extent of each dimension is used
"""
def to_str(seq: Sequence):
return ','.join(map(str, seq))
# BinMD requires non-axis aligned binning when binning an MDHistoWorkspace from the original
# MDEventWorkspace
workspace = self._get_ws()
limits = _binning_limits(workspace, slicepoint, limits)
ndims = workspace.getNumDims()
ws_basis = np.eye(ndims)
output_extents, output_bins = [], []
params = {'AxisAligned': False, 'EnableLogging': LOG_BINMD_CALLS}
for n in range(ndims):
dimension = workspace.getDimension(n)
basis_vec_n = to_str(ws_basis[:, n])
nbins = dimension.getNBins()
dim_min, dim_max = limits[n]
params[
f'BasisVector{n}'] = f'{dimension.name},{dimension.getUnits()},{basis_vec_n}'
output_extents.append(dim_min)
output_extents.append(dim_max)
output_bins.append(nbins)
params['OutputExtents'] = to_str(output_extents)
params['OutputBins'] = to_str(output_bins)
self._ws = BinMD(InputWorkspace=self._get_ws(),
OutputWorkspace=self._rebinned_name,
**params)
def get_histo_ws(self):
"""Converts _raw_ws from MDEventWorkspace to MDHistoWorkspace using BinMD, and caches result in _histo_ws"""
if self._histo_ws is None:
dim_values = []
for x in range(6):
try:
dim = self._raw_ws.getDimension(x)
dim_info = dim.getName() + ',' + str(
dim.getMinimum()) + ',' + str(
dim.getMaximum()) + ',' + str(100)
except RuntimeError:
dim_info = None
dim_values.append(dim_info)
histo_workspace = BinMD(InputWorkspace=self._raw_ws,
OutputWorkspace=str(self),
AlignedDim0=dim_values[0],
AlignedDim1=dim_values[1],
AlignedDim2=dim_values[2],
AlignedDim3=dim_values[3],
AlignedDim4=dim_values[4],
AlignedDim5=dim_values[5])
self._histo_ws = HistogramWorkspace(histo_workspace, self.name)
return self._histo_ws
def test_mdhisto_workspace_many_dims(self):
from mantid.simpleapi import (CreateMDWorkspace, FakeMDEventData,
BinMD)
md_event = CreateMDWorkspace(
Dimensions=4,
Extents=[-10, 10, -10, 10, -10, 10, -10, 10],
Names='deltae,y,z,T',
Units='U,U,U,U',
StoreInADS=False)
FakeMDEventData(InputWorkspace=md_event,
PeakParams=[100000, 0, 0, 0, 0, 1],
StoreInADS=False) # Add Peak
md_histo = BinMD(InputWorkspace=md_event,
AlignedDim0='deltae,-10,10,3',
AlignedDim1='y,-10,10,4',
AlignedDim2='z,-10,10,5',
AlignedDim3='T,-10,10,7',
StoreInADS=False)
histo_data_array = scn.mantid.convert_MDHistoWorkspace_to_data_array(
md_histo)
self.assertEqual(4, len(histo_data_array.dims))
def export_roi_to_workspace_mdevent(self, slicepoint: Sequence[Optional[float]],
bin_params: Sequence[float], limits: tuple,
transpose: bool, dimension_indices: Sequence[int]):
"""
Export 2D roi to a separate workspace.
:param slicepoint: ND sequence of either None or float. A float defines the point
in that dimension for the slice.
:param bin_params: ND sequence containing the number of bins for each dimension
:param limits: An optional ND sequence containing limits for plotting dimensions. If
not provided the full extent of each dimension is used
:param dimension_indices: A list where the value (None, 0, or 1) at index i denotes the index of the axis
dimension i is displayed on
:param transpose: If true the limits are transposed w.r.t the data
"""
workspace = self._get_ws()
params, xindex, yindex = _roi_binmd_parameters(workspace, slicepoint, bin_params, limits, dimension_indices)
params['OutputWorkspace'] = self._roi_name
roi_ws = BinMD(InputWorkspace=self._get_ws(), **params)
roi_ws.clearOriginalWorkspaces()
if transpose:
_inplace_transposemd(self._roi_name, axes=[yindex, xindex])
return f'ROI created: {self._roi_name}'
def reduce(nxsfile,
qaxis,
outfile,
use_ei_guess=False,
ei_guess=None,
eaxis=None,
tof2E=True,
ibnorm='ByCurrent'):
from mantid.simpleapi import DgsReduction, SofQW3, SaveNexus, LoadInstrument, Load, MoveInstrumentComponent, \
MaskBTP, ConvertToMD, BinMD, ConvertMDHistoToMatrixWorkspace, GetEiT0atSNS, GetEi
from mantid import mtd
ws = Load(nxsfile)
if tof2E == 'guess':
axis = ws.getAxis(0).getUnit().caption().lower()
# axis name should be "Time-of-flight"
tof2E = "time" in axis and "flight" in axis
if tof2E:
# mask packs around beam
# MaskBTP(ws, Bank="98-102")
if not use_ei_guess:
run = ws.getRun()
Efixed = run.getLogData('mcvine-Ei').value
T0 = run.getLogData('mcvine-t0').value
else:
Efixed, T0 = ei_guess, 0
DgsReduction(
SampleInputWorkspace=ws,
IncidentEnergyGuess=Efixed,
UseIncidentEnergyGuess=True,
TimeZeroGuess=T0,
OutputWorkspace='reduced',
EnergyTransferRange=eaxis,
IncidentBeamNormalisation=ibnorm,
)
reduced = mtd['reduced']
else:
reduced = Load(nxsfile)
# if eaxis is not specified, use the data in reduced workspace
if eaxis is None:
Edim = reduced.getXDimension()
emin = Edim.getMinimum()
emax = Edim.getMaximum()
de = Edim.getX(1) - Edim.getX(0)
eaxis = emin, de, emax
qmin, dq, qmax = qaxis
nq = int(round((qmax - qmin) / dq))
emin, de, emax = eaxis
ne = int(round((emax - emin) / de))
md = ConvertToMD(
InputWorkspace='reduced',
QDimensions='|Q|',
dEAnalysisMode='Direct',
MinValues="%s,%s" % (qmin, emin),
MaxValues="%s,%s" % (qmax, emax),
SplitInto="%s,%s" % (nq, ne),
)
binned = BinMD(
InputWorkspace=md,
AxisAligned=1,
AlignedDim0="|Q|,%s,%s,%s" % (qmin, qmax, nq),
AlignedDim1="DeltaE,%s,%s,%s" % (emin, emax, ne),
)
# convert to histogram
import histogram as H, histogram.hdf as hh
data = binned.getSignalArray().copy()
err2 = binned.getErrorSquaredArray().copy()
nev = binned.getNumEventsArray()
data /= nev
err2 /= (nev * nev)
qaxis = H.axis('Q',
boundaries=np.arange(qmin, qmax + dq / 2., dq),
unit='1./angstrom')
eaxis = H.axis('E',
boundaries=np.arange(emin, emax + de / 2., de),
unit='meV')
hist = H.histogram('IQE', (qaxis, eaxis), data=data, errors=err2)
if outfile.endswith('.nxs'):
import warnings
warnings.warn(
"reduce function no longer writes iqe.nxs nexus file. it only writes iqe.h5 histogram file"
)
outfile = outfile[:-4] + '.h5'
hh.dump(hist, outfile)
return
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 +
'_MDE_{}_to_{}_every_{}.nxs'.format(first_run, last_run, load_every))
# Convert MD Event workspace to MD Histo workspace
name_MDH = name + '_MDH'
BinMD(InputWorkspace=name_MDE,
AlignedDim0='Q_sample_x,' + BinningDim0,
AlignedDim1='Q_sample_y,' + BinningDim1,
AlignedDim2='Q_sample_z,' + BinningDim2,
OutputWorkspace=name_MDH)
SaveMD(
name_MDH, iptsdir + 'shared/' + name +
'_MDH_{}_to_{}_every_{}.nxs'.format(first_run, last_run, load_every))
Chopper = 0
# generate a nice 2D multi-dimensional workspace
data = LoadNXSPE(datadir + 'ARCS_' + str(RunNumber) +
'_autoreduced.nxspe')
values = ConvertToMDMinMaxLocal('data',
QDimensions='|Q|',
dEAnalysisMode='Direct')
minQ, minE = values.MinValues
maxQ, maxE = values.MaxValues
md = ConvertToMD(InputWorkspace=data,
QDimensions='|Q|',
dEAnalysisMode='Direct')
sqw = BinMD(InputWorkspace=md,
AlignedDim0='|Q|,' + str(minQ) + ',' + str(maxQ) + ',100',
AlignedDim1='DeltaE,' + str(minE) + ',' + str(maxE * 0.8) +
',100')
#2D plot
if PlotTag == 1:
fig, ax = plt.subplots(subplot_kw={'projection': 'mantid'})
c = ax.pcolormesh(sqw, vmin=0., vmax=0.5e-3)
cbar = fig.colorbar(c)
cbar.set_label('Intensity (arb. units)') #add text to colorbar
ax.set_title('Run ' + str(RunNumber) + ',Ei=' + str(Energy) +
'meVChoppers=[' + str(Chopper1) + ',' +
str(Chopper2) + ',' + str(Chopper3) + ']')
fig.show()
# generate a 1D multi-dimensional workspace
ws1 = Load(run1)
ws2 = Load(run2)
SetGoniometer(ws1, Axis0="BL9:Mot:Sample:Axis2,0,1,0,1")
SetGoniometer(ws2, Axis0="BL9:Mot:Sample:Axis2,0,1,0,1")
md1 = ConvertToMD(ws1,
QDimensions='Q3D',
dEAnalysisMode='Elastic',
Q3DFrames='Q_sample',
MinValues=[-10, -10, -10],
MaxValues=[10, 10, 10])
bin1 = BinMD(md1,
AlignedDim0='Q_sample_x,-10,10,1000',
AlignedDim1='Q_sample_z,-10,10,1000',
AlignedDim2='Q_sample_y,-10,10,1')
bin1 /= ws1.run().getProtonCharge()
s1 = bin1.getSignalArray().copy()
x = np.linspace(-1, 1, 1000)
X, Y = np.meshgrid(x, x)
mask = (X**2 + Y**2 > 1) + (X**2 + Y**2 < 0.25)
s1[mask] = 0
s1_mask = s1 < np.percentile(s1, 99.5)
mask[s1_mask[:, :, 0]] = True
plot_zbins = 1
AlignedDim2 = '{},{},{},{}'.format(AxisNames[plot_param['axis3']],
plot_zmin, plot_zmax, plot_zbins)
if have_van:
data, norm = MDNormSCD(InputWorkspace=mde,
FluxWorkspace=fl,
SolidAngleWorkspace=sa,
SkipSafetyCheck=True,
AlignedDim0=AlignedDim0,
AlignedDim1=AlignedDim1,
AlignedDim2=AlignedDim2)
mdh = data / norm
else:
mdh = BinMD(InputWorkspace=mde,
AlignedDim0=AlignedDim0,
AlignedDim1=AlignedDim1,
AlignedDim2=AlignedDim2,
AxisAligned=True)
intensity = mdh.getSignalArray()
vmin = 1
vmax = intensity.max()
if vmax > 1:
fig, ax = plt.subplots()
ax.set_title('{}=[{},{}]'.format(AxisNames[plot_param['axis3']],
plot_zmin, plot_zmax))
logNorm = colors.LogNorm(vmin=vmin, vmax=vmax)
cm = plt.cm.get_cmap('rainbow')
pcm = Plot2DMD(ax, mdh, NumEvNorm=False, norm=logNorm)
fig.colorbar(pcm, ax=ax)
#plotSlice(mdws, xydim=[plot_param['axis1'],plot_param['axis2']], slicepoint=[0,0,0.5*(plot_param['zmin']+plot_param['zmax'])], colorscalelog=True)
with h5py.File(filename, 'r') as f_in:
bc = np.zeros((pixels), dtype=np.int64)
for b in range(8):
bc += np.bincount(f_in['/entry/bank' + str(b + 1) +
'_events/event_id'].value,
minlength=pixels)
bc = bc.reshape((-1, 512)).T
data_array[n] = bc
phi_array[n] = f_in[
'entry/DASlogs/HB2C:Mot:s1.RBV/average_value'].value[0]
t1 = time.time()
print(t1 - t0)
print(data_array.shape)
mdws = CreateMDWorkspace(Dimensions=3,
Extents='-10,10,-10,10,-10,10',
Names='A,B,C',
Units='U,U,U')
t2 = time.time()
binned_ws = BinMD(InputWorkspace=mdws,
AlignedDim0='A,0,10,1801',
AlignedDim1='B,-10,10,512',
AlignedDim2='C,-10,10,3840')
t3 = time.time()
binned_ws.setSignalArray(data_array)
t4 = time.time()
print(t2 - t1)
print(t3 - t2)
print(t4 - t3)
def setUpClass(cls):
"""
Create fake workspaces, 3D and 2D
"""
# 3D
DeltaPDF3DTest_MDE = CreateMDWorkspace(
Dimensions='3',
Extents='-3.1,3.1,-3.1,3.1,-3.1,3.1',
Names='[H,0,0],[0,K,0],[0,0,L]',
Units='rlu,rlu,rlu',
SplitInto='4',
Frames='HKL,HKL,HKL')
# Add Bragg peaks
for h in range(-3, 4):
for k in range(-3, 4):
for l in range(-3, 4):
FakeMDEventData(DeltaPDF3DTest_MDE,
PeakParams='100,' + str(h) + ',' + str(k) +
',' + str(l) + ',0.1',
RandomSeed='1337')
# Add addiontal peaks on [0.5,0.5,0.5] type positions
# This would correspond to negative substitutional correlations
for h in np.arange(-2.5, 3):
for k in np.arange(-2.5, 3):
for l in np.arange(-2.5, 3):
FakeMDEventData(DeltaPDF3DTest_MDE,
PeakParams='20,' + str(h) + ',' + str(k) +
',' + str(l) + ',0.1',
RandomSeed='13337')
BinMD(InputWorkspace='DeltaPDF3DTest_MDE',
AlignedDim0='[H,0,0],-3.05,3.05,61',
AlignedDim1='[0,K,0],-3.05,3.05,61',
AlignedDim2='[0,0,L],-3.05,3.05,61',
OutputWorkspace='DeltaPDF3DTest_MDH')
# 2D
DeltaPDF3DTest_MDE_2 = CreateMDWorkspace(
Dimensions='3',
Extents='-3.1,3.1,-3.1,3.1,-0.1,0.1',
Names='[H,0,0],[0,K,0],[0,0,L]',
Units='rlu,rlu,rlu',
SplitInto='4',
Frames='HKL,HKL,HKL')
# Add Bragg peaks
for h in range(-3, 4):
for k in range(-3, 4):
FakeMDEventData(DeltaPDF3DTest_MDE_2,
PeakParams='100,' + str(h) + ',' + str(k) +
',0,0.1',
RandomSeed='1337')
# Add addiontal peaks on [0.5,0.5,0.5] type positions
# This would correspond to negative substitutional correlations
for h in np.arange(-2.5, 3):
for k in np.arange(-2.5, 3):
FakeMDEventData(DeltaPDF3DTest_MDE_2,
PeakParams='20,' + str(h) + ',' + str(k) +
',0,0.1',
RandomSeed='13337')
BinMD(InputWorkspace='DeltaPDF3DTest_MDE_2',
AlignedDim0='[H,0,0],-3.05,3.05,61',
AlignedDim1='[0,K,0],-3.05,3.05,61',
AlignedDim2='[0,0,L],-0.1,0.1,1',
OutputWorkspace='DeltaPDF3DTest_MDH_2')