def setup(data, begin, end):
background = subtract_background_mean(data, 'tof', begin, end)
del background.coords[
'sample-position'] # ensure unit conversion treats this a monitor
background = sc.neutron.convert(background, 'tof', 'wavelength')
background = sc.rebin(background, 'wavelength', wavelength_bins)
return background
def map_to_bins(data, dim, edges):
data = data.copy()
to_bin_edges(data, dim)
bin_width = data.coords[dim][dim, 1:] - data.coords[dim][dim, :-1]
bin_width.unit = sc.units.one
data *= bin_width
data = sc.rebin(data, dim, edges)
bin_width = edges[dim, 1:] - edges[dim, :-1]
bin_width.unit = sc.units.one
data /= bin_width
return data
def test_rebin():
dataset = sc.Dataset()
dataset['data'] = sc.Variable(['x'],
values=np.array(10 * [1.0]),
unit=sc.units.counts)
dataset.coords['x'] = sc.Variable(['x'], values=np.arange(11.0))
new_coord = sc.Variable(dims=['x'], values=np.arange(0.0, 11, 2))
dataset = sc.rebin(dataset, 'x', new_coord)
np.testing.assert_array_equal(dataset['data'].values, np.array(5 * [2]))
np.testing.assert_array_equal(dataset.coords['x'].values,
np.arange(0, 11, 2))
def process_event_data(file, lambda_binning):
"""
Load and reduce event data (used for Vanadium and Empty instrument)
file: Nexus file to be loaded and its data reduced in this script
lambda_binning: lambda_min, lamba_max, number_of_bins
"""
# load nexus file
event_data = scn.load(file,
advanced_geometry=True,
load_pulse_times=False,
mantid_args={'LoadMonitors': True})
# ################################
# Monitor correction
# extract monitor and convert from tof to wavelength
mon4_lambda = scn.convert(event_data.attrs['monitor4'].values,
'tof',
'wavelength',
scatter=False)
mon4_smooth = smooth_data(mon4_lambda, dim='wavelength', NPoints=40)
del mon4_lambda
# ################################
# vana and EC
# convert to lambda
event_lambda = scn.convert(event_data, 'tof', 'wavelength', scatter=True)
# normalize to monitor
lambda_min, lambda_max, number_bins = lambda_binning
edges_lambda = sc.Variable(['wavelength'],
unit=sc.units.angstrom,
values=np.linspace(lambda_min,
lambda_max,
num=number_bins))
mon_rebin = sc.rebin(mon4_smooth, 'wavelength', edges_lambda)
del mon4_smooth
event_lambda_norm = event_lambda.bins / sc.lookup(func=mon_rebin,
dim='wavelength')
del mon_rebin, event_lambda
return event_lambda_norm
def _stitch_dense_data(
item: sc.DataArray, frames: sc.Dataset, dim: str, new_dim: str,
bins: Union[int, sc.Variable]) -> Union[sc.DataArray, dict]:
# Make empty data container
if isinstance(bins, int):
new_coord = sc.linspace(
dim=new_dim,
start=(frames["time_min"]["frame", 0] -
frames["time_correction"]["frame", 0]).value,
stop=(frames["time_max"]["frame", -1] -
frames["time_correction"]["frame", -1]).value,
num=bins + 1,
unit=frames["time_min"].unit,
)
else:
new_coord = bins
dims = []
shape = []
for dim_ in item.dims:
if dim_ != dim:
dims.append(dim_)
shape.append(item.sizes[dim_])
else:
dims.append(new_dim)
shape.append(new_coord.sizes[new_dim] - 1)
out = sc.DataArray(data=sc.zeros(dims=dims,
shape=shape,
with_variances=item.variances is not None,
unit=item.unit),
coords={new_dim: new_coord})
for group in ["coords", "attrs"]:
for key in getattr(item, group):
if key != dim:
getattr(out, group)[key] = getattr(item, group)[key].copy()
for i in range(frames.sizes["frame"]):
section = item[dim, frames["time_min"].data[
"frame", i]:frames["time_max"].data["frame",
i]].rename_dims({dim: new_dim})
section.coords[new_dim] = section.coords[dim] - frames[
"time_correction"].data["frame", i]
if new_dim != dim:
del section.coords[dim]
out += sc.rebin(section, new_dim, out.coords[new_dim])
return out
def to_wavelength(data, transmission, direct_beam, direct_beam_transmission,
masks, wavelength_bins):
transmission = transmission_fraction(transmission,
direct_beam_transmission,
wavelength_bins)
data = data.copy()
for name, mask in masks.items():
data.masks[name] = mask
data = sc.neutron.convert(data, 'tof', 'wavelength', out=data)
data = sc.rebin(data, 'wavelength', wavelength_bins)
monitor = data.attrs['monitor1'].value
monitor = subtract_background_mean(monitor, 'tof', 40000.0 * sc.units.us,
99000.0 * sc.units.us)
monitor = sc.neutron.convert(monitor, 'tof', 'wavelength', out=monitor)
monitor = sc.rebin(monitor, 'wavelength', wavelength_bins)
direct_beam = contrib.map_to_bins(direct_beam, 'wavelength',
monitor.coords['wavelength'])
direct_beam = monitor * transmission * direct_beam
d = sc.Dataset({'data': data, 'norm': solid_angle(data) * direct_beam})
contrib.to_bin_centers(d, 'wavelength')
return d
def process_vanadium_data(vanadium,
empty_instr,
lambda_binning,
calibration=None,
**absorp):
"""
Create corrected vanadium dataset
Correction applied to Vanadium data only
1. Subtract empty instrument
2. Correct absorption
3. Use calibration for grouping
4. Focus into groups
Parameters
----------
vanadium : Vanadium nexus datafile
empty_instr: Empty instrument nexus file
lambda_binning: format=(lambda_min, lambda_min, number_of_bins)
lambda_min and lambda_max are in Angstroms
calibration: calibration file
Mantid format
**absorp: dictionary containing information to correct absorption for sample and vanadium
only the inputs related to Vanadium will be selected to calculate the correction
see docstrings of powder_reduction for more details
see help of Mantid's algorithm CylinderAbsorption for details
https://docs.mantidproject.org/nightly/algorithms/CylinderAbsorption-v1.html
"""
vana_red = process_event_data(vanadium, lambda_binning)
ec_red = process_event_data(empty_instr, lambda_binning)
# vana - EC
vana_red -= ec_red
del ec_red
# Absorption correction applied
if bool(absorp):
# The values of number_density, scattering and attenuation are hard-coded since they must
# correspond to Vanadium. Only radius and height of the Vanadium cylindrical sample
# shape can be set. The names of these inputs if present have to be renamed to match
# the requirements of Mantid's algorithm CylinderAbsorption
# Create dictionary to calculate absorption correction for Vanadium.
absorp_vana = {
key.replace('Vanadium', 'Sample'): value
for key, value in absorp.items() if 'Vanadium' in key
}
absorp_vana['SampleNumberDensity'] = 0.07118
absorp_vana['ScatteringXSection'] = 5.16
absorp_vana['AttenuationXSection'] = 4.8756
correction = absorption_correction(vanadium, lambda_binning,
**absorp_vana)
# the 3 following lines of code are to place info about source and sample
# position at the right place in the correction dataArray in order to
# proceed to the normalization
del correction.coords['source_position']
del correction.coords['sample_position']
del correction.coords['position']
correction = sc.rebin(
correction, 'wavelength',
sc.Variable(['wavelength'],
values=vana_red.coords['wavelength'].values,
unit=sc.units.angstrom))
vana_red /= correction
del correction
# convert to TOF
vana_red_tof = sc.neutron.convert(vana_red,
'wavelength',
'tof',
realign='linear')
del vana_red
# convert to d-spacing (no calibration applied)
vana_dspacing = sc.neutron.convert(vana_red_tof,
'tof',
'd-spacing',
realign='linear')
del vana_red_tof
# Calibration
# Load
input_load_cal = {'InstrumentFilename': 'WISH_Definition.xml'}
calvana = load_calibration(calibration, mantid_args=input_load_cal)
# Merge table with detector->spectrum mapping from vanadium
# (implicitly checking that detectors between vanadium and calibration are the same)
cal_vana = sc.merge(calvana, vana_dspacing.coords['detector_info'].value)
# Compute spectrum mask from detector mask
maskvana = sc.groupby(cal_vana['mask'], group='spectrum').any('detector')
# Compute spectrum groups from detector groups
gvana = sc.groupby(cal_vana['group'], group='spectrum')
groupvana = gvana.min('detector')
assert groupvana == gvana.max('detector'), \
"Calibration table has mismatching group for detectors in same spectrum"
vana_dspacing.coords['group'] = groupvana.data
vana_dspacing.masks['mask'] = maskvana.data
# Focus
focused_vana = sc.groupby(vana_dspacing, group='group').sum('spectrum')
return focused_vana
def powder_reduction(sample='sample.nxs',
calibration=None,
vanadium=None,
empty_instr=None,
lambda_binning=(0.7, 10.35, 5615),
**absorp):
"""
Simple WISH reduction workflow
Note
----
The sample data were not recorded using the same layout
of WISH as the Vanadium and empty instrument. That's why:
- loading calibration for Vanadium used a different IDF
- the Vanadium correction involved cropping the sample data
to the first 5 groups (panels)
----
Corrections applied:
- Vanadium correction
- Absorption correction
- Normalization by monitors
- Conversion considering calibration
- Masking and grouping detectors into panels
Parameters
----------
sample: Nexus event file
calibration: .cal file following Mantid's standards
The columns correspond to detectors' IDs, offset, selection of detectors
and groups
vanadium: Nexus event file
empty_instr: Nexus event file
lambda_binning: min, max and number of steps for binning in wavelength
min and max are in Angstroms
**absorp: dictionary containing information to correct absorption for Sample and
Vanadium.
There could be only up to two elements related to the correction for Vanadium:
the radius and height of the cylindrical sample shape.
To distinguish them from the inputs related to the sample, their names in the
dictionary are 'CylinderVanadiumRadius' and 'CylinderVanadiumHeight'. The other keys
of the 'absorp' dictionary follow Mantid's syntax and are related to the sample data
only.
see help of Mantid's algorithm CylinderAbsorption for details
https://docs.mantidproject.org/nightly/algorithms/CylinderAbsorption-v1.html
Returns
-------
Scipp dataset containing reduced data in d-spacing
Hints
-----
To plot the output data, one can histogram in d-spacing and sum according to groups
using scipp.histogram and sc.sum, respectively.
"""
# Load counts
sample_data = sc.neutron.load(sample,
advanced_geometry=True,
load_pulse_times=False,
mantid_args={'LoadMonitors': True})
# Load calibration
if calibration is not None:
input_load_cal = {"InstrumentName": "WISH"}
cal = load_calibration(calibration, mantid_args=input_load_cal)
# Merge table with detector->spectrum mapping from sample
# (implicitly checking that detectors between sample and calibration are the same)
cal_sample = sc.merge(cal, sample_data.coords['detector_info'].value)
# Compute spectrum mask from detector mask
mask = sc.groupby(cal_sample['mask'], group='spectrum').any('detector')
# Compute spectrum groups from detector groups
g = sc.groupby(cal_sample['group'], group='spectrum')
group = g.min('detector')
assert group == g.max('detector'), \
"Calibration table has mismatching group for detectors in same spectrum"
sample_data.coords['group'] = group.data
sample_data.masks['mask'] = mask.data
# Correct 4th monitor spectrum
# There are 5 monitors for WISH. Only one, the fourth one, is selected for
# correction (like in the real WISH workflow).
# Select fourth monitor and convert from tof to wavelength
mon4_lambda = sc.neutron.convert(sample_data.attrs['monitor4'].values,
'tof', 'wavelength')
# Spline background
mon4_spline_background = bspline_background(mon4_lambda,
sc.Dim('wavelength'),
smoothing_factor=70)
# Smooth monitor
mon4_smooth = smooth_data(mon4_spline_background,
dim='wavelength',
NPoints=40)
# Delete intermediate data
del mon4_lambda, mon4_spline_background
# Correct data
# 1. Normalize to monitor
# Convert to wavelength (counts)
sample_lambda = sc.neutron.convert(sample_data, 'tof', 'wavelength')
# Rebin monitors' data
lambda_min, lambda_max, number_bins = lambda_binning
edges_lambda = sc.Variable(['wavelength'],
unit=sc.units.angstrom,
values=np.linspace(lambda_min,
lambda_max,
num=number_bins))
mon_rebin = sc.rebin(mon4_smooth, 'wavelength', edges_lambda)
# Realign sample data
sample_lambda.realign({'wavelength': edges_lambda})
sample_lambda /= mon_rebin
del mon_rebin, mon4_smooth
# 2. absorption correction
if bool(absorp):
# Copy dictionary of absorption parameters
absorp_sample = absorp.copy()
# Remove input related to Vanadium if present in absorp dictionary
found_vana_info = [
key for key in absorp_sample.keys() if 'Vanadium' in key
]
for item in found_vana_info:
absorp_sample.pop(item, None)
# Calculate absorption correction for sample data
correction = absorption_correction(sample, lambda_binning,
**absorp_sample)
# the 3 following lines of code are to place info about source and sample
# position at the right place in the correction dataArray in order to
# proceed to the normalization
del correction.coords['source_position']
del correction.coords['sample_position']
del correction.coords['position']
correction_rebin = sc.rebin(correction, 'wavelength', edges_lambda)
del correction
sample_lambda /= correction_rebin
del sample_data
sample_tof = sc.neutron.convert(sample_lambda,
'wavelength',
'tof',
realign='linear')
del sample_lambda
# 3. Convert to d-spacing taking calibration into account
# has to switch to standard conversion in all cases, while support of convert_with_calibration
# for realign='linear' is implemented
sample_dspacing = sc.neutron.convert(sample_tof,
'tof',
'd-spacing',
realign='linear')
del cal_sample
# if calibration is None:
# # No calibration data, use standard convert algorithm
# sample_dspacing = sc.neutron.convert(sample_tof, 'tof', 'd-spacing', realign='linear')
#
# else:
# # Calculate dspacing from calibration file
# sample_dspacing = sc.neutron.diffraction.convert_with_calibration(sample_tof, cal_sample)
# del cal_sample
# 4. Focus panels
# Assuming sample is in d-spacing: Focus into groups
focused = sc.groupby(sample_dspacing, group='group').sum('spectrum')
del sample_dspacing
# 5. Vanadium correction (requires Vanadium and Empty instrument)
if vanadium is not None and empty_instr is not None:
print("Proceed with reduction of Vanadium data ")
vana_red_focused = process_vanadium_data(vanadium, empty_instr,
lambda_binning, calibration,
**absorp)
# The following selection of groups depends on the loaded data for
# Sample, Vanadium and Empty instrument
focused = focused['group', 0:5].copy()
# histogram vanadium for normalizing + cleaning 'metadata'
vana_histo = sc.histogram(vana_red_focused)
del vana_red_focused
vana_histo.coords['detector_info'] = focused.coords[
'detector_info'].copy()
del vana_histo.coords['source_position']
del vana_histo.coords['sample_position']
# normalize by vanadium
focused /= vana_histo
del vana_histo
return focused
def process_vanadium_data(vanadium,
empty_instr,
lambda_binning,
calibration=None,
**absorp):
"""
Create corrected vanadium dataset
Correction applied to Vanadium data only
1. Subtract empty instrument
2. Correct absorption
3. Use calibration for grouping
4. Focus into groups
Parameters
----------
vanadium : Vanadium nexus datafile
empty_instr: Empty instrument nexus file
lambda_binning: format=(lambda_min, lambda_min, number_of_bins)
lambda_min and lambda_max are in Angstroms
calibration: calibration file
Mantid format
**absorp: dictionary containing information to correct absorption for
sample and vanadium
Only the inputs related to Vanadium will be selected to calculate
the correction
see docstrings of powder_reduction for more details
see help of Mantid's algorithm CylinderAbsorption for details
https://docs.mantidproject.org/nightly/algorithms/CylinderAbsorption-v1.html
"""
vana_red = process_event_data(vanadium, lambda_binning)
ec_red = process_event_data(empty_instr, lambda_binning)
# remove 'spectrum' from wavelength coordinate and match this coordinate
# between Vanadium and Empty instrument data
min_lambda = vana_red.coords['wavelength'].values[:, 0].min()
max_lambda = vana_red.coords['wavelength'].values[:, 1].max()
vana_red.coords['wavelength'] = sc.Variable(['wavelength'],
unit=sc.units.angstrom,
values=np.linspace(min_lambda,
max_lambda,
num=2))
ec_red.coords['wavelength'] = sc.Variable(['wavelength'],
unit=sc.units.angstrom,
values=np.linspace(min_lambda,
max_lambda,
num=2))
# vana - EC
ec_red.coords['wavelength'] = vana_red.coords['wavelength']
vana_red.bins.concatenate(-ec_red, out=vana_red)
del ec_red
# Absorption correction applied
if bool(absorp):
# The values of number_density, scattering and attenuation are
# hard-coded since they must correspond to Vanadium. Only radius and
# height of the Vanadium cylindrical sample shape can be set. The
# names of these inputs if present have to be renamed to match
# the requirements of Mantid's algorithm CylinderAbsorption
# Create dictionary to calculate absorption correction for Vanadium.
absorp_vana = {
key.replace('Vanadium', 'Sample'): value
for key, value in absorp.items() if 'Vanadium' in key
}
absorp_vana['SampleNumberDensity'] = 0.07118
absorp_vana['ScatteringXSection'] = 5.16
absorp_vana['AttenuationXSection'] = 4.8756
correction = absorption_correction(vanadium, lambda_binning,
**absorp_vana)
# the 3 following lines of code are to place info about source and
# sample position at the right place in the correction dataArray in
# order to proceed to the normalization
del correction.coords['source_position']
del correction.coords['sample_position']
del correction.coords['position']
lambda_min, lambda_max, number_bins = lambda_binning
edges_lambda = sc.Variable(['wavelength'],
unit=sc.units.angstrom,
values=np.linspace(lambda_min,
lambda_max,
num=number_bins))
correction = sc.rebin(correction, 'wavelength', edges_lambda)
vana_red = vana_red.bins / sc.lookup(func=correction, dim='wavelength')
del correction
# Calibration
# Load
input_load_cal = {'InstrumentFilename': 'WISH_Definition.xml'}
calvana = load_calibration(calibration, mantid_args=input_load_cal)
# Merge table with detector->spectrum mapping from vanadium
# (implicitly checking that detectors between vanadium and
# calibration are the same)
cal_vana = sc.merge(calvana, vana_red.coords['detector_info'].value)
del calvana
# Compute spectrum mask from detector mask
maskvana = sc.groupby(cal_vana['mask'], group='spectrum').any('detector')
# Compute spectrum groups from detector groups
gvana = sc.groupby(cal_vana['group'], group='spectrum')
groupvana = gvana.min('detector')
assert sc.identical(groupvana, gvana.max('detector')), \
("Calibration table has mismatching group "
"for detectors in same spectrum")
vana_red.coords['group'] = groupvana.data
vana_red.masks['mask'] = maskvana.data
# convert to d-spacing with calibration
vana_dspacing = convert_with_calibration(vana_red, cal_vana)
del vana_red, cal_vana
# Focus
focused_vana = \
sc.groupby(vana_dspacing, group='group').bins.concatenate('spectrum')
del vana_dspacing
return focused_vana
def _do_stitching_on_beamline(wavelengths, dim, event_mode=False):
# Make beamline parameters for 6 frames
coords = wfm.make_fake_beamline(nframes=6)
# They are all created half-way through the pulse.
# Compute their arrival time at the detector.
alpha = sc.to_unit(constants.m_n / constants.h, 's/m/angstrom')
dz = sc.norm(coords['position'] - coords['source_position'])
arrival_times = sc.to_unit(alpha * dz * wavelengths,
'us') + coords['source_pulse_t_0'] + (
0.5 * coords['source_pulse_length'])
coords[dim] = arrival_times
# Make a data array that contains the beamline and the time coordinate
tmin = sc.min(arrival_times)
tmax = sc.max(arrival_times)
dt = 0.1 * (tmax - tmin)
if event_mode:
num = 2
else:
num = 2001
time_binning = sc.linspace(dim=dim,
start=(tmin - dt).value,
stop=(tmax + dt).value,
num=num,
unit=dt.unit)
events = sc.DataArray(data=sc.ones(dims=['event'],
shape=arrival_times.shape,
unit=sc.units.counts,
with_variances=True),
coords=coords)
if event_mode:
da = sc.bin(events, edges=[time_binning])
else:
da = sc.histogram(events, bins=time_binning)
# Find location of frames
frames = wfm.get_frames(da)
stitched = wfm.stitch(frames=frames, data=da, dim=dim, bins=2001)
wav = scn.convert(stitched,
origin='tof',
target='wavelength',
scatter=False)
if event_mode:
out = wav
else:
out = sc.rebin(wav,
dim='wavelength',
bins=sc.linspace(dim='wavelength',
start=1.0,
stop=10.0,
num=1001,
unit='angstrom'))
choppers = {key: da.meta[key].value for key in ch.find_chopper_keys(da)}
# Distance between WFM choppers
dz_wfm = sc.norm(choppers["chopper_wfm_2"]["position"].data -
choppers["chopper_wfm_1"]["position"].data)
# Delta_lambda / lambda
dlambda_over_lambda = dz_wfm / sc.norm(
coords['position'] - frames['wfm_chopper_mid_point'].data)
return out, dlambda_over_lambda