def test_linlogspace_linear_log_linear():
q_linloglin = tools.linlogspace(dim='qz',
edges=[0.008, 0.03, 0.08, 0.12],
unit='1/angstrom',
scale=['linear', 'log', 'linear'],
num=[16, 20, 10])
exp_lin = sc.linspace(dim='qz', start=0.008, stop=0.03, num=16, unit='1/angstrom')
exp_log = sc.geomspace(dim='qz', start=0.03, stop=0.08, num=21, unit='1/angstrom')
exp_lin2 = sc.linspace(dim='qz', start=0.08, stop=0.12, num=11, unit='1/angstrom')
expected = sc.concat([exp_lin, exp_log['qz', 1:], exp_lin2['qz', 1:]], 'qz')
assert sc.allclose(q_linloglin, expected)
def params():
dim = 'frame'
return {
'frequency':
sc.scalar(56.0, unit="Hz"),
'phase':
sc.scalar(0.5, unit='rad'),
'position':
sc.vector(value=[0., 0., 5.], unit='m'),
'cutout_angles_center':
sc.linspace(dim=dim, start=0.25, stop=2.0 * np.pi, num=6, unit='rad'),
'cutout_angles_width':
sc.linspace(dim=dim, start=0.1, stop=0.6, num=6, unit='rad'),
'kind':
sc.scalar('wfm')
}
def test_mantid_convert_tof_to_direct_energy_transfer():
efixed = 1000 * sc.Unit('meV')
in_ws = make_workspace('tof', emode='Direct', efixed=efixed)
out_mantid = mantid_convert_units(in_ws,
'energy_transfer',
emode='Direct',
efixed=efixed)
in_da = scn.mantid.from_mantid(in_ws)
out_scipp = scn.convert(data=in_da,
origin='tof',
target='energy_transfer',
scatter=True)
# The conversion consists of multiplications and additions, thus the relative error
# changes with the inputs. In this case, small tof yields a large error due to
# the 1/tof**2 factor in the conversion.
# rtol is chosen to account for linearly changing tof in the input data.
assert sc.allclose(
out_scipp.coords['energy_transfer'],
out_mantid.coords['energy_transfer'],
rtol=sc.linspace(
'energy_transfer', 1e-6, 1e-10,
out_scipp.coords['energy_transfer'].sizes['energy_transfer']))
assert sc.identical(out_scipp.coords['spectrum'],
out_mantid.coords['spectrum'])
def test_basic_stitching():
frames = sc.Dataset()
shift = -5.0
frames['time_min'] = sc.array(dims=['frame'],
values=[0.0],
unit=sc.units.us)
frames['time_max'] = sc.array(dims=['frame'],
values=[10.0],
unit=sc.units.us)
frames['time_correction'] = sc.array(dims=['frame'],
values=[shift],
unit=sc.units.us)
frames["wfm_chopper_mid_point"] = sc.vector(value=[0., 0., 2.0], unit='m')
data = sc.DataArray(data=sc.ones(dims=['t'],
shape=[100],
unit=sc.units.counts),
coords={
't':
sc.linspace(dim='t',
start=0.0,
stop=10.0,
num=101,
unit=sc.units.us),
'source_position':
sc.vector(value=[0., 0., 0.], unit='m')
})
nbins = 10
stitched = wfm.stitch(data=data, dim='t', frames=frames, bins=nbins)
# Note dimension change to TOF as well as shift
assert sc.identical(
sc.values(stitched),
sc.DataArray(
data=sc.ones(dims=['tof'], shape=[nbins], unit=sc.units.counts) *
nbins,
coords={
'tof':
sc.linspace(dim='tof',
start=0.0 - shift,
stop=10.0 - shift,
num=nbins + 1,
unit=sc.units.us),
'source_position':
sc.vector(value=[0., 0., 2.], unit='m')
}))
def test_linlogspace_linear():
q_lin = tools.linlogspace(dim='qz',
edges=[0.008, 0.08],
scale='linear',
num=50,
unit='1/angstrom')
expected = sc.linspace(dim='qz', start=0.008, stop=0.08, num=50, unit='1/angstrom')
assert sc.allclose(q_lin, expected)
def _bin_event_data_for_ploting(data, frame, bins_per_frame):
"""
Bin event data using `bins_per_frame` to make a meaningful plot.
"""
return sc.bin(data,
edges=[
sc.linspace(dim='tof',
start=frame["time_min"].value,
stop=frame["time_max"].value,
num=bins_per_frame,
unit=frame['time_min'].unit)
]).bins.sum()
def test_cutout_angles_from_begin_end(params):
dim = 'frame'
del params['cutout_angles_center']
del params['cutout_angles_width']
params["cutout_angles_begin"] = sc.linspace(dim=dim,
start=0.0,
stop=1.5 * np.pi,
num=6,
unit='rad')
params["cutout_angles_end"] = sc.linspace(dim=dim,
start=0.1,
stop=2.0 * np.pi,
num=6,
unit='rad')
chopper = ch.make_chopper(**params)
assert sc.allclose(
ch.cutout_angles_width(chopper),
params["cutout_angles_end"] - params["cutout_angles_begin"])
assert sc.allclose(
ch.cutout_angles_center(chopper),
0.5 * (params["cutout_angles_begin"] + params["cutout_angles_end"]))
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 _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
