def theta(gravity: sc.Variable, wavelength: sc.Variable, incident_beam: sc.Variable,
scattered_beam: sc.Variable, sample_rotation: sc.Variable) -> sc.Variable:
"""
Compute the theta angle, including gravity correction,
This is similar to the theta calculation in SANS (see
https://docs.mantidproject.org/nightly/algorithms/Q1D-v2.html#q-unit-conversion),
but we ignore the horizontal `x` component.
See the schematic in Fig 5 of doi: 10.1016/j.nima.2016.03.007.
"""
grav = sc.norm(gravity)
L2 = sc.norm(scattered_beam)
y = sc.dot(scattered_beam, gravity) / grav
y_correction = sc.to_unit(wavelength, _elem_unit(L2), copy=True)
y_correction *= y_correction
drop = L2**2
drop *= grav * (m_n**2 / (2 * h**2))
# Optimization when handling either the dense or the event coord of binned data:
# - For the event coord, both operands have same dims, and we can multiply in place
# - For the dense coord, we need to broadcast using non in-place operation
if set(drop.dims).issubset(set(y_correction.dims)):
y_correction *= drop
else:
y_correction = y_correction * drop
y_correction += y
out = sc.abs(y_correction, out=y_correction)
out /= L2
out = sc.asin(out, out=out)
out -= sc.to_unit(sample_rotation, 'rad')
return out
def _furthest_component(det_center, scipp_obj, additional):
distances = [
sc.norm(settings["center"] - det_center).value
for settings in list(additional.values())
]
max_displacement = sorted(distances)[-1]
return max_displacement
def test_norm():
var = sp.Variable(dims=[Dim.X],
values=[[1, 0, 0], [3, 4, 0]],
unit=sp.units.m)
expected = sp.Variable([Dim.X],
values=np.array([1.0, 5.0]),
unit=sp.units.m)
assert sp.norm(var) == expected
def test_convert_beam_length_no_scatter():
original = make_test_data(coords=('position', 'source_position'))
converted = scn.convert(original,
origin='position',
target='Ltotal',
scatter=False)
expected = sc.norm(make_position() - make_source_position())
assert sc.identical(converted.coords['Ltotal'], expected)
def test_norm():
var = sc.Variable(dims=[Dim.X],
values=[[1, 0, 0], [3, 4, 0]],
unit=sc.units.m,
dtype=sc.dtype.vector_3_float64)
expected = sc.Variable([Dim.X],
values=np.array([1.0, 5.0]),
unit=sc.units.m)
assert sc.norm(var) == expected
def _frames_from_slopes(data):
detector_pos_norm = sc.norm(data.meta["position"])
# Get the number of WFM frames
choppers = {
key: data.meta[key].value
for key in ch.find_chopper_keys(data)
}
nframes = ch.cutout_angles_begin(choppers["chopper_wfm_1"]).sizes["frame"]
# Now find frame boundaries
frames = sc.Dataset()
frames["time_min"] = sc.zeros(dims=["frame"],
shape=[nframes],
unit=sc.units.us)
frames["time_max"] = sc.zeros_like(frames["time_min"])
frames["delta_time_min"] = sc.zeros_like(frames["time_min"])
frames["delta_time_max"] = sc.zeros_like(frames["time_min"])
frames["wavelength_min"] = sc.zeros(dims=["frame"],
shape=[nframes],
unit=sc.units.angstrom)
frames["wavelength_max"] = sc.zeros_like(frames["wavelength_min"])
frames["delta_wavelength_min"] = sc.zeros_like(frames["wavelength_min"])
frames["delta_wavelength_max"] = sc.zeros_like(frames["wavelength_min"])
frames["time_correction"] = sc.zeros(dims=["frame"],
shape=[nframes],
unit=sc.units.us)
near_wfm_chopper = choppers["chopper_wfm_1"]
far_wfm_chopper = choppers["chopper_wfm_2"]
# Distance between WFM choppers
dz_wfm = sc.norm(far_wfm_chopper["position"].data -
near_wfm_chopper["position"].data)
# Mid-point between WFM choppers
z_wfm = 0.5 * (near_wfm_chopper["position"].data +
far_wfm_chopper["position"].data)
# Distance between detector positions and wfm chopper mid-point
zdet_minus_zwfm = sc.norm(data.meta["position"] - z_wfm)
# Neutron mass to Planck constant ratio
alpha = sc.to_unit(constants.m_n / constants.h, 'us/m/angstrom')
near_t_open = ch.time_open(near_wfm_chopper)
near_t_close = ch.time_closed(near_wfm_chopper)
far_t_open = ch.time_open(far_wfm_chopper)
for i in range(nframes):
dt_lambda_max = near_t_close["frame", i] - near_t_open["frame", i]
slope_lambda_max = dz_wfm / dt_lambda_max
intercept_lambda_max = sc.norm(
near_wfm_chopper["position"].data
) - slope_lambda_max * near_t_close["frame", i]
t_lambda_max = (detector_pos_norm -
intercept_lambda_max) / slope_lambda_max
slope_lambda_min = sc.norm(near_wfm_chopper["position"].data) / (
near_t_close["frame", i] -
(data.meta["source_pulse_length"] + data.meta["source_pulse_t_0"]))
intercept_lambda_min = sc.norm(
far_wfm_chopper["position"].data
) - slope_lambda_min * far_t_open["frame", i]
t_lambda_min = (detector_pos_norm -
intercept_lambda_min) / slope_lambda_min
t_lambda_min_plus_dt = (
detector_pos_norm -
(sc.norm(near_wfm_chopper["position"].data) -
slope_lambda_min * near_t_close["frame", i])) / slope_lambda_min
dt_lambda_min = t_lambda_min_plus_dt - t_lambda_min
# Compute wavelength information
lambda_min = (t_lambda_min + 0.5 * dt_lambda_min -
far_t_open["frame", i]) / (alpha * zdet_minus_zwfm)
lambda_max = (t_lambda_max - 0.5 * dt_lambda_max -
far_t_open["frame", i]) / (alpha * zdet_minus_zwfm)
dlambda_min = dz_wfm * lambda_min / zdet_minus_zwfm
dlambda_max = dz_wfm * lambda_max / zdet_minus_zwfm
frames["time_min"]["frame", i] = t_lambda_min
frames["delta_time_min"]["frame", i] = dt_lambda_min
frames["time_max"]["frame", i] = t_lambda_max
frames["delta_time_max"]["frame", i] = dt_lambda_max
frames["wavelength_min"]["frame", i] = lambda_min
frames["wavelength_max"]["frame", i] = lambda_max
frames["delta_wavelength_min"]["frame", i] = dlambda_min
frames["delta_wavelength_max"]["frame", i] = dlambda_max
frames["time_correction"]["frame", i] = far_t_open["frame", i]
frames["wfm_chopper_mid_point"] = z_wfm
return frames
def time_distance_diagram(data: sc.DataArray, **kwargs) -> plt.Figure:
"""
Plot the time-distance diagram for a WFM beamline.
The expected input is a Dataset or DataArray containing the chopper cascade
information as well as the description of the source pulse.
This internally calls the `get_frames` method which is used to compute the
frame properties for stitching.
"""
# Get the frame properties
frames = get_frames(data, **kwargs)
# Find detector pixel furthest away from source
source_pos = data.meta["source_position"]
furthest_detector_pos = sc.max(sc.norm(data.meta["position"] -
source_pos)).value
pulse_rectangle_height = furthest_detector_pos / 50.0
tmax_glob = sc.max(frames["time_max"].data).value
# Create figure and axes
fig, ax = plt.subplots(1, 1, figsize=(9, 7))
ax.grid(True, color='lightgray', linestyle="dotted")
ax.set_axisbelow(True)
# Draw a light grey rectangle from the origin to t_0 + pulse_length + t_0
# The second t_0 should in fact be the end of the pulse tail, but since this
# information is not needed for computing the frame properties, it may
# not be present in the description of the beamline.
# So we fake this by simply using t_0 again at the end of the pulse.
ax.add_patch(
Rectangle((0, 0),
(2.0 * data.meta["source_pulse_t_0"] +
data.meta["source_pulse_length"]).value,
-pulse_rectangle_height,
lw=1,
fc='lightgrey',
ec='k',
zorder=10))
# Draw a dark grey rectangle from t_0 to t_0 + pulse_length to represent the usable
# pulse.
ax.add_patch(
Rectangle((data.meta["source_pulse_t_0"].value, 0),
data.meta["source_pulse_length"].value,
-pulse_rectangle_height,
lw=1,
fc='grey',
ec='k',
zorder=11))
# Indicate source pulse and add the duration.
ax.text(data.meta["source_pulse_t_0"].value,
-pulse_rectangle_height,
"Source pulse ({} {})".format(
data.meta["source_pulse_length"].value,
data.meta["source_pulse_length"].unit),
ha="left",
va="top",
fontsize=6)
# Plot the chopper openings as segments
# for name, chopper in data.meta["choppers"].value.items():
for name in ch.find_chopper_keys(data):
chopper = data.meta[name].value
yframe = sc.norm(chopper["position"].data - source_pos).value
time_open = ch.time_open(chopper).values
time_close = ch.time_closed(chopper).values
tmin = 0.0
for fnum in range(len(time_open)):
tmax = time_open[fnum]
ax.plot([tmin, tmax], [yframe] * 2, color='k')
tmin = time_close[fnum]
ax.plot([tmin, tmax_glob], [yframe] * 2, color='k')
ax.text(2.0 * time_close[-1] - time_open[-1],
yframe,
name,
ha="left",
va="bottom")
# Plot the shades of possible neutron paths
for i in range(frames.sizes["frame"]):
col = "C{}".format(i)
frame = frames["frame", i]
for dim in data.meta["position"].dims:
frame = frame[dim, 0]
# Minimum wavelength
lambda_min = np.array(
[[(data.meta["source_pulse_t_0"] +
data.meta["source_pulse_length"] -
frame['delta_time_min']).value, 0],
[(data.meta["source_pulse_t_0"] +
data.meta["source_pulse_length"]).value, 0],
[(frame["time_min"] + frame["delta_time_min"]).value,
furthest_detector_pos],
[frame["time_min"].value, furthest_detector_pos]])
# Maximum wavelength
lambda_max = np.array(
[[data.meta["source_pulse_t_0"].value, 0],
[(data.meta["source_pulse_t_0"] + frame['delta_time_max']).value,
0], [frame["time_max"].value, furthest_detector_pos],
[(frame["time_max"] - frame["delta_time_max"]).value,
furthest_detector_pos]])
ax.plot(np.concatenate((lambda_min[:, 0], lambda_min[0:1, 0])),
np.concatenate((lambda_min[:, 1], lambda_min[0:1, 1])),
color=col,
lw=1)
ax.plot(np.concatenate((lambda_max[:, 0], lambda_max[0:1, 0])),
np.concatenate((lambda_max[:, 1], lambda_max[0:1, 1])),
color=col,
lw=1)
ax.fill([
lambda_max[0, 0], lambda_max[-1, 0], lambda_min[2, 0],
lambda_min[1, 0]
], [
lambda_max[0, 1], lambda_max[-1, 1], lambda_min[2, 1],
lambda_min[1, 1]
],
alpha=0.3,
color=col,
zorder=-5)
ax.fill(lambda_min[:, 0], lambda_min[:, 1], color='w', zorder=-4)
ax.fill(lambda_max[:, 0], lambda_max[:, 1], color='w', zorder=-4)
ax.text(0.5 * (frame["time_min"] + frame["delta_time_min"] +
frame["time_max"] - frame["delta_time_max"]).value,
furthest_detector_pos,
"Frame {}".format(i + 1),
ha="center",
va="top")
# Add thick solid line for the detector position, spanning the entire width
ax.plot([0, tmax_glob], [furthest_detector_pos] * 2, lw=3, color='grey')
ax.text(0.0, furthest_detector_pos, "Detector", va="bottom", ha="left")
# Set axis labels
ax.set_xlabel("Time [microseconds]")
ax.set_ylabel("Distance [m]")
return fig
def frames_analytical(data: Union[sc.DataArray, sc.Dataset]) -> sc.Dataset:
"""
Compute analytical frame boundaries and shifts based on chopper
parameters and detector pixel positions.
A set of frame boundaries is returned for each pixel.
The frame shifts are the same for all pixels.
See Schmakat et al. (2020);
https://www.sciencedirect.com/science/article/pii/S0168900220308640
for a description of the procedure.
TODO: This currently ignores scattering paths, only the distance from
source to pixel.
For imaging, this is what we want, but for scattering techniques, we should
use l1 + l2 in the future.
"""
# Identify the WFM choppers based on their `kind` property
wfm_choppers = {}
for name in ch.find_chopper_keys(data):
chopper = data.meta[name].value
if chopper["kind"].value == "wfm":
wfm_choppers[name] = chopper
if len(wfm_choppers) != 2:
raise RuntimeError("The number of WFM choppers is expected to be 2, "
"found {}".format(len(wfm_choppers)))
# Find the near and far WFM choppers based on their positions relative to the source
wfm_chopper_names = list(wfm_choppers.keys())
if (sc.norm(wfm_choppers[wfm_chopper_names[0]]["position"].data -
data.meta["source_position"]) <
sc.norm(wfm_choppers[wfm_chopper_names[1]]["position"].data -
data.meta["source_position"])).value:
near_index = 0
far_index = 1
else:
near_index = 1
far_index = 0
near_wfm_chopper = wfm_choppers[wfm_chopper_names[near_index]]
far_wfm_chopper = wfm_choppers[wfm_chopper_names[far_index]]
# Compute distances for each detector pixel
detector_positions = data.meta["position"] - data.meta["source_position"]
# Container for frames information
frames = sc.Dataset()
# Distance between WFM choppers
dz_wfm = sc.norm(far_wfm_chopper["position"].data -
near_wfm_chopper["position"].data)
# Mid-point between WFM choppers
z_wfm = 0.5 * (
near_wfm_chopper["position"].data +
far_wfm_chopper["position"].data) - data.meta["source_position"]
# Ratio of WFM chopper distances
z_ratio_wfm = (sc.norm(far_wfm_chopper["position"].data -
data.meta["source_position"]) /
sc.norm(near_wfm_chopper["position"].data -
data.meta["source_position"]))
# Distance between detector positions and wfm chopper mid-point
zdet_minus_zwfm = sc.norm(detector_positions - z_wfm)
# Neutron mass to Planck constant ratio
alpha = sc.to_unit(constants.m_n / constants.h, 'us/m/angstrom')
# Frame time corrections: these are the mid-time point between the WFM choppers,
# which is the same as the opening edge of the second WFM chopper in the case
# of optically blind choppers.
frames["time_correction"] = ch.time_open(far_wfm_chopper)
# Find delta_t for the min and max wavelengths:
# dt_lambda_max is equal to the time width of the WFM choppers windows
dt_lambda_max = ch.time_closed(near_wfm_chopper) - ch.time_open(
near_wfm_chopper)
# t_lambda_max is found from the relation between t and delta_t: equation (2) in
# Schmakat et al. (2020).
t_lambda_max = (dt_lambda_max / dz_wfm) * zdet_minus_zwfm
# t_lambda_min is found from the relation between lambda_N and lambda_N+1,
# equation (3) in Schmakat et al. (2020).
t_lambda_min = t_lambda_max * z_ratio_wfm - data.meta[
"source_pulse_length"] * (zdet_minus_zwfm / sc.norm(
near_wfm_chopper["position"].data - data.meta["source_position"]))
# dt_lambda_min is found from the relation between t and delta_t: equation (2)
# in Schmakat et al. (2020), and using the expression for t_lambda_max.
dt_lambda_min = dt_lambda_max * z_ratio_wfm - data.meta[
"source_pulse_length"] * dz_wfm / sc.norm(
near_wfm_chopper["position"].data - data.meta["source_position"])
# Compute wavelength information
lambda_min = t_lambda_min / (alpha * zdet_minus_zwfm)
lambda_max = t_lambda_max / (alpha * zdet_minus_zwfm)
dlambda_min = dz_wfm * lambda_min / zdet_minus_zwfm
dlambda_max = dz_wfm * lambda_max / zdet_minus_zwfm
# Frame edges and resolutions for each pixel.
# The frames do not stop at t_lambda_min and t_lambda_max, they also include the
# fuzzy areas (delta_t) at the edges.
frames["time_min"] = t_lambda_min - (
0.5 * dt_lambda_min) + frames["time_correction"]
frames["delta_time_min"] = dt_lambda_min
frames["time_max"] = t_lambda_max + (
0.5 * dt_lambda_max) + frames["time_correction"]
frames["delta_time_max"] = dt_lambda_max
frames["wavelength_min"] = lambda_min
frames["wavelength_max"] = lambda_max
frames["delta_wavelength_min"] = dlambda_min
frames["delta_wavelength_max"] = dlambda_max
frames["wfm_chopper_mid_point"] = 0.5 * (
near_wfm_chopper["position"].data + far_wfm_chopper["position"].data)
return frames
def make_fake_beamline(
chopper_wfm_1_position=sc.vector(value=[0.0, 0.0, 6.775], unit='m'),
chopper_wfm_2_position=sc.vector(value=[0.0, 0.0, 7.225], unit='m'),
frequency=sc.scalar(56.0, unit=sc.units.one / sc.units.s),
lambda_min=sc.scalar(1.0, unit='angstrom'),
pulse_length=sc.scalar(2.86e-03, unit='s'),
pulse_t_0=sc.scalar(1.3e-4, unit='s'),
nframes=2):
"""
Fake chopper cascade with 2 optically blind WFM choppers.
Based on mathematical description in Schmakat et al. (2020);
https://www.sciencedirect.com/science/article/pii/S0168900220308640
"""
dim = 'frame'
# Neutron mass to Planck constant ratio
alpha = sc.to_unit(m_n / h, 's/m/angstrom')
omega = (2.0 * np.pi * sc.units.rad) * frequency
cutout_angles_center_wfm_1 = sc.empty(dims=[dim], shape=[nframes], unit='rad')
cutout_angles_center_wfm_2 = sc.empty_like(cutout_angles_center_wfm_1)
cutout_angles_width = sc.empty_like(cutout_angles_center_wfm_1)
for i in range(nframes):
# Equation (3) in Schmakat et al. (2020)
lambda_max = (pulse_length +
alpha * lambda_min * sc.norm(chopper_wfm_1_position)) / (
alpha * sc.norm(chopper_wfm_2_position))
# Equation (4) in Schmakat et al. (2020)
theta = omega * (pulse_length + alpha *
(lambda_min - lambda_max) * sc.norm(chopper_wfm_1_position))
# Equation (5) in Schmakat et al. (2020)
phi_wfm_1 = omega * (
pulse_t_0 + 0.5 * pulse_length + 0.5 * alpha *
(lambda_min + lambda_max) * sc.norm(chopper_wfm_1_position))
# Equation (6) in Schmakat et al. (2020)
phi_wfm_2 = omega * (pulse_t_0 + 1.5 * pulse_length + 0.5 * alpha * (
(3.0 * lambda_min) - lambda_max) * sc.norm(chopper_wfm_1_position))
cutout_angles_width[dim, i] = theta
cutout_angles_center_wfm_1[dim, i] = phi_wfm_1
cutout_angles_center_wfm_2[dim, i] = phi_wfm_2
lambda_min = lambda_max
return {
"chopper_wfm_1":
sc.scalar(
make_chopper(frequency=frequency,
phase=sc.scalar(0.0, unit='deg'),
position=chopper_wfm_1_position,
cutout_angles_center=cutout_angles_center_wfm_1,
cutout_angles_width=cutout_angles_width,
kind=sc.scalar('wfm'))),
"chopper_wfm_2":
sc.scalar(
make_chopper(frequency=frequency,
phase=sc.scalar(0.0, unit='deg'),
position=chopper_wfm_2_position,
cutout_angles_center=cutout_angles_center_wfm_2,
cutout_angles_width=cutout_angles_width,
kind=sc.scalar('wfm'))),
'position':
sc.vector(value=[0., 0., 60.], unit='m'),
"source_pulse_length":
sc.to_unit(pulse_length, 'us'),
"source_pulse_t_0":
sc.to_unit(pulse_t_0, 'us'),
"source_position":
sc.vector(value=[0.0, 0.0, 0.0], unit='m')
}
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
def make_L2():
return sc.norm(make_scattered_beam())
def make_L1():
return sc.norm(make_incident_beam())
