# SPDX-License-Identifier: BSD-3-Clause
# Copyright (c) 2025 Scipp contributors (https://github.com/scipp)
"""
Time-of-flight workflow for unwrapping the time of arrival of the neutron at the
detector.
This workflow is used to convert raw detector data with event_time_zero and
event_time_offset coordinates to data with a time-of-flight coordinate.
"""
from collections.abc import Callable
import numpy as np
import scipp as sc
from scipp._scipp.core import _bins_no_validate
from scippneutron._utils import elem_unit
from .to_events import to_events
from .types import (
DistanceResolution,
LookupTableRelativeErrorThreshold,
Ltotal,
LtotalRange,
PulsePeriod,
PulseStride,
PulseStrideOffset,
RawData,
ResampledTofData,
SimulationResults,
TimeOfFlightLookupTable,
TimeResolution,
TofData,
)
def _mask_large_uncertainty(table: sc.DataArray, error_threshold: float):
"""
Mask regions with large uncertainty with NaNs.
The values are modified in place in the input table.
Parameters
----------
table:
Lookup table with time-of-flight as a function of distance and time-of-arrival.
error_threshold:
Threshold for the relative standard deviation (coefficient of variation) of the
projected time-of-flight above which values are masked.
"""
# Finally, mask regions with large uncertainty with NaNs.
relative_error = sc.stddevs(table.data) / sc.values(table.data)
mask = relative_error > sc.scalar(error_threshold)
# Use numpy for indexing as table is 2D
table.values[mask.values] = np.nan
def _compute_mean_tof_in_distance_range(
simulation: SimulationResults,
distance_bins: sc.Variable,
time_bins: sc.Variable,
distance_unit: str,
time_unit: str,
frame_period: sc.Variable,
time_bins_half_width: sc.Variable,
) -> sc.DataArray:
"""
Compute the mean time-of-flight inside event_time_offset bins for a given range of
distances.
Parameters
----------
simulation:
Results of a time-of-flight simulation used to create a lookup table.
distance_bins:
Bin edges for the distance axis in the lookup table.
time_bins:
Bin edges for the event_time_offset axis in the lookup table.
distance_unit:
Unit of the distance axis.
time_unit:
Unit of the event_time_offset axis.
frame_period:
Period of the source pulses, i.e., time between consecutive pulse starts.
time_bins_half_width:
Half the width of the time bins.
"""
simulation_distance = simulation.distance.to(unit=distance_unit)
distances = sc.midpoints(distance_bins)
# Compute arrival and flight times for all neutrons
toas = simulation.time_of_arrival + (distances / simulation.speed).to(
unit=time_unit, copy=False
)
dist = distances + simulation_distance
tofs = dist * (sc.constants.m_n / sc.constants.h) * simulation.wavelength
data = sc.DataArray(
data=sc.broadcast(simulation.weight, sizes=toas.sizes),
coords={
"toa": toas,
"tof": tofs.to(unit=time_unit, copy=False),
"distance": dist,
},
).flatten(to="event")
# Add the event_time_offset coordinate to the data. We first operate on the
# frame period. The table will later be folded to the pulse period.
data.coords['event_time_offset'] = data.coords['toa'] % frame_period
# Because we staggered the mesh by half a bin width, we want the values above
# the last bin edge to wrap around to the first bin.
# Technically, those values should end up between -0.5*bin_width and 0, but
# a simple modulo also works here because even if they end up between 0 and
# 0.5*bin_width, we are (below) computing the mean between -0.5*bin_width and
# 0.5*bin_width and it yields the same result.
# data.coords['event_time_offset'] %= pulse_period - time_bins_half_width
data.coords['event_time_offset'] %= frame_period - time_bins_half_width
binned = data.bin(
distance=distance_bins + simulation_distance, event_time_offset=time_bins
)
# Weighted mean of tof inside each bin
mean_tof = (
binned.bins.data * binned.bins.coords["tof"]
).bins.sum() / binned.bins.sum()
# Compute the variance of the tofs to track regions with large uncertainty
variance = (
binned.bins.data * (binned.bins.coords["tof"] - mean_tof) ** 2
).bins.sum() / binned.bins.sum()
mean_tof.variances = variance.values
return mean_tof
def _fold_table_to_pulse_period(
table: sc.DataArray, pulse_period: sc.Variable, pulse_stride: int
) -> sc.DataArray:
"""
Fold the lookup table to the pulse period. We make sure the left and right edges of
the table wrap around the ``event_time_offset`` dimension.
Parameters
----------
table:
Lookup table with time-of-flight as a function of distance and time-of-arrival.
pulse_period:
Period of the source pulses, i.e., time between consecutive pulse starts.
pulse_stride:
Stride of used pulses. Usually 1, but may be a small integer when
pulse-skipping.
"""
size = table.sizes['event_time_offset']
if (size % pulse_stride) != 0:
raise ValueError(
"TimeOfFlightLookupTable: the number of time bins must be a multiple of "
f"the pulse stride, but got {size} time bins and a pulse stride of "
f"{pulse_stride}."
)
size = size // pulse_stride
out = sc.concat([table, table['event_time_offset', 0]], dim='event_time_offset')
out = sc.concat(
[
out['event_time_offset', (i * size) : (i + 1) * size + 1]
for i in range(pulse_stride)
],
dim='pulse',
)
return out.assign_coords(
event_time_offset=sc.concat(
[
table.coords['event_time_offset']['event_time_offset', :size],
pulse_period,
],
'event_time_offset',
)
)
[docs]
def compute_tof_lookup_table(
simulation: SimulationResults,
ltotal_range: LtotalRange,
distance_resolution: DistanceResolution,
time_resolution: TimeResolution,
pulse_period: PulsePeriod,
pulse_stride: PulseStride,
error_threshold: LookupTableRelativeErrorThreshold,
) -> TimeOfFlightLookupTable:
"""
Compute a lookup table for time-of-flight as a function of distance and
time-of-arrival.
Parameters
----------
simulation:
Results of a time-of-flight simulation used to create a lookup table.
The results should be a flat table with columns for time-of-arrival, speed,
wavelength, and weight.
ltotal_range:
Range of total flight path lengths from the source to the detector.
distance_resolution:
Resolution of the distance axis in the lookup table.
time_resolution:
Resolution of the time-of-arrival axis in the lookup table. Must be an integer.
pulse_period:
Period of the source pulses, i.e., time between consecutive pulse starts.
pulse_stride:
Stride of used pulses. Usually 1, but may be a small integer when
pulse-skipping.
error_threshold:
Threshold for the relative standard deviation (coefficient of variation) of the
projected time-of-flight above which values are masked.
"""
distance_unit = "m"
time_unit = simulation.time_of_arrival.unit
res = distance_resolution.to(unit=distance_unit)
pulse_period = pulse_period.to(unit=time_unit)
frame_period = pulse_period * pulse_stride
min_dist, max_dist = (
x.to(unit=distance_unit) - simulation.distance.to(unit=distance_unit)
for x in ltotal_range
)
# We need to bin the data below, to compute the weighted mean of the wavelength.
# This results in data with bin edges.
# However, the 2d interpolator expects bin centers.
# We want to give the 2d interpolator a table that covers the requested range,
# hence we need to extend the range by at least half a resolution in each direction.
# Then, we make the choice that the resolution in distance is the quantity that
# should be preserved. Because the difference between min and max distance is
# not necessarily an integer multiple of the resolution, we need to add a pad to
# ensure that the last bin is not cut off. We want the upper edge to be higher than
# the maximum distance, hence we pad with an additional 1.5 x resolution.
pad = 2.0 * res
distance_bins = sc.arange('distance', min_dist - pad, max_dist + pad, res)
# Create some time bins for event_time_offset.
# We want our final table to strictly cover the range [0, frame_period].
# However, binning the data associates mean values inside the bins to the bin
# centers. Instead, we stagger the mesh by half a bin width so we are computing
# values for the final mesh edges (the bilinear interpolation needs values on the
# edges/corners).
nbins = int(frame_period / time_resolution.to(unit=time_unit)) + 1
time_bins = sc.linspace(
'event_time_offset', 0.0, frame_period.value, nbins + 1, unit=pulse_period.unit
)
time_bins_half_width = 0.5 * (time_bins[1] - time_bins[0])
time_bins -= time_bins_half_width
# To avoid a too large RAM usage, we compute the table in chunks, and piece them
# together at the end.
ndist = len(distance_bins) - 1
max_size = 2e7
total_size = ndist * len(simulation.time_of_arrival)
nchunks = total_size / max_size
chunk_size = int(ndist / nchunks) + 1
pieces = []
for i in range(int(nchunks) + 1):
dist_edges = distance_bins[i * chunk_size : (i + 1) * chunk_size + 1]
pieces.append(
_compute_mean_tof_in_distance_range(
simulation=simulation,
distance_bins=dist_edges,
time_bins=time_bins,
distance_unit=distance_unit,
time_unit=time_unit,
frame_period=frame_period,
time_bins_half_width=time_bins_half_width,
)
)
table = sc.concat(pieces, 'distance')
table.coords["distance"] = sc.midpoints(table.coords["distance"])
table.coords["event_time_offset"] = sc.midpoints(table.coords["event_time_offset"])
table = _fold_table_to_pulse_period(
table=table, pulse_period=pulse_period, pulse_stride=pulse_stride
)
# In-place masking for better performance
_mask_large_uncertainty(table, error_threshold)
return TimeOfFlightLookupTable(
table.transpose(('pulse', 'distance', 'event_time_offset'))
)
def _make_tof_interpolator(
lookup: sc.DataArray, distance_unit: str, time_unit: str
) -> Callable:
from scipy.interpolate import RegularGridInterpolator
# TODO: to make use of multi-threading, we could write our own interpolator.
# This should be simple enough as we are making the bins linspace, so computing
# bin indices is fast.
# In the pulse dimension, it could be that for a given event_time_offset and
# distance, a tof value is finite in one pulse and NaN in the other.
# When using the bilinear interpolation, even if the value of the requested point is
# exactly 0 or 1 (in the case of pulse_stride=2), the interpolator will still
# use all 4 corners surrounding the point. This means that if one of the corners
# is NaN, the result will be NaN.
# Here, we use a trick where we duplicate the lookup values in the 'pulse' dimension
# so that the interpolator has values on bin edges for that dimension.
# The interpolator raises an error if axes coordinates are not strictly monotonic,
# so we cannot use e.g. [-0.5, 0.5, 0.5, 1.5] in the case of pulse_stride=2.
# Instead we use [-0.25, 0.25, 0.75, 1.25].
base_grid = np.arange(float(lookup.sizes["pulse"]))
return RegularGridInterpolator(
(
np.sort(np.concatenate([base_grid - 0.25, base_grid + 0.25])),
lookup.coords["distance"].to(unit=distance_unit, copy=False).values,
lookup.coords["event_time_offset"].to(unit=time_unit, copy=False).values,
),
np.repeat(lookup.data.to(unit=time_unit, copy=False).values, 2, axis=0),
method="linear",
bounds_error=False,
fill_value=np.nan,
)
def _time_of_flight_data_histogram(
da: sc.DataArray,
lookup: sc.DataArray,
ltotal: sc.Variable,
pulse_period: sc.Variable,
) -> sc.DataArray:
# In NeXus, 'time_of_flight' is the canonical name in NXmonitor, but in some files,
# it may be called 'tof'.
key = next(iter(set(da.coords.keys()) & {"time_of_flight", "tof"}))
eto_unit = da.coords[key].unit
pulse_period = pulse_period.to(unit=eto_unit)
# In histogram mode, because there is a wrap around at the end of the pulse, we
# need to insert a bin edge at that exact location to avoid having the last bin
# with one finite left edge and a NaN right edge (it becomes NaN as it would be
# outside the range of the lookup table).
new_bins = sc.sort(
sc.concat(
[da.coords[key], sc.scalar(0.0, unit=eto_unit), pulse_period], dim=key
),
key=key,
)
rebinned = da.rebin({key: new_bins})
etos = rebinned.coords[key]
# In histogram mode, the lookup table cannot have a pulse dimension because we
# cannot know in the histogrammed data which pulse the events belong to.
# So we merge the pulse dimension in the lookup table. A quick way to do this
# is to take the mean of the data along the pulse dimension (there should
# only be regions that are NaN in one pulse and finite in the other).
merged = lookup.data.nanmean('pulse')
dim = merged.dims[0]
lookup = sc.DataArray(
data=merged.fold(dim=dim, sizes={'pulse': 1, dim: merged.sizes[dim]}),
coords={
'pulse': sc.arange('pulse', 1.0),
'distance': lookup.coords['distance'],
'event_time_offset': lookup.coords['event_time_offset'],
},
)
pulse_index = sc.zeros(sizes=etos.sizes)
# Create 2D interpolator
interp = _make_tof_interpolator(
lookup, distance_unit=ltotal.unit, time_unit=eto_unit
)
# Compute time-of-flight of the bin edges using the interpolator
tofs = sc.array(
dims=etos.dims,
values=interp((pulse_index.values, ltotal.values, etos.values)),
unit=eto_unit,
)
return rebinned.assign_coords(tof=tofs)
def _time_of_flight_data_events(
da: sc.DataArray,
lookup: sc.DataArray,
ltotal: sc.Variable,
pulse_period: sc.Variable,
pulse_stride: int,
pulse_stride_offset: int,
) -> sc.DataArray:
etos = da.bins.coords["event_time_offset"]
eto_unit = elem_unit(etos)
pulse_period = pulse_period.to(unit=eto_unit)
frame_period = pulse_period * pulse_stride
# TODO: Finding the `tmin` below will not work in the case were data is processed
# in chunks, as taking the minimum time in each chunk will lead to inconsistent
# pulse indices (this will be the case in live data, or when using the
# StreamProcessor). We could instead read it from the first chunk and store it?
# Compute a pulse index for every event: it is the index of the pulse within a
# frame period. When there is no pulse skipping, those are all zero. When there is
# pulse skipping, the index ranges from zero to pulse_stride - 1.
tmin = da.bins.coords['event_time_zero'].min()
pulse_index = (
(
(da.bins.coords['event_time_zero'] - tmin).to(unit=eto_unit)
+ 0.5 * pulse_period
)
% frame_period
) // pulse_period
# Apply the pulse_stride_offset
pulse_index += pulse_stride_offset
pulse_index %= pulse_stride
# Create 2D interpolator
interp = _make_tof_interpolator(
lookup, distance_unit=ltotal.unit, time_unit=eto_unit
)
# Operate on events (broadcast distances to all events)
ltotal = sc.bins_like(etos, ltotal).bins.constituents["data"]
etos = etos.bins.constituents["data"]
pulse_index = pulse_index.bins.constituents["data"]
# Compute time-of-flight for all neutrons using the interpolator
tofs = sc.array(
dims=etos.dims,
values=interp((pulse_index.values, ltotal.values, etos.values)),
unit=eto_unit,
)
parts = da.bins.constituents
parts["data"] = tofs
return da.bins.assign_coords(tof=_bins_no_validate(**parts))
[docs]
def time_of_flight_data(
da: RawData,
lookup: TimeOfFlightLookupTable,
ltotal: Ltotal,
pulse_period: PulsePeriod,
pulse_stride: PulseStride,
pulse_stride_offset: PulseStrideOffset,
) -> TofData:
"""
Convert the time-of-arrival data to time-of-flight data using a lookup table.
The output data will have a time-of-flight coordinate.
Parameters
----------
da:
Raw detector data loaded from a NeXus file, e.g., NXdetector containing
NXevent_data.
lookup:
Lookup table giving time-of-flight as a function of distance and time of
arrival.
ltotal:
Total length of the flight path from the source to the detector.
pulse_period:
Period of the source pulses, i.e., time between consecutive pulse starts.
pulse_stride:
Stride of used pulses. Usually 1, but may be a small integer when
pulse-skipping.
pulse_stride_offset:
When pulse-skipping, the offset of the first pulse in the stride. This is
typically zero but can be a small integer < pulse_stride.
"""
if da.bins is None:
out = _time_of_flight_data_histogram(
da=da, lookup=lookup, ltotal=ltotal, pulse_period=pulse_period
)
else:
out = _time_of_flight_data_events(
da=da,
lookup=lookup,
ltotal=ltotal,
pulse_period=pulse_period,
pulse_stride=pulse_stride,
pulse_stride_offset=pulse_stride_offset,
)
return TofData(out)
[docs]
def resample_tof_data(da: TofData) -> ResampledTofData:
"""
Histogrammed data that has been converted to `tof` will typically have
unsorted bin edges (due to either wrapping of `time_of_flight` or wavelength
overlap between subframes).
This function re-histograms the data to ensure that the bin edges are sorted.
It makes use of the ``to_events`` helper which generates a number of events in each
bin with a uniform distribution. The new events are then histogrammed using a set of
sorted bin edges.
WARNING:
This function is highly experimental, has limitations and should be used with
caution. It is a workaround to the issue that rebinning data with unsorted bin
edges is not supported in scipp.
As such, this function is not part of the default set of providers, and needs to be
inserted manually into the workflow.
Parameters
----------
da:
Histogrammed data with the time-of-flight coordinate.
"""
dim = next(iter(set(da.dims) & {"time_of_flight", "tof"}))
events = to_events(da.rename_dims({dim: "tof"}), "event")
# Define a new bin width, close to the original bin width.
# TODO: this could be a workflow parameter
coord = da.coords["tof"]
bin_width = (coord[dim, 1:] - coord[dim, :-1]).nanmedian()
rehist = events.hist(tof=bin_width)
for key, var in da.coords.items():
if dim not in var.dims:
rehist.coords[key] = var
return ResampledTofData(rehist)
[docs]
def default_parameters() -> dict:
"""
Default parameters of the time-of-flight workflow.
"""
return {
PulsePeriod: 1.0 / sc.scalar(14.0, unit="Hz"),
PulseStride: 1,
PulseStrideOffset: 0,
DistanceResolution: sc.scalar(0.1, unit="m"),
TimeResolution: sc.scalar(250.0, unit='us'),
LookupTableRelativeErrorThreshold: 0.1,
}
[docs]
def providers() -> tuple[Callable]:
"""
Providers of the time-of-flight workflow.
"""
return (compute_tof_lookup_table, extract_ltotal, time_of_flight_data)
