# SPDX-License-Identifier: BSD-3-Clause
# Copyright (c) 2025 Scipp contributors (https://github.com/scipp)
from collections.abc import Mapping, Sequence
from itertools import chain
from typing import Any
import numpy as np
import sciline
import scipp as sc
import scipy.optimize as opt
from orsopy.fileio.orso import OrsoDataset
from ess.reflectometry import orso
from ess.reflectometry.types import ReflectivityOverQ
_STD_TO_FWHM = sc.scalar(2.0) * sc.sqrt(sc.scalar(2.0) * sc.log(sc.scalar(2.0)))
[docs]
def fwhm_to_std(fwhm: sc.Variable) -> sc.Variable:
"""
Convert from full-width half maximum to standard deviation.
Parameters
----------
fwhm:
Full-width half maximum.
Returns
-------
:
Standard deviation.
"""
# Enables the conversion from full width half
# maximum to standard deviation
return fwhm / _STD_TO_FWHM
[docs]
def linlogspace(
dim: str,
edges: list | np.ndarray,
scale: list | str,
num: list | int,
unit: str | None = None,
) -> sc.Variable:
"""
Generate a 1d array of bin edges with a mixture of linear and/or logarithmic
spacings.
Examples:
- Create linearly spaced edges (equivalent to `sc.linspace`):
linlogspace(dim='x', edges=[0.008, 0.08], scale='linear', num=50, unit='m')
- Create logarithmically spaced edges (equivalent to `sc.geomspace`):
linlogspace(dim='x', edges=[0.008, 0.08], scale='log', num=50, unit='m')
- Create edges with a linear and a logarithmic part:
linlogspace(dim='x', edges=[1, 3, 8], scale=['linear', 'log'], num=[16, 20])
Parameters
----------
dim:
The dimension of the output Variable.
edges:
The edges for the different parts of the mesh.
scale:
A string or list of strings specifying the scaling for the different
parts of the mesh. Possible values for the scaling are `"linear"` and `"log"`.
If a list is supplied, the length of the list must be one less than the length
of the `edges` parameter.
num:
An integer or a list of integers specifying the number of points to use
in each part of the mesh. If a list is supplied, the length of the list must be
one less than the length of the `edges` parameter.
unit:
The unit of the output Variable.
Returns
-------
:
Lin-log spaced Q-bin edges.
"""
if not isinstance(scale, list):
scale = [scale]
if not isinstance(num, list):
num = [num]
if len(scale) != len(edges) - 1:
raise ValueError(
"Sizes do not match. The length of edges should be one "
"greater than scale."
)
funcs = {"linear": sc.linspace, "log": sc.geomspace}
grids = []
for i in range(len(edges) - 1):
# Skip the leading edge in the piece when concatenating
start = int(i > 0)
mesh = funcs[scale[i]](
dim=dim, start=edges[i], stop=edges[i + 1], num=num[i] + start, unit=unit
)
grids.append(mesh[dim, start:])
return sc.concat(grids, dim)
def _sort_by(a, by):
return [x for x, _ in sorted(zip(a, by, strict=True), key=lambda x: x[1])]
def _find_interval_overlaps(intervals):
'''Returns the intervals where at least
two or more of the provided intervals
are overlapping.'''
edges = list(chain.from_iterable(intervals))
is_start_edge = list(chain.from_iterable((True, False) for _ in intervals))
edges_sorted = sorted(edges)
is_start_edge_sorted = _sort_by(is_start_edge, edges)
number_overlapping = 0
overlap_intervals = []
for x, is_start in zip(edges_sorted, is_start_edge_sorted, strict=True):
if number_overlapping == 1 and is_start:
start = x
if number_overlapping == 2 and not is_start:
overlap_intervals.append((start, x))
if is_start:
number_overlapping += 1
else:
number_overlapping -= 1
return overlap_intervals
def _searchsorted(a, v):
for i, e in enumerate(a):
if e > v:
return i
return len(a)
def _create_qgrid_where_overlapping(qgrids):
'''Given a number of Q-grids, construct a new grid
covering the regions where (any two of the) provided grids overlap.'''
pieces = []
for start, end in _find_interval_overlaps([(q.min(), q.max()) for q in qgrids]):
interval_sliced_from_qgrids = [
q[max(_searchsorted(q, start) - 1, 0) : _searchsorted(q, end) + 1]
for q in qgrids
]
densest_grid_in_interval = max(interval_sliced_from_qgrids, key=len)
pieces.append(densest_grid_in_interval)
return sc.concat(pieces, dim='Q')
def _same_dtype(arrays):
return [arr.to(dtype='float64') for arr in arrays]
def _interpolate_on_qgrid(curves, grid):
return sc.concat(
_same_dtype([sc.lookup(c, grid.dim)[sc.midpoints(grid)] for c in curves]),
dim='curves',
)
[docs]
def scale_reflectivity_curves_to_overlap(
curves: Sequence[sc.DataArray],
critical_edge_interval: tuple[sc.Variable, sc.Variable] | None = None,
) -> tuple[list[sc.DataArray], list[sc.Variable]]:
'''Make the curves overlap by scaling all except the first by a factor.
The scaling factors are determined by a maximum likelihood estimate
(assuming the errors are normal distributed).
If :code:`critical_edge_interval` is provided then all curves are scaled.
All curves must be have the same unit for data and the Q-coordinate.
Parameters
---------
curves:
the reflectivity curves that should be scaled together
critical_edge_interval:
a tuple denoting an interval that is known to belong
to the critical edge, i.e. where the reflectivity is
known to be 1.
Returns
---------
:
A list of scaled reflectivity curves and a list of the scaling factors.
'''
if critical_edge_interval is not None:
q = next(iter(curves)).coords['Q']
N = (
((q >= critical_edge_interval[0]) & (q < critical_edge_interval[1]))
.sum()
.value
)
edge = sc.DataArray(
data=sc.ones(dims=('Q',), shape=(N,), with_variances=True),
coords={'Q': sc.linspace('Q', *critical_edge_interval, N + 1)},
)
curves, factors = scale_reflectivity_curves_to_overlap([edge, *curves])
return curves[1:], factors[1:]
if len({c.data.unit for c in curves}) != 1:
raise ValueError('The reflectivity curves must have the same unit')
if len({c.coords['Q'].unit for c in curves}) != 1:
raise ValueError('The Q-coordinates must have the same unit for each curve')
qgrid = _create_qgrid_where_overlapping([c.coords['Q'] for c in curves])
r = _interpolate_on_qgrid(map(sc.values, curves), qgrid).values
v = _interpolate_on_qgrid(map(sc.variances, curves), qgrid).values
def cost(scaling_factors):
scaling_factors = np.concatenate([[1.0], scaling_factors])[:, None]
r_scaled = scaling_factors * r
v_scaled = scaling_factors**2 * v
v_scaled[v_scaled == 0] = np.nan
inv_v_scaled = 1 / v_scaled
r_avg = np.nansum(r_scaled * inv_v_scaled, axis=0) / np.nansum(
inv_v_scaled, axis=0
)
return np.nansum((r_scaled - r_avg) ** 2 * inv_v_scaled)
sol = opt.minimize(cost, [1.0] * (len(curves) - 1))
scaling_factors = (1.0, *map(float, sol.x))
return [
scaling_factor * curve
for scaling_factor, curve in zip(scaling_factors, curves, strict=True)
], scaling_factors
[docs]
def combine_curves(
curves: Sequence[sc.DataArray],
q_bin_edges: sc.Variable | None = None,
) -> sc.DataArray:
'''Combines the given curves by interpolating them
on a 1d grid defined by :code:`q_bin_edges` and averaging
over the provided reflectivity curves.
The averaging is done using a weighted mean where the weights
are proportional to the variances.
Unless the curves are already scaled correctly they might
need to be scaled using :func:`scale_reflectivity_curves_to_overlap`
before calling this function.
All curves must be have the same unit for data and the Q-coordinate.
Parameters
----------
curves:
the reflectivity curves that should be combined
q_bin_edges:
the Q bin edges of the resulting combined reflectivity curve
Returns
---------
:
A data array representing the combined reflectivity curve
'''
if len({c.data.unit for c in curves}) != 1:
raise ValueError('The reflectivity curves must have the same unit')
if len({c.coords['Q'].unit for c in curves}) != 1:
raise ValueError('The Q-coordinates must have the same unit for each curve')
r = _interpolate_on_qgrid(map(sc.values, curves), q_bin_edges).values
v = _interpolate_on_qgrid(map(sc.variances, curves), q_bin_edges).values
v[v == 0] = np.nan
inv_v = 1.0 / v
r_avg = np.nansum(r * inv_v, axis=0) / np.nansum(inv_v, axis=0)
v_avg = 1 / np.nansum(inv_v, axis=0)
return sc.DataArray(
data=sc.array(
dims='Q',
values=r_avg,
variances=v_avg,
unit=next(iter(curves)).data.unit,
),
coords={'Q': q_bin_edges},
)
[docs]
def orso_datasets_from_measurements(
workflow: sciline.Pipeline,
runs: Sequence[Mapping[type, Any]],
*,
scale_to_overlap: bool = True,
) -> list[OrsoDataset]:
'''Produces a list of ORSO datasets containing one
reflectivity curve for each of the provided runs.
Each entry of :code:`runs` is a mapping of parameters and
values needed to produce the dataset.
Optionally, the reflectivity curves can be scaled to overlap in
the regions where they have the same Q-value.
Parameters
-----------
workflow:
The sciline workflow used to compute `ReflectivityOverQ` for each of the runs.
runs:
The sciline parameters to be used for each run
scale_to_overlap:
If True the curves will be scaled to overlap.
Note that the curve of the first run is unscaled and
the rest are scaled to match it.
Returns
---------
list of the computed ORSO datasets, containing one reflectivity curve each
'''
reflectivity_curves = []
for parameters in runs:
wf = workflow.copy()
for name, value in parameters.items():
wf[name] = value
reflectivity_curves.append(wf.compute(ReflectivityOverQ))
scale_factors = (
scale_reflectivity_curves_to_overlap([r.hist() for r in reflectivity_curves])[1]
if scale_to_overlap
else (1,) * len(runs)
)
datasets = []
for parameters, curve, scale_factor in zip(
runs, reflectivity_curves, scale_factors, strict=True
):
wf = workflow.copy()
for name, value in parameters.items():
wf[name] = value
wf[ReflectivityOverQ] = scale_factor * curve
dataset = wf.compute(orso.OrsoIofQDataset)
datasets.append(dataset)
return datasets
