from __future__ import annotations
import collections
import copy
import errno
import importlib.metadata
import itertools
import json
import logging
import operator
import os
import pickle
import sys
from typing import Any, Callable, Generator, Iterable
import natsort
import dxtbx
from dxtbx.format.Format import Format
from dxtbx.format.FormatMultiImage import FormatMultiImage
from dxtbx.format.image import ImageBool, ImageDouble
from dxtbx.format.Registry import get_format_class_for_file
from dxtbx.imageset import ImageGrid, ImageSequence, ImageSet, ImageSetFactory
from dxtbx.model import (
BeamFactory,
CrystalFactory,
DetectorFactory,
Experiment,
ExperimentList,
GoniometerFactory,
ProfileModelFactory,
ScanFactory,
)
from dxtbx.sequence_filenames import (
locate_files_matching_template_string,
template_image_range,
template_regex,
template_string_number_index,
)
from dxtbx.serialize import xds
from dxtbx.serialize.filename import resolve_path
from dxtbx.util import get_url_scheme
__all__ = [
"BeamComparison",
"DetectorComparison",
"ExperimentListFactory",
"GoniometerComparison",
]
logger = logging.getLogger(__name__)
# REMOVE and inline when Python 3.10 is minimum
if sys.version_info < (3, 10):
scaling_model_entry_points = importlib.metadata.entry_points().get(
"dxtbx.scaling_model_ext", []
)
else:
scaling_model_entry_points = importlib.metadata.entry_points(
group="dxtbx.scaling_model_ext"
)
class InvalidExperimentListError(RuntimeError):
"""
Indicates an error whilst validating the experiment list.
This means that there is some structural problem that prevents the given data
from representing a well-formed experiment list. This doesn't indicate e.g.
some problem with the data or model consistency.
"""
class FormatChecker:
"""A helper class to speed up identifying the correct image format by first
trying the last format that was used."""
def __init__(self):
"""Set the format class to none."""
self._format_class = None
def find_format(self, filename):
"""Search the registry for the image format class.
Where possible use the last seen format class as a prioritisation hint.
"""
if self._format_class:
self._format_class = get_format_class_for_file(
filename, format_hint=self._format_class.__name__
)
else:
self._format_class = get_format_class_for_file(filename)
if self._format_class:
logger.debug("Using %s for %s", self._format_class.__name__, filename)
else:
logger.debug("No format class found for %s", filename)
return self._format_class
def iter_groups(self, filenames):
group_format = None
group_fnames = []
for filename in filenames:
fmt = self.find_format(filename)
if fmt == group_format:
group_fnames.append(filename)
else:
if group_fnames:
yield group_format, group_fnames
group_fnames = [filename]
group_format = fmt
if fmt is not None:
logger.debug("Using %s for %s", fmt.__name__, filename)
if group_fnames:
yield group_format, group_fnames
[docs]
class BeamComparison:
"""A class to provide simple beam comparison"""
[docs]
def __init__(
self,
wavelength_tolerance=1e-6,
direction_tolerance=1e-6,
polarization_normal_tolerance=1e-6,
polarization_fraction_tolerance=1e-6,
):
self.wavelength_tolerance = wavelength_tolerance
self.direction_tolerance = direction_tolerance
self.polarization_normal_tolerance = polarization_normal_tolerance
self.polarization_fraction_tolerance = polarization_fraction_tolerance
def __call__(self, a, b):
if a is None and b is None:
return True
return a.is_similar_to(
b,
wavelength_tolerance=self.wavelength_tolerance,
direction_tolerance=self.direction_tolerance,
polarization_normal_tolerance=self.polarization_normal_tolerance,
polarization_fraction_tolerance=self.polarization_fraction_tolerance,
)
[docs]
class DetectorComparison:
"""A class to provide simple detector comparison"""
[docs]
def __init__(
self, fast_axis_tolerance=1e-6, slow_axis_tolerance=1e-6, origin_tolerance=1e-6
):
self.fast_axis_tolerance = fast_axis_tolerance
self.slow_axis_tolerance = slow_axis_tolerance
self.origin_tolerance = origin_tolerance
def __call__(self, a, b):
if a is None and b is None:
return True
return a.is_similar_to(
b,
fast_axis_tolerance=self.fast_axis_tolerance,
slow_axis_tolerance=self.slow_axis_tolerance,
origin_tolerance=self.origin_tolerance,
)
[docs]
class GoniometerComparison:
"""A class to provide simple goniometer comparison"""
[docs]
def __init__(
self,
rotation_axis_tolerance=1e-6,
fixed_rotation_tolerance=1e-6,
setting_rotation_tolerance=1e-6,
):
self.rotation_axis_tolerance = rotation_axis_tolerance
self.fixed_rotation_tolerance = fixed_rotation_tolerance
self.setting_rotation_tolerance = setting_rotation_tolerance
def __call__(self, a, b):
if a is None and b is None:
return True
elif a is None or b is None:
return False
return a.is_similar_to(
b,
rotation_axis_tolerance=self.rotation_axis_tolerance,
fixed_rotation_tolerance=self.fixed_rotation_tolerance,
setting_rotation_tolerance=self.setting_rotation_tolerance,
)
class ExperimentListDict:
"""A helper class for serializing the experiment list to dictionary (needed
to save the experiment list to JSON format."""
def __init__(self, obj, check_format=True, directory=None):
"""Initialise. Copy the dictionary."""
# Basic check: This is a dict-like object. This can happen if e.g. we
# were passed a DataBlock list instead of an ExperimentList dictionary
if isinstance(obj, list) or not hasattr(obj, "get"):
raise InvalidExperimentListError(
"Expected dictionary, not {}".format(type(obj))
)
self._obj = copy.deepcopy(obj)
self._check_format = check_format
self._directory = directory
# If this doesn't claim to be an ExperimentList, don't even try
if self._obj.get("__id__") != "ExperimentList":
raise InvalidExperimentListError(
"Expected __id__ 'ExperimentList', but found {}".format(
repr(self._obj.get("__id__"))
)
)
# Extract lists of models referenced by experiments
# Go through all the imagesets and make sure the dictionary
# references by an index rather than a file path.
self._lookups = {
model: self._extract_models(model, function)
for model, function in (
("beam", BeamFactory.from_dict),
("detector", DetectorFactory.from_dict),
("goniometer", GoniometerFactory.from_dict),
("scan", ScanFactory.from_dict),
("crystal", CrystalFactory.from_dict),
("profile", ProfileModelFactory.from_dict),
("imageset", lambda x: x),
("scaling_model", self._scaling_model_from_dict),
)
}
def _extract_models(self, name, from_dict):
"""
Helper function. Extract the models.
if name == imageset: Extract imageset objects from the source.
This function does resolving of an (old) method of imageset lookup
e.g. it was valid to have a string as the imageset value in an
experiment instead of an int - in which case the imageset was
loaded from the named file in the target directory.
If any experiments point to a file in this way, the imageset is
loaded and the experiment is rewritten with an integer pointing
to the new ImageSet in the returned list.
Returns:
The ordered list of serialized-ImageSet dictionaries
that the Experiment list points to.
"""
# Extract all the model list
mlist = self._obj.get(name, [])
# Convert the model from dictionary to concreate
# python class for the model.
mlist = [from_dict(d) for d in mlist]
# Dictionaries for file mappings
mmap = {}
# For each experiment, check the model is not specified by
# a path, if it is then get the dictionary of the model
# and insert it into the list. Replace the path reference
# with an index
for eobj in self._obj["experiment"]:
value = eobj.get(name)
if value is None:
continue
elif isinstance(value, str):
if value not in mmap:
mmap[value] = len(mlist)
mlist.append(
from_dict(_experimentlist_from_file(value, self._directory))
)
eobj[name] = mmap[value]
elif not isinstance(value, int):
raise TypeError("expected int or str, got %s" % type(value))
return mlist
def _load_pickle_path(
self, imageset_data: dict, param: str
) -> tuple[str | None, Any]:
"""
Read a filename from an imageset dict and load if available. This is
used to load mask, gain, pedestal and offset maps. In some situations
(such as tests) these files are not available, in which case the
filename is kept but the data is None.
Args:
imageset_data: The dictionary holding imageset information
param: The key name to lookup in the imageset dictionary
Returns:
A tuple of (filename, data) where data has been loaded from
the pickle file, or is None if the file is inaccessible. If there
is no key entry then ("", None) is returned.
"""
if param not in imageset_data:
return "", None
filename = resolve_path(imageset_data[param], directory=self._directory)
data = None
if filename:
try:
with open(filename, "rb") as fh:
data = pickle.load(fh, encoding="bytes")
except OSError:
pass
else:
filename = ""
return filename, data
def _imageset_from_imageset_data(self, imageset_data, models):
"""Make an imageset from imageset_data - help with refactor decode."""
assert imageset_data is not None
if "params" in imageset_data:
format_kwargs = imageset_data["params"]
else:
format_kwargs = {}
beam = models["beam"]
detector = models["detector"]
goniometer = models["goniometer"]
scan = models["scan"]
# Load the external lookup data
mask_filename, mask = self._load_pickle_path(imageset_data, "mask")
gain_filename, gain = self._load_pickle_path(imageset_data, "gain")
pedestal_filename, pedestal = self._load_pickle_path(imageset_data, "pedestal")
dx_filename, dx = self._load_pickle_path(imageset_data, "dx")
dy_filename, dy = self._load_pickle_path(imageset_data, "dy")
# If dx, dy maps are expected then they must be loaded even when
# self._check_format == False, because they affect the operation of
# programs (dials.index, dials.refine) that do not need the image data.
if (dx_filename or dy_filename) and not all((dx, dx)):
raise RuntimeError(
f"dx ({dx_filename}) and dy ({dy_filename}) maps are expected"
)
if imageset_data["__id__"] == "ImageSet":
imageset = self._make_stills(imageset_data, format_kwargs=format_kwargs)
elif imageset_data["__id__"] == "ImageGrid":
imageset = self._make_grid(imageset_data, format_kwargs=format_kwargs)
elif (
imageset_data["__id__"] == "ImageSequence"
or imageset_data["__id__"] == "ImageSweep"
):
imageset = self._make_sequence(
imageset_data,
beam=beam,
detector=detector,
goniometer=goniometer,
scan=scan,
format_kwargs=format_kwargs,
)
elif imageset_data["__id__"] == "MemImageSet":
imageset = self._make_mem_imageset(imageset_data)
else:
raise RuntimeError("Unknown imageset type")
if imageset is not None:
# Set the external lookup
if mask is None:
mask = ImageBool()
else:
mask = ImageBool(mask)
if gain is None:
gain = ImageDouble()
else:
gain = ImageDouble(gain)
if pedestal is None:
pedestal = ImageDouble()
else:
pedestal = ImageDouble(pedestal)
if dx is None:
dx = ImageDouble()
else:
dx = ImageDouble(dx)
if dy is None:
dy = ImageDouble()
else:
dy = ImageDouble(dy)
if not imageset.external_lookup.mask.data.empty():
if not mask.empty():
mask = tuple(m.data() for m in mask)
for m1, m2 in zip(mask, imageset.external_lookup.mask.data):
m1 &= m2.data()
imageset.external_lookup.mask.data = ImageBool(mask)
else:
imageset.external_lookup.mask.data = mask
imageset.external_lookup.mask.filename = mask_filename
imageset.external_lookup.gain.data = gain
imageset.external_lookup.gain.filename = gain_filename
imageset.external_lookup.pedestal.data = pedestal
imageset.external_lookup.pedestal.filename = pedestal_filename
imageset.external_lookup.dx.data = dx
imageset.external_lookup.dx.filename = dx_filename
imageset.external_lookup.dy.data = dy
imageset.external_lookup.dy.filename = dy_filename
# Update the imageset models
if isinstance(imageset, ImageSequence):
imageset.set_beam(beam)
imageset.set_detector(detector)
imageset.set_goniometer(goniometer)
imageset.set_scan(scan)
elif isinstance(imageset, (ImageSet, ImageGrid)):
for i in range(len(imageset)):
imageset.set_beam(beam, i)
imageset.set_detector(detector, i)
imageset.set_goniometer(goniometer, i)
imageset.set_scan(scan, i)
imageset.update_detector_px_mm_data()
return imageset
def decode(self):
"""Decode the dictionary into a list of experiments."""
# Extract all the experiments - first find all scans belonging to
# same imageset
eobj_scan = {}
for eobj in self._obj["experiment"]:
if self._lookup_model("imageset", eobj) is None:
continue
imageset_ref = eobj.get("imageset")
scan = self._lookup_model("scan", eobj)
if imageset_ref in eobj_scan:
# if there is no scan, or scan is identical, move on, else
# make a scan which encompasses both scans
if not scan or scan == eobj_scan[imageset_ref]:
continue
i = eobj_scan[imageset_ref].get_image_range()
j = scan.get_image_range()
if i[1] + 1 == j[0]:
eobj_scan[imageset_ref] += scan
else:
# make a new bigger scan
o = eobj_scan[imageset_ref].get_oscillation()
s = scan.get_oscillation()
assert abs(o[1] - (s[1])) < 1e-7
scan = copy.deepcopy(scan)
scan.set_image_range((min(i[0], j[0]), max(i[1], j[1])))
scan.set_oscillation((min(o[0], s[0]), o[1]))
eobj_scan[imageset_ref] = scan
else:
eobj_scan[imageset_ref] = copy.deepcopy(scan)
# Map of imageset/scan pairs
imagesets = {}
# For every experiment, use the given input to create
# a sensible experiment.
el = ExperimentList()
for eobj in self._obj["experiment"]:
# Get the models
identifier = eobj.get("identifier", "")
beam = self._lookup_model("beam", eobj)
detector = self._lookup_model("detector", eobj)
goniometer = self._lookup_model("goniometer", eobj)
scan = self._lookup_model("scan", eobj)
crystal = self._lookup_model("crystal", eobj)
profile = self._lookup_model("profile", eobj)
scaling_model = self._lookup_model("scaling_model", eobj)
models = {
"beam": beam,
"detector": detector,
"goniometer": goniometer,
"scan": scan,
"crystal": crystal,
"profile": profile,
"scaling_model": scaling_model,
}
imageset_ref = eobj.get("imageset")
# If not already cached, load this imageset
if imageset_ref not in imagesets:
imageset_data = self._lookup_model("imageset", eobj)
if imageset_data is not None:
# Create the imageset from the input data
models["scan"] = eobj_scan[imageset_ref]
imageset = self._imageset_from_imageset_data(imageset_data, models)
imagesets[imageset_ref] = imageset
else:
# Even if we have an empty entry, this counts as a load
imagesets[imageset_ref] = None
# Append the experiment
el.append(
Experiment(
imageset=imagesets[imageset_ref],
beam=beam,
detector=detector,
goniometer=goniometer,
scan=scan,
crystal=crystal,
profile=profile,
scaling_model=scaling_model,
identifier=identifier,
)
)
return el
def _make_mem_imageset(self, imageset):
"""Can't make a mem imageset from dict."""
return None
def _make_stills(self, imageset, format_kwargs=None):
"""Make a still imageset."""
filenames = [
resolve_path(p, directory=self._directory) if not get_url_scheme(p) else p
for p in imageset["images"]
]
indices = None
if "single_file_indices" in imageset:
indices = imageset["single_file_indices"]
assert len(indices) == len(filenames)
return ImageSetFactory.make_imageset(
filenames,
None,
check_format=self._check_format,
single_file_indices=indices,
format_kwargs=format_kwargs,
)
def _make_grid(self, imageset, format_kwargs=None):
"""Make a still imageset."""
grid_size = imageset["grid_size"]
return ImageGrid.from_imageset(
self._make_stills(imageset, format_kwargs=format_kwargs), grid_size
)
def _make_sequence(
self,
imageset,
beam=None,
detector=None,
goniometer=None,
scan=None,
format_kwargs=None,
):
"""Make an image sequence."""
# Get the template format
template = resolve_path(imageset["template"], directory=self._directory)
# Get the number of images (if no scan is given we'll try
# to find all the images matching the template
if scan is None:
i0, i1 = template_image_range(template)
else:
i0, i1 = scan.get_image_range()
format_class = None
if self._check_format is False:
if "single_file_indices" in imageset:
format_class = FormatMultiImage
# Make a sequence from the input data
return ImageSetFactory.make_sequence(
template,
list(range(i0, i1 + 1)),
format_class=format_class,
check_format=self._check_format,
beam=beam,
detector=detector,
goniometer=goniometer,
scan=scan,
format_kwargs=format_kwargs,
)
def _lookup_model(self, name, experiment_dict):
"""
Find a model by looking up its index from a dictionary
Args:
name (str): The model name e.g. 'beam', 'detector'
experiment_dict (Dict[str, int]):
The experiment dictionary. experiment_dict[name] must
exist and be not None to retrieve a model. If this key
exists, then there *must* be an item with this index
in the ExperimentListDict internal model stores.
Returns:
Optional[Any]:
A model by looking up the index pointed to by
experiment_dict[name]. If not present or empty,
then None is returned.
"""
if experiment_dict.get(name) is None:
return None
return self._lookups[name][experiment_dict[name]]
@staticmethod
def _scaling_model_from_dict(obj):
"""Get the scaling model from a dictionary."""
for entry_point in scaling_model_entry_points:
if entry_point.name == obj["__id__"]:
return entry_point.load().from_dict(obj)
def _experimentlist_from_file(filename, directory=None):
"""Load a model dictionary from a file."""
filename = resolve_path(filename, directory=directory)
try:
with open(filename) as infile:
return json.load(infile)
except OSError:
raise OSError("unable to read file, %s" % filename)
[docs]
class ExperimentListFactory:
"""A class to help instantiate experiment lists."""
[docs]
@staticmethod
def from_args(
args: list[str], unhandled: list[str] | None = None, check_format: bool = True
) -> ExperimentList:
"""Try to load serialised experiments from any recognised format."""
# Create a list for unhandled arguments
if unhandled is None:
unhandled = []
experiments = ExperimentList()
# Try to load from serialized formats
for filename in args:
try:
experiments.extend(
ExperimentListFactory.from_serialized_format(
filename, check_format=check_format
)
)
logger.debug(f"Loaded experiments from {filename}")
except Exception as e:
logger.debug(f"Could not load experiments from {filename}: {e}")
unhandled.append(filename)
raise
return experiments
[docs]
@staticmethod
def from_filenames(
filenames,
unhandled=None,
compare_beam=None,
compare_detector=None,
compare_goniometer=None,
scan_tolerance=None,
format_kwargs=None,
load_models=True,
) -> ExperimentList:
"""Create a list of data blocks from a list of directory or file names."""
experiments = ExperimentList()
# Process each file given by this path list
to_process = _openingpathiterator(filenames)
find_format = FormatChecker()
format_groups = collections.defaultdict(list)
if format_kwargs is None:
format_kwargs = {}
for filename in to_process:
# We now have a file, pre-opened by Format.open_file (therefore
# cached). Determine its type, and prepare to put into a group
format_class = find_format.find_format(filename)
# Verify this makes sense
if not format_class:
# No format class found?
logger.debug("Could not determine format for %s", filename)
if unhandled is not None:
unhandled.append(filename)
elif format_class.is_abstract():
logger.debug(
f"Image file {filename} appears to be a '{format_class.__name__}', but this is an abstract Format"
)
# Invalid format class found?
if unhandled is not None:
unhandled.append(filename)
elif issubclass(format_class, FormatMultiImage):
imageset = format_class.get_imageset(
os.path.abspath(filename), format_kwargs=format_kwargs
)
format_groups[format_class].append(imageset)
logger.debug("Loaded file: %s", filename)
else:
format_object = format_class(filename, **format_kwargs)
meta = ImageMetadataRecord.from_format(format_object)
assert meta.filename == filename
# Add this entry to our table of formats
format_groups[format_class].append(meta)
logger.debug("Loaded metadata of file: %s", filename)
# Now, build experiments from these files. Duplicating the logic of
# the previous implementation:
# - FormatMultiImage files each have their own ImageSet
# - Every set of images forming a scan goes into its own ImageSequence
# - Any consecutive still frames that share any metadata with the
# previous still fram get collected into one ImageSet
all_tof = False
for format_class, records in format_groups.items():
for i in records:
try: # records can be ImageMetadataRecord or ImageSequence
scan = i.get_scan()
if scan is not None and scan.has_property("time_of_flight"):
all_tof = True
elif all_tof:
raise RuntimeError(
"Cannot process mix of ToF and non ToF experiments"
)
except AttributeError:
if all_tof:
raise RuntimeError(
"Cannot process mix of ToF and non ToF experiments"
)
# Treat each format as a separate block of data
for format_class, records in format_groups.items():
if issubclass(format_class, FormatMultiImage):
if all_tof:
_merge_sequence_model_metadata(
records,
compare_beam=compare_beam,
compare_detector=compare_detector,
compare_goniometer=compare_goniometer,
)
for imageset in records:
experiments.extend(
ExperimentListFactory.from_imageset_and_crystal(
imageset, crystal=None, load_models=load_models
)
)
continue
# Merge any consecutive and identical metadata together
_merge_model_metadata(
records,
compare_beam=compare_beam,
compare_detector=compare_detector,
compare_goniometer=compare_goniometer,
)
records = _merge_scans(records, scan_tolerance=scan_tolerance)
imagesets = list(
_convert_to_imagesets(records, format_class, format_kwargs)
)
assert imagesets, "Got no imagesets when constructing ExperimentList?"
for imageset in imagesets:
experiments.extend(
ExperimentListFactory.from_imageset_and_crystal(
imageset, crystal=None, load_models=load_models
)
)
return experiments
[docs]
@staticmethod
def from_imageset_and_crystal(imageset, crystal, load_models=True):
"""Load an experiment list from an imageset and crystal."""
if isinstance(imageset, ImageSequence):
return ExperimentListFactory.from_sequence_and_crystal(
imageset, crystal, load_models
)
else:
return ExperimentListFactory.from_stills_and_crystal(
imageset, crystal, load_models
)
[docs]
@staticmethod
def from_sequence_and_crystal(imageset, crystal, load_models=True):
"""Create an experiment list from sequence and crystal."""
assert isinstance(imageset, ImageSequence)
experiments = ExperimentList()
if load_models:
# if imagesequence is still images, make one experiment for each
# all referencing into the same image set
if imageset.get_scan().is_still():
for j in range(len(imageset)):
subset = imageset[j : j + 1]
experiments.append(
Experiment(
imageset=imageset,
beam=imageset.get_beam(),
detector=imageset.get_detector(),
goniometer=imageset.get_goniometer(),
scan=subset.get_scan(),
crystal=crystal,
)
)
else:
experiments.append(
Experiment(
imageset=imageset,
beam=imageset.get_beam(),
detector=imageset.get_detector(),
goniometer=imageset.get_goniometer(),
scan=imageset.get_scan(),
crystal=crystal,
)
)
return experiments
else:
return ExperimentList([Experiment(imageset=imageset, crystal=crystal)])
[docs]
@staticmethod
def from_stills_and_crystal(imageset, crystal, load_models=True):
"""Create an experiment list from stills and crystal."""
experiments = ExperimentList()
if load_models:
for i in range(len(imageset)):
experiments.append(
Experiment(
imageset=imageset[i : i + 1],
beam=imageset.get_beam(i),
detector=imageset.get_detector(i),
goniometer=imageset.get_goniometer(i),
scan=imageset.get_scan(i),
crystal=crystal,
)
)
else:
for i in range(len(imageset)):
experiments.append(
Experiment(imageset=imageset[i : i + 1], crystal=crystal)
)
return experiments
[docs]
@staticmethod
def from_dict(obj, check_format=True, directory=None):
"""
Load an experiment list from a dictionary.
Args:
obj (dict):
Dictionary containing either ExperimentList or DataBlock
structure.
check_format (bool):
If True, the file will be read to verify metadata.
directory (str):
Returns:
ExperimentList: The dictionary converted
"""
experiments = ExperimentListDict(
obj, check_format=check_format, directory=directory
).decode()
# Check the list is consistent
assert experiments.is_consistent()
return experiments
[docs]
@staticmethod
def from_json(text, check_format=True, directory=None):
"""Load an experiment list from JSON."""
return ExperimentListFactory.from_dict(
json.loads(text),
check_format=check_format,
directory=directory,
)
[docs]
@staticmethod
def from_json_file(filename, check_format=True):
"""Load an experiment list from a json file."""
filename = os.path.abspath(filename)
directory = os.path.dirname(filename)
try:
with open(filename) as infile:
return ExperimentListFactory.from_json(
infile.read(), check_format=check_format, directory=directory
)
except UnicodeDecodeError:
raise InvalidExperimentListError(
f"Cannot interpret {filename} as an ExperimentList"
)
[docs]
@staticmethod
def from_pickle_file(filename):
"""Decode an experiment list from a pickle file."""
with open(filename, "rb") as infile:
obj = pickle.load(infile)
assert isinstance(obj, ExperimentList)
return obj
[docs]
@staticmethod
def from_xds(xds_inp, xds_other):
"""Generate an experiment list from XDS files."""
# Get the sequence from the XDS files
sequence = xds.to_imageset(xds_inp, xds_other)
# Get the crystal from the XDS files
crystal = xds.to_crystal(xds_other)
# Create the experiment list
experiments = ExperimentListFactory.from_imageset_and_crystal(sequence, crystal)
# Set the crystal in the experiment list
assert len(experiments) == 1
return experiments
[docs]
@staticmethod
def from_templates(templates, **kwargs):
"""Import an experiment list from templates"""
assert "verbose" not in kwargs, "The verbose parameter has been removed"
assert len(templates) > 0
experiments = ExperimentList()
find_format = FormatChecker()
# For each template do an import
for template in templates:
template = os.path.normpath(template)
filenames = sorted(locate_files_matching_template_string(template))
if len(filenames):
logger.debug(
"The following files matched the template string:\n%s",
"\n".join(f" {p}" for p in filenames),
)
# Check if we've matched any filenames
if len(filenames) == 0:
raise ValueError(f"Template '{template}' does not match any files")
# Get the format from the first image
format_class = find_format.find_format(filenames[0])
# Verify this makes sense
if format_class is None:
raise ValueError(f"Image file {filenames[0]} format is unknown")
elif format_class.is_abstract():
raise ValueError(
f"Image file {filenames[0]} appears to be a '{type(format_class).__name__}', but this is an abstract Format"
)
else:
image_range = kwargs.get("image_range")
if image_range:
first, last = image_range
else:
first, last = template_image_range(template)
if not kwargs.get("allow_incomplete_sequences", False):
if "#" in template:
# Check all images in range are present - if allowed
i0, i1 = template_string_number_index(template)
prefix = template[:i0]
suffix = template[i1:]
all_numbers = {
int(f.replace(prefix, "").replace(suffix, ""))
for f in filenames
}
missing = set(range(first, last + 1)) - all_numbers
if missing:
raise ValueError(
"Missing image{} {} from imageset ({}-{})".format(
"s" if len(missing) > 1 else "",
", ".join(str(x) for x in sorted(missing)),
first,
last,
)
)
else:
print(
"Warning: Using only one template file: %s. \n "
"`allow_incomplete_sequence` has no effect" % template
)
# Read the image
fmt = format_class(filenames[0], **(kwargs.get("format_kwargs", {})))
# Update the image range
image_range = (first, last)
scan = fmt.get_scan()
scan.set_image_range(image_range)
# Create the sequence and experiment
imageset = dxtbx.imageset.ImageSetFactory.make_sequence(
template,
list(range(first, last + 1)),
format_class,
fmt.get_beam(),
fmt.get_detector(),
fmt.get_goniometer(),
scan,
format_kwargs=kwargs.get("format_kwargs"),
)
experiments.extend(
ExperimentListFactory.from_imageset_and_crystal(
imageset,
crystal=None,
load_models=True,
)
)
return experiments
class ImageMetadataRecord:
"""Object to store metadata information.
This is used whilst building the experiment lists. The metadata for each
image can be read once, and then any grouping/deduplication can happen
later, without re-opening the original file.
"""
def __init__(
self,
beam: dxtbx.model.Beam | None = None,
detector: dxtbx.model.Detector | None = None,
goniometer: dxtbx.model.Goniometer | None = None,
scan: dxtbx.model.Scan | None = None,
template: str | None = None,
filename: str | None = None,
index: int | None = None,
):
"""
Args:
beam: Stores a beam model
detector: Stores a detector model
goniometer: Stores a goniometer model
scan: Stores a scan model
filename: The filename this record was parsed from
template:
The template string parsed from the filename. Usually,
the template is only present if a scan was found and
oscillation width was nonzero.
index:
The index of this file in the template. Applying the
index to the template field should recover the filename
"""
self.beam = beam
self.detector = detector
self.goniometer = goniometer
self.scan = scan
self.template = template
self.filename = filename
self.index = index
def merge_metadata_from(
self,
other_record: ImageMetadataRecord,
compare_beam: Callable = operator.__eq__,
compare_detector: Callable = operator.__eq__,
compare_goniometer: Callable = operator.__eq__,
) -> bool:
"""
Compare two record objects and merge equivalent data.
This method will compare (with optional functions) instance data
for beam, detector and goniometer. If any of the metadata for
this record is equivalent to (but a different instance from) the
other record, then this instance will be altered to match the
other. The function used to compare beams, detectors and
goniometers can be customised - but by default the normal
equality operator is used.
Args:
other_record: Another metadata instance
compare_beam: A function to compare beams
compare_detector: A function to compare detectors
compare_goniometer: A function to compare goniometers
Returns: True if any action was taken
"""
# Allow 'defaults' of None to work - behavior from legacy implementation
compare_beam = compare_beam or operator.__eq__
compare_detector = compare_detector or operator.__eq__
compare_goniometer = compare_goniometer or operator.__eq__
record_altered = False
if self.beam is not other_record.beam and compare_beam(
self.beam, other_record.beam
):
self.beam = other_record.beam
record_altered = True
if self.detector is not other_record.detector and compare_detector(
self.detector, other_record.detector
):
self.detector = other_record.detector
record_altered = True
if self.goniometer is not other_record.goniometer and compare_goniometer(
self.goniometer, other_record.goniometer
):
self.goniometer = other_record.goniometer
record_altered = True
return record_altered
@classmethod
def from_format(cls, fmt: Format) -> Any:
"""
Read metadata information from a Format instance.
This will only pull information out of a single format instance
while it is open - combining metadata records must be done
separately.
Args:
format: The instance of the format class to read data from
Returns:
A new ImageMetadataRecord with the pre-read information
"""
record = cls()
record.filename = fmt.get_image_file()
# Get the metadata from the format
try:
record.beam = fmt.get_beam()
except Exception:
pass
try:
record.detector = fmt.get_detector()
except Exception:
pass
try:
record.goniometer = fmt.get_goniometer()
except Exception:
pass
try:
record.scan = fmt.get_scan()
except Exception:
pass
# Get the template and index if possible - and only if we've got a
# recorded oscillation value
if record.scan is not None:
record.template, record.index = template_regex(record.filename)
return record
def __repr__(self):
items = [
("filename", self.filename),
("beam", self.beam),
("detector", self.detector),
("goiometer", self.goniometer),
("scan", self.scan),
("template", self.template),
("index", self.index),
]
itemstr = ", ".join(x + "=" + repr(y) for x, y in items)
return "<{}{}{}>".format(type(self).__name__, " " if itemstr else "", itemstr)
def __hash__(self):
return hash(
(
self.beam,
self.detector,
self.goniometer,
self.scan,
self.template,
self.filename,
self.index,
)
)
def __eq__(self, other):
if not isinstance(other, ImageMetadataRecord):
return False
return all(
getattr(self, attribute) == getattr(other, attribute)
for attribute in (
"beam",
"detector",
"goniometer",
"scan",
"template",
"filename",
"index",
)
)
def __ne__(self, other):
return not self == other
def _iterate_with_previous(iterable):
"""Convenience iterator to give pairs of (previous, next) items"""
previous = None
for val in iterable:
yield (previous, val)
previous = val
def _groupby_template_is_none(
records: Iterable[ImageMetadataRecord],
) -> Generator[list[ImageMetadataRecord], None, None]:
"""Specialization of groupby that groups records by format=None"""
for _, group in itertools.groupby(
enumerate(records), key=lambda x: -1 if x[1].template is None else x[0]
):
yield [x[1] for x in group]
def _openingpathiterator(pathnames: Iterable[str]):
"""Utility function to efficiently open all paths.
A path is a potential file or directory.
Each path will be opened with :meth:`dxtbx.format.Format.open_file`,
but in order to do so each file will only be opened once, and extraneous
use of :func:`os.stat` will be avoided.
Any path entries that are a directory will be recursed into, once -
any further directories found will be ignored. Any path that is not
a file or directory, or on which IO fails for any reason, will still
be returned.
Args:
pathnames: Paths to attempt to open
"""
# Store a tuple of (recurse, pathname) to track what was root level
paths = collections.deque((True, x) for x in natsort.natsorted(pathnames))
while paths:
# Get the next path from the queue
(do_recurse, pathname) = paths.popleft()
pathname = os.fspath(pathname)
try:
# Attempt to open this 'path'
Format.open_file(pathname)
except OSError as e:
if e.errno == errno.EISDIR:
if do_recurse:
# We've tried to open a directory. Get all the entries...
subdir_paths = sorted(
os.path.join(pathname, x) for x in os.listdir(pathname)
)
# ... and add them to our queue. Make sure not to mark for recursion
paths.extendleft((False, x) for x in reversed(subdir_paths))
logger.debug("Adding %d files from %s", len(subdir_paths), pathname)
else:
logger.debug("Not adding sub-level directory entry %s", pathname)
# Don't return directory instances
continue
else:
# A non-directory-related IO error
logger.debug("Could not import %s: %s", pathname, os.strerror(e.errno))
yield pathname
def _merge_model_metadata(
records: Iterable[ImageMetadataRecord],
compare_beam: Callable | None = None,
compare_detector: Callable | None = None,
compare_goniometer: Callable | None = None,
):
"""
Merge metadata between consecutive record objects.
This will compare each record with the previous one, and make sure
the metadata instances are shared where appropriate.
Args:
records: Records for the images to merge into imagesets
compare_beam: The function to to compare beams
compare_detector: The function to compare detectors
compare_goniometer: The function to compare goniometers
"""
for prev, record in _iterate_with_previous(records):
if prev is None:
continue
record.merge_metadata_from(
prev,
compare_beam=compare_beam,
compare_detector=compare_detector,
compare_goniometer=compare_goniometer,
)
def _merge_sequence_model_metadata(
records: Iterable[ImageSequence],
compare_beam: Callable | None = None,
compare_detector: Callable | None = None,
compare_goniometer: Callable | None = None,
):
record_altered = False
for prev, record in _iterate_with_previous(records):
if prev is None:
continue
record_altered = False
record_beam = record.get_beam()
record_detector = record.get_detector()
record_goniometer = record.get_goniometer()
prev_beam = prev.get_beam()
prev_detector = prev.get_detector()
prev_goniometer = prev.get_goniometer()
if record_beam is not prev_beam and compare_beam(record_beam, prev_beam):
record.set_beam(prev_beam)
record_altered = True
if record_detector is not prev_detector and compare_detector(
record_detector, prev_detector
):
record.set_detector(prev_detector)
record_altered = True
if record_goniometer is not prev_goniometer and compare_goniometer(
record_goniometer, prev_goniometer
):
record.set_goniometer(prev_goniometer)
record_altered = True
return record_altered
def _merge_scans(
records: Iterable[ImageMetadataRecord], scan_tolerance: float | None = None
) -> list[ImageMetadataRecord]:
"""
Merge consecutive scan records with identical metadata.
The records should have previously had their model metadata merged,
as identity will be used to compare metadata identity at this stage.
Args:
records: Records to merge
scan_tolerance: Fraction of oscillation range to tolerate
when merging scan records
Returns:
A (potentially shorter) list of records with scans merged
"""
merged_records = []
logger.debug("Merging scans")
for prev, record in _iterate_with_previous(records):
# The first record always gets recorded
if prev is None:
merged_records.append(record)
logger.debug(" Saving initial record %s", record)
continue
# Compare metadata instances
same_metadata = [
prev.beam is record.beam,
prev.detector is record.detector,
prev.goniometer is record.goniometer,
]
# Condition for combining:
# - All metadata must match
# - Previous record must be templated
# - This record must be templated
if (
all(same_metadata)
and prev.template is not None
and record.template is not None
):
# Attempt to append to scan
try:
if scan_tolerance is None:
prev.scan.append(record.scan)
else:
prev.scan.append(record.scan, scan_tolerance=scan_tolerance)
except RuntimeError as e:
logger.debug(
" Failed to merge record %s with previous - writing new scan",
str(e),
)
else:
# If we appended, then we don't need to keep this record's scan
record.scan = prev.scan
logger.debug(" Appended record %s to previous", record)
continue
merged_records.append(record)
logger.debug("Result of merging record scans: %d records", len(merged_records))
return merged_records
def _convert_to_imagesets(
records: Iterable[ImageMetadataRecord],
format_class: type[Format],
format_kwargs: dict | None = None,
) -> Generator[dxtbx.imageset.ImageSet, None, None]:
"""
Convert records into imagesets.
The records should have been metadata- and scan-merged by this point.
Rules:
- Any groups of template=None where any of the metadata objects
are shared, go into a single imageset
- Anything with a template goes into a single sequence
Args:
records: The records to convert
format_class: The format class for the data in this record
format_kwargs: Any format configuration arguments to pass
to the format imageset creator.
Returns:
Imagesets representing the records
"""
# Iterate over images/sets such that template=None are clustered
for setgroup in _groupby_template_is_none(records):
if setgroup[0].template is not None:
# If we have a template, then it's a sequence
assert len(setgroup) == 1, "Got group of metadata records in template?"
logger.debug("Creating Imagesequence from %s", setgroup[0].template)
yield _create_imagesequence(setgroup[0], format_class, format_kwargs)
else:
# Without a template, it was never identified as a sequence, so an imageset
logger.debug("Creating ImageSet from %d files", len(setgroup))
yield _create_imageset(setgroup, format_class, format_kwargs)
def _create_imageset(
records: Iterable[ImageMetadataRecord],
format_class: type[Format],
format_kwargs: dict | None = None,
) -> dxtbx.imageset.ImageSet:
"""
Create an ImageSet object from a set of single-image records.
Args:
records: Single-image metadata records to merge into a single imageset
format_class: The format class object for these image records
format_kwargs: Extra arguments to pass to the format class when
creating an ImageSet
Returns:
An imageset for all the image records
"""
records = list(records)
# Nothing here should have been assigned a template parameter
assert all(x.template is None for x in records)
# Everything should have a filename
assert all(x.filename for x in records)
# Extract the filenames from the records
filenames = [
x.filename if get_url_scheme(x.filename) else os.path.abspath(x.filename)
for x in records
if x.filename
]
# Create the imageset
imageset = dxtbx.imageset.ImageSetFactory.make_imageset(
filenames, format_class, format_kwargs=format_kwargs, check_format=False
)
# Update all of the metadata for each record
for i, r in enumerate(records):
imageset.set_beam(r.beam, i)
imageset.set_detector(r.detector, i)
imageset.set_goniometer(r.goniometer, i)
imageset.set_scan(r.scan, i)
return imageset
def _create_imagesequence(
record: ImageMetadataRecord,
format_class: type[Format],
format_kwargs: dict | None = None,
) -> dxtbx.imageset.ImageSequence:
"""
Create an ImageSequence object from a single rotation data image.
Args:
record: Single-image metadata records to merge into a single imageset
format_class: The format class object for these image records
format_kwargs: Extra arguments to pass to the format class when
creating an ImageSet
Returns:
An imageset representing the sequence of data
"""
assert record.scan
assert record.template
index_start, index_end = record.scan.get_image_range()
# Create the sequence
sequence = dxtbx.imageset.ImageSetFactory.make_sequence(
template=os.path.abspath(record.template),
indices=list(range(index_start, index_end + 1)),
format_class=format_class,
beam=record.beam,
detector=record.detector,
goniometer=record.goniometer,
scan=record.scan,
format_kwargs=format_kwargs,
# check_format=False,
)
return sequence
