Source code for dxtbx.serialize.imageset

from __future__ import annotations

import os
import pickle

from dxtbx.format.image import ImageBool, ImageDouble
from dxtbx.imageset import ImageSequence, ImageSet, ImageSetFactory
from dxtbx.model import BeamFactory, DetectorFactory, GoniometerFactory, ScanFactory
from dxtbx.serialize.filename import resolve_path


[docs] def filename_to_absolute(filename): """Convert filenames to absolute form.""" if isinstance(filename, list): return [os.path.abspath(f) for f in filename] return os.path.abspath(filename)
[docs] def filename_or_none(filename): if filename is None or filename == "": return None return filename_to_absolute(filename)
[docs] def basic_imageset_to_dict(imageset): """Convert an imageset to a dictionary Params: imageset The imageset Returns: A dictionary of the parameters """ return { "__id__": "imageset", "filenames": filename_to_absolute(imageset.paths()), "mask": filename_or_none(imageset.external_lookup.mask.filename), "gain": filename_or_none(imageset.external_lookup.gain.filename), "pedestal": filename_or_none(imageset.external_lookup.pedestal.filename), "beam": imageset.get_beam(0).to_dict(), "detector": imageset.get_detector(0).to_dict(), }
[docs] def imagesequence_to_dict(sequence): """Convert a sequence to a dictionary Params: sequence The sequence Returns: A dictionary of the parameters """ return { "__id__": "imageset", "template": filename_to_absolute(sequence.get_template()), "mask": filename_or_none(sequence.external_lookup.mask.filename), "gain": filename_or_none(sequence.external_lookup.gain.filename), "pedestal": filename_or_none(sequence.external_lookup.pedestal.filename), "beam": sequence.get_beam().to_dict(), "detector": sequence.get_detector().to_dict(), "goniometer": sequence.get_goniometer().to_dict(), "scan": sequence.get_scan().to_dict(), }
[docs] def imageset_to_dict(imageset): """Convert the imageset to a dictionary Params: imageset The imageset Returns: A dictionary of the parameters """ # If this is an imageset then return a list of filenames if isinstance(imageset, ImageSequence): return imagesequence_to_dict(imageset) elif isinstance(imageset, ImageSet): return basic_imageset_to_dict(imageset) else: raise TypeError("Unknown ImageSet Type")
[docs] def basic_imageset_from_dict(d, directory=None): """Construct an ImageSet class from the dictionary.""" # Get the filename list and create the imageset filenames = [resolve_path(str(p), directory=directory) for p in d["filenames"]] imageset = ImageSetFactory.new(filenames)[0] # Set some external lookups if "mask" in d and d["mask"] is not None and d["mask"] != "": path = resolve_path(d["mask"], directory=directory) with open(path) as infile: imageset.external_lookup.mask.filename = path imageset.external_lookup.mask.data = ImageBool(pickle.load(infile)) if "gain" in d and d["gain"] is not None and d["gain"] != "": path = resolve_path(d["gain"], directory=directory) with open(path) as infile: imageset.external_lookup.gain.filename = path imageset.external_lookup.gain.data = ImageDouble(pickle.load(infile)) if "pedestal" in d and d["pedestal"] is not None and d["pedestal"] != "": path = resolve_path(d["pedestal"], directory=directory) with open(path) as infile: imageset.external_lookup.pedestal.filename = path imageset.external_lookup.pedestal.data = ImageDouble(pickle.load(infile)) # Get the existing models as dictionaries beam_dict = imageset.get_beam(0).to_dict() detector_dict = imageset.get_detector(0).to_dict() # Set models imageset.set_beam(BeamFactory.from_dict(d.get("beam"), beam_dict)) imageset.set_detector(DetectorFactory.from_dict(d.get("detector"), detector_dict)) return imageset
[docs] def imagesequence_from_dict(d, check_format=True, directory=None): """Construct and image sequence from the dictionary.""" # Get the template (required) template = resolve_path(str(d["template"]), directory=directory) # If the scan isn't set, find all available files scan_dict = d.get("scan") if scan_dict is None: image_range = None else: image_range = scan_dict.get("image_range") # Set the models with the existing models as templates beam = BeamFactory.from_dict(d.get("beam")) goniometer = GoniometerFactory.from_dict(d.get("goniometer")) detector = DetectorFactory.from_dict(d.get("detector")) scan = ScanFactory.from_dict(d.get("scan")) # Construct the sequence try: sequence = ImageSetFactory.from_template( template, image_range, beam=beam, detector=detector, goniometer=goniometer, scan=scan, check_format=check_format, )[0] except Exception: indices = list(range(image_range[0], image_range[1] + 1)) sequence = ImageSetFactory.make_sequence( template, indices, beam=beam, detector=detector, goniometer=goniometer, scan=scan, check_format=check_format, ) # Set some external lookups if "mask" in d and d["mask"] is not None and d["mask"] != "": path = resolve_path(d["mask"], directory=directory) with open(path) as infile: sequence.external_lookup.mask.filename = path sequence.external_lookup.mask.data = ImageBool(pickle.load(infile)) if "gain" in d and d["gain"] is not None and d["gain"] != "": path = resolve_path(d["gain"], directory=directory) with open(path) as infile: sequence.external_lookup.gain.filename = path sequence.external_lookup.gain.data = ImageDouble(pickle.load(infile)) if "pedestal" in d and d["pedestal"] is not None and d["pedestal"] != "": path = resolve_path(d["pedestal"], directory=directory) with open(path) as infile: sequence.external_lookup.pedestal.filename = path sequence.external_lookup.pedestal.data = ImageDouble(pickle.load(infile)) return sequence
[docs] def imageset_from_dict(d, check_format=True, directory=None): """Convert the dictionary to a sequence Params: d The dictionary of parameters Returns: The sequence """ # Check the input if d is None: return None # Check the version and id if str(d["__id__"]) != "imageset": raise ValueError('"__id__" does not equal "imageset"') if "filenames" in d: return basic_imageset_from_dict(d, directory=directory) elif "template" in d: return imagesequence_from_dict( d, check_format=check_format, directory=directory ) else: raise TypeError("Unable to deserialize given imageset")