def update(self): '''Check to see if any more frames have appeared - if they have update myself and reset.''' images = find_matching_images(self._template, self._directory) if len(images) > len(self._images): self._images = images from xia2.Schema import load_imagesets imagesets = load_imagesets( self._template, self._directory, id_image=self._id_image, use_cache=False, reversephi=Flags.get_reversephi()) max_images = 0 best_sweep = None for imageset in imagesets: scan = imageset.get_scan() if scan is None: continue if imageset.get_scan().get_num_images() > max_images: best_sweep = imageset self._imageset = best_sweep return
def _setup_from_image(self, image): """Configure myself from an image name.""" template, directory = image2template_directory(image) self._fp_matching_images = find_matching_images(template, directory) # trim this down to only allowed images... if self._fp_wedge: start, end = self._fp_wedge images = [] for j in self._fp_matching_images: if j < start or j > end: continue images.append(j) self._fp_matching_images = images from xia2.Schema import load_imagesets imagesets = load_imagesets( template, directory, image_range=(self._fp_matching_images[0], self._fp_matching_images[-1]), ) assert len(imagesets) == 1, "multiple imagesets match %s" % template imageset = imagesets[0] self._setup_from_imageset(imageset)
def update(self): '''Check to see if any more frames have appeared - if they have update myself and reset.''' from xia2.Applications.xia2setup import is_hd5f_name if is_hd5f_name(os.path.join(self._directory, self._template)): return images = find_matching_images(self._template, self._directory) if len(images) > len(self._images): self._images = images from xia2.Schema import load_imagesets imagesets = load_imagesets( self._template, self._directory, id_image=self._id_image, use_cache=False, reversephi=PhilIndex.params.xia2.settings.input.reverse_phi) max_images = 0 best_sweep = None for imageset in imagesets: scan = imageset.get_scan() if scan is None: continue if imageset.get_scan().get_num_images() > max_images: best_sweep = imageset self._imageset = best_sweep return
def _setup_from_image(self, image): '''Configure myself from an image name.''' template, directory = image2template_directory(image) self._fp_matching_images = find_matching_images(template, directory) # trim this down to only allowed images... if self._fp_wedge: start, end = self._fp_wedge images = [] for j in self._fp_matching_images: if j < start or j > end: continue images.append(j) self._fp_matching_images = images from xia2.Schema import load_imagesets imagesets = load_imagesets( template, directory, image_range=(self._fp_matching_images[0], self._fp_matching_images[-1])) assert len(imagesets) == 1, 'multiple imagesets match %s' % template imageset = imagesets[0] self._setup_from_imageset(imageset) return
'thau_lores_1_###.mccd': 1000, '27032_1_E1_###.mccd': 0, '27032_1_E2_###.mccd': 100, '27032_2_###.mccd': 200 } batches = {} from xia2.Experts.FindImages import find_matching_images, \ template_directory_number2image, image2template_directory image_names = [] for image in sys.argv[1:]: template, directory = image2template_directory(image) images = find_matching_images(template, directory) batch_images = [] for i in images: image = template_directory_number2image(template, directory, i) image_names.append(image) batch_images.append(image) epochs = epocher(batch_images) for j in range(len(epochs)): batch = images[j] + offsets[template] batches[epochs[j][1]] = batch dose = accumulate(image_names) epochs = sorted(dose.keys())
} batches = { } from xia2.Experts.FindImages import find_matching_images, \ template_directory_number2image, image2template_directory from xia2.Handlers.Flags import Flags # just cos we can... Flags.set_trust_timestamps(True) image_names = [] for image in sys.argv[1:]: template, directory = image2template_directory(image) images = find_matching_images(template, directory) batch_images = [] for i in images: image = template_directory_number2image( template, directory, i) image_names.append(image) batch_images.append(image) epochs = epocher(batch_images) for j in range(len(epochs)): batch = images[j] + offsets[template] batches[epochs[j][1]] = batch dose = accumulate(image_names)