def _init_models(self):
"""
Descript. :
"""
self._char = queue_model_objects.Characterisation()
self._char_params = self._char.characterisation_parameters
self._processing_parameters = queue_model_objects.ProcessingParameters(
)
self._set_space_group(self._processing_parameters.space_group)
if self._beamline_setup_hwobj is not None:
self._acquisition_parameters = self._beamline_setup_hwobj.\
get_default_char_acq_parameters()
self._char_params = self._beamline_setup_hwobj.\
get_default_characterisation_parameters()
else:
self._acquisition_parameters = queue_model_objects.AcquisitionParameters(
)
self._path_template.reference_image_prefix = 'ref'
# The num images drop down default value is 1
# we would like it to be 2
self._acquisition_parameters.num_images = 2
self._char.characterisation_software =\
queue_model_enumerables.COLLECTION_ORIGIN.EDNA
self._path_template.num_files = 2
self._path_template.compression = False
self._acquisition_parameters.shutterless = False
def _init_models(self):
"""
Descript. :
"""
self._char = queue_model_objects.Characterisation()
self._char_params = self._char.characterisation_parameters
self._processing_parameters = queue_model_objects.ProcessingParameters(
)
if self._beamline_setup_hwobj is not None:
self._acquisition_parameters = self._beamline_setup_hwobj.\
get_default_char_acq_parameters()
self._char_params = self._beamline_setup_hwobj.\
get_default_characterisation_parameters()
try:
transmission = self._beamline_setup_hwobj.transmission_hwobj.getAttFactor(
)
transmission = round(float(transmission), 1)
except AttributeError:
transmission = 0
try:
resolution = self._beamline_setup_hwobj.resolution_hwobj.getPosition(
)
resolution = round(float(resolution), 3)
except AttributeError:
resolution = 0
try:
energy = self._beamline_setup_hwobj.energy_hwobj.getCurrentEnergy(
)
energy = round(float(energy), 4)
except AttributeError:
energy = 0
self._acquisition_parameters.resolution = resolution
self._acquisition_parameters.energy = energy
self._acquisition_parameters.transmission = transmission
else:
self._acquisition_parameters = queue_model_objects.AcquisitionParameters(
)
return
self._path_template.reference_image_prefix = 'ref'
# The num images drop down default value is 1
# we would like it to be 2
self._acquisition_parameters.num_images = 2
self._char.characterisation_software =\
queue_model_enumerables.COLLECTION_ORIGIN.EDNA
self._path_template.num_files = 2
self._acquisition_parameters.shutterless = False
def _create_task(self, sample, shape):
"""
Descript. :
"""
tasks = []
if not shape or not isinstance(shape, GraphicsItemPoint):
cpos = queue_model_objects.CentredPosition()
cpos.snapshot_image = self._graphics_manager_hwobj.get_scene_snapshot(
)
else:
# Shapes selected and sample is mounted, get the
# centred positions for the shapes
snapshot = self._graphics_manager_hwobj.\
get_scene_snapshot(shape)
cpos = copy.deepcopy(shape.get_centred_position())
cpos.snapshot_image = snapshot
char_params = copy.deepcopy(self._char_params)
acq = self._create_acq(sample)
dc = queue_model_objects.\
DataCollection([acq], sample.crystals[0],
self._processing_parameters)
# Reference images for characterisations should be taken 90 deg apart
# this is achived by setting overap to -89
acq.acquisition_parameters.overlap = -89
acq.acquisition_parameters.centred_position = cpos
dc.acquisitions[0] = acq
dc.experiment_type = queue_model_enumerables.EXPERIMENT_TYPE.EDNA_REF
dc.run_processing_parallel = False
char = queue_model_objects.Characterisation(dc, char_params)
char.set_name(dc.acquisitions[0].\
path_template.get_prefix())
char.set_number(dc.acquisitions[0].\
path_template.run_number)
char.run_characterisation = self._char_widget.characterisation_gbox.isChecked(
)
char.wait_result = self._char_widget.wait_result_cbx.isChecked()
char.run_diffraction_plan = self._char_widget.execute_plan_cbx.isChecked(
)
tasks.append(char)
self._path_template.run_number += 1
return tasks
def _create_task(self, sample, shape):
tasks = []
if not shape:
cpos = queue_model_objects.CentredPosition()
cpos.snapshot_image = self._shape_history.get_snapshot([])
else:
# Shapes selected and sample is mounted, get the
# centred positions for the shapes
if isinstance(shape, shape_history.Point):
snapshot = self._shape_history.\
get_snapshot([shape.qub_point])
#cpos = shape.get_centred_positions()[0]
cpos = copy.deepcopy(shape.get_centred_positions()[0])
cpos.snapshot_image = snapshot
char_params = copy.deepcopy(self._char_params)
acq = self._create_acq(sample)
dc = queue_model_objects.\
DataCollection([acq], sample.crystals[0],
self._processing_parameters)
# Reference images for characterisations should be taken 90 deg apart
# this is achived by setting overap to -89
acq.acquisition_parameters.overlap = -89
acq.acquisition_parameters.centred_position = cpos
dc.acquisitions[0] = acq
dc.experiment_type = queue_model_enumerables.EXPERIMENT_TYPE.EDNA_REF
char = queue_model_objects.Characterisation(dc, char_params)
char.set_name(dc.acquisitions[0].\
path_template.get_prefix())
char.set_number(dc.acquisitions[0].\
path_template.run_number)
tasks.append(char)
self._path_template.run_number += 1
return tasks
def add_characterisation(node_id, task):
"""
Adds a data characterisation task to the sample with id: <id>
:param int id: id of the sample to which the task belongs
:param dict task: Task data (parameters)
:returns: The queue id of the Data collection
:rtype: int
"""
sample_model, sample_entry = get_entry(node_id)
params = task['parameters']
refdc_model, refdc_entry = _create_dc(task)
refdc_model.set_name('refdc')
char_params = qmo.CharacterisationParameters().set_from_dict(params)
char_model = qmo.Characterisation(refdc_model, char_params)
char_entry = qe.CharacterisationGroupQueueEntry(Mock(), char_model)
char_entry.queue_model_hwobj = mxcube.queue
# Set the characterisation and reference collection parameters
set_char_params(char_model, char_entry, task)
# A characterisation has two TaskGroups one for the characterisation itself
# and its reference collection and one for the resulting diffraction plans.
# But we only create a reference group if there is a result !
refgroup_model = qmo.TaskGroup()
mxcube.queue.add_child(sample_model, refgroup_model)
mxcube.queue.add_child(refgroup_model, char_model)
refgroup_entry = qe.TaskGroupQueueEntry(Mock(), refgroup_model)
refgroup_entry.set_enabled(True)
sample_entry.enqueue(refgroup_entry)
refgroup_entry.enqueue(char_entry)
char_model.set_enabled(task['checked'])
char_entry.set_enabled(task['checked'])
return char_model._node_id
def _create_task(self, sample, shape):
tasks = []
if not shape:
cpos = queue_model_objects.CentredPosition()
cpos.snapshot_image = self._shape_history.get_snapshot([])
else:
# Shapes selected and sample is mounted, get the
# centred positions for the shapes
if isinstance(shape, shape_history.Point):
snapshot = self._shape_history.\
get_snapshot([shape.qub_point])
cpos = shape.get_centred_positions()[0]
cpos.snapshot_image = snapshot
char_params = copy.deepcopy(self._char_params)
# Acquisition for start position
acq = queue_model_objects.Acquisition()
acq.acquisition_parameters = \
copy.deepcopy(self._acquisition_parameters)
acq.acquisition_parameters.collect_agent = \
queue_model_enumerables.COLLECTION_ORIGIN.MXCUBE
acq.acquisition_parameters.centred_position = cpos
acq.path_template = copy.deepcopy(self._path_template)
if self._beamline_setup_hwobj.in_plate_mode():
acq.acquisition_parameters.take_snapshots = False
else:
acq.acquisition_parameters.take_snapshots = True
if '<sample_name>' in acq.path_template.directory:
name = sample.get_name().replace(':', '-')
acq.path_template.directory = acq.path_template.directory.\
replace('<sample_name>', name)
acq.path_template.process_directory = acq.path_template.process_directory.\
replace('<sample_name>', name)
if '<acronym>-<name>' in acq.path_template.base_prefix:
acq.path_template.base_prefix = self.get_default_prefix(sample)
acq.path_template.run_numer = self._beamline_setup_hwobj.queue_model_hwobj.\
get_next_run_number(acq.path_template)
processing_parameters = copy.deepcopy(self._processing_parameters)
data_collection = queue_model_objects.\
DataCollection([acq], sample.crystals[0],
self._processing_parameters)
# Referance images for characterisations should be taken 90 deg apart
# this is achived by setting overap to -89
acq.acquisition_parameters.overlap = -89
data_collection.acquisitions[0] = acq
data_collection.experiment_type = queue_model_enumerables.EXPERIMENT_TYPE.EDNA_REF
char = queue_model_objects.Characterisation(data_collection,
char_params)
char.set_name(data_collection.acquisitions[0].\
path_template.get_prefix())
char.set_number(data_collection.acquisitions[0].\
path_template.run_number)
tasks.append(char)
self._path_template.run_number += 1
return tasks
