def make_scan(image_range,
exposure_times,
oscillation,
epochs,
batch_offset=0,
deg=True):
if not isinstance(exposure_times, list):
num_images = image_range[1] - image_range[0] + 1
exposure_times = [exposure_times for i in range(num_images)]
else:
num_images = image_range[1] - image_range[0] + 1
num_exp = len(exposure_times)
if num_exp != num_images:
if num_exp == 0:
exposure_times = [0 for i in range(num_images)]
else:
exposure_times = exposure_times.extend([
exposure_times[-1] for i in range(num_images - num_exp)
])
epoch_list = [epochs[j] for j in sorted(epochs)]
return Scan(
tuple(map(int, image_range)),
tuple(map(float, oscillation)),
flex.double(list(map(float, exposure_times))),
flex.double(list(map(float, epoch_list))),
batch_offset,
deg,
)
def from_dict(d, t=None):
''' Convert the dictionary to a scan model
Params:
d The dictionary of parameters
t The template dictionary to use
Returns:
The scan model
'''
from dxtbx.model import Scan
from scitbx.array_family import flex # import dependency
# If None, return None
if d == None:
if t == None: return None
else: return from_dict(t, None)
elif t != None:
d = dict(t.items() + d.items())
if not isinstance(d['exposure_time'], list):
d['exposure_time'] = [d['exposure_time']]
d.setdefault('batch_offset', 0) # backwards compatibility 20180205
if 'valid_image_ranges' not in d:
d['valid_image_ranges'] = {} # backwards compatibility 20181113
# Create the model from the dictionary
return Scan.from_dict(d)
def from_dict(d, t=None):
''' Convert the dictionary to a scan model
Params:
d The dictionary of parameters
t The template dictionary to use
Returns:
The scan model
'''
from dxtbx.model import Scan
from scitbx.array_family import flex # import dependency
# If None, return None
if d == None:
if t == None: return None
else: return from_dict(t, None)
elif t != None:
d = dict(t.items() + d.items())
if not isinstance(d['exposure_time'], list):
d['exposure_time'] = [d['exposure_time']]
# Create the model from the dictionary
return Scan.from_dict(d)
def from_dict(d, t=None):
"""Convert the dictionary to a scan model
Params:
d The dictionary of parameters
t The template dictionary to use
Returns:
The scan model
"""
if d is None and t is None:
return None
joint = t.copy() if t else {}
joint.update(d)
if not isinstance(joint["exposure_time"], list):
joint["exposure_time"] = [joint["exposure_time"]]
joint.setdefault("batch_offset", 0) # backwards compatibility 20180205
joint.setdefault("valid_image_ranges", {}) # backwards compatibility 20181113
# Create the model from the joint dictionary
return Scan.from_dict(joint)
def from_phil(params, reference=None):
"""
Generate a scan model from phil parameters
"""
if reference is None:
if params.scan.image_range is None and params.scan.oscillation is None:
return None
if params.scan.image_range is None:
raise RuntimeError("No image range set")
if params.scan.oscillation is None:
raise RuntimeError("No oscillation set")
scan = Scan(params.scan.image_range, params.scan.oscillation)
else:
scan = reference
if params.scan.image_range is not None:
most_recent_image_index = (scan.get_image_range()[1] -
scan.get_image_range()[0])
scan.set_oscillation(
scan.get_image_oscillation(params.scan.image_range[0]))
scan.set_image_range(params.scan.image_range)
if (params.scan.extrapolate_scan and
(params.scan.image_range[1] - params.scan.image_range[0]) >
most_recent_image_index):
exposure_times = scan.get_exposure_times()
epochs = scan.get_epochs()
exposure_time = exposure_times[most_recent_image_index]
epoch_correction = epochs[most_recent_image_index]
for i in range(
most_recent_image_index + 1,
params.scan.image_range[1] -
params.scan.image_range[0] + 1,
):
exposure_times[i] = exposure_time
epoch_correction += exposure_time
epochs[i] = epoch_correction
scan.set_epochs(epochs)
scan.set_exposure_times(exposure_times)
if params.scan.oscillation is not None:
scan.set_oscillation(params.scan.oscillation)
if params.scan.batch_offset is not None:
scan.set_batch_offset(params.scan.batch_offset)
# Return the model
return scan