def from_files(cls,
raw_input=None,
pickle_list=[],
dials_refls=[],
dials_expts=[],
_prefix='cluster_from_file',
_message='Made from list of individual files',
n_images=None,
dials=False,
json=False,
**kwargs):
"""Constructor to get a cluster from a list of individual files.
:param pickle_list: list of pickle files
:param dials_refls: list of DIALS integrated reflections
:param dials_expts: list of DIALS experiment jsons
:param n_images: find at most this number of images
:param dials: use the dials_refls and dials_expts arguments to construct the clusters (default: False)
:param use_b: Boolean. If True, intialise Scale and B. If False, use only
mean intensity scalling.
"""
data = []
def sort_dials_raw_input(raw):
expts = []
refls = []
for path in raw:
if path.endswith(".pickle"):
refls.append(path)
elif path.endswith(".json"):
expts.append(path)
return (refls, expts)
def done():
if n_images is None:
return False
return len(data) >= n_images
if dials:
if raw_input is not None:
r, e = sort_dials_raw_input(raw_input)
dials_refls.extend(r)
dials_expts.extend(e)
for r, e in zip(dials_refls, dials_expts):
this_frame = SingleDialsFrameFromFiles(refls_path=r, expts_path=e, **kwargs)
if hasattr(this_frame, 'miller_array'):
data.append(this_frame)
if done():
break
else:
logger.info('skipping reflections {} and experiments {}'.format(r, e))
elif json:
if raw_input is not None:
r, e = sort_dials_raw_input(raw_input)
dials_expts.extend(e)
dials_expts_ids = [os.path.join(os.path.dirname(e), os.path.basename(e).split("_")[0])
for e in dials_expts]
for e in dials_expts:
name = os.path.join(os.path.dirname(e), os.path.basename(e).split("_")[0])
this_frame = SingleDialsFrameFromJson(expts_path=e, **kwargs)
this_frame.name=name
data.append(this_frame)
if done():
break
else:
if raw_input is not None:
pickle_list.extend(raw_input)
print "There are %d input files"%(len(pickle_list))
from xfel.command_line.print_pickle import generate_data_from_streams
for data_dict in generate_data_from_streams(pickle_list):
this_frame = SingleFrame(dicti=data_dict, **kwargs)
if hasattr(this_frame, 'miller_array'):
data.append(this_frame)
if done():
break
else:
logger.info('skipping file {}'.format(os.path.basename(path)))
print "%d lattices will be analyzed"%(len(data))
return cls(data, _prefix, _message)
def from_files(cls,
raw_input=None,
pickle_list=[],
dials_refls=[],
dials_expts=[],
_prefix='cluster_from_file',
_message='Made from list of individual files',
n_images=None,
dials=False,
**kwargs):
"""Constructor to get a cluster from a list of individual files.
:param pickle_list: list of pickle files
:param dials_refls: list of DIALS integrated reflections
:param dials_expts: list of DIALS experiment jsons
:param n_images: find at most this number of images
:param dials: use the dials_refls and dials_expts arguments to construct the clusters (default: False)
:param use_b: Boolean. If True, intialise Scale and B. If False, use only
mean intensity scalling.
"""
data = []
def sort_dials_raw_input(raw):
expts = []
refls = []
for path in raw:
if path.endswith(".pickle"):
refls.append(path)
elif path.endswith(".json"):
expts.append(path)
return (refls, expts)
def done():
if n_images is None:
return False
return len(data) >= n_images
if dials:
if raw_input is not None:
r, e = sort_dials_raw_input(raw_input)
dials_refls.extend(r)
dials_expts.extend(e)
dials_refls_ids = [
os.path.join(os.path.dirname(r),
os.path.basename(r).split("_")[0])
for r in dials_refls
]
dials_expts_ids = [
os.path.join(os.path.dirname(e),
os.path.basename(e).split("_")[0])
for e in dials_expts
]
matches = [(dials_refls[i],
dials_expts[dials_expts_ids.index(dials_refls_ids[i])])
for i in xrange(len(dials_refls_ids))
if dials_refls_ids[i] in dials_expts_ids]
for (r, e) in matches:
this_frame = SingleDialsFrameFromFiles(refls_path=r,
expts_path=e,
**kwargs)
if hasattr(this_frame, 'miller_array'):
data.append(this_frame)
if done():
break
else:
logging.info(
'skipping reflections {} and experiments {}'.format(
r, e))
else:
if raw_input is not None:
pickle_list.extend(raw_input)
for path in pickle_list:
this_frame = SingleFrame(path, os.path.basename(path),
**kwargs)
if hasattr(this_frame, 'miller_array'):
data.append(this_frame)
if done():
break
else:
logging.info('skipping file {}'.format(
os.path.basename(path)))
return cls(data, _prefix, _message)