def execute_merge_files(self, mm):
input_files = []
output_fd, output_file = tempfile.mkstemp(".mat")
pipeline = cpp.Pipeline()
li = LoadImages()
li.module_num = 1
pipeline.add_module(li)
for m in mm:
input_fd, input_file = tempfile.mkstemp(".mat")
pipeline.save_measurements(input_file, m)
input_files.append((input_fd, input_file))
M.MergeOutputFiles.merge_files(output_file, [x[1] for x in input_files])
m = cpmeas.load_measurements(output_file)
os.close(output_fd)
os.remove(output_file)
for fd, filename in input_files:
os.close(fd)
os.remove(filename)
return m
def run_pipeline_headless(options, args):
'''Run a CellProfiler pipeline in headless mode'''
if sys.platform == 'darwin':
if options.start_awt:
from javabridge import activate_awt
activate_awt()
if not options.first_image_set is None:
if not options.first_image_set.isdigit():
raise ValueError(
"The --first-image-set option takes a numeric argument")
else:
image_set_start = int(options.first_image_set)
else:
image_set_start = None
image_set_numbers = None
if not options.last_image_set is None:
if not options.last_image_set.isdigit():
raise ValueError(
"The --last-image-set option takes a numeric argument")
else:
image_set_end = int(options.last_image_set)
if image_set_start is None:
image_set_numbers = np.arange(1, image_set_end + 1)
else:
image_set_numbers = np.arange(image_set_start,
image_set_end + 1)
else:
image_set_end = None
if ((options.pipeline_filename is not None)
and (not options.pipeline_filename.lower().startswith('http'))):
options.pipeline_filename = os.path.expanduser(
options.pipeline_filename)
from cellprofiler.pipeline import Pipeline, EXIT_STATUS, M_PIPELINE
import cellprofiler.measurement as cpmeas
import cellprofiler.preferences as cpprefs
pipeline = Pipeline()
initial_measurements = None
try:
if h5py.is_hdf5(options.pipeline_filename):
initial_measurements = cpmeas.load_measurements(
options.pipeline_filename, image_numbers=image_set_numbers)
except:
logging.root.info("Failed to load measurements from pipeline")
if initial_measurements is not None:
pipeline_text = \
initial_measurements.get_experiment_measurement(
M_PIPELINE)
pipeline_text = pipeline_text.encode('us-ascii')
pipeline.load(StringIO(pipeline_text))
if not pipeline.in_batch_mode():
#
# Need file list in order to call prepare_run
#
from cellprofiler.utilities.hdf5_dict import HDF5FileList
with h5py.File(options.pipeline_filename, "r") as src:
if HDF5FileList.has_file_list(src):
HDF5FileList.copy(src,
initial_measurements.hdf5_dict.hdf5_file)
else:
pipeline.load(options.pipeline_filename)
if options.groups is not None:
kvs = [x.split('=') for x in options.groups.split(',')]
groups = dict(kvs)
else:
groups = None
file_list = cpprefs.get_image_set_file()
if file_list is not None:
pipeline.read_file_list(file_list)
#
# Fixup CreateBatchFiles with any command-line input or output directories
#
if pipeline.in_batch_mode():
create_batch_files = [
m for m in pipeline.modules() if m.is_create_batch_module()
]
if len(create_batch_files) > 0:
create_batch_files = create_batch_files[0]
if options.output_directory is not None:
create_batch_files.custom_output_directory.value = \
options.output_directory
if options.image_directory is not None:
create_batch_files.default_image_directory.value = \
options.image_directory
use_hdf5 = len(args) > 0 and not args[0].lower().endswith(".mat")
measurements = pipeline.run(
image_set_start=image_set_start,
image_set_end=image_set_end,
grouping=groups,
measurements_filename=None if not use_hdf5 else args[0],
initial_measurements=initial_measurements)
if len(args) > 0 and not use_hdf5:
pipeline.save_measurements(args[0], measurements)
if options.done_file is not None:
if (measurements is not None
and measurements.has_feature(cpmeas.EXPERIMENT, EXIT_STATUS)):
done_text = measurements.get_experiment_measurement(EXIT_STATUS)
exit_code = (0 if done_text == "Complete" else -1)
else:
done_text = "Failure"
exit_code = -1
fd = open(options.done_file, "wt")
fd.write("%s\n" % done_text)
fd.close()
if measurements is not None:
measurements.close()
return exit_code
def merge_files(destination, sources, force_headless=False):
is_headless = force_headless or get_headless()
if not is_headless:
import wx
if len(sources) == 0:
return
if not is_headless:
progress = wx.ProgressDialog("Writing " + destination,
"Loading " + sources[0],
maximum=len(sources) * 4 + 1,
style=wx.PD_CAN_ABORT |
wx.PD_APP_MODAL |
wx.PD_ELAPSED_TIME |
wx.PD_REMAINING_TIME)
count = 0
try:
pipeline = cpp.Pipeline()
has_error = [False]
def callback(caller, event):
if isinstance(event, cpp.LoadExceptionEvent):
has_error = True
wx.MessageBox(
message="Could not load %s: %s" % (
sources[0], event.error),
caption="Failed to load %s" % sources[0])
has_error[0] = True
pipeline.add_listener(callback)
pipeline.load(sources[0])
if has_error[0]:
return
if destination.lower().endswith(".h5"):
mdest = cpmeas.Measurements(filename=destination,
multithread=False)
h5_dest = True
else:
mdest = cpmeas.Measurements(multithread=False)
h5_dest = False
for source in sources:
if not is_headless:
count += 1
keep_going, skip = progress.Update(count, "Loading " + source)
if not keep_going:
return
if h5py.is_hdf5(source):
msource = cpmeas.Measurements(filename=source,
mode="r",
multithread=False)
else:
msource = cpmeas.load_measurements(source)
dest_image_numbers = mdest.get_image_numbers()
source_image_numbers = msource.get_image_numbers()
if (len(dest_image_numbers) == 0 or
len(source_image_numbers) == 0):
offset_source_image_numbers = source_image_numbers
else:
offset_source_image_numbers = (
np.max(dest_image_numbers) -
np.min(source_image_numbers) + source_image_numbers + 1)
for object_name in msource.get_object_names():
if object_name in mdest.get_object_names():
destfeatures = mdest.get_feature_names(object_name)
else:
destfeatures = []
for feature in msource.get_feature_names(object_name):
if object_name == cpmeas.EXPERIMENT:
if not mdest.has_feature(object_name, feature):
src_value = msource.get_experiment_measurement(
feature)
mdest.add_experiment_measurement(feature,
src_value)
continue
src_values = msource.get_measurement(
object_name,
feature,
image_set_number=source_image_numbers)
mdest[object_name,
feature,
offset_source_image_numbers] = src_values
destset = set(destfeatures)
if not is_headless:
keep_going, skip = progress.Update(count + 1, "Saving to " + destination)
if not keep_going:
return
if not h5_dest:
pipeline.save_measurements(destination, mdest)
finally:
if not is_headless:
progress.Destroy()
def merge_files(destination, sources, force_headless=False):
is_headless = force_headless or get_headless()
if not is_headless:
import wx
if len(sources) == 0:
return
if not is_headless:
progress = wx.ProgressDialog("Writing " + destination,
"Loading " + sources[0],
maximum=len(sources) * 4 + 1,
style=wx.PD_CAN_ABORT |
wx.PD_APP_MODAL |
wx.PD_ELAPSED_TIME |
wx.PD_REMAINING_TIME)
count = 0
try:
pipeline = cpp.Pipeline()
has_error = [False]
def callback(caller, event):
if isinstance(event, cpp.LoadExceptionEvent):
has_error = True
wx.MessageBox(
message="Could not load %s: %s" % (
sources[0], event.error),
caption="Failed to load %s" % sources[0])
has_error[0] = True
pipeline.add_listener(callback)
pipeline.load(sources[0])
if has_error[0]:
return
if destination.lower().endswith(".h5"):
mdest = cpmeas.Measurements(filename=destination,
multithread=False)
h5_dest = True
else:
mdest = cpmeas.Measurements(multithread=False)
h5_dest = False
for source in sources:
if not is_headless:
count += 1
keep_going, skip = progress.Update(count, "Loading " + source)
if not keep_going:
return
if h5py.is_hdf5(source):
msource = cpmeas.Measurements(filename=source,
mode="r",
multithread=False)
else:
msource = cpmeas.load_measurements(source)
dest_image_numbers = mdest.get_image_numbers()
source_image_numbers = msource.get_image_numbers()
if (len(dest_image_numbers) == 0 or
len(source_image_numbers) == 0):
offset_source_image_numbers = source_image_numbers
else:
offset_source_image_numbers = (
np.max(dest_image_numbers) -
np.min(source_image_numbers) + source_image_numbers + 1)
for object_name in msource.get_object_names():
if object_name in mdest.get_object_names():
destfeatures = mdest.get_feature_names(object_name)
else:
destfeatures = []
for feature in msource.get_feature_names(object_name):
if object_name == cpmeas.EXPERIMENT:
if not mdest.has_feature(object_name, feature):
src_value = msource.get_experiment_measurement(
feature)
mdest.add_experiment_measurement(feature,
src_value)
continue
src_values = msource.get_measurement(
object_name,
feature,
image_set_number=source_image_numbers)
mdest[object_name,
feature,
offset_source_image_numbers] = src_values
destset = set(destfeatures)
if not is_headless:
keep_going, skip = progress.Update(count + 1, "Saving to " + destination)
if not keep_going:
return
if not h5_dest:
pipeline.save_measurements(destination, mdest)
finally:
if not is_headless:
progress.Destroy()
def run_pipeline_headless(options, args):
'''Run a CellProfiler pipeline in headless mode'''
if sys.platform == 'darwin':
if options.start_awt:
from javabridge import activate_awt
activate_awt()
if not options.first_image_set is None:
if not options.first_image_set.isdigit():
raise ValueError("The --first-image-set option takes a numeric argument")
else:
image_set_start = int(options.first_image_set)
else:
image_set_start = None
image_set_numbers = None
if not options.last_image_set is None:
if not options.last_image_set.isdigit():
raise ValueError("The --last-image-set option takes a numeric argument")
else:
image_set_end = int(options.last_image_set)
if image_set_start is None:
image_set_numbers = np.arange(1, image_set_end + 1)
else:
image_set_numbers = np.arange(image_set_start, image_set_end + 1)
else:
image_set_end = None
if ((options.pipeline_filename is not None) and
(not options.pipeline_filename.lower().startswith('http'))):
options.pipeline_filename = os.path.expanduser(options.pipeline_filename)
from cellprofiler.pipeline import Pipeline, EXIT_STATUS, M_PIPELINE
import cellprofiler.measurement as cpmeas
import cellprofiler.preferences as cpprefs
pipeline = Pipeline()
initial_measurements = None
try:
if h5py.is_hdf5(options.pipeline_filename):
initial_measurements = cpmeas.load_measurements(
options.pipeline_filename,
image_numbers=image_set_numbers)
except:
logging.root.info("Failed to load measurements from pipeline")
if initial_measurements is not None:
pipeline_text = \
initial_measurements.get_experiment_measurement(
M_PIPELINE)
pipeline_text = pipeline_text.encode('us-ascii')
pipeline.load(StringIO(pipeline_text))
if not pipeline.in_batch_mode():
#
# Need file list in order to call prepare_run
#
from cellprofiler.utilities.hdf5_dict import HDF5FileList
with h5py.File(options.pipeline_filename, "r") as src:
if HDF5FileList.has_file_list(src):
HDF5FileList.copy(
src, initial_measurements.hdf5_dict.hdf5_file)
else:
pipeline.load(options.pipeline_filename)
if options.groups is not None:
kvs = [x.split('=') for x in options.groups.split(',')]
groups = dict(kvs)
else:
groups = None
file_list = cpprefs.get_image_set_file()
if file_list is not None:
pipeline.read_file_list(file_list)
#
# Fixup CreateBatchFiles with any command-line input or output directories
#
if pipeline.in_batch_mode():
create_batch_files = [
m for m in pipeline.modules()
if m.is_create_batch_module()]
if len(create_batch_files) > 0:
create_batch_files = create_batch_files[0]
if options.output_directory is not None:
create_batch_files.custom_output_directory.value = \
options.output_directory
if options.image_directory is not None:
create_batch_files.default_image_directory.value = \
options.image_directory
use_hdf5 = len(args) > 0 and not args[0].lower().endswith(".mat")
measurements = pipeline.run(
image_set_start=image_set_start,
image_set_end=image_set_end,
grouping=groups,
measurements_filename=None if not use_hdf5 else args[0],
initial_measurements=initial_measurements)
if len(args) > 0 and not use_hdf5:
pipeline.save_measurements(args[0], measurements)
if options.done_file is not None:
if (measurements is not None and measurements.has_feature(cpmeas.EXPERIMENT, EXIT_STATUS)):
done_text = measurements.get_experiment_measurement(EXIT_STATUS)
exit_code = (0 if done_text == "Complete" else -1)
else:
done_text = "Failure"
exit_code = -1
fd = open(options.done_file, "wt")
fd.write("%s\n" % done_text)
fd.close()
if measurements is not None:
measurements.close()
return exit_code
