def test_load_v4():
data = """CellProfiler Pipeline: http://www.cellprofiler.org
Version:3
DateRevision:20141017202435
GitHash:b261e94
ModuleCount:3
HasImagePlaneDetails:False
MeasureTexture:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Hidden:2
Hidden:2
Hidden:2
Select an image to measure:rawDNA
Select an image to measure:rawGFP
Select objects to measure:Cells
Select objects to measure:Nuclei
Texture scale to measure:3
Angles to measure:Horizontal,Vertical
Texture scale to measure:5
Angles to measure:Diagonal,Anti-diagonal
Measure Gabor features?:Yes
Number of angles to compute for Gabor:6
Measure images or objects?:Images
MeasureTexture:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Hidden:2
Hidden:2
Hidden:2
Select an image to measure:rawDNA
Select an image to measure:rawGFP
Select objects to measure:Cells
Select objects to measure:Nuclei
Texture scale to measure:3
Angles to measure:Horizontal,Vertical
Texture scale to measure:5
Angles to measure:Diagonal,Anti-diagonal
Measure Gabor features?:No
Number of angles to compute for Gabor:6
Measure images or objects?:Objects
MeasureTexture:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Hidden:2
Hidden:2
Hidden:2
Select an image to measure:rawDNA
Select an image to measure:rawGFP
Select objects to measure:Cells
Select objects to measure:Nuclei
Texture scale to measure:3
Angles to measure:Horizontal,Vertical
Texture scale to measure:5
Angles to measure:Diagonal,Anti-diagonal
Measure Gabor features?:No
Number of angles to compute for Gabor:6
Measure images or objects?:Both
"""
pipeline = cellprofiler_core.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event,
cellprofiler_core.pipeline.event.LoadException)
pipeline.add_listener(callback)
pipeline.load(io.StringIO(data))
assert len(pipeline.modules()) == 3
for i, (wants_gabor, io_choice) in enumerate((
(True, cellprofiler.modules.measuretexture.IO_IMAGES),
(False, cellprofiler.modules.measuretexture.IO_OBJECTS),
(False, cellprofiler.modules.measuretexture.IO_BOTH),
)):
module = pipeline.modules()[i]
assert isinstance(module,
cellprofiler.modules.measuretexture.MeasureTexture)
assert len(module.images_list.value) == 2
assert {"rawDNA", "rawGFP"}.issubset(module.images_list.value)
assert len(module.objects_list.value) == 2
assert {"Cells", "Nuclei"}.issubset(module.objects_list.value)
assert len(module.scale_groups) == 2
assert module.scale_groups[0].scale == 3
assert module.scale_groups[1].scale == 5
def test_load_v5():
with open(
"./tests/resources/modules/measureobjectintensitydistribution/v5.pipeline",
"r") as fd:
data = fd.read()
pipeline = cellprofiler_core.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event,
cellprofiler_core.pipeline.event.LoadException)
pipeline.add_listener(callback)
pipeline.load(io.StringIO(data))
assert len(pipeline.modules()) == 2
module = pipeline.modules()[0]
assert isinstance(
module,
cellprofiler.modules.measureobjectintensitydistribution.
MeasureObjectIntensityDistribution,
)
assert (module.wants_zernikes == cellprofiler.modules.
measureobjectintensitydistribution.Z_MAGNITUDES)
assert module.zernike_degree == 7
assert len(module.images_list.value) == 2
assert {'CropGreen', 'CropRed'}.issubset(module.images_list.value)
assert len(module.objects) == 2
for group, (object_name, center_choice, center_object_name) in zip(
module.objects,
(
(
"Nuclei",
cellprofiler.modules.measureobjectintensitydistribution.C_SELF,
"Ichthyosaurs",
),
(
"Cells",
cellprofiler.modules.measureobjectintensitydistribution.
C_EDGES_OF_OTHER,
"Nuclei",
),
),
):
assert group.object_name.value == object_name
assert group.center_choice.value == center_choice
assert group.center_object_name == center_object_name
assert len(module.bin_counts) == 2
for group, (bin_count, scale, max_radius) in zip(module.bin_counts,
((5, True, 100),
(4, False, 100))):
assert group.wants_scaled == scale
assert group.bin_count == bin_count
assert group.maximum_radius == max_radius
for (
group,
(
image_name,
object_name,
bin_count,
measurement,
colormap,
wants_to_save,
output_image_name,
),
) in zip(
module.heatmaps,
(
(
"CropRed",
"Cells",
5,
cellprofiler.modules.measureobjectintensitydistribution.
A_FRAC_AT_D,
"Default",
True,
"Heat",
),
(
"CropGreen",
"Nuclei",
4,
cellprofiler.modules.measureobjectintensitydistribution.
A_MEAN_FRAC,
"Spectral",
False,
"A",
),
(
"CropRed",
"Nuclei",
5,
cellprofiler.modules.measureobjectintensitydistribution.
A_RADIAL_CV,
"Default",
False,
"B",
),
),
):
assert group.image_name.value == image_name
assert group.object_name.value == object_name
assert int(group.bin_count.value) == bin_count
assert group.measurement == measurement
assert group.colormap == colormap
assert group.wants_to_save_display == wants_to_save
assert group.display_name == output_image_name
module = pipeline.modules()[1]
assert (module.wants_zernikes == cellprofiler.modules.
measureobjectintensitydistribution.Z_MAGNITUDES_AND_PHASE)
def test_load_v4():
file = tests.modules.test_resources_directory(
"measureimagequality/v4.pipeline")
with open(file, "r") as fd:
data = fd.read()
pipeline = cellprofiler_core.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event,
cellprofiler_core.pipeline.event.LoadException)
pipeline.add_listener(callback)
pipeline.load(io.StringIO(data))
assert len(pipeline.modules()) == 5
for module in pipeline.modules():
assert isinstance(
module,
cellprofiler.modules.measureimagequality.MeasureImageQuality)
module = pipeline.modules()[0]
assert len(module.image_groups) == 1
group = module.image_groups[0]
assert group.threshold_groups == []
assert module.images_choice == cellprofiler.modules.measureimagequality.O_ALL_LOADED
assert group.check_blur
assert group.scale_groups[0].scale == 20
assert group.check_saturation
assert group.check_intensity
assert group.calculate_threshold
assert group.use_all_threshold_methods
module = pipeline.modules()[1]
assert len(module.image_groups) == 1
group = module.image_groups[0]
assert module.images_choice == cellprofiler.modules.measureimagequality.O_SELECT
assert group.image_names.value_text == "Alpha"
module = pipeline.modules()[2]
assert len(module.image_groups) == 1
group = module.image_groups[0]
assert module.images_choice == cellprofiler.modules.measureimagequality.O_SELECT
assert "Delta" in group.image_names.value
assert "Beta" in group.image_names.value
assert len(group.image_names.value) == 2
module = pipeline.modules()[3]
assert len(module.image_groups) == 2
group = module.image_groups[0]
assert module.images_choice == cellprofiler.modules.measureimagequality.O_SELECT
assert group.image_names.value_text == "Delta"
assert group.check_intensity
assert not group.use_all_threshold_methods
thr = group.threshold_groups[0]
assert thr.threshold_method == centrosome.threshold.TM_OTSU
assert (thr.use_weighted_variance == O_WEIGHTED_VARIANCE)
assert thr.two_class_otsu == O_TWO_CLASS
group = module.image_groups[1]
assert group.image_names.value_text == "Epsilon"
module = pipeline.modules()[4]
assert len(module.image_groups) == 1
group = module.image_groups[0]
assert module.images_choice == cellprofiler.modules.measureimagequality.O_SELECT
assert group.image_names.value_text == "Zeta"
assert not group.use_all_threshold_methods
thr = group.threshold_groups[0]
assert thr.threshold_method == centrosome.threshold.TM_OTSU
assert (thr.use_weighted_variance == O_WEIGHTED_VARIANCE)
assert thr.two_class_otsu == O_TWO_CLASS
thr = group.threshold_groups[1]
assert thr.threshold_method == centrosome.threshold.TM_OTSU
assert (thr.use_weighted_variance == O_WEIGHTED_VARIANCE)
assert thr.two_class_otsu == O_THREE_CLASS
assert (thr.assign_middle_to_foreground == O_FOREGROUND)
def test_load_v2():
file = tests.modules.get_test_resources_directory(
"enhanceorsuppressfeatures/v2.pipeline")
with open(file, "r") as fd:
data = fd.read()
pipeline = cellprofiler_core.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event,
cellprofiler_core.pipeline.event.LoadException)
pipeline.add_listener(callback)
pipeline.load(io.StringIO(data))
assert len(pipeline.modules()) == 5
for (
module,
(
input_name,
output_name,
operation,
feature_size,
feature_type,
min_range,
max_range,
),
) in zip(
pipeline.modules(),
(
(
"Initial",
"EnhancedSpeckles",
cellprofiler.modules.enhanceorsuppressfeatures.ENHANCE,
11,
cellprofiler.modules.enhanceorsuppressfeatures.E_SPECKLES,
1,
10,
),
(
"EnhancedSpeckles",
"EnhancedNeurites",
cellprofiler.modules.enhanceorsuppressfeatures.ENHANCE,
9,
cellprofiler.modules.enhanceorsuppressfeatures.E_NEURITES,
1,
10,
),
(
"EnhancedNeurites",
"EnhancedDarkHoles",
cellprofiler.modules.enhanceorsuppressfeatures.ENHANCE,
9,
cellprofiler.modules.enhanceorsuppressfeatures.E_DARK_HOLES,
4,
11,
),
(
"EnhancedDarkHoles",
"EnhancedCircles",
cellprofiler.modules.enhanceorsuppressfeatures.ENHANCE,
9,
cellprofiler.modules.enhanceorsuppressfeatures.E_CIRCLES,
4,
11,
),
(
"EnhancedCircles",
"Suppressed",
cellprofiler.modules.enhanceorsuppressfeatures.SUPPRESS,
13,
cellprofiler.modules.enhanceorsuppressfeatures.E_CIRCLES,
4,
11,
),
),
):
assert module.module_name == "EnhanceOrSuppressFeatures"
assert isinstance(
module,
cellprofiler.modules.enhanceorsuppressfeatures.
EnhanceOrSuppressFeatures,
)
assert module.x_name == input_name
assert module.y_name == output_name
assert module.method == operation
assert module.enhance_method == feature_type
assert module.object_size == feature_size
assert module.hole_size.min == min_range
assert module.hole_size.max == max_range
def test_load_v11():
pipeline_txt = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:3
DateRevision:300
GitHash:
ModuleCount:4
HasImagePlaneDetails:False
SaveImages:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Select the type of image to save:Image
Select the image to save:DNA
Select the objects to save:None
Select the module display window to save:None
Select method for constructing file names:From image filename
Select image name for file prefix:DNA
Enter single file name:OrigBlue
Number of digits:4
Append a suffix to the image file name?:No
Text to append to the image name:
Saved file format:png
Output file location:Default Output Folder\x7C
Image bit depth:8-bit integer
Overwrite existing files without warning?:No
When to save:Every cycle
Rescale the images? :No
Save as grayscale or color image?:Grayscale
Select colormap:gray
Record the file and path information to the saved image?:No
Create subfolders in the output folder?:No
Base image folder:Elsewhere...\x7C
Saved movie format:avi
SaveImages:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Select the type of image to save:Image
Select the image to save:DNA
Select the objects to save:None
Select the module display window to save:None
Select method for constructing file names:From image filename
Select image name for file prefix:DNA
Enter single file name:OrigBlue
Number of digits:4
Append a suffix to the image file name?:No
Text to append to the image name:
Saved file format:jpg
Output file location:Default Output Folder\x7C
Image bit depth:8-bit integer
Overwrite existing files without warning?:No
When to save:Every cycle
Rescale the images? :No
Save as grayscale or color image?:Grayscale
Select colormap:gray
Record the file and path information to the saved image?:No
Create subfolders in the output folder?:No
Base image folder:Elsewhere...\x7C
Saved movie format:avi
SaveImages:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Select the type of image to save:Image
Select the image to save:DNA
Select the objects to save:None
Select the module display window to save:None
Select method for constructing file names:From image filename
Select image name for file prefix:DNA
Enter single file name:OrigBlue
Number of digits:4
Append a suffix to the image file name?:No
Text to append to the image name:
Saved file format:tif
Output file location:Default Output Folder\x7C
Image bit depth:8-bit integer
Overwrite existing files without warning?:No
When to save:Every cycle
Rescale the images? :No
Save as grayscale or color image?:Grayscale
Select colormap:gray
Record the file and path information to the saved image?:No
Create subfolders in the output folder?:No
Base image folder:Elsewhere...\x7C
Saved movie format:avi
SaveImages:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Select the type of image to save:Movie
Select the image to save:DNA
Select the objects to save:None
Select the module display window to save:None
Select method for constructing file names:From image filename
Select image name for file prefix:DNA
Enter single file name:OrigBlue
Number of digits:4
Append a suffix to the image file name?:No
Text to append to the image name:
Saved file format:tif
Output file location:Default Output Folder\x7C
Image bit depth:8-bit integer
Overwrite existing files without warning?:No
When to save:Every cycle
Rescale the images? :No
Save as grayscale or color image?:Grayscale
Select colormap:gray
Record the file and path information to the saved image?:No
Create subfolders in the output folder?:No
Base image folder:Elsewhere...\x7C
Saved movie format:avi
"""
def callback(caller, event):
assert not isinstance(event,
cellprofiler_core.pipeline.event.LoadException)
pipeline = cellprofiler_core.pipeline.Pipeline()
pipeline.add_listener(callback)
pipeline.load(io.StringIO(pipeline_txt))
module = pipeline.modules()[0]
assert module.save_image_or_figure.value == cellprofiler.modules.saveimages.IF_IMAGE
assert module.image_name.value == "DNA"
assert (module.file_name_method.value ==
cellprofiler.modules.saveimages.FN_FROM_IMAGE)
assert module.file_image_name.value == "DNA"
assert module.single_file_name.value == "OrigBlue"
assert module.number_of_digits.value == 4
assert module.wants_file_name_suffix.value == False
assert module.file_format.value == "png"
assert module.pathname.value == "Default Output Folder|"
assert module.bit_depth.value == cellprofiler.modules.saveimages.BIT_DEPTH_8
assert module.overwrite.value == False
assert module.when_to_save.value == cellprofiler.modules.saveimages.WS_EVERY_CYCLE
assert module.update_file_names.value == False
assert module.create_subdirectories.value == False
assert module.root_dir.value == "Elsewhere...|"
module = pipeline.modules()[1]
assert module.file_format.value == "jpeg"
module = pipeline.modules()[2]
assert module.file_format.value == "tiff"
module = pipeline.modules()[3]
assert module.file_format.value == "tiff"
def test_load_v1():
with open("./tests/resources/modules/maskobjects/v1.pipeline", "r") as fd:
data = fd.read()
pipeline = cellprofiler_core.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event, cellprofiler_core.pipeline.event.LoadException)
pipeline.add_listener(callback)
pipeline.load(six.moves.StringIO(data))
assert len(pipeline.modules()) == 4
module = pipeline.modules()[0]
assert isinstance(module, cellprofiler.modules.maskobjects.MaskObjects)
assert module.object_name.value == "Nuclei"
assert module.mask_choice.value == cellprofiler.modules.maskobjects.MC_OBJECTS
assert module.masking_objects.value == "Wells"
assert module.remaining_objects.value == "MaskedNuclei"
assert (
module.retain_or_renumber.value == cellprofiler.modules.maskobjects.R_RENUMBER
)
assert module.overlap_choice.value == cellprofiler.modules.maskobjects.P_MASK
assert not module.wants_inverted_mask
module = pipeline.modules()[1]
assert isinstance(module, cellprofiler.modules.maskobjects.MaskObjects)
assert module.object_name.value == "Cells"
assert module.mask_choice.value == cellprofiler.modules.maskobjects.MC_IMAGE
assert module.masking_image.value == "WellBoundary"
assert module.remaining_objects.value == "MaskedCells"
assert module.retain_or_renumber.value == cellprofiler.modules.maskobjects.R_RETAIN
assert module.overlap_choice.value == cellprofiler.modules.maskobjects.P_KEEP
assert not module.wants_inverted_mask
module = pipeline.modules()[2]
assert isinstance(module, cellprofiler.modules.maskobjects.MaskObjects)
assert module.object_name.value == "Cytoplasm"
assert module.mask_choice.value == cellprofiler.modules.maskobjects.MC_OBJECTS
assert module.masking_objects.value == "Cells"
assert module.remaining_objects.value == "MaskedCytoplasm"
assert (
module.retain_or_renumber.value == cellprofiler.modules.maskobjects.R_RENUMBER
)
assert module.overlap_choice.value == cellprofiler.modules.maskobjects.P_REMOVE
assert not module.wants_inverted_mask
module = pipeline.modules()[3]
assert isinstance(module, cellprofiler.modules.maskobjects.MaskObjects)
assert module.object_name.value == "Speckles"
assert module.mask_choice.value == cellprofiler.modules.maskobjects.MC_OBJECTS
assert module.masking_objects.value == "Cells"
assert module.remaining_objects.value == "MaskedSpeckles"
assert (
module.retain_or_renumber.value == cellprofiler.modules.maskobjects.R_RENUMBER
)
assert (
module.overlap_choice.value
== cellprofiler.modules.maskobjects.P_REMOVE_PERCENTAGE
)
assert round(abs(module.overlap_fraction.value - 0.3), 7) == 0
assert not module.wants_inverted_mask
def test_load_v3():
file = tests.modules.get_test_resources_directory("flagimages/v3.pipeline")
with open(file, "r") as fd:
data = fd.read()
pipeline = cellprofiler_core.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event,
cellprofiler_core.pipeline.event.LoadException)
pipeline.add_listener(callback)
pipeline.load(six.moves.StringIO(data))
assert len(pipeline.modules()) == 1
module = pipeline.modules()[0]
assert isinstance(module, cellprofiler.modules.flagimage.FlagImage)
expected = (
(
"QCFlag",
cellprofiler.modules.flagimage.C_ANY,
False,
(
("Intensity_MaxIntensity_DNA", None, 0.95, "foo.txt"),
("Intensity_MinIntensity_Cytoplasm", 0.05, None, "bar.txt"),
("Intensity_MeanIntensity_DNA", 0.1, 0.9, "baz.txt"),
),
),
(
"HighCytoplasmIntensity",
None,
True,
(("Intensity_MeanIntensity_Cytoplasm", None, 0.8, "dunno.txt"), ),
),
)
assert len(expected) == module.flag_count.value
for flag, (feature_name, combine, skip,
measurements) in zip(module.flags, expected):
assert isinstance(flag, cellprofiler_core.setting.SettingsGroup)
assert flag.category == "Metadata"
assert flag.feature_name == feature_name
assert flag.wants_skip == skip
if combine is not None:
assert flag.combination_choice == combine
assert len(measurements) == flag.measurement_count.value
for (measurement, (measurement_name, min_value, max_value,
rules_file)) in zip(flag.measurement_settings,
measurements):
assert isinstance(measurement,
cellprofiler_core.setting.SettingsGroup)
assert measurement.source_choice == cellprofiler.modules.flagimage.S_IMAGE
assert measurement.measurement == measurement_name
assert measurement.wants_minimum.value == (min_value is not None)
if measurement.wants_minimum.value:
assert round(abs(measurement.minimum_value.value - min_value),
7) == 0
assert measurement.wants_maximum.value == (max_value is not None)
if measurement.wants_maximum.value:
assert round(abs(measurement.maximum_value.value - max_value),
7) == 0
assert measurement.rules_file_name == rules_file
assert measurement.rules_class == "1"
def run_pipeline_headless(options, args):
"""
Run a CellProfiler pipeline in headless mode
"""
if options.first_image_set is not 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 = 1
image_set_numbers = None
if options.last_image_set is not 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 = numpy.arange(1, image_set_end + 1)
else:
image_set_numbers = numpy.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)
pipeline = cellprofiler_core.pipeline.Pipeline()
initial_measurements = None
try:
if h5py.is_hdf5(options.pipeline_filename):
initial_measurements = cellprofiler_core.measurement.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(
cellprofiler_core.pipeline.M_PIPELINE)
pipeline_text = pipeline_text
pipeline.load(six.moves.StringIO(pipeline_text))
if not pipeline.in_batch_mode():
#
# Need file list in order to call prepare_run
#
with h5py.File(options.pipeline_filename, "r") as src:
if cellprofiler_core.utilities.hdf5_dict.HDF5FileList.has_file_list(
src):
cellprofiler_core.utilities.hdf5_dict.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 = cellprofiler_core.preferences.get_image_set_file()
if file_list is not None:
pipeline.read_file_list(file_list)
elif options.image_directory is not None:
pathnames = []
for dirname, _, fnames in os.walk(
os.path.abspath(options.image_directory)):
pathnames.append([
os.path.join(dirname, fname) for fname in fnames
if os.path.isfile(os.path.join(dirname, fname))
])
pathnames = sum(pathnames, [])
pipeline.add_pathnames_to_file_list(pathnames)
#
# 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(
cellprofiler_core.measurement.EXPERIMENT,
cellprofiler_core.pipeline.EXIT_STATUS,
):
done_text = measurements.get_experiment_measurement(
cellprofiler_core.pipeline.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()
elif not measurements.has_feature(cellprofiler_core.measurement.EXPERIMENT,
cellprofiler_core.pipeline.EXIT_STATUS):
# The pipeline probably failed
exit_code = 1
else:
exit_code = 0
if measurements is not None:
measurements.close()
return exit_code
