def get_measurements_for_good_pipeline(nimages=1, group_numbers=None):
"""Get an appropriately initialized measurements structure for the good pipeline"""
path = os.path.join(tests.modules.example_images_directory(),
"ExampleSBSImages")
m = cellprofiler.measurement.Measurements()
if group_numbers is None:
group_numbers = [1] * nimages
group_indexes = [1]
last_group_number = group_numbers[0]
group_index = 1
for group_number in group_numbers:
if group_number == last_group_number:
group_index += 1
else:
group_index = 1
group_indexes.append(group_index)
for i in range(1, nimages + 1):
filename = "Channel2-%02d-%s-%02d.tif" % (
i,
"ABCDEFGH"[int((i - 1) / 12)],
((i - 1) % 12) + 1,
)
url = cellprofiler.modules.loadimages.pathname2url(
os.path.join(path, filename))
m[cellprofiler.measurement.IMAGE,
cellprofiler.measurement.C_FILE_NAME + "_DNA", i, ] = filename
m[cellprofiler.measurement.IMAGE,
cellprofiler.measurement.C_PATH_NAME + "_DNA", i, ] = path
m[cellprofiler.measurement.IMAGE,
cellprofiler.measurement.C_URL + "_DNA", i] = url
m[cellprofiler.measurement.IMAGE,
cellprofiler.measurement.GROUP_NUMBER, i] = group_numbers[i - 1]
m[cellprofiler.measurement.IMAGE, cellprofiler.measurement.GROUP_INDEX,
i] = group_indexes[i - 1]
jblob = javabridge.run_script(
"""
importPackage(Packages.org.cellprofiler.imageset);
importPackage(Packages.org.cellprofiler.imageset.filter);
var imageFile=new ImageFile(new java.net.URI(url));
var imageFileDetails = new ImageFileDetails(imageFile);
var imageSeries=new ImageSeries(imageFile, 0);
var imageSeriesDetails = new ImageSeriesDetails(imageSeries, imageFileDetails);
var imagePlane=new ImagePlane(imageSeries, 0, ImagePlane.ALWAYS_MONOCHROME);
var ipd = new ImagePlaneDetails(imagePlane, imageSeriesDetails);
var stack = ImagePlaneDetailsStack.makeMonochromeStack(ipd);
var stacks = java.util.Collections.singletonList(stack);
var keys = java.util.Collections.singletonList(imageNumber);
var imageSet = new ImageSet(stacks, keys);
imageSet.compress(java.util.Collections.singletonList("DNA"), null);
""",
dict(url=url, imageNumber=str(i)),
)
blob = javabridge.get_env().get_byte_array_elements(jblob)
m[cellprofiler.measurement.IMAGE,
cellprofiler.modules.namesandtypes.M_IMAGE_SET, i,
blob.dtype, ] = blob
pipeline = cellprofiler.pipeline.Pipeline()
pipeline.loadtxt(six.moves.StringIO(GOOD_PIPELINE))
pipeline.write_pipeline_measurement(m)
return m
def get_measurements_for_good_pipeline(nimages=1,
group_numbers=None):
'''Get an appropriately initialized measurements structure for the good pipeline'''
path = os.path.join(tests.modules.example_images_directory(), "ExampleSBSImages")
m = cellprofiler.measurement.Measurements()
if group_numbers is None:
group_numbers = [1] * nimages
group_indexes = [1]
last_group_number = group_numbers[0]
group_index = 1
for group_number in group_numbers:
if group_number == last_group_number:
group_index += 1
else:
group_index = 1
group_indexes.append(group_index)
for i in range(1, nimages + 1):
filename = ("Channel2-%02d-%s-%02d.tif" %
(i, "ABCDEFGH"[int((i - 1) / 12)], ((i - 1) % 12) + 1))
url = cellprofiler.modules.loadimages.pathname2url(os.path.join(path, filename))
m[cellprofiler.measurement.IMAGE, cellprofiler.measurement.C_FILE_NAME + "_DNA", i] = filename
m[cellprofiler.measurement.IMAGE, cellprofiler.measurement.C_PATH_NAME + "_DNA", i] = path
m[cellprofiler.measurement.IMAGE, cellprofiler.measurement.C_URL + "_DNA", i] = url
m[cellprofiler.measurement.IMAGE, cellprofiler.measurement.GROUP_NUMBER, i] = group_numbers[i - 1]
m[cellprofiler.measurement.IMAGE, cellprofiler.measurement.GROUP_INDEX, i] = group_indexes[i - 1]
jblob = javabridge.run_script("""
importPackage(Packages.org.cellprofiler.imageset);
importPackage(Packages.org.cellprofiler.imageset.filter);
var imageFile=new ImageFile(new java.net.URI(url));
var imageFileDetails = new ImageFileDetails(imageFile);
var imageSeries=new ImageSeries(imageFile, 0);
var imageSeriesDetails = new ImageSeriesDetails(imageSeries, imageFileDetails);
var imagePlane=new ImagePlane(imageSeries, 0, ImagePlane.ALWAYS_MONOCHROME);
var ipd = new ImagePlaneDetails(imagePlane, imageSeriesDetails);
var stack = ImagePlaneDetailsStack.makeMonochromeStack(ipd);
var stacks = java.util.Collections.singletonList(stack);
var keys = java.util.Collections.singletonList(imageNumber);
var imageSet = new ImageSet(stacks, keys);
imageSet.compress(java.util.Collections.singletonList("DNA"), null);
""", dict(url=url, imageNumber=str(i)))
blob = javabridge.get_env().get_byte_array_elements(jblob)
m[cellprofiler.measurement.IMAGE, cellprofiler.modules.namesandtypes.M_IMAGE_SET, i, blob.dtype] = blob
pipeline = cellprofiler.pipeline.Pipeline()
pipeline.loadtxt(cStringIO.StringIO(GOOD_PIPELINE))
pipeline.write_pipeline_measurement(m)
return m
def test_03_09_flag_image_abort(self):
#
# Regression test of issue #1210
# Make a pipeline that aborts during FlagImage
#
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:3
DateRevision:20140918122611
GitHash:ded6939
ModuleCount:6
HasImagePlaneDetails:False
Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
:
Filter images?:Images only
Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "\x5B\\\\\\\\\\\\\\\\/\x5D\\\\\\\\.")
Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Extract metadata?:No
Metadata data type:Text
Metadata types:{}
Extraction method count:1
Metadata extraction method:Extract from file/folder names
Metadata source:File name
Regular expression:^(?P<Plate>.*)_(?P<Well>\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P<Site>\x5B0-9\x5D)_w(?P<ChannelNumber>\x5B0-9\x5D)
Regular expression:(?P<Date>\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$
Extract metadata from:All images
Select the filtering criteria:and (file does contain "")
Metadata file location:
Match file and image metadata:\x5B\x5D
Use case insensitive matching?:No
NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:5|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Assign a name to:All images
Select the image type:Grayscale image
Name to assign these images:DNA
Match metadata:\x5B\x5D
Image set matching method:Order
Set intensity range from:Image metadata
Assignments count:1
Single images count:0
Select the rule criteria:and (file does contain "")
Name to assign these images:DNA
Name to assign these objects:Cell
Select the image type:Grayscale image
Set intensity range from:Image metadata
Retain outlines of loaded objects?:No
Name the outline image:LoadedOutlines
Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Do you want to group your images?:No
grouping metadata count:1
Metadata category:None
FlagImage:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Hidden:1
Hidden:1
Name the flag\'s category:Metadata
Name the flag:QCFlag
Flag if any, or all, measurement(s) fails to meet the criteria?:Flag if any fail
Skip image set if flagged?:Yes
Flag is based on:Whole-image measurement
Select the object to be used for flagging:None
Which measurement?:Height_DNA
Flag images based on low values?:No
Minimum value:0.0
Flag images based on high values?:Yes
Maximum value:1.0
Rules file location:Elsewhere...\x7C
Rules file name:rules.txt
Class number:
MeasureImageIntensity:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Select the image to measure:DNA
Measure the intensity only from areas enclosed by objects?:No
Select the input objects:None
"""
self.awthread = self.AWThread(self.announce_addr)
self.awthread.start()
self.set_work_socket()
self.awthread.ex(self.awthread.aw.do_job,
cellprofiler.analysis.WorkReply(
image_set_numbers = [1],
worker_runs_post_group = False,
wants_dictionary = True))
#
# The worker should ask for the pipeline and preferences next.
#
req = self.awthread.recv(self.work_socket)
self.assertIsInstance(req, cellprofiler.analysis.PipelinePreferencesRequest)
self.assertEqual(req.analysis_id, self.analysis_id)
input_dir = os.path.join(tests.modules.example_images_directory(), "ExampleSBSImages")
cellprofiler.preferences.set_default_image_directory(input_dir)
preferences = {cellprofiler.preferences.DEFAULT_IMAGE_DIRECTORY:
cellprofiler.preferences.config_read(cellprofiler.preferences.DEFAULT_IMAGE_DIRECTORY)}
rep = cellprofiler.analysis.Reply(
pipeline_blob = numpy.array(data),
preferences = preferences)
req.reply(rep)
#
# The worker asks for the initial measurements.
#
req = self.awthread.recv(self.work_socket)
self.assertIsInstance(req, cellprofiler.analysis.InitialMeasurementsRequest)
self.assertEqual(req.analysis_id, self.analysis_id)
m = get_measurements_for_good_pipeline()
pipeline = cellprofiler.pipeline.Pipeline()
pipeline.loadtxt(cStringIO.StringIO(data))
pipeline.write_pipeline_measurement(m)
try:
req.reply(cellprofiler.analysis.Reply(buf = m.file_contents()))
finally:
m.close()
#
# Next, the worker asks for the shared dictionary
#
req = self.awthread.recv(self.work_socket)
self.assertIsInstance(req, cellprofiler.analysis.SharedDictionaryRequest)
shared_dictionaries = [{ ("foo%d" % i):"bar%d" % i} for i in range(1,7)]
rep = cellprofiler.analysis.SharedDictionaryReply(
dictionaries = shared_dictionaries)
req.reply(rep)
#
# MeasureImageIntensity follows FlagImage and it is poised to ask
# for a display. So if we get that, we know the module has been run
# and we fail the test.
#
req = self.awthread.recv(self.work_socket)
self.assertFalse(isinstance(req, cellprofiler.analysis.DisplayRequest))
self.assertFalse(isinstance(req, cellprofiler.analysis.ExceptionReport))
def test_03_09_flag_image_abort(self):
#
# Regression test of issue #1210
# Make a pipeline that aborts during FlagImage
#
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:3
DateRevision:20140918122611
GitHash:ded6939
ModuleCount:6
HasImagePlaneDetails:False
Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
:
Filter images?:Images only
Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "\x5B\\\\\\\\\\\\\\\\/\x5D\\\\\\\\.")
Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Extract metadata?:No
Metadata data type:Text
Metadata types:{}
Extraction method count:1
Metadata extraction method:Extract from file/folder names
Metadata source:File name
Regular expression:^(?P<Plate>.*)_(?P<Well>\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P<Site>\x5B0-9\x5D)_w(?P<ChannelNumber>\x5B0-9\x5D)
Regular expression:(?P<Date>\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$
Extract metadata from:All images
Select the filtering criteria:and (file does contain "")
Metadata file location:
Match file and image metadata:\x5B\x5D
Use case insensitive matching?:No
NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:5|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Assign a name to:All images
Select the image type:Grayscale image
Name to assign these images:DNA
Match metadata:\x5B\x5D
Image set matching method:Order
Set intensity range from:Image metadata
Assignments count:1
Single images count:0
Select the rule criteria:and (file does contain "")
Name to assign these images:DNA
Name to assign these objects:Cell
Select the image type:Grayscale image
Set intensity range from:Image metadata
Retain outlines of loaded objects?:No
Name the outline image:LoadedOutlines
Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Do you want to group your images?:No
grouping metadata count:1
Metadata category:None
FlagImage:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Hidden:1
Hidden:1
Name the flag\'s category:Metadata
Name the flag:QCFlag
Flag if any, or all, measurement(s) fails to meet the criteria?:Flag if any fail
Skip image set if flagged?:Yes
Flag is based on:Whole-image measurement
Select the object to be used for flagging:None
Which measurement?:Height_DNA
Flag images based on low values?:No
Minimum value:0.0
Flag images based on high values?:Yes
Maximum value:1.0
Rules file location:Elsewhere...\x7C
Rules file name:rules.txt
Class number:
MeasureImageIntensity:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Select the image to measure:DNA
Measure the intensity only from areas enclosed by objects?:No
Select the input objects:None
"""
self.awthread = self.AWThread(self.announce_addr)
self.awthread.start()
self.set_work_socket()
self.awthread.ex(
self.awthread.aw.do_job,
cellprofiler.analysis.WorkReply(image_set_numbers=[1],
worker_runs_post_group=False,
wants_dictionary=True))
#
# The worker should ask for the pipeline and preferences next.
#
req = self.awthread.recv(self.work_socket)
self.assertIsInstance(req,
cellprofiler.analysis.PipelinePreferencesRequest)
self.assertEqual(req.analysis_id, self.analysis_id)
input_dir = os.path.join(tests.modules.example_images_directory(),
"ExampleSBSImages")
cellprofiler.preferences.set_default_image_directory(input_dir)
preferences = {
cellprofiler.preferences.DEFAULT_IMAGE_DIRECTORY:
cellprofiler.preferences.config_read(
cellprofiler.preferences.DEFAULT_IMAGE_DIRECTORY)
}
rep = cellprofiler.analysis.Reply(pipeline_blob=numpy.array(data),
preferences=preferences)
req.reply(rep)
#
# The worker asks for the initial measurements.
#
req = self.awthread.recv(self.work_socket)
self.assertIsInstance(req,
cellprofiler.analysis.InitialMeasurementsRequest)
self.assertEqual(req.analysis_id, self.analysis_id)
m = get_measurements_for_good_pipeline()
pipeline = cellprofiler.pipeline.Pipeline()
pipeline.loadtxt(cStringIO.StringIO(data))
pipeline.write_pipeline_measurement(m)
try:
req.reply(cellprofiler.analysis.Reply(buf=m.file_contents()))
finally:
m.close()
#
# Next, the worker asks for the shared dictionary
#
req = self.awthread.recv(self.work_socket)
self.assertIsInstance(req,
cellprofiler.analysis.SharedDictionaryRequest)
shared_dictionaries = [{
("foo%d" % i): "bar%d" % i
} for i in range(1, 7)]
rep = cellprofiler.analysis.SharedDictionaryReply(
dictionaries=shared_dictionaries)
req.reply(rep)
#
# MeasureImageIntensity follows FlagImage and it is poised to ask
# for a display. So if we get that, we know the module has been run
# and we fail the test.
#
req = self.awthread.recv(self.work_socket)
self.assertFalse(isinstance(req, cellprofiler.analysis.DisplayRequest))
self.assertFalse(isinstance(req,
cellprofiler.analysis.ExceptionReport))
