def test_load_v3():
with open("./tests/resources/modules/measurecolocalization/v3.pipeline",
"r") as fd:
data = fd.read()
fd = six.moves.StringIO(data)
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event, cellprofiler.pipeline.LoadExceptionEvent)
pipeline.add_listener(callback)
pipeline.load(fd)
assert len(pipeline.modules()) == 1
module = pipeline.modules()[-1]
assert (module.images_or_objects.value ==
cellprofiler.modules.measurecolocalization.M_IMAGES_AND_OBJECTS)
assert module.image_count.value == 2
assert module.thr == 25.0
for name in [x.image_name.value for x in module.image_groups]:
assert name in ["DNA", "Cytoplasm"]
assert module.object_count.value == 2
for name in [x.object_name.value for x in module.object_groups]:
assert name in ["Nuclei", "Cells"]
def test_load_v2():
data = """CellProfiler Pipeline: http://www.cellprofiler.org
Version:1
SVNRevision:10479
IdentifyDeadWorms:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D]
Input image:BinaryWorms
Objects name:DeadWorms
Worm width:6
Worm length:114
Number of angles:180
Automatically calculate distance parameters?:No
Spatial distance:6
Angular distance:45
"""
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event, cellprofiler.pipeline.LoadExceptionEvent)
pipeline.load(six.moves.StringIO(data))
assert len(pipeline.modules()) == 1
module = pipeline.modules()[0]
assert isinstance(module,
cellprofiler.modules.identifydeadworms.IdentifyDeadWorms)
assert module.image_name == "BinaryWorms"
assert module.object_name == "DeadWorms"
assert module.worm_width == 6
assert module.worm_length == 114
assert module.angle_count == 180
assert not module.wants_automatic_distance
assert module.space_distance == 6
assert module.angular_distance == 45
def write_schema(pipeline_filename):
if pipeline_filename is None:
raise ValueError(
"The --write-schema-and-exit switch must be used in conjunction\nwith the -p or --pipeline switch to load a pipeline with an\n"
"ExportToDatabase module.")
pipeline = cellprofiler.pipeline.Pipeline()
pipeline.load(pipeline_filename)
pipeline.turn_off_batch_mode()
for module in pipeline.modules():
if module.module_name == "ExportToDatabase":
break
else:
raise ValueError(
"The pipeline, \"%s\", does not have an ExportToDatabase module" %
pipeline_filename)
m = cellprofiler.measurement.Measurements()
workspace = cellprofiler.workspace.Workspace(pipeline, module, m,
cellprofiler.object.ObjectSet,
m, None)
module.prepare_run(workspace)
def test_load_v3():
with open("./tests/resources/modules/align/v3.pipeline", "r") as fd:
data = fd.read()
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event, cellprofiler.pipeline.LoadExceptionEvent)
pipeline.add_listener(callback)
pipeline.load(six.moves.StringIO(data))
assert len(pipeline.modules()) == 3
for module, crop_method in zip(
pipeline.modules(),
(
cellprofiler.modules.align.C_SAME_SIZE,
cellprofiler.modules.align.C_CROP,
cellprofiler.modules.align.C_PAD,
),
):
assert isinstance(module, cellprofiler.modules.align.Align)
assert (module.alignment_method ==
cellprofiler.modules.align.M_MUTUAL_INFORMATION)
assert module.crop_mode == crop_method
assert module.first_input_image == "Image1"
assert module.second_input_image == "Image2"
assert module.first_output_image, "AlignedImage1"
assert module.second_output_image, "AlignedImage2"
def test_load_v1():
with open("./tests/resources/modules/labelimages/v1.pipeline", "r") as fd:
data = fd.read()
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event, cellprofiler.pipeline.LoadExceptionEvent)
pipeline.add_listener(callback)
pipeline.load(six.moves.StringIO(data))
assert len(pipeline.modules()) == 2
module = pipeline.modules()[0]
assert isinstance(module, cellprofiler.modules.labelimages.LabelImages)
assert module.site_count == 3
assert module.row_count == 32
assert module.column_count == 48
assert module.order == cellprofiler.modules.labelimages.O_COLUMN
module = pipeline.modules()[1]
assert isinstance(module, cellprofiler.modules.labelimages.LabelImages)
assert module.site_count == 1
assert module.row_count == 8
assert module.column_count == 12
assert module.order == cellprofiler.modules.labelimages.O_ROW
def set_file_chooser_path(self, path):
file_names = []
for file_name in os.listdir(path):
ext = os.path.splitext(file_name)[1].lower()
if len(ext) > 0 and ext[1:] in cellprofiler.preferences.EXT_PIPELINE_CHOICES:
file_names.append(file_name)
self.file_chooser.DeleteAllItems()
module_count = [None]
def on_pipeline_event(caller, event):
if isinstance(event, cellprofiler.pipeline.LoadExceptionEvent):
module_count[0] = None
elif isinstance(event, cellprofiler.pipeline.PipelineLoadedEvent):
module_count[0] = len(caller.modules())
pipeline = cellprofiler.pipeline.Pipeline()
pipeline.add_listener(on_pipeline_event)
for file_name in file_names:
file_path = os.path.join(path, file_name)
mtime = os.path.getmtime(file_path)
mtime = datetime.datetime.fromtimestamp(mtime)
mtime = mtime.ctime()
pipeline.load(file_path)
if module_count[0] is not None:
index = self.file_chooser.InsertStringItem(
sys.maxint, file_name)
self.file_chooser.SetStringItem(index, FC_DATE_COLUMN, mtime)
self.file_chooser.SetStringItem(index, FC_MODULE_COUNT_COLUMN,
str(module_count[0]))
self.file_chooser.SetColumnWidth(FC_FILENAME_COLUMN, wx.LIST_AUTOSIZE)
self.file_chooser.SetColumnWidth(FC_DATE_COLUMN, wx.LIST_AUTOSIZE)
def set_file_chooser_path(self, path):
file_names = []
for file_name in os.listdir(path):
ext = os.path.splitext(file_name)[1].lower()
if len(ext) > 0 and ext[
1:] in cellprofiler.preferences.EXT_PIPELINE_CHOICES:
file_names.append(file_name)
self.file_chooser.DeleteAllItems()
module_count = [None]
def on_pipeline_event(caller, event):
if isinstance(event, cellprofiler.pipeline.LoadExceptionEvent):
module_count[0] = None
elif isinstance(event, cellprofiler.pipeline.PipelineLoadedEvent):
module_count[0] = len(caller.modules())
pipeline = cellprofiler.pipeline.Pipeline()
pipeline.add_listener(on_pipeline_event)
for file_name in file_names:
file_path = os.path.join(path, file_name)
mtime = os.path.getmtime(file_path)
mtime = datetime.datetime.fromtimestamp(mtime)
mtime = mtime.ctime()
pipeline.load(file_path)
if module_count[0] is not None:
index = self.file_chooser.InsertStringItem(
sys.maxint, file_name)
self.file_chooser.SetStringItem(index, FC_DATE_COLUMN, mtime)
self.file_chooser.SetStringItem(index, FC_MODULE_COUNT_COLUMN,
str(module_count[0]))
self.file_chooser.SetColumnWidth(FC_FILENAME_COLUMN, wx.LIST_AUTOSIZE)
self.file_chooser.SetColumnWidth(FC_DATE_COLUMN, wx.LIST_AUTOSIZE)
def test_01_03_load_v3(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:1
SVNRevision:9524
LoadSingleImage:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D]
Folder containing the image file:Default Input Folder\x7CNone
Filename of the image to load (Include the extension, e.g., .tif):foo.tif
Name the image that will be loaded:DNA
Filename of the image to load (Include the extension, e.g., .tif):bar.tif
Name the image that will be loaded:Cytoplasm
"""
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
self.assertFalse(
isinstance(event, cellprofiler.pipeline.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO(data))
self.assertEqual(len(pipeline.modules()), 1)
module = pipeline.modules()[0]
self.assertTrue(
isinstance(module,
cellprofiler.modules.loadsingleimage.LoadSingleImage))
self.assertEqual(len(module.file_settings), 2)
fs = module.file_settings[0]
self.assertEqual(fs.file_name, "foo.tif")
self.assertEqual(fs.image_name, "DNA")
fs = module.file_settings[1]
self.assertEqual(fs.file_name, "bar.tif")
self.assertEqual(fs.image_name, "Cytoplasm")
self.assertTrue(fs.rescale)
def test_load_v6():
with open("./tests/resources/modules/loaddata/v6.pipeline", "r") as fd:
data = fd.read()
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event, cellprofiler.pipeline.LoadExceptionEvent)
pipeline.add_listener(callback)
pipeline.load(io.StringIO(data))
assert len(pipeline.modules()) == 1
module = pipeline.modules()[0]
assert isinstance(module, cellprofiler.modules.loaddata.LoadData)
assert module.csv_file_name == "1049_Metadata.csv"
assert module.csv_directory.dir_choice == cellprofiler.setting.ABSOLUTE_FOLDER_NAME
assert (module.csv_directory.custom_path ==
r"x:\projects\NightlyBuild\trunk\ExampleImages\ExampleSBSImages")
assert module.wants_images
assert (module.image_directory.dir_choice ==
cellprofiler.setting.DEFAULT_INPUT_FOLDER_NAME)
assert module.rescale
assert module.wants_image_groupings
assert not module.wants_rows
assert module.row_range.min == 1
assert module.row_range.max == 100000
assert len(module.metadata_fields.selections) == 2
assert module.metadata_fields.selections[0] == "Column"
assert module.metadata_fields.selections[1] == "Row"
def test_load_v4():
with open("./tests/resources/modules/loaddata/v4.pipeline", "r") as fd:
data = fd.read()
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event, cellprofiler.pipeline.LoadExceptionEvent)
pipeline.add_listener(callback)
pipeline.load(io.StringIO(data))
assert len(pipeline.modules()) == 1
module = pipeline.modules()[0]
assert isinstance(module, cellprofiler.modules.loaddata.LoadData)
assert module.csv_file_name == "1049_Metadata.csv"
assert (module.csv_directory.dir_choice ==
cellprofiler.setting.DEFAULT_INPUT_FOLDER_NAME)
assert module.wants_images
assert module.rescale
assert not module.wants_image_groupings
assert not module.wants_rows
assert module.row_range.min == 10
assert module.row_range.max == 36
assert len(module.metadata_fields.selections) == 1
assert module.metadata_fields.selections[0] == "Well"
def test_load_v1():
data = ('eJztWNFO2zAUdUqBsUkr28v26Ee60aotQ4NqKu0oEtUIVLRiQohtpnXba'
'EkcOQlrNyHtcZ+0x33OHvcJs4NDUhMIbccmpqay2nt9zz3Xx0lsV600dy'
'qv4Wo2B9VKM9PRdAzrOnI6hBpFaDrLcJNi5OA2JGYRqsSEKhrAfB7mV4o'
'rheLqOizkcutgvEupqQ/Z19pjAObY9z3WEqJrVthKqHG7gR1HM7v2LEiC'
'p8L/g7UDRDV0ouMDpLvYDih8f83skObAuuhSSdvV8S4ywsHs2nWNE0ztv'
'Y4PFN11rY/1hvYZS0Pww/bxqWZrxBR4kV/2XvASR+LlOnyfD3RQJB24Lq'
'mQn8dvgyA+GaHbo1D8orA1s62dam0X6VAzUPeiCm8eYvLNS/m4rQ5qPI2'
'HL8fgFyU8b03cdzJbfdRyoIGcVu8meVJSnpRXx5bZQ2YLt4N6cjF5lKE8'
'CliZQAfBfiMd7kt4blcJNIkDXRsH8xFXf2IoTwLkX46H2yWXcXMSzr983'
'AII6oy7D59I4+V2FXeQqzvQmy1Y1ShuOYQOJqrjX+Kixj0zNO4ZcMietr'
'vK9zdwk75/bktX+T2R/4P6RPElh/iS7Pk08SR8X2P43oBhXbn9bmmj/op'
'vBHAp+zz9nltvsa7vk0+lo0qmfpz2PZtEdw2zdJTLrB9/yS8Xzs6DGxpD'
'es505LhHqb8XU/+aVD+3eQ2HGFFR2IuzdIa72AbG6QlfQfiqaBB4Jqnz5'
'9xo6/e4POUYPaLWF2+x71LiWrfPH7XOB/yQbUGwdZfeS1PcFPc/4soh3P'
'Q5nuJGxS0qV6938jmDx38A199vz8Dw/cbtFttiWJTw/yVo1vAOz3ZWJ6h'
'9fnrN7rCftdBBlvP0Y3i2JZ7tq3jw+aGOUNu1LIpt27Zw6yPvEce9PdoQ'
'PQ3RI+u5EMEf1iXBPqnk9fMg6x/My6+NcfgSymW+BzG4pFCS476B0eZ96'
'Zp4f2zjxv8G/FcCeg==')
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event, cellprofiler.pipeline.LoadExceptionEvent)
pipeline.add_listener(callback)
pipeline.load(StringIO.StringIO(zlib.decompress(base64.b64decode(data))))
assert len(pipeline.modules()) == 2
module = pipeline.modules()[1]
assert module.module_name == 'EnhanceOrSuppressFeatures'
assert module.x_name.value == 'MyImage'
assert module.y_name.value == 'MyEnhancedImage'
assert module.method.value == cellprofiler.modules.enhanceorsuppressfeatures.ENHANCE
assert module.object_size == 17
def test_01_04_load_v4(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:1
SVNRevision:9524
LoadSingleImage:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D]
Folder containing the image file:Default Input Folder\x7CNone
Filename of the image to load (Include the extension, e.g., .tif):foo.tif
Name the image that will be loaded:DNA
Rescale image?:No
Filename of the image to load (Include the extension, e.g., .tif):bar.tif
Name the image that will be loaded:Cytoplasm
Rescale image?:Yes
"""
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cellprofiler.pipeline.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO(data))
self.assertEqual(len(pipeline.modules()), 1)
module = pipeline.modules()[0]
self.assertTrue(isinstance(module, cellprofiler.modules.loadsingleimage.LoadSingleImage))
self.assertEqual(len(module.file_settings), 2)
fs = module.file_settings[0]
self.assertEqual(fs.file_name, "foo.tif")
self.assertEqual(fs.image_objects_choice, cellprofiler.modules.loadsingleimage.IO_IMAGES)
self.assertEqual(fs.image_name, "DNA")
self.assertFalse(fs.rescale)
fs = module.file_settings[1]
self.assertEqual(fs.file_name, "bar.tif")
self.assertEqual(fs.image_name, "Cytoplasm")
self.assertTrue(fs.rescale)
def test_01_02_load_v1(self):
data = ('eJztWF9v0zAQd7vuH0ioiAd4tPayFdYo7TZpq9C2siJRWEu1VWPTNMBdndZS'
'EleJM1bQJB75WHykfQTsLmkSE5a03RBITRWld7nf/e7OzsVOrdzcL7+CG4oK'
'a+VmXiM6hg0dMY1aRgmabBXuWRgx3IbULMFm14FvHR0W12Bho1TcLBXWYVFV'
't8B4R6pae8Qv8AkAc/y6wM+0e2vWlVOBU8iHmDFiduxZkAHPXP1Pfh4hi6CW'
'jo+Q7mDbp/D0VVOjzX5veKtG246O68gIGvOj7hgtbNnvNQ/o3m6QS6wfkq9Y'
'SsEzO8AXxCbUdPGuf1k75KVM4hV1aCz4dUhJdRB1yQb0wv4N8O0zEXV7HLDP'
'ujIx2+SCtB2kQ2KgzjAK4U+N8TcT8jcDKvXyALcbg8uCcBzibOJLln99ic4Z'
'NBA77ybx80DyI+QDbPNBaYtQEueRCvlJgTUXdxyDW5b4l4f8sNWHCGo8G2pB'
'qkHWxZBapENMXmdhkKhOUflVKDQpg46Nk+eXCfnJAFUpbiTBpUO4NKjT8eZF'
'QVUTzc+nUr5CrmANOTqDVTE5YYVYWNS0P1EcQdychPMOD7cI/DrvxvBFzes6'
'Rha2Gaxj0um2qDVO3Cf8qZwk7n+NL8m8GoVvM4ZvDoTHRch7OjVFs/ubdfX6'
'433xyX2sEIG7Sz65r9R5SSfh+x7D9w6Ex1HIH1d2Gi/FAgVvKy9yn4T0Aev6'
'Af2yfVrON85ynmaP6o5hbp+q+a2zb4XV4tWN8SHhyIEyF5n3KPF3Y+LflOIX'
'sojhhPcIN7D1q1xeqGrUZF1XV3R1FdT3NZPEeTw/2rrivvpl1PttsAjpWNTp'
'3T9/VL/2+SFfGuHeXT0/U9wUN8X9H7jdAG7aN6a4UXHXAZz8fpXXv8L+M7h9'
'vj0H4fkm5HO+pOlZVHyfsRRj8BHBVnSK2je7eGWf/60GNvRJ9rNLEs/Sn3is'
'wSZXudnrRtdrMcJ/MO80/2Xnb6+zXF+/7tc74/DNpH7nexiDy7iVErgfYLRx'
'XbnF3sttXPtf+3UFIg==')
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cellprofiler.pipeline.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO.StringIO(zlib.decompress(base64.b64decode(data))))
self.assertEqual(len(pipeline.modules()), 2)
module = pipeline.modules()[1]
self.assertEqual(module.x_name, 'DNA')
self.assertEqual(module.y_name, 'ResizedDNA')
self.assertEqual(module.size_method, cellprofiler.modules.resize.R_BY_FACTOR)
self.assertAlmostEqual(module.resizing_factor.value, .25)
self.assertEqual(module.interpolation, cellprofiler.modules.resize.I_NEAREST_NEIGHBOR)
def test_01_02_load_v2(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:3
DateRevision:20120209212234
ModuleCount:1
HasImagePlaneDetails:False
Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)]
:{"ShowFiltered"\x3A false}
Filter choice:%s
Filter:or (directory does startwith "foo") (file does contain "bar")
"""
for fc, fctext in ((cellprofiler.modules.images.FILTER_CHOICE_CUSTOM, "Custom"),
(cellprofiler.modules.images.FILTER_CHOICE_IMAGES, "Images only"),
(cellprofiler.modules.images.FILTER_CHOICE_NONE, "No filtering")):
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cellprofiler.pipeline.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(cStringIO.StringIO(data % fctext))
self.assertEqual(len(pipeline.modules()), 1)
module = pipeline.modules()[0]
self.assertTrue(isinstance(module, cellprofiler.modules.images.Images))
self.assertEqual(module.filter_choice, fc)
self.assertEqual(module.filter.value, 'or (directory does startwith "foo") (file does contain "bar")')
def test_01_02_load_v2(self):
data = ('eJztVd1KwzAUTuv82QTxTi9zJV5oycTf3eimiAM3xQ3RK4lbOgJpM9J0bD6B'
'j+Jj+Cg+go9gMtKtrWOdeiPogZCc5PvOyfk4bWrl5mW5AvccBGvl5rZLGYHX'
'DEuXC68EfbkFTwXBkrQh90vwXFBYDjsQHcDifqmISmgX7iB0BL5nVrW2oqa3'
'RQAW1Lykhm2O5o1vxYb2G0RK6neCeZAD62b/VY1bLCh+ZOQWs5AE4xTRftV3'
'eXPQHR3VeDtkpI69OFhZPfQeiQiu3Ihojq9pn7AGfSKpEiLYDenRgHLf8E38'
'9O4oL5epvFqHF3usgzVBh0JsX+MvwBifm4Bfi+FXjX9GXBwyCase7hB4RgVp'
'SS4Gw3goI56ViGcBx9zjMIO3BJL30H4R7R45raA3S965BH8O3CvtflP9WTw7'
'wbNBnf9At60i0vYT3U4yePlUXu03Ko2HNg9GXavj3H2xX+P3XUjhI4vw+X/e'
'n+c9g+n9Ff8eh/0Ipvf1Bkj2tfZbhLGu4PrdE443/DkHDuO4LUlfOpdq0VSL'
'z3XkJ8SP38dWq0JG/em6x3q8H38nnz0h33IGL2de3LR+s+i9OQUPUvgPhsiG'
'ig==')
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cellprofiler.pipeline.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO.StringIO(zlib.decompress(base64.b64decode(data))))
self.assertEqual(len(pipeline.modules()), 1)
module = pipeline.modules()[0]
self.assertTrue(isinstance(module, cellprofiler.modules.loaddata.LoadText))
self.assertEqual(module.csv_file_name, "1049.csv")
self.assertTrue(module.wants_images.value)
self.assertTrue(module.wants_image_groupings.value)
self.assertEqual(len(module.metadata_fields.selections), 1)
self.assertEqual(module.metadata_fields.selections[0], "SBS_doses")
self.assertEqual(module.image_directory.dir_choice,
cellprofiler.setting.DEFAULT_INPUT_FOLDER_NAME)
self.assertFalse(module.wants_rows.value)
def test_01_04_load_v4(self):
data = """CellProfiler Pipeline: http://www.cellprofiler.org
Version:3
DateRevision:20141015195823
GitHash:051040e
ModuleCount:1
HasImagePlaneDetails:False
MeasureImageOverlap:[module_num:1|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]
Compare segmented objects, or foreground/background?:Foreground/background segmentation
Select the image to be used as the ground truth basis for calculating the amount of overlap:Foo
Select the image to be used to test for overlap:Bar
Select the objects to be used as the ground truth basis for calculating the amount of overlap:Cell2_0
Select the objects to be tested for overlap against the ground truth:Cell2_1
Calculate earth mover\'s distance?:Yes
Maximum # of points:101
Point selection method:Skeleton
Maximum distance:102
Penalize missing pixels:Yes
"""
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cellprofiler.pipeline.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO.StringIO(data))
module = pipeline.modules()[0]
self.assertTrue(isinstance(module, cellprofiler.modules.measureimageoverlap.MeasureImageOverlap))
self.assertEqual(module.ground_truth, "Foo")
self.assertEqual(module.test_img, "Bar")
def test_01_03_load_v3(self):
data = ('eJztVUtPg0AQXihtfCTGgwePezIelICv2F5MqzE2kWqE+Dhu28WSLGwDS338'
'An+CP9Of4FKX8pAUbT2Y6AQyO7PzzTczLGA0rfNmC+6rGjSa1rbtEAwvCWI2'
'9d0G9NgWPPYxYrgPqdeAN1yf4B7UD/jV0Hcbe/twR9PqYDaR2sYKV68LANS4'
'5grIYqsqbCl1R7aJGXO8+6AKFLAu/G/8vka+g7oEXyMS4iChiP1tz6bW03Cy'
'ZdB+SHAHuelgLp3Q7WI/uLBjoNi+dB4xMZ1nnGshDrvCIydwqCfwIn/eO+Gl'
'LMdrDujDqc/LyeVvIdYbmIw/gax/PDc5mZtUMLellD+KPwNJvFIQv5aKXxX2'
'CbZRSBhse8OQwVNK+tiP82kl+aRMPgmoAndYglsA2ToiW9f26movGH2Ft5LB'
'V8Adn/Vv6r8MJ2dwMujQOea2pWuRzMObxtVyuFhi3KLQEe72m+dzVp5/3N/E'
'vYDp5yv9/o3PI5h+/jdA9v2J7B4mZOjT6L/oq+744x2ohKI+w49MPecLiy8+'
'97FYkD9dj8xXSyX95/tO5vF2NAtfpYBvuQSniD9yfn5fmffmlHhQEP/dfqQf'
'qCvhUSY1feT/kHfzN4ip')
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cellprofiler.pipeline.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO.StringIO(zlib.decompress(base64.b64decode(data))))
self.assertEqual(len(pipeline.modules()), 1)
module = pipeline.modules()[0]
self.assertTrue(isinstance(module, cellprofiler.modules.loaddata.LoadText))
self.assertEqual(module.csv_file_name, "1049.csv")
self.assertTrue(module.wants_images.value)
self.assertFalse(module.wants_image_groupings.value)
def test_01_03_load_v3(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:3
DateRevision:20131210175632
GitHash:63ec479
ModuleCount:1
HasImagePlaneDetails:False
MeasureImageOverlap:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True]
Compare segmented objects, or foreground/background?:Foreground/background segmentation
Select the image to be used as the ground truth basis for calculating the amount of overlap:Foo
Select the image to be used to test for overlap:Bar
Select the objects to be used as the ground truth basis for calculating the amount of overlap:Cell2_0
Select the objects to be tested for overlap against the ground truth:Cell2_1
"""
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
self.assertFalse(
isinstance(event, cellprofiler.pipeline.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO.StringIO(data))
module = pipeline.modules()[0]
self.assertTrue(
isinstance(
module,
cellprofiler.modules.measureimageoverlap.MeasureImageOverlap))
self.assertEqual(module.ground_truth, "Foo")
self.assertEqual(module.test_img, "Bar")
# self.assertEqual(module.object_name_GT, "Cell2_0")
# self.assertEqual(module.object_name_ID, "Cell2_1")
self.assertFalse(module.wants_emd)
def test_load_v5():
data = r'''CellProfiler Pipeline: http://www.cellprofiler.org
Version:3
DateRevision:20150414135713
GitHash:3bad577
ModuleCount:2
HasImagePlaneDetails:False
EnhanceOrSuppressFeatures:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:5|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Select the input image:Dendrite
Name the output image:EnhancedDendrite
Select the operation:Enhance
Feature size:10
Feature type:Neurites
Range of hole sizes:1,10
Smoothing scale:2.0
Shear angle:0
Decay:0.95
Enhancement method:Tubeness
Speed and accuracy:Slow / circular
EnhanceOrSuppressFeatures:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:5|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Select the input image:Axon
Name the output image:EnhancedAxon
Select the operation:Enhance
Feature size:10
Feature type:Neurites
Range of hole sizes:1,10
Smoothing scale:2.0
Shear angle:0
Decay:0.95
Enhancement method:Line structures
Speed and accuracy:Fast / hexagonal
'''
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event, cellprofiler.pipeline.LoadExceptionEvent)
pipeline.add_listener(callback)
pipeline.load(StringIO.StringIO(data))
assert len(pipeline.modules()) == 2
module = pipeline.modules()[0]
assert isinstance(module, cellprofiler.modules.enhanceorsuppressfeatures.EnhanceOrSuppressFeatures)
assert module.x_name == "Dendrite"
assert module.y_name == "EnhancedDendrite"
assert module.method == cellprofiler.modules.enhanceorsuppressfeatures.ENHANCE
assert module.enhance_method == cellprofiler.modules.enhanceorsuppressfeatures.E_NEURITES
assert module.smoothing == 2.0
assert module.object_size == 10
assert module.hole_size.min == 1
assert module.hole_size.max == 10
assert module.angle == 0
assert module.decay == .95
assert module.neurite_choice == cellprofiler.modules.enhanceorsuppressfeatures.N_TUBENESS
assert module.speckle_accuracy == cellprofiler.modules.enhanceorsuppressfeatures.S_SLOW
module = pipeline.modules()[1]
assert isinstance(module, cellprofiler.modules.enhanceorsuppressfeatures.EnhanceOrSuppressFeatures)
assert module.speckle_accuracy == cellprofiler.modules.enhanceorsuppressfeatures.S_FAST
def test_load_v1():
with open("./tests/resources/modules/straightenworms/v1.pipeline",
"r") as fd:
data = fd.read()
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event, cellprofiler.pipeline.LoadExceptionEvent)
pipeline.add_listener(callback)
pipeline.load(six.moves.StringIO(data))
assert len(pipeline.modules()) == 1
module = pipeline.modules()[0]
assert isinstance(module,
cellprofiler.modules.straightenworms.StraightenWorms)
assert module.objects_name == "OverlappingWorms"
assert module.straightened_objects_name == "StraightenedWorms"
assert module.width == 20
assert (module.training_set_directory.dir_choice ==
cellprofiler.setting.DEFAULT_OUTPUT_FOLDER_NAME)
assert module.training_set_file_name == "TrainingSet.xml"
assert module.image_count.value == 2
for group, input_name, output_name in (
(module.images[0], "Brightfield", "StraightenedBrightfield"),
(module.images[1], "Fluorescence", "StraightenedFluorescence"),
):
assert group.image_name == input_name
assert group.straightened_image_name == output_name
def test_01_01_load_v1(self):
data = ('eJztV01v2jAYdvgabBPith59mnrootANqeWyMtAEU6EVRdV2qlwwzJITR46'
'DYL9gP2nHHfdz9hNm06QkHiUQsXXSsGQlr/0+z/tlJ3a3MThvvIM104Ldxu'
'DVmFAMLykSY8btOnTEEWxyjAQeQebU4eCzDz/4DqyewGqt/vq4btXgsWWdg'
'nTN6HTL8nFYAaAgn0XZM8FUPpCNSFfyFRaCOBMvD3LgIBj/Lvs14gTdUnyN'
'qI+9pYlwvOOM2WDu3k912cinuIfsqLJsPd++xdy7GIfAYPqSzDC9Il+wFkK'
'o1sdT4hHmBPiAXx+9t8uEZlfl4aCwzIOh5SGrjSv9Nljq51bk7UVEvxLILT'
'xGPhWwY6MJhi3C8VAwPl/wWQl82RhfFrR6jQXuLAFX0vxQcnMumEuRZ0fiS'
'bJvxHgMYAa4kwRcEcTtK/m9rOdIBrCJ/2UNXw7xkSDS+lG13pyaQ2+6Cf6J'
'hldyk3G+aR1WxdG86PdvtDi2XQef5FrexP+CZl/JLTIlI7xZ/pLwu94PSfl'
'8rvEpuccgx94QUfmRAkGOd8WTdn9EcQUNF7YQVwqeu8Kt8jMT8zMjY30cP1'
'Pt16Oqpdpj+Psjv93/Ia2ds4S8PNXyomSi9s+EM9/dPc+fiqOi2a/E7EPij'
'LC7S57/vR573B63xy1xX42Hv+f6OUvpfwTr9+9LEN+/Sh5iSl3O1L2Om/bi'
'8uGZlKGRwDNhnsuXgXy5458l8Lc1/vZD/EN5KJXnKEKpbxMHCXkDukGuS+d'
'm826mE51pqJl/wX60jqUV9qP1yEipmM+vrb9e9+V6+Pk2jT3DMH47tzxLwO'
'UiPqmm8N/AduvucI1+GOPf0v8FsdkNYQ==')
fd = StringIO.StringIO(zlib.decompress(base64.b64decode(data)))
pipeline = cellprofiler.pipeline.Pipeline()
pipeline.load(fd)
self.assertEqual(len(pipeline.modules()), 3)
module = pipeline.modules()[0]
self.assertTrue(isinstance(module, cellprofiler.modules.loaddata.LoadText))
self.assertEqual(module.csv_directory.dir_choice,
cellprofiler.setting.DEFAULT_INPUT_FOLDER_NAME)
self.assertEqual(module.csv_file_name, "1049.csv")
self.assertTrue(module.wants_images.value)
self.assertFalse(module.wants_image_groupings.value)
self.assertEqual(module.image_directory.dir_choice,
cellprofiler.setting.DEFAULT_INPUT_FOLDER_NAME)
self.assertFalse(module.wants_rows.value)
def test_04_02_load_saturation_blur(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:1
SVNRevision:8925
FromMatlab:True
MeasureImageSaturationBlur:[module_num:1|svn_version:\'8913\'|variable_revision_number:4|show_window:False|notes:\x5B\x5D]
What did you call the image you want to check for saturation?:Image1
What did you call the image you want to check for saturation?:Image2
What did you call the image you want to check for saturation?:Image3
What did you call the image you want to check for saturation?:Do not use
What did you call the image you want to check for saturation?:Do not use
What did you call the image you want to check for saturation?:Do not use
Do you want to also check the above images for image quality (called blur earlier)?:Yes
If you chose to check images for image quality above, enter the window size of LocalFocusScore measurement (A suggested value is 2 times ObjectSize)?:25
"""
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cellprofiler.pipeline.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO.StringIO(data))
self.assertEqual(len(pipeline.modules()), 1)
module = pipeline.modules()[0]
self.assertTrue(isinstance(module, M.MeasureImageQuality))
self.assertEqual(len(module.image_groups), 3)
for i in range(3):
group = module.image_groups[i]
self.assertEqual(group.image_names, "Image%d" % (i + 1))
self.assertTrue(group.check_blur)
self.assertEqual(group.scale_groups[0].scale, 25)
self.assertTrue(group.check_saturation)
self.assertFalse(group.calculate_threshold)
def test_01_02_load_v1(self):
data = ('eJztWF9v0zAQd7vuH0ioiAd4tPayFdYo7TZpq9C2siJRWEu1VWPTNMBdndZS'
'EleJM1bQJB75WHykfQTsLmkSE5a03RBITRWld7nf/e7OzsVOrdzcL7+CG4oK'
'a+VmXiM6hg0dMY1aRgmabBXuWRgx3IbULMFm14FvHR0W12Bho1TcLBXWYVFV'
't8B4R6pae8Qv8AkAc/y6wM+0e2vWlVOBU8iHmDFiduxZkAHPXP1Pfh4hi6CW'
'jo+Q7mDbp/D0VVOjzX5veKtG246O68gIGvOj7hgtbNnvNQ/o3m6QS6wfkq9Y'
'SsEzO8AXxCbUdPGuf1k75KVM4hV1aCz4dUhJdRB1yQb0wv4N8O0zEXV7HLDP'
'ujIx2+SCtB2kQ2KgzjAK4U+N8TcT8jcDKvXyALcbg8uCcBzibOJLln99ic4Z'
'NBA77ybx80DyI+QDbPNBaYtQEueRCvlJgTUXdxyDW5b4l4f8sNWHCGo8G2pB'
'qkHWxZBapENMXmdhkKhOUflVKDQpg46Nk+eXCfnJAFUpbiTBpUO4NKjT8eZF'
'QVUTzc+nUr5CrmANOTqDVTE5YYVYWNS0P1EcQdychPMOD7cI/DrvxvBFzes6'
'Rha2Gaxj0um2qDVO3Cf8qZwk7n+NL8m8GoVvM4ZvDoTHRch7OjVFs/ubdfX6'
'433xyX2sEIG7Sz65r9R5SSfh+x7D9w6Ex1HIH1d2Gi/FAgVvKy9yn4T0Aev6'
'Af2yfVrON85ynmaP6o5hbp+q+a2zb4XV4tWN8SHhyIEyF5n3KPF3Y+LflOIX'
'sojhhPcIN7D1q1xeqGrUZF1XV3R1FdT3NZPEeTw/2rrivvpl1PttsAjpWNTp'
'3T9/VL/2+SFfGuHeXT0/U9wUN8X9H7jdAG7aN6a4UXHXAZz8fpXXv8L+M7h9'
'vj0H4fkm5HO+pOlZVHyfsRRj8BHBVnSK2je7eGWf/60GNvRJ9rNLEs/Sn3is'
'wSZXudnrRtdrMcJ/MO80/2Xnb6+zXF+/7tc74/DNpH7nexiDy7iVErgfYLRx'
'XbnF3sttXPtf+3UFIg==')
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cellprofiler.pipeline.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO.StringIO(zlib.decompress(base64.b64decode(data))))
self.assertEqual(len(pipeline.modules()), 2)
module = pipeline.modules()[1]
self.assertEqual(module.x_name, 'DNA')
self.assertEqual(module.y_name, 'ResizedDNA')
self.assertEqual(module.size_method, cellprofiler.modules.resize.R_BY_FACTOR)
self.assertAlmostEqual(module.resizing_factor.value, .25)
self.assertEqual(module.interpolation, cellprofiler.modules.resize.I_NEAREST_NEIGHBOR)
def test_load_v4():
data = """CellProfiler Pipeline: http://www.cellprofiler.org
Version:3
DateRevision:20141015195823
GitHash:051040e
ModuleCount:1
HasImagePlaneDetails:False
MeasureImageOverlap:[module_num:1|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]
Compare segmented objects, or foreground/background?:Foreground/background segmentation
Select the image to be used as the ground truth basis for calculating the amount of overlap:Foo
Select the image to be used to test for overlap:Bar
Select the objects to be used as the ground truth basis for calculating the amount of overlap:Cell2_0
Select the objects to be tested for overlap against the ground truth:Cell2_1
Calculate earth mover\'s distance?:Yes
Maximum # of points:101
Point selection method:Skeleton
Maximum distance:102
Penalize missing pixels:Yes
"""
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event, cellprofiler.pipeline.LoadExceptionEvent)
pipeline.add_listener(callback)
pipeline.load(io.StringIO(data))
module = pipeline.modules()[0]
assert isinstance(
module, cellprofiler.modules.measureimageoverlap.MeasureImageOverlap)
assert module.ground_truth == "Foo"
assert module.test_img == "Bar"
def test_load_v4(self):
with open("./tests/resources/modules/loadsingleimage/v4.pipeline",
"r") as fd:
data = fd.read()
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event,
cellprofiler.pipeline.LoadExceptionEvent)
pipeline.add_listener(callback)
pipeline.load(six.moves.StringIO(data))
assert len(pipeline.modules()) == 1
module = pipeline.modules()[0]
assert isinstance(module,
cellprofiler.modules.loadsingleimage.LoadSingleImage)
assert len(module.file_settings) == 2
fs = module.file_settings[0]
assert fs.file_name == "foo.tif"
assert fs.image_objects_choice == cellprofiler.modules.loadsingleimage.IO_IMAGES
assert fs.image_name == "DNA"
assert not fs.rescale
fs = module.file_settings[1]
assert fs.file_name == "bar.tif"
assert fs.image_name == "Cytoplasm"
assert fs.rescale
def test_load_v5():
with open("./tests/resources/modules/displaydataonimage/v5.pipeline",
"r") as fd:
data = fd.read()
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event, cellprofiler.pipeline.LoadExceptionEvent)
pipeline.add_listener(callback)
pipeline.load(six.moves.StringIO(data))
assert len(pipeline.modules()) == 1
module = pipeline.modules()[0]
assert isinstance(
module, cellprofiler.modules.displaydataonimage.DisplayDataOnImage)
assert module.objects_or_image == cellprofiler.modules.displaydataonimage.OI_OBJECTS
assert module.measurement == "AreaShape_Zernike_0_0"
assert module.image_name == "DNA"
assert module.text_color == "green"
assert module.objects_name == "Nuclei"
assert module.display_image == "Zernike"
assert module.font_size == 10
assert module.decimals == 2
assert module.saved_image_contents == cellprofiler.modules.displaydataonimage.E_AXES
assert module.offset == 5
assert module.color_or_text == cellprofiler.modules.displaydataonimage.CT_COLOR
assert module.colormap == "jet"
assert not module.wants_image
assert (module.color_map_scale_choice ==
cellprofiler.modules.displaydataonimage.CMS_USE_MEASUREMENT_RANGE)
assert module.color_map_scale.min == 0
assert module.color_map_scale.max == 1
def test_load_v2():
with open("./tests/resources/modules/makeprojection/v2.pipeline", "r") as fd:
data = fd.read()
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event, cellprofiler.pipeline.LoadExceptionEvent)
pipeline.load(six.moves.StringIO(data))
methods = (
cellprofiler.modules.makeprojection.P_AVERAGE,
cellprofiler.modules.makeprojection.P_MAXIMUM,
cellprofiler.modules.makeprojection.P_MINIMUM,
cellprofiler.modules.makeprojection.P_SUM,
cellprofiler.modules.makeprojection.P_VARIANCE,
cellprofiler.modules.makeprojection.P_POWER,
cellprofiler.modules.makeprojection.P_BRIGHTFIELD,
)
assert len(pipeline.modules()) == len(methods)
for method, module in zip(methods, pipeline.modules()):
assert isinstance(module, cellprofiler.modules.makeprojection.MakeProjection)
assert module.image_name == "ch02"
assert module.projection_type == method
assert module.projection_image_name == "ProjectionCh00Scale6"
assert module.frequency == 6
def test_load_v3():
with open("./tests/resources/modules/resize/v3.pipeline", "r") as fd:
data = fd.read()
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event, cellprofiler.pipeline.LoadExceptionEvent)
pipeline.add_listener(callback)
pipeline.load(io.StringIO(data))
assert len(pipeline.modules()) == 2
module = pipeline.modules()[0]
assert isinstance(module, cellprofiler.modules.resize.Resize)
assert module.x_name == "DNA"
assert module.y_name == "ResizedDNA"
assert module.size_method == cellprofiler.modules.resize.R_TO_SIZE
assert round(abs(module.resizing_factor.value - 0.25), 7) == 0
assert module.specific_width == 141
assert module.specific_height == 169
assert module.interpolation == cellprofiler.modules.resize.I_BILINEAR
assert module.additional_image_count.value == 1
additional_image = module.additional_images[0]
assert additional_image.input_image_name == "Actin"
assert additional_image.output_image_name == "ResizedActin"
module = pipeline.modules()[1]
assert isinstance(module, cellprofiler.modules.resize.Resize)
assert module.interpolation == cellprofiler.modules.resize.I_BICUBIC
def test_load_v3():
"""
Tests a pipeline that was produced with module revision 3.
The channel names are named according to the schema:
Channel(#new_imagenumber)_(#channel_number),
e.g. Channel3_2 would be the image number 3 that contains channel
number 2.
Thus it can be easily checked via the new image name, if the channel
number is correctly parsed.
"""
with open("./tests/resources/modules/colortogray/v3.pipeline", "r") as fd:
data = fd.read()
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event, cellprofiler.pipeline.LoadExceptionEvent)
pipeline.add_listener(callback)
pipeline.load(six.moves.StringIO(data))
assert len(pipeline.modules()) == 1
module = pipeline.modules()[0]
assert isinstance(module, cellprofiler.modules.colortogray.ColorToGray)
assert module.image_name == "DNA"
assert module.channel_count.value == 8
for i in range(module.channel_count.value):
c = module.channels[i].image_name.value.split("_")[1]
assert module.channels[i].channel_choice.value == int(c)
assert module.channels[6].image_name.value == "Channel7_7"
def test_01_02_load_v2(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:1
SVNRevision:9063
OverlayOutlines:[module_num:5|svn_version:\'9000\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D]
Display outlines on a blank image?:No
Select image on which to display outlines:DNA
Name the output image\x3A:PrimaryOverlay
Select outline display mode\x3A:Color
Select method to determine brightness of outlines\x3A:Max of image
Line width\x3A:1.5
Select outlines to display\x3A:PrimaryOutlines
Select outline color\x3A:Red
Select outlines to display\x3A:SecondaryOutlines
Select outline color\x3A:Green
"""
pipeline = cellprofiler.pipeline.Pipeline()
pipeline.load(StringIO.StringIO(data))
self.assertEqual(len(pipeline.modules()), 1)
module = pipeline.modules()[0]
self.assertTrue(isinstance(module, cellprofiler.modules.overlayoutlines.OverlayOutlines))
self.assertFalse(module.blank_image)
self.assertEqual(module.image_name, "DNA")
self.assertEqual(module.output_image_name, "PrimaryOverlay")
self.assertEqual(module.wants_color, "Color")
self.assertEqual(module.max_type, cellprofiler.modules.overlayoutlines.MAX_IMAGE)
self.assertEqual(module.line_mode.value, "Inner")
self.assertEqual(len(module.outlines), 2)
for outline, name, color in zip(module.outlines,
("PrimaryOutlines", "SecondaryOutlines"),
("Red", "Green")):
self.assertEqual(outline.objects_name.value, cellprofiler.setting.NONE)
self.assertEqual(outline.color, color)
def test_load_v1():
with open("./tests/resources/modules/tile/v1.pipeline", "r") as fd:
data = fd.read()
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event, cellprofiler.pipeline.LoadExceptionEvent)
pipeline.add_listener(callback)
pipeline.load(six.moves.StringIO(data))
assert len(pipeline.modules()) == 1
module = pipeline.modules()[0]
assert isinstance(module, cellprofiler.modules.tile.Tile)
assert module.input_image == "ResizedColorImage"
assert module.output_image == "TiledImage"
assert module.tile_method == cellprofiler.modules.tile.T_ACROSS_CYCLES
assert module.rows == 2
assert module.columns == 12
assert module.wants_automatic_rows
assert not module.wants_automatic_columns
assert module.place_first == cellprofiler.modules.tile.P_TOP_LEFT
assert module.tile_style == cellprofiler.modules.tile.S_ROW
assert not module.meander
assert len(module.additional_images) == 3
for g, expected in zip(module.additional_images,
("Cytoplasm", "ColorImage", "DNA")):
assert g.input_image_name == expected
def test_01_03_load_v3(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:3
DateRevision:20131210175632
GitHash:63ec479
ModuleCount:1
HasImagePlaneDetails:False
MeasureImageOverlap:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True]
Compare segmented objects, or foreground/background?:Foreground/background segmentation
Select the image to be used as the ground truth basis for calculating the amount of overlap:Foo
Select the image to be used to test for overlap:Bar
Select the objects to be used as the ground truth basis for calculating the amount of overlap:Cell2_0
Select the objects to be tested for overlap against the ground truth:Cell2_1
"""
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cellprofiler.pipeline.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO.StringIO(data))
module = pipeline.modules()[0]
self.assertTrue(isinstance(module, cellprofiler.modules.measureimageoverlap.MeasureImageOverlap))
self.assertEqual(module.ground_truth, "Foo")
self.assertEqual(module.test_img, "Bar")
# self.assertEqual(module.object_name_GT, "Cell2_0")
# self.assertEqual(module.object_name_ID, "Cell2_1")
self.assertFalse(module.wants_emd)
def test_01_02_load_v1(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:1
SVNRevision:9169
MeasureImageOverlap:[module_num:1|svn_version:\'9000\'|variable_revision_number:1|show_window:True|notes:\x5B\x5D]
Which image do you want to use as the basis for calculating the amount of overlap? :GroundTruth
Which image do you want to compare for overlap?:Segmentation
"""
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
self.assertFalse(
isinstance(event, cellprofiler.pipeline.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO.StringIO(data))
self.assertEqual(len(pipeline.modules()), 1)
module = pipeline.modules()[0]
self.assertTrue(
isinstance(
module,
cellprofiler.modules.measureimageoverlap.MeasureImageOverlap))
self.assertEqual(module.ground_truth, "GroundTruth")
self.assertEqual(module.test_img, "Segmentation")
def test_01_01_01_load_v1(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:1
SVNRevision:8957
MeasureObjectSizeShape:[module_num:1|svn_version:\'1\'|variable_revision_number:1|show_window:True|notes:\x5B\x5D]
Select objects to measure:Nuclei
Select objects to measure:Cells
Calculate the Zernike features?:Yes
"""
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
self.assertFalse(
isinstance(event, cellprofiler.pipeline.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO.StringIO(data))
self.assertEqual(len(pipeline.modules()), 1)
module = pipeline.modules()[0]
self.assertTrue(
isinstance(
module, cellprofiler.modules.measureobjectsizeshape.
MeasureObjectSizeShape))
self.assertEqual(len(module.object_groups), 2)
for og, expected in zip(module.object_groups, ("Nuclei", "Cells")):
self.assertEqual(og.name, expected)
self.assertTrue(module.calculate_zernikes)
def test_04_02_load_saturation_blur(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:1
SVNRevision:8925
FromMatlab:True
MeasureImageSaturationBlur:[module_num:1|svn_version:\'8913\'|variable_revision_number:4|show_window:False|notes:\x5B\x5D]
What did you call the image you want to check for saturation?:Image1
What did you call the image you want to check for saturation?:Image2
What did you call the image you want to check for saturation?:Image3
What did you call the image you want to check for saturation?:Do not use
What did you call the image you want to check for saturation?:Do not use
What did you call the image you want to check for saturation?:Do not use
Do you want to also check the above images for image quality (called blur earlier)?:Yes
If you chose to check images for image quality above, enter the window size of LocalFocusScore measurement (A suggested value is 2 times ObjectSize)?:25
"""
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
self.assertFalse(
isinstance(event, cellprofiler.pipeline.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO.StringIO(data))
self.assertEqual(len(pipeline.modules()), 1)
module = pipeline.modules()[0]
self.assertTrue(isinstance(module, M.MeasureImageQuality))
self.assertEqual(len(module.image_groups), 3)
for i in range(3):
group = module.image_groups[i]
self.assertEqual(group.image_names, "Image%d" % (i + 1))
self.assertTrue(group.check_blur)
self.assertEqual(group.scale_groups[0].scale, 25)
self.assertTrue(group.check_saturation)
self.assertFalse(group.calculate_threshold)
def test_load_v1():
data = ('eJztWNFO2zAUdUqBsUkr28v26Ee60aotQ4NqKu0oEtUIVLRiQohtpnXba'
'EkcOQlrNyHtcZ+0x33OHvcJs4NDUhMIbccmpqay2nt9zz3Xx0lsV600dy'
'qv4Wo2B9VKM9PRdAzrOnI6hBpFaDrLcJNi5OA2JGYRqsSEKhrAfB7mV4o'
'rheLqOizkcutgvEupqQ/Z19pjAObY9z3WEqJrVthKqHG7gR1HM7v2LEiC'
'p8L/g7UDRDV0ouMDpLvYDih8f83skObAuuhSSdvV8S4ywsHs2nWNE0ztv'
'Y4PFN11rY/1hvYZS0Pww/bxqWZrxBR4kV/2XvASR+LlOnyfD3RQJB24Lq'
'mQn8dvgyA+GaHbo1D8orA1s62dam0X6VAzUPeiCm8eYvLNS/m4rQ5qPI2'
'HL8fgFyU8b03cdzJbfdRyoIGcVu8meVJSnpRXx5bZQ2YLt4N6cjF5lKE8'
'CliZQAfBfiMd7kt4blcJNIkDXRsH8xFXf2IoTwLkX46H2yWXcXMSzr983'
'AII6oy7D59I4+V2FXeQqzvQmy1Y1ShuOYQOJqrjX+Kixj0zNO4ZcMietr'
'vK9zdwk75/bktX+T2R/4P6RPElh/iS7Pk08SR8X2P43oBhXbn9bmmj/op'
'vBHAp+zz9nltvsa7vk0+lo0qmfpz2PZtEdw2zdJTLrB9/yS8Xzs6DGxpD'
'es505LhHqb8XU/+aVD+3eQ2HGFFR2IuzdIa72AbG6QlfQfiqaBB4Jqnz5'
'9xo6/e4POUYPaLWF2+x71LiWrfPH7XOB/yQbUGwdZfeS1PcFPc/4soh3P'
'Q5nuJGxS0qV6938jmDx38A199vz8Dw/cbtFttiWJTw/yVo1vAOz3ZWJ6h'
'9fnrN7rCftdBBlvP0Y3i2JZ7tq3jw+aGOUNu1LIpt27Zw6yPvEce9PdoQ'
'PQ3RI+u5EMEf1iXBPqnk9fMg6x/My6+NcfgSymW+BzG4pFCS476B0eZ96'
'Zp4f2zjxv8G/FcCeg==')
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event, cellprofiler.pipeline.LoadExceptionEvent)
pipeline.add_listener(callback)
pipeline.load(StringIO.StringIO(zlib.decompress(base64.b64decode(data))))
assert len(pipeline.modules()) == 2
module = pipeline.modules()[1]
assert module.module_name == 'EnhanceOrSuppressFeatures'
assert module.x_name.value == 'MyImage'
assert module.y_name.value == 'MyEnhancedImage'
assert module.method.value == cellprofiler.modules.enhanceorsuppressfeatures.ENHANCE
assert module.object_size == 17
def test_load_v2():
with open("./tests/resources/modules/measureobjectneighbors/v2.pipeline",
"r") as fd:
data = fd.read()
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event, cellprofiler.pipeline.LoadExceptionEvent)
pipeline.add_listener(callback)
pipeline.load(six.moves.StringIO(data))
assert len(pipeline.modules()) == 1
module = pipeline.modules()[0]
assert isinstance(
module,
cellprofiler.modules.measureobjectneighbors.MeasureObjectNeighbors)
assert module.object_name == "glia"
assert module.neighbors_name == "neurites"
assert (module.distance_method ==
cellprofiler.modules.measureobjectneighbors.D_EXPAND)
assert module.distance == 2
assert not module.wants_count_image
assert module.count_image_name == "countimage"
assert module.count_colormap == "pink"
assert not module.wants_percent_touching_image
assert module.touching_image_name == "touchingimage"
assert module.touching_colormap == "purple"
def test_01_03_load_v3(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:3
DateRevision:20140505183007
GitHash:c675ec6
ModuleCount:1
HasImagePlaneDetails:False
OverlayOutlines:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Display outlines on a blank image?:No
Select image on which to display outlines:DNA
Name the output image:PrimaryOverlay
Outline display mode:Color
Select method to determine brightness of outlines:Max of image
Width of outlines:1.5
Select outlines to display:PrimaryOutlines
Select outline color:Red
Load outlines from an image or objects?:Image
Select objects to display:Nuclei
Select outlines to display\x3A:SecondaryOutlines
Select outline color\x3A:Green
Load outlines from an image or objects?:Objects
Select objects to display:Cells
"""
pipeline = cellprofiler.pipeline.Pipeline()
pipeline.load(io.StringIO(data))
self.assertEqual(len(pipeline.modules()), 1)
module = pipeline.modules()[0]
self.assertTrue(
isinstance(module, cellprofiler.modules.overlayoutlines.OverlayOutlines)
)
self.assertFalse(module.blank_image)
self.assertEqual(module.image_name, "DNA")
self.assertEqual(module.output_image_name, "PrimaryOverlay")
self.assertEqual(module.wants_color, "Color")
self.assertEqual(
module.max_type, cellprofiler.modules.overlayoutlines.MAX_IMAGE
)
self.assertEqual(module.line_mode.value, "Inner")
self.assertEqual(len(module.outlines), 2)
for outline, name, color, choice, objects_name in (
(
module.outlines[0],
"PrimaryOutlines",
"Red",
cellprofiler.modules.overlayoutlines.FROM_IMAGES,
"Nuclei",
),
(
module.outlines[1],
"SecondaryOutlines",
"Green",
cellprofiler.modules.overlayoutlines.FROM_OBJECTS,
"Cells",
),
):
self.assertEqual(outline.color, color)
self.assertEqual(outline.objects_name, objects_name)
def test_01_03_load_v3(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:3
DateRevision:20120112154631
ModuleCount:1
HasImagePlaneDetails:False
Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)]
Extract metadata?:Yes
Extraction method count:2
Metadata extraction method:Extract from file/folder names
Metadata source:File name
Regular expression:^Channel(?P<ChannelNumber>\x5B12\x5D)-(?P<Index>\x5B0-9\x5D+)-(?P<WellRow>\x5BA-H\x5D)-(?P<WellColumn>\x5B0-9\x5D{2}).tif$
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:or (file does contain "Channel2")
Metadata file location:
Match file and image metadata:\x5B\x5D
Case insensitive matching:No
Metadata extraction method:Import from file
Metadata source:Folder 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:Example(?P<Project>\x5B^\\\\\\\\\x5D+)Images
Extract metadata from:Images matching a rule
Select the filtering criteria:or (file does contain "")
Metadata file location\x3A:/imaging/analysis/metadata.csv
Match file and image metadata:\x5B{\'Image Metadata\'\x3A u\'ChannelNumber\', \'CSV Metadata\'\x3A u\'Wavelength\'}\x5D
Case insensitive matching:Yes
"""
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cellprofiler.pipeline.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(cStringIO.StringIO(data))
self.assertEqual(len(pipeline.modules()), 1)
module = pipeline.modules()[0]
self.assertTrue(isinstance(module, cellprofiler.modules.metadata.Metadata))
self.assertTrue(module.wants_metadata)
self.assertEqual(module.data_type_choice, cellprofiler.modules.metadata.DTC_TEXT)
self.assertEqual(len(module.extraction_methods), 2)
em0, em1 = module.extraction_methods
self.assertEqual(em0.extraction_method, cellprofiler.modules.metadata.X_MANUAL_EXTRACTION)
self.assertEqual(em0.source, cellprofiler.modules.metadata.XM_FILE_NAME)
self.assertEqual(em0.file_regexp.value,
r"^Channel(?P<ChannelNumber>[12])-(?P<Index>[0-9]+)-(?P<WellRow>[A-H])-(?P<WellColumn>[0-9]{2}).tif$")
self.assertEqual(em0.folder_regexp.value,
r"(?P<Date>[0-9]{4}_[0-9]{2}_[0-9]{2})$")
self.assertEqual(em0.filter_choice, cellprofiler.modules.metadata.F_ALL_IMAGES)
self.assertEqual(em0.filter, 'or (file does contain "Channel2")')
self.assertFalse(em0.wants_case_insensitive)
self.assertEqual(em1.extraction_method, cellprofiler.modules.metadata.X_IMPORTED_EXTRACTION)
self.assertEqual(em1.source, cellprofiler.modules.metadata.XM_FOLDER_NAME)
self.assertEqual(em1.filter_choice, cellprofiler.modules.metadata.F_FILTERED_IMAGES)
self.assertEqual(em1.csv_location.get_dir_choice(), cellprofiler.setting.ABSOLUTE_FOLDER_NAME)
self.assertEqual(em1.csv_location.get_custom_path(), "/imaging/analysis")
self.assertEqual(em1.csv_filename.value, "metadata.csv")
self.assertEqual(em1.csv_joiner.value, "[{'Image Metadata': u'ChannelNumber', 'CSV Metadata': u'Wavelength'}]")
self.assertTrue(em1.wants_case_insensitive)
def test_load_v4():
with open("./tests/resources/modules/flagimages/v4.pipeline", "r") as fd:
data = fd.read()
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event, cellprofiler.pipeline.LoadExceptionEvent)
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", "4"),
("Intensity_MinIntensity_Cytoplasm", 0.05, None, "bar.txt", "2"),
("Intensity_MeanIntensity_DNA", 0.1, 0.9, "baz.txt", "1"),
),
),
(
"HighCytoplasmIntensity",
None,
True,
(("Intensity_MeanIntensity_Cytoplasm", None, 0.8, "dunno.txt", "3"),),
),
)
assert len(expected) == module.flag_count.value
for flag, (feature_name, combine, skip, measurements) in zip(
module.flags, expected
):
assert isinstance(flag, cellprofiler.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, rules_class),
) in zip(flag.measurement_settings, measurements):
assert isinstance(measurement, cellprofiler.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 == rules_class
def test_01_05_load_v4():
data = r'''CellProfiler Pipeline: http://www.cellprofiler.org
Version:2
DateRevision:20120516145742
EnhanceOrSuppressFeatures:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)]
Select the input image:Dendrite
Name the output image:EnhancedDendrite
Select the operation:Enhance
Feature size:10
Feature type:Neurites
Range of hole sizes:1,10
Smoothing scale:2.0
Shear angle:0
Decay:0.95
Enhancement method:Tubeness
EnhanceOrSuppressFeatures:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)]
Select the input image:Axon
Name the output image:EnhancedAxon
Select the operation:Enhance
Feature size:10
Feature type:Neurites
Range of hole sizes:1,10
Smoothing scale:2.0
Shear angle:0
Decay:0.95
Enhancement method:Line structures
'''
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event, cellprofiler.pipeline.LoadExceptionEvent)
pipeline.add_listener(callback)
pipeline.load(StringIO.StringIO(data))
assert len(pipeline.modules()) == 2
module = pipeline.modules()[0]
assert isinstance(
module, cellprofiler.modules.enhanceorsuppressfeatures.
EnhanceOrSuppressFeatures)
assert module.x_name == "Dendrite"
assert module.y_name == "EnhancedDendrite"
assert module.method == cellprofiler.modules.enhanceorsuppressfeatures.ENHANCE
assert module.enhance_method == cellprofiler.modules.enhanceorsuppressfeatures.E_NEURITES
assert module.smoothing == 2.0
assert module.object_size == 10
assert module.hole_size.min == 1
assert module.hole_size.max == 10
assert module.angle == 0
assert module.decay == .95
assert module.neurite_choice == cellprofiler.modules.enhanceorsuppressfeatures.N_TUBENESS
module = pipeline.modules()[1]
assert isinstance(
module, cellprofiler.modules.enhanceorsuppressfeatures.
EnhanceOrSuppressFeatures)
assert module.neurite_choice == cellprofiler.modules.enhanceorsuppressfeatures.N_GRADIENT
def test_load_v2():
data = ('eJztW0Fv2zYUpl0nWFagS08dhhXgoYckiDXZq9E0KFo78boZqB2jNtoNadYx'
'Mm1zkEVDotK4Re/9qTvmuONEW7IlTolkS4qdVAIE+VH8+L33+PhIyVS90n5V'
'OYAlSYb1SjvfJSqGTRWxLtUH+1Bju/BQx4jhDqTaPmybGDboGSw+hoXifqm0'
'X5BhUZafgsWOTK1+z7pcPANg3bp+Y51Z+9aaLWdcJ5dbmDGi9Yw1kAPf2+UX'
'1vkG6QSdqvgNUk1szCic8prWpe3RcHqrTjumihto4K5sHQ1zcIp146jrAO3b'
'TXKO1Rb5iAUTnGqv8RkxCNVsvN2+WDrlpUzgbfXph5e6pY7Q/gFiSr/FrB7w'
'lnO/bf0481tG8FvOOh+6ynn938Csfs7Hz/dd9TdtmWgdckY6JlIhGaDeVGve'
'nhzQ3h1Pe3dAtVEZ4/YCcOuCHlxumIqKSTTecgBuU+DlZxufs/wv50hhcMC7'
'Ig79g/BrAp7Lh1hVDbA4vqJYwwaE81/Gg8+AnyPwFuTdx7KND/L/XQHP5aZO'
'h6iHmDWIxuVh2vlWaIfLVQo1yqBp2OMoznbmjcc/rFEURz9GjaMgvbMefBY0'
'aDRckL/9+v+IGSb8VaWnSJ36O+r4C8qDDwQ8l6u4i0yVwRpPgrBKdKwwqo8i'
'9f+8uIIk/w+3LuCcw8Ft2NcouHKAnmH7bZH5R5bk8bFbsH/EYM8y+8sPl/Pg'
'ctzmQpL2XTY+k+yfMHoU5GT7ddF8FqZ/QuJKqzh+N4C3X7lc0xjWDMJGrnYW'
'iY8WVqjWQfroyGQq0S5dPyZtz2EfaRpWC/kE/DJvXpBjzOPzrH/jGI9h4rxB'
'NZykfeL6tLAg7klIXJi8Ead9Yfw5z7xVDsD5zd/tDxQqKjIMe8RGsbcfwP9E'
'4Ofyn1svms/4iwj8XNrZfs+lt9bS9flxJd88OZbzT08+FT9vvzf4jRaxao3L'
'tkPZ+53Ax+Wmbj3eurLUovnuLSa9Pn9dcsZfDGiK89gexX+/B+jxSNCDy9LO'
'8bt3P51w91RtJ04LXpsalx/56RVnXPk9P72kOu7p1NQ60f0SxH/FPFB0zwNx'
'rAevYx5P+rl/VeycN48XF7Qvznl4FfJ/kvPwKuf7Zc7ftyGPLzP+k1z3ylJp'
'6Xom/Z4lznXadeNWZX21av2c9Lpp1cftbctL4nqltCQ9v/www2UEnN//hdcZ'
'3+M/F3mAD8O345cP6enfWGGzhuLU5zrznMsOSLQOHibY3rL9k+Juxji6LX67'
'bfamuBSX4lJc1Ly4Cbx5kZ+z+WSybLhJ9n5t/g3iT9dxKS4JXBksN+5T3NeJ'
'K4M07lJcOu+luBSX4lJciktxNwn3b2aGywg4Lrv3s/D6f7l4/Ob5HVf9TVtW'
'sKoOdcq/a9SlwfjjO0NSKepMvmaTXlk/a64P2zjPMICnLPCUL+MhHawx0h0N'
'+eZCk9EBYkSRanYp33JYcUo5bz+Ad0/g3QviNZzN11PO6XZszncewHcg8B1c'
'xjfAyDB1PHmHTpwd0lJ9Unw0LhY2Totxs+HD7+7/rCU9eHh/7ap4A8AbZ7P4'
'u3ixCF8ul83eA959d3cDcDngjXuO/wfMF+dbV9R3bFzl+vP6OWMdUf0048lN'
'dZq0v5r1/wNnl0Vu')
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event, cellprofiler.pipeline.LoadExceptionEvent)
pipeline.add_listener(callback)
pipeline.load(StringIO.StringIO(zlib.decompress(base64.b64decode(data))))
assert len(pipeline.modules()) == 4
module = pipeline.modules()[-1]
assert isinstance(
module,
cellprofiler.modules.measureobjectintensity.MeasureObjectIntensity)
assert len(module.images) == 2
for expected, actual in zip(("DNA", "Actin"),
[img.name for img in module.images]):
assert expected == actual
assert len(module.objects) == 2
for expected, actual in zip(("Cells", "Nuclei"),
[obj.name for obj in module.objects]):
assert expected == actual
def test_01_02_load_v2(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:1
SVNRevision:9524
LoadSingleImage:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D]
Folder containing the image file:Default Input Folder\x7CNone
Filename of the image to load (Include the extension, e.g., .tif):foo.tif
Name the image that will be loaded:DNA
Filename of the image to load (Include the extension, e.g., .tif):bar.tif
Name the image that will be loaded:Cytoplasm
LoadSingleImage:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D]
Folder containing the image file:Default Output Folder\x7CNone
Filename of the image to load (Include the extension, e.g., .tif):baz.tif
Name the image that will be loaded:GFP
LoadSingleImage:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D]
Folder containing the image file:Elsewhere...\x7CNone
Filename of the image to load (Include the extension, e.g., .tif):baz.tif
Name the image that will be loaded:GFP
LoadSingleImage:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D]
Folder containing the image file:URL\x7Chttps\x3A//svn.broadinstitute.org/CellProfiler/trunk/ExampleImages/ExampleSBSImages
Filename of the image to load (Include the extension, e.g., .tif):Channel1-01-A-01.tif
Name the image that will be loaded:DNA1
"""
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cellprofiler.pipeline.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO(data))
self.assertEqual(len(pipeline.modules()), 4)
module = pipeline.modules()[0]
self.assertTrue(isinstance(module, cellprofiler.modules.loadsingleimage.LoadSingleImage))
self.assertEqual(len(module.file_settings), 2)
fs = module.file_settings[0]
self.assertEqual(fs.file_name, "foo.tif")
self.assertEqual(fs.image_name, "DNA")
fs = module.file_settings[1]
self.assertEqual(fs.file_name, "bar.tif")
self.assertEqual(fs.image_name, "Cytoplasm")
module = pipeline.modules()[3]
fs = module.file_settings[0]
self.assertEqual(
fs.file_name,
"https://svn.broadinstitute.org/CellProfiler/trunk/ExampleImages/"
"ExampleSBSImages/Channel1-01-A-01.tif")
dir_choice = [
cellprofiler.setting.DEFAULT_INPUT_FOLDER_NAME, cellprofiler.setting.DEFAULT_OUTPUT_FOLDER_NAME,
cellprofiler.setting.ABSOLUTE_FOLDER_NAME, cellprofiler.setting.URL_FOLDER_NAME]
for i, module in enumerate(pipeline.modules()):
self.assertTrue(isinstance(module, cellprofiler.modules.loadsingleimage.LoadSingleImage))
self.assertEqual(module.directory.dir_choice, dir_choice[i])
def test_01_03_load_v2(self):
data = """CellProfiler Pipeline: http://www.cellprofiler.org
Version:1
SVNRevision:10865
MeasureTexture:[module_num:1|svn_version:\'1\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D]
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
Texture scale to measure:5
Measure Gabor features?:Yes
Number of angles to compute for Gabor:6
MeasureTexture:[module_num:2|svn_version:\'1\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D]
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
Texture scale to measure:5
Measure Gabor features?:No
Number of angles to compute for Gabor:6
"""
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cellprofiler.pipeline.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO.StringIO(data))
self.assertEqual(len(pipeline.modules()), 2)
for i, wants_gabor in enumerate((True, False)):
module = pipeline.modules()[i]
self.assertTrue(isinstance(module, cellprofiler.modules.measuretexture.MeasureTexture))
self.assertEqual(len(module.image_groups), 2)
self.assertEqual(module.image_groups[0].image_name, "rawDNA")
self.assertEqual(module.image_groups[1].image_name, "rawGFP")
self.assertEqual(len(module.object_groups), 2)
self.assertEqual(module.object_groups[0].object_name, "Cells")
self.assertEqual(module.object_groups[1].object_name, "Nuclei")
self.assertEqual(len(module.scale_groups), 2)
self.assertEqual(module.scale_groups[0].scale, 3)
self.assertEqual(len(module.scale_groups[0].angles.get_selections()), 1)
self.assertEqual(module.scale_groups[0].angles.get_selections()[0], cellprofiler.modules.measuretexture.H_HORIZONTAL)
self.assertEqual(module.scale_groups[1].scale, 5)
self.assertEqual(len(module.scale_groups[1].angles.get_selections()), 1)
self.assertEqual(module.scale_groups[1].angles.get_selections()[0], cellprofiler.modules.measuretexture.H_HORIZONTAL)
self.assertEqual(module.wants_gabor, wants_gabor)
self.assertEqual(module.gabor_angles, 6)
def test_load_v1():
with open("./tests/resources/modules/maskobjects/v1.pipeline", "r") as fd:
data = fd.read()
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event, cellprofiler.pipeline.LoadExceptionEvent)
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_01_01_load_v1(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:1
SVNRevision:9524
LoadSingleImage:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:1|show_window:True|notes:\x5B\x5D]
Folder containing the image file:Default Input Folder
Name of the folder containing the image file:path1
Filename of the image to load (Include the extension, e.g., .tif):foo.tif
Name the image that will be loaded:DNA
Filename of the image to load (Include the extension, e.g., .tif):bar.tif
Name the image that will be loaded:Cytoplasm
LoadSingleImage:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:1|show_window:True|notes:\x5B\x5D]
Folder containing the image file:Default Output Folder
Name of the folder containing the image file:path2
Filename of the image to load (Include the extension, e.g., .tif):baz.tif
Name the image that will be loaded:GFP
LoadSingleImage:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:1|show_window:True|notes:\x5B\x5D]
Folder containing the image file:Custom folder
Name of the folder containing the image file:path3
Filename of the image to load (Include the extension, e.g., .tif):baz.tif
Name the image that will be loaded:GFP
LoadSingleImage:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:1|show_window:True|notes:\x5B\x5D]
Folder containing the image file:Custom with metadata
Name of the folder containing the image file:path4
Filename of the image to load (Include the extension, e.g., .tif):baz.tif
Name the image that will be loaded:GFP
"""
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cellprofiler.pipeline.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO(data))
self.assertEqual(len(pipeline.modules()), 4)
dir_choice = [
cellprofiler.setting.DEFAULT_INPUT_FOLDER_NAME, cellprofiler.setting.DEFAULT_OUTPUT_FOLDER_NAME,
cellprofiler.setting.ABSOLUTE_FOLDER_NAME, cellprofiler.setting.ABSOLUTE_FOLDER_NAME]
for i, module in enumerate(pipeline.modules()):
self.assertTrue(isinstance(module, cellprofiler.modules.loadsingleimage.LoadSingleImage))
self.assertEqual(module.directory.dir_choice, dir_choice[i])
self.assertEqual(module.directory.custom_path,
"path%d" % (i + 1))
module = pipeline.modules()[0]
self.assertTrue(isinstance(module, cellprofiler.modules.loadsingleimage.LoadSingleImage))
self.assertEqual(len(module.file_settings), 2)
fs = module.file_settings[0]
self.assertEqual(fs.file_name, "foo.tif")
self.assertEqual(fs.image_name, "DNA")
fs = module.file_settings[1]
self.assertEqual(fs.file_name, "bar.tif")
self.assertEqual(fs.image_name, "Cytoplasm")
def test_01_05_load_v4():
data = r'''CellProfiler Pipeline: http://www.cellprofiler.org
Version:2
DateRevision:20120516145742
EnhanceOrSuppressFeatures:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)]
Select the input image:Dendrite
Name the output image:EnhancedDendrite
Select the operation:Enhance
Feature size:10
Feature type:Neurites
Range of hole sizes:1,10
Smoothing scale:2.0
Shear angle:0
Decay:0.95
Enhancement method:Tubeness
EnhanceOrSuppressFeatures:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)]
Select the input image:Axon
Name the output image:EnhancedAxon
Select the operation:Enhance
Feature size:10
Feature type:Neurites
Range of hole sizes:1,10
Smoothing scale:2.0
Shear angle:0
Decay:0.95
Enhancement method:Line structures
'''
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event, cellprofiler.pipeline.LoadExceptionEvent)
pipeline.add_listener(callback)
pipeline.load(StringIO.StringIO(data))
assert len(pipeline.modules()) == 2
module = pipeline.modules()[0]
assert isinstance(module, cellprofiler.modules.enhanceorsuppressfeatures.EnhanceOrSuppressFeatures)
assert module.x_name == "Dendrite"
assert module.y_name == "EnhancedDendrite"
assert module.method == cellprofiler.modules.enhanceorsuppressfeatures.ENHANCE
assert module.enhance_method == cellprofiler.modules.enhanceorsuppressfeatures.E_NEURITES
assert module.smoothing == 2.0
assert module.object_size == 10
assert module.hole_size.min == 1
assert module.hole_size.max == 10
assert module.angle == 0
assert module.decay == .95
assert module.neurite_choice == cellprofiler.modules.enhanceorsuppressfeatures.N_TUBENESS
module = pipeline.modules()[1]
assert isinstance(module, cellprofiler.modules.enhanceorsuppressfeatures.EnhanceOrSuppressFeatures)
assert module.neurite_choice == cellprofiler.modules.enhanceorsuppressfeatures.N_GRADIENT
def test_01_03_load_v3(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:1
SVNRevision:10104
Resize:[module_num:1|svn_version:\'10104\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D]
Select the input image:DNA
Name the output image:ResizedDNA
Select resizing method:Resize by specifying desired final dimensions
Resizing factor:0.25
Width of the final image, in pixels:141
Height of the final image, in pixels:169
Interpolation method:Bilinear
Additional image count:1
Select the additional image?:Actin
Name the output image:ResizedActin
Resize:[module_num:2|svn_version:\'10104\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D]
Select the input image:DNA
Name the output image:ResizedDNA
Select resizing method:Resize by specifying desired final dimensions
Resizing factor:0.25
Width of the final image, in pixels:100
Height of the final image, in pixels:100
Interpolation method:Bicubic
Additional image count:0
"""
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cellprofiler.pipeline.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO.StringIO(data))
self.assertEqual(len(pipeline.modules()), 2)
module = pipeline.modules()[0]
self.assertTrue(isinstance(module, cellprofiler.modules.resize.Resize))
self.assertEqual(module.x_name, "DNA")
self.assertEqual(module.y_name, "ResizedDNA")
self.assertEqual(module.size_method, cellprofiler.modules.resize.R_TO_SIZE)
self.assertAlmostEqual(module.resizing_factor.value, 0.25)
self.assertEqual(module.specific_width, 141)
self.assertEqual(module.specific_height, 169)
self.assertEqual(module.interpolation, cellprofiler.modules.resize.I_BILINEAR)
self.assertEqual(module.additional_image_count.value, 1)
additional_image = module.additional_images[0]
self.assertEqual(additional_image.input_image_name, "Actin")
self.assertEqual(additional_image.output_image_name, "ResizedActin")
module = pipeline.modules()[1]
self.assertTrue(isinstance(module, cellprofiler.modules.resize.Resize))
self.assertEqual(module.interpolation, cellprofiler.modules.resize.I_BICUBIC)
def test_test_load_v3():
data = r'''CellProfiler Pipeline: http://www.cellprofiler.org
Version:1
SVNRevision:10999
EnhanceOrSuppressFeatures:[module_num:1|svn_version:\'10591\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D]
Select the input image:DNA
Name the output image:EnhancedTexture
Select the operation:Enhance
Feature size:10
Feature type:Texture
Range of hole sizes:1,10
Smoothing scale:3.5
Shear angle:45
Decay:0.90
EnhanceOrSuppressFeatures:[module_num:2|svn_version:\'10591\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D]
Select the input image:EnhancedTexture
Name the output image:EnhancedDIC
Select the operation:Enhance
Feature size:10
Feature type:DIC
Range of hole sizes:1,10
Smoothing scale:1.5
Shear angle:135
Decay:0.99
'''
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event, cellprofiler.pipeline.LoadExceptionEvent)
pipeline.add_listener(callback)
pipeline.load(StringIO.StringIO(data))
assert len(pipeline.modules()) == 2
module = pipeline.modules()[0]
assert isinstance(module, cellprofiler.modules.enhanceorsuppressfeatures.EnhanceOrSuppressFeatures)
assert module.x_name == "DNA"
assert module.y_name == "EnhancedTexture"
assert module.method == cellprofiler.modules.enhanceorsuppressfeatures.ENHANCE
assert module.enhance_method == cellprofiler.modules.enhanceorsuppressfeatures.E_TEXTURE
assert module.smoothing == 3.5
assert module.object_size == 10
assert module.hole_size.min == 1
assert module.hole_size.max == 10
assert module.angle == 45
assert module.decay == .9
assert module.speckle_accuracy == cellprofiler.modules.enhanceorsuppressfeatures.S_SLOW
module = pipeline.modules()[1]
assert isinstance(module, cellprofiler.modules.enhanceorsuppressfeatures.EnhanceOrSuppressFeatures)
assert module.enhance_method == cellprofiler.modules.enhanceorsuppressfeatures.E_DIC
def test_01_05_load_v5(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:3
DateRevision:300
GitHash:
ModuleCount:1
HasImagePlaneDetails:False
Metadata:[module_num:1|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?:Yes
Metadata data type:Text
Metadata types:{}
Extraction method count:2
Metadata extraction method:Import from file
Metadata source:File name
Regular expression to extract from file name:^(?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 to extract from folder name:(?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:/imaging/analysis/metadata.csv
Match file and image metadata:\x5B{\'Image Metadata\'\x3A u\'FileLocation\', \'CSV Metadata\'\x3A u\'None\'}\x5D
Use case insensitive matching?:No
Metadata extraction method:Import from file
Metadata source:File name
Regular expression to extract from file name:^(?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 to extract from folder name:(?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:https\x3A//cellprofiler.org/metadata.csv
Match file and image metadata:\x5B{\'Image Metadata\'\x3A u\'FileLocation\', \'CSV Metadata\'\x3A u\'None\'}\x5D
Use case insensitive matching?:No
"""
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cellprofiler.pipeline.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(cStringIO.StringIO(data))
self.assertEqual(len(pipeline.modules()), 1)
module = pipeline.modules()[0]
em0, em1 = module.extraction_methods
self.assertEqual(em0.csv_location.get_dir_choice(), cellprofiler.setting.ABSOLUTE_FOLDER_NAME)
self.assertEqual(em0.csv_location.get_custom_path(), "/imaging/analysis")
self.assertEqual(em0.csv_filename.value, "metadata.csv")
self.assertEqual(em1.csv_location.get_dir_choice(), cellprofiler.setting.URL_FOLDER_NAME)
self.assertEqual(em1.csv_location.get_custom_path(), "https://cellprofiler.org")
self.assertEqual(em1.csv_filename.value, "metadata.csv")
def test_load_v3(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:3
DateRevision:300
GitHash:
ModuleCount:1
HasImagePlaneDetails:False
MeasureImageAreaOccupied:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Hidden:3
Measure the area occupied in a binary image, or in objects?:Binary Image
Select objects to measure:None
Retain a binary image of the object regions?:No
Name the output binary image:Stain
Select a binary image to measure:DNA
Measure the area occupied in a binary image, or in objects?:Objects
Select objects to measure:Cells
Retain a binary image of the object regions?:Yes
Name the output binary image:Stain
Select a binary image to measure:None
Measure the area occupied in a binary image, or in objects?:Objects
Select objects to measure:Nuclei
Retain a binary image of the object regions?:No
Name the output binary image:Stain
Select a binary image to measure:None
"""
def callback(caller, event):
self.assertFalse(isinstance(event, cellprofiler.pipeline.LoadExceptionEvent))
pipeline = cellprofiler.pipeline.Pipeline()
pipeline.add_listener(callback)
pipeline.load(six.StringIO(data))
module = pipeline.modules()[0]
assert module.count.value == 3
assert module.operands[0].operand_choice == "Binary Image"
assert module.operands[1].operand_choice == "Objects"
assert module.operands[1].operand_objects == "Cells"
assert module.operands[2].operand_choice == "Objects"
assert module.operands[2].operand_objects == "Nuclei"
def test_01_01_load_matlab(self):
data = ('eJzzdQzxcXRSMNUzUPB1DNFNy8xJ1VEIyEksScsvyrVSCHAO9/TTUXAuSk0s'
'SU1RyM+zUgjJKFXwKs1RMDJWMDS1MjWyAjKMDAwsFUgGDIyevvwMDAwBTAwM'
'FXPeTr/rd9tA5Li/V1CCc8fG2bFvlKbHMHh3a73dJKLGm3lazcFjctljNbWt'
'U26F2gUk6h9/KpfYv+H4DN6fW5zm1mWsFejTeW1ht7/4T3+y+m2Ggv3cF568'
'ZVvPW6HjlpTdqvWGW8a1pWoho9Ue71/vK/c6MTrWyscr2h9TTFF9a7/4+ptP'
'ZVPu/jp158ZxMykx9s9Jxw/b8R3Kv74yuei3tWTy/6Y7iRrTS25Y7lNUTDT5'
'6P+jKF9sumvQndy4/znPrDIm8Vmtz+f+m1Qj/uljVWuedetHa8+9Rx6/eHBZ'
'wnKipctEYwEZ74OR2oXXHlyxa0v9sfLZ76hZ6zf4s3UEvdnOvyYs926cp0zi'
'/McT7GvClwTN3qxauK3Ir3/9pbJYv6t7PtX0bar5d/2zRU551s9MVXveKONn'
'M48Fvwz5esdvpmdkyrHET4+4zrmzpsc+4tidGvl7z7l39x/+m2v5h/uj+NLm'
'ruCt9TckWC16fKSF8y+kO879meMhk/i9w18j/ewq936LwvkW3Fcfr/zWaj/T'
'/q1dwZYnNfbaP/JV9m3gLD3JW5j8ZHZKx543X+a5T3fT9GPO2RYxl1/0Z6no'
'Hl9P9sRqxl28sk8ec93b8vPU7z/8615pvdR87m6/oNMm9Hf0/D+14THln/fx'
'83df4Y6wiYr1i427ajnls8+/rEcXi5+dqLqaG/9I3/xfcb7u25d/fW5YTfm+'
'Wc9nf35N4glbbZkdTYt/xeV4ySTa/9Nf21b/8U6mVkl2QfZ1g57Ic/9X/V9r'
'9ZHH9/KTylV/viz5//x3PP/0D/+5L6/t38v5+PKmVY//M5ac/+oEAKjKSvo=')
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cellprofiler.pipeline.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO.StringIO(zlib.decompress(base64.b64decode(data))))
self.assertEqual(len(pipeline.modules()), 4)
#
# 4 modules - first resizes by factor, second resizes to a fixed size
# interpolation is different for each
#
for module in pipeline.modules()[1:]:
self.assertTrue(isinstance(module, cellprofiler.modules.resize.Resize))
module = pipeline.modules()[1]
self.assertEqual(module.x_name, 'DNA')
self.assertEqual(module.y_name, 'ResizedDNA')
self.assertEqual(module.size_method, cellprofiler.modules.resize.R_BY_FACTOR)
self.assertAlmostEqual(module.resizing_factor.value, .25)
self.assertEqual(module.interpolation, cellprofiler.modules.resize.I_NEAREST_NEIGHBOR)
module = pipeline.modules()[2]
self.assertEqual(module.size_method, cellprofiler.modules.resize.R_TO_SIZE)
self.assertEqual(module.specific_width.value, 150)
self.assertEqual(module.specific_height.value, 150)
self.assertEqual(module.interpolation, cellprofiler.modules.resize.I_BILINEAR)
module = pipeline.modules()[3]
self.assertEqual(module.interpolation, cellprofiler.modules.resize.I_BICUBIC)
def test_01_03_load_v3(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:3
DateRevision:20140505183007
GitHash:c675ec6
ModuleCount:1
HasImagePlaneDetails:False
OverlayOutlines:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Display outlines on a blank image?:No
Select image on which to display outlines:DNA
Name the output image:PrimaryOverlay
Outline display mode:Color
Select method to determine brightness of outlines:Max of image
Width of outlines:1.5
Select outlines to display:PrimaryOutlines
Select outline color:Red
Load outlines from an image or objects?:Image
Select objects to display:Nuclei
Select outlines to display\x3A:SecondaryOutlines
Select outline color\x3A:Green
Load outlines from an image or objects?:Objects
Select objects to display:Cells
"""
pipeline = cellprofiler.pipeline.Pipeline()
pipeline.load(StringIO.StringIO(data))
self.assertEqual(len(pipeline.modules()), 1)
module = pipeline.modules()[0]
self.assertTrue(isinstance(module, cellprofiler.modules.overlayoutlines.OverlayOutlines))
self.assertFalse(module.blank_image)
self.assertEqual(module.image_name, "DNA")
self.assertEqual(module.output_image_name, "PrimaryOverlay")
self.assertEqual(module.wants_color, "Color")
self.assertEqual(module.max_type, cellprofiler.modules.overlayoutlines.MAX_IMAGE)
self.assertEqual(module.line_mode.value, "Inner")
self.assertEqual(len(module.outlines), 2)
for outline, name, color, choice, objects_name in (
(module.outlines[0], "PrimaryOutlines", "Red",
cellprofiler.modules.overlayoutlines.FROM_IMAGES, "Nuclei"),
(module.outlines[1], "SecondaryOutlines", "Green",
cellprofiler.modules.overlayoutlines.FROM_OBJECTS, "Cells")):
self.assertEqual(outline.color, color)
self.assertEqual(outline.objects_name, objects_name)
def test_01_00_load_matlab(self):
data = ('eJzzdQzxcXRSMNUzUPB1DNFNy8xJ1VEIyEksScsvyrVSCHAO9/TTUXAuSk0s'
'SU1RyM+zUggpTVXwTSxSMDRTMDSxMjW3MrJQMDIwNFAgGTAwevryMzAwrGZk'
'YKiY8zbM1v+wgcDeFpapDEuFuIOvchpu3WDY9MJBVaNj0boTqn6pmp2LaxRO'
'6Sc86S9JT3mSnrjd77VExqRLnF8XBX+ZU/1+nl78OqYDt80OqFmHR7wy4A1O'
'8PXqq7bo67Tv8TF44LCmfsObxRMWNHb/PuFbwLLZ47r1Puf37gffXDLdKixe'
'PlFdfPMLtXsM7Rd7JwsdfRr9qeeuXOXBCb1b3vDZT+wIiP/Qum+X1Wvv5KX5'
'U5+utpzfvOxM4/mjk65V/jU887pX/tk2xavXJT5Fv/Dfc1lm3syHvoWbnwZo'
'/dE7bJ/DG6DxI93yT2zr+Y1vF7M/WqiYd+yI5orNi18U3Hk3rzzG/GPLmaDi'
'FKnWZwGNOf+7rsz/rF/84zfX/MfHA32YxV3j0qSOPkvUrJLZnnl4eaFy5xHu'
'QJd074sPfyh9ZT1aGvY0fe3ma5FLPq+c/ltuuu3zn2s07Sr97t1L9Ji8wLFG'
'mn31lcjfv+//u/fw+/OZybKbzhfH3bddqn88XOSm7TbHZGu9dwlLrT79LzM+'
'vv8c32mtb/OvObxbf5Cz5rnoy3SJp5Vs1se+FtZu+t9c9P15hmOt1Olr9YzH'
'iy8IAQDsQ/za')
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cellprofiler.pipeline.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO(zlib.decompress(base64.b64decode(data))))
self.assertEqual(len(pipeline.modules()), 2)
module = pipeline.modules()[0]
self.assertTrue(isinstance(module, cellprofiler.modules.loadsingleimage.LoadSingleImage))
self.assertEqual(module.directory.dir_choice, cellprofiler.setting.DEFAULT_INPUT_SUBFOLDER_NAME)
self.assertEqual(module.directory.custom_path, "./foo")
self.assertEqual(len(module.file_settings), 2)
fs = module.file_settings[0]
self.assertEqual(fs.file_name, "dna_image.tif")
self.assertEqual(fs.image_name, "DNA")
fs = module.file_settings[1]
self.assertEqual(fs.file_name, "cytoplasm_image.tif")
self.assertEqual(fs.image_name, "Cytoplasm")
module = pipeline.modules()[1]
self.assertTrue(isinstance(module, cellprofiler.modules.loadsingleimage.LoadSingleImage))
self.assertEqual(module.directory.dir_choice, cellprofiler.setting.DEFAULT_OUTPUT_SUBFOLDER_NAME)
self.assertEqual(module.directory.custom_path, "./bar")
self.assertEqual(len(module.file_settings), 1)
fs = module.file_settings[0]
self.assertEqual(fs.file_name, "DNAIllum.tif")
self.assertEqual(fs.image_name, "DNAIllum")
def test_01_06_load_v6(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:1
SVNRevision:10536
LoadData:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:6|show_window:True|notes:\x5B\x5D]
Input data file location:Elsewhere...\x7Cx\x3A\\projects\\NightlyBuild\\trunk\\ExampleImages\\ExampleSBSImages
Name of the file:1049_Metadata.csv
Load images based on this data?:Yes
Base image location:Default Input Folder\x7C.
Process just a range of rows?:No
Rows to process:1,100000
Group images by metadata?:Yes
Select metadata fields for grouping:Column,Row
Rescale intensities?:Yes
"""
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cellprofiler.pipeline.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO.StringIO(data))
self.assertEqual(len(pipeline.modules()), 1)
module = pipeline.modules()[0]
self.assertTrue(isinstance(module, cellprofiler.modules.loaddata.LoadData))
self.assertEqual(module.csv_file_name, "1049_Metadata.csv")
self.assertEqual(module.csv_directory.dir_choice,
cellprofiler.setting.ABSOLUTE_FOLDER_NAME)
self.assertEqual(module.csv_directory.custom_path,
r"x:\projects\NightlyBuild\trunk\ExampleImages\ExampleSBSImages")
self.assertTrue(module.wants_images)
self.assertEqual(module.image_directory.dir_choice,
cellprofiler.setting.DEFAULT_INPUT_FOLDER_NAME)
self.assertTrue(module.rescale)
self.assertTrue(module.wants_image_groupings)
self.assertFalse(module.wants_rows)
self.assertEqual(module.row_range.min, 1)
self.assertEqual(module.row_range.max, 100000)
self.assertEqual(len(module.metadata_fields.selections), 2)
self.assertEqual(module.metadata_fields.selections[0], "Column")
self.assertEqual(module.metadata_fields.selections[1], "Row")
def test_load_v1(pipeline):
data = ('eJztWltv2jAUNpeidpW6rnvYpL74cZvaKKFF2ngZFNaNqVy0ok57WxoM9WR'
'slDgU9kv2U/a4n7SfsBhCSbzQcGm5SIlkwXH8ne+c4xM7OXI5X7/In8GMos'
'Jyvn7cxATBGtF5k5ntLKT8CBZMpHPUgIxm4bmJ4WebQi0NtZOspmZPMzCtq'
'u/AfFesVN5zfn69BiDl/G47Le7e2nLlmKcJ+RJxjmnL2gJJ8NLt/+O0K93E'
'+jVBVzqxkTWmGPWXaJPV+527W2XWsAmq6G3vYOeq2O1rZFrV5gjo3q7hHiK'
'X+CeSXBgN+4K62MKMunhXv9x7x8u4xCviAJ6P4xCT4pBw2qGnX4z/BMbjkw'
'Fxe+YZv+/KmDZwFzdsnUDc1lt3Vgh9b0P0bUv6hFw1cevMCfk0+JSETw3ib'
'RCEh/y5EPy+hBetjnr8+ENPNzhs69y4WYYdOxJeyAVESEkE1BNPNURPzKcn'
'Bk6mnIctiV/Imnp0qi6ALzDCTDCd/08kvJCLDFLGoW2h6f1P+PQkwDcnG+e'
'dv1tMOTKn44378HFQYQ+HS0m40TXC7YBxfMKe3xeSn0IuoqZuEw4HuQaL2E'
'QGZ2Z/oXjPa/+s+a2A6fJrV/JbyFVu2fAjYdc6CeR/SLvnzQ/ZX21N7ZTzQ'
'D3SHtXOpI8vCYr5WmkenKqo2iJ2Lrq/5ULwQftCyVmWqIV5f0K8lhHnTbO7'
'wiiaJ/81dT3tlNeFTADfOtj5mPvDLLhciJ17wJ+vQh6+v1VtTjAVL7WrsHt'
'T4hvZuVo7nX1sLZ+rZb53RXmwvnbK+6qSWc2850LsDMrX+i2DBtEty61srM'
'LusO+6oLrMV4RbN6LM1hUFJWpMqiOsQ9yDvv/PmYlaJrNpY3H+7wez1cGW6'
'eegaCYc7UyvJyhP2fUP58t9rGjZ+ebhh5g2UGeGuATV4cZxGapb9boW4SJc'
'hItwm4zLeXDROhzhItxqcWHvWQfA/zwKmQ0rUv+9aG2S3xEuwq3Dfjft99i'
'm+BvhIlyEWx2uFxvj5DqTXK8d1KU8PEHr0xvgX5+EbCBCOiYT5+pMpT04/G'
'UphOmN4ekr5cL5W/IcxBI8nRCenMSTm8SDG4hy3Ox3TIfN5qytc2woJbe35'
'vTmR72C9yaENy3xpifxGox2kck5GzipFIZinXkOSMnzthPA541/3JGeHibv'
'nW8A/PM8nv+/7+fhiydiAz7vuYHdEFzSY9PIz99gtjx7dc/4kY/LGv8PmsR'
'r2g==')
fd = StringIO.StringIO(zlib.decompress(base64.b64decode(data)))
pipeline.load(fd)
assert len(pipeline.modules()) == 3
module = pipeline.modules()[2]
assert isinstance(module, cellprofiler.modules.convertobjectstoimage.ConvertToImage)
assert module.object_name.value == "Nuclei"
assert module.image_name.value == "CellImage"
assert module.image_mode.value == "Color"
assert module.colormap.value == "winter"
def test_01_02_load_v1(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:1
SVNRevision:9169
MeasureImageOverlap:[module_num:1|svn_version:\'9000\'|variable_revision_number:1|show_window:True|notes:\x5B\x5D]
Which image do you want to use as the basis for calculating the amount of overlap? :GroundTruth
Which image do you want to compare for overlap?:Segmentation
"""
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cellprofiler.pipeline.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO.StringIO(data))
self.assertEqual(len(pipeline.modules()), 1)
module = pipeline.modules()[0]
self.assertTrue(isinstance(module, cellprofiler.modules.measureimageoverlap.MeasureImageOverlap))
self.assertEqual(module.ground_truth, "GroundTruth")
self.assertEqual(module.test_img, "Segmentation")
def print_measurements(options):
"""Print the measurements that would be output by a pipeline
This function calls Pipeline.get_measurement_columns() to get the
measurements that would be output by a pipeline. This can be used in
a workflow tool or LIMS to find the outputs of a pipeline without
running it. For instance, someone might want to integrate CellProfiler
with Knime and write a Knime node that let the user specify a pipeline
file. The node could then execute CellProfiler with the --measurements
switch and display the measurements as node outputs.
"""
if options.pipeline_filename is None:
raise ValueError("Can't print measurements, no pipeline file")
import cellprofiler.pipeline
pipeline = cellprofiler.pipeline.Pipeline()
def callback(pipeline, event):
if isinstance(event, cellprofiler.pipeline.LoadExceptionEvent):
raise ValueError("Failed to load %s" % options.pipeline_filename)
pipeline.add_listener(callback)
pipeline.load(os.path.expanduser(options.pipeline_filename))
columns = pipeline.get_measurement_columns()
print "--- begin measurements ---"
print "Object,Feature,Type"
for column in columns:
object_name, feature, data_type = column[:3]
print "%s,%s,%s" % (object_name, feature, data_type)
print "--- end measurements ---"
def test_load_matlab(pipeline):
data = ('eJzzdQzxcXRSMNUzUPB1DNFNy8xJ1VEIyEksScsvyrVSCHAO9/TTUXAuSk0'
'sSU1RyM+zUnArylTwKs1TMDRSMDS2Mja3MjBUMDIwsFQgGTAwevryMzAwrG'
'BiYKiY8zbcMf+WgUiZgsI1v45A31uNzn3WJbI5l7c81rLK9ZAO0V5lkikuz'
'PP1yQ2NQoE307/umL/p9b+jSR5Nj5kMPa+8M/Ce9nXO933f9z6fLr6cj2FH'
'LlMA77UoA+8EW62FOR7BJ8I7in2Wx7HOeBC547/2jOsXjha4XDBVsfvkYLP'
'or9zcDQ+LxOx/HTpm51j74sSpgy9+n+PYeS9BSLKtX/8j00TtP8KNTzqu7F'
'tk+c1g8cLajvXvjtVJhv+51vzzd8ZkdueTzpYz1C/tu1DPb/br2bQ9pip/3'
'SzaMw7G10Q4zI7me/Kt90Dm8gdThC1SCy7ax/+sn54UHvonPPznVs5DDqf3'
'LbvBvbOaWdP1Sajgv8ufytfckD/+zZbdL7CGL/Lz9LR0tTc+H7598Y1Wf9d'
'1KPpErNsM7qLneyer3st3XJveWe70IOT5HDnNmtY7sUVmyheKD9XK/l/K4s'
'f55eYtvU3+jwuXfBD4xLpZ4/G094q1crs/hZv0m8zJ/Wr/YF5+rUoOi2dP8'
'f2fhkIWbC9sBPi/zrm1fC5Lv/PjirWZy/epv91c9lzxVfLt6uuLZQL5Py89'
'oh5lv/SBbeCHlp6XEuc/3mg/cmjRyyqhuIt/Nn1r17E5V3uuid9gm/GHgNf'
'PdNztvn080WRe+vDSZkVHIb97ddOv2vXuf1e94LRydq3IWeez7LWc3Jbtey'
'o73kg/+1e97g77p29bQy/P2HVm/svHOb/fbbzIn/KZN+DePrWHkY79zpuZH'
'90pW3//yM42bSH33vLrv+7Hb/z376rp2nuzFWayKxrPF/a5OLdpemH2sXf7'
'FnxbvubvA+Pmx/NKJl7ofL6ySfnJPYnAPvXT3uLWn8xD2sNjDwoXTb9oWfR'
'zQuv+aVnz1y57sljE46yw8fW/PwPqpx4/Nvf/SRNr4ys/70lt+bflWl3+wc'
'Tn/2r660zeef/XfzaV/REATdyFtA==')
fd = StringIO.StringIO(zlib.decompress(base64.b64decode(data)))
pipeline.load(fd)
assert len(pipeline.modules()) == 3
module = pipeline.modules()[2]
assert isinstance(module, cellprofiler.modules.convertobjectstoimage.ConvertToImage)
assert module.object_name.value == "Nuclei"
assert module.image_name.value == "NucleiImage"
assert module.image_mode.value == "Color"
assert module.colormap.value == "flag"