def test_dont_load_file():
csv_text = """"Image_FileName_DNA","Image_PathName_DNA"
"%s","%s"
""" % (
test_filename,
test_path,
)
pipeline, module, filename = make_pipeline(csv_text)
c0_ran = [False]
def callback(workspace):
imgset = workspace.image_set
assert len(imgset.names) == 0
c0_ran[0] = True
c0 = C0()
c0.callback = callback
c0.set_module_num(1)
pipeline.add_module(c0)
try:
module.wants_images.value = False
pipeline.run()
assert c0_ran[0]
finally:
os.remove(filename)
def test_scaling():
"""Test loading an image scaled and unscaled"""
folder = "loaddata"
file_name = "1-162hrh2ax2.tif"
path = tests.modules.make_12_bit_image(folder, file_name, (22, 18))
csv_text = (
"Image_PathName_MyFile,Image_FileName_MyFile\n" "%s,%s\n" % os.path.split(path)
)
c0_image = []
for rescale in (False, True):
pipeline, module, filename = make_pipeline(csv_text)
try:
module.rescale.value = rescale
def callback(workspace):
imgset = workspace.image_set
image = imgset.get_image("MyFile")
pixels = image.pixel_data
c0_image.append(pixels.copy())
c0 = C0()
c0.callback = callback
c0.set_module_num(2)
pipeline.add_module(c0)
pipeline.run()
finally:
os.remove(filename)
unscaled, scaled = c0_image
numpy.testing.assert_almost_equal(unscaled * 65535.0 / 4095.0, scaled)
def test_metadata_row_and_column_and_well():
csv_text = """"Metadata_Row","Metadata_Column","Metadata_Well"
"C","03","B14"
"""
pipeline, module, filename = make_pipeline(csv_text)
columns = module.get_measurement_columns(pipeline)
assert any([
c[0] == cellprofiler_core.measurement.IMAGE and c[1] == "Metadata_Row"
and c[2] == "varchar(1)" for c in columns
])
assert any([
c[0] == cellprofiler_core.measurement.IMAGE
and c[1] == "Metadata_Column" and c[2] == "varchar(2)" for c in columns
])
assert any([
c[0] == cellprofiler_core.measurement.IMAGE and c[1] == "Metadata_Well"
and c[2] == "varchar(3)" for c in columns
])
m = pipeline.run()
features = module.get_measurements(
pipeline,
cellprofiler_core.measurement.IMAGE,
cellprofiler_core.measurement.C_METADATA,
)
for feature, expected in (("Row", "C"), ("Column", "03"), ("Well", "B14")):
assert feature in features
value = m.get_current_image_measurement("_".join(
(cellprofiler_core.measurement.C_METADATA, feature)))
assert value == expected
def test_load_file():
csv_text = """"Image_FileName_DNA","Image_PathName_DNA"
"%s","%s"
""" % (
test_filename,
test_path,
)
pipeline, module, filename = make_pipeline(csv_text)
c0_ran = [False]
def callback(workspace):
imgset = workspace.image_set
image = imgset.get_image("DNA")
pixels = image.pixel_data
assert pixels.shape[0] == test_shape[0]
c0_ran[0] = True
c0 = C0()
c0.callback = callback
c0.set_module_num(2)
pipeline.add_module(c0)
try:
m = pipeline.run()
assert isinstance(m, cellprofiler_core.measurement.Measurements)
assert c0_ran[0]
hexdigest = m.get_current_image_measurement("MD5Digest_DNA")
assert hexdigest == test_md5
assert "PathName_DNA" in m.get_feature_names(cellprofiler_core.measurement.IMAGE)
assert m.get_current_image_measurement("PathName_DNA") == test_path
assert "FileName_DNA" in m.get_feature_names(cellprofiler_core.measurement.IMAGE)
assert m.get_current_image_measurement("FileName_DNA") == test_filename
finally:
os.remove(filename)
def test_metadata():
csv_text = """"Metadata_Plate"
"P-12345"
"""
pipeline, module, filename = make_pipeline(csv_text)
m = pipeline.run()
data = m.get_current_image_measurement("Metadata_Plate")
assert data == "P-12345"
os.remove(filename)
def test_int_image_measurement():
csv_text = """"Test_Measurement"
1
"""
pipeline, module, filename = make_pipeline(csv_text)
m = pipeline.run()
data = m.get_current_image_measurement("Test_Measurement")
assert isinstance(data, numpy.int), "data is type %s, not np.int" % (type(data))
assert data == 1
os.remove(filename)
def test_float_image_measurement():
csv_text = """"Test_Measurement"
1.5
"""
pipeline, module, filename = make_pipeline(csv_text)
m = pipeline.run()
data = m.get_current_image_measurement("Test_Measurement")
assert numpy.isreal(data)
assert round(abs(data - 1.5), 7) == 0
os.remove(filename)
def test_string_image_measurement():
csv_text = """"Test_Measurement"
"Hello, world"
"""
pipeline, module, filename = make_pipeline(csv_text)
m = pipeline.run()
data = m.get_current_image_measurement("Test_Measurement")
assert not numpy.isreal(data)
assert data == "Hello, world"
os.remove(filename)
def test_long_int_image_measurement():
csv_text = """"Test_Measurement"
1234567890123
"""
pipeline, module, filename = make_pipeline(csv_text)
m = pipeline.run()
data = m.get_current_image_measurement("Test_Measurement")
assert isinstance(
data, six.text_type
), "Expected <type 'six.text_type'> got %s" % type(data)
assert data == "1234567890123"
os.remove(filename)
def test_load_bcb_file():
csv_text = """ELN_RUN_ID,CBIP_RUN_ID,ASSAY_PLATE_BARCODE,\
MX_PLATE_ID,ASSAY_WELL_POSITION,ASSAY_WELL_ROLE,SITE_X,SITE_Y,\
MICROSCOPE,SOURCE_DESCRIPTION,DATE_CREATED,FILE_PATH,FILE_NAME,\
CPD_PLATE_MAP_NAME,CPD_WELL_POSITION,BROAD_ID,\
CPD_MMOL_CONC,SOURCE_NAME,SOURCE_COMPOUND_NAME,CPD_SMILES
"4012-10-W01-01-02","4254","BR00021547","20777","N01","COMPOUND",\
"2","2","GS IX Micro","DAPI","2010/03/19 06:01:12","%s",\
"%s","C-4012-00-D80-001_Rev3","N01",\
"BRD-K71194192-001-01-6","2.132352941","ChemBridge","",\
"Oc1ccnc(SCC(=O)Nc2ccc(Oc3ccccc3)cc2)n1"
""" % (
test_path,
test_filename,
)
pipeline, module, filename = make_pipeline(csv_text)
c0_ran = [False]
def callback(workspace):
imgset = workspace.image_set
image = imgset.get_image("DAPI")
pixels = image.pixel_data
assert pixels.shape[0] == test_shape[0]
c0_ran[0] = True
c0 = C0()
c0.callback = callback
c0.set_module_num(2)
pipeline.add_module(c0)
try:
m = pipeline.run()
assert isinstance(m, cellprofiler_core.measurement.Measurements)
assert c0_ran[0]
hexdigest = m.get_current_image_measurement("MD5Digest_DAPI")
assert hexdigest == test_md5
assert "PathName_DAPI" in m.get_feature_names(
cellprofiler_core.measurement.IMAGE)
assert m.get_current_image_measurement("PathName_DAPI") == test_path
assert "FileName_DAPI" in m.get_feature_names(
cellprofiler_core.measurement.IMAGE)
assert m.get_current_image_measurement(
"FileName_DAPI") == test_filename
finally:
os.remove(filename)
def test_alternate_image_start():
csv_text = """"Metadata_Measurement"
1
2
3
4
5
6
7
8
9
10
"""
pipeline, module, filename = make_pipeline(csv_text)
m = pipeline.run(image_set_start=2)
data = m.get_all_measurements(cellprofiler_core.measurement.IMAGE,
"Metadata_Measurement")
assert all([data[i - 2] == i for i in range(2, 11)])
os.remove(filename)
def test_some_rows():
csv_text = """"Test_Measurement"
1
2
3
4
5
6
7
8
9
10
"""
pipeline, module, filename = make_pipeline(csv_text)
module.wants_rows.value = True
module.row_range.min = 4
module.row_range.max = 6
m = pipeline.run()
assert isinstance(m, cellprofiler_core.measurement.Measurements)
data = m.get_all_measurements(cellprofiler_core.measurement.IMAGE, "Test_Measurement")
assert numpy.all(data == numpy.arange(4, 7))
os.remove(filename)
def test_img_717():
"""Regression test of img-717, column without underbar"""
csv_text = """"Image","Test_Measurement"
"foo",1
"foo",2
"foo",3
"foo",4
"foo",5
"foo",6
"foo",7
"foo",8
"foo",9
"foo",10
"""
pipeline, module, filename = make_pipeline(csv_text)
module.wants_rows.value = True
module.row_range.min = 4
module.row_range.max = 6
m = pipeline.run()
assert isinstance(m, cellprofiler_core.measurement.Measurements)
data = m.get_all_measurements(cellprofiler_core.measurement.IMAGE, "Test_Measurement")
assert numpy.all(data == numpy.arange(4, 7))
os.remove(filename)
def run_pipeline_headless(options, args):
"""
Run a CellProfiler pipeline in headless mode
"""
if options.first_image_set is not None:
if not options.first_image_set.isdigit():
raise ValueError(
"The --first-image-set option takes a numeric argument")
else:
image_set_start = int(options.first_image_set)
else:
image_set_start = 1
image_set_numbers = None
if options.last_image_set is not None:
if not options.last_image_set.isdigit():
raise ValueError(
"The --last-image-set option takes a numeric argument")
else:
image_set_end = int(options.last_image_set)
if image_set_start is None:
image_set_numbers = numpy.arange(1, image_set_end + 1)
else:
image_set_numbers = numpy.arange(image_set_start,
image_set_end + 1)
else:
image_set_end = None
if (options.pipeline_filename is not None) and (
not options.pipeline_filename.lower().startswith("http")):
options.pipeline_filename = os.path.expanduser(
options.pipeline_filename)
pipeline = cellprofiler_core.pipeline.Pipeline()
initial_measurements = None
try:
if h5py.is_hdf5(options.pipeline_filename):
initial_measurements = cellprofiler_core.measurement.load_measurements(
options.pipeline_filename, image_numbers=image_set_numbers)
except:
logging.root.info("Failed to load measurements from pipeline")
if initial_measurements is not None:
pipeline_text = initial_measurements.get_experiment_measurement(
cellprofiler_core.pipeline.M_PIPELINE)
pipeline_text = pipeline_text
pipeline.load(six.moves.StringIO(pipeline_text))
if not pipeline.in_batch_mode():
#
# Need file list in order to call prepare_run
#
with h5py.File(options.pipeline_filename, "r") as src:
if cellprofiler_core.utilities.hdf5_dict.HDF5FileList.has_file_list(
src):
cellprofiler_core.utilities.hdf5_dict.HDF5FileList.copy(
src, initial_measurements.hdf5_dict.hdf5_file)
else:
pipeline.load(options.pipeline_filename)
if options.groups is not None:
kvs = [x.split("=") for x in options.groups.split(",")]
groups = dict(kvs)
else:
groups = None
file_list = cellprofiler_core.preferences.get_image_set_file()
if file_list is not None:
pipeline.read_file_list(file_list)
elif options.image_directory is not None:
pathnames = []
for dirname, _, fnames in os.walk(
os.path.abspath(options.image_directory)):
pathnames.append([
os.path.join(dirname, fname) for fname in fnames
if os.path.isfile(os.path.join(dirname, fname))
])
pathnames = sum(pathnames, [])
pipeline.add_pathnames_to_file_list(pathnames)
#
# Fixup CreateBatchFiles with any command-line input or output directories
#
if pipeline.in_batch_mode():
create_batch_files = [
m for m in pipeline.modules() if m.is_create_batch_module()
]
if len(create_batch_files) > 0:
create_batch_files = create_batch_files[0]
if options.output_directory is not None:
create_batch_files.custom_output_directory.value = (
options.output_directory)
if options.image_directory is not None:
create_batch_files.default_image_directory.value = (
options.image_directory)
use_hdf5 = len(args) > 0 and not args[0].lower().endswith(".mat")
measurements = pipeline.run(
image_set_start=image_set_start,
image_set_end=image_set_end,
grouping=groups,
measurements_filename=None if not use_hdf5 else args[0],
initial_measurements=initial_measurements,
)
if len(args) > 0 and not use_hdf5:
pipeline.save_measurements(args[0], measurements)
if options.done_file is not None:
if measurements is not None and measurements.has_feature(
cellprofiler_core.measurement.EXPERIMENT,
cellprofiler_core.pipeline.EXIT_STATUS,
):
done_text = measurements.get_experiment_measurement(
cellprofiler_core.pipeline.EXIT_STATUS)
exit_code = 0 if done_text == "Complete" else -1
else:
done_text = "Failure"
exit_code = -1
fd = open(options.done_file, "wt")
fd.write("%s\n" % done_text)
fd.close()
elif not measurements.has_feature(cellprofiler_core.measurement.EXPERIMENT,
cellprofiler_core.pipeline.EXIT_STATUS):
# The pipeline probably failed
exit_code = 1
else:
exit_code = 0
if measurements is not None:
measurements.close()
return exit_code
