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 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 ---")
