def test_12_00_load_version_0(self):
data = ('eJzr9HBx4+WS4mIAAQ4OBhYGAQZk8B8KTiig8mHyCVCaEUp3QOkVTDBxRrCc'
'BFRcEGo+urqQIFdXkOr/aABmzwNWCM3BMApGIvBwdQwA0RFQPiw9nWBCVeeb'
'mlhcWpSam5pXUgziw9KlA5n24kq/HswQfgY0XRJKvy84IfRo+h2ZAD39wkrZ'
'DlZUdZ65iempDIh060GhvcF+/i6gFAxLdwrMFBo4QgGuciCAHcIvgOZvQuXA'
'B14IDTNnFIwsgF4OKEDpCZyo6sDlgF9pblJqEZiflp8PpmHlggWJ9qKXAxrs'
'JBowCsAAVzkQwQ3hV0DzN6FywANqAGprexSMFICrPTuDF1VdSmJJIojOzEtJ'
'rWCgvD0LKQeY4OWAATd+9TB3LZAi08JhChgZ2CBxAY0QVmCIgphMTExgEWZg'
'zgYxBIABzQJWoQDVB8nxjIwwcyCAAxjSIDYzowe0SGBBsU8IGmMgNbzNhn6I'
'HvTgAIwMCL+AaEsDVP4oGAWjYBSMglEwCoYfQG9XWvDjVw/r90yQpKWrhh4g'
'v10JaakzwuddIAA23IdoX/pAg54HxV7k9qU8uH3ZQEVfkQ9w9bdPQPsjL2Qg'
'NKH+doEchB7tb49MgKu/zSGLqo7a/W3a9RMjCPYTwwdhPxG9nkiQxascnl93'
'yNPSVUMP0L6eiCFYT+QMonoCAIv/qlY=')
data = zlib.decompress(base64.b64decode(data))
fid, name = tempfile.mkstemp(".h5")
m = None
try:
fd = os.fdopen(fid, "wb")
fd.write(data)
fd.close()
m = cpmeas.load_measurements(name)
self.assertEqual(tuple(m.get_image_numbers()), (1,))
self.assertEqual(m.get_measurement(cpmeas.IMAGE, "foo",
image_set_number = 1), 12345)
finally:
if m is not None:
del m
os.unlink(name)
def test_12_00_load_version_0(self):
data = ('eJzr9HBx4+WS4mIAAQ4OBhYGAQZk8B8KTiig8mHyCVCaEUp3QOkVTDBxRrCc'
'BFRcEGo+urqQIFdXkOr/aABmzwNWCM3BMApGIvBwdQwA0RFQPiw9nWBCVeeb'
'mlhcWpSam5pXUgziw9KlA5n24kq/HswQfgY0XRJKvy84IfRo+h2ZAD39wkrZ'
'DlZUdZ65iempDIh060GhvcF+/i6gFAxLdwrMFBo4QgGuciCAHcIvgOZvQuXA'
'B14IDTNnFIwsgF4OKEDpCZyo6sDlgF9pblJqEZiflp8PpmHlggWJ9qKXAxrs'
'JBowCsAAVzkQwQ3hV0DzN6FywANqAGprexSMFICrPTuDF1VdSmJJIojOzEtJ'
'rWCgvD0LKQeY4OWAATd+9TB3LZAi08JhChgZ2CBxAY0QVmCIgphMTExgEWZg'
'zgYxBIABzQJWoQDVB8nxjIwwcyCAAxjSIDYzowe0SGBBsU8IGmMgNbzNhn6I'
'HvTgAIwMCL+AaEsDVP4oGAWjYBSMglEwCoYfQG9XWvDjVw/r90yQpKWrhh4g'
'v10JaakzwuddIAA23IdoX/pAg54HxV7k9qU8uH3ZQEVfkQ9w9bdPQPsjL2Qg'
'NKH+doEchB7tb49MgKu/zSGLqo7a/W3a9RMjCPYTwwdhPxG9nkiQxascnl93'
'yNPSVUMP0L6eiCFYT+QMonoCAIv/qlY=')
data = zlib.decompress(base64.b64decode(data))
fid, name = tempfile.mkstemp(".h5")
m = None
try:
fd = os.fdopen(fid, "wb")
fd.write(data)
fd.close()
m = cpmeas.load_measurements(name)
self.assertEqual(tuple(m.get_image_numbers()), (1, ))
self.assertEqual(
m.get_measurement(cpmeas.IMAGE, "foo", image_set_number=1),
12345)
finally:
if m is not None:
del m
os.unlink(name)
def test_12_01_load_version_1(self):
data = (
"eJzt3E9sFFUcwPH3Zrft0lIpDUGCBWqiUg8124ZAQQslUqwxtkSQlAvplu7a"
"Ymlr/yQ9mWKIKZgoRw5GS2JMI2rqQSFeEE94MZyM8WBKwMDBIkFDMGDrzr73"
"dneGzi4sq7vd/X44/Hbe/HZmOrx582aT+R1v27W7snxtubAFAsIvqkSyBW3/"
"UelYNuu7dJQ6Tuo4bZl2GVu3Rrev1Nt35+17rbXVzl5wMfsJ+lUMCBSjttad"
"e+zYqZdrdbxoOfP2h4dH+gYHxKvh0MjYcPhIeGB0xG43/bPpIfcrRan6rt5A"
"SbQH2h8ty4q1+KL92f5QFe2Y/qQjk7qfS2m2owSiV4L92Se7dFf2O/ZXrXu4"
"nfPr8Yb2xJWSH/a2d+ySwoqPEuMlqfPN9Vrr+y+PaunxGhen9Pn8rkzFdOPi"
"nWoVGReLk9e4OFvmzGsMNjTUB7fUNzbVNzTaHzY1xtozHxcX779demCYqDZ5"
"0THi4NwFWSnEcmvq7vzpucsT9+zWK+U75oX/czH7ijjstbUvdbf+Uc1P0l4N"
"+5er6JzDoFi4rwbTn26UO/N6QqMhO/YN9ITHReIqaMlwv1KULT5L8Ik0swR1"
"hFJPE8xkxvTriuh1aa+ReoUV264UI71jkUh/OD53kLq9JxzpD40m2uOzDcvq"
"/FC3+ZOmHMmzDd8Je7aR6RlwMrMEc1faXZE631yvH1VmZfcFw3P2mbZf1alo"
"OfuVmYSZ/mWWE/3EV9ut2/wqPbbZ+/vJUJb/0gcTu5Msi36wpq4vXH/72C+l"
"0c9NWzrmRUl01cUnpQgGxMxL32+v+G3yxEarf/j8+qF9V2uit6UzpU81yLDn"
"0+gnann1ChXd40hQx80rnMfTHRqOxZePhN4It48d6Q6r5Y7uw+FDo4kGM77U"
"pfn7vI5v+lO1/I3H8Zn8n1zHl+1xzj7JDStFbUC0bH3vqwMnjz3z1sZ37n3w"
"8bWr1u5vXz+7Ifji7+v+ERPnm5uX+VXfSnfHFo+rwB27OHn15KlqZ17sCovG"
"9rFD/eG+R+/J6g4l43eoJh6gMuI1YvWuUsuTq1VMNw4EntDxfzpu5Bev59jp"
"1c68yOBgLLrvuJk+x7pnqi2rUueb+9SNxx5yRwXOaxwY0g0n16qYbhzoWqci"
"84Hi5DUfmFnrzMv2zNb9u/a7aW5EZnXbmpRpRWcJ/BJy84Buy8UvIXtqUueb"
"/je9Piu7Lxg5+CXk3Abdlo+/hNj3UbnYfTSY+ns3n3U16CvA51wEAABAgZBJ"
"0UqKAAAAAAAAAAAAAAAAAAAAAAAgf3i9N/q0R/4P7joXfkcAAAAFKNM6E539"
"rgbqTAAAABQ06kwAAAAAAAAAAAAAAAAAAAAAAJD/vN4brfHI7wm4GqgzAQBA"
"wbPnC2KxOhPaxWkV3dMEFId0/aP2jIpVuTpA5NTe9o5dUvji//+zf6TOr9Vx"
"qEmmSouPN++veISDW0KkKFPvbuvTUhI9A7F3t32qxT7D9oeqgHk2q9XfW6Oi"
"VF80z23mOq0QTbHvSf0SuBXbrhQjvWORSH84ni91e0840h8aTbQHotu3v++z"
"rJlrus2f9HhYrf+n7JwdJxrahWjJyvlQ/cqK94Oqz1Lnm/7X+3lWdl8wpCjN"
"sF/VqWipL5oaAqaOlOlfZjnRT3zjX+g2v0qPbfb+fjKU5b/0wWRaV2vmrKuB"
"uloAAAAFzUqKpqYWcz4AAAAAAAAAAAAAAAAAAAAAAPKL13uj9R75z7kLIlBX"
"CwCAgidj/4SuziLESl2hJHBTLdfdUjFdfaWZP1WkvlJxamvduceOnXrZ9Ke2"
"W868ntBoyI59Az3hcREv95JxNZ4lUI+oJaiPLRf1iKZupUyPX693/srK7gtG"
"DuoRBW7rtkKqR9QSv8I16hEBAAAUNOoRAQAAAAAAAAAAAAAAAAAAAACQ/7ze"
"G13nkV9zdPH3RXlnAACAwuVVj+hUk5oXnNumYrp6RC0vqDzqERUnr3pEP29z"
"zi+LsB7RpfEc1iMKPi9T5pvrdbI5dV6xyUE9olPbE/2kYOoRXWqjHhEAAEAx"
"oR4RAAAAAAAAAAAAAAAAAAAAAAD572HrEW2lHhEAAEVn/ODcBVkpxHJr6u78"
"6bnLE/fs+cCV8h3zwp+07s2FS7fvTGzeGl03+7Uc/FuU3bdyU7NeeVuU/Qv/"
"J+C3"
)
data = zlib.decompress(base64.b64decode(data))
fid, name = tempfile.mkstemp(".h5")
m = None
try:
fd = os.fdopen(fid, "wb")
fd.write(data)
fd.close()
m = cpmeas.load_measurements(name)
self.assertEqual(tuple(m.get_image_numbers()), (1, 2))
self.assertEqual(m.get_measurement(cpmeas.IMAGE, "foo", image_set_number=1), 12345)
self.assertEqual(m.get_measurement(cpmeas.IMAGE, "foo", image_set_number=2), 23456)
for image_number, expected in ((1, np.array([234567, 123456])), (2, np.array([123456, 234567]))):
values = m.get_measurement("Nuclei", "bar", image_set_number=image_number)
self.assertEqual(len(expected), len(values))
self.assertTrue(np.all(expected == values))
finally:
if m is not None:
del m
os.unlink(name)
def run_with_yield(self):
# this function acts like a CP pipeline object, allowing us to
# use the same code path as a non-distributed computation for
# tracking results and updating the GUI.
# Returned results are combined as they are received in
# an HDF5 dict via self.measurements instance
jobs_finished = 0
while True:
finished_job = self.work_server.fetch_result()
if finished_job is not None:
if finished_job['pipeline_hash'][0] == self.pipeline_blob_hash:
#Read data, write to temp file, load into HDF5_dict instance
raw_dat = finished_job['measurements'][0]
meas_str = zlib.decompress(raw_dat)
temp_hdf5 = tempfile.NamedTemporaryFile(dir=os.path.dirname(self.output_file))
temp_hdf5.write(meas_str)
temp_hdf5.flush()
curr_meas = cpmeas.load_measurements(filename=temp_hdf5.name)
self.measurements.combine_measurements(curr_meas,can_overwrite = True)
del curr_meas
jobs_finished += 1
if self.status_callback:
self.status_callback(self.total_jobs, jobs_finished)
else:
# out of date result?
print "ignored mismatched pipeline hash", finished_job['pipeline_hash'][0], self.pipeline_blob_hash
else:
# pretend to be busy
time.sleep(0.1)
if jobs_finished == self.total_jobs:
# when finished, stop serving
self.stop_serving()
return
# this is part of the pipeline mimicry
if self.frame:
post_module_runner_done_event(self.frame)
# continue to yield None until the work is finished
yield None
def test_08_01_load(self):
data = ('eJzt3M1LFGEcwPFnZtZ2VcSXS29Gewm8BJu9KHgxUtvAl6UX8GDpmmMWauIL'
'SPQH6M2jl6Bb0qGgCK92M+rWRZAg6BJB4LFL2D7zPM/uztS4smtYu9/P5fH5'
'zTPP7M7+5pnxML/lZFdPXc2JGiHFYiIiGkS+XW31jb9vto/o1tLtkm7XbBO3'
'vG3HdLxRzx8cd/N6d7ccvRtgjvO5SrUxgUqU7L6cku2g7pt82rT94/rc9NzC'
'rDvlTs/Pyb7Jy84ijxuWv0lH9Sd0XhbK36/VqiV/K1Mwf80qu1TlH3dtKn3P'
'Fbm8TZZ43Bv9A10yg03exZ0SJ6xQYetAKqr6M/r6LrQO7NSp1syDyhJcB1p0'
'u1LtH+etA/0LU6PurNfvc+fTY+n59HBqMj3v5taHxD6PG1wHWqLFfoPKFrYO'
'DNaq/qK+vgutA0l9A/A/baNShD3Prtb5x8lrXrb3p8fcRVH686xaB+zsOpCo'
'3Xu8+Vyx20UesExZIqp+C/2DVGXOqPzTdlTEyVzZ8o+GzImOeCPiej91Ri1L'
'7Wgex8z6UCvavf0s/X+N7c1ribmJhfHxSVfPJbyVXMbH3HHvhmDiscz8cn/H'
'trd6dSwisptFk/7l5Zi+nub+4jPJL5hX7fV7jzfr3krjgRy+bFjiSJF5pZ4k'
'LNufV8H8Mv1cnjhPm3QsooZ70/6eJzMH/E33R95HrT/dRws8+Gw1BwL6CnD8'
'XQAAAJQJK6+181onrwUAAAAAAAAAAAAAAAAAAAAAAIcr7L3RUyHjY7cCgYiv'
'AQAAZWjxzve3Vl3mOcCJv/zZsf3FedWWiXauRx/+ENHMxs11S8RjIvF+Y/dk'
'/c6zjtNr71aXZCmXje3W10d3lp9/04X8hh68+CRr6ix+sB49dj4e4lcCAAAF'
'hNV1TOi6d6lh1Raq67g1olrqOlamsLqOM8P+cQdd17FwPUJV7+24pXKzITvU'
'1HkrrS6hyfd91CWceKJjkbzLJL/e3JBXb669yDOhBOsRbg7vOTz7QeKjJR22'
'7BxCPcLEXR0Lq0c49B/WI5yYCgSoRwgAAFDWqEcIAAAAAAAAAAAAAAAAAAAA'
'AMC/L+y90Y6Q8WdC6hFSVwAAgPJ19cpAr/fOoL7hm/cHo7pNnT3Xev7CRWH/'
'FpfhS9n3CXNxGW7Lzr9W/5c+OAAAAAAA2LdfZABKkA==')
data = zlib.decompress(base64.b64decode(data))
fd, filename = tempfile.mkstemp('.h5')
try:
f = os.fdopen(fd, "wb")
f.write(data)
f.close()
m = cpmeas.load_measurements(filename)
for i in range(1, 4):
self.assertEqual(
m.get_measurement(cpmeas.IMAGE, 'ImageNumber', i), i)
for i, plate in enumerate(('P-12345', 'P-23456', 'P-34567')):
self.assertEqual(
m.get_measurement(cpmeas.IMAGE, 'Metadata_Plate', i + 1),
plate)
finally:
try:
os.unlink(filename)
except:
print "Failed to remove file %s" % filename
def test_08_01_load(self):
data = ('eJzt3M1LFGEcwPFnZtZ2VcSXS29Gewm8BJu9KHgxUtvAl6UX8GDpmmMWauIL'
'SPQH6M2jl6Bb0qGgCK92M+rWRZAg6BJB4LFL2D7zPM/uztS4smtYu9/P5fH5'
'zTPP7M7+5pnxML/lZFdPXc2JGiHFYiIiGkS+XW31jb9vto/o1tLtkm7XbBO3'
'vG3HdLxRzx8cd/N6d7ccvRtgjvO5SrUxgUqU7L6cku2g7pt82rT94/rc9NzC'
'rDvlTs/Pyb7Jy84ijxuWv0lH9Sd0XhbK36/VqiV/K1Mwf80qu1TlH3dtKn3P'
'Fbm8TZZ43Bv9A10yg03exZ0SJ6xQYetAKqr6M/r6LrQO7NSp1syDyhJcB1p0'
'u1LtH+etA/0LU6PurNfvc+fTY+n59HBqMj3v5taHxD6PG1wHWqLFfoPKFrYO'
'DNaq/qK+vgutA0l9A/A/baNShD3Prtb5x8lrXrb3p8fcRVH686xaB+zsOpCo'
'3Xu8+Vyx20UesExZIqp+C/2DVGXOqPzTdlTEyVzZ8o+GzImOeCPiej91Ri1L'
'7Wgex8z6UCvavf0s/X+N7c1ribmJhfHxSVfPJbyVXMbH3HHvhmDiscz8cn/H'
'trd6dSwisptFk/7l5Zi+nub+4jPJL5hX7fV7jzfr3krjgRy+bFjiSJF5pZ4k'
'LNufV8H8Mv1cnjhPm3QsooZ70/6eJzMH/E33R95HrT/dRws8+Gw1BwL6CnD8'
'XQAAAJQJK6+181onrwUAAAAAAAAAAAAAAAAAAAAAAIcr7L3RUyHjY7cCgYiv'
'AQAAZWjxzve3Vl3mOcCJv/zZsf3FedWWiXauRx/+ENHMxs11S8RjIvF+Y/dk'
'/c6zjtNr71aXZCmXje3W10d3lp9/04X8hh68+CRr6ix+sB49dj4e4lcCAAAF'
'hNV1TOi6d6lh1Raq67g1olrqOlamsLqOM8P+cQdd17FwPUJV7+24pXKzITvU'
'1HkrrS6hyfd91CWceKJjkbzLJL/e3JBXb669yDOhBOsRbg7vOTz7QeKjJR22'
'7BxCPcLEXR0Lq0c49B/WI5yYCgSoRwgAAFDWqEcIAAAAAAAAAAAAAAAAAAAA'
'AMC/L+y90Y6Q8WdC6hFSVwAAgPJ19cpAr/fOoL7hm/cHo7pNnT3Xev7CRWH/'
'FpfhS9n3CXNxGW7Lzr9W/5c+OAAAAAAA2LdfZABKkA==')
data = zlib.decompress(base64.b64decode(data))
fd, filename = tempfile.mkstemp('.h5')
try:
f = os.fdopen(fd, "wb")
f.write(data)
f.close()
m = cpmeas.load_measurements(filename)
for i in range(1, 4):
self.assertEqual(m.get_measurement(
cpmeas.IMAGE, 'ImageNumber', i), i)
for i, plate in enumerate(('P-12345', 'P-23456', 'P-34567')):
self.assertEqual(m.get_measurement(
cpmeas.IMAGE, 'Metadata_Plate', i+1), plate)
finally:
try:
os.unlink(filename)
except:
print "Failed to remove file %s" % filename
def execute_merge_files(self, mm):
input_files = []
output_fd, output_file = tempfile.mkstemp(".mat")
pipeline = cpp.Pipeline()
li = LoadImages()
li.module_num = 1
pipeline.add_module(li)
for m in mm:
input_fd, input_file = tempfile.mkstemp(".mat")
pipeline.save_measurements(input_file, m)
input_files.append((input_fd, input_file))
M.MergeOutputFiles.merge_files(output_file, [ x[1] for x in input_files])
m = cpmeas.load_measurements(output_file)
gc.collect()
os.close(output_fd)
os.remove(output_file)
for fd, filename in input_files:
os.close(fd)
os.remove(filename)
return m
def execute_merge_files(self, mm):
input_files = []
output_fd, output_file = tempfile.mkstemp(".mat")
pipeline = cpp.Pipeline()
li = LoadImages()
li.module_num = 1
pipeline.add_module(li)
for m in mm:
input_fd, input_file = tempfile.mkstemp(".mat")
pipeline.save_measurements(input_file, m)
input_files.append((input_fd, input_file))
M.MergeOutputFiles.merge_files(output_file,
[x[1] for x in input_files])
m = cpmeas.load_measurements(output_file)
gc.collect()
os.close(output_fd)
os.remove(output_file)
for fd, filename in input_files:
os.close(fd)
os.remove(filename)
return m
def load_measurements(self, measurements_file_name):
self.measurements = cpmeas.load_measurements(
measurements_file_name, can_overwrite = True)
# Start on the first image
self.measurements.next_image_set(1)
def run_pipeline_headless(options, args):
'''Run a CellProfiler pipeline in headless mode'''
if sys.platform == 'darwin':
if options.start_awt:
import bioformats
from cellprofiler.utilities.jutil import activate_awt
activate_awt()
if not options.first_image_set is 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 = None
image_set_numbers = None
if not options.last_image_set is 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 = np.arange(1, image_set_end+1)
else:
image_set_numbers = np.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)
from cellprofiler.pipeline import Pipeline, EXIT_STATUS, M_PIPELINE
import cellprofiler.measurements as cpmeas
pipeline = Pipeline()
initial_measurements = None
try:
if h5py.is_hdf5(options.pipeline_filename):
initial_measurements = cpmeas.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(
M_PIPELINE)
pipeline_text = pipeline_text.encode('us-ascii')
pipeline.load(StringIO(pipeline_text))
if not pipeline.in_batch_mode():
#
# Need file list in order to call prepare_run
#
from cellprofiler.utilities.hdf5_dict import HDF5FileList
with h5py.File(options.pipeline_filename, "r") as src:
if HDF5FileList.has_file_list(src):
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
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(cpmeas.EXPERIMENT, EXIT_STATUS)):
done_text = measurements.get_experiment_measurement(EXIT_STATUS)
else:
done_text = "Failure"
fd = open(options.done_file, "wt")
fd.write("%s\n"%done_text)
fd.close()
if measurements is not None:
measurements.close()
def load_measurements(self, measurements_file_name):
self.measurements = cpmeas.load_measurements(measurements_file_name,
can_overwrite=True)
# Start on the first image
self.measurements.next_image_set(1)
options.pipeline_filename = os.path.expanduser(
options.pipeline_filename)
if options.worker_mode_URL is not None:
last_success = time.time(
) # timeout checking for distributed workers.
continue_looping = True # for distributed workers
while continue_looping:
from cellprofiler.pipeline import Pipeline, EXIT_STATUS
import cellprofiler.measurements as cpmeas
continue_looping = False # distributed workers reset this, below
pipeline = Pipeline()
measurements = None
try:
import h5py
if h5py.is_hdf5(options.pipeline_filename):
measurements = cpmeas.load_measurements(
options.pipeline_filename)
except:
logging.root.info("Failed to load measurements from pipeline")
if options.worker_mode_URL is None:
# normal behavior
pipeline.load(options.pipeline_filename)
else:
# distributed worker
continue_looping = True
if time.time() - last_success > worker_timeout:
logging.root.info("Worker timed out. Exiting.")
break
try:
jobinfo = cpdistributed.fetch_work(options.worker_mode_URL)
except:
def test_12_01_load_version_1(self):
data = ('eJzt3E9sFFUcwPH3Zrft0lIpDUGCBWqiUg8124ZAQQslUqwxtkSQlAvplu7a'
'Ymlr/yQ9mWKIKZgoRw5GS2JMI2rqQSFeEE94MZyM8WBKwMDBIkFDMGDrzr73'
'dneGzi4sq7vd/X44/Hbe/HZmOrx582aT+R1v27W7snxtubAFAsIvqkSyBW3/'
'UelYNuu7dJQ6Tuo4bZl2GVu3Rrev1Nt35+17rbXVzl5wMfsJ+lUMCBSjttad'
'e+zYqZdrdbxoOfP2h4dH+gYHxKvh0MjYcPhIeGB0xG43/bPpIfcrRan6rt5A'
'SbQH2h8ty4q1+KL92f5QFe2Y/qQjk7qfS2m2owSiV4L92Se7dFf2O/ZXrXu4'
'nfPr8Yb2xJWSH/a2d+ySwoqPEuMlqfPN9Vrr+y+PaunxGhen9Pn8rkzFdOPi'
'nWoVGReLk9e4OFvmzGsMNjTUB7fUNzbVNzTaHzY1xtozHxcX779demCYqDZ5'
'0THi4NwFWSnEcmvq7vzpucsT9+zWK+U75oX/czH7ijjstbUvdbf+Uc1P0l4N'
'+5er6JzDoFi4rwbTn26UO/N6QqMhO/YN9ITHReIqaMlwv1KULT5L8Ik0swR1'
'hFJPE8xkxvTriuh1aa+ReoUV264UI71jkUh/OD53kLq9JxzpD40m2uOzDcvq'
'/FC3+ZOmHMmzDd8Je7aR6RlwMrMEc1faXZE631yvH1VmZfcFw3P2mbZf1alo'
'OfuVmYSZ/mWWE/3EV9ut2/wqPbbZ+/vJUJb/0gcTu5Msi36wpq4vXH/72C+l'
'0c9NWzrmRUl01cUnpQgGxMxL32+v+G3yxEarf/j8+qF9V2uit6UzpU81yLDn'
'0+gnann1ChXd40hQx80rnMfTHRqOxZePhN4It48d6Q6r5Y7uw+FDo4kGM77U'
'pfn7vI5v+lO1/I3H8Zn8n1zHl+1xzj7JDStFbUC0bH3vqwMnjz3z1sZ37n3w'
'8bWr1u5vXz+7Ifji7+v+ERPnm5uX+VXfSnfHFo+rwB27OHn15KlqZ17sCovG'
'9rFD/eG+R+/J6g4l43eoJh6gMuI1YvWuUsuTq1VMNw4EntDxfzpu5Bev59jp'
'1c68yOBgLLrvuJk+x7pnqi2rUueb+9SNxx5yRwXOaxwY0g0n16qYbhzoWqci'
'84Hi5DUfmFnrzMv2zNb9u/a7aW5EZnXbmpRpRWcJ/BJy84Buy8UvIXtqUueb'
'/je9Piu7Lxg5+CXk3Abdlo+/hNj3UbnYfTSY+ns3n3U16CvA51wEAABAgZBJ'
'0UqKAAAAAAAAAAAAAAAAAAAAAAAgf3i9N/q0R/4P7joXfkcAAAAFKNM6E539'
'rgbqTAAAABQ06kwAAAAAAAAAAAAAAAAAAAAAAJD/vN4brfHI7wm4GqgzAQBA'
'wbPnC2KxOhPaxWkV3dMEFId0/aP2jIpVuTpA5NTe9o5dUvji//+zf6TOr9Vx'
'qEmmSouPN++veISDW0KkKFPvbuvTUhI9A7F3t32qxT7D9oeqgHk2q9XfW6Oi'
'VF80z23mOq0QTbHvSf0SuBXbrhQjvWORSH84ni91e0840h8aTbQHotu3v++z'
'rJlrus2f9HhYrf+n7JwdJxrahWjJyvlQ/cqK94Oqz1Lnm/7X+3lWdl8wpCjN'
'sF/VqWipL5oaAqaOlOlfZjnRT3zjX+g2v0qPbfb+fjKU5b/0wWRaV2vmrKuB'
'uloAAAAFzUqKpqYWcz4AAAAAAAAAAAAAAAAAAAAAAPKL13uj9R75z7kLIlBX'
'CwCAgidj/4SuziLESl2hJHBTLdfdUjFdfaWZP1WkvlJxamvduceOnXrZ9Ke2'
'W868ntBoyI59Az3hcREv95JxNZ4lUI+oJaiPLRf1iKZupUyPX693/srK7gtG'
'DuoRBW7rtkKqR9QSv8I16hEBAAAUNOoRAQAAAAAAAAAAAAAAAAAAAACQ/7ze'
'G13nkV9zdPH3RXlnAACAwuVVj+hUk5oXnNumYrp6RC0vqDzqERUnr3pEP29z'
'zi+LsB7RpfEc1iMKPi9T5pvrdbI5dV6xyUE9olPbE/2kYOoRXWqjHhEAAEAx'
'oR4RAAAAAAAAAAAAAAAAAAAAAAD572HrEW2lHhEAAEVn/ODcBVkpxHJr6u78'
'6bnLE/fs+cCV8h3zwp+07s2FS7fvTGzeGl03+7Uc/FuU3bdyU7NeeVuU/Qv/'
'J+C3')
data = zlib.decompress(base64.b64decode(data))
fid, name = tempfile.mkstemp(".h5")
m = None
try:
fd = os.fdopen(fid, "wb")
fd.write(data)
fd.close()
m = cpmeas.load_measurements(name)
self.assertEqual(tuple(m.get_image_numbers()), (1, 2))
self.assertEqual(
m.get_measurement(cpmeas.IMAGE, "foo", image_set_number=1),
12345)
self.assertEqual(
m.get_measurement(cpmeas.IMAGE, "foo", image_set_number=2),
23456)
for image_number, expected in ((1, np.array([234567, 123456])),
(2, np.array([123456, 234567]))):
values = m.get_measurement("Nuclei",
"bar",
image_set_number=image_number)
self.assertEqual(len(expected), len(values))
self.assertTrue(np.all(expected == values))
finally:
if m is not None:
del m
os.unlink(name)
elif options.run_pipeline: # this includes distributed workers
if (options.pipeline_filename is not None) and (not options.pipeline_filename.lower().startswith('http')):
options.pipeline_filename = os.path.expanduser(options.pipeline_filename)
if options.worker_mode_URL is not None:
last_success = time.time() # timeout checking for distributed workers.
continue_looping = True # for distributed workers
while continue_looping:
from cellprofiler.pipeline import Pipeline, EXIT_STATUS
import cellprofiler.measurements as cpmeas
continue_looping = False # distributed workers reset this, below
pipeline = Pipeline()
measurements = None
try:
import h5py
if h5py.is_hdf5(options.pipeline_filename):
measurements = cpmeas.load_measurements(options.pipeline_filename)
except:
logging.root.info("Failed to load measurements from pipeline")
if options.worker_mode_URL is None:
# normal behavior
pipeline.load(options.pipeline_filename)
else:
# distributed worker
continue_looping = True
if time.time() - last_success > worker_timeout:
logging.root.info("Worker timed out. Exiting.")
break
try:
jobinfo = cpdistributed.fetch_work(options.worker_mode_URL)
except:
elif options.run_pipeline: # this includes distributed workers
if (options.pipeline_filename is not None) and (not options.pipeline_filename.lower().startswith('http')):
options.pipeline_filename = os.path.expanduser(options.pipeline_filename)
if options.worker_mode_URL is not None:
last_success = time.time() # timeout checking for distributed workers.
continue_looping = True # for distributed workers
while continue_looping:
from cellprofiler.pipeline import Pipeline, EXIT_STATUS
import cellprofiler.measurements as cpmeas
continue_looping = False # distributed workers reset this, below
pipeline = Pipeline()
initial_measurements = None
try:
import h5py
if h5py.is_hdf5(options.pipeline_filename):
initial_measurements = cpmeas.load_measurements(options.pipeline_filename)
except:
logging.root.info("Failed to load measurements from pipeline")
if options.worker_mode_URL is None:
# normal behavior
pipeline.load(options.pipeline_filename)
else:
# distributed worker
continue_looping = True
if time.time() - last_success > worker_timeout:
logging.root.info("Worker timed out. Exiting.")
break
try:
jobinfo = cpdistributed.fetch_work(options.worker_mode_URL)
except:
if (options.pipeline_filename is not None) and (not options.pipeline_filename.lower().startswith('http')):
options.pipeline_filename = os.path.expanduser(options.pipeline_filename)
if options.worker_mode_URL is not None:
last_success = time.time() # timeout checking for distributed workers.
continue_looping = True # for distributed workers
while continue_looping:
from cellprofiler.pipeline import Pipeline, EXIT_STATUS, M_PIPELINE
import cellprofiler.measurements as cpmeas
continue_looping = False # distributed workers reset this, below
pipeline = Pipeline()
initial_measurements = None
try:
import h5py
if h5py.is_hdf5(options.pipeline_filename):
initial_measurements = cpmeas.load_measurements(
options.pipeline_filename,
image_numbers=image_set_numbers)
except:
logging.root.info("Failed to load measurements from pipeline")
if options.worker_mode_URL is None:
# normal behavior
if initial_measurements is not None:
pipeline_text = \
initial_measurements.get_experiment_measurement(
M_PIPELINE)
pipeline_text = pipeline_text.encode('us-ascii')
pipeline.load(StringIO(pipeline_text))
else:
pipeline.load(options.pipeline_filename)
else:
# distributed worker
options.pipeline_filename = os.path.expanduser(
options.pipeline_filename)
if options.worker_mode_URL is not None:
last_success = time.time(
) # timeout checking for distributed workers.
continue_looping = True # for distributed workers
while continue_looping:
from cellprofiler.pipeline import Pipeline, EXIT_STATUS
import cellprofiler.measurements as cpmeas
continue_looping = False # distributed workers reset this, below
pipeline = Pipeline()
initial_measurements = None
try:
import h5py
if h5py.is_hdf5(options.pipeline_filename):
initial_measurements = cpmeas.load_measurements(
options.pipeline_filename)
except:
logging.root.info("Failed to load measurements from pipeline")
if options.worker_mode_URL is None:
# normal behavior
pipeline.load(options.pipeline_filename)
else:
# distributed worker
continue_looping = True
if time.time() - last_success > worker_timeout:
logging.root.info("Worker timed out. Exiting.")
break
try:
jobinfo = cpdistributed.fetch_work(options.worker_mode_URL)
except:
def test_13_01_load_matlab(self):
data = ('eJztHEtzHEd5pCghVkhi3gmVQ5NylS2XJe3KWlueCpFkSbYV9CpLsRMss+nd'
'6d3teF7MQ7tLTBXHcM2JOweuUFw4pfIDuFHAkRTwA3wCDqSK7+vu2XnsrPYh'
'aYuAxrX2dvf36u/VX/f0env1YGv1NinNFcj26sFsjZuM7Jk0qDmepRM7uEbW'
'PEYDZhDH1slBIySrYZ0UFkmxoC8s6cUlslAoFrXRnonN7Vfw33c07QX450X4'
'TKqh51V7IvH5Cnwa1DZM5sP4lPay6l+Ez1roecwOJPI2o37oMQs6fG3PYzUG'
'Y1XA0vZZEHC77ndEQP73pgbjL/untO+o9hfw2QmtCvN2a5sWrTOgrijDt9sM'
'GK3a1Gz/hBkKO4LedowQZ6GefXrEdsPADYN7TnO3FjBbdB9wi+0H1As6+BIa'
'u4B2xDLZHbrr3GPVwPHaqnud1WhoBpJ8PKi6BY0EimgfOI7p3wFPsKklhex0'
'32OmmzIh6m9Ji/X3Qh/9XVD96lk5DfxCAv+rA9hvUsk9LN6EwttL4E3l4H03'
'gXdRtTFKZgtLs3Hk3Jy7daNUKBRiPY4qT1J/efK8qKXlwfaafhh4of1E+H8f'
'/G9m8LFdBXxmuYcVahxyqz5bXFwqRPM4bXpugt5EDr2kXhB+pQ//lzL8sS08'
'nATo+bn2yPpl9CT9MsLbe2O4fBLJ8z34bLRc5nHMXFIkCbUTVk3GE2wFH/Lq'
'cHy+rtqXUEbuMpPbrBx9Ac48KEMSCUI/w+feK8Pp/7VXjtf/319O6x/ba8w0'
'9zwH1x+PRDLppBEErj4/32w256oA4SqIOcerTz9gns9hVVqY3n+wc58dcdEq'
'FhcLi9PTWw41hAJ9/ZElsm3ZDi29+NQ/sstHCvPwcrF4/Vbp8PLTI+pxWgEg'
'T9FBaEjVQO6p33Ca5Sa3DaepH3ghe2o7AZA9bJVuw2f9aYUG1UbZB80xnXoe'
'bV+Jhq4RI2i77Psht4OlmcfTBB7MqwR7wdlIhRETBGWGDuT5ETdCahIuxI6B'
'fWZCegaZiMWChmPoB6wVzG60aDUgFvIWoHJpIU5N4RNuE0arDVL3nNBd1q8L'
'qAPBuAEfoAFfaCBarFU1QxAjwm3AgoQEqo5lgZ7WHQJzJqHPBBG1qBFqmsQP'
'KzXHNEChpMmDBuAgPSky0JNjy/qOY0vcTRuWIcmGiGLDdKoUJ6erFUlB3BGI'
'h62bax3UtQarPlGoPqy0QNwjFvd9WAsJfDVC1+RATBL2l/X3lRbvogaiqVXa'
'qEZq0ICiWAJgQ06fVJkXUJiCwlej+y6r8lqMBsPMNHy0n9AtkNQloJh1UiUA'
'QqWyJITMMlUntAO9KA2StIPP8g1ArlSpz2Z9Zvs84EdsRvdhpeeGoLDnYCcA'
'genBAL7SUMr8itlGK/CE13Rm4jkWaTagPEqY6D6rhyb1wClcj/kYDFLAGvio'
'H+MqUwsjYqGg/+jK8t5bWDuyt+euzpSx9RBi9m2MhtXZPQwI/FqYvYVfP1r4'
'6UzZR6B9DhiJkZm0p3pDidNRPnFp0NDnropYVM88kkeW60rI3MH7oY1jl5TT'
'UdtmZspm0p+Qm+UccVCBh3VSx1/EMPqkCtdNO2CeycCiRiZSq5K4TyDnK0PJ'
'MMXsJRhwEQzUj/wC3NypfAheBuw24zyxAwJI48t0IsH19Z3V/GFJQ5fLijK6'
'8HwnDDDx+ihdCjaeniLGIljFa0tAC5l2FZGUAqN8qrj5VWri9ALp01G4Tm8a'
'sPRBtO3BIki99q5knsrhC905/HrxuBx+65RTuIrz2D652mbElXOINKgSPpcz'
'5JD0E/oHd4fcZRKDA3Yg3UPhXUPfdnkLlBjaHOhc2eb2tW3amtGLhWuLBYG/'
'zkGjXsdaaBwfOAk5OjQ9atdZx5AHIBqIZDFPVj7EtzCjRxQwnRObUQ8yJsRf'
'nXldrpBlGjhhtYGej1wrjmfIeQhFoYrilJxQYdCAsG5AzCZCZjfwQ3LXdCrU'
'lKJGMBCHnqdWwxrFTYvyqC2nCcxgc0hCF6OpAvEK6UEkC4WrF+YK4rlWVF/k'
'auZCVdEC+QIRydUol3KVq8HRIAoq7Xj2QEcy3RbSohIN7uOWLOR+g8BaYrlx'
'4Ij4RydvZ3E82iRi1ceZy7CrsKDJmN2Hxj6H1RdE9C3HCYTCIQsHGF1ySvug'
'A8yVYnE1oUjA6cmsST2Yk+n4oCJo2zIrWNzmVmjhgg5qNMRsKGyY9ZvRAshQ'
'r6iEUKy2CObNAgvHDIW+uCqfRVZOsY1t3iNxRJGeTBpQ95gEbCbLmDhiYhtE'
'RFfDwAHTYeyYbQJ/V0NcgYjfQ0X9MXspo2Ob/NltUxuLt5S3JV29wm3MBXLO'
'ibW2K+2KaMmZsYq5XmKnQkmZCftEKMmwioXdwCWJbFHXpFVOhbvfpSEsrjQd'
'LyUZfU1ntmpSXyw/OMxUMxm6yzqAEdEfaQQViWZAMZqM1xtYEYosjeoUtKD0'
'tAPPcdvL+sMshJwuCFVX6c+PFnmLG0Zi9WgTJY4jKxIHSgYP4z/iUaHVJ7Jn'
'Wb/TGe2rzsiHovRWU+Ty9Zb1L6Vj564a6aRhXWr1Hp5kidI15sBrWIpjlQPl'
'n92pEzrD8dKx5sA3O2TK91rCZbsw9FKhkM23Vnw4JjUW+Qwm/Ngz4zwVKwUq'
'LRfrTyKXUqEgWYaIr6nclIHF1BTtDxvacPvJkc43HMs1QduI/6/p4fbJl1X7'
'00mkA3VfuXv/nXlEzVeWld0xYJ11rHwH0oEZN9MMttdL6xxqu6AMNUVvavdE'
'xBwP05Ft0zZY6ziwWJgoEh+oQExJh1tSzOR92O5B9T0AWMx0P7R2axsiGUA1'
'mFHIRotVxUKDR6LlQjHe3aeoPeRG0OirjwTTXY/Xexkhy3QhvzTV5Fnuhuc5'
'Xm8gTXv3/lZ/yTRtH6INkkI/0BTTXurA56TxNtx57LNlxP9gSH6jnnuOY15/'
'+vG/v3hV+7WY18+G5NfvnJho6byF7VK1xJboYmmpslC7sUhLxVKhyG5cv1Uq'
'FW4slYzFceh3S+GdNZ9x2lFL4J/2efK0lrYjtuUBzZxr12M6rSH5nvY5+jj0'
'/MmFvzyr/+H1z8Zm19//9aPSP341lrzz7hj1mMw7Y9Hje5+89ed//nYseozw'
'Ph6Sz3va8fFwOQF/UbXxeFKfn59f0+cxLuYhLuajuJhPBOlY89Dnzso49fzZ'
'xHD177dUe1mL3liXZR0TFbhb6sy+vMZwk1N+HzGzne91+J/1PMe5jvxwZueP'
'P59aWxkXv998+2+//ME7q4Lfs+dGv6ewhxto3JtpqUe9dJHlZNftgEEvDUT7'
'8DXHdLxtqm4IbNEKM9Nd+OwHHnc7bx3j7ifcFfVsuoZd5z5sttsgIHtAzTBC'
'uAPb3+7ZRNAPxZYzReg06toV7fj8czGBf1F9ovfsh+rKx5fhfXuhD72pFL0p'
're7R9iB4z6XwntM+ZMFA/LJ478M+ZxC8yRTepLbjnCy/DIs3rXX7XRZvsgsv'
'TQ/xP780XNy/ptrP4PNAvRQRwZOIiKh/0645+KotGpJuuhNd/Ok8MvP7u7UI'
'UQ33yCsd+tF7eYWv6Gd7O3zxtUyKzn7Dad7B92sZ+rfxjQ3eVsgMoL5+d7F3'
'Xkb9vZnoHyR+vqal4wfbXe/rE/SG9Wfc64+aX7J3AQY5L3shQwfb0dnHIPJP'
'pPAntOvaYOd0z2f4Ylu8wdJG32fFVxMG1/8geWGQvIdnpSfh1+984PXMfLGd'
'd0viKQqiDT7/UfNpL7yVPnh5960Sb/gGlntUu2X9tXhCfqPki653j2r8JPfM'
'hvVXfG95En4rffhdyMwb253acAi5e9nrtPiPkh/lVnE0vykWztZPs3g3B8Qb'
'NZ/1ygONPng3M3rFdq9LQ49WZ/cePyrM3nqcuSgk+mYG8odXM/ywnXnbPNC5'
'wqUMHWzPXX10eDj/OHmZqNMRXSDSBrTDqPl4VH85y3r6rNeNxGUqbVT9Qh48'
'UZ0zalyNknei1zrjtE9Uj45q17OuP7vtWTqRflb64OX5YeKyg+gfpC74RoYO'
'tnOuzQ1ML6/O6LpFocbPct3Nq8vjaxaD8z/tuvSs6qqsnko5eMPwG+1+g7z/'
'cZbzzOq1VOjOm2dZr+bVI6dZr+b5bXybJV/u05xvrzrx48TvOiYyeHnnF+PU'
'jzjsEFeVT85/lPwmLzbJe/fcNpiboHcadjrHO8c7xzvHO8f778VbSeANek4e'
'r1ty2fgyzfccb7z+c9Z1z5fV7/9f67WVPvM+zzfneOd4/7t4FydivOx+PLuP'
'R/gPEnzy8sVVLZ0vsJ363w3kj139OfwFrrxdMJe94o98Wn343M7wud2Lj/pd'
'UVv9YFX9eGiu528ZcvR5IYd/Ui+T8OeNN0/nHtawfF+60C3vS33wpoa4p5q1'
'/5Vj4KPnJPDDzn9iQuL9Ysh5HMcnK9fkCfH+A86LP/E=')
data = zlib.decompress(base64.b64decode(data))
fid, name = tempfile.mkstemp(".mat")
m = None
try:
fd = os.fdopen(fid, "wb")
fd.write(data)
fd.close()
m = cpmeas.load_measurements(name)
self.assertEqual(tuple(m.get_image_numbers()), (1,))
self.assertEqual(m.get_measurement(cpmeas.IMAGE, "Count_Nuclei",
image_set_number = 1), 1)
values = m.get_measurement("Nuclei", "Location_Center_X", 1)
self.assertEqual(len(values), 1)
self.assertAlmostEqual(values[0], 34.580433355219959)
finally:
gc.collect()
if m is not None:
del m
os.unlink(name)
def test_03_01_save_and_load(self):
'''Save a pipeline to batch data, open it to check and load it'''
data = ('eJztWW1PGkEQXhC1WtPYTzb9tB+llROoGiWNgi9NSYUSIbZGbbvCApvu7ZJ7'
'UWlj0o/9Wf1J/QndxTs4tsoBRS3JHbkcMzfPPDOzs8uxl8uU9jPbcFWLw1ym'
'FKsSimGBIqvKDT0FmbUEdwyMLFyBnKVgycYwY9dgIgET8dTqRmolCZPx+AYY'
'7ghlc0/EJf4cgClxfSTOsHNr0pFDnlPKRWxZhNXMSRABzxz9L3EeIoOgM4oP'
'EbWx2aFw9VlW5aVmo30rxys2xXmke43Fkbf1M2yY76su0LldIJeYFsk3rKTg'
'mh3gc2ISzhy841/Vtnm5pfDKOhTmOnUIKXWQdVnw6KX9W9Cxj9xQt6ce+3lH'
'JqxCzknFRhQSHdXaUbTGwcffRJe/CXAk0BKX9sHNK3HIs4QvrdjeJSpbUEdW'
'uS79rPv4mVb8SLmcOrGw3ugr/lAXPgRe9Zl3uAsXBnkO+sp7VolXyrscMm5B'
'28T91/02/lHgphSce7i4GdCJ0y/fOSVfKe9RE1/UsYE1TXP91H38rCh+pCzG'
'uYwpbRhcLlHGiWXY7OuJaCC9ISZ3q5NdqbhdzLZb+yH4hpmXy3I2ioVtGTFE'
'myYZxbwcdpzvu68eku8+cGmf/GZAdz9IeaeOGMM0ERtx3P30z24+c6d86jqc'
'uOP8Il18EdE/DP8L3w8fvnegezyl/Glxq/BaPljhTe1l9LOUPoj15YBfbB5n'
'YoXTqKvZ4dTW2eZxPLZx+j2xlLy6Ni4SgWwpozfmPUj8fuvhuhK/lGUMRxgZ'
'TmArV9GYVOU4s+qOLunodlGzo3mgeZMcxbwZir9p8QZFpj4C/kHnUfKO+YJ5'
'NJ7z6OPsYP8rxuV3NcAFuAAX4P43XNqD63c/pLUZUzO43YCEVXBjnPINcOON'
'S4OgXwPc8DipvO35Ut2/kfZfQO9+ewG6+03K3s04TW9topsa5ahyvYut7Yuv'
'Wc+Gdj/P52sKz9ptPOXWK5AzuU8tb5ja9TuRbal4Q1rvCNT6zdzA561DWHwW'
'pnvXXa13Zxx+bw3DFwn9zffYBxdxKidxP8Fg47zYw97NbVj7P/nFW+E=')
def callback(caller,event):
self.assertFalse(isinstance(event, cpp.LoadExceptionEvent))
self.assertFalse(isinstance(event, cpp.RunExceptionEvent))
for windows_mode in ((False, True) if sys.platform.startswith("win")
else (False,)):
pipeline = cpp.Pipeline()
pipeline.add_listener(callback)
pipeline.load(StringIO(zlib.decompress(base64.b64decode(data))))
ipath = os.path.join(T.example_images_directory(),'ExampleSBSImages')
bpath = tempfile.mkdtemp()
bfile = os.path.join(bpath,C.F_BATCH_DATA)
hfile = os.path.join(bpath, C.F_BATCH_DATA_H5)
try:
li = pipeline.modules()[0]
self.assertTrue(isinstance(li, LI.LoadImages))
module = pipeline.modules()[1]
self.assertTrue(isinstance(module, C.CreateBatchFiles))
li.location.dir_choice = LI.ABSOLUTE_FOLDER_NAME
li.location.custom_path = ipath
module.wants_default_output_directory.value = False
module.custom_output_directory.value = bpath
module.remote_host_is_windows.value = windows_mode
self.assertEqual(len(module.mappings), 1)
mapping = module.mappings[0]
mapping.local_directory.value = ipath
self.assertFalse(pipeline.in_batch_mode())
measurements = cpmeas.Measurements()
image_set_list = cpi.ImageSetList()
result = pipeline.prepare_run(
cpw.Workspace(pipeline, None, None, None,
measurements, image_set_list))
self.assertFalse(pipeline.in_batch_mode())
self.assertFalse(result)
self.assertFalse(module.batch_mode.value)
pipeline = cpp.Pipeline()
pipeline.add_listener(callback)
fd = open(hfile,'rb')
try:
pipeline.load(fd)
fd.seek(0)
finally:
fd.close()
measurements = cpmeas.load_measurements(hfile)
image_set_list = cpi.ImageSetList()
self.assertTrue(pipeline.in_batch_mode())
module = pipeline.modules()[1]
self.assertTrue(isinstance(module, C.CreateBatchFiles))
self.assertTrue(module.batch_mode.value)
image_numbers = measurements.get_image_numbers()
self.assertTrue([x == i+1 for i, x in enumerate(image_numbers)])
workspace = cpw.Workspace(pipeline, None, None, None,
measurements, image_set_list)
pipeline.prepare_run(workspace)
pipeline.prepare_group(workspace, {}, range(1,97))
for i in range(96):
image_set = image_set_list.get_image_set(i)
for image_name in ('DNA', 'Cytoplasm'):
pathname = measurements.get_measurement(
cpmeas.IMAGE, "PathName_"+image_name, i+1)
self.assertEqual(pathname,
'\\imaging\\analysis' if windows_mode
else '/imaging/analysis')
del measurements
finally:
if os.path.exists(bfile):
os.unlink(bfile)
if os.path.exists(hfile):
os.unlink(hfile)
os.rmdir(bpath)
def run_pipeline_headless(options, args):
'''Run a CellProfiler pipeline in headless mode'''
#
# Start Ilastik's workers
#
try:
from ilastik.core.jobMachine import GLOBAL_WM
GLOBAL_WM.set_thread_count(1)
except:
logging.root.warn("Failed to stop Ilastik")
if sys.platform == 'darwin':
if options.start_awt:
import bioformats
from javabridge import activate_awt
activate_awt()
if not options.first_image_set is 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 = None
image_set_numbers = None
if not options.last_image_set is 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 = np.arange(1, image_set_end + 1)
else:
image_set_numbers = np.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)
from cellprofiler.pipeline import Pipeline, EXIT_STATUS, M_PIPELINE
import cellprofiler.measurements as cpmeas
import cellprofiler.preferences as cpprefs
pipeline = Pipeline()
initial_measurements = None
try:
if h5py.is_hdf5(options.pipeline_filename):
initial_measurements = cpmeas.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(
M_PIPELINE)
pipeline_text = pipeline_text.encode('us-ascii')
pipeline.load(StringIO(pipeline_text))
if not pipeline.in_batch_mode():
#
# Need file list in order to call prepare_run
#
from cellprofiler.utilities.hdf5_dict import HDF5FileList
with h5py.File(options.pipeline_filename, "r") as src:
if HDF5FileList.has_file_list(src):
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 = cpprefs.get_image_set_file()
if file_list is not None:
pipeline.read_file_list(file_list)
#
# 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(cpmeas.EXPERIMENT, EXIT_STATUS)):
done_text = measurements.get_experiment_measurement(EXIT_STATUS)
else:
done_text = "Failure"
fd = open(options.done_file, "wt")
fd.write("%s\n" % done_text)
fd.close()
if measurements is not None:
measurements.close()
def test_03_01_save_and_load(self):
'''Save a pipeline to batch data, open it to check and load it'''
data = ('eJztWW1PGkEQXhC1WtPYTzb9tB+llROoGiWNgi9NSYUSIbZGbbvCApvu7ZJ7'
'UWlj0o/9Wf1J/QndxTs4tsoBRS3JHbkcMzfPPDOzs8uxl8uU9jPbcFWLw1ym'
'FKsSimGBIqvKDT0FmbUEdwyMLFyBnKVgycYwY9dgIgET8dTqRmolCZPx+AYY'
'7ghlc0/EJf4cgClxfSTOsHNr0pFDnlPKRWxZhNXMSRABzxz9L3EeIoOgM4oP'
'EbWx2aFw9VlW5aVmo30rxys2xXmke43Fkbf1M2yY76su0LldIJeYFsk3rKTg'
'mh3gc2ISzhy841/Vtnm5pfDKOhTmOnUIKXWQdVnw6KX9W9Cxj9xQt6ce+3lH'
'JqxCzknFRhQSHdXaUbTGwcffRJe/CXAk0BKX9sHNK3HIs4QvrdjeJSpbUEdW'
'uS79rPv4mVb8SLmcOrGw3ugr/lAXPgRe9Zl3uAsXBnkO+sp7VolXyrscMm5B'
'28T91/02/lHgphSce7i4GdCJ0y/fOSVfKe9RE1/UsYE1TXP91H38rCh+pCzG'
'uYwpbRhcLlHGiWXY7OuJaCC9ISZ3q5NdqbhdzLZb+yH4hpmXy3I2ioVtGTFE'
'myYZxbwcdpzvu68eku8+cGmf/GZAdz9IeaeOGMM0ERtx3P30z24+c6d86jqc'
'uOP8Il18EdE/DP8L3w8fvnegezyl/Glxq/BaPljhTe1l9LOUPoj15YBfbB5n'
'YoXTqKvZ4dTW2eZxPLZx+j2xlLy6Ni4SgWwpozfmPUj8fuvhuhK/lGUMRxgZ'
'TmArV9GYVOU4s+qOLunodlGzo3mgeZMcxbwZir9p8QZFpj4C/kHnUfKO+YJ5'
'NJ7z6OPsYP8rxuV3NcAFuAAX4P43XNqD63c/pLUZUzO43YCEVXBjnPINcOON'
'S4OgXwPc8DipvO35Ut2/kfZfQO9+ewG6+03K3s04TW9topsa5ahyvYut7Yuv'
'Wc+Gdj/P52sKz9ptPOXWK5AzuU8tb5ja9TuRbal4Q1rvCNT6zdzA561DWHwW'
'pnvXXa13Zxx+bw3DFwn9zffYBxdxKidxP8Fg47zYw97NbVj7P/nFW+E=')
def callback(caller,event):
self.assertFalse(isinstance(event, cpp.LoadExceptionEvent))
self.assertFalse(isinstance(event, cpp.RunExceptionEvent))
for windows_mode in ((False, True) if sys.platform.startswith("win")
else (False,)):
pipeline = cpp.Pipeline()
pipeline.add_listener(callback)
pipeline.load(StringIO(zlib.decompress(base64.b64decode(data))))
ipath = os.path.join(T.example_images_directory(),'ExampleSBSImages')
bpath = tempfile.mkdtemp()
bfile = os.path.join(bpath,C.F_BATCH_DATA)
hfile = os.path.join(bpath, C.F_BATCH_DATA_H5)
try:
li = pipeline.modules()[0]
self.assertTrue(isinstance(li, LI.LoadImages))
module = pipeline.modules()[1]
self.assertTrue(isinstance(module, C.CreateBatchFiles))
li.location.dir_choice = LI.ABSOLUTE_FOLDER_NAME
li.location.custom_path = ipath
module.wants_default_output_directory.value = False
module.custom_output_directory.value = bpath
module.remote_host_is_windows.value = windows_mode
self.assertEqual(len(module.mappings), 1)
mapping = module.mappings[0]
mapping.local_directory.value = ipath
self.assertFalse(pipeline.in_batch_mode())
measurements = cpmeas.Measurements()
image_set_list = cpi.ImageSetList()
result = pipeline.prepare_run(
cpw.Workspace(pipeline, None, None, None,
measurements, image_set_list))
self.assertFalse(pipeline.in_batch_mode())
self.assertFalse(result)
self.assertFalse(module.batch_mode.value)
pipeline = cpp.Pipeline()
pipeline.add_listener(callback)
fd = open(hfile,'rb')
try:
pipeline.load(fd)
fd.seek(0)
finally:
fd.close()
measurements = cpmeas.load_measurements(hfile)
image_set_list = cpi.ImageSetList()
self.assertTrue(pipeline.in_batch_mode())
module = pipeline.modules()[1]
self.assertTrue(isinstance(module, C.CreateBatchFiles))
self.assertTrue(module.batch_mode.value)
image_numbers = measurements.get_image_numbers()
self.assertTrue([x == i+1 for i, x in enumerate(image_numbers)])
workspace = cpw.Workspace(pipeline, None, None, None,
measurements, image_set_list)
pipeline.prepare_run(workspace)
pipeline.prepare_group(workspace, {}, range(1,97))
for i in range(96):
image_set = image_set_list.get_image_set(i)
for image_name in ('DNA', 'Cytoplasm'):
pathname = measurements.get_measurement(
cpmeas.IMAGE, "PathName_"+image_name, i+1)
self.assertEqual(pathname,
'\\imaging\\analysis' if windows_mode
else '/imaging/analysis')
del measurements
finally:
if os.path.exists(bfile):
os.unlink(bfile)
if os.path.exists(hfile):
os.unlink(hfile)
os.rmdir(bpath)
def merge_files(destination, sources, force_headless=False):
is_headless = force_headless or get_headless()
if not is_headless:
import wx
if len(sources) == 0:
return
if not is_headless:
progress = wx.ProgressDialog("Writing " + destination,
"Loading " + sources[0],
maximum = len(sources) * 4 + 1,
style = wx.PD_CAN_ABORT |
wx.PD_APP_MODAL |
wx.PD_ELAPSED_TIME |
wx.PD_REMAINING_TIME)
count = 0
try:
pipeline = cpp.Pipeline()
has_error = [False]
def callback(caller, event):
if isinstance(event, cpp.LoadExceptionEvent):
has_error = True
wx.MessageBox(
message = "Could not load %s: %s" % (
sources[0], event.error),
caption = "Failed to load %s" % sources[0])
has_error[0] = True
pipeline.add_listener(callback)
pipeline.load(sources[0])
if has_error[0]:
return
if destination.lower().endswith(".h5"):
mdest = cpmeas.Measurements(filename=destination,
multithread=False)
h5_dest = True
else:
mdest = cpmeas.Measurements(multithread=False)
h5_dest = False
for source in sources:
if not is_headless:
count += 1
keep_going, skip = progress.Update(count, "Loading " + source)
if not keep_going:
return
if h5py.is_hdf5(source):
msource = cpmeas.Measurements(filename=source,
mode="r",
multithread=False)
else:
msource = cpmeas.load_measurements(source)
dest_image_numbers = mdest.get_image_numbers()
source_image_numbers = msource.get_image_numbers()
if (len(dest_image_numbers) == 0 or
len(source_image_numbers) == 0):
offset_source_image_numbers = source_image_numbers
else:
offset_source_image_numbers = (
np.max(dest_image_numbers) -
np.min(source_image_numbers) + source_image_numbers + 1)
for object_name in msource.get_object_names():
if object_name in mdest.get_object_names():
destfeatures = mdest.get_feature_names(object_name)
else:
destfeatures = []
for feature in msource.get_feature_names(object_name):
if object_name == cpmeas.EXPERIMENT:
if not mdest.has_feature(object_name, feature):
src_value = msource.get_experiment_measurement(
feature)
mdest.add_experiment_measurement(feature,
src_value)
continue
src_values = msource.get_measurement(
object_name,
feature,
image_set_number = source_image_numbers)
mdest[object_name,
feature,
offset_source_image_numbers] = src_values
destset = set(destfeatures)
if not is_headless:
keep_going, skip = progress.Update(count+1, "Saving to "+destination)
if not keep_going:
return
if not h5_dest:
pipeline.save_measurements(destination, mdest)
finally:
if not is_headless:
progress.Destroy()
def merge_files(destination, sources, force_headless=False):
is_headless = force_headless or get_headless()
if not is_headless:
import wx
if len(sources) == 0:
return
if not is_headless:
progress = wx.ProgressDialog("Writing " + destination,
"Loading " + sources[0],
maximum=len(sources) * 4 + 1,
style=wx.PD_CAN_ABORT
| wx.PD_APP_MODAL | wx.PD_ELAPSED_TIME
| wx.PD_REMAINING_TIME)
count = 0
try:
pipeline = cpp.Pipeline()
has_error = [False]
def callback(caller, event):
if isinstance(event, cpp.LoadExceptionEvent):
has_error = True
wx.MessageBox(message="Could not load %s: %s" %
(sources[0], event.error),
caption="Failed to load %s" % sources[0])
has_error[0] = True
pipeline.add_listener(callback)
pipeline.load(sources[0])
if has_error[0]:
return
if destination.lower().endswith(".h5"):
mdest = cpmeas.Measurements(filename=destination,
multithread=False)
h5_dest = True
else:
mdest = cpmeas.Measurements(multithread=False)
h5_dest = False
for source in sources:
if not is_headless:
count += 1
keep_going, skip = progress.Update(count,
"Loading " + source)
if not keep_going:
return
if h5py.is_hdf5(source):
msource = cpmeas.Measurements(filename=source,
mode="r",
multithread=False)
else:
msource = cpmeas.load_measurements(source)
dest_image_numbers = mdest.get_image_numbers()
source_image_numbers = msource.get_image_numbers()
if (len(dest_image_numbers) == 0
or len(source_image_numbers) == 0):
offset_source_image_numbers = source_image_numbers
else:
offset_source_image_numbers = (
np.max(dest_image_numbers) -
np.min(source_image_numbers) + source_image_numbers +
1)
for object_name in msource.get_object_names():
if object_name in mdest.get_object_names():
destfeatures = mdest.get_feature_names(object_name)
else:
destfeatures = []
for feature in msource.get_feature_names(object_name):
if object_name == cpmeas.EXPERIMENT:
if not mdest.has_feature(object_name, feature):
src_value = msource.get_experiment_measurement(
feature)
mdest.add_experiment_measurement(
feature, src_value)
continue
src_values = msource.get_measurement(
object_name,
feature,
image_set_number=source_image_numbers)
mdest[object_name, feature,
offset_source_image_numbers] = src_values
destset = set(destfeatures)
if not is_headless:
keep_going, skip = progress.Update(count + 1,
"Saving to " + destination)
if not keep_going:
return
if not h5_dest:
pipeline.save_measurements(destination, mdest)
finally:
if not is_headless:
progress.Destroy()
def run_pipeline_headless(options, args):
'''Run a CellProfiler pipeline in headless mode'''
#
# Start Ilastik's workers
#
try:
from ilastik.core.jobMachine import GLOBAL_WM
GLOBAL_WM.set_thread_count(1)
except:
logging.root.warn("Failed to stop Ilastik")
if sys.platform == 'darwin':
if options.start_awt:
import bioformats
from javabridge import activate_awt
activate_awt()
if not options.first_image_set is 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 = None
image_set_numbers = None
if not options.last_image_set is 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 = np.arange(1, image_set_end+1)
else:
image_set_numbers = np.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)
from cellprofiler.pipeline import Pipeline, EXIT_STATUS, M_PIPELINE
import cellprofiler.measurements as cpmeas
import cellprofiler.preferences as cpprefs
pipeline = Pipeline()
initial_measurements = None
try:
if h5py.is_hdf5(options.pipeline_filename):
initial_measurements = cpmeas.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(
M_PIPELINE)
pipeline_text = pipeline_text.encode('us-ascii')
pipeline.load(StringIO(pipeline_text))
if not pipeline.in_batch_mode():
#
# Need file list in order to call prepare_run
#
from cellprofiler.utilities.hdf5_dict import HDF5FileList
with h5py.File(options.pipeline_filename, "r") as src:
if HDF5FileList.has_file_list(src):
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 = cpprefs.get_image_set_file()
if file_list is not None:
pipeline.read_file_list(file_list)
#
# 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(cpmeas.EXPERIMENT, EXIT_STATUS)):
done_text = measurements.get_experiment_measurement(EXIT_STATUS)
else:
done_text = "Failure"
fd = open(options.done_file, "wt")
fd.write("%s\n"%done_text)
fd.close()
if measurements is not None:
measurements.close()
def merge_files(destination, sources, force_headless=False):
is_headless = force_headless or get_headless()
if not is_headless:
import wx
if len(sources) == 0:
return
if not is_headless:
progress = wx.ProgressDialog("Writing " + destination,
"Loading " + sources[0],
maximum = len(sources) * 4 + 1,
style = wx.PD_CAN_ABORT |
wx.PD_APP_MODAL |
wx.PD_ELAPSED_TIME |
wx.PD_REMAINING_TIME)
count = 0
try:
pipeline = cpp.Pipeline()
has_error = [False]
def callback(caller, event):
if isinstance(event, cpp.LoadExceptionEvent):
has_error = True
wx.MessageBox(
message = "Could not load %s: %s" % (
sources[0], event.error),
caption = "Failed to load %s" % sources[0])
has_error[0] = True
pipeline.add_listener(callback)
if has_error[0]:
return
# distributed processing passes a list of functions, not a
# list of filenames.
pipeline.load(sources[0]() if callable(sources[0]) else sources[0])
mdest = cpmeas.Measurements()
image_set_count = 0
for source in sources:
if not is_headless:
count += 1
keep_going, skip = progress.Update(count, "Loading " + source)
if not keep_going:
return
# distributed processing passes a list of functions
msource = cpmeas.load_measurements(source() if callable(source) else source)
source_image_set_count = 0
for object_name in msource.get_object_names():
if object_name in mdest.get_object_names():
destfeatures = mdest.get_feature_names(object_name)
else:
destfeatures = []
for feature in msource.get_feature_names(object_name):
src_values = msource.get_all_measurements(object_name,
feature)
if np.isscalar(src_values):
# For something like "Experiment", there is a single
# value. Keep the first value seen among all sources.
#
if not feature in destfeatures:
mdest.add_all_measurements(object_name, feature,
[src_values])
else:
source_image_count = max(source_image_set_count,
len(src_values))
if feature in destfeatures:
dest_values = mdest.get_all_measurements(object_name,
feature)
if not isinstance(dest_values, list):
dest_values = dest_values.tolist()
else:
dest_values = [None] * image_set_count
if isinstance(src_values, list):
dest_values += src_values
else:
dest_values += src_values.tolist()
mdest.add_all_measurements(object_name, feature, dest_values)
destset = set(destfeatures)
#
# These are features that are in the destination, but not
# in the source. We have to add Nones to the destination.
#
for feature in destset.difference(
msource.get_feature_names(object_name)):
dest_values = mdest.get_all_measurements(
object_name, feature)
dest_values += [None] * source_image_count
#
# The source may not have all of the objects that are in
# the destination. Add blanks here.
#
destobjects = set(mdest.get_object_names())
for object_name in destobjects.difference(
msource.get_object_names()):
for feature in mdest.get_feature_names(object_name):
dest_values = mdest.get_all_measurements(
object_name, feature)
dest_values += [None] * source_image_count
mdest.add_all_measurements(object_name, feature, dest_values)
image_set_count += source_image_count
mdest.image_set_number = image_set_count
if not is_headless:
keep_going, skip = progress.Update(count+1, "Saving to "+destination)
if not keep_going:
return
pipeline.save_measurements(destination, mdest)
finally:
if not is_headless:
progress.Destroy()
def merge_files(destination, sources, force_headless=False):
is_headless = force_headless or get_headless()
if not is_headless:
import wx
if len(sources) == 0:
return
if not is_headless:
progress = wx.ProgressDialog("Writing " + destination,
"Loading " + sources[0],
maximum = len(sources) * 4 + 1,
style = wx.PD_CAN_ABORT |
wx.PD_APP_MODAL |
wx.PD_ELAPSED_TIME |
wx.PD_REMAINING_TIME)
count = 0
try:
pipeline = cpp.Pipeline()
has_error = [False]
def callback(caller, event):
if isinstance(event, cpp.LoadExceptionEvent):
has_error = True
wx.MessageBox(
message = "Could not load %s: %s" % (
sources[0], event.error),
caption = "Failed to load %s" % sources[0])
has_error[0] = True
pipeline.add_listener(callback)
# distributed processing passes a list of functions, not a
# list of filenames.
pipeline.load(sources[0]() if callable(sources[0]) else sources[0])
if has_error[0]:
return
mdest = cpmeas.Measurements()
for source in sources:
if not is_headless:
count += 1
keep_going, skip = progress.Update(count, "Loading " + source)
if not keep_going:
return
# distributed processing passes a list of functions
msource = cpmeas.load_measurements(source() if callable(source) else source)
dest_image_numbers = mdest.get_image_numbers()
source_image_numbers = msource.get_image_numbers()
if len(dest_image_numbers) == 0:
offset_source_image_numbers = source_image_numbers
else:
offset_source_image_numbers = (
np.max(dest_image_numbers) + source_image_numbers)
for object_name in msource.get_object_names():
if object_name in mdest.get_object_names():
destfeatures = mdest.get_feature_names(object_name)
else:
destfeatures = []
for feature in msource.get_feature_names(object_name):
if object_name == cpmeas.EXPERIMENT:
if not mdest.has_feature(object_name, feature):
src_value = msource.get_experiment_measurement(
feature)
mdest.add_experiment_measurement(feature,
src_value)
continue
src_values = msource.get_measurement(
object_name,
feature,
image_set_number = source_image_numbers)
for image_number, value in zip(
offset_source_image_numbers, src_values):
mdest.add_measurement(
object_name, feature, value,
image_set_number = image_number)
destset = set(destfeatures)
if not is_headless:
keep_going, skip = progress.Update(count+1, "Saving to "+destination)
if not keep_going:
return
pipeline.save_measurements(destination, mdest)
finally:
if not is_headless:
progress.Destroy()
def test_13_01_load_matlab(self):
data = ('eJztHEtzHEd5pCghVkhi3gmVQ5NylS2XJe3KWlueCpFkSbYV9CpLsRMss+nd'
'6d3teF7MQ7tLTBXHcM2JOweuUFw4pfIDuFHAkRTwA3wCDqSK7+vu2XnsrPYh'
'aYuAxrX2dvf36u/VX/f0env1YGv1NinNFcj26sFsjZuM7Jk0qDmepRM7uEbW'
'PEYDZhDH1slBIySrYZ0UFkmxoC8s6cUlslAoFrXRnonN7Vfw33c07QX450X4'
'TKqh51V7IvH5Cnwa1DZM5sP4lPay6l+Ez1roecwOJPI2o37oMQs6fG3PYzUG'
'Y1XA0vZZEHC77ndEQP73pgbjL/untO+o9hfw2QmtCvN2a5sWrTOgrijDt9sM'
'GK3a1Gz/hBkKO4LedowQZ6GefXrEdsPADYN7TnO3FjBbdB9wi+0H1As6+BIa'
'u4B2xDLZHbrr3GPVwPHaqnud1WhoBpJ8PKi6BY0EimgfOI7p3wFPsKklhex0'
'32OmmzIh6m9Ji/X3Qh/9XVD96lk5DfxCAv+rA9hvUsk9LN6EwttL4E3l4H03'
'gXdRtTFKZgtLs3Hk3Jy7daNUKBRiPY4qT1J/efK8qKXlwfaafhh4of1E+H8f'
'/G9m8LFdBXxmuYcVahxyqz5bXFwqRPM4bXpugt5EDr2kXhB+pQ//lzL8sS08'
'nATo+bn2yPpl9CT9MsLbe2O4fBLJ8z34bLRc5nHMXFIkCbUTVk3GE2wFH/Lq'
'cHy+rtqXUEbuMpPbrBx9Ac48KEMSCUI/w+feK8Pp/7VXjtf/319O6x/ba8w0'
'9zwH1x+PRDLppBEErj4/32w256oA4SqIOcerTz9gns9hVVqY3n+wc58dcdEq'
'FhcLi9PTWw41hAJ9/ZElsm3ZDi29+NQ/sstHCvPwcrF4/Vbp8PLTI+pxWgEg'
'T9FBaEjVQO6p33Ca5Sa3DaepH3ghe2o7AZA9bJVuw2f9aYUG1UbZB80xnXoe'
'bV+Jhq4RI2i77Psht4OlmcfTBB7MqwR7wdlIhRETBGWGDuT5ETdCahIuxI6B'
'fWZCegaZiMWChmPoB6wVzG60aDUgFvIWoHJpIU5N4RNuE0arDVL3nNBd1q8L'
'qAPBuAEfoAFfaCBarFU1QxAjwm3AgoQEqo5lgZ7WHQJzJqHPBBG1qBFqmsQP'
'KzXHNEChpMmDBuAgPSky0JNjy/qOY0vcTRuWIcmGiGLDdKoUJ6erFUlB3BGI'
'h62bax3UtQarPlGoPqy0QNwjFvd9WAsJfDVC1+RATBL2l/X3lRbvogaiqVXa'
'qEZq0ICiWAJgQ06fVJkXUJiCwlej+y6r8lqMBsPMNHy0n9AtkNQloJh1UiUA'
'QqWyJITMMlUntAO9KA2StIPP8g1ArlSpz2Z9Zvs84EdsRvdhpeeGoLDnYCcA'
'genBAL7SUMr8itlGK/CE13Rm4jkWaTagPEqY6D6rhyb1wClcj/kYDFLAGvio'
'H+MqUwsjYqGg/+jK8t5bWDuyt+euzpSx9RBi9m2MhtXZPQwI/FqYvYVfP1r4'
'6UzZR6B9DhiJkZm0p3pDidNRPnFp0NDnropYVM88kkeW60rI3MH7oY1jl5TT'
'UdtmZspm0p+Qm+UccVCBh3VSx1/EMPqkCtdNO2CeycCiRiZSq5K4TyDnK0PJ'
'MMXsJRhwEQzUj/wC3NypfAheBuw24zyxAwJI48t0IsH19Z3V/GFJQ5fLijK6'
'8HwnDDDx+ihdCjaeniLGIljFa0tAC5l2FZGUAqN8qrj5VWri9ALp01G4Tm8a'
'sPRBtO3BIki99q5knsrhC905/HrxuBx+65RTuIrz2D652mbElXOINKgSPpcz'
'5JD0E/oHd4fcZRKDA3Yg3UPhXUPfdnkLlBjaHOhc2eb2tW3amtGLhWuLBYG/'
'zkGjXsdaaBwfOAk5OjQ9atdZx5AHIBqIZDFPVj7EtzCjRxQwnRObUQ8yJsRf'
'nXldrpBlGjhhtYGej1wrjmfIeQhFoYrilJxQYdCAsG5AzCZCZjfwQ3LXdCrU'
'lKJGMBCHnqdWwxrFTYvyqC2nCcxgc0hCF6OpAvEK6UEkC4WrF+YK4rlWVF/k'
'auZCVdEC+QIRydUol3KVq8HRIAoq7Xj2QEcy3RbSohIN7uOWLOR+g8BaYrlx'
'4Ij4RydvZ3E82iRi1ceZy7CrsKDJmN2Hxj6H1RdE9C3HCYTCIQsHGF1ySvug'
'A8yVYnE1oUjA6cmsST2Yk+n4oCJo2zIrWNzmVmjhgg5qNMRsKGyY9ZvRAshQ'
'r6iEUKy2CObNAgvHDIW+uCqfRVZOsY1t3iNxRJGeTBpQ95gEbCbLmDhiYhtE'
'RFfDwAHTYeyYbQJ/V0NcgYjfQ0X9MXspo2Ob/NltUxuLt5S3JV29wm3MBXLO'
'ibW2K+2KaMmZsYq5XmKnQkmZCftEKMmwioXdwCWJbFHXpFVOhbvfpSEsrjQd'
'LyUZfU1ntmpSXyw/OMxUMxm6yzqAEdEfaQQViWZAMZqM1xtYEYosjeoUtKD0'
'tAPPcdvL+sMshJwuCFVX6c+PFnmLG0Zi9WgTJY4jKxIHSgYP4z/iUaHVJ7Jn'
'Wb/TGe2rzsiHovRWU+Ty9Zb1L6Vj564a6aRhXWr1Hp5kidI15sBrWIpjlQPl'
'n92pEzrD8dKx5sA3O2TK91rCZbsw9FKhkM23Vnw4JjUW+Qwm/Ngz4zwVKwUq'
'LRfrTyKXUqEgWYaIr6nclIHF1BTtDxvacPvJkc43HMs1QduI/6/p4fbJl1X7'
'00mkA3VfuXv/nXlEzVeWld0xYJ11rHwH0oEZN9MMttdL6xxqu6AMNUVvavdE'
'xBwP05Ft0zZY6ziwWJgoEh+oQExJh1tSzOR92O5B9T0AWMx0P7R2axsiGUA1'
'mFHIRotVxUKDR6LlQjHe3aeoPeRG0OirjwTTXY/Xexkhy3QhvzTV5Fnuhuc5'
'Xm8gTXv3/lZ/yTRtH6INkkI/0BTTXurA56TxNtx57LNlxP9gSH6jnnuOY15/'
'+vG/v3hV+7WY18+G5NfvnJho6byF7VK1xJboYmmpslC7sUhLxVKhyG5cv1Uq'
'FW4slYzFceh3S+GdNZ9x2lFL4J/2efK0lrYjtuUBzZxr12M6rSH5nvY5+jj0'
'/MmFvzyr/+H1z8Zm19//9aPSP341lrzz7hj1mMw7Y9Hje5+89ed//nYseozw'
'Ph6Sz3va8fFwOQF/UbXxeFKfn59f0+cxLuYhLuajuJhPBOlY89Dnzso49fzZ'
'xHD177dUe1mL3liXZR0TFbhb6sy+vMZwk1N+HzGzne91+J/1PMe5jvxwZueP'
'P59aWxkXv998+2+//ME7q4Lfs+dGv6ewhxto3JtpqUe9dJHlZNftgEEvDUT7'
'8DXHdLxtqm4IbNEKM9Nd+OwHHnc7bx3j7ifcFfVsuoZd5z5sttsgIHtAzTBC'
'uAPb3+7ZRNAPxZYzReg06toV7fj8czGBf1F9ovfsh+rKx5fhfXuhD72pFL0p'
're7R9iB4z6XwntM+ZMFA/LJ478M+ZxC8yRTepLbjnCy/DIs3rXX7XRZvsgsv'
'TQ/xP780XNy/ptrP4PNAvRQRwZOIiKh/0645+KotGpJuuhNd/Ok8MvP7u7UI'
'UQ33yCsd+tF7eYWv6Gd7O3zxtUyKzn7Dad7B92sZ+rfxjQ3eVsgMoL5+d7F3'
'Xkb9vZnoHyR+vqal4wfbXe/rE/SG9Wfc64+aX7J3AQY5L3shQwfb0dnHIPJP'
'pPAntOvaYOd0z2f4Ylu8wdJG32fFVxMG1/8geWGQvIdnpSfh1+984PXMfLGd'
'd0viKQqiDT7/UfNpL7yVPnh5960Sb/gGlntUu2X9tXhCfqPki653j2r8JPfM'
'hvVXfG95En4rffhdyMwb253acAi5e9nrtPiPkh/lVnE0vykWztZPs3g3B8Qb'
'NZ/1ygONPng3M3rFdq9LQ49WZ/cePyrM3nqcuSgk+mYG8odXM/ywnXnbPNC5'
'wqUMHWzPXX10eDj/OHmZqNMRXSDSBrTDqPl4VH85y3r6rNeNxGUqbVT9Qh48'
'UZ0zalyNknei1zrjtE9Uj45q17OuP7vtWTqRflb64OX5YeKyg+gfpC74RoYO'
'tnOuzQ1ML6/O6LpFocbPct3Nq8vjaxaD8z/tuvSs6qqsnko5eMPwG+1+g7z/'
'cZbzzOq1VOjOm2dZr+bVI6dZr+b5bXybJV/u05xvrzrx48TvOiYyeHnnF+PU'
'jzjsEFeVT85/lPwmLzbJe/fcNpiboHcadjrHO8c7xzvHO8f778VbSeANek4e'
'r1ty2fgyzfccb7z+c9Z1z5fV7/9f67WVPvM+zzfneOd4/7t4FydivOx+PLuP'
'R/gPEnzy8sVVLZ0vsJ363w3kj139OfwFrrxdMJe94o98Wn343M7wud2Lj/pd'
'UVv9YFX9eGiu528ZcvR5IYd/Ui+T8OeNN0/nHtawfF+60C3vS33wpoa4p5q1'
'/5Vj4KPnJPDDzn9iQuL9Ysh5HMcnK9fkCfH+A86LP/E=')
data = zlib.decompress(base64.b64decode(data))
fid, name = tempfile.mkstemp(".mat")
m = None
try:
fd = os.fdopen(fid, "wb")
fd.write(data)
fd.close()
m = cpmeas.load_measurements(name)
self.assertEqual(tuple(m.get_image_numbers()), (1, ))
self.assertEqual(
m.get_measurement(cpmeas.IMAGE,
"Count_Nuclei",
image_set_number=1), 1)
values = m.get_measurement("Nuclei", "Location_Center_X", 1)
self.assertEqual(len(values), 1)
self.assertAlmostEqual(values[0], 34.580433355219959)
finally:
gc.collect()
if m is not None:
del m
os.unlink(name)
def merge_files(destination, sources, force_headless=False):
is_headless = force_headless or get_headless()
if not is_headless:
import wx
if len(sources) == 0:
return
if not is_headless:
progress = wx.ProgressDialog("Writing " + destination,
"Loading " + sources[0],
maximum = len(sources) * 4 + 1,
style = wx.PD_CAN_ABORT |
wx.PD_APP_MODAL |
wx.PD_ELAPSED_TIME |
wx.PD_REMAINING_TIME)
count = 0
try:
pipeline = cpp.Pipeline()
has_error = [False]
def callback(caller, event):
if isinstance(event, cpp.LoadExceptionEvent):
has_error = True
wx.MessageBox(
message = "Could not load %s: %s" % (
sources[0], event.error),
caption = "Failed to load %s" % sources[0])
has_error[0] = True
pipeline.add_listener(callback)
# distributed processing passes a list of functions, not a
# list of filenames.
pipeline.load(sources[0]() if callable(sources[0]) else sources[0])
if has_error[0]:
return
mdest = cpmeas.Measurements()
for source in sources:
if not is_headless:
count += 1
keep_going, skip = progress.Update(count, "Loading " + source)
if not keep_going:
return
# distributed processing passes a list of functions
msource = cpmeas.load_measurements(source() if callable(source) else source)
dest_image_numbers = mdest.get_image_numbers()
source_image_numbers = msource.get_image_numbers()
if len(dest_image_numbers) == 0:
offset_source_image_numbers = source_image_numbers
else:
offset_source_image_numbers = (
np.max(dest_image_numbers) + source_image_numbers)
for object_name in msource.get_object_names():
if object_name in mdest.get_object_names():
destfeatures = mdest.get_feature_names(object_name)
else:
destfeatures = []
for feature in msource.get_feature_names(object_name):
if object_name == cpmeas.EXPERIMENT:
if not mdest.has_feature(object_name, feature):
src_value = msource.get_experiment_measurement(
feature)
mdest.add_experiment_measurement(feature,
src_value)
continue
src_values = msource.get_measurement(
object_name,
feature,
image_set_number = source_image_numbers)
for image_number, value in zip(
offset_source_image_numbers, src_values):
mdest.add_measurement(
object_name, feature, value,
image_set_number = image_number)
destset = set(destfeatures)
if not is_headless:
keep_going, skip = progress.Update(count+1, "Saving to "+destination)
if not keep_going:
return
pipeline.save_measurements(destination, mdest)
finally:
if not is_headless:
progress.Destroy()
