def test_02_01_prepare_to_create_batch_single(self):
n = N.NamesAndTypes()
n.module_num = 1
n.assignment_method.value = N.ASSIGN_ALL
n.single_image_provider.value = IMAGE_NAME
m = cpmeas.Measurements(mode="memory")
pathnames = ["foo", "fuu"]
expected_pathnames = ["bar", "fuu"]
filenames = ["boo", "foobar"]
expected_filenames = ["boo", "barbar"]
urlnames = ["file:/foo/bar", "http://foo/bar"]
expected_urlnames = ["file:/bar/bar", "http://foo/bar"]
m.add_all_measurements(cpmeas.IMAGE,
cpmeas.C_FILE_NAME + "_" + IMAGE_NAME,
filenames)
m.add_all_measurements(cpmeas.IMAGE,
cpmeas.C_PATH_NAME + "_" + IMAGE_NAME,
pathnames)
m.add_all_measurements(cpmeas.IMAGE, cpmeas.C_URL + "_" + IMAGE_NAME,
urlnames)
pipeline = cpp.Pipeline()
pipeline.add_module(n)
workspace = cpw.Workspace(pipeline, n, m, None, m, None)
n.prepare_to_create_batch(workspace, lambda x: x.replace("foo", "bar"))
for feature, expected in ((cpmeas.C_FILE_NAME, expected_filenames),
(cpmeas.C_PATH_NAME, expected_pathnames),
(cpmeas.C_URL, expected_urlnames)):
values = m.get_measurement(cpmeas.IMAGE,
feature + "_" + IMAGE_NAME,
np.arange(len(expected)) + 1)
self.assertSequenceEqual(expected, list(values))
def test_01_06_02_missing(self):
n = N.NamesAndTypes()
n.assignment_method.value = N.ASSIGN_RULES
n.matching_choice.value = N.MATCH_BY_METADATA
n.add_assignment()
n.assignments[0].image_name.value = C0
n.assignments[1].image_name.value = C1
n.assignments[0].rule_filter.value = 'file does contain "%s"' % C0
n.assignments[1].rule_filter.value = 'file does contain "%s"' % C1
n.join.build("[{'%s':'%s','%s':'%s'},{'%s':'%s','%s':'%s'}]" %
(C0, M0, C1, M2, C0, M1, C1, M3))
data = {
C0: [("%s%s%s" % (C0, m[M0], m[M1]), m)
for i, m in enumerate(md([(M1, 3), (M0, 2)]))],
C1: [("%s%s%s" % (C1, m[M2], m[M3]), m)
for i, m in enumerate(md([(M3, 3), (M2, 2)]))]
}
bad_row = 3
# Steal the bad row's metadata
additional = [data[C1][bad_row]]
del data[C0][bad_row]
del data[C1][bad_row]
self.do_teest(n, data, [(M0, M2), (M1, M3)], [(M0, C0), (M1, C0),
(M2, C1), (M3, C1)],
additional)
def test_01_08_some_against_all(self):
#
# Permute both the order of the columns and the order of joins
#
joins = [{C0: M0, C1: M1}, {C0: None, C1: M2}]
for cA, cB in ((C0, C1), (C1, C0)):
for j0, j1 in ((0, 1), (1, 0)):
n = N.NamesAndTypes()
n.assignment_method.value = N.ASSIGN_RULES
n.matching_choice.value = N.MATCH_BY_METADATA
n.add_assignment()
n.assignments[0].image_name.value = cA
n.assignments[1].image_name.value = cB
n.assignments[
0].rule_filter.value = 'file does contain "%s"' % cA
n.assignments[
1].rule_filter.value = 'file does contain "%s"' % cB
n.join.build(repr([joins[j0], joins[j1]]))
mA0 = [M0, M3][j0]
mB0 = [M0, M3][j1]
mA1 = joins[j0][C1]
mB1 = joins[j1][C1]
data = {
C0: [("%s%s" % (C0, m[M0]), m)
for i, m in enumerate(md([(mB0, 2), (mA0, 3)]))],
C1: [("%s%s%s" % (C1, m[M1], m[M2]), m)
for i, m in enumerate(md([(mB1, 2), (mA1, 3)]))]
}
expected_keys = [[(M0, M1), (M2, )][i] for i in j0, j1]
self.do_teest(n, data, expected_keys, [(C0, M0), (C0, M3),
(C1, M1), (C1, M2)])
def run_workspace(self,
path,
load_as_type,
series=None,
index=None,
channel=None,
single=False):
'''Run a workspace to load a file
path - path to the file
load_as_type - one of the LOAD_AS... constants
series, index, channel - pick a plane from within a file
returns the workspace after running
'''
n = N.NamesAndTypes()
n.assignment_method.value = N.ASSIGN_ALL if single else N.ASSIGN_RULES
n.single_image_provider.value = IMAGE_NAME
n.single_load_as_choice.value = load_as_type
n.assignments[0].image_name.value = IMAGE_NAME
n.assignments[0].object_name.value = OBJECTS_NAME
n.assignments[0].load_as_choice.value = load_as_type
n.module_num = 1
pipeline = cpp.Pipeline()
pipeline.add_module(n)
url = pathname2url(path)
pathname, filename = os.path.split(path)
m = cpmeas.Measurements(mode="memory")
if load_as_type == N.LOAD_AS_OBJECTS:
url_feature = cpmeas.C_OBJECTS_URL + "_" + OBJECTS_NAME
path_feature = cpmeas.C_OBJECTS_PATH_NAME + "_" + OBJECTS_NAME
file_feature = cpmeas.C_OBJECTS_FILE_NAME + "_" + OBJECTS_NAME
series_feature = cpmeas.C_OBJECTS_SERIES + "_" + OBJECTS_NAME
frame_feature = cpmeas.C_OBJECTS_FRAME + "_" + OBJECTS_NAME
channel_feature = cpmeas.C_OBJECTS_CHANNEL + "_" + OBJECTS_NAME
else:
url_feature = cpmeas.C_URL + "_" + IMAGE_NAME
path_feature = cpmeas.C_PATH_NAME + "_" + IMAGE_NAME
file_feature = cpmeas.C_FILE_NAME + "_" + IMAGE_NAME
series_feature = cpmeas.C_SERIES + "_" + IMAGE_NAME
frame_feature = cpmeas.C_FRAME + "_" + IMAGE_NAME
channel_feature = cpmeas.C_CHANNEL + "_" + IMAGE_NAME
m.image_set_number = 1
m.add_measurement(cpmeas.IMAGE, url_feature, url)
m.add_measurement(cpmeas.IMAGE, path_feature, pathname)
m.add_measurement(cpmeas.IMAGE, file_feature, filename)
if series is not None:
m.add_measurement(cpmeas.IMAGE, series_feature, series)
if index is not None:
m.add_measurement(cpmeas.IMAGE, frame_feature, index)
if channel is not None:
m.add_measurement(cpmeas.IMAGE, channel_feature, channel)
m.add_measurement(cpmeas.IMAGE, cpmeas.GROUP_NUMBER, 1)
m.add_measurement(cpmeas.IMAGE, cpmeas.GROUP_INDEX, 1)
workspace = cpw.Workspace(pipeline, n, m, N.cpo.ObjectSet(), m, None)
n.run(workspace)
return workspace
def test_02_02_prepare_to_create_batch_multiple(self):
n = N.NamesAndTypes()
n.module_num = 1
n.assignment_method.value = N.ASSIGN_RULES
n.add_assignment()
n.assignments[0].load_as_choice.value = N.LOAD_AS_GRAYSCALE_IMAGE
n.assignments[0].image_name.value = IMAGE_NAME
n.assignments[1].load_as_choice.value = N.LOAD_AS_OBJECTS
n.assignments[1].object_name.value = OBJECTS_NAME
m = cpmeas.Measurements(mode="memory")
pathnames = ["foo", "fuu"]
expected_pathnames = ["bar", "fuu"]
filenames = ["boo", "foobar"]
expected_filenames = ["boo", "barbar"]
urlnames = ["file:/foo/bar", "http://foo/bar"]
expected_urlnames = ["file:/bar/bar", "http://foo/bar"]
for feature, name, values in ((cpmeas.C_FILE_NAME, IMAGE_NAME,
filenames), (cpmeas.C_OBJECTS_FILE_NAME,
OBJECTS_NAME,
reversed(filenames)),
(cpmeas.C_PATH_NAME, IMAGE_NAME,
pathnames), (cpmeas.C_OBJECTS_PATH_NAME,
OBJECTS_NAME,
reversed(pathnames)),
(cpmeas.C_URL, IMAGE_NAME, urlnames),
(cpmeas.C_OBJECTS_URL, OBJECTS_NAME,
reversed(urlnames))):
m.add_all_measurements(cpmeas.IMAGE, feature + "_" + name, values)
pipeline = cpp.Pipeline()
pipeline.add_module(n)
workspace = cpw.Workspace(pipeline, n, m, None, m, None)
n.prepare_to_create_batch(workspace, lambda x: x.replace("foo", "bar"))
for feature, name, expected in ((cpmeas.C_FILE_NAME, IMAGE_NAME,
expected_filenames),
(cpmeas.C_OBJECTS_FILE_NAME,
OBJECTS_NAME,
reversed(expected_filenames)),
(cpmeas.C_PATH_NAME, IMAGE_NAME,
expected_pathnames),
(cpmeas.C_OBJECTS_PATH_NAME,
OBJECTS_NAME,
reversed(expected_pathnames)),
(cpmeas.C_URL, IMAGE_NAME,
expected_urlnames),
(cpmeas.C_OBJECTS_URL, OBJECTS_NAME,
reversed(expected_urlnames))):
values = m.get_measurement(cpmeas.IMAGE, feature + "_" + name,
np.arange(1, 3))
self.assertSequenceEqual(list(expected), list(values))
def test_01_07_one_against_all(self):
n = N.NamesAndTypes()
n.assignment_method.value = N.ASSIGN_RULES
n.matching_choice.value = N.MATCH_BY_METADATA
n.add_assignment()
n.assignments[0].image_name.value = C0
n.assignments[1].image_name.value = C1
n.assignments[0].rule_filter.value = 'file does contain "%s"' % C0
n.assignments[1].rule_filter.value = 'file does contain "%s"' % C1
n.join.build("[{'%s':None,'%s':'%s'}]" % (C0, C1, M0))
data = {
C0: [(C0, {})] * 3,
C1: [("%s%d" % (C1, i), m) for i, m in enumerate(md([(M0, 3)]))]
}
self.do_teest(n, data, [(M0, )], [(M0, C1)])
def test_01_10_by_order(self):
n = N.NamesAndTypes()
n.assignment_method.value = N.ASSIGN_RULES
n.matching_choice.value = N.MATCH_BY_ORDER
n.add_assignment()
n.assignments[0].image_name.value = C0
n.assignments[1].image_name.value = C1
n.assignments[0].rule_filter.value = 'file does contain "%s"' % C0
n.assignments[1].rule_filter.value = 'file does contain "%s"' % C1
data = {
C0:
[("%s%d" % (C0, i + 1), m) for i, m in enumerate(md([(M0, 2)]))],
C1:
[("%s%d" % (C1, i + 1), m) for i, m in enumerate(md([(M1, 2)]))]
}
self.do_teest(n, data, [(cpmeas.IMAGE_NUMBER, )], [(C0, M0), (C1, M1)])
def test_01_01_one(self):
n = N.NamesAndTypes()
n.assignment_method.value = N.ASSIGN_RULES
n.matching_choice.value = N.MATCH_BY_METADATA
n.assignments[0].image_name.value = C0
n.join.build("[{\"%s\":\"%s\"}]" % (C0, M0))
# It should match by order, even if match by metadata and the joiner
# are set up.
data = {
C0: [("images/1.jpg", {
M0: "k1"
}), ("images/2.jpg", {
M0: "k3"
}), ("images/3.jpg", {
M0: "k2"
})]
}
self.do_teest(n, data, [(cpmeas.IMAGE_NUMBER, )], [(M0, C0)])
def test_01_05_match_two_and_two(self):
n = N.NamesAndTypes()
n.assignment_method.value = N.ASSIGN_RULES
n.matching_choice.value = N.MATCH_BY_METADATA
n.add_assignment()
n.assignments[0].image_name.value = C0
n.assignments[1].image_name.value = C1
n.assignments[0].rule_filter.value = 'file does contain "%s"' % C0
n.assignments[1].rule_filter.value = 'file does contain "%s"' % C1
n.join.build("[{'%s':'%s','%s':'%s'},{'%s':'%s','%s':'%s'}]" %
(C0, M0, C1, M2, C0, M1, C1, M3))
data = {
C0: [("%s%s%s" % (C0, m[M0], m[M1]), m)
for i, m in enumerate(md([(M1, 3), (M0, 2)]))],
C1: [("%s%s%s" % (C1, m[M2], m[M3]), m)
for i, m in enumerate(md([(M3, 3), (M2, 2)]))]
}
self.do_teest(n, data, [(M0, M2), (M1, M3)], [(M0, C0), (M1, C0),
(M2, C1), (M3, C1)])
def test_01_03_match_one_different_key(self):
n = N.NamesAndTypes()
n.assignment_method.value = N.ASSIGN_RULES
n.matching_choice.value = N.MATCH_BY_METADATA
n.add_assignment()
n.assignments[0].image_name.value = C0
n.assignments[1].image_name.value = C1
n.assignments[0].rule_filter.value = 'file does contain "1"'
n.assignments[1].rule_filter.value = 'file doesnot contain "1"'
n.join.build("[{'%s':'%s','%s':'%s'}]" % (C0, M0, C1, M1))
data = {
C0: [("images/1.jpg", {
M0: "k1"
})],
C1: [("images/2.jpg", {
M1: "k1"
})]
}
self.do_teest(n, data, [(M0, M1)], [(M0, C0), (M1, C1)])
def test_01_11_by_order_bad(self):
# Regression test of issue #392: columns of different lengths
n = N.NamesAndTypes()
n.assignment_method.value = N.ASSIGN_RULES
n.matching_choice.value = N.MATCH_BY_ORDER
n.add_assignment()
n.assignments[0].image_name.value = C0
n.assignments[1].image_name.value = C1
n.assignments[0].rule_filter.value = 'file does contain "%s"' % C0
n.assignments[1].rule_filter.value = 'file does contain "%s"' % C1
n.ipd_columns = \
[[cpp.ImagePlaneDetails("%s%d" % (C0, (3-i)), None, None, None, **m)
for i, m in enumerate(md([(M0, 3)]))],
[cpp.ImagePlaneDetails("%s%d" % (C1, i+1), None, None, None, **m)
for i, m in enumerate(md([(M1, 2)]))]]
data = {
C0:
[("%s%d" % (C0, i + 1), m) for i, m in enumerate(md([(M0, 2)]))],
C1:
[("%s%d" % (C1, i + 1), m) for i, m in enumerate(md([(M1, 2)]))]
}
additional = [("%sBad" % C0, {})]
self.do_teest(n, data, [(cpmeas.IMAGE_NUMBER, )], [(C0, M0), (C1, M1)])
def test_01_00_01_all(self):
n = N.NamesAndTypes()
n.assignment_method.value = N.ASSIGN_ALL
n.single_image_provider.value = C0
data = {C0: [("images/1.jpg", {M0: "1"})]}
self.do_teest(n, data, [(cpmeas.IMAGE_NUMBER, )], [(M0, C0)])
