def setUp(self):
# create a couple Observations to use and a corresponding serializer
self.el1 = Observation('sampleA', {'phenotype': StringAttribute('WT')})
self.el1_serializer = ObservationSerializer(self.el1)
self.el2 = Observation('sampleB', {'phenotype': StringAttribute('KO')})
self.el2_serializer = ObservationSerializer(self.el2)
# a duplicate of el1 above, for testing addition of duplicate elements:
self.duplicate_element = Observation('sampleA', {})
self.dup_element_serializer = ObservationSerializer(
self.duplicate_element)
# the correct serialized representation of an ElementSet instance
self.expected_element_set_data = {
'multiple': True,
'elements': [self.el1_serializer.data, self.el2_serializer.data]
}
# a correctly formed instance of an ObservationSet
self.element_set = ObservationSet([self.el1, self.el2])
# the class that will execute the tests
self.tester_class = ElementSetSerializerTester(
ObservationSetSerializer)
def setUp(self):
# create a couple Observations to use
self.el1 = Observation('sampleA', {'phenotype': StringAttribute('WT')})
self.el2 = Observation('sampleB', {'phenotype': StringAttribute('KO')})
# a duplicate of element above:
self.duplicate_element = Observation('sampleA', {})
# instantiate the class that will actually execute the tests
self.tester_class = ElementSetTester(ObservationSet)
def test_observation_set_csv_converter(self):
obs1 = Observation('foo')
obs2 = Observation('bar')
obs_set = ObservationSet([obs1, obs2])
d = obs_set.to_dict()
c = ObservationSetCsvConverter()
# order doesn't matter, so need to check both orders:
converted_input = c.convert('xyz', d, '', '')
self.assertTrue(({
'xyz': 'foo,bar'
} == converted_input)
| ({
'xyz': 'bar,foo'
} == converted_input))
def create_observation_set():
# create a couple Observations to use and a corresponding serializer
el1 = Observation('sampleA', {'phenotype': StringAttribute('WT')})
el1_serializer = ObservationSerializer(el1)
el2 = Observation('sampleB', {'phenotype': StringAttribute('KO')})
el2_serializer = ObservationSerializer(el2)
# the correct serialized representation of an ElementSet instance
observation_set_data = {
'multiple': True,
'elements': [el1_serializer.data, el2_serializer.data]
}
return observation_set_data
def create(self, validated_data):
'''
Returns an Observation instance from the validated
data.
'''
attr_dict = self._gather_attributes(validated_data)
return Observation(validated_data['id'], attr_dict)
def test_metadata_correct_case2(self):
'''
Typically, the metadata is collected following a successful
validation. Do that here
'''
m = IntegerMatrix()
resource_path = os.path.join(TESTDIR, 'test_integer_matrix.tsv')
metadata = m.extract_metadata(resource_path)
# Parse the test file to ensure we extracted the right content.
line = open(resource_path).readline()
contents = line.strip().split('\t')
samplenames = contents[1:]
obs_list = [Observation(x) for x in samplenames]
gene_list = []
for i, line in enumerate(open(resource_path)):
if i > 0:
g = line.split('\t')[0]
gene_list.append(g)
feature_list = [Feature(x) for x in gene_list]
obs_set = ObservationSetSerializer(ObservationSet(obs_list)).data
feature_set = FeatureSetSerializer(FeatureSet(feature_list)).data
self.assertEqual(obs_set, metadata[OBSERVATION_SET_KEY])
self.assertEqual(feature_set, metadata[FEATURE_SET_KEY])
self.assertIsNone(metadata[PARENT_OP_KEY])
def test_metadata_correct(self):
resource_path = os.path.join(TESTDIR, 'three_column_annotation.tsv')
t = AnnotationTable()
column_dict = {}
obs_list = []
for i, line in enumerate(open(resource_path)):
if i == 0:
contents = line.strip().split('\t')
for j, c in enumerate(contents[1:]):
column_dict[j] = c
else:
contents = line.strip().split('\t')
samplename = contents[0]
attr_dict = {}
for j, v in enumerate(contents[1:]):
attr = UnrestrictedStringAttribute(v)
attr_dict[column_dict[j]] = attr
obs = Observation(samplename, attr_dict)
obs_list.append(obs)
expected_obs_set = ObservationSetSerializer(
ObservationSet(obs_list)).data
metadata = t.extract_metadata(resource_path, 'tsv')
self.assertEqual(metadata[OBSERVATION_SET_KEY], expected_obs_set)
self.assertIsNone(metadata[FEATURE_SET_KEY])
self.assertIsNone(metadata[PARENT_OP_KEY])
def test_metadata_correct_case2(self):
'''
Typically, the metadata is collected following a successful
validation. However, here we don't validate. Check that
it goes and collects the table in the process
'''
m = Matrix()
resource_path = os.path.join(TESTDIR, 'test_matrix.tsv')
metadata = m.extract_metadata(resource_path, 'tsv')
# Parse the test file to ensure we extracted the right content.
line = open(resource_path).readline()
contents = line.strip().split('\t')
samplenames = contents[1:]
obs_list = [Observation(x) for x in samplenames]
gene_list = []
for i, line in enumerate(open(resource_path)):
if i > 0:
g = line.split('\t')[0]
gene_list.append(g)
feature_list = [Feature(x) for x in gene_list]
obs_set = ObservationSetSerializer(ObservationSet(obs_list)).data
feature_set = FeatureSetSerializer(FeatureSet(feature_list)).data
self.assertEqual(obs_set, metadata[OBSERVATION_SET_KEY])
# Commented out when removed the feature metadata, as it was causing database
# issues due to the size of the json object.
#self.assertEqual(feature_set, metadata[FEATURE_SET_KEY])
self.assertIsNone(metadata[FEATURE_SET_KEY])
self.assertIsNone(metadata[PARENT_OP_KEY])
def setUp(self):
float_attr = FloatAttribute(0.01)
int_attr = IntegerAttribute(3)
self.demo_element = Observation('my_identifier', {
'keyA': float_attr,
'keyB': int_attr
})
# the class that will execute the tests
self.tester_class = ElementTester(Observation)
def test_observation_set_list_converter(self):
'''
Tests that we get properly formatted JSON-compatible
arrays (of strings in this case). Used when we need to
supply a WDL job with a list of relevant samples as an
array of strings, for instance.
'''
obs1 = Observation('foo')
obs2 = Observation('bar')
obs_set = ObservationSet([obs1, obs2])
d = obs_set.to_dict()
c = ObservationSetListConverter()
# order doesn't matter, so need to check both orders:
converted_input = c.convert('xyz', d, '', '')
self.assertTrue(({
'xyz': ['foo', 'bar']
} == converted_input)
| ({
'xyz': ['bar', 'foo']
} == converted_input))
def extract_metadata(self, resource_path, parent_op_pk=None):
super().extract_metadata(resource_path, parent_op_pk)
# the FeatureSet comes from the rows:
f_set = FeatureSet([Feature(x) for x in self.table.index])
self.metadata[DataResource.FEATURE_SET] = FeatureSetSerializer(
f_set).data
# the ObservationSet comes from the cols:
o_set = ObservationSet([Observation(x) for x in self.table.columns])
self.metadata[DataResource.OBSERVATION_SET] = ObservationSetSerializer(
o_set).data
return self.metadata
def extract_metadata(self,
resource_path,
file_extension,
parent_op_pk=None):
super().extract_metadata(resource_path, file_extension, parent_op_pk)
# Note: removed the addition of FeatureSets to the metadata as it was causing
# issues with large json objects being inserted into the database.
# the FeatureSet comes from the rows:
# f_set = FeatureSet([Feature(x) for x in self.table.index])
# self.metadata[DataResource.FEATURE_SET] = FeatureSetSerializer(f_set).data
# the ObservationSet comes from the cols:
o_set = ObservationSet([Observation(x) for x in self.table.columns])
self.metadata[DataResource.OBSERVATION_SET] = ObservationSetSerializer(
o_set).data
return self.metadata
def setUp(self):
float_attr = FloatAttribute(0.01)
int_attr = IntegerAttribute(3)
boolean_attr = BooleanAttribute(True)
bounded_float_attr = BoundedFloatAttribute(0.1, min=0.0, max=1.0)
self.demo_element = Observation('my_identifier', {
'keyA': float_attr,
'keyB': int_attr
})
self.demo_element2 = Observation('my_identifier', {})
self.demo_element_data = {
'id': 'my_identifier',
'attributes': {
'keyA': {
'attribute_type': 'Float',
'value': 0.01
},
'keyB': {
'attribute_type': 'Integer',
'value': 3
}
}
}
self.demo_element_data2 = {'id': 'my_identifier', 'attributes': {}}
self.bad_element_data = {
'id': 'my_identifier',
'attributes': {
'keyA': {
'attribute_type': 'Float',
'value': 'abc'
},
'keyB': {
'attribute_type': 'Integer',
'value': 3
}
}
}
self.demo_element_data_w_bounds = {
'id': 'my_identifier',
'attributes': {
'pvalue': {
'attribute_type': 'BoundedFloat',
'value': 0.1,
'min': 0.0,
'max': 1.0
},
'keyB': {
'attribute_type': 'Integer',
'value': 3
}
}
}
self.demo_element_w_bounds = Observation('my_identifier', {
'pvalue': bounded_float_attr,
'keyB': int_attr
})
self.bad_demo_element_data_w_bounds = {
'id': 'my_identifier',
'attributes': {
'pvalue': {
'attribute_type': 'BoundedFloat',
'value': 1.1, # out of bounds!!
'min': 0.0,
'max': 1.0
},
'keyB': {
'attribute_type': 'Integer',
'value': 3
}
}
}
self.demo_element_w_bool = Observation('my_identifier', {
'keyA': int_attr,
'some_bool': boolean_attr
})
self.demo_element_data_w_bool1 = {
'id': 'my_identifier',
'attributes': {
'keyA': {
'attribute_type': 'Integer',
'value': 3
},
'some_bool': {
'attribute_type': 'Boolean',
'value': 'true'
}
}
}
self.demo_element_data_w_bool2 = {
'id': 'my_identifier',
'attributes': {
'keyA': {
'attribute_type': 'Integer',
'value': 3
},
'some_bool': {
'attribute_type': 'Boolean',
'value': 1
}
}
}
self.demo_element_data_w_bool3 = {
'id': 'my_identifier',
'attributes': {
'keyA': {
'attribute_type': 'Integer',
'value': 3
},
'some_bool': {
'attribute_type': 'Boolean',
'value': True
}
}
}
self.bad_demo_element_data_w_bool = {
'id': 'my_identifier',
'attributes': {
'keyA': {
'attribute_type': 'Integer',
'value': 3
},
'some_bool': {
'attribute_type': 'Boolean',
'value': -1
}
}
}
# the class that will execute the tests
self.tester_class = ElementSerializerTester(ObservationSerializer)
def setUp(self):
self.establish_clients()
self.new_resource1 = Resource.objects.create(
name = 'foo.txt',
owner = self.regular_user_1,
is_active=True
)
self.new_resource2 = Resource.objects.create(
name = 'bar.txt',
owner = self.regular_user_1,
is_active=True
)
self.new_resource3 = Resource.objects.create(
name = 'baz.txt',
owner = self.regular_user_1,
is_active=True
)
# create a workspace to which we will eventually add resources
self.workspace = Workspace.objects.create(
owner = self.regular_user_1
)
self.empty_workspace = Workspace.objects.create(
owner = self.regular_user_1
)
# create a few Observations to use with the different Resources
obs1 = Observation('sampleA', {
'phenotype': StringAttribute('WT')
})
obs1_serializer = ObservationSerializer(obs1)
obs2 = Observation('sampleB', {
'phenotype': StringAttribute('KO')
})
obs2_serializer = ObservationSerializer(obs2)
obs3 = Observation('sampleC', {
'phenotype': StringAttribute('KO')
})
obs3_serializer = ObservationSerializer(obs3)
# create Features to use and a corresponding serializer
feature1 = Feature('featureA', {
'pathway': StringAttribute('foo')
})
feature1_serializer = FeatureSerializer(feature1)
feature2 = Feature('featureB', {
'pathway': StringAttribute('bar')
})
feature2_serializer = FeatureSerializer(feature2)
feature3 = Feature('featureC', {
'pathway': StringAttribute('bar3')
})
feature3_serializer = FeatureSerializer(feature3)
feature4 = Feature('featureD', {
'pathway': StringAttribute('bar')
})
feature4_serializer = FeatureSerializer(feature4)
# create an ObservationSet for resource1
observation_set_data1 = {
'multiple': True,
'elements': [
obs1_serializer.data,
obs2_serializer.data
]
}
# create an ObservationSet for resource2
observation_set_data2 = {
'multiple': True,
'elements': [
obs3_serializer.data,
]
}
# create a FeatureSet for resource1
feature_set_data1 = {
'multiple': True,
'elements': [
feature1_serializer.data,
feature2_serializer.data
]
}
# create a FeatureSet for resource2
feature_set_data2 = {
'multiple': True,
'elements': [
feature3_serializer.data,
feature4_serializer.data
]
}
metadata1 = {
RESOURCE_KEY: self.new_resource1.pk,
OBSERVATION_SET_KEY: observation_set_data1,
FEATURE_SET_KEY: feature_set_data1,
PARENT_OP_KEY: None
}
metadata2 = {
RESOURCE_KEY: self.new_resource2.pk,
OBSERVATION_SET_KEY: observation_set_data2,
FEATURE_SET_KEY: feature_set_data2,
PARENT_OP_KEY: None
}
rms1 = ResourceMetadataSerializer(data=metadata1)
if rms1.is_valid(raise_exception=True):
rms1.save()
rms2 = ResourceMetadataSerializer(data=metadata2)
if rms2.is_valid(raise_exception=True):
rms2.save()
