def setUp(self):
ConfigTestCase.setUp(self)
TempfileTestCase.setUp(self)
self.no_resources_repo = AlphaRepo()
resource_filename1 = self.create_tempfile(suffix='.qza').name
resource_filename2 = self.create_tempfile(suffix='.qza').name
test_series1 = pd.Series({
'sample1': 7.15,
'sample2': 9.04
},
name='chao1')
test_series2 = pd.Series(
{
'sample3': 7.24,
'sample2': 9.04,
'sample4': 8.25
},
name='faith_pd')
imported_artifact = Artifact.import_data("SampleData[AlphaDiversity]",
test_series1)
imported_artifact.save(resource_filename1)
imported_artifact = Artifact.import_data("SampleData[AlphaDiversity]",
test_series2)
imported_artifact.save(resource_filename2)
config.resources.update({
'alpha_resources': {
'chao1': resource_filename1,
'faith_pd': resource_filename2,
}
})
resources.update(config.resources)
self.repo = AlphaRepo()
def setUp(self):
super().setUp()
self.series1_filename = self.create_tempfile(suffix='.qza').name
self.series2_filename = self.create_tempfile(suffix='.qza').name
self.series_1 = pd.Series(
{
'sample-foo-bar': 7.24,
'sample-baz-qux': 8.25,
'sample-3': 6.4,
},
name='observed_otus')
self.series_2 = pd.Series(
{
'sample-foo-bar': 9.01,
'sample-qux-quux': 9.04
}, name='chao1')
imported_artifact = Artifact.import_data("SampleData[AlphaDiversity]",
self.series_1)
imported_artifact.save(self.series1_filename)
imported_artifact = Artifact.import_data("SampleData[AlphaDiversity]",
self.series_2)
imported_artifact.save(self.series2_filename)
config.resources.update({
'alpha_resources': {
'observed_otus': self.series1_filename,
'chao1': self.series2_filename,
}
})
resources.update(config.resources)
def start(self):
self.metadata_file = self.create_tempfile(suffix='.txt')
metadata_path = self.metadata_file.name
Metadata(self.metadata_table).save(metadata_path)
self.faith_pd_file = self.create_tempfile(suffix='.qza')
faith_pd_path = self.faith_pd_file.name
faith_pd_artifact = Artifact.import_data(
"SampleData[AlphaDiversity]",
self.faith_pd_data,
)
faith_pd_artifact.save(faith_pd_path)
self.taxonomy_file = self.create_tempfile(suffix='.qza')
taxonomy_path = self.taxonomy_file.name
imported_artifact = Artifact.import_data("FeatureData[Taxonomy]",
self.taxonomy_greengenes_df)
imported_artifact.save(taxonomy_path)
self.table_file = self.create_tempfile(suffix='.qza')
table_path = self.table_file.name
imported_artifact = Artifact.import_data("FeatureTable[Frequency]",
self.table)
imported_artifact.save(table_path)
config.resources.update({
'metadata': metadata_path,
'alpha_resources': {
'faith-pd': faith_pd_path,
},
'table_resources': {
'greengenes': {
'table': table_path,
'feature-data-taxonomy': taxonomy_path,
}
},
})
resources.update(config.resources)
def build_app():
app = connexion.FlaskApp(__name__)
app.app.json_encoder = NumPySafeJSONEncoder
resource_config = SERVER_CONFIG.get('resources', {})
# default configuration for resources is provided in
# microsetta.config.resources, this config can be updated by a json file
# passed to `build_app`.
config_resources.update(resource_config)
resources.update(config_resources)
resource = copy.deepcopy(config_resources)
resource = schema.make_elements(resource)
load_data = _pool.submit(atomic_update_resources, resource)
futures.add(load_data)
load_data.add_done_callback(lambda fut: futures.remove(load_data))
app_file = resource_filename('microsetta_public_api.api',
'microsetta_public_api.yml')
# validate_responses needs to be False to support sending binary
# files it seems, see https://github.com/zalando/connexion/issues/401
app.add_api(app_file,
validate_responses=SERVER_CONFIG.get('validate', True))
app.app.register_error_handler(UnknownMetric, handle_404)
app.app.register_error_handler(UnknownResource, handle_404)
app.app.register_error_handler(UnknownID, handle_404)
app.app.register_error_handler(UnknownCategory, handle_404)
app.app.register_error_handler(IncompatibleOptions, handle_400)
app.app.register_error_handler(InvalidParameter, handle_400)
CORS(app.app)
return app
def setUp(self):
super().setUp()
self.plotting_metadata_path = self.create_tempfile(suffix='.txt').name
self.plotting_metadata_table = pd.DataFrame(
{
'age_cat': ['30s', '40s', '50s', '30s', '30s', '50s'],
'bmi_cat': ['normal', 'not', 'not', 'normal', 'not', 'normal'],
'num_cat': [20, 30, 7.15, 8.25, 30, 7.15],
},
index=pd.Series([
'sample-1', 'sample-2', 'sample-3', 'sample-4', 'sample-5',
'sample-6'
],
name='#SampleID'))
Metadata(self.plotting_metadata_table).save(
self.plotting_metadata_path)
config.resources.update({'metadata': self.plotting_metadata_path})
self.plotting_series1_filename = self.create_tempfile(
suffix='.qza').name
self.plotting_series2_filename = self.create_tempfile(
suffix='.qza').name
self.plotting_series_1 = pd.Series(
{
'sample-2': 7.24,
'sample-4': 8.25,
'sample-3': 6.4,
},
name='observed_otus')
self.plotting_series_2 = pd.Series({
'sample-2': 9.01,
'sample-5': 9.04
},
name='chao1')
imported_artifact = Artifact.import_data("SampleData[AlphaDiversity]",
self.plotting_series_1)
imported_artifact.save(self.plotting_series1_filename)
imported_artifact = Artifact.import_data("SampleData[AlphaDiversity]",
self.plotting_series_2)
imported_artifact.save(self.plotting_series2_filename)
config.resources.update({
'alpha_resources': {
'observed_otus': self.plotting_series1_filename,
'chao1': self.plotting_series2_filename,
}
})
resources.update(config.resources)
def setUp(self):
TempfileTestCase.setUp(self)
ConfigTestCase.setUp(self)
self.metadata_filename = self.create_tempfile(suffix='.qza').name
self.test_metadata = pd.DataFrame(
{
'age_cat': ['30s', '40s', '50s', '30s', np.nan],
'num_cat': [7.24, 7.24, 8.25, 7.24, np.nan],
'other': [1, 2, 3, 4, np.nan],
},
index=pd.Series(['a', 'b', 'c', 'd', 'e'], name='#SampleID'))
Metadata(self.test_metadata).save(self.metadata_filename)
config.resources.update({'metadata': self.metadata_filename})
resources.update(config.resources)
self.repo = MetadataRepo()
def build_app(resources_config_json=None):
app = connexion.FlaskApp(__name__)
# default configuration for resources is provided in
# microsetta.config.resources, this config can be updated by a json file
# passed to `build_app`.
if resources_config_json is not None:
with open(resources_config_json) as fp:
resource_updates = json.load(fp)
config.resources.update(resource_updates)
resources.update(config.resources)
app_file = resource_filename('microsetta_public_api.api',
'microsetta_public_api.yml')
app.add_api(app_file, validate_responses=True)
return app
def setUp(self):
super().setUp()
self.metadata_path = self.create_tempfile(suffix='.txt').name
self.metadata_table = pd.DataFrame(
{
'age_cat': ['30s', '40s', '50s', '30s', '30s', '50s', '50s'],
'bmi_cat': [
'normal', 'not', 'not', 'normal', 'not', 'normal',
'overweight'
],
'num_cat': [20, 30, 7.15, 8.25, 30, 7.15, np.nan],
},
index=pd.Series([
'sample-1',
'sample-2',
'sample-3',
'sample-4',
'sample-5',
'sample-6',
'sample-7',
],
name='#SampleID'))
Metadata(self.metadata_table).save(self.metadata_path)
config.resources.update({'metadata': self.metadata_path})
resources.update(config.resources)
self.sample_querybuilder = {
"condition":
"AND",
"rules": [
{
"id": "age_cat",
"field": "age_cat",
"type": "string",
"input": "select",
"operator": "equal",
"value": "30s"
},
]
}
def setUp(self):
super().setUp()
self.table1_filename = self.create_tempfile(suffix='.qza').name
self.taxonomy1_filename = self.create_tempfile(suffix='.qza').name
self.table2_filename = self.create_tempfile(suffix='.qza').name
self.taxonomy2_filename = self.create_tempfile(suffix='.qza').name
self.table3_filename = self.create_tempfile(suffix='.qza').name
self.var_table_filename = self.create_tempfile(suffix='.qza').name
self.table_biom = self.create_tempfile(suffix='.biom').name
self.taxonomy_greengenes_df_filename = self.create_tempfile(
suffix='.qza').name
self.table = biom.Table(np.array([[0, 1, 2], [2, 4, 6], [3, 0, 1]]),
['feature-1', 'feature-2', 'feature-3'],
['sample-1', 'sample-2', 'sample-3'])
self.taxonomy_df = pd.DataFrame(
[['feature-1', 'a; b; c', 0.123],
['feature-2', 'a; b; c; d; e', 0.345],
['feature-3', 'a; f; g; h', 0.678]],
columns=['Feature ID', 'Taxon', 'Confidence'])
self.taxonomy_greengenes_df = pd.DataFrame(
[['feature-1', 'k__a;p__b; o__c', 0.123],
['feature-2', 'k__a; p__b; o__c; f__d; g__e', 0.34],
['feature-3', 'k__a; p__f; o__g; f__h', 0.678]],
columns=['Feature ID', 'Taxon', 'Confidence'])
self.taxonomy_greengenes_df.set_index('Feature ID', inplace=True)
self.taxonomy_df.set_index('Feature ID', inplace=True)
self.table2 = biom.Table(np.array([[0, 1, 2], [2, 4, 6], [3, 0, 1]]),
['feature-1', 'feature-X', 'feature-3'],
['sample-1', 'sample-2', 'sample-3'])
self.taxonomy2_df = pd.DataFrame(
[['feature-1', 'a; b; c', 0.123],
['feature-X', 'a; b; c; d; e', 0.34],
['feature-3', 'a; f; g; h', 0.678]],
columns=['Feature ID', 'Taxon', 'Confidence'])
self.taxonomy2_df.set_index('Feature ID', inplace=True)
self.table3 = biom.Table(np.array([[1, 2], [0, 1]]),
['feature-X', 'feature-3'],
['sample-2', 'sample-3'])
imported_artifact = Artifact.import_data("FeatureTable[Frequency]",
self.table)
imported_artifact.save(self.table1_filename)
imported_artifact = Artifact.import_data("FeatureData[Taxonomy]",
self.taxonomy_df)
imported_artifact.save(self.taxonomy1_filename)
imported_artifact = Artifact.import_data("FeatureTable[Frequency]",
self.table2)
imported_artifact.save(self.table2_filename)
imported_artifact = Artifact.import_data("FeatureData[Taxonomy]",
self.taxonomy2_df)
imported_artifact.save(self.taxonomy2_filename)
imported_artifact = Artifact.import_data("FeatureTable[Frequency]",
self.table3)
imported_artifact.save(self.table3_filename)
with biom_open(self.table_biom, 'w') as f:
self.table.to_hdf5(f, 'test-table')
imported_artifact = Artifact.import_data("FeatureData[Taxonomy]",
self.taxonomy_greengenes_df)
imported_artifact.save(self.taxonomy_greengenes_df_filename)
config.resources.update({
'table_resources': {
'table1': {
'table': self.table1_filename,
},
'table2': {
'table': self.table1_filename,
'feature-data-taxonomy': self.taxonomy1_filename,
},
'table2-greengenes': {
'table': self.table1_filename,
'feature-data-taxonomy':
self.taxonomy_greengenes_df_filename,
},
'table-fish': {
'table': self.table_biom,
'feature-data-taxonomy': self.taxonomy1_filename,
'table-format': 'biom'
},
'table5': {
'table': self.table2_filename,
},
'table6': {
'table': self.table_biom,
'table-format': 'biom',
},
'table-cached-model': {
'table': self.table1_filename,
'feature-data-taxonomy': self.taxonomy1_filename,
'cache-taxonomy': True,
},
}
})
resources.update(config.resources)
def setUp(self):
TempfileTestCase.setUp(self)
ConfigTestCase.setUp(self)
self.no_resources_repo = TaxonomyRepo()
self.table1_filename = self.create_tempfile(suffix='.qza').name
self.taxonomy1_filename = self.create_tempfile(suffix='.qza').name
self.table2_filename = self.create_tempfile(suffix='.qza').name
self.taxonomy2_filename = self.create_tempfile(suffix='.qza').name
self.table3_filename = self.create_tempfile(suffix='.qza').name
self.var_table_filename = self.create_tempfile(suffix='.qza').name
self.table_biom = self.create_tempfile(suffix='.biom').name
self.table = biom.Table(np.array([[0, 1, 2], [2, 4, 6], [3, 0, 1]]),
['feature-1', 'feature-2', 'feature-3'],
['sample-1', 'sample-2', 'sample-3'])
self.taxonomy_df = pd.DataFrame(
[['feature-1', 'a; b; c', 0.123],
['feature-2', 'a; b; c; d; e', 0.345],
['feature-3', 'a; f; g; h', 0.678]],
columns=['Feature ID', 'Taxon', 'Confidence'])
self.taxonomy_df.set_index('Feature ID', inplace=True)
self.table2 = biom.Table(np.array([[0, 1, 2], [2, 4, 6], [3, 0, 1]]),
['feature-1', 'feature-X', 'feature-3'],
['sample-1', 'sample-2', 'sample-3'])
self.taxonomy2_df = pd.DataFrame(
[['feature-1', 'a; b; c', 0.123],
['feature-X', 'a; b; c; d; e', 0.34],
['feature-3', 'a; f; g; h', 0.678]],
columns=['Feature ID', 'Taxon', 'Confidence'])
self.taxonomy2_df.set_index('Feature ID', inplace=True)
self.table3 = biom.Table(np.array([[1, 2], [0, 1]]),
['feature-X', 'feature-3'],
['sample-2', 'sample-3'])
imported_artifact = Artifact.import_data("FeatureTable[Frequency]",
self.table)
imported_artifact.save(self.table1_filename)
imported_artifact = Artifact.import_data("FeatureData[Taxonomy]",
self.taxonomy_df)
imported_artifact.save(self.taxonomy1_filename)
imported_artifact = Artifact.import_data("FeatureTable[Frequency]",
self.table2)
imported_artifact.save(self.table2_filename)
imported_artifact = Artifact.import_data("FeatureData[Taxonomy]",
self.taxonomy2_df)
imported_artifact.save(self.taxonomy2_filename)
imported_artifact = Artifact.import_data("FeatureTable[Frequency]",
self.table3)
imported_artifact.save(self.table3_filename)
with biom_open(self.table_biom, 'w') as f:
self.table.to_hdf5(f, 'test-table')
config.resources.update({
'table_resources': {
'table1': {
'table': self.table1_filename,
},
'table2': {
'table': self.table1_filename,
'feature-data-taxonomy': self.taxonomy1_filename,
'cache-taxonomy': False,
},
'table3': {
'table': self.table3_filename,
'feature-data-taxonomy': self.taxonomy2_filename,
'cache-taxonomy': False,
'variances': self.table3_filename,
},
'table4': {
'table': self.table_biom,
'feature-data-taxonomy': self.taxonomy1_filename,
'cache-taxonomy': False,
'table-format': 'biom'
},
'table5': {
'table': self.table2_filename,
},
'table6': {
'table': self.table_biom,
'table-format': 'biom',
},
'cached-taxonomy-table': {
'table': self.table1_filename,
'feature-data-taxonomy': self.taxonomy1_filename,
'cache-taxonomy': True,
},
}
})
resources.update(config.resources)
self.repo = TaxonomyRepo()