def _run_pyfibre(self):
input_files = []
for file_path in self.file_paths:
input_files += parse_file_path(file_path, self.key)
file_sets = []
for label, parser in self.supported_parsers.items():
file_sets += parser.get_file_sets(input_files)
for label, reader in self.supported_readers.items():
logger.info(f"Analysing {label} images")
analyser = self.supported_analysers[label]
image_databases = [pd.DataFrame() for _ in analyser.database_names]
generator = self.runner.run(file_sets, analyser, reader)
for databases in generator:
for index, database in enumerate(databases):
if isinstance(database, pd.Series):
image_databases[index] = image_databases[index].append(
database, ignore_index=True)
elif isinstance(database, pd.DataFrame):
image_databases[index] = pd.concat(
[image_databases[index], database])
if self.database_name:
for index, name in enumerate(analyser.database_names):
save_database(image_databases[index], self.database_name,
name)
def test_load_database(self):
with NamedTemporaryFile() as temp_file:
save_database(self.database, temp_file.name)
database = load_database(temp_file.name)
self.assertArrayAlmostEqual(self.database['one'], database['one'])
self.assertArrayAlmostEqual(self.database['two'], database['two'])
def test_save_database(self):
with NamedTemporaryFile() as temp_file:
save_database(self.database, temp_file.name)
self.assertTrue(os.path.exists(f'{temp_file.name}.h5'))
self.assertTrue(os.path.exists(f'{temp_file.name}.xls'))
save_database(self.database, temp_file.name, 'extra')
self.assertTrue(os.path.exists(f'{temp_file.name}_extra.h5'))
self.assertTrue(os.path.exists(f'{temp_file.name}_extra.xls'))
def save_database(self, filename):
"""Save databases successfully generated by all loaded images"""
for tag, analyser in self.supported_analysers.items():
for index, name in enumerate(analyser.database_names):
try:
save_database(self.image_databases[tag][index], filename,
name)
except IOError:
logger.exception("Error when saving databases")
return False
return True
def save_databases(self, databases):
"""Save pandas DataFrame instances created during the analysis"""
for index, name in enumerate(self.database_names):
save_database(databases[index], self._data_file, f'{name}_metric')