def test_load_plugins(self):
"""Verify the main model loads available plugins"""
plugin_list = model.load_plugins()
for plugin in plugin_list:
plugin_instance = plugin[1]
self.assertTrue(
issubclass(plugin_instance, abstractplugin.AbstractPlugin))
def get_available_plugins(self):
"""Returns a tuple of available NDIToolbox plugins:
(plugin_names, plugin_classes)
"""
available_plugins = mainmodel.load_plugins()
plugin_names = [plugin[0] for plugin in available_plugins]
plugin_classes = [plugin[1] for plugin in available_plugins]
return plugin_names, plugin_classes
def get_plugin_class(self):
"""Returns the plugin class with the specified name, or None if not found."""
available_plugins = mainmodel.load_plugins()
plugin_names = [plugin[0] for plugin in available_plugins]
plugin_classes = [plugin[1] for plugin in available_plugins]
if self.toolkit in plugin_names:
plugin_class = plugin_classes[plugin_names.index(self.toolkit)]
return plugin_class
def get_available_plugins(self):
"""Returns a tuple of available NDIToolbox plugins:
(plugin_names, plugin_classes)
"""
available_plugins = mainmodel.load_plugins()
plugin_names = [plugin[0] for plugin in available_plugins]
plugin_classes = [plugin[1] for plugin in available_plugins]
return plugin_names, plugin_classes
def get_normalize_plugin(self):
"""Returns NDIToolbox's NormalizePlugin plugin"""
normalize_plugin_name = "NormalizePlugin"
plugin_list = model.load_plugins()
plugin_names = [plugin[0] for plugin in plugin_list]
plugin_classes = [plugin[1] for plugin in plugin_list]
# Ensure that the normalize plugin was found
self.assertTrue(normalize_plugin_name in plugin_names)
return plugin_classes[plugin_names.index(normalize_plugin_name)]
def test_get_plugins(self):
"""Verify a list of available plugins is returned"""
expected_plugin_list = mainmodel.load_plugins()
expected_plugin_names = [plugin[0] for plugin in expected_plugin_list]
retrieved_plugin_list = self.basic_model.get_plugins()
self.assertEqual(len(expected_plugin_list), len(retrieved_plugin_list))
for plugin in retrieved_plugin_list:
plugin_name = plugin[0]
plugin_instance = plugin[1]
self.assertTrue(plugin_name in expected_plugin_names)
self.assertTrue(issubclass(plugin_instance, abstractplugin.AbstractPlugin))
def test_get_plugins(self):
"""Verify a list of available plugins is returned"""
expected_plugin_list = mainmodel.load_plugins()
expected_plugin_names = [plugin[0] for plugin in expected_plugin_list]
retrieved_plugin_list = self.basic_model.get_plugins()
self.assertEqual(len(expected_plugin_list), len(retrieved_plugin_list))
for plugin in retrieved_plugin_list:
plugin_name = plugin[0]
plugin_instance = plugin[1]
self.assertTrue(plugin_name in expected_plugin_names)
self.assertTrue(
issubclass(plugin_instance, abstractplugin.AbstractPlugin))
def test_load_plugins(self):
"""Verify the main model loads available plugins"""
plugin_list = model.load_plugins()
for plugin in plugin_list:
plugin_instance = plugin[1]
self.assertTrue(issubclass(plugin_instance, abstractplugin.AbstractPlugin))
multiprocessing.freeze_support()
if len(sys.argv) > 1:
# Enter headless batch mode - given a list of input files, run a specified toolkit on each in turn
print("\nNDIToolbox Batch Mode")
parser = argparse.ArgumentParser(description='Run NDIToolbox toolkit in batch mode')
parser.add_argument('-t', '--toolkit', help='Name of toolkit to run')
parser.add_argument('-c', '--toolkit_config', help='Config file for toolkit')
parser.add_argument('-f', '--filetype', help='Specify type of input file (default: guess from file extension)')
parser.add_argument('-i', '--input_files', nargs=argparse.REMAINDER, help='Specify input file or files')
parser.add_argument('-s', '--save_output', action='store_true', default=False,
help="Save plugin output to new HDF5 data file")
parser.add_argument('-m', '--multiprocess', action='store_true', default=False,
help="Use multiple simultaneous processes for analysis")
args = parser.parse_args()
mainmodel.MainModel.check_user_path()
available_plugins = mainmodel.load_plugins()
available_plugins_names = [plugin[0] for plugin in available_plugins]
if args.toolkit and args.toolkit not in available_plugins_names:
print("** Unable to locate plugin '{0}'. Available plugins:".format(args.toolkit))
for plugin_name in available_plugins_names:
print("\t{0}".format(plugin_name))
sys.exit(1)
workers = multiprocessing.Pool()
if args.multiprocess:
print("Using multiprocessing mode, {0} simultaneous processes".format(multiprocessing.cpu_count()))
if args.input_files:
for _f in args.input_files:
paths = glob.glob(_f)
for _p in paths:
if not args.multiprocess:
print("\nProcessing {0}...".format(_p))
nargs=argparse.REMAINDER,
help='Specify input file or files')
parser.add_argument('-s',
'--save_output',
action='store_true',
default=False,
help="Save plugin output to new HDF5 data file")
parser.add_argument(
'-m',
'--multiprocess',
action='store_true',
default=False,
help="Use multiple simultaneous processes for analysis")
args = parser.parse_args()
mainmodel.MainModel.check_user_path()
available_plugins = mainmodel.load_plugins()
available_plugins_names = [plugin[0] for plugin in available_plugins]
if args.toolkit and args.toolkit not in available_plugins_names:
print(
"** Unable to locate plugin '{0}'. Available plugins:".format(
args.toolkit))
for plugin_name in available_plugins_names:
print("\t{0}".format(plugin_name))
sys.exit(1)
workers = multiprocessing.Pool()
if args.multiprocess:
print("Using multiprocessing mode, {0} simultaneous processes".
format(multiprocessing.cpu_count()))
if args.input_files:
for _f in args.input_files:
paths = glob.glob(_f)
