def get_calculator(cls, matrix_params):
# Get all available calculators
available_calculators = PluginHandler.get_classes('pyproct.data.matrix',
selection_keyword = "MatrixCalculator",
skip_list = ["test", "cases"],
plugin_name = "matrix")
# Choose the calculator we need
calculation_method = matrix_params["method"]
calculator_classes = filter(lambda x: x.CALCULATION_METHOD == calculation_method, available_calculators)
if len(calculator_classes) == 0:
print "[ERROR][MatrixCalculator::calculate] %s is not a registered matrix calculation method."%(calculation_method)
exit()
else:
return calculator_classes[0]
def get_loader(self, data_type):
"""
Chooses the best loader for the type of data we have.
"""
# Get all available loaders
available_loaders = PluginHandler.get_classes('pyproct.data.handler',
selection_keyword = "DataLoader",
skip_list = ["test"],
plugin_name = "data_loader")
loaders = filter(lambda x: x.LOADER_TYPE == data_type, available_loaders)
if len(loaders) == 0:
print "[ERROR][DataHandler::get_loader] There is not a registered data loader for %s data type."%(data_type)
exit()
else:
return loaders[0]
def run(self, clustering, postprocessing_parameters, data_handler,
workspace_handler, matrix_handler, generated_files):
available_action_classes = PluginHandler.get_classes(
'pyproct.postprocess.actions', "PostAction", ["test"],
"postprocess")
for postprocessing_action_class in available_action_classes:
if postprocessing_action_class.KEYWORD in postprocessing_parameters:
try:
postprocessing_action_class().run(
clustering, postprocessing_parameters[
postprocessing_action_class.KEYWORD], data_handler,
workspace_handler, matrix_handler, generated_files)
except Exception, e:
print "[ERROR][Driver::postprocess] Impossible to perform '%s' postprocessing action." % (
postprocessing_action_class.KEYWORD)
print "Message: %s" % str(e)
traceback.print_exc()
def run(self, clustering, postprocessing_parameters, data_handler, workspace_handler, matrix_handler, generated_files):
available_action_classes = PluginHandler.get_classes('pyproct.postprocess.actions',
"PostAction",
["test","confSpaceComparison"],
"postprocess")
for postprocessing_action_class in available_action_classes:
if postprocessing_action_class.KEYWORD in postprocessing_parameters:
try:
postprocessing_action_class().run(clustering,
postprocessing_parameters[postprocessing_action_class.KEYWORD],
data_handler,
workspace_handler,
matrix_handler,
generated_files)
except Exception, e:
print "[ERROR][Driver::postprocess] Impossible to perform '%s' postprocessing action."%(postprocessing_action_class.KEYWORD)
print "Message: %s"%str(e)
traceback.print_exc()
def get_calculator(cls, matrix_params):
# Get all available calculators
available_calculators = PluginHandler.get_classes(
'pyproct.data.matrix',
selection_keyword="MatrixCalculator",
skip_list=["test", "cases"],
plugin_name="matrix")
# Choose the calculator we need
calculation_method = matrix_params["method"]
calculator_classes = filter(
lambda x: x.CALCULATION_METHOD == calculation_method,
available_calculators)
if len(calculator_classes) == 0:
print "[ERROR][MatrixCalculator::calculate] %s is not a registered matrix calculation method." % (
calculation_method)
exit()
else:
return calculator_classes[0]
def test_plugin_entry_points(self):
classes = PluginHandler.get_classes_from_plugins("testplugin1","pyproct.test.plugin")
self.assertItemsEqual([x.__name__ for x in classes], ["MatrixTestClass", "AnotherMatrixTestClass"])
classes = PluginHandler.get_classes_from_plugins("testplugin2","pyproct.test.plugin")
self.assertEqual(classes[0].__name__, "AnalysisTestClass")
