def get_raw_data(input_data, file_name, group_name, mpi=False, new_shape=None):
"""
Gets a file backed, Raw data object
:returns: a RawTimeseriesData Object containing the example data.
"""
data = RawTimeseriesData()
data.create_backing_h5(file_name, group_name, input_data, mpi, new_shape)
return data
def get_raw_data(input_data, file_name, group_name, mpi=False, new_shape=None):
"""
Gets a file backed, Raw data object
:returns: a RawTimeseriesData Object containing the example data.
"""
data = RawTimeseriesData()
data.create_backing_h5(file_name, group_name, input_data, mpi, new_shape)
return data
def get_nx_tomo_test_data():
"""Gets the nx_tomo test data and returns it in the RawData Structure
:returns: a RawTimeseriesData Object containing the example data.
"""
path = get_test_data_path('24737.nxs')
raw_timeseries_data = RawTimeseriesData()
raw_timeseries_data.populate_from_nx_tomo(path)
return raw_timeseries_data
def get_nx_tomo_test_data():
"""Gets the nx_tomo test data and returns it in the RawData Structure
:returns: a RawTimeseriesData Object containing the example data.
"""
path = get_test_data_path("24737.nxs")
raw_timeseries_data = RawTimeseriesData()
raw_timeseries_data.populate_from_nx_tomo(path)
return raw_timeseries_data
def test_pipeline(self):
logging.debug("Starting test_pipeline")
if not hasattr(self, 'temp_dir'):
self.temp_dir = tempfile.gettempdir()
input_data = None
first_plugin = pu.load_plugin(self.plugin_list[0])
if self.plugin_list[0] == base_class_name:
return
if not hasattr(self, 'input_file'):
input_data = tu.get_appropriate_input_data(first_plugin)[0]
else:
input_data = RawTimeseriesData()
input_data.populate_from_nexus(self.input_file)
logging.debug("Starting to run the processing chain")
process.run_plugin_chain(input_data, self.plugin_list, self.temp_dir)
def test_pipeline(self):
logging.debug("Starting test_pipeline")
if not hasattr(self, 'temp_dir'):
self.temp_dir = tempfile.gettempdir()
input_data = None
first_plugin = pu.load_plugin(self.plugin_list[0])
if self.plugin_list[0] == base_class_name:
return
if not hasattr(self, 'input_file'):
input_data = tu.get_appropriate_input_data(first_plugin)[0]
else:
input_data = RawTimeseriesData()
input_data.populate_from_nexus(self.input_file)
logging.debug("Starting to run the processing chain")
process.run_plugin_chain(input_data, self.plugin_list, self.temp_dir)
def load_raw_data(filename):
data = RawTimeseriesData()
data.populate_from_nx_tomo(filename)
return data
def load_raw_data(filename):
data = RawTimeseriesData()
data.populate_from_nx_tomo(filename)
return data
