def testGetDataReturnObject(self):
"""Make sure correct data object type is returned"""
from pyfusion.acquisition.FakeData.acq import FakeDataAcquisition
from pyfusion import conf
# make sure the requested data type is returned using config reference
test_acq = FakeDataAcquisition('test_fakedata')
from pyfusion.data.timeseries import TimeseriesData
data_instance_1 = test_acq.getdata(self.shot_number,
timeseries_test_channel_1)
self.assertTrue(isinstance(data_instance_1, TimeseriesData))
# ...and for kwargs
# read config as dict and pass as kwargs
config_dict = conf.utils.get_config_as_dict('Diagnostic',
timeseries_test_channel_1)
data_instance_2 = test_acq.getdata(self.shot_number, **config_dict)
self.assertTrue(isinstance(data_instance_2, TimeseriesData))
# check that the two signals are the same
from numpy.testing import assert_array_almost_equal
assert_array_almost_equal(data_instance_1.signal,
data_instance_2.signal)
assert_array_almost_equal(data_instance_1.timebase,
data_instance_2.timebase)
def test_multichannel_single_channels(self):
from pyfusion.acquisition.FakeData.acq import FakeDataAcquisition
from pyfusion import config
test_acq = FakeDataAcquisition('test_fakedata')
multichannel_data = test_acq.getdata(self.shot_number,
multichannel_name)
channel_1_data = test_acq.getdata(self.shot_number,
timeseries_test_channel_1)
channel_2_data = test_acq.getdata(self.shot_number,
timeseries_test_channel_2)
from numpy.testing import assert_array_almost_equal
assert_array_almost_equal(multichannel_data.signal[0, :],
channel_1_data.signal)
assert_array_almost_equal(multichannel_data.signal[1, :],
channel_2_data.signal)
self.assertEqual(channel_1_data.meta.get('shot'), self.shot_number)
assert_array_almost_equal(multichannel_data.signal.get_channel(0),
channel_1_data.signal)
