def setup_class(cls):
"""
Called once for all tests for Pipelines.
Sets rounding_precision, and does a one-time load
of dynamic and static trial data as dictionaries from
SampleData/Sample_2/.
"""
cls.rounding_precision = 8
cwd = os.getcwd()
if(cwd.split(os.sep)[-1]=="pyCGM_Single"):
parent = os.path.dirname(cwd)
os.chdir(parent)
cls.cwd = os.getcwd()
cur_dir = cls.cwd
dynamic_trial, static_trial,_,_,_ = getfilenames(3)
dynamic_trial = os.path.join(cur_dir, dynamic_trial)
static_trial = os.path.join(cur_dir, static_trial)
motionData = loadData(dynamic_trial)
staticData = loadData(static_trial)
cls.data_original = dataAsDict(motionData, npArray=True)
cls.static_original = dataAsDict(staticData, npArray=True)
for frame in motionData:
frame['SACR'] = pelvisJointCenter(frame)[2]
cls.data_with_sacrum_original = dataAsDict(motionData, npArray=True)
Pipelines.clearMarker(motionData, 'LFHD')
cls.data_with_sacrum_clear_marker = dataAsDict(motionData, npArray=True)
def test_clearMarker_accuracy(self, data, name, expected_result):
"""
This function tests Pipelines.clearMarker(data, name), where
data is an array of dictionary of markers lists and name is the
name of a marker to clear.
We test cases where the values in data are lists and numpy arrays.
We test the case where the marker name is not a key in data.
"""
result = Pipelines.clearMarker(data, name)
np.testing.assert_equal(result, expected_result)