def test_no_scaling(self):
"""Test that raw data is returned unscaled when there is no scaling"""
tdms_obj = TdmsObject("/'group'/'channel'")
expected_data = np.array([1.0, 2.0, 3.0])
tdms_obj._data = expected_data
self.assertIs(expected_data, tdms_obj.data)
def test_unsupported_scaling_type(self):
"""Raw data is returned unscaled when the scaling type is unsupported.
"""
tdms_obj = TdmsObject("/'group'/'channel'")
expected_data = np.array([1.0, 2.0, 3.0])
tdms_obj.properties["NI_Number_Of_Scales"] = 1
tdms_obj.properties["NI_Scale[0]_Scale_Type"] = "UnknownScaling"
tdms_obj._data = expected_data
self.assertIs(expected_data, tdms_obj.data)
def test_polynomial_scaling_with_3_coefficients(self):
"""Test polynomial scaling"""
tdms_obj = TdmsObject("/'group'/'channel'")
tdms_obj._data = np.array([1.0, 2.0, 3.0])
tdms_obj.properties["NI_Number_Of_Scales"] = 1
tdms_obj.properties["NI_Scale[0]_Scale_Type"] = "Polynomial"
tdms_obj.properties["NI_Scale[0]_Polynomial_Coefficients_Size"] = 3
tdms_obj.properties["NI_Scale[0]_Polynomial_Coefficients[0]"] = 10.0
tdms_obj.properties["NI_Scale[0]_Polynomial_Coefficients[1]"] = 1.0
tdms_obj.properties["NI_Scale[0]_Polynomial_Coefficients[2]"] = 2.0
expected_data = np.array([13.0, 20.0, 31.0])
np.testing.assert_almost_equal(expected_data, tdms_obj.data)
def test_linear_scaling(self):
"""Test linear scaling"""
tdms_obj = TdmsObject("/'group'/'channel'")
tdms_obj._data = np.array([1.0, 2.0, 3.0])
tdms_obj.properties["NI_Number_Of_Scales"] = 1
tdms_obj.properties["NI_Scale[0]_Scale_Type"] = "Linear"
tdms_obj.properties["NI_Scale[0]_Linear_Slope"] = 2.0
tdms_obj.properties["NI_Scale[0]_Linear_Y_Intercept"] = 10.0
expected_data = np.array([12.0, 14.0, 16.0])
np.testing.assert_almost_equal(expected_data, tdms_obj.data)
def test_scaling_from_root(self):
"""Test linear scaling in the root object"""
tdms_file = TestTdmsFile()
tdms_channel = TdmsObject("/'group'/'channel'", tdms_file)
tdms_channel._data = np.array([1.0, 2.0, 3.0])
tdms_root = TdmsObject("/", tdms_file)
tdms_root.properties["NI_Number_Of_Scales"] = 1
tdms_root.properties["NI_Scale[0]_Scale_Type"] = "Linear"
tdms_root.properties["NI_Scale[0]_Linear_Slope"] = 2.0
tdms_root.properties["NI_Scale[0]_Linear_Y_Intercept"] = 10.0
tdms_file.objects[tdms_channel.path] = tdms_channel
tdms_file.objects[tdms_root.path] = tdms_root
expected_data = np.array([12.0, 14.0, 16.0])
np.testing.assert_almost_equal(expected_data, tdms_channel.data)
def test_add_scaling(self):
""" Test scaling that adds two input scalings"""
tdms_obj = TdmsObject("/'group'/'channel'")
tdms_obj._scaler_data = {
0: np.array([1.0, 2.0, 3.0]),
1: np.array([2.0, 4.0, 6.0]),
}
expected_scaled_data = np.array([3.0, 6.0, 9.0])
tdms_obj.properties["NI_Number_Of_Scales"] = 3
tdms_obj.properties["NI_Scale[2]_Scale_Type"] = "Add"
tdms_obj.properties["NI_Scale[2]_Add_Left_Operand_Input_Source"] = 0
tdms_obj.properties["NI_Scale[2]_Add_Right_Operand_Input_Source"] = 1
np.testing.assert_almost_equal(expected_scaled_data, tdms_obj.data)
def test_thermocouple_scaling_temperature_to_voltage(self):
"""Test thermocouple scaling from a temperature to voltage in uV"""
tdms_obj = TdmsObject("/'group'/'channel'")
tdms_obj._data = np.array([0.0, 10.0, 50.0, 100.0])
expected_scaled_data = np.array(
[0.0, 396.8619078, 2023.0778862, 4096.2302187])
tdms_obj.properties["NI_Number_Of_Scales"] = 1
tdms_obj.properties["NI_Scale[0]_Scale_Type"] = "Thermocouple"
tdms_obj.properties[
"NI_Scale[0]_Thermocouple_Thermocouple_Type"] = 10073
tdms_obj.properties["NI_Scale[0]_Thermocouple_Scaling_Direction"] = 1
tdms_obj.properties[
"NI_Scale[0]_Thermocouple_Input_Source"] = 0xFFFFFFFF
np.testing.assert_almost_equal(expected_scaled_data,
tdms_obj.data,
decimal=3)
def test_thermocouple_scaling_voltage_to_temperature(self):
"""Test thermocouple scaling from a voltage in uV to temperature"""
tdms_obj = TdmsObject("/'group'/'channel'")
tdms_obj._data = np.array([0.0, 10.0, 100.0, 1000.0])
expected_scaled_data = np.array(
[0.0, 0.2534448, 2.5309141, 24.9940185])
tdms_obj.properties["NI_Number_Of_Scales"] = 1
tdms_obj.properties["NI_Scale[0]_Scale_Type"] = "Thermocouple"
tdms_obj.properties[
"NI_Scale[0]_Thermocouple_Thermocouple_Type"] = 10073
tdms_obj.properties["NI_Scale[0]_Thermocouple_Scaling_Direction"] = 0
tdms_obj.properties[
"NI_Scale[0]_Thermocouple_Input_Source"] = 0xFFFFFFFF
np.testing.assert_almost_equal(expected_scaled_data,
tdms_obj.data,
decimal=3)
def test_subtract_scaling(self):
""" Test scaling that subtracts an input scaling from another"""
# This behaves the opposite to what you'd expect, the left operand
# is subtracted from the right operand.
tdms_obj = TdmsObject("/'group'/'channel'")
tdms_obj._scaler_data = {
0: np.array([1.0, 2.0, 3.0]),
1: np.array([2.0, 4.0, 6.0]),
}
expected_scaled_data = np.array([1.0, 2.0, 3.0])
tdms_obj.properties["NI_Number_Of_Scales"] = 3
tdms_obj.properties["NI_Scale[2]_Scale_Type"] = "Subtract"
tdms_obj.properties[
"NI_Scale[2]_Subtract_Left_Operand_Input_Source"] = 0
tdms_obj.properties[
"NI_Scale[2]_Subtract_Right_Operand_Input_Source"] = 1
np.testing.assert_almost_equal(expected_scaled_data, tdms_obj.data)
def test_multiple_scalings_with_unscaled_status(self):
"""Test all scalings applied from multiple scalings
when the scaling status is unscaled.
"""
tdms_obj = TdmsObject("/'group'/'channel'")
tdms_obj._data = np.array([1.0, 2.0, 3.0])
tdms_obj.properties["NI_Number_Of_Scales"] = 3
tdms_obj.properties["NI_Scaling_Status"] = "unscaled"
tdms_obj.properties["NI_Scale[0]_Scale_Type"] = "Linear"
tdms_obj.properties["NI_Scale[0]_Linear_Slope"] = 1.0
tdms_obj.properties["NI_Scale[0]_Linear_Y_Intercept"] = 1.0
tdms_obj.properties["NI_Scale[1]_Scale_Type"] = "Linear"
tdms_obj.properties["NI_Scale[1]_Linear_Slope"] = 2.0
tdms_obj.properties["NI_Scale[1]_Linear_Y_Intercept"] = 2.0
tdms_obj.properties["NI_Scale[2]_Scale_Type"] = "Linear"
tdms_obj.properties["NI_Scale[2]_Linear_Slope"] = 3.0
tdms_obj.properties["NI_Scale[2]_Linear_Y_Intercept"] = 3.0
expected_data = np.array([21.0, 27.0, 33.0])
np.testing.assert_almost_equal(expected_data, tdms_obj.data)
def test_rtd_scaling(self):
"""Test RTD scaling"""
tdms_obj = TdmsObject("/'group'/'channel'")
tdms_obj._data = np.array([0.5, 0.6])
tdms_obj.properties["NI_Number_Of_Scales"] = 1
tdms_obj.properties["NI_Scale[0]_Scale_Type"] = "RTD"
tdms_obj.properties["NI_Scale[0]_RTD_Current_Excitation"] = 0.001
tdms_obj.properties["NI_Scale[0]_RTD_R0_Nominal_Resistance"] = 100.0
tdms_obj.properties["NI_Scale[0]_RTD_A"] = 0.0039083
tdms_obj.properties["NI_Scale[0]_RTD_B"] = -5.775e-07
tdms_obj.properties["NI_Scale[0]_RTD_C"] = -4.183e-12
tdms_obj.properties["NI_Scale[0]_RTD_Lead_Wire_Resistance"] = 0.0
tdms_obj.properties["NI_Scale[0]_RTD_Resistance_Configuration"] = 3
tdms_obj.properties["NI_Scale[0]_RTD_Input_Source"] = 0xFFFFFFFF
expected_data = np.array([1256.89628, 1712.83429])
np.testing.assert_almost_equal(expected_data, tdms_obj.data, decimal=3)
def test_multiple_scalings_but_all_with_raw_data_input(self):
"""Test that only the last scaling is applied from multiple scalings
when it has the raw data as the input source
"""
tdms_obj = TdmsObject("/'group'/'channel'")
tdms_obj._data = np.array([1.0, 2.0, 3.0])
tdms_obj.properties["NI_Number_Of_Scales"] = 3
tdms_obj.properties["NI_Scale[0]_Scale_Type"] = "Linear"
tdms_obj.properties["NI_Scale[0]_Linear_Slope"] = 1.0
tdms_obj.properties["NI_Scale[0]_Linear_Y_Intercept"] = 1.0
tdms_obj.properties["NI_Scale[0]_Linear_Input_Source"] = 0xFFFFFFFF
tdms_obj.properties["NI_Scale[1]_Scale_Type"] = "Linear"
tdms_obj.properties["NI_Scale[1]_Linear_Slope"] = 2.0
tdms_obj.properties["NI_Scale[1]_Linear_Y_Intercept"] = 2.0
tdms_obj.properties["NI_Scale[1]_Linear_Input_Source"] = 0xFFFFFFFF
tdms_obj.properties["NI_Scale[2]_Scale_Type"] = "Linear"
tdms_obj.properties["NI_Scale[2]_Linear_Slope"] = 3.0
tdms_obj.properties["NI_Scale[2]_Linear_Y_Intercept"] = 3.0
tdms_obj.properties["NI_Scale[2]_Linear_Input_Source"] = 0xFFFFFFFF
expected_data = np.array([6.0, 9.0, 12.0])
np.testing.assert_almost_equal(expected_data, tdms_obj.data)
def test_table_scaling(self):
"""Test table scaling"""
tdms_obj = TdmsObject("/'group'/'channel'")
tdms_obj._data = np.array([0.5, 1.0, 1.5, 2.5, 3.0, 3.5])
expected_scaled_data = np.array([2.0, 2.0, 3.0, 6.0, 8.0, 8.0])
# The scaled values are actually the range of inputs into the scaling,
# which are mapped to the pre-scaled values. This makes no sense but
# matches the behaviour of the Excel TDMS plugin.
tdms_obj.properties["NI_Number_Of_Scales"] = 1
tdms_obj.properties["NI_Scale[0]_Scale_Type"] = "Table"
tdms_obj.properties["NI_Scale[0]_Table_Scaled_Values_Size"] = 3
tdms_obj.properties["NI_Scale[0]_Table_Scaled_Values[0]"] = 1.0
tdms_obj.properties["NI_Scale[0]_Table_Scaled_Values[1]"] = 2.0
tdms_obj.properties["NI_Scale[0]_Table_Scaled_Values[2]"] = 3.0
tdms_obj.properties["NI_Scale[0]_Table_Pre_Scaled_Values_Size"] = 3
tdms_obj.properties["NI_Scale[0]_Table_Pre_Scaled_Values[0]"] = 2.0
tdms_obj.properties["NI_Scale[0]_Table_Pre_Scaled_Values[1]"] = 4.0
tdms_obj.properties["NI_Scale[0]_Table_Pre_Scaled_Values[2]"] = 8.0
np.testing.assert_almost_equal(expected_scaled_data, tdms_obj.data)