def test_rtd_scaling_with_negative_temperature():
""" Test RTD scaling with negative temperature values, which requires
solving the full quartic Callendar-Van Dusen equation
"""
data = StubTdmsData(
np.array(
[0.08, 0.09, 0.095, 0.1, 0.12, 0.14, 0.16, 0.18, 0.2, 0.22, 0.24]))
expected_data = np.array([
-50.77114, -25.48835, -12.76894, -0., 51.56605, 103.94273, 157.1695,
211.28915, 266.34819, 322.3973, 379.49189
])
properties = {
"NI_Number_Of_Scales": 1,
"NI_Scale[0]_Scale_Type": "RTD",
"NI_Scale[0]_RTD_Current_Excitation": 0.001,
"NI_Scale[0]_RTD_R0_Nominal_Resistance": 100.0,
"NI_Scale[0]_RTD_A": 0.0039083,
"NI_Scale[0]_RTD_B": -5.775e-07,
"NI_Scale[0]_RTD_C": -4.183e-12,
"NI_Scale[0]_RTD_Lead_Wire_Resistance": 0,
"NI_Scale[0]_RTD_Resistance_Configuration": 2,
"NI_Scale[0]_RTD_Input_Source": 0xFFFFFFFF,
}
scaling = get_scaling(properties, {}, {})
scaled_data = scaling.scale(data)
assert scaling.get_dtype(types.DoubleFloat, None) == np.dtype('float64')
assert scaled_data.dtype == np.dtype('float64')
np.testing.assert_almost_equal(scaled_data, expected_data, decimal=5)
def test_multiple_scalings_applied_in_order():
"""Test all scalings applied from multiple scalings
"""
data = StubTdmsData(np.array([1.0, 2.0, 3.0]))
expected_scaled_data = np.array([21.0, 27.0, 33.0])
properties = {
"NI_Number_Of_Scales": 3,
"NI_Scaling_Status": "unscaled",
"NI_Scale[0]_Scale_Type": "Linear",
"NI_Scale[0]_Linear_Slope": 1.0,
"NI_Scale[0]_Linear_Y_Intercept": 1.0,
"NI_Scale[0]_Linear_Input_Source": 0xFFFFFFFF,
"NI_Scale[1]_Scale_Type": "Linear",
"NI_Scale[1]_Linear_Slope": 2.0,
"NI_Scale[1]_Linear_Y_Intercept": 2.0,
"NI_Scale[1]_Linear_Input_Source": 0,
"NI_Scale[2]_Scale_Type": "Linear",
"NI_Scale[2]_Linear_Slope": 3.0,
"NI_Scale[2]_Linear_Y_Intercept": 3.0,
"NI_Scale[2]_Linear_Input_Source": 1,
}
scaling = get_scaling(properties, {}, {})
scaled_data = scaling.scale(data)
np.testing.assert_almost_equal(scaled_data, expected_scaled_data)
def test_add_scaling():
""" Test scaling that adds two input scalings"""
scaler_data = StubDaqmxData({
0:
np.array([1, 2, 3], dtype=np.dtype('int32')),
1:
np.array([2, 4, 6], dtype=np.dtype('uint32')),
})
expected_scaled_data = np.array([3.0, 6.0, 9.0])
properties = {
"NI_Number_Of_Scales": 3,
"NI_Scale[2]_Scale_Type": "Add",
"NI_Scale[2]_Add_Left_Operand_Input_Source": 0,
"NI_Scale[2]_Add_Right_Operand_Input_Source": 1,
}
scaling = get_scaling(properties, {}, {})
scaled_data = scaling.scale(scaler_data)
assert scaling.get_dtype(None, {
0: types.Int32,
1: types.Uint32
}) == np.dtype('int64')
assert scaled_data.dtype == np.dtype('int64')
np.testing.assert_almost_equal(scaled_data, expected_scaled_data)
def test_multiple_scalings_but_all_with_raw_data_input():
"""Test that only the last scaling is applied from multiple scalings
when it has the raw data as the input source
"""
data = StubTdmsData(np.array([1.0, 2.0, 3.0]))
expected_scaled_data = np.array([6.0, 9.0, 12.0])
properties = {
"NI_Number_Of_Scales": 3,
"NI_Scale[0]_Scale_Type": "Linear",
"NI_Scale[0]_Linear_Slope": 1.0,
"NI_Scale[0]_Linear_Y_Intercept": 1.0,
"NI_Scale[0]_Linear_Input_Source": 0xFFFFFFFF,
"NI_Scale[1]_Scale_Type": "Linear",
"NI_Scale[1]_Linear_Slope": 2.0,
"NI_Scale[1]_Linear_Y_Intercept": 2.0,
"NI_Scale[1]_Linear_Input_Source": 0xFFFFFFFF,
"NI_Scale[2]_Scale_Type": "Linear",
"NI_Scale[2]_Linear_Slope": 3.0,
"NI_Scale[2]_Linear_Y_Intercept": 3.0,
"NI_Scale[2]_Linear_Input_Source": 0xFFFFFFFF,
}
scaling = get_scaling(properties, {}, {})
scaled_data = scaling.scale(data)
np.testing.assert_almost_equal(scaled_data, expected_scaled_data)
def test_table_scaling():
"""Test table scaling"""
data = StubTdmsData(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.
properties = {
"NI_Number_Of_Scales": 1,
"NI_Scale[0]_Scale_Type": "Table",
"NI_Scale[0]_Table_Scaled_Values_Size": 3,
"NI_Scale[0]_Table_Scaled_Values[0]": 1.0,
"NI_Scale[0]_Table_Scaled_Values[1]": 2.0,
"NI_Scale[0]_Table_Scaled_Values[2]": 3.0,
"NI_Scale[0]_Table_Pre_Scaled_Values_Size": 3,
"NI_Scale[0]_Table_Pre_Scaled_Values[0]": 2.0,
"NI_Scale[0]_Table_Pre_Scaled_Values[1]": 4.0,
"NI_Scale[0]_Table_Pre_Scaled_Values[2]": 8.0,
}
scaling = get_scaling(properties, {}, {})
scaled_data = scaling.scale(data)
assert scaling.get_dtype(types.DoubleFloat, None) == np.dtype('float64')
np.testing.assert_almost_equal(scaled_data, expected_scaled_data)
def test_subtract_scaling():
""" 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.
scaler_data = StubDaqmxData({
0:
np.array([1, 2, 3], dtype=np.dtype('int32')),
1:
np.array([2, 4, 6], dtype=np.dtype('uint32')),
})
expected_scaled_data = np.array([1.0, 2.0, 3.0])
properties = {
"NI_Number_Of_Scales": 3,
"NI_Scale[2]_Scale_Type": "Subtract",
"NI_Scale[2]_Subtract_Left_Operand_Input_Source": 0,
"NI_Scale[2]_Subtract_Right_Operand_Input_Source": 1,
}
scaling = get_scaling(properties, {}, {})
scaled_data = scaling.scale(scaler_data)
assert scaling.get_dtype(None, {
0: types.Int32,
1: types.Uint32
}) == np.dtype('int64')
assert scaled_data.dtype == np.dtype('int64')
np.testing.assert_almost_equal(scaled_data, expected_scaled_data)
def test_unsupported_scaling_type():
"""Raw data is returned unscaled when the scaling type is unsupported.
"""
properties = {
"NI_Number_Of_Scales": 1,
"NI_Scale[0]_Scale_Type": "UnknownScaling"
}
scaling = get_scaling(properties, {}, {})
assert scaling is None
def test_scaling_status_scaled():
""" When the scaling status is scaled, data is already scaled so scaling should not be applied
"""
properties = {
"NI_Number_Of_Scales": 1,
"NI_Scale[0]_Scale_Type": "Linear",
"NI_Scale[0]_Linear_Slope": 2.0,
"NI_Scale[0]_Linear_Y_Intercept": 10.0,
"NI_Scaling_Status": "scaled",
}
scaling = get_scaling(properties, {}, {})
assert scaling is None
def data(self):
"""
NumPy array containing data if there is data for this object,
otherwise None.
"""
if self._data is None:
# self._data is None if data segment is empty
return np.empty((0, 1))
if self._data_scaled is None:
scale = scaling.get_scaling(self)
if scale is None:
self._data_scaled = self._data
else:
self._data_scaled = scale.scale(self._data)
return self._data_scaled
def test_polynomial_scaling_with_no_coefficients():
"""Test polynomial scaling when there are no coefficients, so data should be all zero
"""
data = StubTdmsData(np.array([1.0, 2.0, 3.0]))
expected_scaled_data = np.array([0.0, 0.0, 0.0])
properties = {
"NI_Number_Of_Scales": 1,
"NI_Scale[0]_Scale_Type": "Polynomial",
"NI_Scale[0]_Polynomial_Coefficients_Size": 0
}
scaling = get_scaling(properties, {}, {})
scaled_data = scaling.scale(data)
np.testing.assert_almost_equal(scaled_data, expected_scaled_data)
def test_scaling_from_root():
"""Test linear scaling in the root object"""
data = StubTdmsData(np.array([1.0, 2.0, 3.0]))
expected_scaled_data = np.array([12.0, 14.0, 16.0])
root_properties = {
"NI_Number_Of_Scales": 1,
"NI_Scale[0]_Scale_Type": "Linear",
"NI_Scale[0]_Linear_Slope": 2.0,
"NI_Scale[0]_Linear_Y_Intercept": 10.0,
}
scaling = get_scaling({}, {}, root_properties)
scaled_data = scaling.scale(data)
np.testing.assert_almost_equal(scaled_data, expected_scaled_data)
def data(self):
"""
NumPy array containing data if there is data for this object,
otherwise None.
"""
if self._data is None:
# self._data is None if data segment is empty
return np.empty((0, 1))
if self._data_scaled is None:
scale = scaling.get_scaling(self)
if scale is None:
self._data_scaled = self._data
else:
self._data_scaled = scale.scale(self._data)
return self._data_scaled
def test_thermocouple_scaling_voltage_to_temperature():
"""Test thermocouple scaling from a voltage in uV to temperature"""
data = StubTdmsData(np.array([0.0, 10.0, 100.0, 1000.0]))
expected_scaled_data = np.array([0.0, 0.2534448, 2.5309141, 24.9940185])
properties = {
"NI_Number_Of_Scales": 1,
"NI_Scale[0]_Scale_Type": "Thermocouple",
"NI_Scale[0]_Thermocouple_Thermocouple_Type": 10073,
"NI_Scale[0]_Thermocouple_Scaling_Direction": 0,
"NI_Scale[0]_Thermocouple_Input_Source": 0xFFFFFFFF,
}
scaling = get_scaling(properties, {}, {})
scaled_data = scaling.scale(data)
np.testing.assert_almost_equal(
scaled_data, expected_scaled_data, decimal=3)
def test_polynomial_scaling_with_3_coefficients():
"""Test polynomial scaling"""
data = StubTdmsData(np.array([1, 2, 3], dtype=np.dtype('int32')))
expected_scaled_data = np.array([13.0, 20.0, 31.0])
properties = {
"NI_Number_Of_Scales": 1,
"NI_Scale[0]_Scale_Type": "Polynomial",
"NI_Scale[0]_Polynomial_Coefficients_Size": 3,
"NI_Scale[0]_Polynomial_Coefficients[0]": 10.0,
"NI_Scale[0]_Polynomial_Coefficients[1]": 1.0,
"NI_Scale[0]_Polynomial_Coefficients[2]": 2.0,
}
scaling = get_scaling(properties, {}, {})
scaled_data = scaling.scale(data)
np.testing.assert_almost_equal(scaled_data, expected_scaled_data)
def test_add_scaling_with_default_inputs():
""" Test scaling that adds two input scalings"""
data = StubTdmsData(np.array([1, 2, 3], dtype=np.dtype('int32')))
expected_scaled_data = np.array([2, 4, 6])
properties = {
"NI_Number_Of_Scales": 1,
"NI_Scale[0]_Scale_Type": "Add",
"NI_Scale[0]_Add_Left_Operand_Input_Source": 0xFFFFFFFF,
"NI_Scale[0]_Add_Right_Operand_Input_Source": 0xFFFFFFFF,
}
scaling = get_scaling(properties, {}, {})
scaled_data = scaling.scale(data)
assert scaling.get_dtype(types.Int32, None) == np.dtype('int32')
assert scaled_data.dtype == np.dtype('int32')
np.testing.assert_equal(expected_scaled_data, scaled_data)
def test_thermocouple_scaling_temperature_to_voltage():
"""Test thermocouple scaling from a temperature to voltage in uV"""
data = StubTdmsData(np.array([0.0, 10.0, 50.0, 100.0]))
expected_scaled_data = np.array([
0.0, 396.8619078, 2023.0778862, 4096.2302187])
properties = {
"NI_Number_Of_Scales": 1,
"NI_Scale[0]_Scale_Type": "Thermocouple",
"NI_Scale[0]_Thermocouple_Thermocouple_Type": 10073,
"NI_Scale[0]_Thermocouple_Scaling_Direction": 1,
"NI_Scale[0]_Thermocouple_Input_Source": 0xFFFFFFFF,
}
scaling = get_scaling(properties, {}, {})
scaled_data = scaling.scale(data)
np.testing.assert_almost_equal(
scaled_data, expected_scaled_data, decimal=3)
def test_linear_scaling():
"""Test linear scaling"""
data = StubTdmsData(np.array([1, 2, 3], dtype=np.dtype('int32')))
expected_scaled_data = np.array([12.0, 14.0, 16.0])
properties = {
"NI_Scaling_Status": "unscaled",
"NI_Number_Of_Scales": 1,
"NI_Scale[0]_Scale_Type": "Linear",
"NI_Scale[0]_Linear_Slope": 2.0,
"NI_Scale[0]_Linear_Y_Intercept": 10.0
}
scaling = get_scaling(properties, {}, {})
scaled_data = scaling.scale(data)
assert scaling.get_dtype(types.Int32, None) == np.dtype('float64')
assert scaled_data.dtype == np.dtype('float64')
np.testing.assert_almost_equal(scaled_data, expected_scaled_data)
def test_polynomial_scaling():
"""Test polynomial scaling"""
data = StubTdmsData(np.array([1, 2, 3], dtype=np.dtype('int32')))
expected_scaled_data = np.array([16.0, 44.0, 112.0])
properties = {
"NI_Number_Of_Scales": 1,
"NI_Scale[0]_Scale_Type": "Polynomial",
"NI_Scale[0]_Polynomial_Coefficients[0]": 10.0,
"NI_Scale[0]_Polynomial_Coefficients[1]": 1.0,
"NI_Scale[0]_Polynomial_Coefficients[2]": 2.0,
"NI_Scale[0]_Polynomial_Coefficients[3]": 3.0,
}
scaling = get_scaling(properties, {}, {})
scaled_data = scaling.scale(data)
assert scaling.get_dtype(types.Int32, None) == np.dtype('float64')
assert scaled_data.dtype == np.dtype('float64')
np.testing.assert_almost_equal(scaled_data, expected_scaled_data)
def test_thermocouple_scaling_voltage_to_temperature_benchmark(benchmark):
"""Test thermocouple scaling from a voltage in uV to temperature"""
data = StubTdmsData(np.tile(np.array([0.0, 10.0, 100.0, 1000.0]), 100))
expected_scaled_data = np.tile(
np.array([0.000000, 0.250843, 2.508899, 24.983648]), 100)
properties = {
"NI_Number_Of_Scales": 1,
"NI_Scale[0]_Scale_Type": "Thermocouple",
"NI_Scale[0]_Thermocouple_Thermocouple_Type": 10073,
"NI_Scale[0]_Thermocouple_Scaling_Direction": 0,
"NI_Scale[0]_Thermocouple_Input_Source": 0xFFFFFFFF,
}
scaling = get_scaling(properties, {}, {})
scaled_data = benchmark(scaling.scale, data)
np.testing.assert_almost_equal(scaled_data,
expected_scaled_data,
decimal=3)
def test_thermistor_scaling_with_invalid_excitation_type():
data = StubTdmsData(np.array([1.1, 1.0, 0.9]))
properties = {
"NI_Number_Of_Scales": 1,
"NI_Scale[0]_Scale_Type": "Thermistor",
"NI_Scale[0]_Thermistor_Resistance_Configuration": 3,
"NI_Scale[0]_Thermistor_Excitation_Type": 12345,
"NI_Scale[0]_Thermistor_Excitation_Value": 2.5,
"NI_Scale[0]_Thermistor_R1_Reference_Resistance": 10000.0,
"NI_Scale[0]_Thermistor_Lead_Wire_Resistance": 0.0,
"NI_Scale[0]_Thermistor_A": 0.0012873851,
"NI_Scale[0]_Thermistor_B": 0.00023575235,
"NI_Scale[0]_Thermistor_C": 9.497806e-8,
"NI_Scale[0]_Thermistor_Temperature_Offset": 1.0,
"NI_Scale[0]_Thermistor_Input_Source": 0xFFFFFFFF,
}
scaling = get_scaling(properties, {}, {})
with pytest.raises(ValueError) as exc_info:
_ = scaling.scale(data)
assert "Invalid excitation type: 12345" in str(exc_info.value)
def test_thermistor_scaling_with_current_excitation(
resistance_configuration, lead_resistance, expected_data):
data = StubTdmsData(np.array([1.1, 1.0, 0.9]))
properties = {
"NI_Number_Of_Scales": 1,
"NI_Scale[0]_Scale_Type": "Thermistor",
"NI_Scale[0]_Thermistor_Resistance_Configuration": resistance_configuration,
"NI_Scale[0]_Thermistor_Excitation_Type": 10134,
"NI_Scale[0]_Thermistor_Excitation_Value": 1.0e-3,
"NI_Scale[0]_Thermistor_R1_Reference_Resistance": 0.0,
"NI_Scale[0]_Thermistor_Lead_Wire_Resistance": lead_resistance,
"NI_Scale[0]_Thermistor_A": 0.0012873851,
"NI_Scale[0]_Thermistor_B": 0.00023575235,
"NI_Scale[0]_Thermistor_C": 9.497806e-8,
"NI_Scale[0]_Thermistor_Temperature_Offset": 1.0,
"NI_Scale[0]_Thermistor_Input_Source": 0xFFFFFFFF,
}
scaling = get_scaling(properties, {}, {})
scaled_data = scaling.scale(data)
np.testing.assert_almost_equal(scaled_data, expected_data)
def test_rtd_scaling(resistance_configuration, lead_resistance, expected_data):
"""Test RTD scaling"""
data = StubTdmsData(np.array([0.5, 0.6]))
properties = {
"NI_Number_Of_Scales": 1,
"NI_Scale[0]_Scale_Type": "RTD",
"NI_Scale[0]_RTD_Current_Excitation": 0.001,
"NI_Scale[0]_RTD_R0_Nominal_Resistance": 100.0,
"NI_Scale[0]_RTD_A": 0.0039083,
"NI_Scale[0]_RTD_B": -5.775e-07,
"NI_Scale[0]_RTD_C": -4.183e-12,
"NI_Scale[0]_RTD_Lead_Wire_Resistance": lead_resistance,
"NI_Scale[0]_RTD_Resistance_Configuration": resistance_configuration,
"NI_Scale[0]_RTD_Input_Source": 0xFFFFFFFF,
}
scaling = get_scaling(properties, {}, {})
scaled_data = scaling.scale(data)
assert scaling.get_dtype(types.DoubleFloat, None) == np.dtype('float64')
np.testing.assert_almost_equal(scaled_data, expected_data, decimal=3)
def _scaling(self):
return scaling.get_scaling(self.properties, self._group_properties,
self._file_properties)
def _scaling(self):
group_properties = self._tdms_file[self._path.group].properties
file_properties = self._tdms_file.properties
return scaling.get_scaling(self.properties, group_properties,
file_properties)
