Handle scaled scaling status

When the scaling status is scaled, scaling has
already been applied so should not be applied again.

nptdms/scaling.py

@@ -446,6 +446,10 @@ def _get_channel_scaling(properties):
     num_scalings = _get_number_of_scalings(properties)
     if num_scalings is None or num_scalings == 0:
         return None
+    scaling_status = properties.get("NI_Scaling_Status", "unscaled")
+    if scaling_status == "scaled":
+        # Data is written with scaling already applied
+        return None
 
     scalings = [None] * num_scalings
     for scale_index in range(num_scalings):

nptdms/test/test_scaling.py

@@ -36,6 +36,7 @@ def test_linear_scaling():
     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,
@@ -454,6 +455,20 @@ def test_scaling_from_root():
     np.testing.assert_almost_equal(expected_scaled_data, scaled_data)
 
 
+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
+
+
 class StubTdmsData(object):
     def __init__(self, data):
         self.data = data