raising error on wrong normalization task

matthiasprobst · Nov 30, 2023 · 9201f13 · 9201f13
1 parent bed9c4b
commit 9201f13
Show file tree

Hide file tree

Showing 2 changed files with 27 additions and 2 deletions.
diff --git a/h5rdmtoolbox/extensions/normalize.py b/h5rdmtoolbox/extensions/normalize.py
@@ -6,6 +6,8 @@
 
 import h5rdmtoolbox as h5tbx
 
+from numpy.core._exceptions import _UFuncBinaryResolutionError
+
 
 def to_base_units(da: xr.DataArray) -> xr.DataArray:
     """Turns the units of an xarray to the base units, e.g. m/mm turns to dimensionless
@@ -39,11 +41,18 @@ def _normalize(obj, name, value, rename):
         if isinstance(value, str):
             qobj = obj.pint.quantify(unit_registry=h5tbx.get_ureg())
             q = h5tbx.get_ureg()(value)
-            norm_obj = (qobj/q).pint.dequantify()
+            norm_obj = (qobj / q).pint.dequantify()
         elif isinstance(value, (int, float)):
             # user indicates a float or int, which is interpreted as dimensionless
             with xr.set_options(keep_attrs=True):
-                norm_obj = obj / value
+                try:
+                    norm_obj = obj / value
+                except _UFuncBinaryResolutionError:
+                    raise RuntimeError(f'A error occurred while normalizing {obj.name} by {name}={value}. '
+                                       'This is likely due to a mismatch of data types. '
+                                       'you might specify the dimension to be normalized by '
+                                       'because not every dimension might of of a numeric type. '
+                                       'Orig. error: {e}')
         else:
             raise TypeError(f'Normalization must be either a string or a float, not {type(v)}.')
 

diff --git a/tests/test_extensions.py b/tests/test_extensions.py
@@ -1,3 +1,4 @@
+import datetime
 import numpy as np
 import unittest
 import xarray as xr
@@ -62,6 +63,21 @@ def test_Vector(self):
             self.assertEqual(vec2.attrs['test'], 1)
             self.assertIsInstance(vec2, xr.DataArray)
 
+    def test_normalize_issue_with_time_vector(self):
+        with h5tbx.File() as h5:
+            h5.create_dataset('x', data=[1, 2, 3], make_scale=True)
+            now = datetime.datetime.now()
+            h5.create_time_dataset('t',
+                                   data=[now, now + datetime.timedelta(seconds=1), now + datetime.timedelta(seconds=2)],
+                                   make_scale=True)
+            u = h5.create_dataset('u', data=[-4, 10, 0], attach_scales=[('t', 'x'), ])
+
+            with self.assertRaises(RuntimeError):
+                u[()].normalize.coords(rename=True, L=5)
+            unorm = u[()].normalize.coords(rename=True, x=dict(L=5))
+            np.testing.assert_array_equal(unorm.x.values, u[()].x.values / 5)
+            np.testing.assert_array_equal(unorm.values, u[()].values)
+
     def test_norm_one_for_all_without_unit(self):
         with h5tbx.File() as h5:
             h5.create_dataset('x', data=[1, 2, 3], make_scale=True)