bugfixes

h5rdmtoolbox/conventions/standard_attributes.py

@@ -282,6 +282,7 @@ def validate(self, value, parent, attrs=None):
         if value is not None:
             if isinstance(value, str) and value.startswith('{') and value.endswith('}'):
                 value = json.loads(value)
+            if isinstance(value, dict):
                 try:
                     model_fields = self.validator.model_fields
                     if 'value' in model_fields and 'typing.Dict' in str(model_fields['value'].annotation):

h5rdmtoolbox/wrapper/core.py

@@ -549,7 +549,12 @@ def create_time_dataset(self,
         if isinstance(data, np.ndarray):
             return self.create_string_dataset(name, data=[t.astype(datetime).isoformat() for t in data],
                                               overwrite=overwrite, attrs=attrs, **kwargs)
-        return self.create_string_dataset(name, data=[t.isoformat() for t in data],
+        _data = np.asarray(data)
+        _orig_shape = _data.shape
+        _flat_data = _data.flatten()
+        _flat_data = np.asarray([t.isoformat() for t in _flat_data])
+        _reshaped_data = _flat_data.reshape(_orig_shape)
+        return self.create_string_dataset(name, data=_reshaped_data.tolist(),
                                           overwrite=overwrite, attrs=attrs, **kwargs)
 
     def create_string_dataset(self,
@@ -570,7 +575,7 @@ def create_string_dataset(self,
         if isinstance(data, str):
             n_letter = len(data)
         elif isinstance(data, (tuple, list)):
-            n_letter = max([len(d) for d in data])
+            n_letter = max([len(d) for d in np.asarray(data).flatten()])
         else:
             raise TypeError(f'Unexpected type for parameter "data": {type(data)}. Expected str or List/Tuple of str')
         dtype = f'S{n_letter}'
@@ -1668,7 +1673,8 @@ def __getitem__(self, args, new_dtype=None, nparray=False) -> Union[xr.DataArray
                     if dim_ds_data.dtype.kind == 'S':
                         # decode string array
                         if dim_ds_attrs.get('ISTIMEDS', False):
-                            dim_ds_data = np.array([datetime.fromisoformat(t) for t in dim_ds_data.astype(str)]).astype(datetime)
+                            dim_ds_data = np.array([datetime.fromisoformat(t) for t in dim_ds_data.astype(str)]).astype(
+                                datetime)
                     if dim_ds_data.ndim == 0:
                         if isinstance(arg, int):
                             coords[coord_name] = xr.DataArray(name=coord_name,
@@ -1718,7 +1724,15 @@ def __getitem__(self, args, new_dtype=None, nparray=False) -> Union[xr.DataArray
             # decode string array
             _arr = arr.astype(str)
             if self.attrs.get('ISTIMEDS', False):
-                return xr.DataArray([datetime.fromisoformat(t) for t in _arr], attrs=attrs)
+                if _arr.ndim == 0:
+                    _arr = np.asarray(datetime.fromisoformat(_arr))
+                elif _arr.ndim == 1:
+                    _arr = [datetime.fromisoformat(t) for t in _arr]
+                else:  # _arr.ndim > 1:
+                    orig_shape = _arr.shape
+                    _flat_arr = np.asarray([datetime.fromisoformat(t) for t in _arr.flatten()])
+                    _arr = _flat_arr.reshape(orig_shape)
+                return xr.DataArray(_arr, attrs=attrs)
             else:
                 if isinstance(_arr, np.ndarray):
                     return tuple(_arr)

tests/wrapper/test_core.py

@@ -6,7 +6,7 @@
 import pathlib
 import unittest
 import xarray as xr
-from datetime import datetime
+from datetime import datetime, timedelta
 from numpy import linspace as ls
 
 import h5rdmtoolbox as h5tbx
@@ -730,9 +730,8 @@ def test_create_dataset_with_ancillary_ds(self):
                 h5.create_dataset('vel3', data=[1.5, 2.5], attach_scale='3D')
 
     def test_time(self):
-        import datetime
-        tdata = [datetime.datetime.now(),
-                 (datetime.datetime.now() + datetime.timedelta(hours=1))]
+        tdata = [datetime.now(),
+                 (datetime.now() + timedelta(hours=1))]
         tdata_np = np.asarray(tdata, dtype=np.datetime64)
         with h5tbx.File() as h5:
             h5.create_string_dataset('time', data=[t.isoformat() for t in tdata],
@@ -761,14 +760,27 @@ def test_time(self):
             np.testing.assert_equal(t.values, np.datetime64(tdata[it]))
 
     def test_time_as_coord(self):
-        import datetime
         with h5tbx.File() as h5:
-            h5.create_time_dataset('time', data=[datetime.datetime.now(),
-                                                 datetime.datetime.now() + datetime.timedelta(hours=1),
-                                                 datetime.datetime.now() + datetime.timedelta(hours=3)],
+            h5.create_time_dataset('time', data=[datetime.now(),
+                                                 datetime.now() + timedelta(hours=1),
+                                                 datetime.now() + timedelta(hours=3)],
                                    attrs={'ISTIMEDS': True,
                                           'TIMEFORMAT': 'ISO'}, make_scale=True)
             h5.create_dataset('vel', data=[1, 2, -3], attach_scale='time')
             v = h5.vel[()]
 
-        print(v.time)
+    def test_multidim_time_ds(self):
+        with h5tbx.File() as h5:
+            h5.create_time_dataset('time', data=[[datetime.now(),
+                                                  datetime.now() + timedelta(hours=1),
+                                                  datetime.now() + timedelta(hours=3)],
+                                                 [datetime.now(),
+                                                  datetime.now() + timedelta(hours=6),
+                                                  datetime.now() + timedelta(hours=10)]
+                                                 ],
+                                   attrs={'ISTIMEDS': True,
+                                          'TIMEFORMAT': 'ISO'})
+            t = h5.time[()]
+            self.assertIsInstance(t, xr.DataArray)
+            self.assertEqual(t.shape, (2, 3))
+            self.assertIsInstance(t[0, 0].values, np.datetime64)