Bug timestamps

parcels/field.py

@@ -98,14 +98,7 @@ def __init__(self, name, data, lon=None, lat=None, depth=None, time=None, grid=N
             self.units = unitconverters_map[self.fieldtype]
         else:
             raise ValueError("Unsupported mesh type. Choose either: 'spherical' or 'flat'")
-        if timestamps is not None:
-            # Check whether flattened or not
-            if all(isinstance(f, np.ndarray) for f in timestamps):
-                self.timestamps = np.array([stamp for f in timestamps for stamp in f])
-            if all(isinstance(stamp, np.datetime64) for stamp in timestamps):
-                self.timestamps = timestamps
-        else:
-            self.timestamps = timestamps
+        self.timestamps = timestamps
         if type(interp_method) is dict:
             if self.name in interp_method:
                 self.interp_method = interp_method[self.name]
@@ -313,8 +306,7 @@ def from_netcdf(cls, filenames, variable, dimensions, indices=None, grid=None,
                 for findex in range(len(data_filenames)):
                     for f in [data_filenames[findex]] * len(timestamps[findex]):
                         dataFiles.append(f)
-                timestamps = np.array([stamp for file in timestamps for stamp in file])
-                timeslices = timestamps
+                timeslices = np.array([stamp for file in timestamps for stamp in file])
                 time = timeslices
             elif netcdf_engine == 'xarray':
                 with NetcdfFileBuffer(data_filenames, dimensions, indices, netcdf_engine) as filebuffer:
@@ -1083,7 +1075,11 @@ def advancetime(self, field_new, advanceForward):
 
     def computeTimeChunk(self, data, tindex):
         g = self.grid
-        timestamp = None if self.timestamps is None else self.timestamps[tindex]
+        timestamp = self.timestamps
+        if timestamp is not None:
+            summedlen = np.cumsum([len(ls) for ls in self.timestamps])
+            timestamp = self.timestamps[np.where(g.ti + tindex < summedlen)[0][0]]
+
         filebuffer = NetcdfFileBuffer(self.dataFiles[g.ti + tindex], self.dimensions, self.indices,
                                       self.netcdf_engine, timestamp=timestamp,
                                       interp_method=self.interp_method,

parcels/fieldset.py

@@ -257,14 +257,6 @@ def from_netcdf(cls, filenames, variables, dimensions, indices=None, fieldtype=N
             logger.warning_once("Time already provided, defaulting to dimensions['time'] over timestamps.")
             timestamps = None
 
-        # Typecast timestamps to (nested) numpy array.
-        if timestamps is not None:
-            if isinstance(timestamps, list):
-                timestamps = np.array(timestamps)
-                if any(isinstance(i, list) for i in timestamps):
-                    timestamps = np.array([np.array(sub) for sub in timestamps])
-            assert isinstance(timestamps, np.ndarray), "Timestamps must be nested list or array"
-            assert all(isinstance(file, np.ndarray) for file in timestamps), "Timestamps must be nested list or array"
         fields = {}
         if 'creation_log' not in kwargs.keys():
             kwargs['creation_log'] = 'from_netcdf'

tests/test_fieldset.py

@@ -346,16 +346,23 @@ def test_timestaps(datetype, tmpdir):
         dims2['time'] = np.arange('2005-02-11', '2005-02-15', dtype='datetime64[D]')
 
     fieldset1 = FieldSet.from_data(data1, dims1)
+    fieldset1.U.data[0, :, :] = 2.
     fieldset1.write(tmpdir.join('file1'))
 
     fieldset2 = FieldSet.from_data(data2, dims2)
+    fieldset2.U.data[0, :, :] = 0.
     fieldset2.write(tmpdir.join('file2'))
 
     fieldset3 = FieldSet.from_parcels(tmpdir.join('file*'))
     timestamps = [dims1['time'], dims2['time']]
     fieldset4 = FieldSet.from_parcels(tmpdir.join('file*'), timestamps=timestamps)
     assert np.allclose(fieldset3.U.grid.time_full, fieldset4.U.grid.time_full)
 
+    for d in [0, 8, 10]:
+        fieldset3.computeTimeChunk(d*3600, 1)
+        fieldset4.computeTimeChunk(d*3600, 1)
+        assert np.allclose(fieldset3.U.data, fieldset4.U.data)
+
 
 @pytest.mark.parametrize('mode', ['scipy', 'jit'])
 @pytest.mark.parametrize('time_periodic', [86400., False])