Fix some remaining bugs in previous commit

Author: knutfrode

examples/data_dir/test.nc

@@ -5,14 +5,14 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import xarray as xr
-import trajan as _
+import trajan as ta
 
 #%%
 # Demonstrating calculation and plotting of gridded concentrations
 
 #%%
 # Importing a trajectory dataset from an oil drift simulation (OpenOil)
-ds = xr.open_dataset('opv2025_subset.nc')
+ds = xr.open_dataset(ta.DATA_DIR + 'opv2025_subset.nc')
 
 #%%
 # Make a grid covering this dataset with a horizontal resolution of 200m

examples/example_concentration.py

@@ -129,10 +129,7 @@
 # **Speed filter** — positions from which the speed to the next observation
 # exceeds a given threshold (here 3 m/s) are masked. We reload the original
 # (uncleaned) dataset to illustrate the effect.
-with lzma.open('barents.nc.xz') as barents:
-    ds_raw = xr.open_dataset(barents)
-    ds_raw.load()
-
+ds_raw = xr.open_dataset(ta.DATA_DIR + 'barents.nc')
 ds_speed = ds_raw.traj.filter(method='speed', max_speed=3.)
 
 #%%

examples/example_drifters.py

@@ -633,12 +633,12 @@ def assign_cf_attrs(self,
             'time_coverage_start':
             pd.to_datetime(
                 np.nanmin(ds[self.time_varname].values[ds[
-                    self.time_varname].values != np.datetime64('NaT', 'ns')])).
+                    self.time_varname].values != np.datetime64('NaT', 's')])).
             isoformat() if self.ds.sizes[self.obs_dim] > 0 else np.nan,
             'time_coverage_end':
             pd.to_datetime(
                 np.nanmax(ds[self.time_varname].values[ds[
-                    self.time_varname].values != np.datetime64('NaT', 'ns')])
+                    self.time_varname].values != np.datetime64('NaT', 's')])
             ).isoformat() if self.ds.sizes[self.obs_dim] > 0 else np.nan,
         })
 

trajan/traj/__init__.py

@@ -100,7 +100,7 @@ def from_contiguous(ds, trajectory_dim, obs_dim, time_varname,
         # the time var (special case as it is of a different type)
 
         array_time = np.full((nbr_trajectories, longest_trajectory),
-                             np.datetime64('NaT', 'ns'),
+                             np.datetime64('NaT', 's'),
                              dtype='datetime64[ns]')
 
         start_index = 0
@@ -389,7 +389,7 @@ def insert_nan_where(self, condition):
                     s = np.split(olddata, insert_indices)
 
                     if np.issubdtype(var.dtype, np.datetime64):
-                        na = np.atleast_1d(np.datetime64('NaT', 'ns'))
+                        na = np.atleast_1d(np.datetime64('NaT', 's'))
                     else:
                         na = np.atleast_1d(np.nan)
                     newdata = np.concatenate(