FIX: Fix for more warnings.

act/io/armfiles.py

@@ -196,10 +196,8 @@ def read_netcdf(
     # https://github.com/pydata/xarray/issues/3644
     # To ensure the times are read in correctly need to set use_cftime=True.
     # This will read in time as cftime object. But Xarray uses numpy datetime64
-    # natively. This will convert the cftime time values to numpy datetime64. cftime
-    # does not preserve the time past ms precision. We will use ms precision for
-    # the conversion.
-    desired_time_precision = 'datetime64[ms]'
+    # natively. This will convert the cftime time values to numpy datetime64.
+    desired_time_precision = 'datetime64[ns]'
     for var_name in ['time', 'time_offset']:
         try:
             if 'time' in ds.dims and type(ds[var_name].values[0]).__module__.startswith('cftime.'):

act/io/sodar.py

@@ -9,6 +9,7 @@
 import fsspec
 import numpy as np
 import pandas as pd
+import xarray as xr
 
 from act.io.noaapsl import filter_list
 
@@ -74,7 +75,7 @@ def read_mfas_sodar(filepath):
     # tmp_columns is used to removed '#' column that causes
     # columns to move over by one.
     df = pd.read_table(filepath,
-                       sep='\s+',
+                       sep=r'\s+',
                        skiprows=skip_full_ind,
                        names=tmp_columns,
                        usecols=columns)
@@ -103,7 +104,8 @@ def read_mfas_sodar(filepath):
 
     # Use unique time and height values to reindex data to be two dimensional.
     ind = pd.MultiIndex.from_product((time_dim, height_dim), names=('time', 'height'))
-    ds = ds.assign(Dates=ind).unstack("Dates")
+    mindex_coords = xr.Coordinates.from_pandas_multiindex(ind, 'Dates')
+    ds = ds.assign_coords(mindex_coords).unstack("Dates")
 
     # Add file metadata.
     for key in file_dict.keys():