trying to circumvent issue numpy/netcdf runtime warning issue #4

src/efts_io/__init__.py

@@ -5,6 +5,6 @@
 
 from __future__ import annotations
 
-import netCDF4  # noqa: I001
+# import netCDF4
 
 __all__: list[str] = []

src/efts_io/conventions.py

@@ -1,9 +1,9 @@
 """Naming conventions for the EFTS netCDF file format."""
 
 from datetime import datetime
-from typing import Iterable, List, Optional, Union
+from typing import Any, Iterable, List, Optional, Union
 
-import netCDF4 as nc  # noqa: N813
+# import netCDF4 as nc
 import numpy as np
 import pandas as pd
 import xarray as xr
@@ -123,20 +123,30 @@ def check_index_found(
         )
 
 
+# MdDatasetsType = Union[nc.Dataset, xr.Dataset, xr.DataArray]
+MdDatasetsType = Union[xr.Dataset, xr.DataArray]
+
+
+def _is_nc_dataset(d: Any) -> bool:
+    # Have to disable using directly netCDF4 for now due to issue #4
+    return False
+    # return isinstance(d, nc.Dataset)
+
+
 def _has_required_dimensions(
-    d: Union[nc.Dataset, xr.Dataset, xr.DataArray],
+    d: MdDatasetsType,
     mandatory_dimensions: Iterable[str],
 ) -> bool:
-    if isinstance(d, nc.Dataset):
+    if _is_nc_dataset(d):
         return set(d.dimensions.keys()) == set(mandatory_dimensions)
     return set(d.dims.keys()) == set(mandatory_dimensions)
 
 
-def has_required_stf2_dimensions(d: Union[nc.Dataset, xr.Dataset, xr.DataArray]) -> bool:
+def has_required_stf2_dimensions(d: MdDatasetsType) -> bool:
     return _has_required_dimensions(d, mandatory_netcdf_dimensions)
 
 
-def has_required_xarray_dimensions(d: Union[nc.Dataset, xr.Dataset, xr.DataArray]) -> bool:
+def has_required_xarray_dimensions(d: MdDatasetsType) -> bool:
     return _has_required_dimensions(d, mandatory_xarray_dimensions)
 
 
@@ -145,8 +155,8 @@ def _has_all_members(tested: Iterable[str], reference: Iterable[str]) -> bool:
     return set(tested).intersection(r) == r
 
 
-def has_required_global_attributes(d: Union[nc.Dataset, xr.Dataset, xr.DataArray]) -> bool:
-    if isinstance(d, nc.Dataset):
+def has_required_global_attributes(d: MdDatasetsType) -> bool:
+    if _is_nc_dataset(d):
         a = d.ncattrs()
         tested = set(a)
     else:
@@ -155,7 +165,7 @@ def has_required_global_attributes(d: Union[nc.Dataset, xr.Dataset, xr.DataArray
     return _has_all_members(tested, mandatory_global_attributes)
 
 
-def has_required_variables(d: Union[nc.Dataset, xr.Dataset, xr.DataArray]) -> bool:
+def has_required_variables(d: MdDatasetsType) -> bool:
     a = d.variables.keys()
     tested = set(a)
     # Note: even if xarray, we do not need to check for the 'data_vars' attribute here.

src/efts_io/dimensions.py

@@ -3,7 +3,7 @@
 from datetime import datetime
 from typing import Any, Dict, Optional, Tuple, Union
 
-import netCDF4  # noqa: F401
+# import netCDF4
 import numpy as np
 import pandas as pd
 

src/efts_io/variables.py

@@ -2,7 +2,7 @@
 
 from typing import Any, Dict, List, Optional
 
-import netCDF4  # noqa: F401
+# import netCDF4
 import numpy as np
 import pandas as pd
 

src/efts_io/wrapper.py

@@ -3,7 +3,7 @@
 import os
 from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
 
-import netCDF4  # noqa: F401
+# import netCDF4
 import numpy as np
 import pandas as pd
 import xarray as xr

tests/test_create.py

@@ -0,0 +1,44 @@
+# import netCDF4
+import numpy as np
+import pandas as pd
+
+
+def test_create_new_efts():
+    import efts_io.wrapper as wrap
+
+    issue_times = pd.date_range("2010-01-01", periods=31, freq="D")
+    station_ids = ["a", "b"]
+    lead_times = np.arange(start=1, stop=4, step=1)
+    lead_time_tstep = "hours"
+    ensemble_size = 10
+    station_names = None
+    nc_attributes = None
+    latitudes = None
+    longitudes = None
+    areas = None
+    d = wrap.xr_efts(
+        issue_times,
+        station_ids,
+        lead_times,
+        lead_time_tstep,
+        ensemble_size,
+        station_names,
+        nc_attributes,
+        latitudes,
+        longitudes,
+        areas,
+    )
+    from efts_io.conventions import (
+        has_required_global_attributes,
+        has_required_variables,
+        has_required_xarray_dimensions,
+    )
+
+    assert has_required_xarray_dimensions(d)
+    assert has_required_global_attributes(d)
+    assert has_required_variables(d)
+
+
+if __name__ == "__main__":
+    # test_read_thing()
+    test_create_new_efts()

tests/test_read_file.py

@@ -1,16 +1,12 @@
 import os
 
+# import netCDF4
 import numpy as np
 import pandas as pd
 
 from efts_io.wrapper import EftsDataSet
 
 pkg_dir = os.path.join(os.path.dirname(__file__), "..")
-# sys.path.insert(0, pkg_dir)
-
-# from efts_io.attributes import *
-# from efts_io.dimensions import *
-# from efts_io.wrapper import *
 
 variable_names = ["variable_1", "variable_2"]
 stations_ids = [123, 456]