Fix bandit issues

.bandit

@@ -1,3 +1,3 @@
 [bandit]
 skips: B506
-exclude: pyresample/test,pyresample/version.py
+exclude: pyresample/test,pyresample/version.py,versioneer.py

pyresample/ewa/dask_ewa.py

@@ -186,8 +186,8 @@ class DaskEWAResampler(BaseResampler):
     def __init__(self, source_geo_def, target_geo_def):
         """Initialize in-memory cache."""
         super(DaskEWAResampler, self).__init__(source_geo_def, target_geo_def)
-        assert isinstance(source_geo_def, SwathDefinition), \
-            "EWA resampling can only operate on SwathDefinitions"
+        if not isinstance(source_geo_def, SwathDefinition):
+            raise ValueError("EWA resampling can only operate on SwathDefinitions")
         self.cache = {}
 
     def _new_chunks(self, in_arr, rows_per_scan):

pyresample/future/resamplers/nearest.py

@@ -127,8 +127,8 @@ def __init__(self,
             raise ImportError("Missing 'xarray' and 'dask' dependencies")
         super().__init__(source_geo_def, target_geo_def, cache=cache)
         self._internal_cache = {}
-        assert (self.target_geo_def.ndim == 2), \
-            "Target area definition must be 2 dimensions"
+        if self.target_geo_def.ndim != 2:
+            raise ValueError("Target area definition must be 2 dimensions")
 
     @property
     def version(self) -> str:
@@ -218,8 +218,8 @@ def _get_neighbor_info(self, mask, neighbors, radius_of_influence, epsilon):
         valid_output_idx = ((target_lons >= -180) & (target_lons <= 180) & (target_lats <= 90) & (target_lats >= -90))
 
         if mask is not None:
-            assert (mask.shape == self.source_geo_def.shape), \
-                "'mask' must be the same shape as the source geo definition"
+            if mask.shape != self.source_geo_def.shape:
+                raise ValueError("'mask' must be the same shape as the source geo definition")
             mask = mask.data
         index_arr = self._query_resample_kdtree(
             resample_kdtree, target_lons, target_lats, valid_input_idx,

pyresample/future/resamplers/resampler.py

@@ -38,7 +38,7 @@
 def hash_dict(the_dict: dict, existing_hash: Optional[HashType] = None) -> HashType:
     """Calculate a hash for a dictionary and optionally update an existing hash."""
     if existing_hash is None:
-        existing_hash = hashlib.sha1()
+        existing_hash = hashlib.sha1()  # nosec: B324
     existing_hash.update(json.dumps(the_dict, sort_keys=True).encode('utf-8'))
     return existing_hash
 

pyresample/geometry.py

@@ -123,7 +123,7 @@ def __hash__(self):
     def update_hash(self, existing_hash: Optional[HashType] = None) -> HashType:
         """Update the hash."""
         if existing_hash is None:
-            existing_hash = hashlib.sha1()
+            existing_hash = hashlib.sha1()  # nosec: B324
         existing_hash.update(get_array_hashable(self.lons))
         existing_hash.update(get_array_hashable(self.lats))
         try:
@@ -2058,7 +2058,7 @@ def __ne__(self, other):
     def update_hash(self, existing_hash: Optional[HashType] = None) -> HashType:
         """Update a hash, or return a new one if needed."""
         if existing_hash is None:
-            existing_hash = hashlib.sha1()
+            existing_hash = hashlib.sha1()  # nosec: B324
         existing_hash.update(self.crs_wkt.encode('utf-8'))
         existing_hash.update(np.array(self.shape))
         existing_hash.update(np.array(self.area_extent))

pyresample/kd_tree.py

@@ -905,8 +905,8 @@ def __init__(self,
         if radius_of_influence is None:
             radius_of_influence = self._compute_radius_of_influence()
         self.radius_of_influence = radius_of_influence
-        assert (self.target_geo_def.ndim == 2), \
-            "Target area definition must be 2 dimensions"
+        if self.target_geo_def.ndim != 2:
+            raise ValueError("Target area definition must be 2 dimensions")
 
     def _compute_radius_of_influence(self):
         """Estimate a good default radius_of_influence."""
@@ -989,8 +989,8 @@ def get_neighbour_info(self, mask=None):
         valid_output_idx = ((target_lons >= -180) & (target_lons <= 180) & (target_lats <= 90) & (target_lats >= -90))
 
         if mask is not None:
-            assert (mask.shape == self.source_geo_def.shape), \
-                "'mask' must be the same shape as the source geo definition"
+            if mask.shape != self.source_geo_def.shape:
+                raise ValueError("'mask' must be the same shape as the source geo definition")
             mask = mask.data
         index_arr, distance_arr = self.query_resample_kdtree(
             resample_kdtree, target_lons, target_lats, valid_output_idx, mask)
@@ -1056,13 +1056,13 @@ def get_sample_from_neighbour_info(self, data, fill_value=np.nan):
         dst_geo_dims = ('y', 'x')
         # verify that source dims are the same between geo and data
         data_geo_dims = tuple(d for d in data.dims if d in src_geo_dims)
-        assert (data_geo_dims == src_geo_dims), \
-            "Data dimensions do not match source area dimensions"
+        if data_geo_dims != src_geo_dims:
+            raise ValueError("Data dimensions do not match source area dimensions")
         # verify that the dims are next to each other
         first_dim_idx = data.dims.index(src_geo_dims[0])
         num_dims = len(src_geo_dims)
-        assert (data.dims[first_dim_idx:first_dim_idx + num_dims] == data_geo_dims), \
-            "Data's geolocation dimensions are not consecutive."
+        if data.dims[first_dim_idx:first_dim_idx + num_dims] != data_geo_dims:
+            raise ValueError("Data's geolocation dimensions are not consecutive.")
 
         # FIXME: Can't include coordinates whose dimensions depend on the geo
         #        dims either