Annika comments

Author: VictorGervilla

engine/tolerance.py

@@ -45,7 +45,7 @@
     "--minimum-tolerance",
     type=float,
     default="0.0",
-    help=cli_help["member_type"],
+    help=cli_help["minimum_tolerance"],
 )
 def tolerance(stats_file_name, tolerance_file_name, member_ids, member_type, minimum_tolerance):
 
@@ -79,7 +79,7 @@ def tolerance(stats_file_name, tolerance_file_name, member_ids, member_type, min
     # Take the cumulative maximum to only allow monotonically growing tolerances
     force_monotonic(df_max)
 
-    # Set a fixed non-zero tolerance value in case these became exactly zero
+    # Set a fixed tolerance value in case these became exactly zero
     df_max = df_max.map(lambda x: minimum_tolerance if x < minimum_tolerance else x)
 
     tolerance_dir = os.path.dirname(tolerance_file_name)

tests/cli/test_tolerance_cli.py

@@ -6,6 +6,7 @@
 """
 
 import os
+import pytest
 
 from tests.helpers import (
     assert_empty_df,
@@ -16,13 +17,25 @@
 )
 
 
-def test_tolerance_cli(ref_data, df_ref_tolerance, tmp_dir, new_ref):
+@pytest.mark.parametrize("use_minimum_tolerance", [True, False])
+def test_tolerance_cli(ref_data, df_ref_tolerance, tmp_dir, new_ref, use_minimum_tolerance):
     stats_file_name = os.path.join(ref_data, "stats_{member_id}.csv")
     tolerance_file_name = os.path.join(tmp_dir, "tolerance.csv")
-    run_tolerance_cli(stats_file_name, tolerance_file_name, member_type="dp", minimum_tolerance=1e-14)
-    df_test = load_pandas(tolerance_file_name, index_col=[0, 1])
-    err = pandas_error(df_ref_tolerance, df_test)
 
+    if use_minimum_tolerance:
+        run_tolerance_cli(stats_file_name, tolerance_file_name, member_type="dp", minimum_tolerance=1e-14)
+        df_test = load_pandas(tolerance_file_name, index_col=[0, 1])
+        err = pandas_error(df_ref_tolerance, df_test)
+    else:
+        run_tolerance_cli(stats_file_name, tolerance_file_name, member_type="dp")
+
+        # Create modified tolerance df with T tolerances equal to 0.0 to match `minimum_tolerance` default
+        df_test = load_pandas(tolerance_file_name, index_col=[0, 1])
+        modified_df_ref_tolerance = df_ref_tolerance
+        modified_df_ref_tolerance.loc[("NetCDF:*.nc", "T")] = 0.0
+        
+        err = pandas_error(modified_df_ref_tolerance, df_test)
+    
     store_as_potential_new_ref(tolerance_file_name, new_ref)
 
     assert_empty_df(err, "Tolerance datasets are not equal!")