Merge branch 'develop' into update/subrepos

ndsl/stencils/corners.py

@@ -1003,6 +1003,7 @@ def fill_corners_dgrid_defn(
     with computation(PARALLEL), interval(...):
         # this line of code is used to fix the missing symbol crash due to the node visitor depth limitation
         acoef = mysign
+        x_out = x_out
         # sw corner
         with horizontal(region[i_start - 1, j_start - 1]):
             x_out = mysign * y_in[0, 1, 0]

ndsl/stencils/testing/translate.py

@@ -167,6 +167,8 @@ def make_storage_data_input_vars(self, inputs, storage_vars=None):
         for p in self.in_vars["parameters"]:
             if type(inputs_in[p]) in [np.int64, np.int32]:
                 inputs_out[p] = int(inputs_in[p])
+            elif type(inputs_in[p]) is bool:
+                inputs_out[p] == inputs_in[p]
             else:
                 inputs_out[p] = Float(inputs_in[p])
         for d, info in storage_vars.items():

ndsl/testing/comparison.py

@@ -39,22 +39,23 @@ def __init__(
     ):
         super().__init__(reference_values, computed_values)
         self.eps = eps
+        self._calculated_metric = np.empty_like(self.references)
         self.success = self._compute_errors(
             ignore_near_zero_errors,
             near_zero,
         )
         self.check = np.all(self.success)
-        self._calculated_metric = np.empty_like(self.references)
 
     def _compute_errors(
         self,
         ignore_near_zero_errors,
         near_zero,
     ) -> npt.NDArray[np.bool_]:
         if self.references.dtype in (np.float64, np.int64, np.float32, np.int32):
-            denom = np.abs(self.references) + np.abs(self.computed)
+            denom = self.references
+            denom[self.references == 0] = self.computed[self.references == 0]
             self._calculated_metric = np.asarray(
-                2.0 * np.abs(self.computed - self.references) / denom
+                np.abs((self.computed - self.references) / denom)
             )
             self._calculated_metric[denom == 0] = 0.0
         elif self.references.dtype in (np.bool_, bool):
@@ -123,7 +124,7 @@ def __repr__(self) -> str:
                 f"{reference_failures[b]}  {abs_errs[-1]:.3e}  {metric_err:.3e}"
             )
 
-            if np.isnan(metric_err) or (metric_err > worst_metric_err):
+            if np.isnan(metric_err) or (abs(metric_err) > abs(worst_metric_err)):
                 worst_metric_err = metric_err
                 worst_full_idx = full_index
                 worst_abs_err = abs_errs[-1]
@@ -249,7 +250,7 @@ def __repr__(self) -> str:
             f"All failures ({bad_indices_count}/{full_count}) ({failures_pct}%),\n",
             f"Index   Computed   Reference   "
             f"Absolute E(<{self.absolute_eps:.2e})  "
-            f"Relative E(<{self.relative_fraction*100:.2e}%)   "
+            f"Relative E(<{self.relative_fraction * 100:.2e}%)   "
             f"ULP E(<{self.ulp_threshold})",
         ]
         # Summary and worst result