paramfit: use sum(ssr) rather than mean(rmsd) for best-fit selection

1 files changed, 21 insertions, 9 deletions

diff --git a/lib/paramfit.py b/lib/paramfit.py
index eb0e141..e6539a4 100644
--- a/lib/paramfit.py
+++ b/lib/paramfit.py
@@ -16,7 +16,7 @@ from .utils import (
 )
 
 logger = logging.getLogger(__name__)
-best_fit_metric = os.getenv("DFATOOL_ULS_ERROR_METRIC", "rmsd")
+best_fit_metric = os.getenv("DFATOOL_ULS_ERROR_METRIC", "ssr")
 
 
 class ParamFit:
@@ -255,16 +255,28 @@ def _try_fits(
         if len(result) > 0:
             results[function_name] = {}
             for measure in result.keys():
-                results[function_name][measure] = np.mean(result[measure])
+                if measure == "ssr":
+                    results[function_name][measure] = np.sum(result[measure])
+                else:
+                    results[function_name][measure] = np.mean(result[measure])
             err = results[function_name][best_fit_metric]
             if err < best_fit_val:
                 best_fit_val = err
                 best_fit_name = function_name
 
-    return {
-        "best": best_fit_name,
-        "best_err": best_fit_val,
-        "mean_err": np.mean(ref_results["mean"]),
-        "median_err": np.mean(ref_results["median"]),
-        "results": results,
-    }
+    if best_fit_metric == "ssr":
+        return {
+            "best": best_fit_name,
+            "best_err": best_fit_val,
+            "mean_err": np.sum(ref_results["mean"]),
+            "median_err": np.sum(ref_results["median"]),
+            "results": results,
+        }
+    else:
+        return {
+            "best": best_fit_name,
+            "best_err": best_fit_val,
+            "mean_err": np.mean(ref_results["mean"]),
+            "median_err": np.mean(ref_results["median"]),
+            "results": results,
+        }