some proper fix to the code

Author: stevenhua0320

src/diffpy/srmise/applications/extract.py

@@ -18,6 +18,21 @@
 import numpy as np
 
 
+def _baseline_namespace():
+    """Return the baseline classes supported by the CLI."""
+    from diffpy.srmise.baselines.arbitrary import Arbitrary
+    from diffpy.srmise.baselines.fromsequence import FromSequence
+    from diffpy.srmise.baselines.nanospherical import NanoSpherical
+    from diffpy.srmise.baselines.polynomial import Polynomial
+
+    return {
+        "Arbitrary": Arbitrary,
+        "FromSequence": FromSequence,
+        "NanoSpherical": NanoSpherical,
+        "Polynomial": Polynomial,
+    }
+
+
 def main():
     """Default SrMise entry-point."""
 
@@ -483,12 +498,16 @@ def main():
 
         bl = NanoSpherical()
         options.baseline = parsepars(bl, options.bspherical)
-        try:
-            options.baseline = eval("baselines." + options.baseline)
-        except Exception as err:
-            print(err)
-            print("Could not create baseline '%s'. Exiting." % options.baseline)
-            return
+    try:
+        options.baseline = eval(
+            options.baseline,
+            {"__builtins__": {}},
+            _baseline_namespace(),
+        )
+    except Exception as err:
+        print(err)
+        print("Could not create baseline '%s'. Exiting." % options.baseline)
+        return
 
     filename = args[0]
 

src/diffpy/srmise/modelcluster.py

@@ -1538,8 +1538,8 @@ def prune(self):
                     # Create model with ith peak removed, and distant peaks effectively fixed
                     lo = max(i - peak_range, 0)
                     hi = min(i + peak_range + 1, len(best_model))
-                    check_models[i] = type(best_model)(best_model[lo:i]).copy()
-                    check_models[i].extend(type(best_model)(best_model[i + 1 : hi]).copy())
+                    check_models[i] = best_model[lo:i].copy()
+                    check_models[i].extend(best_model[i + 1: hi].copy())
                     prune_mc.model = check_models[i]
 
                     msg = [

src/diffpy/srmise/modelevaluators/aic.py

@@ -85,7 +85,7 @@ def evaluate(self, fit, count_fixed=False, kshift=0):
         if self.chisq is None:
             self.chisq = self.chi_squared(fit.value(), fit.y_cluster, fit.error_cluster)
 
-        self.stat = self.chisq + self.parpenalty(k, n)
+        self.stat = self.chisq + self.parpenalty(k)
 
         return self.stat
 
@@ -106,7 +106,7 @@ def minpoints(self, npars):
 
         return 1
 
-    def parpenalty(self, k, n=None):
+    def parpenalty(self, k):
         """Returns the cost for adding k parameters to the current model
         cluster.
 
@@ -169,7 +169,7 @@ def growth_justified(self, fit, k_prime):
             logger.warning("AIC.growth_justified(): too few data to evaluate quality reliably.")
             n = self.minpoints(k_actual)
 
-        penalty = self.parpenalty(k_test, n) - self.parpenalty(k_actual, n)
+        penalty = self.parpenalty(k_test) - self.parpenalty(k_actual)
 
         return penalty < self.chisq
 

src/diffpy/srmise/peaks/gaussianoverr.py

@@ -534,7 +534,7 @@ def max(self, pars):
 
     guesspars = [[2.7, 0.15, 5], [3.7, 0.3, 5]]
     guess_peaks = Peaks([pf.actualize(p, "pwa") for p in guesspars])
-    cluster = ModelCluster(guess_peaks, r, y, err, None, AICc, [pf])
+    cluster = ModelCluster(guess_peaks, None, r, y, err, None, AICc, [pf])
 
     qual1 = cluster.quality()
     print(qual1.stat)

src/diffpy/srmise/peaks/terminationripples.py

@@ -416,12 +416,12 @@ def extend_grid(self, r, dr):
 
     guesspars = [[2.7, 0.15, 5], [3.7, 0.3, 5]]
     guess_peaks = Peaks([pf2.actualize(p, "pwa") for p in guesspars])
-    cluster = ModelCluster(guess_peaks, r, y_ripple, err, None, AICc, [pf2])
+    cluster = ModelCluster(guess_peaks, None, r, y_ripple, err, None, AICc, [pf2])
 
     qual1 = cluster.quality()
     print(qual1.stat)
     cluster.fit()
-    yfit = cluster.calc()
+    yfit = cluster.valuebl()
     qual2 = cluster.quality()
     print(qual2.stat)