fix: fix load single peak in py3 support

Author: stevenhua0320

news/fix-single-peak.rst

@@ -0,0 +1,23 @@
+**Added:**
+
+* <news item>
+
+**Changed:**
+
+* <news item>
+
+**Deprecated:**
+
+* <news item>
+
+**Removed:**
+
+* <news item>
+
+**Fixed:**
+
+* Fixed extracting single peak with `py2` legacy cleanup
+
+**Security:**
+
+* <news item>

src/diffpy/srmise/applications/extract.py

@@ -18,6 +18,63 @@
 import numpy as np
 
 
+def _resolve_cli_expression(expression, namespace):
+    """Resolve a CLI expression against an explicit namespace.
+
+    Parameters
+    ----------
+    expression : str
+        The user-supplied CLI expression.
+    namespace : dict
+        The explicit namespace allowed during evaluation.
+
+    Returns
+    -------
+    object
+        The resolved class or instance.
+    """
+    return eval(expression, {"__builtins__": {}}, namespace)
+
+
+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 _peakfunction_namespace():
+    """Return the peak-function classes supported by the CLI."""
+    from diffpy.srmise.peaks.gaussian import Gaussian
+    from diffpy.srmise.peaks.gaussianoverr import GaussianOverR
+    from diffpy.srmise.peaks.terminationripples import TerminationRipples
+
+    return {
+        "Gaussian": Gaussian,
+        "GaussianOverR": GaussianOverR,
+        "TerminationRipples": TerminationRipples,
+    }
+
+
+def _modelevaluator_namespace():
+    """Return the model evaluators supported by the CLI."""
+    from diffpy.srmise.modelevaluators.aic import AIC
+    from diffpy.srmise.modelevaluators.aicc import AICc
+
+    return {
+        "AIC": AIC,
+        "AICc": AICc,
+    }
+
+
 def main():
     """Default SrMise entry-point."""
 
@@ -433,15 +490,21 @@ def main():
 
     if options.peakfunction:
         try:
-            options.peakfunction = eval("peaks." + options.peakfunction)
+            options.peakfunction = _resolve_cli_expression(
+                options.peakfunction,
+                _peakfunction_namespace(),
+            )
         except Exception as err:
             print(err)
             print("Could not create peak function '%s'. Exiting." % options.peakfunction)
             return
 
     if options.modelevaluator:
         try:
-            options.modelevaluator = eval("modelevaluators." + options.modelevaluator)
+            options.modelevaluator = _resolve_cli_expression(
+                options.modelevaluator,
+                _modelevaluator_namespace(),
+            )
         except Exception as err:
             print(err)
             print("Could not find ModelEvaluator '%s'. Exiting." % options.modelevaluator)
@@ -483,10 +546,12 @@ def main():
 
         bl = NanoSpherical()
         options.baseline = parsepars(bl, options.bspherical)
-
+    elif options.baseline:
         try:
-            options.baseline = eval("baselines." + options.baseline)
-
+            options.baseline = _resolve_cli_expression(
+                options.baseline,
+                _baseline_namespace(),
+            )
         except Exception as err:
             print(err)
             print("Could not create baseline '%s'. Exiting." % options.baseline)

src/diffpy/srmise/dataclusters.py

@@ -206,19 +206,19 @@ def __next__(self):
 
         if np.abs(nearest_cluster[1]) <= self.res:
             # Add to an existing cluster
-            self.lastcluster_idx = nearest_cluster[0]
-            if test_idx < self.clusters[nearest_cluster[0], 0]:
-                self.clusters[nearest_cluster[0], 0] = test_idx
+            self.lastcluster_idx = int(nearest_cluster[0])
+            if test_idx < self.clusters[int(nearest_cluster[0]), 0]:
+                self.clusters[int(nearest_cluster[0]), 0] = test_idx
             else:
-                self.clusters[nearest_cluster[0], 1] = test_idx
+                self.clusters[int(nearest_cluster[0]), 1] = test_idx
         else:
             # Make a new cluster
             if nearest_cluster[1] < 0:
                 # Insert left of nearest cluster
-                self.lastcluster_idx = nearest_cluster[0]
+                self.lastcluster_idx = int(nearest_cluster[0])
             else:
                 # insert right of nearest cluster
-                self.lastcluster_idx = nearest_cluster[0] + 1
+                self.lastcluster_idx = int(nearest_cluster[0]) + 1
             self.clusters = np.insert(
                 self.clusters,
                 int(self.lastcluster_idx),

src/diffpy/srmise/modelcluster.py

@@ -1174,7 +1174,7 @@ def cleanfit(self):
         pos = np.array([p["position"] for p in self.model])
         left_idx = pos.searchsorted(self.r_cluster[0])
         right_idx = pos.searchsorted(self.r_cluster[-1])
-        outside_idx = range(0, left_idx)
+        outside_idx = list(range(0, left_idx))
         outside_idx.extend(range(right_idx, len(self.model)))
         # inside_idx = range(left_idx, right_idx)
 
@@ -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] = best_model[lo:i].copy()
-                    check_models[i].extend(best_model[i + 1 : hi].copy())
+                    check_models[i] = type(best_model)(best_model[lo:i]).copy()
+                    check_models[i].extend(type(best_model)(best_model[i + 1: hi]).copy())
                     prune_mc.model = check_models[i]
 
                     msg = [

src/diffpy/srmise/modelevaluators/aic.py

@@ -106,7 +106,7 @@ def minpoints(self, npars):
 
         return 1
 
-    def parpenalty(self, k):
+    def parpenalty(self, k, n=None):
         """Returns the cost for adding k parameters to the current model
         cluster.
 

src/diffpy/srmise/modelparts.py

@@ -430,8 +430,9 @@ def __getitem__(self, index):
         if isinstance(index, tuple) and len(index) == 2:
             start, end = index
             return self.__class__(super().__getitem__(slice(start, end)))
-        else:
-            return super().__getitem__(index)
+        if isinstance(index, slice):
+            return self.__class__(super().__getitem__(index))
+        return super().__getitem__(index)
 
     def transform(self, in_format="internal", out_format="internal"):
         """Transforms format of parameters in this modelpart.

src/diffpy/srmise/peakextraction.py

@@ -905,7 +905,7 @@ def extract_single(self, recursion_depth=1):
                 )
             else:
                 # Update an existing cluster
-                mclusters[step.lastcluster_idx].change_slice(step.cut(step.lastcluster_idx))
+                mclusters[int(step.lastcluster_idx)].change_slice(step.cut(step.lastcluster_idx))
 
             # Find newly adjacent clusters
             adjacent = step.find_adjacent_clusters().ravel()
@@ -969,7 +969,7 @@ def extract_single(self, recursion_depth=1):
                 # near_peaks: array containing the indices of two nearest peaks on either side of border_x
                 # other_peaks: all the other peaks in full_cluster
                 # left_data, right_data: indices defining the extent of the "interpeak range" for x, etc.
-                near_peaks = np.array([], dtype=np.int)
+                near_peaks = np.array([], dtype=np.int64)
 
                 # interpeak range goes from peak to peak of next nearest peaks, although their contributions
                 # to the data are still removed.
@@ -1122,7 +1122,7 @@ def extract_single(self, recursion_depth=1):
                 # near_peaks: array containing the indices of two nearest peaks on either side of border_x
                 # other_peaks: all the other peaks in new_cluster
                 # left_data, right_data: indices defining the extent of the "interpeak range" for x, etc.
-                near_peaks = np.array([], dtype=np.int)
+                near_peaks = np.array([], dtype=np.int64)
 
                 # interpeak range goes from peak to peak of next nearest peaks, although their contributions
                 # to the data are still removed.

src/diffpy/srmise/peaks/terminationripples.py

@@ -316,7 +316,7 @@ def value(self, peak, r, rng=None):
             # issues is difficult to determine without detailed knowledge
             # of the underlying function.
             dr = (r[-1] - r[0]) / (len(r) - 1)
-            segments = np.ceil(dr / dr_super)
+            segments = int(np.ceil(dr / dr_super))
             dr_segmented = dr / segments
 
             rpart = r[rng]