improve Transformation return_ranked_list kwarg doc string

dev_scripts/chemenv/get_plane_permutations_optimized.py

@@ -235,9 +235,9 @@ def random_permutations_iterator(initial_permutation, n_permutations):
                     points_perfect=points_perfect,
                 )
 
-                mycsms = [c["symmetry_measure"] for c in csms]
+                my_csms = [c["symmetry_measure"] for c in csms]
                 prt1(string="Continuous symmetry measures", printing_volume=printing_volume)
-                prt1(string=mycsms, printing_volume=printing_volume)
+                prt1(string=my_csms, printing_volume=printing_volume)
                 csms_with_recorded_permutation = []
                 explicit_permutations = []
                 for icsm, csm in enumerate(csms):
@@ -378,19 +378,19 @@ def random_permutations_iterator(initial_permutation, n_permutations):
                         points_perfect=points_perfect,
                     )
 
-                    mycsms = [c["symmetry_measure"] for c in csms]
-                    imin = np.argmin(mycsms)
-                    mincsm = min(mycsms)
+                    my_csms = [c["symmetry_measure"] for c in csms]
+                    imin = np.argmin(my_csms)
+                    mincsm = min(my_csms)
                     if not mincsm < 1.0:
-                        print("Following is not close enough to 0.0 ...")
-                        input(mycsms)
+                        print("Following is not close enough to 0 ...")
+                        input(my_csms)
                     mincsm_indices = []
-                    for icsm, csm in enumerate(mycsms):
+                    for icsm, csm in enumerate(my_csms):
                         if np.isclose(mincsm, csm, rtol=0.0):
                             mincsm_indices.append(icsm)
                     this_plane_sep_perm = tuple(sep_perms[imin])
                     prt2(
-                        string=f"  permutation {'-'.join(map(str, this_plane_sep_perm))} gives csm={mycsms[imin]:.6f}",
+                        string=f"  permutation {'-'.join(map(str, this_plane_sep_perm))} gives csm={my_csms[imin]:.6f}",
                         printing_volume=printing_volume,
                     )
 

dev_scripts/chemenv/test_algos.py

@@ -86,7 +86,7 @@
             print("Number of permutations tested : ", len(results[0]))
             algos_results.append(min(results[0]))
 
-            if not np.isclose(min(results[0]), 0.0):
+            if not np.isclose(min(results[0]), 0):
                 print("Following is not 0.0 ...")
                 input(results)
         print("   => ", algos_results)

dev_scripts/chemenv/view_environment.py

@@ -53,27 +53,27 @@
             print(algo)
             print()
         # Visualize the separation plane of a given algorithm
-        sepplane = False
+        sep_plane = False
         if any(algo.algorithm_type == SEPARATION_PLANE for algo in cg.algorithms):
             test = input("Enter index of the algorithm for which you want to visualize the plane : ")
             if test != "":
                 try:
                     ialgo = int(test)
                     algo = cg.algorithms[ialgo]
-                    sepplane = True
+                    sep_plane = True
                 except Exception:
                     print(
                         "Unable to determine the algorithm/separation_plane you want "
                         "to visualize for this geometry. Continues without ..."
                     )
-        myfactor = 3.0
+        my_factor = 3
         if vis is None:
-            vis = visualize(cg=cg, zoom=1.0, myfactor=myfactor)
+            vis = visualize(cg=cg, zoom=1, myfactor=my_factor)
         else:
-            vis = visualize(cg=cg, vis=vis, myfactor=myfactor)
-        cg_points = [myfactor * np.array(pp) for pp in cg.points]
-        cg_central_site = myfactor * np.array(cg.central_site)
-        if sepplane:
+            vis = visualize(cg=cg, vis=vis, myfactor=my_factor)
+        cg_points = [my_factor * np.array(pp) for pp in cg.points]
+        cg_central_site = my_factor * np.array(cg.central_site)
+        if sep_plane:
             pts = [cg_points[ii] for ii in algo.plane_points]
             if algo.minimum_number_of_points == 2:
                 pts.append(cg_central_site)
@@ -92,23 +92,23 @@
             p3 = centre - factor * (pts[0] - centre)
             p4 = centre - perp
 
-            vis.add_faces([[p1, p2, p3, p4]], [1.0, 0.0, 0.0], opacity=0.5)
+            vis.add_faces([[p1, p2, p3, p4]], [1, 0, 0], opacity=0.5)
 
             target_radius = 0.25
             radius = 1.5 * target_radius
 
             if algo.minimum_number_of_points == 2:
                 vis.add_partial_sphere(
-                    coords=cg_central_site, radius=radius, color=[1.0, 0.0, 0.0], start=0, end=360, opacity=0.5
+                    coords=cg_central_site, radius=radius, color=[1, 0, 0], start=0, end=360, opacity=0.5
                 )
             for pp in pts:
-                vis.add_partial_sphere(coords=pp, radius=radius, color=[1.0, 0.0, 0.0], start=0, end=360, opacity=0.5)
+                vis.add_partial_sphere(coords=pp, radius=radius, color=[1, 0, 0], start=0, end=360, opacity=0.5)
 
             ps1 = [cg_points[ii] for ii in algo.point_groups[0]]
             ps2 = [cg_points[ii] for ii in algo.point_groups[1]]
 
             for pp in ps1:
-                vis.add_partial_sphere(coords=pp, radius=radius, color=[0.0, 1.0, 0.0], start=0, end=360, opacity=0.5)
+                vis.add_partial_sphere(coords=pp, radius=radius, color=[0, 1, 0], start=0, end=360, opacity=0.5)
             for pp in ps2:
-                vis.add_partial_sphere(coords=pp, radius=radius, color=[0.0, 0.0, 1.0], start=0, end=360, opacity=0.5)
+                vis.add_partial_sphere(coords=pp, radius=radius, color=[0, 0, 1], start=0, end=360, opacity=0.5)
         vis.show()

pymatgen/analysis/local_env.py

@@ -178,7 +178,7 @@ def _get_valences(self):
             valences = bv.get_valences(self._structure)
         except Exception:
             try:
-                bv = BVAnalyzer(symm_tol=0.0)
+                bv = BVAnalyzer(symm_tol=0)
                 self._structure = bv.get_oxi_state_decorated_structure(self._structure)
                 valences = bv.get_valences(self._structure)
             except Exception:
@@ -1313,13 +1313,13 @@ class MinimumDistanceNN(NearNeighbors):
     (relative) distance tolerance parameter.
     """
 
-    def __init__(self, tol: float = 0.1, cutoff=10.0, get_all_sites=False):
+    def __init__(self, tol: float = 0.1, cutoff=10, get_all_sites=False):
         """
         Args:
             tol (float): tolerance parameter for neighbor identification
                 (default: 0.1).
             cutoff (float): cutoff radius in Angstrom to look for trial
-                near-neighbor sites (default: 10.0).
+                near-neighbor sites (default: 10).
             get_all_sites (bool): If this is set to True then the neighbor
                 sites are only determined by the cutoff radius, tol is ignored
         """
@@ -1718,13 +1718,13 @@ class MinimumOKeeffeNN(NearNeighbors):
     to calculate relative distances.
     """
 
-    def __init__(self, tol: float = 0.1, cutoff=10.0):
+    def __init__(self, tol: float = 0.1, cutoff=10):
         """
         Args:
             tol (float): tolerance parameter for neighbor identification
                 (default: 0.1).
             cutoff (float): cutoff radius in Angstrom to look for trial
-                near-neighbor sites (default: 10.0).
+                near-neighbor sites (default: 10).
         """
         self.tol = tol
         self.cutoff = cutoff
@@ -1813,13 +1813,13 @@ class MinimumVIRENN(NearNeighbors):
     to calculate relative distances.
     """
 
-    def __init__(self, tol: float = 0.1, cutoff=10.0):
+    def __init__(self, tol: float = 0.1, cutoff=10):
         """
         Args:
             tol (float): tolerance parameter for neighbor identification
                 (default: 0.1).
             cutoff (float): cutoff radius in Angstrom to look for trial
-                near-neighbor sites (default: 10.0).
+                near-neighbor sites (default: 10).
         """
         self.tol = tol
         self.cutoff = cutoff
@@ -2020,7 +2020,7 @@ def get_okeeffe_distance_prediction(el1, el2):
     return r1 + r2 - r1 * r2 * pow(sqrt(c1) - sqrt(c2), 2) / (c1 * r1 + c2 * r2)
 
 
-def get_neighbors_of_site_with_index(struct, n, approach="min_dist", delta=0.1, cutoff=10.0):
+def get_neighbors_of_site_with_index(struct, n, approach="min_dist", delta=0.1, cutoff=10):
     """
     Returns the neighbors of a given site using a specific neighbor-finding
     method.
@@ -2052,7 +2052,7 @@ def get_neighbors_of_site_with_index(struct, n, approach="min_dist", delta=0.1,
     raise RuntimeError(f"unsupported neighbor-finding method ({approach}).")
 
 
-def site_is_of_motif_type(struct, n, approach="min_dist", delta=0.1, cutoff=10.0, thresh=None):
+def site_is_of_motif_type(struct, n, approach="min_dist", delta=0.1, cutoff=10, thresh=None):
     """
     Returns the motif type of the site with index n in structure struct;
     currently featuring "tetrahedral", "octahedral", "bcc", and "cp"