Breaking: remove single-use PolarizationLattice which inherited from Structure (antipattern) (#3585)

* breaking: remove PolarizationLattice which inherited from Structure (antipattern) and refactor get_same_branch_polarization_data()

* rename d|s vars

Author: janosh

pymatgen/analysis/elasticity/elastic.py

@@ -1002,9 +1002,9 @@ def generate_pseudo(strain_states, order=3):
             difference derivative of the stress with respect to the strain state
         absent_syms: symbols of the tensor absent from the PI expression
     """
-    s = sp.Symbol("s")
+    symb = sp.Symbol("s")
     nstates = len(strain_states)
-    ni = np.array(strain_states) * s
+    ni = np.array(strain_states) * symb
     pseudo_inverses, absent_symbols = [], []
     for degree in range(2, order + 1):
         cvec, carr = get_symbol_list(degree)
@@ -1015,14 +1015,14 @@ def generate_pseudo(strain_states, order=3):
             for _ in range(degree - 1):
                 exps = np.dot(exps, strain_v)
             exps /= math.factorial(degree - 1)
-            sarr[n] = [sp.diff(exp, s, degree - 1) for exp in exps]
+            sarr[n] = [sp.diff(exp, symb, degree - 1) for exp in exps]
         svec = sarr.ravel()
         present_symbols = set.union(*(exp.atoms(sp.Symbol) for exp in svec))
         absent_symbols += [set(cvec) - present_symbols]
-        m = np.zeros((6 * nstates, len(cvec)))
+        pseudo_mat = np.zeros((6 * nstates, len(cvec)))
         for n, c in enumerate(cvec):
-            m[:, n] = v_diff(svec, c)
-        pseudo_inverses.append(np.linalg.pinv(m))
+            pseudo_mat[:, n] = v_diff(svec, c)
+        pseudo_inverses.append(np.linalg.pinv(pseudo_mat))
     return pseudo_inverses, absent_symbols
 
 

pymatgen/analysis/ferroelectricity/polarization.py

@@ -46,12 +46,20 @@
 
 from __future__ import annotations
 
+from typing import TYPE_CHECKING
+
 import numpy as np
 from scipy.interpolate import UnivariateSpline
 
 from pymatgen.core.lattice import Lattice
 from pymatgen.core.structure import Structure
 
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+    from pymatgen.core.sites import PeriodicSite
+
+
 __author__ = "Tess Smidt"
 __copyright__ = "Copyright 2017, The Materials Project"
 __version__ = "1.0"
@@ -73,7 +81,7 @@ def zval_dict_from_potcar(potcar):
     return zval_dict
 
 
-def calc_ionic(site, structure: Structure, zval):
+def calc_ionic(site: PeriodicSite, structure: Structure, zval: float) -> np.ndarray:
     """
     Calculate the ionic dipole moment using ZVAL from pseudopotential.
 
@@ -103,31 +111,27 @@ def get_total_ionic_dipole(structure, zval_dict):
     return np.sum(tot_ionic, axis=0)
 
 
-class PolarizationLattice(Structure):
-    """TODO Why is a Lattice inheriting a structure? This is ridiculous."""
-
-    def get_nearest_site(self, coords, site, r=None):
-        """
-        Given coords and a site, find closet site to coords.
+def get_nearest_site(struct: Structure, coords: Sequence[float], site: PeriodicSite, r: float | None = None):
+    """
+    Given coords and a site, find closet site to coords.
 
-        Args:
-            coords (3x1 array): Cartesian coords of center of sphere
-            site: site to find closest to coords
-            r: radius of sphere. Defaults to diagonal of unit cell
+    Args:
+        coords (3x1 array): Cartesian coords of center of sphere
+        site: site to find closest to coords
+        r (float): radius of sphere. Defaults to diagonal of unit cell
 
-        Returns:
-            Closest site and distance.
-        """
-        index = self.index(site)
-        if r is None:
-            r = np.linalg.norm(np.sum(self.lattice.matrix, axis=0))
-        ns = self.get_sites_in_sphere(coords, r, include_index=True)
-        # Get sites with identical index to site
-        ns = [n for n in ns if n[2] == index]
-        # Sort by distance to coords
-        ns.sort(key=lambda x: x[1])
-        # Return PeriodicSite and distance of closest image
-        return ns[0][0:2]
+    Returns:
+        Closest site and distance.
+    """
+    index = struct.index(site)
+    r = r or np.linalg.norm(np.sum(struct.lattice.matrix, axis=0))
+    ns = struct.get_sites_in_sphere(coords, r, include_index=True)
+    # Get sites with identical index to site
+    ns = [n for n in ns if n[2] == index]
+    # Sort by distance to coords
+    ns.sort(key=lambda x: x[1])
+    # Return PeriodicSite and distance of closest image
+    return ns[0][0:2]
 
 
 class Polarization:
@@ -298,18 +302,18 @@ def get_same_branch_polarization_data(self, convert_to_muC_per_cm2=True, all_in_
         for idx in range(n_elecs):
             lattice = lattices[idx]
             frac_coord = np.divide(np.array([p_tot[idx]]), np.array(lattice.lengths))
-            d = PolarizationLattice(lattice, ["C"], [np.array(frac_coord).ravel()])
-            d_structs.append(d)
-            site = d[0]
+            struct = Structure(lattice, ["C"], [np.array(frac_coord).ravel()])
+            d_structs.append(struct)
+            site = struct[0]
             # Adjust nonpolar polarization to be closest to zero.
             # This is compatible with both a polarization of zero or a half quantum.
             prev_site = [0, 0, 0] if idx == 0 else sites[-1].coords
-            new_site = d.get_nearest_site(prev_site, site)
+            new_site = get_nearest_site(struct, prev_site, site)
             sites.append(new_site[0])
 
         adjust_pol = []
-        for site, d in zip(sites, d_structs):
-            adjust_pol.append(np.multiply(site.frac_coords, np.array(d.lattice.lengths)).ravel())
+        for site, struct in zip(sites, d_structs):
+            adjust_pol.append(np.multiply(site.frac_coords, np.array(struct.lattice.lengths)).ravel())
         return np.array(adjust_pol)
 
     def get_lattice_quanta(self, convert_to_muC_per_cm2=True, all_in_polar=True):

pymatgen/analysis/graphs.py

@@ -1290,11 +1290,11 @@ def __str__(self):
         return out
 
     def __repr__(self):
-        s = "Structure Graph"
-        s += f"\nStructure: \n{self.structure!r}"
-        s += f"\nGraph: {self.name}\n"
-        s += self._edges_to_str(self.graph)
-        return s
+        out = "Structure Graph"
+        out += f"\nStructure: \n{self.structure!r}"
+        out += f"\nGraph: {self.name}\n"
+        out += self._edges_to_str(self.graph)
+        return out
 
     def __len__(self):
         """length of Structure / number of nodes in graph"""

pymatgen/analysis/structure_analyzer.py

@@ -250,13 +250,15 @@ def __init__(self, structure: Structure, cutoff=10):
             cutoff (float) Cutoff distance.
         """
         self.cutoff = cutoff
-        self.s = structure
-        recp_len = np.array(self.s.lattice.reciprocal_lattice.abc)
-        i = np.ceil(cutoff * recp_len / (2 * pi))
-        offsets = np.mgrid[-i[0] : i[0] + 1, -i[1] : i[1] + 1, -i[2] : i[2] + 1].T
+        self.structure = structure
+        recip_vec = np.array(self.structure.lattice.reciprocal_lattice.abc)
+        cutoff_vec = np.ceil(cutoff * recip_vec / (2 * pi))
+        offsets = np.mgrid[
+            -cutoff_vec[0] : cutoff_vec[0] + 1, -cutoff_vec[1] : cutoff_vec[1] + 1, -cutoff_vec[2] : cutoff_vec[2] + 1
+        ].T
         self.offsets = np.reshape(offsets, (-1, 3))
         # shape = [image, axis]
-        self.cart_offsets = self.s.lattice.get_cartesian_coords(self.offsets)
+        self.cart_offsets = self.structure.lattice.get_cartesian_coords(self.offsets)
 
     @property
     def connectivity_array(self):
@@ -271,12 +273,12 @@ def connectivity_array(self):
             solid angle of polygon between atom_i and image_j of atom_j
         """
         # shape = [site, axis]
-        cart_coords = np.array(self.s.cart_coords)
+        cart_coords = np.array(self.structure.cart_coords)
         # shape = [site, image, axis]
         all_sites = cart_coords[:, None, :] + self.cart_offsets[None, :, :]
         vt = Voronoi(all_sites.reshape((-1, 3)))
         n_images = all_sites.shape[1]
-        cs = (len(self.s), len(self.s), len(self.cart_offsets))
+        cs = (len(self.structure), len(self.structure), len(self.cart_offsets))
         connectivity = np.zeros(cs)
         vts = np.array(vt.vertices)
         for (ki, kj), v in vt.ridge_dict.items():
@@ -321,7 +323,7 @@ def get_connections(self):
         for ii in range(max_conn.shape[0]):
             for jj in range(max_conn.shape[1]):
                 if max_conn[ii][jj] != 0:
-                    dist = self.s.get_distance(ii, jj)
+                    dist = self.structure.get_distance(ii, jj)
                     con.append([ii, jj, dist])
         return con
 
@@ -335,9 +337,9 @@ def get_sitej(self, site_index, image_index):
             site_index (int): index of the site (3 in the example)
             image_index (int): index of the image (12 in the example)
         """
-        atoms_n_occu = self.s[site_index].species
-        lattice = self.s.lattice
-        coords = self.s[site_index].frac_coords + self.offsets[image_index]
+        atoms_n_occu = self.structure[site_index].species
+        lattice = self.structure.lattice
+        coords = self.structure[site_index].frac_coords + self.offsets[image_index]
         return PeriodicSite(atoms_n_occu, coords, lattice)
 
 

pymatgen/analysis/surface_analysis.py

@@ -861,8 +861,8 @@ def chempot_vs_gamma_plot_one(
         # w.r.t. bulk. Label with formula if non-stoichiometric
         ucell_comp = self.ucell_entry.composition.reduced_composition
         if entry.adsorbates:
-            s = entry.cleaned_up_slab
-            clean_comp = s.composition.reduced_composition
+            struct = entry.cleaned_up_slab
+            clean_comp = struct.composition.reduced_composition
         else:
             clean_comp = entry.composition.reduced_composition
 

pymatgen/core/composition.py

@@ -365,8 +365,8 @@ def get_reduced_formula_and_factor(self, iupac_ordering: bool = False) -> tuple[
         all_int = all(abs(x - round(x)) < Composition.amount_tolerance for x in self.values())
         if not all_int:
             return self.formula.replace(" ", ""), 1
-        d = {key: int(round(val)) for key, val in self.get_el_amt_dict().items()}
-        formula, factor = reduce_formula(d, iupac_ordering=iupac_ordering)
+        el_amt_dict = {key: int(round(val)) for key, val in self.get_el_amt_dict().items()}
+        formula, factor = reduce_formula(el_amt_dict, iupac_ordering=iupac_ordering)
 
         if formula in Composition.special_formulas:
             formula = Composition.special_formulas[formula]

pymatgen/core/ion.py

@@ -136,8 +136,8 @@ def get_reduced_formula_and_factor(self, iupac_ordering: bool = False, hydrates:
                 nH2O = int(nO) if nH >= 2 * nO else int(nH) // 2
                 comp = self.composition - nH2O * Composition("H2O")
 
-        d = {k: int(round(v)) for k, v in comp.get_el_amt_dict().items()}
-        (formula, factor) = reduce_formula(d, iupac_ordering=iupac_ordering)
+        el_amt_dict = {k: int(round(v)) for k, v in comp.get_el_amt_dict().items()}
+        (formula, factor) = reduce_formula(el_amt_dict, iupac_ordering=iupac_ordering)
 
         if self.composition.get("H") == self.composition.get("O") is not None:
             formula = formula.replace("HO", "OH")

pymatgen/io/abinit/inputs.py

@@ -858,18 +858,18 @@ def to_str(self, post=None, with_structure=True, with_pseudos=True, exclude=None
             vname = name + post
             app(str(InputVariable(vname, value)))
 
-        s = "\n".join(lines)
+        out = "\n".join(lines)
         if not with_pseudos:
-            return s
+            return out
 
         # Add JSON section with pseudo potentials.
         ppinfo = ["\n\n\n#<JSON>"]
-        d = {"pseudos": [p.as_dict() for p in self.pseudos]}
-        ppinfo.extend(json.dumps(d, indent=4).splitlines())
+        psp_dict = {"pseudos": [p.as_dict() for p in self.pseudos]}
+        ppinfo.extend(json.dumps(psp_dict, indent=4).splitlines())
         ppinfo.append("</JSON>")
 
-        s += "\n#".join(ppinfo)
-        return s
+        out += "\n#".join(ppinfo)
+        return out
 
     @property
     def comment(self):

pymatgen/io/adf.py

@@ -533,18 +533,18 @@ def _setup_task(self, geo_subkeys):
                 self.geo.remove_subkey("Frequencies")
 
     def __str__(self):
-        s = f"""TITLE {self.title}\n
+        out = f"""TITLE {self.title}\n
 {self.units}
 {self.xc}
 {self.basis_set}
 {self.scf}
 {self.geo}"""
-        s += "\n"
+        out += "\n"
         for block_key in self.other_directives:
             if not isinstance(block_key, AdfKey):
                 raise ValueError(f"{block_key} is not an AdfKey!")
-            s += str(block_key) + "\n"
-        return s
+            out += str(block_key) + "\n"
+        return out
 
     def as_dict(self):
         """A JSON-serializable dict representation of self."""

pymatgen/io/lammps/data.py

@@ -563,10 +563,10 @@ def disassemble(
                 }
                 ff_df = self.force_field[kw]
                 for t in ff_df.itertuples(index=True, name=None):
-                    d = {"coeffs": list(t[1:]), "types": []}
+                    coeffs_dict = {"coeffs": list(t[1:]), "types": []}
                     if class2_coeffs:
-                        d.update({k: list(v[t[0] - 1]) for k, v in class2_coeffs.items()})
-                    topo_coeffs[kw].append(d)
+                        coeffs_dict.update({k: list(v[t[0] - 1]) for k, v in class2_coeffs.items()})
+                    topo_coeffs[kw].append(coeffs_dict)
 
         if self.topology:
 
@@ -591,8 +591,8 @@ def label_topo(t) -> tuple:
 
         if any(topo_coeffs):
             for v in topo_coeffs.values():
-                for d in v:
-                    d["types"] = list(set(d["types"]))
+                for coeffs_dict in v:
+                    coeffs_dict["types"] = list(set(coeffs_dict["types"]))
 
         ff = ForceField(
             mass_info=mass_info,