remove unnecessary dict.keys()

pymatgen/analysis/diffraction/tem.py

@@ -271,10 +271,9 @@ def cell_intensity(
             dict of hkl plane to cell intensity
         """
         csf = self.cell_scattering_factors(structure, bragg_angles)
-        plane = bragg_angles.keys()
         csf_val = np.array(list(csf.values()))
         cell_intensity_val = (csf_val * csf_val.conjugate()).real
-        cell_intensity = dict(zip(plane, cell_intensity_val))
+        cell_intensity = dict(zip(bragg_angles, cell_intensity_val))
         return cell_intensity
 
     def get_pattern(

pymatgen/analysis/disorder.py

@@ -32,7 +32,7 @@ def get_warren_cowley_parameters(structure: Structure, r: float, dr: float) -> d
             n_neighbors[site.specie] += 1
 
     alpha_ij = {}  # type: ignore
-    for sp1, sp2 in itertools.product(comp.keys(), comp.keys()):
+    for sp1, sp2 in itertools.product(comp, comp):
         pij = n_ij.get((sp1, sp2), 0) / n_neighbors[sp1]
         conc2 = comp.get_atomic_fraction(sp2)
         alpha_ij[(sp1, sp2)] = (pij - conc2) / ((1 if sp1 == sp2 else 0) - conc2)

pymatgen/analysis/phase_diagram.py

@@ -1099,23 +1099,23 @@ def get_element_profile(self, element, comp, comp_tol=1e-5):
         if element not in self.elements:
             raise ValueError("get_transition_chempots can only be called with elements in the phase diagram.")
 
-        gccomp = Composition({el: amt for el, amt in comp.items() if el != element})
-        elref = self.el_refs[element]
-        elcomp = Composition(element.symbol)
+        gc_comp = Composition({el: amt for el, amt in comp.items() if el != element})
+        el_ref = self.el_refs[element]
+        el_comp = Composition(element.symbol)
         evolution = []
 
-        for cc in self.get_critical_compositions(elcomp, gccomp)[1:]:
-            decomp_entries = list(self.get_decomposition(cc).keys())
+        for cc in self.get_critical_compositions(el_comp, gc_comp)[1:]:
+            decomp_entries = list(self.get_decomposition(cc))
             decomp = [k.composition for k in decomp_entries]
-            rxn = Reaction([comp], decomp + [elcomp])
+            rxn = Reaction([comp], decomp + [el_comp])
             rxn.normalize_to(comp)
-            c = self.get_composition_chempots(cc + elcomp * 1e-5)[element]
-            amt = -rxn.coeffs[rxn.all_comp.index(elcomp)]
+            c = self.get_composition_chempots(cc + el_comp * 1e-5)[element]
+            amt = -rxn.coeffs[rxn.all_comp.index(el_comp)]
             evolution.append(
                 {
                     "chempot": c,
                     "evolution": amt,
-                    "element_reference": elref,
+                    "element_reference": el_ref,
                     "reaction": rxn,
                     "entries": decomp_entries,
                     "critical_composition": cc,

pymatgen/analysis/structure_prediction/tests/test_substitution_probability.py

@@ -72,13 +72,13 @@ class SubstitutionPredictorTest(unittest.TestCase):
     def test_prediction(self):
         sp = SubstitutionPredictor(threshold=8e-3)
         result = sp.list_prediction(["Na+", "Cl-"], to_this_composition=True)[5]
-        cprob = sp.p.cond_prob_list(result["substitutions"].keys(), result["substitutions"].values())
-        assert result["probability"] == approx(cprob)
+        cond_prob = sp.p.cond_prob_list(list(result["substitutions"]), result["substitutions"].values())
+        assert result["probability"] == approx(cond_prob)
         assert set(result["substitutions"].values()) == {"Na+", "Cl-"}
 
         result = sp.list_prediction(["Na+", "Cl-"], to_this_composition=False)[5]
-        cprob = sp.p.cond_prob_list(result["substitutions"].keys(), result["substitutions"].values())
-        assert result["probability"] == approx(cprob)
+        cond_prob = sp.p.cond_prob_list(list(result["substitutions"]), result["substitutions"].values())
+        assert result["probability"] == approx(cond_prob)
         assert set(result["substitutions"].values()) != {"Na+", "Cl-"}
 
         c = Composition({"Ag2+": 1, "Cl-": 2})

pymatgen/analysis/surface_analysis.py

@@ -1296,7 +1296,7 @@ def surface_chempot_range_map(
 
             # Plot the edge along the max x value
             pt = v[-1]
-            delu1, delu2 = pt.keys()
+            delu1, delu2 = pt
             xvals.extend([pt[delu1], pt[delu1]])
             yvals.extend(pt[delu2][0])
             if not show_unphyiscal_only:
@@ -1849,7 +1849,7 @@ def scaled_wulff(self, wulffshape, r):
         """
         # get the scaling ratio for the energies
         r_ratio = r / wulffshape.effective_radius
-        miller_list = wulffshape.miller_energy_dict.keys()
+        miller_list = list(wulffshape.miller_energy_dict)
         # Normalize the magnitude of the facet normal vectors
         # of the Wulff shape by the minimum surface energy.
         se_list = np.array(list(wulffshape.miller_energy_dict.values()))

pymatgen/io/cp2k/outputs.py

@@ -659,9 +659,9 @@ def parse_dft_params(self):
 
         # Functional
         if self.input and self.input.check("FORCE_EVAL/DFT/XC/XC_FUNCTIONAL"):
-            xcfuncs = list(self.input["force_eval"]["dft"]["xc"]["xc_functional"].subsections.keys())
-            if xcfuncs:
-                self.data["dft"]["functional"] = xcfuncs
+            xc_funcs = list(self.input["force_eval"]["dft"]["xc"]["xc_functional"].subsections)
+            if xc_funcs:
+                self.data["dft"]["functional"] = xc_funcs
             else:
                 for v in self.input["force_eval"]["dft"]["xc"].subsections.values():
                     if v.name.upper() == "XC_FUNCTIONAL":
@@ -1614,7 +1614,7 @@ def read_pattern(self, patterns, reverse=False, terminate_on_match=False, postpr
             terminate_on_match=terminate_on_match,
             postprocess=postprocess,
         )
-        for k in patterns.keys():
+        for k in patterns:
             self.data[k] = [i[0] for i in matches.get(k, [])]
 
     def read_table_pattern(

pymatgen/io/vasp/inputs.py

@@ -2014,7 +2014,7 @@ def verify_potcar(self) -> tuple[bool, bool]:
             # file with known potcar file hashes.
             md5_file_hash = self.file_hash
             hash_db = loadfn(os.path.join(cwd, "vasp_potcar_file_hashes.json"))
-            if md5_file_hash in hash_db.keys():
+            if md5_file_hash in hash_db:
                 passed_hash_check = True
             else:
                 passed_hash_check = False