fix typos

pymatgen/analysis/eos.py

@@ -42,7 +42,7 @@ def __init__(self, volumes, energies):
         self.volumes = np.array(volumes)
         self.energies = np.array(energies)
         # minimum energy(e0), buk modulus(b0),
-        # derivative of bulk modulus wrt pressure(b1), minimum volume(v0)
+        # derivative of bulk modulus w.r.t. pressure(b1), minimum volume(v0)
         self._params = None
         # the eos function parameters. It is the same as _params except for
         # equation of states that uses polynomial fits(delta_factor and
@@ -147,7 +147,7 @@ def b0_GPa(self):
 
     @property
     def b1(self):
-        """Returns the derivative of bulk modulus wrt pressure(dimensionless)."""
+        """Returns the derivative of bulk modulus w.r.t. pressure(dimensionless)."""
         return self._params[2]
 
     @property
@@ -191,7 +191,7 @@ def plot(self, width=8, height=None, ax: plt.Axes = None, dpi=None, **kwargs):
             f"Minimum energy = {self.e0:1.2f} eV",
             f"Minimum or reference volume = {self.v0:1.2f} Ang^3",
             f"Bulk modulus = {self.b0:1.2f} eV/Ang^3 = {self.b0_GPa:1.2f} GPa",
-            f"Derivative of bulk modulus wrt pressure = {self.b1:1.2f}",
+            f"Derivative of bulk modulus w.r.t. pressure = {self.b1:1.2f}",
         ]
         text = "\n".join(lines)
         text = kwargs.get("text", text)
@@ -239,7 +239,7 @@ def plot_ax(self, ax: plt.Axes = None, fontsize=12, **kwargs):
             f"Minimum energy = {self.e0:1.2f} eV",
             f"Minimum or reference volume = {self.v0:1.2f} Ang^3",
             f"Bulk modulus = {self.b0:1.2f} eV/Ang^3 = {self.b0_GPa:1.2f} GPa",
-            f"Derivative of bulk modulus wrt pressure = {self.b1:1.2f}",
+            f"Derivative of bulk modulus w.r.t. pressure = {self.b1:1.2f}",
         ]
         text = "\n".join(lines)
         text = kwargs.get("text", text)
@@ -357,8 +357,7 @@ def _set_params(self):
         and set to the _params attribute.
         """
         fit_poly = np.poly1d(self.eos_params)
-        # the volume at min energy, used as the initial guess for the
-        # optimization wrt volume.
+        # the volume at min energy, used as the initial guess for the optimization w.r.t. volume.
         v_e_min = self.volumes[np.argmin(self.energies)]
         # evaluate e0, v0, b0 and b1
         min_wrt_v = minimize(fit_poly, v_e_min)

pymatgen/analysis/phase_diagram.py

@@ -2374,7 +2374,7 @@ class (pymatgen.analysis.chempot_diagram).
         Args:
             elements: Sequence of elements to be considered as independent
                 variables. E.g., if you want to show the stability ranges of
-                all Li-Co-O phases wrt to uLi and uO, you will supply
+                all Li-Co-O phases w.r.t. to uLi and uO, you will supply
                 [Element("Li"), Element("O")]
             referenced: if True, gives the results with a reference being the
                         energy of the elemental phase. If False, gives absolute values.
@@ -2392,7 +2392,7 @@ class (pymatgen.analysis.chempot_diagram).
         Args:
             elements: Sequence of elements to be considered as independent
                 variables. E.g., if you want to show the stability ranges of
-                all Li-Co-O phases wrt to uLi and uO, you will supply
+                all Li-Co-O phases w.r.t. to uLi and uO, you will supply
                 [Element("Li"), Element("O")]
             referenced: if True, gives the results with a reference being the
                 energy of the elemental phase. If False, gives absolute values.

pymatgen/analysis/quasiharmonic.py

@@ -121,7 +121,7 @@ def __init__(
     def optimize_gibbs_free_energy(self):
         """
         Evaluate the Gibbs free energy as a function of V, T and P i.e
-        G(V, T, P), minimize G(V, T, P) wrt V for each T and store the
+        G(V, T, P), minimize G(V, T, P) w.r.t. V for each T and store the
         optimum values.
 
         Note: The data points for which the equation of state fitting fails
@@ -146,8 +146,7 @@ def optimize_gibbs_free_energy(self):
 
     def optimizer(self, temperature):
         """
-        Evaluate G(V, T, P) at the given temperature(and pressure) and
-        minimize it wrt V.
+        Evaluate G(V, T, P) at the given temperature(and pressure) and minimize it w.r.t. V.
 
         1. Compute the  vibrational Helmholtz free energy, A_vib.
         2. Compute the Gibbs free energy as a function of volume, temperature
@@ -171,7 +170,7 @@ def optimizer(self, temperature):
 
         # fit equation of state, G(V, T, P)
         eos_fit = self.eos.fit(self.volumes, G_V)
-        # minimize the fit eos wrt volume
+        # minimize the fit EoS w.r.t. volume
         # Note: the ref energy and the ref volume(E0 and V0) not necessarily
         # the same as minimum energy and min volume.
         volume_guess = eos_fit.volumes[np.argmin(eos_fit.energies)]
@@ -288,13 +287,13 @@ def gruneisen_parameter(self, temperature, volume):
         """
         if isinstance(self.eos, PolynomialEOS):
             p = np.poly1d(self.eos.eos_params)
-            # first derivative of energy at 0K wrt volume evaluated at the
+            # first derivative of energy at 0K w.r.t. volume evaluated at the
             # given volume, in eV/Ang^3
             dEdV = np.polyder(p, 1)(volume)
-            # second derivative of energy at 0K wrt volume evaluated at the
+            # second derivative of energy at 0K w.r.t. volume evaluated at the
             # given volume, in eV/Ang^6
             d2EdV2 = np.polyder(p, 2)(volume)
-            # third derivative of energy at 0K wrt volume evaluated at the
+            # third derivative of energy at 0K w.r.t. volume evaluated at the
             # given volume, in eV/Ang^9
             d3EdV3 = np.polyder(p, 3)(volume)
         else:
@@ -314,7 +313,7 @@ def gruneisen_parameter(self, temperature, volume):
             )
 
         # Slater-gamma formulation
-        # first derivative of bulk modulus wrt volume, eV/Ang^6
+        # first derivative of bulk modulus w.r.t. volume, eV/Ang^6
         dBdV = d2EdV2 + d3EdV3 * volume
         return -(1.0 / 6.0 + 0.5 * volume * dBdV / FloatWithUnit(self.ev_eos_fit.b0_GPa, "GPa").to("eV ang^-3"))
 

pymatgen/analysis/surface_analysis.py

@@ -858,7 +858,7 @@ def chempot_vs_gamma_plot_one(
         chempot_range = sorted(chempot_range)
 
         # use dashed lines for slabs that are not stoichiometric
-        # wrt bulk. Label with formula if non-stoichiometric
+        # 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
@@ -1134,7 +1134,7 @@ def surface_chempot_range_map(
         delu_dict=None,
         ax=None,
         annotate=True,
-        show_unphyiscal_only=False,
+        show_unphysical_only=False,
         fontsize=10,
     ) -> plt.Axes:
         """
@@ -1143,14 +1143,14 @@ def surface_chempot_range_map(
             energy stability. Currently works only for 2-component PDs. At
             the moment uses a brute force method by enumerating through the
             range of the first element chempot with a specified increment
-            and determines the chempot rangeo fht e second element for each
+            and determines the chempot range of the second element for each
             SlabEntry. Future implementation will determine the chempot range
             map first by solving systems of equations up to 3 instead of 2.
 
         Args:
             elements (list): Sequence of elements to be considered as independent
                 variables. E.g., if you want to show the stability ranges of
-                all Li-Co-O phases wrt to duLi and duO, you will supply
+                all Li-Co-O phases w.r.t. to duLi and duO, you will supply
                 [Element("Li"), Element("O")]
             miller_index ([h, k, l]): Miller index of the surface we are interested in
             ranges ([[range1], [range2]]): List of chempot ranges (max and min values)
@@ -1164,7 +1164,7 @@ def surface_chempot_range_map(
             ax (plt.Axes): Axes object to plot on. If None, will create a new plot.
             annotate (bool): Whether to annotate each "phase" with the label of
                 the entry. If no label, uses the reduced formula
-            show_unphyiscal_only (bool): Whether to only show the shaded region where
+            show_unphysical_only (bool): Whether to only show the shaded region where
                 surface energy is negative. Useful for drawing other chempot range maps.
             fontsize (int): Font size of the annotation
         """
@@ -1219,15 +1219,15 @@ def surface_chempot_range_map(
                         neg_dmu_range = [pt1[delu2][0][1], pt1[delu2][0][2]]
                     # Shade the threshold and region at which se<=0
                     ax.plot([pt1[delu1], pt1[delu1]], neg_dmu_range, "k--")
-                elif pt1[delu2][1][0] < 0 and pt1[delu2][1][1] < 0 and not show_unphyiscal_only:
+                elif pt1[delu2][1][0] < 0 and pt1[delu2][1][1] < 0 and not show_unphysical_only:
                     # Any chempot at this point will result
                     # in se<0, shade the entire y range
                     ax.plot([pt1[delu1], pt1[delu1]], range2, "k--")
 
                 if ii == len(vertex) - 1:
                     break
                 pt2 = vertex[ii + 1]
-                if not show_unphyiscal_only:
+                if not show_unphysical_only:
                     ax.plot(
                         [pt1[delu1], pt2[delu1]],
                         [pt1[delu2][0][0], pt2[delu2][0][0]],
@@ -1243,7 +1243,7 @@ def surface_chempot_range_map(
             delu1, delu2 = pt
             xvals.extend([pt[delu1], pt[delu1]])
             yvals.extend(pt[delu2][0])
-            if not show_unphyiscal_only:
+            if not show_unphysical_only:
                 ax.plot([pt[delu1], pt[delu1]], [pt[delu2][0][0], pt[delu2][0][-1]], "k")
 
             if annotate:
@@ -1595,7 +1595,7 @@ class NanoscaleStability:
     polymorphs with respect to size. The Wulff shape will be the model for the
     nanoparticle. Stability will be determined by an energetic competition between the
     weighted surface energy (surface energy of the Wulff shape) and the bulk energy. A
-    future release will include a 2D phase diagram (e.g. wrt size vs chempot for adsorbed
+    future release will include a 2D phase diagram (e.g. w.r.t. size vs chempot for adsorbed
     or non-stoichiometric surfaces). Based on the following work:
 
     Kang, S., Mo, Y., Ong, S. P., & Ceder, G. (2014). Nanoscale

pymatgen/command_line/gulp_caller.py

@@ -176,7 +176,7 @@
     "spatial",
     "storevectors",
     "nomolecularinternalke",
-    "voight",
+    "voigt",
     "zsisa",
     # Optimization method
     "conjugate",

pymatgen/core/surface.py

@@ -1125,23 +1125,23 @@ def repair_broken_bonds(self, slab, bonds):
             (Slab) A Slab object with a particular shifted oriented unit cell.
         """
         for pair in bonds:
-            blength = bonds[pair]
+            bond_len = bonds[pair]
 
             # First lets determine which element should be the
             # reference (center element) to determine broken bonds.
             # e.g. P for a PO4 bond. Find integer coordination
-            # numbers of the pair of elements wrt to each other
+            # numbers of the pair of elements w.r.t. to each other
             cn_dict = {}
             for i, el in enumerate(pair):
-                cnlist = []
+                cn_list = []
                 for site in self.oriented_unit_cell:
                     poly_coord = 0
                     if site.species_string == el:
-                        for nn in self.oriented_unit_cell.get_neighbors(site, blength):
+                        for nn in self.oriented_unit_cell.get_neighbors(site, bond_len):
                             if nn[0].species_string == pair[i - 1]:
                                 poly_coord += 1
-                    cnlist.append(poly_coord)
-                cn_dict[el] = cnlist
+                    cn_list.append(poly_coord)
+                cn_dict[el] = cn_list
 
             # We make the element with the higher coordination our reference
             if max(cn_dict[pair[0]]) > max(cn_dict[pair[1]]):
@@ -1153,7 +1153,7 @@ def repair_broken_bonds(self, slab, bonds):
                 # Determine the coordination of our reference
                 if site.species_string == element1:
                     poly_coord = 0
-                    for neighbor in slab.get_neighbors(site, blength):
+                    for neighbor in slab.get_neighbors(site, bond_len):
                         poly_coord += 1 if neighbor.species_string == element2 else 0
 
                     # suppose we find an undercoordinated reference atom
@@ -1164,7 +1164,7 @@ def repair_broken_bonds(self, slab, bonds):
 
                         # find its NNs with the corresponding
                         # species it should be coordinated with
-                        neighbors = slab.get_neighbors(slab[i], blength, include_index=True)
+                        neighbors = slab.get_neighbors(slab[i], bond_len, include_index=True)
                         to_move = [nn[2] for nn in neighbors if nn[0].species_string == element2]
                         to_move.append(i)
                         # and then move those NNs along with the central
@@ -1237,7 +1237,7 @@ def nonstoichiometric_symmetrized_slab(self, init_slab):
         if init_slab.is_symmetric():
             return [init_slab]
 
-        nonstoich_slabs = []
+        non_stoich_slabs = []
         # Build an equivalent surface slab for each of the different surfaces
         for top in [True, False]:
             asym = True
@@ -1261,12 +1261,12 @@ def nonstoichiometric_symmetrized_slab(self, init_slab):
                 # Check if the altered surface is symmetric
                 if slab.is_symmetric():
                     asym = False
-                    nonstoich_slabs.append(slab)
+                    non_stoich_slabs.append(slab)
 
         if len(slab) <= len(self.parent):
             warnings.warn("Too many sites removed, please use a larger slab size.")
 
-        return nonstoich_slabs
+        return non_stoich_slabs
 
 
 module_dir = os.path.dirname(os.path.abspath(__file__))

pymatgen/core/tensors.py

@@ -271,7 +271,7 @@ def symmetrized(self):
     @property
     def voigt_symmetrized(self):
         """Returns a "voigt"-symmetrized tensor, i. e. a voigt-notation
-        tensor such that it is invariant wrt permutation of indices.
+        tensor such that it is invariant w.r.t. permutation of indices.
         """
         if not (self.rank % 2 == 0 and self.rank >= 2):
             raise ValueError("V-symmetrization requires rank even and >= 2")
@@ -567,7 +567,7 @@ def populate(
 
         Args:
             structure (Structure): structure to base population on
-            prec (float): precision for determining a non-zero value
+            prec (float): precision for determining a non-zero value. Defaults to 1e-5.
             maxiter (int): maximum iterations for populating the tensor
             verbose (bool): whether to populate verbosely
             precond (bool): whether to precondition by cycling through
@@ -747,7 +747,7 @@ def is_fit_to_structure(self, structure: Structure, tol: float = 1e-2):
 
     @property
     def voigt(self):
-        """TensorCollection where all tensors are in voight form."""
+        """TensorCollection where all tensors are in Voigt form."""
         return [t.voigt for t in self]
 
     @property
@@ -804,7 +804,7 @@ def voigt_symmetrized(self):
         return self.__class__([t.voigt_symmetrized for t in self])
 
     def as_dict(self, voigt=False):
-        """:param voigt: Whether to use voight form.
+        """:param voigt: Whether to use Voigt form.
 
         Returns:
             Dict representation of TensorCollection.

pymatgen/io/abinit/abiobjects.py

@@ -65,7 +65,7 @@ def lattice_from_abivars(cls=None, *args, **kwargs):
             and abs(ang_deg[0] - 90.0) + abs(ang_deg[1] - 90.0) + abs(ang_deg[2] - 90) > tol12
         ):
             # Treat the case of equal angles (except all right angles):
-            # generates trigonal symmetry wrt third axis
+            # generates trigonal symmetry w.r.t. third axis
             cos_ang = cos(pi * ang_deg[0] / 180.0)
             a2 = 2.0 / 3.0 * (1.0 - cos_ang)
             aa = sqrt(a2)