fix doc str indentation

pymatgen/analysis/chemenv/connectivity/connected_components.py

@@ -574,32 +574,28 @@ def is_0d(self) -> bool:
         """Whether this connected component is 0-dimensional."""
         if self._periodicity_vectors is None:
             self.compute_periodicity()
-        assert self._periodicity_vectors is not None  # fix mypy arg 1 to len has incompatible type Optional
-        return len(self._periodicity_vectors) == 0
+        return len(self._periodicity_vectors) == 0  # type: ignore[arg-type]
 
     @property
     def is_1d(self) -> bool:
         """Whether this connected component is 1-dimensional."""
         if self._periodicity_vectors is None:
             self.compute_periodicity()
-        assert self._periodicity_vectors is not None  # fix mypy arg 1 to len has incompatible type Optional
-        return len(self._periodicity_vectors) == 1
+        return len(self._periodicity_vectors) == 1  # type: ignore[arg-type]
 
     @property
     def is_2d(self) -> bool:
         """Whether this connected component is 2-dimensional."""
         if self._periodicity_vectors is None:
             self.compute_periodicity()
-        assert self._periodicity_vectors is not None  # fix mypy arg 1 to len has incompatible type Optional
-        return len(self._periodicity_vectors) == 2
+        return len(self._periodicity_vectors) == 2  # type: ignore[arg-type]
 
     @property
     def is_3d(self) -> bool:
         """Whether this connected component is 3-dimensional."""
         if self._periodicity_vectors is None:
             self.compute_periodicity()
-        assert self._periodicity_vectors is not None  # fix mypy arg 1 to len has incompatible type Optional
-        return len(self._periodicity_vectors) == 3
+        return len(self._periodicity_vectors) == 3  # type: ignore[arg-type]
 
     @staticmethod
     def _order_vectors(vectors):
@@ -654,10 +650,10 @@ def elastic_centered_graph(self, start_node=None):
         logging.info("In elastic centering")
         # Loop on start_nodes, sometimes some nodes cannot be elastically taken
         # inside the cell if you start from a specific node
-        ntest_nodes = 0
+        n_test_nodes = 0
         start_node = next(iter(self.graph.nodes()))
 
-        ntest_nodes += 1
+        n_test_nodes += 1
         centered_connected_subgraph = nx.MultiGraph()
         centered_connected_subgraph.add_nodes_from(self.graph.nodes())
         centered_connected_subgraph.add_edges_from(self.graph.edges(data=True))
@@ -782,12 +778,21 @@ def _retuplify_edgedata(edata):
         """
         Private method used to cast back lists to tuples where applicable in an edge data.
 
-        The format of the edge data is :
-        {'start': STARTINDEX, 'end': ENDINDEX, 'delta': TUPLE(DELTAX, DELTAY, DELTAZ),
-         'ligands': [TUPLE(LIGAND_1_INDEX, TUPLE(DELTAX_START_LIG_1, DELTAY_START_LIG_1, DELTAZ_START_LIG_1),
-                                           TUPLE(DELTAX_END_LIG_1, DELTAY_END_LIG_1, DELTAZ_END_LIG_1)),
-                     TUPLE(LIGAND_2_INDEX, ...),
-                     ... ]}
+        The format of the edge data is:
+        {
+            "start": STARTINDEX,
+            "end": ENDINDEX,
+            "delta": TUPLE(DELTAX, DELTAY, DELTAZ),
+            "ligands": [
+                TUPLE(
+                    LIGAND_1_INDEX,
+                    TUPLE(DELTAX_START_LIG_1, DELTAY_START_LIG_1, DELTAZ_START_LIG_1),
+                    TUPLE(DELTAX_END_LIG_1, DELTAY_END_LIG_1, DELTAZ_END_LIG_1),
+                ),
+                TUPLE(LIGAND_2_INDEX, ...),
+                ...,
+            ],
+        }
         When serializing to json/bson, these tuples are transformed into lists. This method transforms these lists
         back to tuples.
 

pymatgen/analysis/chemenv/coordination_environments/coordination_geometry_finder.py

@@ -1584,9 +1584,9 @@ def coordination_geometry_symmetry_measures_separation_plane_optim(
 
         Returns:
             tuple: Continuous symmetry measures for the given coordination geometry for each plane and permutation
-                   investigated, corresponding permutations, corresponding algorithms,
-                   corresponding mappings from local to perfect environment and corresponding mappings
-                   from perfect to local environment.
+                investigated, corresponding permutations, corresponding algorithms,
+                corresponding mappings from local to perfect environment and corresponding mappings
+                from perfect to local environment.
         """
         if optimization == 2:
             logging.log(level=5, msg="... using optimization = 2")

pymatgen/electronic_structure/plotter.py

@@ -3298,7 +3298,7 @@ def plot_seebeck_dop(self, temps="all", output="average"):
 
         Args:
             temps: the default 'all' plots all the temperatures in the analyzer.
-                   Specify a list of temperatures if you want to plot only some.
+                Specify a list of temperatures if you want to plot only some.
             output: with 'average' you get an average of the three directions
                 with 'eigs' you get all the three directions.
 
@@ -3343,7 +3343,7 @@ def plot_conductivity_dop(self, temps="all", output="average", relaxation_time=1
 
         Args:
             temps: the default 'all' plots all the temperatures in the analyzer.
-                   Specify a list of temperatures if you want to plot only some.
+                Specify a list of temperatures if you want to plot only some.
             output: with 'average' you get an average of the three directions
                 with 'eigs' you get all the three directions.
             relaxation_time: specify a constant relaxation time value
@@ -3392,7 +3392,7 @@ def plot_power_factor_dop(self, temps="all", output="average", relaxation_time=1
 
         Args:
             temps: the default 'all' plots all the temperatures in the analyzer.
-                   Specify a list of temperatures if you want to plot only some.
+                Specify a list of temperatures if you want to plot only some.
             output: with 'average' you get an average of the three directions
                 with 'eigs' you get all the three directions.
             relaxation_time: specify a constant relaxation time value
@@ -3438,7 +3438,7 @@ def plot_zt_dop(self, temps="all", output="average", relaxation_time=1e-14):
 
         Args:
             temps: the default 'all' plots all the temperatures in the analyzer.
-                   Specify a list of temperatures if you want to plot only some.
+                Specify a list of temperatures if you want to plot only some.
             output: with 'average' you get an average of the three directions
                 with 'eigs' you get all the three directions.
             relaxation_time: specify a constant relaxation time value
@@ -3489,7 +3489,7 @@ def plot_eff_mass_dop(self, temps="all", output="average"):
 
         Args:
             temps: the default 'all' plots all the temperatures in the analyzer.
-                   Specify a list of temperatures if you want to plot only some.
+                Specify a list of temperatures if you want to plot only some.
             output: with 'average' you get an average of the three directions
                 with 'eigs' you get all the three directions.
             relaxation_time: specify a constant relaxation time value

pymatgen/io/abinit/abiobjects.py

@@ -105,9 +105,7 @@ def structure_from_abivars(cls=None, *args, **kwargs):
     Example:
         al_structure = structure_from_abivars(
             acell=3*[7.5],
-            rprim=[0.0, 0.5, 0.5,
-                   0.5, 0.0, 0.5,
-                   0.5, 0.5, 0.0],
+            rprim=[0.0, 0.5, 0.5, 0.5, 0.0, 0.5, 0.5, 0.5, 0.0],
             typat=1,
             xred=[0.0, 0.0, 0.0],
             ntypat=1,

pymatgen/io/abinit/inputs.py

@@ -247,7 +247,7 @@ def _get_shifts(shift_mode, structure):
             return shifts
         return ((0, 0, 0),)
 
-    raise ValueError(f"invalid {shift_mode=}")
+    raise ValueError(f"Invalid {shift_mode=}")
 
 
 def gs_input(
@@ -272,9 +272,9 @@ def gs_input(
         kppa: Defines the sampling used for the SCF run. Defaults to 1000 if not given.
         ecut: cutoff energy in Ha (if None, ecut is initialized from the pseudos according to accuracy)
         pawecutdg: cutoff energy in Ha for PAW double-grid (if None, pawecutdg is initialized from the pseudos
-                   according to accuracy)
+            according to accuracy)
         scf_nband: Number of bands for SCF run. If scf_nband is None, nband is automatically initialized
-                   from the list of pseudos, the structure and the smearing option.
+            from the list of pseudos, the structure and the smearing option.
         accuracy: Accuracy of the calculation.
         spin_mode: Spin polarization.
         smearing: Smearing technique.

pymatgen/io/vasp/outputs.py

@@ -4138,12 +4138,12 @@ def get_adjusted_fermi_level(efermi, cbm, band_structure):
     # make a working copy of band_structure
     bs_working = BandStructureSymmLine.from_dict(band_structure.as_dict())
     if bs_working.is_metal():
-        e = efermi
-        while e < cbm:
-            e += 0.01
-            bs_working._efermi = e
+        energy = efermi
+        while energy < cbm:
+            energy += 0.01
+            bs_working._efermi = energy
             if not bs_working.is_metal():
-                return e
+                return energy
     return efermi
 
 
@@ -4249,7 +4249,7 @@ def __init__(self, filename="WAVECAR", verbose=False, precision="normal", vasp_t
                 # and occupations in way that does not span FORTRAN records, but
                 # reader below appears to assume that record boundaries can be ignored
                 # (see OUTWAV vs. OUTWAV_4 in vasp fileio.F)
-                raise ValueError(f"invalid {rtag=}, must be one of {valid_rtags}")
+                raise ValueError(f"Invalid {rtag=}, must be one of {valid_rtags}")
 
             # padding to end of fortran REC=1
             np.fromfile(f, dtype=np.float64, count=recl8 - 3)