np.newaxis -> None

json->JSON, yaml->YAML in doc strings

.pre-commit-config.yaml

@@ -8,7 +8,7 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.4.7
+    rev: v0.4.8
     hooks:
       - id: ruff
         args: [--fix, --unsafe-fixes]
@@ -64,6 +64,6 @@ repos:
         args: [--drop-empty-cells, --keep-output]
 
   - repo: https://github.com/RobertCraigie/pyright-python
-    rev: v1.1.365
+    rev: v1.1.366
     hooks:
       - id: pyright

dev_scripts/regen_libxcfunc.py

@@ -2,7 +2,7 @@
 """
 This script regenerates the enum values in pymatgen.core.libxc_func.py.
 It requires in input the path of the `libxc_docs.txt` file contained in libxc/src
-The script parses this file, creates a new json file inside pymatgen.core
+The script parses this file, creates a new JSON file inside pymatgen.core
 and update the enum values declared in LibxcFunc.
 The script must be executed inside pymatgen/dev_scripts.
 """
@@ -16,10 +16,7 @@
 
 
 def parse_libxc_docs(path):
-    """
-    Parse libxc_docs.txt file, return dictionary with mapping:
-    libxc_id --> info_dict.
-    """
+    """Parse libxc_docs.txt file, return dictionary {libxc_id: info_dict}."""
 
     def parse_section(section):
         dct = {}
@@ -46,7 +43,7 @@ def parse_section(section):
 
 
 def write_libxc_docs_json(xc_funcs, json_path):
-    """Write json file with libxc metadata to path jpath."""
+    """Write JSON file with libxc metadata to path jpath."""
     xc_funcs = deepcopy(xc_funcs)
 
     # Remove XC_FAMILY from Family and XC_ from Kind to make strings more human-readable.
@@ -85,7 +82,7 @@ def main():
 
     xc_funcs = parse_libxc_docs(path)
 
-    # Generate new json file in pycore
+    # Generate new JSON file in pycore
     pmg_core = os.path.abspath("../pymatgen/core/")
     json_path = f"{pmg_core}/libxc_docs.json"
     write_libxc_docs_json(xc_funcs, json_path)

pymatgen/analysis/bond_valence.py

@@ -29,7 +29,7 @@
 for key, val in loadfn(f"{module_dir}/bvparam_1991.yaml").items():
     BV_PARAMS[Element(key)] = val
 
-# Read in yaml containing data-mined ICSD BV data.
+# Read in YAML containing data-mined ICSD BV data.
 all_data = loadfn(f"{module_dir}/icsd_bv.yaml")
 ICSD_BV_DATA = {Species.from_str(sp): data for sp, data in all_data["bvsum"].items()}
 PRIOR_PROB = {Species.from_str(sp): data for sp, data in all_data["occurrence"].items()}

pymatgen/analysis/dimensionality.py

@@ -403,9 +403,9 @@ def find_connected_atoms(struct, tolerance=0.45, ldict=None):
 
     n_atoms = len(struct.species)
     fc = np.array(struct.frac_coords)
-    fc_copy = np.repeat(fc[:, :, np.newaxis], 27, axis=2)
+    fc_copy = np.repeat(fc[:, :, None], 27, axis=2)
     neighbors = np.array(list(itertools.product([0, 1, -1], [0, 1, -1], [0, 1, -1]))).T
-    neighbors = np.repeat(neighbors[np.newaxis, :, :], 1, axis=0)
+    neighbors = np.repeat(neighbors[None, :, :], 1, axis=0)
     fc_diff = fc_copy - neighbors
     species = list(map(str, struct.species))
     # in case of charged species

pymatgen/analysis/ferroelectricity/polarization.py

@@ -146,7 +146,9 @@ class Polarization:
     electron Angstroms along the three lattice directions (a,b,c).
     """
 
-    def __init__(self, p_elecs, p_ions, structures, p_elecs_in_cartesian=True, p_ions_in_cartesian=False):
+    def __init__(
+        self, p_elecs, p_ions, structures: Sequence[Structure], p_elecs_in_cartesian=True, p_ions_in_cartesian=False
+    ):
         """
         p_elecs: np.array of electronic contribution to the polarization with shape [N, 3]
         p_ions: np.array of ionic contribution to the polarization with shape [N, 3]
@@ -260,8 +262,8 @@ def get_same_branch_polarization_data(self, convert_to_muC_per_cm2=True, all_in_
         p_tot = p_elec + p_ion
         p_tot = np.array(p_tot)
 
-        lattices = [s.lattice for s in self.structures]
-        volumes = np.array([s.lattice.volume for s in self.structures])
+        lattices = [struct.lattice for struct in self.structures]
+        volumes = np.array([latt.volume for latt in lattices])
 
         n_elecs = len(p_elec)
 
@@ -273,7 +275,7 @@ def get_same_branch_polarization_data(self, convert_to_muC_per_cm2=True, all_in_
         # convert polarizations and lattice lengths prior to adjustment
         if convert_to_muC_per_cm2 and not all_in_polar:
             # Convert the total polarization
-            p_tot = np.multiply(units.T[:, np.newaxis], p_tot)
+            p_tot = np.multiply(units.T[:, None], p_tot)
             # adjust lattices
             for idx in range(n_elecs):
                 lattice = lattices[idx]
@@ -316,7 +318,7 @@ def get_lattice_quanta(self, convert_to_muC_per_cm2=True, all_in_polar=True):
         """Get the dipole / polarization quanta along a, b, and c for
         all structures.
         """
-        lattices = [s.lattice for s in self.structures]
+        lattices = [struct.lattice for struct in self.structures]
         volumes = np.array([struct.volume for struct in self.structures])
 
         n_structs = len(self.structures)

pymatgen/analysis/graphs.py

@@ -150,7 +150,7 @@ def __init__(self, structure: Structure, graph_data: dict | None = None) -> None
         self.structure = structure
         self.graph = nx.readwrite.json_graph.adjacency_graph(graph_data)
 
-        # tidy up edge attr dicts, reading to/from json duplicates information
+        # tidy up edge attr dicts, reading to/from JSON duplicates information
         for _, _, _, data in self.graph.edges(keys=True, data=True):
             for key in ("id", "key"):
                 data.pop(key, None)
@@ -1585,7 +1585,7 @@ def __init__(self, molecule, graph_data=None):
         self.molecule = molecule
         self.graph = nx.readwrite.json_graph.adjacency_graph(graph_data)
 
-        # tidy up edge attr dicts, reading to/from json duplicates
+        # tidy up edge attr dicts, reading to/from JSON duplicates
         # information
         for _, _, _, data in self.graph.edges(keys=True, data=True):
             for key in ("id", "key"):

pymatgen/analysis/magnetism/heisenberg.py

@@ -555,18 +555,18 @@ def get_interaction_graph(self, filename=None):
             logging.warning(warning_msg)
 
         # J_ij exchange interaction matrix
-        for i, _node in enumerate(sgraph.graph.nodes):
-            connections = sgraph.get_connected_sites(i)
+        for idx in range(len(sgraph.graph.nodes)):
+            connections = sgraph.get_connected_sites(idx)
             for c in connections:
                 jimage = c[1]  # relative integer coordinates of atom j
                 j = c[2]  # index of neighbor
                 dist = c[-1]  # i <-> j distance
 
-                j_exc = self._get_j_exc(i, j, dist)
+                j_exc = self._get_j_exc(idx, j, dist)
 
-                igraph.add_edge(i, j, to_jimage=jimage, weight=j_exc, warn_duplicates=False)
+                igraph.add_edge(idx, j, to_jimage=jimage, weight=j_exc, warn_duplicates=False)
 
-        # Save to a json file if desired
+        # Save to a JSON file if desired
         if filename:
             if not filename.endswith(".json"):
                 filename += ".json"
@@ -641,7 +641,7 @@ def get_heisenberg_model(self):
         hm_nni = self.nn_interactions
         hm_d = self.dists
 
-        # Exchange matrix DataFrame in json format
+        # Exchange matrix DataFrame in JSON format
         hm_em = self.ex_mat.to_json()
         hm_ep = self.get_exchange()
         hm_javg = self.estimate_exchange()
@@ -860,29 +860,27 @@ def __init__(
         self.igraph = igraph
 
     def as_dict(self):
-        """Because some dicts have tuple keys, some sanitization is required for json compatibility."""
-        dct = {}
-        dct["@module"] = type(self).__module__
-        dct["@class"] = type(self).__name__
-        dct["@version"] = __version__
-        dct["formula"] = self.formula
-        dct["structures"] = [s.as_dict() for s in self.structures]
-        dct["energies"] = self.energies
-        dct["cutoff"] = self.cutoff
-        dct["tol"] = self.tol
-        dct["sgraphs"] = [sgraph.as_dict() for sgraph in self.sgraphs]
-        dct["dists"] = self.dists
-        dct["ex_params"] = self.ex_params
-        dct["javg"] = self.javg
-        dct["igraph"] = self.igraph.as_dict()
-
-        # Sanitize tuple & int keys
-        dct["ex_mat"] = jsanitize(self.ex_mat)
-        dct["nn_interactions"] = jsanitize(self.nn_interactions)
-        dct["unique_site_ids"] = jsanitize(self.unique_site_ids)
-        dct["wyckoff_ids"] = jsanitize(self.wyckoff_ids)
-
-        return dct
+        """Because some dicts have tuple keys, some sanitization is required for JSON compatibility."""
+        return {
+            "@module": type(self).__module__,
+            "@class": type(self).__name__,
+            "@version": __version__,
+            "formula": self.formula,
+            "structures": [struct.as_dict() for struct in self.structures],
+            "energies": self.energies,
+            "cutoff": self.cutoff,
+            "tol": self.tol,
+            "sgraphs": [sgraph.as_dict() for sgraph in self.sgraphs],
+            "dists": self.dists,
+            "ex_params": self.ex_params,
+            "javg": self.javg,
+            "igraph": self.igraph.as_dict(),
+            # Sanitize tuple & int keys
+            "ex_mat": jsanitize(self.ex_mat),
+            "nn_interactions": jsanitize(self.nn_interactions),
+            "unique_site_ids": jsanitize(self.unique_site_ids),
+            "wyckoff_ids": jsanitize(self.wyckoff_ids),
+        }
 
     @classmethod
     def from_dict(cls, dct: dict) -> Self:

pymatgen/analysis/structure_prediction/substitution_probability.py

@@ -50,11 +50,9 @@ class SubstitutionProbability:
     def __init__(self, lambda_table=None, alpha=-5):
         """
         Args:
-            lambda_table:
-                json table of the weight functions lambda if None,
+            lambda_table: JSON table of the weight functions lambda if None,
                 will use the default lambda.json table
-            alpha:
-                weight function for never observed substitutions.
+            alpha (float): weight function for never observed substitutions.
         """
         if lambda_table is not None:
             self._lambda_table = lambda_table
@@ -94,8 +92,8 @@ def get_lambda(self, s1, s2):
         Returns:
             Lambda values
         """
-        k = frozenset([get_el_sp(s1), get_el_sp(s2)])
-        return self._l.get(k, self.alpha)
+        key = frozenset([get_el_sp(s1), get_el_sp(s2)])
+        return self._l.get(key, self.alpha)
 
     def get_px(self, sp):
         """

pymatgen/analysis/transition_state.py

@@ -274,7 +274,7 @@ def as_dict(self):
             "r": jsanitize(self.r),
             "energies": jsanitize(self.energies),
             "forces": jsanitize(self.forces),
-            "structures": [s.as_dict() for s in self.structures],
+            "structures": [struct.as_dict() for struct in self.structures],
         }
 
 

pymatgen/cli/pmg.py

@@ -131,7 +131,7 @@ def main():
         metavar="dir_name",
         nargs=2,
         help="Initial directory where the CP2K data is located and the output directory where the "
-        "CP2K yaml data files will be written",
+        "CP2K YAML data files will be written",
     )
 
     parser_config.add_argument(

pymatgen/command_line/critic2_caller.py

@@ -180,7 +180,7 @@ def from_chgcar(
                 critical points to a file 'table.cml' in the working directory
                 useful for visualization
             write_json (bool): Whether to write out critical points
-                and YT json. YT integration will be performed with this setting.
+                and YT JSON. YT integration will be performed with this setting.
             zpsp (dict): Dict of element/symbol name to number of electrons
                 (ZVAL in VASP pseudopotential), with which to properly augment core regions
                 and calculate charge transfer. Optional.
@@ -453,8 +453,8 @@ class with bonding information. By default, this returns
             structure: associated Structure
             stdout: stdout from running critic2 in automatic mode
             stderr: stderr from running critic2 in automatic mode
-            cpreport: json output from CPREPORT command
-            yt: json output from YT command
+            cpreport: JSON output from CPREPORT command
+            yt: JSON output from YT command
             zpsp (dict): Dict of element/symbol name to number of electrons
                 (ZVAL in VASP pseudopotential), with which to calculate charge transfer.
                 Optional.

pymatgen/command_line/vampire_caller.py

@@ -400,7 +400,7 @@ class VampireOutput(MSONable):
     def __init__(self, parsed_out=None, nmats=None, critical_temp=None):
         """
         Args:
-            parsed_out (json): json rep of parsed stdout DataFrame.
+            parsed_out (str): JSON rep of parsed stdout DataFrame.
             nmats (int): Number of distinct materials (1 for each specie and up/down spin).
             critical_temp (float): Monte Carlo Tc result.
         """

pymatgen/core/__init__.py

@@ -50,7 +50,7 @@ def _load_pmg_settings() -> dict[str, Any]:
         except Exception as exc:
             # If there are any errors, default to using environment variables
             # if present.
-            warnings.warn(f"Error loading {file_path}: {exc}.\nYou may need to reconfigure your yaml file.")
+            warnings.warn(f"Error loading {file_path}: {exc}.\nYou may need to reconfigure your YAML file.")
 
     # Override .pmgrc.yaml with env vars (if present)
     for key, val in os.environ.items():

pymatgen/core/libxcfunc.py

@@ -26,7 +26,7 @@
 __status__ = "Production"
 __date__ = "May 16, 2016"
 
-# Load libxc info from json file
+# Load libxc info from JSON file
 with open(os.path.join(os.path.dirname(__file__), "libxc_docs.json"), encoding="utf-8") as file:
     _all_xcfuncs = {int(k): v for k, v in json.load(file).items()}
 
@@ -497,5 +497,5 @@ def from_dict(cls, dct: dict) -> Self:
         return cls[dct["name"]]
 
     def to_json(self) -> str:
-        """Get a json string representation of the LibxcFunc."""
+        """Get a JSON string representation of the LibxcFunc."""
         return json.dumps(self.as_dict(), cls=MontyEncoder)

pymatgen/core/periodic_table.py

@@ -29,7 +29,7 @@
 
     from pymatgen.util.typing import SpeciesLike
 
-# Load element data from json file
+# Load element data from JSON file
 with open(Path(__file__).absolute().parent / "periodic_table.json", encoding="utf-8") as ptable_json:
     _pt_data = json.load(ptable_json)
 

pymatgen/core/surface.py

@@ -1679,7 +1679,7 @@ def generate_all_slabs(
     return all_slabs
 
 
-# Load the reconstructions_archive json file
+# Load the reconstructions_archive JSON file
 module_dir = os.path.dirname(os.path.abspath(__file__))
 with open(f"{module_dir}/reconstructions_archive.json", encoding="utf-8") as data_file:
     RECONSTRUCTIONS_ARCHIVE = json.load(data_file)
@@ -1715,7 +1715,7 @@ class ReconstructionGenerator:
             the reconstructed slab. Only the a and b lattice vectors are
             actually changed while the c vector remains the same.
             This matrix is what the Wood's notation is based on.
-        reconstruction_json (dict): The full json or dictionary containing
+        reconstruction_json (dict): The full JSON or dictionary containing
             the instructions for building the slab.
 
     Todo:

pymatgen/electronic_structure/plotter.py

@@ -4062,7 +4062,7 @@ def plot_fermi_surface(
 
         polydata = cp.actor.actors[0].mapper.input
         pts = np.array(polydata.points)  # - 1
-        polydata.points = np.dot(pts, cell / np.array(data.shape)[:, np.newaxis])
+        polydata.points = np.dot(pts, cell / np.array(data.shape)[:, None])
 
         cx, cy, cz = (np.mean(np.array(polydata.points)[:, i]) for i in range(3))
 

pymatgen/ext/matproj_legacy.py

@@ -364,7 +364,7 @@ def get_doc(self, materials_id):
             materials_id (str): e.g. mp-1143 for Al2O3
 
         Returns:
-            Dict of json document of all data that is displayed on a materials
+            Dict of JSON document of all data that is displayed on a materials
             details page.
         """
         return self._make_request(f"/materials/{materials_id}/doc", mp_decode=False)

pymatgen/io/common.py

@@ -276,7 +276,7 @@ def to_hdf5(self, filename):
             f["fcoords"]: Fractional coords
             f["lattice"]: Lattice in the pymatgen.core.Lattice matrix
                 format
-            f.attrs["structure_json"]: String of json representation
+            f.attrs["structure_json"]: String of JSON representation
 
         Args:
             filename (str): Filename to output to.

pymatgen/io/phonopy.py

@@ -241,7 +241,7 @@ def get_displaced_structures(pmg_structure, atom_disp=0.01, supercell_matrix=Non
         atom_disp (float): Atomic displacement. Default is 0.01 $\\AA$.
         supercell_matrix (3x3 array): Scaling matrix for supercell.
         yaml_fname (str): If not None, it represents the full path to
-            the outputting displacement yaml file, e.g. disp.yaml.
+            the outputting displacement YAML file, e.g. disp.yaml.
         **kwargs: Parameters used in Phonopy.generate_displacement method.
 
     Returns:
@@ -411,7 +411,7 @@ def get_phonon_band_structure_symm_line_from_fc(
 def get_gruneisenparameter(gruneisen_path, structure=None, structure_path=None) -> GruneisenParameter:
     """Get Gruneisen object from gruneisen.yaml file, as obtained from phonopy (Frequencies in THz!).
     The order is structure > structure path > structure from gruneisen dict.
-    Newer versions of phonopy include the structure in the yaml file,
+    Newer versions of phonopy include the structure in the YAML file,
     the structure/structure_path is kept for compatibility.
 
     Args: