DEV: linting.

.pre-commit-config.yaml

@@ -64,6 +64,7 @@ repos:
     additional_dependencies:
     - flake8-typing-imports==1.10.1
     - flake8-rst==0.8.0
+    - Flake8-pyproject
 
 - repo: https://github.com/pycqa/pydocstyle
   rev: 6.3.0  # pick a git hash / tag to point to

WFacer/convergence.py

@@ -22,6 +22,7 @@ def compare_min_energy_structures_by_composition(min_e1, min_e2, matcher=None):
         matcher (StructureMatcher): optional
             A StructureMatcher used compare structures.
             wrangler.cluster_subspace._site_matcher is recommended.
+
     Return:
         float, bool:
             maximum energy difference in eV/site,
@@ -52,6 +53,7 @@ def compare_fitted_coefs(cluster_subspace, coefs_prev, coefs_now):
             Not ECIs because not divided by multiplicity!
         coefs_now(1d arrayLike):
             Cluster coefficients fitted in the latest iteration.
+
     Returns:
         float:
             || ECI' - ECI ||_1 / ||ECI||_1.
@@ -83,6 +85,7 @@ def ce_converged(
             must be equal to that of current iter_id - 1.
         convergence_options(dict):
             Pre-processed convergence criterion.
+
     Returns:
         bool.
     """

WFacer/enumeration.py

@@ -63,6 +63,7 @@ def enumerate_matrices(
             to 30, to prevent over-skewing.
         kwargs:
             keyword arguments to pass into SpaceGroupAnalyzer.
+
     Returns:
         List of 2D lists.
     """
@@ -222,6 +223,7 @@ def enumerate_compositions_as_counts(
 
     Results will be returned in "counts" format
     (see smol.moca.CompositionSpace).
+
     Args:
         sc_size(int):
             The super-cell size in the number of prim cells.
@@ -239,6 +241,7 @@ def enumerate_compositions_as_counts(
             Default to 1.
         kwargs:
             Other keyword arguments to initialize CompositionSpace.
+
     Returns:
         Enumerated possible compositions in "counts" format, not normalized:
             2D np.ndarray[int]
@@ -427,6 +430,7 @@ def generate_training_structures(
             before matching. It should be used with caution.
         kwargs:
             Keyword arguments for utils.selection.select_initial_rows.
+
     Returns:
         list[Structure], list[3*3 list[list[int]]], list[list[float]]:
             Initial training structures, super-cell matrices,

WFacer/fit.py

@@ -28,6 +28,7 @@ def fit_ecis_from_wrangler(
     """Fit ECIs from a fully processed wrangler.
 
     No weights will be used.
+
     Args:
         wrangler(CeDataWrangler):
             A CeDataWrangler storing all training structures.
@@ -60,6 +61,7 @@ def fit_ecis_from_wrangler(
             Other keyword arguments to initialize an optimizer.
         kwargs:
             Keyword arguments used by estimator._fit. For example, solver arguments.
+
     Returns:
         Estimator, 1D np.ndarray, float, float, float, 1D np.ndarray:
             Fitted estimator, coefficients (not ECIs), cross validation error (meV/site),

WFacer/jobs.py

@@ -195,6 +195,7 @@ def enumerate_structures(last_ce_document):
     Args:
         last_ce_document(CeOutputsDocument):
             The last cluster expansion outputs document.
+
     Returns:
         dict:
            Newly enumerated structures, super-cell matrices
@@ -266,6 +267,7 @@ def get_structure_calculation_flows(enum_output, last_ce_document):
             Output by enumeration job.
         last_ce_document(CeOutputsDocument):
             The last cluster expansion outputs document.
+
     Returns:
         list[Flow], list[OutputReference]:
             Flows for each structure and their output references pointing
@@ -325,6 +327,7 @@ def calculate_structures_job(enum_output, last_ce_document):
             Output by enumeration job.
         last_ce_document(CeOutputsDocument):
             The last cluster expansion outputs document.
+
     Returns:
         list[TaskDoc]:
             Results of VASP calculations as TaskDoc.
@@ -353,6 +356,7 @@ def parse_calculations(taskdocs, enum_output, last_ce_document):
             Output by enumeration job.
         last_ce_document(CeOutputsDocument):
             The last cluster expansion outputs document.
+
     Returns:
         dict
             Updated wrangler, all entries before decoration,
@@ -471,6 +475,7 @@ def fit_calculations(parse_output, last_ce_document):
             Output by parse job.
         last_ce_document(CeOutputsDocument):
             The last cluster expansion outputs document.
+
     Returns:
         dict:
            Dictionary containing fitted CE information.
@@ -506,6 +511,7 @@ def update_document(enum_output, parse_output, fit_output, last_ce_document):
             Output by parse job.
         last_ce_document(CeOutputsDocument):
             The last cluster expansion outputs document.
+
     Returns:
         CeOutputDocument:
             The updated document.
@@ -550,6 +556,7 @@ def initialize_document(prim, project_name="ace-work", options=None):
 
     In this job, a cluster subspace will be created, super-cells
     and compositions will also be enumerated.
+
     Args:
         prim(structure):
             A primitive cell structure (no need to be reduced) with

WFacer/maker.py

@@ -22,6 +22,7 @@ def ce_step_trigger(last_ce_document):
         last_ce_document(CeOutputsDocument):
             The cluster expansion outputs document from the
             latest step.
+
     Returns:
         Response:
             Either a CeOutputsDocument if converged, or a
@@ -120,6 +121,7 @@ def make(self, prim, last_document=None, add_num_iterations=None):
                 number of iterations, add this many more iterations.
                 Default is None. When given None or 0, will simply double
                 max_iter in options.
+
         Returns:
             Flow:
                 The iterative cluster expansion workflow.

WFacer/preprocessing.py

@@ -22,6 +22,7 @@ def reduce_prim(prim, **kwargs):
             A primitive cell with partial occupancy to be expanded.
         kwargs:
             Keyword arguments for SpacegroupAnalyzer.
+
     Returns:
         Structure
     """
@@ -168,6 +169,7 @@ def get_cluster_subspace(
             for extensibility.)
         kwargs:
             Other keyword arguments for ClusterSubspace.from_cutoffs.
+
     Returns:
         ClusterSubspace:
             A cluster subspace generated from cutoffs.
@@ -194,6 +196,7 @@ def process_supercell_options(d):
     Args:
         d(dict):
             An input dictionary containing various options in the input file.
+
     Returns:
         dict:
             A dict containing supercell matrix options, including the following keys:
@@ -246,6 +249,7 @@ def process_composition_options(d):
     Args:
         d(dict):
             An input dictionary containing various options in the input file.
+
     Returns:
         dict:
             A dict containing composition options, including the following keys:
@@ -321,6 +325,7 @@ def process_structure_options(d):
     Args:
         d(dict):
             An input dictionary containing various options in the input file.
+
     Returns:
         dict:
             A dict containing structure options, including the following keys:
@@ -377,6 +382,7 @@ def process_calculation_options(d):
     Args:
         d(dict):
             An input dictionary containing various options in the input file.
+
     Returns:
         dict:
             A dict containing calculation options, including the following keys:
@@ -459,6 +465,7 @@ def process_decorator_options(d):
     Args:
         d(dict):
             An input dictionary containing various options in the input file.
+
     Returns:
         dict:
             A dict containing calculation options, including the following keys:
@@ -509,6 +516,7 @@ def process_subspace_options(d):
     Args:
         d(dict):
             An input dictionary containing various options in the input file.
+
     Returns:
         dict:
             A dict containing fit options, including the following keys:
@@ -541,6 +549,7 @@ def process_fit_options(d):
     Args:
         d(dict):
             An input dictionary containing various options in the input file.
+
     Returns:
         dict:
             A dict containing fit options, including the following keys:
@@ -603,6 +612,7 @@ def process_convergence_options(d):
     Args:
         d(dict):
             An input dictionary containing various options in the input file.
+
     Returns:
         dict:
             A dict containing convergence options, including the following keys:
@@ -658,9 +668,11 @@ def get_initial_ce_coefficients(cluster_subspace):
     Any coefficient, except those for external terms, will be initialized to 0.
     This guarantees that for ionic systems, structures with lower ewald energy
     are always selected first.
+
     Args:
         cluster_subspace(ClusterSubspace):
             The initial cluster subspace.
+
     Returns:
         np.ndarray[float].
     """

WFacer/specie_decorators/base.py

@@ -103,6 +103,7 @@ def group_site_by_species(entries):
         Args:
             entries(List[ComputedStructureEntry]):
                 Entries of computed structures.
+
         Return:
             (Entry index, site index) occupied by each species:
             defaultdict
@@ -125,6 +126,7 @@ def is_trained(self):
         """Gives whether this decorator is trained before.
 
         If trained, will be blocked from training again.
+
         Returns:
             bool.
         """
@@ -154,9 +156,11 @@ def decorate(self, entries):
         If an assigned entry is not valid,
         for example, in charge assignment, if an assigned structure is not
         charge neutral, then this entry will be returned as None.
+
         Args:
             entries(List[ComputedStructureEntry]):
                 Entries of computed structures.
+
         Returns:
             List[NoneType|ComputedStructureEntry]
         """
@@ -422,9 +426,11 @@ def decorate(self, entries):
         If an assigned entry is not valid,
         for example, in charge assignment, if an assigned structure is not
         charge neutral, then this entry will be returned as None.
+
         Args:
             entries(List[ComputedStructureEntry]):
                 Entries of computed structures.
+
         Returns:
             Entries with structures decorated. Returns None if decoration
             failed (not charge balanced, etc.)
@@ -515,7 +521,7 @@ def __init__(self, labels, cuts=None, **kwargs):
                GuessChargeDecorator.
                Be sure to provide labels for all the species you wish to assign
                a property to, otherwise, you are the cause of your own error!
-            cuts(dict{str|Species: list}): optional
+           cuts(dict{str|Species: list}): optional
                Cuts to divide required property value into sectors, so as
                to decide the label they belong to. Keys are the same
                as argument "labels".
@@ -547,6 +553,7 @@ def is_trained(self):
         """Gives whether this decorator is trained before.
 
         If trained, will be blocked from training again.
+
         Returns:
             bool.
         """
@@ -657,6 +664,7 @@ def train(self, entries, reset=False, n_calls=50):
 
         First initialize with mixture of gaussian, then
         optimize some objective function with gaussian process.
+
         Args:
             entries(List[ComputedStructureEntry]):
                 Entries of computed structures.
@@ -707,9 +715,11 @@ def decorate(self, entries):
         If an assigned entry is not valid,
         for example, in charge assignment, if an assigned structure is not
         charge neutral, then this entry will be returned as None.
+
         Args:
             entries(List[ComputedStructureEntry]):
                 Entries of computed structures.
+
         Returns:
             List[NoneType|ComputedStructureEntry]
         """
@@ -796,6 +806,7 @@ def get_site_property_query_names_from_decorator(decname):
     Args:
         decname(str):
             Decorator name.
+
     Returns:
         list[str]:
             List of names of required site properties by the

WFacer/specie_decorators/charge.py

@@ -101,9 +101,11 @@ def decorate(self, entries):
         Warning: Do not use this with multi-valent
         elements, unless you know what you want
         clearly!!!
+
         Args:
             entries(List[ComputedStructureEntry]):
                 Entries of computed structures.
+
         Returns:
             List[NoneType|ComputedStructureEntry]
         """
@@ -143,9 +145,11 @@ def decorate(self, entries):
         Warning: Do not use this with multi-valent
         elements, unless you know what you want
         clearly!!!
+
         Args:
             entries(List[ComputedStructureEntry]):
                 Entries of computed structures.
+
         Returns:
             List[NoneType|ComputedStructureEntry]
         """

WFacer/utils/convex_hull.py

@@ -25,6 +25,7 @@ def get_min_energy_structures_by_composition(wrangler, max_iter_id=None):
         max_iter_id(int): optional
             Maximum iteration index included in the energy comparison.
             If none given, will read existing maximum iteration number.
+
     Returns:
         defaultdict:
             element compositions as keys, energy per site and structure

WFacer/utils/duplicacy.py

@@ -9,9 +9,11 @@ def clean_up_decoration(s):
     """Remove all decoration from a structure.
 
     Used before comparing two structures before sending to compute.
+
     Args:
         s(Structure):
             A structure.
+
     Returns:
         Structure:
             The cleaned up structure containing only Element.
@@ -55,6 +57,7 @@ def is_duplicate(s1, s2, remove_decorations=False, matcher=None):
         matcher(StructureMatcher): optional
             A StructureMatcher to compare two structures. Using the same
             _site_matcher as cluster_subspace is highly recommended.
+
     Returns:
         bool
     """
@@ -80,6 +83,7 @@ def is_corr_duplicate(s1, proc1, s2=None, proc2=None, features2=None):
     faster that comparing two structures, because comparing two
     structures might involve reducing them to primitive cells
     in advance, which can occasionally be very slow.
+
     Args:
         s1 (Structure):
            A structure to be checked.

WFacer/utils/occu.py

@@ -11,6 +11,7 @@ def get_random_occupancy_from_counts(ensemble, counts):
         counts(1D arrayLike):
             Species composition in "counts" format.
             See smol.moca.composition.
+
     Returns:
         np.ndarray:
             An encoded occupancy array.