[pre-commit.ci] pre-commit autoupdate (#1231)

* [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/psf/black: 22.12.0 → 23.1.0](psf/black@22.12.0...23.1.0)

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

.pre-commit-config.yaml

@@ -36,7 +36,7 @@ repos:
         types_or: [python, pyi]
 
   - repo: https://github.com/psf/black
-    rev: 22.12.0
+    rev: 23.1.0
     hooks:
       - id: black
         types: [file]

benchmark/pytest/analysis/test_optimization_group.py

@@ -99,7 +99,6 @@ def setup_optimization_group(scheme):
 
 
 def test_benchmark_align_data(benchmark):
-
     model = setup_model(False, True)
     assert model.valid()
 
@@ -111,7 +110,6 @@ def test_benchmark_align_data(benchmark):
 @pytest.mark.parametrize("link_clp", [True, False])
 @pytest.mark.parametrize("index_dependent", [True, False])
 def test_benchmark_calculate_matrix(benchmark, link_clp, index_dependent):
-
     model = setup_model(index_dependent, link_clp)
     assert model.valid()
 
@@ -124,7 +122,6 @@ def test_benchmark_calculate_matrix(benchmark, link_clp, index_dependent):
 @pytest.mark.parametrize("link_clp", [True, False])
 @pytest.mark.parametrize("index_dependent", [True, False])
 def test_benchmark_calculate_residual(benchmark, link_clp, index_dependent):
-
     model = setup_model(index_dependent, link_clp)
     assert model.valid()
 
@@ -139,7 +136,6 @@ def test_benchmark_calculate_residual(benchmark, link_clp, index_dependent):
 @pytest.mark.parametrize("link_clp", [True, False])
 @pytest.mark.parametrize("index_dependent", [True, False])
 def test_benchmark_calculate_result_data(benchmark, link_clp, index_dependent):
-
     model = setup_model(index_dependent, link_clp)
     assert model.valid()
 
@@ -155,7 +151,6 @@ def test_benchmark_calculate_result_data(benchmark, link_clp, index_dependent):
 @pytest.mark.parametrize("link_clp", [True, False])
 @pytest.mark.parametrize("index_dependent", [True, False])
 def test_benchmark_optimize_20_runs(benchmark, link_clp, index_dependent):
-
     model = setup_model(index_dependent, link_clp)
     assert model.valid()
 
@@ -164,7 +159,6 @@ def test_benchmark_optimize_20_runs(benchmark, link_clp, index_dependent):
 
     @benchmark
     def run():
-
         for _ in range(20):
             optimization_group.calculate(scheme.parameters)
 

glotaran/builtin/io/sdt/sdt_file_reader.py

@@ -99,7 +99,6 @@ def load_dataset(
             )
 
         if flim:
-
             if orig_time_axis_index != 2:
                 np.swapaxes(raw_data, 2, orig_time_axis_index)
 

glotaran/builtin/io/sdt/test/test_file_readers.py

@@ -16,7 +16,6 @@
 )
 @pytest.mark.filterwarnings("ignore:There was no `index`:UserWarning")
 def test_read_sdt(test_file_path, result_file_path, index):
-
     sdt_reader = SdtDataIo("sdt")
     test_dataset = sdt_reader.load_dataset(test_file_path, index=index)
     result_df = pd.read_csv(

glotaran/builtin/io/yml/test/test_model_parser.py

@@ -143,7 +143,6 @@ def test_k_matrices(model):
 
 
 def test_weight(model):
-
     weight = model.weights[0]
     assert isinstance(weight, Weight)
     assert weight.datasets == ["d1", "d2"]
@@ -153,7 +152,6 @@ def test_weight(model):
 
 
 def test_shapes(model):
-
     assert "shape1" in model.shape
 
     shape = model.shape["shape1"]

glotaran/builtin/megacomplexes/clp_guide/test/test_clp_guide_megacomplex.py

@@ -11,7 +11,6 @@
 
 
 def test_clp_guide():
-
     model = Model.create_class_from_megacomplexes(
         [DecaySequentialMegacomplex, ClpGuideMegacomplex]
     )(

glotaran/builtin/megacomplexes/coherent_artifact/coherent_artifact_megacomplex.py

@@ -122,7 +122,6 @@ def _calculate_coherent_artifact_matrix(
 
 @nb.jit(nopython=True, parallel=True)
 def _calculate_coherent_artifact_matrix_on_index(matrix, center, width, axis, order):
-
     matrix[:, 0] = np.exp(-1 * (axis - center) ** 2 / (2 * width**2))
     if order > 1:
         matrix[:, 1] = matrix[:, 0] * (center - axis) / width**2

glotaran/builtin/megacomplexes/coherent_artifact/test/test_coherent_artifact.py

@@ -70,7 +70,6 @@ def test_coherent_artifact(spectral_dependence: str):
             ["irf_disp2", 0.001, {"vary": False, "non-negative": False}],
         ]
     elif spectral_dependence == "shifted":
-
         irf_spec["shift"] = ["irf_shift1", "irf_shift2", "irf_shift3"]
         parameter_list += [
             ["irf_shift1", -2],

glotaran/builtin/megacomplexes/damped_oscillation/damped_oscillation_megacomplex.py

@@ -77,7 +77,6 @@ def calculate_matrix(
         model_axis: ArrayLike,
         **kwargs,
     ):
-
         clp_label = [f"{label}_cos" for label in self.labels] + [
             f"{label}_sin" for label in self.labels
         ]
@@ -191,7 +190,6 @@ def finalize_data(
 
 @nb.jit(nopython=True, parallel=True)
 def calculate_damped_oscillation_matrix_no_irf(matrix, frequencies, rates, axis):
-
     idx = 0
     for frequency, rate in zip(frequencies, rates):
         osc = np.exp(-rate * axis - 1j * frequency * axis)

glotaran/builtin/megacomplexes/damped_oscillation/test/test_doas_model.py

@@ -343,7 +343,6 @@ class OneOscillationWithSequentialModel:
     ],
 )
 def test_doas_model(suite):
-
     print(suite.sim_model.validate())
     assert suite.sim_model.valid()
 

glotaran/builtin/megacomplexes/decay/test/test_decay_megacomplex.py

@@ -253,7 +253,6 @@ class ThreeComponentSequential:
 )
 @pytest.mark.parametrize("nnls", [True, False])
 def test_kinetic_model(suite, nnls):
-
     model = suite.model
     print(model.validate())
     assert model.valid()

glotaran/builtin/megacomplexes/decay/test/test_k_matrix.py

@@ -184,7 +184,6 @@ class ParallelModelWithEquilibria:
     [SequentialModel, SequentialModelWithBacktransfer, ParallelModel, ParallelModelWithEquilibria],
 )
 def test_a_matrix_general(matrix):
-
     params = Parameters.from_list(matrix.params)
 
     mat = KMatrix(label="", matrix=matrix.matrix)
@@ -218,7 +217,6 @@ def test_a_matrix_general(matrix):
 
 
 def test_a_matrix_sequential():
-
     compartments = ["s1", "s2", "s3"]
     matrix = {
         ("s2", "s1"): "1",
@@ -262,7 +260,6 @@ def test_a_matrix_sequential():
 
 
 def test_combine_matrices():
-
     matrix1 = {
         ("s1", "s1"): "1",
         ("s2", "s2"): "2",

glotaran/builtin/megacomplexes/decay/test/test_spectral_irf.py

@@ -184,7 +184,6 @@ class MultiCenterIrfDispersion:
     ],
 )
 def test_spectral_irf(suite):
-
     model = suite.model
     assert model.valid(), model.validate()
 

glotaran/builtin/megacomplexes/decay/util.py

@@ -47,7 +47,6 @@ def calculate_matrix(
     model_axis: ArrayLike,
     **kwargs,
 ):
-
     compartments = megacomplex.get_compartments(dataset_model)
     initial_concentration = megacomplex.get_initial_concentration(dataset_model)
     k_matrix = megacomplex.get_k_matrix()
@@ -160,7 +159,6 @@ def decay_matrix_implementation_index_independent(
     dataset_model: DatasetModel,
 ):
     if isinstance(dataset_model.irf, IrfMultiGaussian):
-
         (
             centers,
             widths,
@@ -285,7 +283,6 @@ def retrieve_initial_concentration(
     dataset: xr.Dataset,
     species_dimension: str,
 ):
-
     if (
         not hasattr(dataset_model, "initial_concentration")
         or dataset_model.initial_concentration is None

glotaran/builtin/megacomplexes/spectral/spectral_megacomplex.py

@@ -36,7 +36,6 @@ def calculate_matrix(
         model_axis: ArrayLike,
         **kwargs,
     ):
-
         compartments = []
         for compartment in self.shape:
             if compartment in compartments:

glotaran/builtin/megacomplexes/spectral/test/test_spectral_model.py

@@ -250,7 +250,6 @@ class ThreeCompartmentModel:
     ],
 )
 def test_spectral_model(suite):
-
     model = suite.spectral_model
     print(model.validate())
     assert model.valid()

glotaran/builtin/megacomplexes/test/test_spectral_decay.py

@@ -241,7 +241,6 @@ class ThreeComponentSequential:
 )
 @pytest.mark.parametrize("nnls", [True, False])
 def test_decay_model(suite, nnls):
-
     model = suite.model
     print(model.validate())
     assert model.valid()

glotaran/builtin/megacomplexes/test/test_spectral_decay_full_model.py

@@ -177,7 +177,6 @@ class ThreeComponentSequential:
 )
 @pytest.mark.parametrize("nnls", [True, False])
 def test_kinetic_model(suite, nnls):
-
     model = suite.model
     print(model.validate())
     assert model.valid()

glotaran/cli/commands/explore.py

@@ -57,7 +57,6 @@ def export(filename: str, select, out: str, name: str):
             stop = True
 
     while not stop:
-
         echo(f"Selected dataset '{name}'.")
         echo(f"\nDataset Content\n\n{data}\n")
 

glotaran/cli/commands/util.py

@@ -80,7 +80,6 @@ def loader(filename):
 
 
 def select_name(filename, dataset):
-
     names = list(dataset)
     echo(f"\nDataset names in in '{filename}':\n")
     for i, n in enumerate(names):

glotaran/cli/commands/validate.py

@@ -13,7 +13,6 @@ def validate_cmd(parameters_file: str, model_file: str, scheme_file: str):
         return
 
     if model_file is not None:
-
         model = util.load_model_file(model_file, verbose=True)
         parameters = None
         if parameters_file is not None:

glotaran/cli/main.py

@@ -11,7 +11,6 @@ class Cli(click.Group):
     """The glotaran CLI implementation of :class:`click.group`"""
 
     def __init__(self, *args, **kwargs):
-
         click.echo(
             "[DEPRECATED] The pyglotaran command line interface will be removed without "
             "replacement in version '0.8.0' since it lacks a lot of essential functionality and "

glotaran/deprecation/deprecation_utils.py

@@ -752,7 +752,6 @@ def deprecate_submodule(
     )
 
     def warn_getattr(attribute_name: str):
-
         if attribute_name == "__file__":
             return new_module.__file__
 

glotaran/deprecation/modules/test/test_changed_imports.py

@@ -16,7 +16,6 @@
 
 
 def check_recwarn(records: WarningsRecorder, warn_nr=1):
-
     for record in records:
         print(record)
 

glotaran/deprecation/test/test_deprecation_utils.py

@@ -494,7 +494,6 @@ def test_deprecate_module_attribute():
     """Same code as the original import and warning"""
 
     with pytest.warns(GlotaranApiDeprecationWarning) as record:
-
         from glotaran.deprecation.test.dummy_package.deprecated_module_attribute import (
             deprecated_attribute,
         )
@@ -508,7 +507,6 @@ def test_deprecate_module_attribute_overwrite():
     """Qualname was only used for the warning"""
 
     with pytest.warns(GlotaranApiDeprecationWarning) as record:
-
         from glotaran.deprecation.test.dummy_package.deprecated_module_attribute import foo_bar
 
         assert foo_bar.__code__ == parse_version.__code__
@@ -560,7 +558,6 @@ def test_deprecate_submodule_import_error(recwarn: WarningsRecorder):
     """Raise warning when Attribute of fake module is imported"""
 
     with pytest.raises(ImportError) as excinfo:
-
         from glotaran.deprecation.test.dummy_package.deprecated_module import (  # noqa: F401
             does_not_exists,
         )

glotaran/model/megacomplex.py

@@ -14,7 +14,6 @@
 from glotaran.plugin_system.megacomplex_registration import register_megacomplex
 
 if TYPE_CHECKING:
-
     from glotaran.model import DatasetModel
     from glotaran.typing.types import ArrayLike
 
@@ -42,7 +41,6 @@ def megacomplex(
     """
 
     def decorator(cls):
-
         megacomplex_type = item(cls)
         megacomplex_type.__dataset_model_type__ = dataset_model_type
         megacomplex_type.__is_exclusive__ = exclusive

glotaran/model/test/test_model.py

@@ -153,7 +153,6 @@ def test_model_create_class():
 
 
 def test_global_items():
-
     m = Model.create_class([])(
         **{
             "clp_penalties": [

glotaran/optimization/data_provider.py

@@ -53,7 +53,6 @@ def __init__(self, scheme: Scheme, dataset_group: DatasetGroup):
         self._global_dimensions: dict[str, str] = {}
 
         for label, dataset_model in dataset_group.dataset_models.items():
-
             dataset = scheme.data[label]
             model_dimension = get_dataset_model_model_dimension(dataset_model)
             self._model_axes[label] = dataset.coords[model_dimension].data
@@ -197,7 +196,6 @@ def add_model_weight(
             ),
         )
         for model_weight in model_weights:
-
             idx = {}
             if model_weight.global_interval is not None:
                 idx[global_dimension] = self.get_axis_slice_from_interval(
@@ -499,7 +497,6 @@ def create_aligned_global_axes(self, scheme: Scheme) -> dict[str, ArrayLike]:
         aligned_axis_values = None
         aligned_global_axes = {}
         for label, global_axis in self._global_axes.items():
-
             aligned_global_axis = global_axis
             if aligned_axis_values is None:
                 aligned_axis_values = aligned_global_axis

glotaran/optimization/matrix_provider.py

@@ -389,7 +389,6 @@ def apply_relations(
                 if relation.target in clp_labels and self.does_interval_item_apply(
                     relation, index
                 ):
-
                     if relation.source not in clp_labels:
                         continue
 

glotaran/optimization/optimization_history.py

@@ -10,7 +10,6 @@
 from glotaran.utils.regex import RegexPattern
 
 if TYPE_CHECKING:
-
     from glotaran.typing import StrOrPath
 
 

glotaran/optimization/test/models.py

@@ -28,7 +28,6 @@ def calculate_matrix(
         model_axis: ArrayLike,
         **kwargs,
     ):
-
         compartments = ["s1", "s2"]
         array = np.zeros((model_axis.size, len(compartments)))
 

glotaran/optimization/test/test_optimization.py

@@ -216,7 +216,7 @@ def test_result_data(model_weight: bool, index_dependent: bool):
         ("fitted_data", ("model", "global")),
     ]
 
-    for (label, dims) in wanted:
+    for label, dims in wanted:
         print("Check label", label)
         assert label in result_data
         print("Check dims", result_data[label].dims, dims)

glotaran/parameter/parameter.py

@@ -25,7 +25,6 @@
 from glotaran.utils.sanitize import sanitize_parameter_list
 
 if TYPE_CHECKING:
-
     from glotaran.parameter import Parameters
 
 RESERVED_LABELS: list[str] = list(asteval.make_symbol_table().keys()) + ["parameters", "iteration"]