style: misc style fixes and missing imports

src/invert4geom/cross_validation.py

@@ -1,4 +1,4 @@
-from __future__ import annotations
+from __future__ import annotations  # pylint: disable=too-many-lines
 
 import itertools
 import logging
@@ -8,13 +8,18 @@
 import typing
 
 import deprecation
+import harmonica as hm
 import numpy as np
 import pandas as pd
+import sklearn
 import verde as vd
 import xarray as xr
+from nptyping import NDArray
+from polartoolkit import maps
 from polartoolkit import utils as polar_utils
 from tqdm.autonotebook import tqdm
 
+import invert4geom
 from invert4geom import inversion, plotting, regional, utils
 
 

src/invert4geom/inversion.py

@@ -984,8 +984,8 @@ def run_inversion(
         # update the l2 and delta l2 norms
         previous_delta_l2_norm = copy.copy(delta_l2_norm)
         l2_norm, delta_l2_norm = update_l2_norms(
-            current_rmse = updated_rmse,
-            last_l2_norm = l2_norm,
+            current_rmse=updated_rmse,
+            last_l2_norm=l2_norm,
         )
         final_l2_norm = l2_norm
 
@@ -1431,7 +1431,6 @@ def run_inversion_workflow(  # equivalent to monte_carlo_full_workflow
         # use the best damping parameter
         inversion_kwargs["solver_damping"] = best_damping
 
-
         if run_zref_or_density_cv is False:
             if fname is not None:
                 # save results to pickle

src/invert4geom/optimization.py

@@ -1,24 +1,27 @@
-from __future__ import annotations
+from __future__ import annotations  # pylint: disable=too-many-lines
 
+import itertools
 import logging
 import math
 import multiprocessing
 import os
 import pathlib
+import pickle
 import random
 import re
 import subprocess
 import typing
 import warnings
 
 import harmonica as hm
-import optuna
+import numpy as np
 import optuna
 import pandas as pd
+import xarray as xr
 from nptyping import NDArray
+from tqdm.autonotebook import tqdm
 
-from invert4geom import plotting, utils
-
+from invert4geom import cross_validation, plotting, regional, utils
 
 try:
     import joblib
@@ -346,14 +349,14 @@ def optuna_parallel(
 
     # set up parallel processing and run optimization
     if parallel is True:
-        # @utils.supress_stdout
+
         def optimize_study(
             study_name: str,
-            storage: typing.Any,
+            storage: optuna.storages.BaseStorage,
             objective: typing.Callable[..., float],
             n_trials: int,
         ) -> None:
-            storage: optuna.storages.BaseStorage,
+            study = optuna.load_study(study_name=study_name, storage=storage)
             optuna.logging.set_verbosity(optuna.logging.WARNING)
             study.optimize(
                 objective,
@@ -399,11 +402,11 @@ def optuna_max_cores(
     n_trials: int,
     optimize_study: typing.Callable[..., None],
     study_name: str,
-    study_storage: typing.Any,
+    study_storage: optuna.storages.BaseStorage,
     objective: typing.Callable[..., float],
 ) -> None:
     """
-    study_storage: optuna.storages.BaseStorage,
+    Set up optuna optimization in parallel splitting up the number of trials over all
     available cores.
     """
     if joblib is None:

src/invert4geom/plotting.py

@@ -1,5 +1,6 @@
 from __future__ import annotations  # pylint: disable=too-many-lines
 
+import logging
 import typing
 
 import numpy as np
@@ -17,8 +18,8 @@
     clear_output = None
 
 try:
+    import matplotlib as mpl
     import matplotlib.pyplot as plt
-    from matplotlib.ticker import MaxNLocator
 except ImportError:
     plt = None
 
@@ -32,8 +33,10 @@
     import pyvista
 except ImportError:
     pyvista = None
+
 import verde as vd
 import xarray as xr
+from polartoolkit import maps
 from polartoolkit import utils as polar_utils
 
 from invert4geom import utils
@@ -363,7 +366,7 @@ def plot_convergence(
         if i == 0:
             delta_l2_norms.append(np.nan)
         else:
-            delta_l2_norms.append(l2_norms[i-1]/m)
+            delta_l2_norms.append(l2_norms[i - 1] / m)
 
     # get tolerance values
     l2_norm_tolerance = float(params["L2 norm tolerance"])
@@ -478,11 +481,11 @@ def plot_dynamic_convergence(
     ax2 = ax1.twinx()
 
     # plot L2-norm convergence
-    ax1.plot([i for i in range(len(l2_norms))], l2_norms, "b-")
+    ax1.plot(list(range(len(l2_norms))), l2_norms, "b-")
 
     # plot delta L2-norm convergence
     if iters > 1:
-        ax2.plot([i for i in range(len(delta_l2_norms))], delta_l2_norms, "g-")
+        ax2.plot(list(range(len(delta_l2_norms))), delta_l2_norms, "g-")
 
     # set axis labels, ticks and gridlines
     ax1.set_xlabel("Iteration")
@@ -502,7 +505,7 @@ def plot_dynamic_convergence(
 
     # plot current L2-norm and Δ L2-norm
     ax1.plot(
-        iters-1,
+        iters - 1,
         l2_norms[-1],
         "^",
         markersize=6,
@@ -511,7 +514,7 @@ def plot_dynamic_convergence(
     )
     if iters > 1:
         ax2.plot(
-            iters-1,
+            iters - 1,
             delta_l2_norms[-1],
             "^",
             markersize=6,
@@ -541,17 +544,22 @@ def plot_dynamic_convergence(
     plt.show()
 
 
-def align_yaxis(ax1, v1, ax2, v2):
+def align_yaxis(
+    ax1: mpl.axes.Axes,
+    v1: float,
+    ax2: mpl.axes.Axes,
+    v2: float,
+) -> None:
     """
     adjust ax2 ylimit so that v2 in ax2 is aligned to v1 in ax1.
     From https://stackoverflow.com/a/10482477/18686384
     """
     _, y1 = ax1.transData.transform((0, v1))
     _, y2 = ax2.transData.transform((0, v2))
     inv = ax2.transData.inverted()
-    _, dy = inv.transform((0, 0)) - inv.transform((0, y1-y2))
+    _, dy = inv.transform((0, 0)) - inv.transform((0, y1 - y2))
     miny, maxy = ax2.get_ylim()
-    ax2.set_ylim(miny+dy, maxy+dy)
+    ax2.set_ylim(miny + dy, maxy + dy)
 
 
 def grid_inversion_results(
@@ -1211,7 +1219,7 @@ def show_prism_layers(
         prisms = [prisms]
 
     for i, j in enumerate(prisms):
-        # turn prisms into pyvist object
+        # turn prisms into pyvista object
         pv_grid = j.prism_layer.to_pyvista()
 
         trans = opacity[i] if opacity is not None else None
@@ -1263,7 +1271,7 @@ def combined_history(
     study: optuna.study.Study,
     target_names: list[str],
     include_duration: bool = False,
-) -> typing.Any:
+) -> plotly.graph_objects.Figure:
     """
     plot combined optimization history for multiobjective optimizations.
 
@@ -1278,7 +1286,7 @@ def combined_history(
 
     Returns
     -------
-    typing.Any
+    plotly.graph_objects.Figure
         a plotly figure
     """
 
@@ -1361,7 +1369,7 @@ def plot_optuna_figures(
     target_names: list[str],
     include_duration: bool = False,
     # params=None,
-    # seperate_param_importances=False,
+    # separate_param_importances=False,
     plot_history: bool = True,
     plot_slice: bool = True,
     plot_importance: bool = True,
@@ -1417,7 +1425,7 @@ def plot_optuna_figures(
     #         pass
     #     else:
     #         try:
-    #             if seperate_param_importances is True:
+    #             if separate_param_importances is True:
     #                 combined_importance(
     #                     study,
     #                     target_names,

src/invert4geom/utils.py

@@ -6,6 +6,7 @@
 import warnings
 
 import dask
+import deprecation
 import harmonica as hm
 import numpy as np
 import pandas as pd
@@ -16,6 +17,9 @@
 from nptyping import NDArray
 from pykdtree.kdtree import KDTree  # pylint: disable=no-name-in-module
 
+import invert4geom
+from invert4geom import cross_validation
+
 
 def rmse(data: NDArray, as_median: bool = False) -> float:
     """
@@ -1011,8 +1015,8 @@ def best_spline_cv(
     current_version=invert4geom.__version__,
     details="function eq_sources_score has been moved to the cross_validation model.",
 )
-def eq_sources_score(kwargs) -> float:
+def eq_sources_score(kwargs: typing.Any) -> float:
     """
     deprecated function, use cross_validation.eq_sources_score instead.
     """
-    return cross_validation.eq_sources_score(kwargs)
+    return cross_validation.eq_sources_score(**kwargs)

tests/test_cross_validation.py

@@ -9,6 +9,7 @@
 def test_zref_density_optimal_parameter():
     cross_validation.zref_density_optimal_parameter()
 
+
 @deprecation.fail_if_not_removed
 def test_grav_optimal_parameter():
     cross_validation.grav_optimal_parameter()

tests/test_regional.py

@@ -221,7 +221,6 @@ def test_regional_constraints(test_input):
         grav_df=anomalies,
         grav_data_column="misfit",
         grid_method=test_input,
-        eqs_gridding_trials=2,
         grav_obs_height=1e3,
     )
 

tests/test_utils.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import deprecation
 import harmonica as hm
 import numpy as np
 import numpy.testing as npt