Using ruff for linting #02

.python-version

@@ -0,0 +1 @@
+3.9

pyproject.toml

@@ -85,7 +85,7 @@ select = [
     # "I",   # isort (Ensures imports are sorted properly)
     # "B",   # flake8-bugbear (Detects likely bugs and bad practices)
     # "TID", # flake8-tidy-imports (Checks for banned or misplaced imports)
-    # "UP",  # pyupgrade (Automatically updates old Python syntax)
+    "UP",  # pyupgrade (Automatically updates old Python syntax)
     # "YTT", # flake8-2020 (Detects outdated Python 2/3 compatibility issues)
     # "FLY", # flynt (Converts old-style string formatting to f-strings)
     # "PIE", # flake8-pie

src/lasso/diffcrash/diffcrash_run.py

@@ -10,7 +10,7 @@
 import time
 import typing
 from concurrent import futures
-from typing import List, Union
+from typing import Union
 from pathlib import Path
 import psutil
 
@@ -688,9 +688,7 @@ def run_import(self, pool: futures.ThreadPoolExecutor):
 
             if n_imports_finished != n_new_imports_finished:
                 # pylint: disable = consider-using-f-string
-                msg = "Running Imports ... [{0}/{1}] - {2:3.2f}%\r".format(
-                    n_new_imports_finished, len(return_code_futures), percentage
-                )
+                msg = f"Running Imports ... [{n_new_imports_finished}/{len(return_code_futures)}] - {percentage:3.2f}%\r"
                 print(str_running(msg), end="", flush=True)
                 self.logger.info(msg)
 
@@ -1091,7 +1089,7 @@ def is_logfile_successful(self, logfile: Path) -> bool:
         success : `bool`
         """
 
-        with open(logfile, "r", encoding="utf-8") as fp:
+        with open(logfile, encoding="utf-8") as fp:
             for line in fp:
                 if "successfully" in line:
                     return True
@@ -1181,7 +1179,7 @@ def clear_project_dir(self):
         # reinit logger
         self.logger = self._setup_logger()
 
-    def read_config_file(self, config_file: str) -> List[str]:
+    def read_config_file(self, config_file: str) -> list[str]:
         """Read a diffcrash config file
 
         Parameters
@@ -1203,7 +1201,7 @@ def read_config_file(self, config_file: str) -> List[str]:
         # pylint: disable = too-many-branches
         # pylint: disable = too-many-statements
 
-        with open(config_file, "r", encoding="utf-8") as conf:
+        with open(config_file, encoding="utf-8") as conf:
             conf_lines = conf.readlines()
         line = 0
 
@@ -1303,7 +1301,7 @@ def read_config_file(self, config_file: str) -> List[str]:
 
         return export_item_list
 
-    def check_if_logfiles_show_success(self, pattern: str) -> List[str]:
+    def check_if_logfiles_show_success(self, pattern: str) -> list[str]:
         """Check if a logfiles with given pattern show success
 
         Parameters

src/lasso/dimred/dimred_run.py

@@ -7,7 +7,8 @@
 import sys
 import time
 from concurrent.futures.process import ProcessPoolExecutor
-from typing import Sequence, Tuple, Union
+from typing import Union
+from collections.abc import Sequence
 
 import h5py
 import numpy as np
@@ -529,7 +530,7 @@ def _parse_simulation_and_reference_runs(
         reference_run_pattern: Union[None, str],
         exclude_runs: Sequence[str],
         table: Table,
-    ) -> Tuple[Sequence[str], str, Sequence[str]]:
+    ) -> tuple[Sequence[str], str, Sequence[str]]:
         # pylint: disable = too-many-locals
 
         # search all denoted runs

src/lasso/dimred/hashing.py

@@ -1,7 +1,8 @@
 import multiprocessing
 import os
 import time
-from typing import List, Tuple, Union, Sequence
+from typing import Union
+from collections.abc import Sequence
 
 import h5py
 import numpy as np
@@ -104,8 +105,8 @@ def _compute_mode_similarities(
     hashes2: np.ndarray,
     eigenvectors_sub1: np.ndarray,
     eigenvectors_sub2: np.ndarray,
-    matches: List[Tuple[int, int]],
-) -> List[float]:
+    matches: list[tuple[int, int]],
+) -> list[float]:
     """Compute the mode similarity between different meshes
 
     Parameters
@@ -217,7 +218,7 @@ def _join_hash_comparison_thread_files(
 
 def run_hash_comparison(
     comparison_filepath: str,
-    hashes_filepaths: List[str],
+    hashes_filepaths: list[str],
     n_threads: int = 1,
     print_progress: bool = False,
 ):
@@ -597,7 +598,7 @@ def curve_normalizer(x: np.ndarray, y: np.ndarray):
 
 def compute_hashes(
     eig_vecs: np.ndarray, result_field: np.ndarray, n_points: int = 100, bandwidth: float = 0.05
-) -> List[Tuple[np.ndarray, np.ndarray]]:
+) -> list[tuple[np.ndarray, np.ndarray]]:
     """Compute hashes for a result field
 
     Parameters

src/lasso/dimred/hashing_sphere.py

@@ -1,5 +1,4 @@
 import os
-import typing
 import warnings
 
 import h5py
@@ -15,7 +14,7 @@
 warnings.simplefilter(action="ignore", category=FutureWarning)
 
 
-def _create_sphere_mesh(diameter: np.ndarray) -> typing.Tuple[np.ndarray, np.ndarray]:
+def _create_sphere_mesh(diameter: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
     """Compute the alpha and beta increments for a
         meshed sphere for binning the projected values
 
@@ -70,7 +69,7 @@ def _create_sphere_mesh(diameter: np.ndarray) -> typing.Tuple[np.ndarray, np.nda
 
 def _project_to_sphere(
     points: np.ndarray, centroid: np.ndarray, axis: str = "Z"
-) -> typing.Tuple[np.ndarray, np.ndarray]:
+) -> tuple[np.ndarray, np.ndarray]:
     """compute the projection vectors of centroid to each point in terms of spherical coordinates
 
     Parameters

src/lasso/dimred/svd/clustering_betas.py

@@ -1,4 +1,5 @@
-from typing import Sequence, Tuple, Union
+from typing import Union
+from collections.abc import Sequence
 
 import numpy as np
 from sklearn.cluster import DBSCAN, OPTICS, KMeans, SpectralClustering
@@ -418,7 +419,7 @@ def document_algorithm(keyword):
 }
 
 
-def create_cluster_arg_dict(args: Sequence[str]) -> Union[Tuple[str, dict], str]:
+def create_cluster_arg_dict(args: Sequence[str]) -> Union[tuple[str, dict], str]:
     """Determines which cluster to use and creates a python dictionary to use as cluster_params
 
     Parameters
@@ -493,7 +494,7 @@ def create_cluster_arg_dict(args: Sequence[str]) -> Union[Tuple[str, dict], str]
     return cluster_type, cluster_arg_dict
 
 
-def create_detector_arg_dict(args: Sequence[str]) -> Union[Tuple[str, dict], str]:
+def create_detector_arg_dict(args: Sequence[str]) -> Union[tuple[str, dict], str]:
     """Determines which detector to use and creates a python dictionary to use as detector_params
 
     Parameters
@@ -579,7 +580,7 @@ def group_betas(
     detector=None,
     cluster_params=None,
     detector_params=None,
-) -> Union[Tuple[list, list], str]:
+) -> Union[tuple[list, list], str]:
     """
     Base function to to group betas into groups, detect outliers. Provides that all different
     clustering and outlier detection algorithms are implemented in an easy to access environment.

src/lasso/dimred/svd/keyword_types.py

@@ -1,6 +1,3 @@
-import typing
-
-
 class ClusterType:
     """Specifies names of specific clustering algorithms
 
@@ -22,7 +19,7 @@ class ClusterType:
     SpectralClustering = "SpectralClustering"
 
     @staticmethod
-    def get_cluster_type_name() -> typing.List[str]:
+    def get_cluster_type_name() -> list[str]:
         """Get the name of the clustering algorithms"""
         return [
             ClusterType.OPTICS,
@@ -51,7 +48,7 @@ class DetectorType:
     # Experimental = "Experimental"
 
     @staticmethod
-    def get_detector_type_name() -> typing.List[str]:
+    def get_detector_type_name() -> list[str]:
         """Get the name of the detector algorithms"""
         return [
             DetectorType.IsolationForest,

src/lasso/dimred/svd/plot_beta_clusters.py

@@ -2,7 +2,8 @@
 import re
 import time
 import webbrowser
-from typing import Sequence, Union
+from typing import Union
+from collections.abc import Sequence
 
 import numpy as np
 
@@ -118,11 +119,13 @@ def plot_clusters_js(
         id_nr.append(id_group)
 
     # pylint: disable = consider-using-f-string
-    _three_min_ = '<script type="text/javascript">%s</script>' % _read_file(
-        os.path.join(
-            # move path to "~/lasso/"
-            os.path.split(os.path.split(os.path.dirname(__file__))[0])[0],
-            "plotting/resources/three_latest.min.js",
+    _three_min_ = '<script type="text/javascript">{}</script>'.format(
+        _read_file(
+            os.path.join(
+                # move path to "~/lasso/"
+                os.path.split(os.path.split(os.path.dirname(__file__))[0])[0],
+                "plotting/resources/three_latest.min.js",
+            )
         )
     )
 
@@ -136,10 +139,10 @@ def plot_clusters_js(
             name = "outliers"
             color = "black"
         else:
-            name = "cluster {i}".format(i=index)
+            name = f"cluster {index}"
             color = colorlist[(index - 1) % 10]
         formatted_trace = TRACE_STRING.format(
-            _traceNr_="trace{i}".format(i=index),
+            _traceNr_=f"trace{index}",
             _name_=name,
             _color_=color,
             _runIDs_=id_cluster[index].tolist(),

src/lasso/dimred/svd/pod_functions.py

@@ -1,4 +1,4 @@
-from typing import Tuple, Union
+from typing import Union
 
 import numpy as np
 from scipy.sparse import csc_matrix
@@ -37,7 +37,7 @@ def calculate_v_and_betas(
     stacked_sub_displ: np.ndarray,
     progress_bar: Union[None, Progress, PlaceHolderBar] = None,
     task_id: Union[None, TaskID] = None,
-) -> Union[str, Tuple[np.ndarray, np.ndarray]]:
+) -> Union[str, tuple[np.ndarray, np.ndarray]]:
     """
     Calculates the right reduced order Basis V and up to 10 eigenvalues of the subsamples
 

src/lasso/dimred/svd/subsampling_methods.py

@@ -1,7 +1,8 @@
 import os
 import random
 import time
-from typing import List, Sequence, Tuple, Union
+from typing import Union
+from collections.abc import Sequence
 
 import numpy as np
 from sklearn.neighbors import NearestNeighbors
@@ -45,7 +46,7 @@ def _mark_dead_eles(node_indexes: np.ndarray, alive_shells: np.ndarray) -> np.nd
 
 def _extract_shell_parts(
     part_list: Sequence[int], d3plot: D3plot
-) -> Union[Tuple[np.ndarray, np.ndarray], str]:
+) -> Union[tuple[np.ndarray, np.ndarray], str]:
     """
     Extracts a shell part defined by its part ID out of the given d3plot.
     Returns a new node index, relevant coordinates and displacement
@@ -123,7 +124,7 @@ def _extract_shell_parts(
                 return err_msg.format(part)
 
         def mask_parts(
-            part_list2: List[int], element_part_index: np.ndarray, element_node_index: np.ndarray
+            part_list2: list[int], element_part_index: np.ndarray, element_node_index: np.ndarray
         ) -> np.ndarray:
             element_part_filter = np.full(element_part_index.shape, False)
             proc_parts = []
@@ -180,7 +181,7 @@ def mask_parts(
 
 def create_reference_subsample(
     load_path: str, parts: Sequence[int], nr_samples=2000
-) -> Union[Tuple[np.ndarray, float, float], str]:
+) -> Union[tuple[np.ndarray, float, float], str]:
     """
     Loads the D3plot at load_path, extracts the node coordinates of part 13, returns
     a random subsample of these nodes
@@ -238,7 +239,7 @@ def create_reference_subsample(
 
 def remap_random_subsample(
     load_path: str, parts: list, reference_subsample: np.ndarray
-) -> Union[Tuple[np.ndarray, float, float], str]:
+) -> Union[tuple[np.ndarray, float, float], str]:
     """
     Remaps the specified sample onto a new mesh provided by reference subsampl, using knn matching
 

src/lasso/dyna/array_type.py

@@ -1,6 +1,3 @@
-import typing
-
-
 class ArrayType:
     """Specifies the names for specific arrays
 
@@ -494,7 +491,7 @@ class ArrayType:
     rigid_wall_position = "rigid_wall_position"
 
     @staticmethod
-    def get_state_array_names() -> typing.List[str]:
+    def get_state_array_names() -> list[str]:
         """Get the names of all state arrays
 
         Returns:

src/lasso/dyna/binout.py

@@ -1,5 +1,5 @@
 import glob
-from typing import List, Union
+from typing import Union
 
 import h5py
 import numpy as np
@@ -67,13 +67,13 @@ def __init__(self, filepath: str):
 
         # check file existence
         if not self.filelist:
-            raise IOError("No file was found.")
+            raise OSError("No file was found.")
 
         # open lsda buffer
         self.lsda = Lsda(self.filelist, "r")
         self.lsda_root = self.lsda.root
 
-    def read(self, *path) -> Union[List[str], str, np.ndarray]:
+    def read(self, *path) -> Union[list[str], str, np.ndarray]:
         """Read all data from Binout (top to low level)
 
         Parameters