fmt

src/depiction/spectrum/peak_picking/basic_interpolated_peak_picker.py

@@ -93,5 +93,6 @@ def _find_local_maxima_indices(self, mz_arr: NDArray[float], int_arr: NDArray[fl
         )
         return local_maxima_indices
 
+
 # TODO the interpolation could be much faster, if it were implemented in numba for our specific case of 3 points,
 #      since in general the scipy library will do everything much moe general than is actually required.

src/depiction_targeted_preproc/example_compare/run_compare.py

@@ -16,9 +16,11 @@ def prepare_tasks(input_imzml_path: Path, work_dir: Path) -> list[Path]:
     requested_files = get_all_output_files(folders)
 
     combined_dir = work_dir / input_imzml_path.stem
-    exp_files = [combined_dir / "exp_compare_cluster_stats.pdf",
-                 combined_dir / "exp_plot_compare_peak_density.pdf",
-                 combined_dir / "exp_plot_map_comparison.pdf"]
+    exp_files = [
+        combined_dir / "exp_compare_cluster_stats.pdf",
+        combined_dir / "exp_plot_compare_peak_density.pdf",
+        combined_dir / "exp_plot_map_comparison.pdf",
+    ]
     return requested_files + exp_files
 
 

src/depiction_targeted_preproc/workflow/exp/plot_map_comparison.py

@@ -22,5 +22,6 @@ def exp_plot_map_comparison(
 
     plt.savefig(output_pdf_path, bbox_inches="tight")
 
+
 if __name__ == "__main__":
     typer.run(exp_plot_map_comparison)

src/depiction_targeted_preproc/workflow/proc/cluster_hdbscan.py

@@ -5,7 +5,7 @@
 import numpy as np
 import typer
 import xarray
-from hdbscan.flat import (HDBSCAN_flat)
+from hdbscan.flat import HDBSCAN_flat
 from loguru import logger
 from sklearn.preprocessing import StandardScaler
 from typer import Option
@@ -30,9 +30,8 @@ def cluster_dbscan(input_netcdf_path: Annotated[Path, Option()], output_netcdf_p
     data_scaled = scaler.transform(reduced_data.values)
 
     try:
-        clusterer = HDBSCAN_flat(data_scaled,
-                                 n_clusters=10)
-                                 #min_cluster_size=math.ceil(0.02 * data_scaled.shape[0]))
+        clusterer = HDBSCAN_flat(data_scaled, n_clusters=10)
+        # min_cluster_size=math.ceil(0.02 * data_scaled.shape[0]))
         clusters = clusterer.labels_
     except IndexError:
         logger.error("No clusters found")

src/depiction_targeted_preproc/workflow/proc/cluster_stats.py

@@ -54,7 +54,7 @@ def compute_metrics(cluster_data: np.ndarray, cluster_coords: np.ndarray) -> dic
         "PAS": pas,
         "Silhouette": silhouette,
         "Davies-Bouldin": davies_bouldin,
-        "Calinski-Harabasz": calinski_harabasz
+        "Calinski-Harabasz": calinski_harabasz,
     }
 
 
@@ -71,9 +71,7 @@ def cluster_stats(input_netcdf_path: Annotated[Path, Option()], output_csv_path:
 
     metrics = compute_metrics(cluster_data, cluster_coords)
 
-    metrics_df = pl.DataFrame(
-        {"metric": list(metrics.keys()), "value": list(metrics.values())}
-    )
+    metrics_df = pl.DataFrame({"metric": list(metrics.keys()), "value": list(metrics.values())})
     metrics_df.write_csv(output_csv_path)
 
 

src/depiction_targeted_preproc/workflow/vis/clustering.py

@@ -15,7 +15,7 @@ def vis_clustering(input_netcdf_path: Annotated[Path, Option()], output_png_path
     source_image.plot(cmap="tab10", ax=ax)
     ax.set_aspect("equal")
 
-    #n_classes = len(set(source_image.values.ravel()))
+    # n_classes = len(set(source_image.values.ravel()))
     n_classes = len(np.unique(source_image.values))
     fig.suptitle(f"Clustering with {n_classes} classes")
     fig.tight_layout()

src/depiction_targeted_preproc/workflow/vis/test_mass_shifts.py

@@ -30,8 +30,9 @@ def vis_test_mass_shifts(
 
     # compute the shifts
     def compute_shifts(coef):
-        result = calibration.apply_spectrum_model(spectrum_mz_arr=test_masses, spectrum_int_arr=test_masses_int,
-                                                  model_coef=xr.DataArray(coef, dims=["c"]))
+        result = calibration.apply_spectrum_model(
+            spectrum_mz_arr=test_masses, spectrum_int_arr=test_masses_int, model_coef=xr.DataArray(coef, dims=["c"])
+        )
         return xr.DataArray(result[0] - test_masses, dims=["m"])
 
     shifts = xr.apply_ufunc(

tests/unit/calibration/spectrum/test_calibration_method_mcc.py

@@ -11,4 +11,4 @@ def test_get_distances_nearest_when_invalid(self) -> None:
 
 
 if __name__ == "__main__":
-   unittest.main()
+    unittest.main()

tests/unit/spectrum/peak_picking/test_basic_interpolated_peak_picker.py

@@ -29,8 +29,8 @@ def test_interpolate_max_mz_and_intensity_when_success_and_exact(self) -> None:
         """In this very simple case, interpolation should return the same value as the intensity on both sides is
         symmetric."""
         local_max_index = 2
-        mz_arr = np.array([1., 2, 3, 4, 5])
-        int_arr = np.array([0., 0, 10, 0, 0])
+        mz_arr = np.array([1.0, 2, 3, 4, 5])
+        int_arr = np.array([0.0, 0, 10, 0, 0])
 
         interpolated_mz, interpolated_int = self.basic_interpolated_peak_picker._interpolate_max_mz_and_intensity(
             local_max_index, mz_arr, int_arr
@@ -41,8 +41,8 @@ def test_interpolate_max_mz_and_intensity_when_success_and_exact(self) -> None:
 
     def test_interpolate_max_mz_and_intensity_when_success_and_not_exact(self) -> None:
         local_max_index = 2
-        mz_arr = np.array([1., 2, 3, 4, 5])
-        int_arr = np.array([0., 0, 10, 5, 0])
+        mz_arr = np.array([1.0, 2, 3, 4, 5])
+        int_arr = np.array([0.0, 0, 10, 5, 0])
 
         interpolated_mz, interpolated_int = self.basic_interpolated_peak_picker._interpolate_max_mz_and_intensity(
             local_max_index, mz_arr, int_arr
@@ -53,8 +53,8 @@ def test_interpolate_max_mz_and_intensity_when_success_and_not_exact(self) -> No
 
     def test_interpolate_max_mz_and_intensity_when_failure(self) -> None:
         local_max_index = 2
-        mz_arr = np.array([1., 2, 3, 4, 5])
-        int_arr = np.array([0., 10, 10, 10, 0])
+        mz_arr = np.array([1.0, 2, 3, 4, 5])
+        int_arr = np.array([0.0, 10, 10, 10, 0])
 
         with Logot().capturing(capturer=LoguruCapturer) as logot:
             interpolated_mz, interpolated_int = self.basic_interpolated_peak_picker._interpolate_max_mz_and_intensity(