black formatting

src/wf_psf/psf_models/tf_psf_field.py

@@ -448,7 +448,7 @@ def assign_S_mat(self, S_mat):
     def project_DD_features(self, tf_zernike_cube):
         """Project data-driven features to parametric part.
 
-        This method projects non-parametric wavefront onto the first `n_poly_param` Zernikes, 
+        This method projects non-parametric wavefront onto the first `n_poly_param` Zernikes,
         which is defined by the order of zernike polynomials used in the parametric part.
         Then, it transfers their parameters to the parametric model.
 

src/wf_psf/tests/test_utils/utils_test.py

@@ -42,7 +42,9 @@ def test_unobscured_zernike_projection():
     tf_unobscured_opd = tf_zernike_opd(tf_zk_array)
 
     # Compute normalisation factor
-    norm_factor = unobscured_zernike_projection(tf_zernike_cube[0, :, :], tf_zernike_cube[0, :, :])
+    norm_factor = unobscured_zernike_projection(
+        tf_zernike_cube[0, :, :], tf_zernike_cube[0, :, :]
+    )
 
     # Compute projections for each zernike
     estimated_zk_array = np.array(

src/wf_psf/training/train.py

@@ -312,7 +312,9 @@ def train(
                 psf_model.project_DD_features(
                     psf_model.zernike_maps
                 )  # make this a callable function
-                logger.info("Projected non-parametric DD features onto the parametric model.")
+                logger.info(
+                    "Projected non-parametric DD features onto the parametric model."
+                )
         if psf_model.reset_dd_features:
             psf_model.tf_np_poly_opd.init_vars()
             logger.info("DataDriven features were reset to random initialisation.")

src/wf_psf/utils/utils.py

@@ -487,7 +487,7 @@ def load_multi_cycle_params_click(args):
 
 def unobscured_zernike_projection(tf_z1, tf_z2, norm_factor=None):
     """Compute a zernike projection for unobscured wavefronts (OPDs).
-    
+
     Compute internal product between zernikes and OPDs.
 
     Defined such that Zernikes are orthonormal to each other.