style: clean up several lines and remove noqa

Author: John Halloran

doc/source/conf.py

@@ -20,7 +20,8 @@
 
 # If extensions (or modules to document with autodoc) are in another directory,
 # add these directories to sys.path here. If the directory is relative to the
-# documentation root, use Path().resolve() to make it absolute, like shown here. # noqa: E501
+# documentation root, use Path().resolve() to make it absolute,
+# like shown here.
 # sys.path.insert(0, str(Path(".").resolve()))
 sys.path.insert(0, str(Path("../..").resolve()))
 sys.path.insert(0, str(Path("../../src").resolve()))
@@ -204,7 +205,7 @@
 # html_file_suffix = None
 
 # Output file base name for HTML help builder.
-basename = "diffpy.snmf".replace(" ", "").replace(".", "")  # noqa: E501
+basename = "diffpy.snmf".replace(" ", "").replace(".", "")
 htmlhelp_basename = basename + "doc"
 
 

src/diffpy/snmf/snmf_class.py

@@ -628,7 +628,7 @@ def apply_transformation_matrix(
         # Expand row indices
         repm = np.tile(
             np.arange(self.n_components),
-            (self.signal_length, self.n_signals),  # noqa E501
+            (self.signal_length, self.n_signals),
         )
 
         # Compute transformations
@@ -747,9 +747,7 @@ def update_components(self):
                 * (self.components_ - self._prev_components)
             )  # Element-wise multiplication
             denom = (
-                np.linalg.norm(
-                    self.components_ - self._prev_components, "fro"
-                )  # noqa E501
+                np.linalg.norm(self.components_ - self._prev_components, "fro")
                 ** 2
             )  # Frobenius norm squared
             step_size = num / denom if denom > 0 else initial_step_size
@@ -946,8 +944,7 @@ def _compute_objective_function(
         regularization_term = (
             0.5
             * rho
-            * np.linalg.norm(spline_smooth_operator @ stretch.T, "fro")
-            ** 2  # noqa E501
+            * np.linalg.norm(spline_smooth_operator @ stretch.T, "fro") ** 2
         )
         sparsity_term = eta * np.sum(np.sqrt(components))
         return residual_term + regularization_term + sparsity_term