From dba338d6cdb9faca85d44d329de418235bbcac4c Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Fri, 14 Feb 2025 01:31:52 +0000
Subject: [PATCH] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 hera_filters/dspec.py            | 12 ++++++------
 hera_filters/tests/test_dspec.py |  4 ++--
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/hera_filters/dspec.py b/hera_filters/dspec.py
index 4758c04..8b26e64 100644
--- a/hera_filters/dspec.py
+++ b/hera_filters/dspec.py
@@ -2933,11 +2933,11 @@ def sparse_linear_fit_2D(
         If True, the solver will apply a preconditioner to the basis matrices before
         solving the least-squares problem. This option is useful when the input weights
         are frequency or time dependent and are either very large or very small, or when
-        the basis matrices are ill-conditioned due to large stretches of zeros. 
-        The preconditioner is computed using the the inverse of the regularized Gramian 
-        matrix of the basis matrices. Prior computing the inverse, the eigenvalues of the 
-        Gramian matrix are regularized by adding a small value proportional to the smallest 
-        eigenvalue. This helps to stabilize the computation of the inverse. The regularization 
+        the basis matrices are ill-conditioned due to large stretches of zeros.
+        The preconditioner is computed using the the inverse of the regularized Gramian
+        matrix of the basis matrices. Prior computing the inverse, the eigenvalues of the
+        Gramian matrix are regularized by adding a small value proportional to the smallest
+        eigenvalue. This helps to stabilize the computation of the inverse. The regularization
         factor is computed as the minimum eigenvalue of the Gramian matrix multiplied by the
         `eig_scaling_factor` parameter.
     eig_scaling_factor : float, optional, default 1e-1
@@ -2994,7 +2994,7 @@ def sparse_linear_fit_2D(
         axis_1_pcond = np.linalg.pinv(
             XTX_axis_1 + np.eye(XTX_axis_1.shape[0]) * axis_1_lambda
         )
-        
+
         # Compute the preconditioner for the second axis
         XTX_axis_2 = np.dot(axis_2_basis.T.conj() * axis_2_wgts, axis_2_basis)
         eigenval, _ = np.linalg.eig(XTX_axis_2)
diff --git a/hera_filters/tests/test_dspec.py b/hera_filters/tests/test_dspec.py
index 4efa554..7c3c0a7 100644
--- a/hera_filters/tests/test_dspec.py
+++ b/hera_filters/tests/test_dspec.py
@@ -1557,7 +1557,7 @@ def test_precondition_sparse_solver():
     axis_1_weights = (~time_flags[:, 0]).astype(float) * rng.integers(1, 10, size=(ntimes,))
     axis_2_weights = (~freq_flags[0]).astype(float)
     wgts = np.outer(axis_1_weights, axis_2_weights)
-    
+
     # Add frequency dependence to the weights to make the problem more ill-conditioned
     wgts *= (freqs / 150e6) ** -3.5
 
@@ -1589,4 +1589,4 @@ def test_precondition_sparse_solver():
     # Check that the fit closely matches to the separable fit
     np.testing.assert_allclose(sol, sol_sparse, atol=1e-8, rtol=1e-6)
     np.testing.assert_allclose(sol, sol_sparse_precond, atol=1e-8, rtol=1e-6)
-    np.testing.assert_array_less(meta_precond['iter_num'], meta['iter_num'])
\ No newline at end of file
+    np.testing.assert_array_less(meta_precond['iter_num'], meta['iter_num'])