From e0a883123b239d5ecbc3a439ea37a24fe2db983e Mon Sep 17 00:00:00 2001
From: Steven Murray <steven.g.murray@asu.edu>
Date: Fri, 5 Jul 2024 15:25:44 +0200
Subject: [PATCH] style: run pre-commit

---
 .github/workflows/warnings_tests.yaml | 2 +-
 src/linsolve/linsolve.py              | 4 ++--
 tests/test_linsolve.py                | 4 +++-
 3 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/warnings_tests.yaml b/.github/workflows/warnings_tests.yaml
index dec1e99..cf66876 100644
--- a/.github/workflows/warnings_tests.yaml
+++ b/.github/workflows/warnings_tests.yaml
@@ -20,4 +20,4 @@ jobs:
 
       - name: Run Tests
         run: |
-          pytest -W error
\ No newline at end of file
+          pytest -W error
diff --git a/src/linsolve/linsolve.py b/src/linsolve/linsolve.py
index 89aa7cf..550c6c7 100644
--- a/src/linsolve/linsolve.py
+++ b/src/linsolve/linsolve.py
@@ -564,7 +564,7 @@ def _invert_solve(self, A, y, rcond):
         methods.
         """
         # As of numpy 1.8, solve works on stacks of matrices
-        # Change in numpy 2.0:            
+        # Change in numpy 2.0:
         # The b array is only treated as a shape (M,) column vector if it is
         # exactly 1-dimensional. In all other instances it is treated as a stack
         # of (M, K) matrices. Previously b would be treated as a stack of (M,)
@@ -696,7 +696,7 @@ def eval(self, sol, keys=None):
     def _chisq(self, sol, data, wgts, evaluator):
         """Internal adaptable chisq calculator."""
         if len(wgts) == 0:
-            sigma2 = {k: 1.0 for k in list(data.keys())}  # equal weights
+            sigma2 = dict.fromkeys(data.keys(), value=1.0)  # equal weights
         else:
             sigma2 = {k: wgts[k] ** -1 for k in list(wgts.keys())}
         evaluated = evaluator(sol, keys=data)
diff --git a/tests/test_linsolve.py b/tests/test_linsolve.py
index 64ae692..df14d97 100644
--- a/tests/test_linsolve.py
+++ b/tests/test_linsolve.py
@@ -490,7 +490,9 @@ def test_init(self):
             np.testing.assert_almost_equal(eval(k), 0.002)
         assert len(ls.ls.prms) == 3
 
-        ls = linsolve.LinProductSolver(d, sol0, w, sparse=self.sparse, build_solver=False)
+        ls = linsolve.LinProductSolver(
+            d, sol0, w, sparse=self.sparse, build_solver=False
+        )
         assert not hasattr(ls, "ls")
         assert ls.dtype == np.complex64