From 338bf8906a4097aab6acb840c708fde598356183 Mon Sep 17 00:00:00 2001
From: Fabian Joswig <fabian.joswig@ed.ac.uk>
Date: Fri, 24 Jun 2022 12:28:49 +0100
Subject: [PATCH] refactor: maintainability issues in tests fixed.

---
 tests/correlators_test.py | 4 ++--
 tests/fits_test.py        | 7 +++----
 tests/linalg_test.py      | 4 ++--
 3 files changed, 7 insertions(+), 8 deletions(-)

diff --git a/tests/correlators_test.py b/tests/correlators_test.py
index 3cfa230b..c1c79975 100644
--- a/tests/correlators_test.py
+++ b/tests/correlators_test.py
@@ -249,9 +249,9 @@ def test_matrix_corr():
 
 def test_corr_none_entries():
     a = pe.pseudo_Obs(1.0, 0.1, 'a')
-    l = np.asarray([[a, a], [a, a]])
+    la = np.asarray([[a, a], [a, a]])
     n = np.asarray([[None, None], [None, None]])
-    x = [l, n]
+    x = [la, n]
     matr = pe.Corr(x)
     matr.projected(np.asarray([1.0, 0.0]))
 
diff --git a/tests/fits_test.py b/tests/fits_test.py
index 8dd6677d..6cb806d2 100644
--- a/tests/fits_test.py
+++ b/tests/fits_test.py
@@ -194,7 +194,7 @@ def test_linear_fit_guesses():
         lin_func = lambda a, x: a[0] + a[1] * x
         with pytest.raises(Exception):
             pe.least_squares(xvals, yvals, lin_func)
-        [o.gamma_method() for o in yvals];
+        [o.gamma_method() for o in yvals]
         with pytest.raises(Exception):
             pe.least_squares(xvals, yvals, lin_func, initial_guess=[5])
 
@@ -414,7 +414,7 @@ def test_fit_vs_jackknife():
 
     for i, cov in enumerate([cov1, cov2, cov3]):
         dat = pe.misc.gen_correlated_data(np.arange(1, 4), cov, 'test', 0.5, samples=samples)
-        [o.gamma_method(S=0) for o in dat];
+        [o.gamma_method(S=0) for o in dat]
         func = lambda a, x: a[0] + a[1] * x
         fr = pe.least_squares(np.arange(1, 4), dat, func)
         fr.gamma_method(S=0)
@@ -448,8 +448,7 @@ def test_correlated_fit_vs_jackknife():
     x_val = np.arange(1, 6, 2)
     for i, cov in enumerate([cov1, cov2, cov3]):
         dat = pe.misc.gen_correlated_data(x_val + x_val ** 2 + np.random.normal(0.0, 0.1, 3), cov, 'test', 0.5, samples=samples)
-        [o.gamma_method(S=0) for o in dat];
-        dat
+        [o.gamma_method(S=0) for o in dat]
         func = lambda a, x: a[0] * x + a[1] * x ** 2
         fr = pe.least_squares(x_val, dat, func, correlated_fit=True, silent=True)
         [o.gamma_method(S=0) for o in fr]
diff --git a/tests/linalg_test.py b/tests/linalg_test.py
index 09db0ac5..59dc898c 100644
--- a/tests/linalg_test.py
+++ b/tests/linalg_test.py
@@ -258,8 +258,8 @@ def test_complex_matrix_inverse():
     inverse_matrix = np.linalg.inv(matrix)
     inverse_obs_matrix = pe.linalg.inv(obs_matrix)
     for (n, m), entry in np.ndenumerate(inverse_matrix):
-        assert np.isclose(inverse_matrix[n, m].real,  inverse_obs_matrix[n, m].real.value)
-        assert np.isclose(inverse_matrix[n, m].imag,  inverse_obs_matrix[n, m].imag.value)
+        assert np.isclose(inverse_matrix[n, m].real, inverse_obs_matrix[n, m].real.value)
+        assert np.isclose(inverse_matrix[n, m].imag, inverse_obs_matrix[n, m].imag.value)
 
 
 def test_matrix_functions():