Apply pyupgrade to tests.sandbox

tests/sandbox/linalg/test_linalg.py

@@ -45,7 +45,7 @@ def test_rop_lop():
     v1 = rop_f(vx, vv)
     v2 = scan_f(vx, vv)
 
-    assert _allclose(v1, v2), "ROP mismatch: %s %s" % (v1, v2)
+    assert _allclose(v1, v2), "ROP mismatch: {} {}".format(v1, v2)
 
     raised = False
     try:
@@ -54,10 +54,8 @@ def test_rop_lop():
         raised = True
     if not raised:
         raise Exception(
-            (
-                "Op did not raised an error even though the function"
-                " is not differentiable"
-            )
+            "Op did not raised an error even though the function"
+            " is not differentiable"
         )
 
     vv = np.asarray(rng.uniform(size=(4,)), theano.config.floatX)
@@ -69,7 +67,7 @@ def test_rop_lop():
 
     v1 = lop_f(vx, vv)
     v2 = scan_f(vx, vv)
-    assert _allclose(v1, v2), "LOP mismatch: %s %s" % (v1, v2)
+    assert _allclose(v1, v2), "LOP mismatch: {} {}".format(v1, v2)
 
 
 def test_spectral_radius_bound():

tests/sandbox/test_rng_mrg.py

@@ -217,7 +217,9 @@ def check_basics(
     if hasattr(target_avg, "shape"):  # looks if target_avg is an array
         diff = np.mean(abs(mean - target_avg))
         # print prefix, 'mean diff with mean', diff
-        assert np.all(diff < mean_rtol * (1 + abs(target_avg))), "bad mean? %s %s" % (
+        assert np.all(
+            diff < mean_rtol * (1 + abs(target_avg))
+        ), "bad mean? {} {}".format(
             mean,
             target_avg,
         )
@@ -228,7 +230,7 @@ def check_basics(
         # print prefix, 'mean', mean
         assert abs(mean - target_avg) < mean_rtol * (
             1 + abs(target_avg)
-        ), "bad mean? %f %f" % (mean, target_avg)
+        ), "bad mean? {:f} {:f}".format(mean, target_avg)
 
     std = np.sqrt(avg_var)
     # print prefix, 'var', avg_var
@@ -236,7 +238,7 @@ def check_basics(
     if target_std is not None:
         assert abs(std - target_std) < std_tol * (
             1 + abs(target_std)
-        ), "bad std? %f %f %f" % (std, target_std, std_tol)
+        ), "bad std? {:f} {:f} {:f}".format(std, target_std, std_tol)
     # print prefix, 'time', dt
     # print prefix, 'elements', steps * sample_size[0] * sample_size[1]
     # print prefix, 'samples/sec', steps * sample_size[0] * sample_size[1] / dt
@@ -598,11 +600,11 @@ def test_normal_truncation():
 
         # check if truncated at 2*std
         samples = f(*input)
-        assert np.all(avg + 2 * std - samples >= 0), "bad upper bound? %s %s" % (
+        assert np.all(avg + 2 * std - samples >= 0), "bad upper bound? {} {}".format(
             samples,
             avg + 2 * std,
         )
-        assert np.all(samples - (avg - 2 * std) >= 0), "bad lower bound? %s %s" % (
+        assert np.all(samples - (avg - 2 * std) >= 0), "bad lower bound? {} {}".format(
             samples,
             avg - 2 * std,
         )