cosmetics

test/test_field.py

@@ -272,9 +272,9 @@ def test_err():
     with assert_raises(TypeError):
         ift.full(s1, [2, 3])
     with assert_raises(TypeError):
-        ift.Field(s2, [0,1])
+        ift.Field(s2, [0, 1])
     with assert_raises(TypeError):
-        f1.outer([0,1])
+        f1.outer([0, 1])
     with assert_raises(ValueError):
         f1.extract(s2)
     with assert_raises(TypeError):
@@ -340,12 +340,14 @@ def test_funcs(num, dom, func):
     res2 = getattr(np, func)(num)
     assert_allclose(res.local_data, res2)
 
+
 @pmp('rtype', ['normal', 'pm1', 'uniform'])
 @pmp('dtype', [np.float64, np.complex128])
 def test_from_random(rtype, dtype):
     sp = ift.RGSpace(3)
     f = ift.Field.from_random(rtype, sp, dtype=dtype)
 
+
 def test_field_of_objects():
     arr = np.array(['x', 'y', 'z'])
     sp = ift.RGSpace(3)

test/test_minimizers.py

@@ -72,6 +72,7 @@ def test_quadratic_minimization(minimizer, space):
         rtol=1e-3,
         atol=1e-3)
 
+
 @pmp('space', spaces)
 def test_WF_curvature(space):
     np.random.seed(42)
@@ -85,7 +86,8 @@ def test_WF_curvature(space):
     n = ift.Field.from_random('uniform', domain=space) + 0.5
     N = ift.DiagonalOperator(n)
     all_diag = 1./s + r**2/n
-    curv = ift.WienerFilterCurvature(R,N,S, iteration_controller=IC, iteration_controller_sampling=IC)
+    curv = ift.WienerFilterCurvature(R, N, S, iteration_controller=IC,
+                                     iteration_controller_sampling=IC)
     m = curv.inverse(required_result)
     assert_allclose(
         m.local_data,
@@ -100,7 +102,9 @@ def test_WF_curvature(space):
         n = ift.from_random('uniform', R.domain) + 0.5
         N = ift.DiagonalOperator(n)
         all_diag = 1./s + R(1/n)
-        curv = ift.WienerFilterCurvature(R.adjoint,N,S, iteration_controller=IC, iteration_controller_sampling=IC)
+        curv = ift.WienerFilterCurvature(R.adjoint, N, S,
+                                         iteration_controller=IC,
+                                         iteration_controller_sampling=IC)
         m = curv.inverse(required_result)
         assert_allclose(
             m.local_data,
@@ -111,8 +115,6 @@ def test_WF_curvature(space):
         curv.draw_sample(from_inverse=True)
 
 
-
-
 @pmp('minimizer', minimizers + newton_minimizers)
 def test_rosenbrock(minimizer):
     try:

test/test_multi_field.py

@@ -34,13 +34,13 @@ def test_func():
     assert_allclose(
         ift.log(ift.exp((f1)))["d1"].local_data, f1["d1"].local_data)
 
+
 def test_multifield_field_consistency():
     f1 = ift.full(dom, 27)
     f2 = ift.from_global_data(dom, f1.to_global_data())
     assert_equal(f1.sum(), f2.sum())
     assert_equal(-f1, (-f2)['d1'])
-    assert_equal(f1.__abs__(), (f2.__abs__())['d1'])
-
+    assert_equal(abs(f1), (abs(f2))['d1'])
 
 
 def test_dataconv():
@@ -48,7 +48,7 @@ def test_dataconv():
     f2 = ift.from_global_data(dom, f1.to_global_data())
     for key, val in f1.items():
         assert_equal(val.local_data, f2[key].local_data)
-    if not "d1" in f2:
+    if "d1" not in f2:
         raise KeyError()
     assert_equal({"d1": f1}, f2.to_dict())
     f3 = ift.full(dom, 27+1.j)

test/test_operators/test_gradients.py

@@ -81,10 +81,10 @@ def testBinary(type1, type2, space, seed):
     pos = ift.from_random("normal", dom)
     model = ift.OuterProduct(pos['s1'], ift.makeDomain(space))
     ift.extra.check_value_gradient_consistency(model, pos['s2'], ntries=20)
-    model = select_s1 **2
+    model = select_s1**2
     pos = ift.from_random("normal", dom1)
     ift.extra.check_value_gradient_consistency(model, pos, ntries=20)
-    model = select_s1.clip(-1,1)
+    model = select_s1.clip(-1, 1)
     pos = ift.from_random("normal", dom1)
     ift.extra.check_value_gradient_consistency(model, pos, ntries=20)
     if isinstance(space, ift.RGSpace):
@@ -127,9 +127,9 @@ def testPointModel(space, seed):
 
 
 @pmp('target', [
-    ift.RGSpace(64, distances=.789,harmonic=True),
-    ift.RGSpace([32, 32], distances=.789,harmonic=True),
-    ift.RGSpace([32, 32, 8], distances=.789,harmonic=True)
+    ift.RGSpace(64, distances=.789, harmonic=True),
+    ift.RGSpace([32, 32], distances=.789, harmonic=True),
+    ift.RGSpace([32, 32, 8], distances=.789, harmonic=True)
 ])
 @pmp('causal', [True, False])
 @pmp('minimum_phase', [True, False])

test/test_sugar.py

@@ -25,13 +25,14 @@ import nifty5 as ift
 def test_get_signal_variance():
     space = ift.RGSpace(3)
     hspace = space.get_default_codomain()
-    spec1 = lambda x : np.ones_like(x)
+    spec1 = lambda x: np.ones_like(x)
     assert_equal(ift.get_signal_variance(spec1, hspace), 3.)
 
     space = ift.RGSpace(3, distances=1.)
     hspace = space.get_default_codomain()
+
     def spec2(k):
         t = np.zeros_like(k)
-        t[k==0] = 1.
+        t[k == 0] = 1.
         return t
     assert_equal(ift.get_signal_variance(spec2, hspace), 1/9.)