Formatting

test/test_operators/test_fisher_metric.py

@@ -26,17 +26,19 @@ spaces = [ift.GLSpace(5),
           (ift.RGSpace(3, distances=.789), ift.UnstructuredDomain(2))]
 pmp = pytest.mark.parametrize
 field = list2fixture([ift.from_random(sp, 'normal') for sp in spaces] +
-        [ift.from_random(sp, 'normal', dtype=np.complex128) for sp in spaces])
+                     [ift.from_random(sp, 'normal', dtype=np.complex128) for sp in spaces])
 
 Nsamp = 2000
 np.random.seed(42)
 
+
 def _to_array(d):
     if isinstance(d, np.ndarray):
         return d
     assert isinstance(d, dict)
     return np.concatenate(list(d.values()))
 
+
 def _complex2real(sp):
     tup = tuple([d for d in sp])
     rsp = ift.DomainTuple.make((ift.UnstructuredDomain(2),) + tup)
@@ -45,6 +47,7 @@ def _complex2real(sp):
     x = ift.ScalingOperator(sp, 1)
     return rl(x.real)+im(x.imag)
 
+
 def test_complex2real():
     sp = ift.UnstructuredDomain(3)
     op = _complex2real(ift.makeDomain(sp))
@@ -53,14 +56,16 @@ def test_complex2real():
     assert op(f).dtype == np.float64
     f = ift.from_random(op.target, 'normal')
     assert np.all((f == op(op.adjoint_times(f))).val)
-    
+
+
 def energy_tester_complex(pos, get_noisy_data, energy_initializer):
     op = _complex2real(pos.domain)
     npos = op(pos)
-    nget_noisy_data = lambda mean : get_noisy_data(op.adjoint_times(mean))
-    nenergy_initializer = lambda mean : energy_initializer(mean) @ op.adjoint
+    nget_noisy_data = lambda mean: get_noisy_data(op.adjoint_times(mean))
+    nenergy_initializer = lambda mean: energy_initializer(mean) @ op.adjoint
     energy_tester(npos, nget_noisy_data, nenergy_initializer)
 
+
 def energy_tester(pos, get_noisy_data, energy_initializer):
     if np.issubdtype(pos.dtype, np.complexfloating):
         energy_tester_complex(pos, get_noisy_data, energy_initializer)
@@ -83,25 +88,24 @@ def energy_tester(pos, get_noisy_data, energy_initializer):
     res2 = _to_array(energy(lin).metric(test_vec).val)
     np.testing.assert_allclose(res/std, res2/std, atol=6)
 
+
 def test_GaussianEnergy(field):
     dtype = field.dtype
-
     icov = ift.from_random(field.domain, 'normal')**2
     icov = ift.makeOp(icov)
-    get_noisy_data = lambda mean : mean + icov.draw_sample_with_dtype(
-            from_inverse=True, dtype=dtype)
-    E_init = lambda mean : ift.GaussianEnergy(mean=mean,
-            inverse_covariance=icov)
+    get_noisy_data = lambda mean: mean + icov.draw_sample_with_dtype(
+        from_inverse=True, dtype=dtype)
+    E_init = lambda mean: ift.GaussianEnergy(mean=mean, inverse_covariance=icov)
     energy_tester(field, get_noisy_data, E_init)
 
+
 def test_PoissonEnergy(field):
     if not isinstance(field, ift.Field):
         return
     if np.iscomplexobj(field.val):
         return
-    def get_noisy_data(mean):
-        return ift.makeField(mean.domain, np.random.poisson(mean.val))
-    lam = 10*(field**2).clip(0.1,None) # make rate positive and high enough to avoid bad statistic
-    E_init = lambda mean : ift.PoissonianEnergy(mean)
+    get_noisy_data = lambda mean: ift.makeField(mean.domain, np.random.poisson(mean.val))
+    # Make rate positive and high enough to avoid bad statistic
+    lam = 10*(field**2).clip(0.1, None)
+    E_init = lambda mean: ift.PoissonianEnergy(mean)
     energy_tester(lam, get_noisy_data, E_init)
-