cosmetics

nifty5/extra.py

@@ -72,7 +72,7 @@ def _full_implementation(op, domain_dtype, target_dtype, atol, rtol,
 def _check_linearity(op, domain_dtype, atol, rtol):
     fld1 = from_random("normal", op.domain, dtype=domain_dtype)
     fld2 = from_random("normal", op.domain, dtype=domain_dtype)
-    alpha = np.random.random() # FIXME: this can break badly with MPI!
+    alpha = np.random.random()  # FIXME: this can break badly with MPI!
     val1 = op(alpha*fld1+fld2)
     val2 = alpha*op(fld1)+op(fld2)
     _assert_allclose(val1, val2, atol=atol, rtol=rtol)
@@ -81,8 +81,9 @@ def _check_linearity(op, domain_dtype, atol, rtol):
 def _domain_check(op):
     for dd in [op.domain, op.target]:
         if not isinstance(dd, (DomainTuple, MultiDomain)):
-            raise TypeError('The domain and the target of an operator need to',
-                            'be instances of either DomainTuple or MultiDomain.')
+            raise TypeError(
+                'The domain and the target of an operator need to',
+                'be instances of either DomainTuple or MultiDomain.')
 
 
 def consistency_check(op, domain_dtype=np.float64, target_dtype=np.float64,

nifty5/operators/diagonal_operator.py

@@ -158,7 +158,7 @@ class DiagonalOperator(EndomorphicOperator):
     def process_sample(self, samp, from_inverse):
         if (self._complex or (self._diagmin < 0.) or
                 (self._diagmin == 0. and from_inverse)):
-                    raise ValueError("operator not positive definite")
+            raise ValueError("operator not positive definite")
         if from_inverse:
             res = samp.local_data/np.sqrt(self._ldiag)
         else:

nifty5/operators/scaling_operator.py

@@ -87,7 +87,7 @@ class ScalingOperator(EndomorphicOperator):
         fct = self._factor
         if (fct.imag != 0. or fct.real < 0. or
                 (fct.real == 0. and from_inverse)):
-                    raise ValueError("operator not positive definite")
+            raise ValueError("operator not positive definite")
         return 1./np.sqrt(fct) if from_inverse else np.sqrt(fct)
 
 #     def process_sample(self, samp, from_inverse):

nifty5/plot.py

@@ -423,7 +423,7 @@ def _plot(f, ax, **kwargs):
     if len(f) == 0:
         raise ValueError("need something to plot")
     if not isinstance(f[0], Field):
-            raise TypeError("incorrect data type")
+        raise TypeError("incorrect data type")
     dom1 = f[0].domain
     if (len(dom1) == 1 and
         (isinstance(dom1[0], PowerSpace) or

test/test_operators/test_adjoint.py

@@ -39,6 +39,7 @@ dtype = list2fixture([np.float64, np.complex128])
 
 np.random.seed(42)
 
+
 @pmp('sp', _p_RG_spaces)
 def testLOSResponse(sp, dtype):
     starts = np.random.randn(len(sp.shape), 10)
@@ -76,14 +77,14 @@ def testLinearInterpolator():
 def testRealizer(sp):
     op = ift.Realizer(sp)
     ift.extra.consistency_check(op, np.complex128, np.float64,
-    only_r_linear=True)
+                                only_r_linear=True)
 
 
 @pmp('sp', _h_spaces + _p_spaces + _pow_spaces)
 def testConjugationOperator(sp):
     op = ift.ConjugationOperator(sp)
     ift.extra.consistency_check(op, np.complex128, np.complex128,
-    only_r_linear=True)
+                                only_r_linear=True)
 
 
 @pmp('args', [(ift.RGSpace(10, harmonic=True), 4, 0), (ift.RGSpace(

test/test_operators/test_nft.py

@@ -17,7 +17,7 @@
 
 import numpy as np
 import pytest
-from numpy.testing import assert_allclose
+from numpy.testing import assert_allclose, assert_
 
 import nifty5 as ift
 
@@ -26,11 +26,11 @@ np.random.seed(40)
 pmp = pytest.mark.parametrize
 
 
-def _l2error(a,b):
+def _l2error(a, b):
     return np.sqrt(np.sum(np.abs(a-b)**2)/np.sum(np.abs(a)**2))
 
 
-@pmp('eps', [ 1e-2, 1e-4, 1e-7, 1e-10, 1e-11, 1e-12, 2e-13])
+@pmp('eps', [1e-2, 1e-4, 1e-7, 1e-10, 1e-11, 1e-12, 2e-13])
 @pmp('nu', [12, 128])
 @pmp('nv', [4, 12, 128])
 @pmp('N', [1, 10, 100])
@@ -39,7 +39,7 @@ def test_gridding(nu, nv, N, eps):
     vis = np.random.randn(N) + 1j*np.random.randn(N)
 
     # Nifty
-    GM = ift.GridderMaker(ift.RGSpace((nu, nv)),eps=eps)
+    GM = ift.GridderMaker(ift.RGSpace((nu, nv)), eps=eps)
     # re-order for performance
     idx = GM.getReordering(uv)
     uv, vis = uv[idx], vis[idx]
@@ -53,7 +53,7 @@ def test_gridding(nu, nv, N, eps):
     dft = pynu*0.
     for i in range(N):
         dft += (vis[i]*np.exp(2j*np.pi*(x*uv[i, 0] + y*uv[i, 1]))).real
-    assert(_l2error(dft,pynu)<eps)
+    assert_(_l2error(dft, pynu) < eps)
 
 
 @pmp('eps', [1e-2, 1e-6, 2e-13])

test/test_operators/test_simplification.py

@@ -23,23 +23,23 @@ import nifty5 as ift
 
 def test_simplification():
     from nifty5.operators.operator import _ConstantOperator
-    f1 = ift.Field.full(ift.RGSpace(10),2.)
+    f1 = ift.Field.full(ift.RGSpace(10), 2.)
     op = ift.FFTOperator(f1.domain)
     _, op2 = op.simplify_for_constant_input(f1)
     assert_equal(isinstance(op2, _ConstantOperator), True)
     assert_allclose(op(f1).local_data, op2(f1).local_data)
 
     dom = {"a": ift.RGSpace(10)}
-    f1 = ift.full(dom,2.)
+    f1 = ift.full(dom, 2.)
     op = ift.FFTOperator(f1.domain["a"]).ducktape("a")
     _, op2 = op.simplify_for_constant_input(f1)
     assert_equal(isinstance(op2, _ConstantOperator), True)
     assert_allclose(op(f1).local_data, op2(f1).local_data)
 
     dom = {"a": ift.RGSpace(10), "b": ift.RGSpace(5)}
-    f1 = ift.full(dom,2.)
+    f1 = ift.full(dom, 2.)
     pdom = {"a": ift.RGSpace(10)}
-    f2 = ift.full(pdom,2.)
+    f2 = ift.full(pdom, 2.)
     o1 = ift.FFTOperator(f1.domain["a"])
     o2 = ift.FFTOperator(f1.domain["b"])
     op = (o1.ducktape("a").ducktape_left("a") +