cosmetics and slightly better tests

nifty5/data_objects/distributed_do.py

@@ -559,8 +559,7 @@ def absmax(arr):
     if arr._data.size == 0:
         tmp = np.array(0, dtype=arr._data.dtype)
     else:
-        tmp = np.asarray(np.linalg.norm(np.atleast_1d(arr._data).reshape(-1),
-                                        ord=np.inf))
+        tmp = np.asarray(np.linalg.norm(arr._data.reshape(-1), ord=np.inf))
     res = np.empty_like(tmp)
     _comm.Allreduce(tmp, res, MPI.MAX)
     return res[()]
@@ -569,8 +568,7 @@ def absmax(arr):
 def norm(arr, ord=2):
     if ord == np.inf:
         return absmax(arr)
-    tmp = np.asarray(np.linalg.norm(np.atleast_1d(arr._data).reshape(-1),
-                                    ord=ord) ** ord)
+    tmp = np.asarray(np.linalg.norm(arr._data.reshape(-1), ord=ord) ** ord)
     res = np.empty_like(tmp)
     _comm.Allreduce(tmp, res, MPI.SUM)
     return res[()] ** (1./ord)

nifty5/data_objects/numpy_do.py

@@ -144,8 +144,8 @@ def ensure_default_distributed(arr):
 
 
 def absmax(arr):
-    return np.linalg.norm(np.atleast_1d(arr).rehape(-1), ord=np.inf)
+    return np.linalg.norm(arr.rehape(-1), ord=np.inf)
 
 
 def norm(arr, ord=2):
-    return np.linalg.norm(np.atleast_1d(arr).reshape(-1), ord=ord)
+    return np.linalg.norm(arr.reshape(-1), ord=ord)

nifty5/library/adjust_variances.py

@@ -23,7 +23,8 @@ from ..operators.energy_operators import Hamiltonian, InverseGammaLikelihood
 from ..operators.scaling_operator import ScalingOperator
 
 
-def make_adjust_variances(a, xi, position, samples=[], scaling=None, ic_samp=None):
+def make_adjust_variances(a, xi, position, samples=[], scaling=None,
+                          ic_samp=None):
     """ Creates a Hamiltonian for constant likelihood optimizations.
 
     Constructs a Hamiltonian to solve constant likelihood optimizations of the

test/test_energies/test_consistency.py

@@ -69,7 +69,6 @@ class Energy_Tests(unittest.TestCase):
         energy = ift.InverseGammaLikelihood(ift.exp, d)
         ift.extra.check_value_gradient_consistency(energy, model, tol=1e-7)
 
-
     @expand(product(
         [ift.GLSpace(15),
          ift.RGSpace(64, distances=.789),

test/test_field.py

@@ -110,10 +110,12 @@ class Test_Functionality(unittest.TestCase):
         assert_allclose(sc1.mean.local_data, fp1.local_data, rtol=0.2)
         assert_allclose(sc2.mean.local_data, fp2.local_data, rtol=0.2)
 
-    def test_norm(self):
-        s = ift.RGSpace((10,))
-        f = ift.Field.from_random("normal", domain=s, dtype=np.complex128)
-        gd = f.to_global_data()
+    @expand(product([ift.RGSpace((8,), harmonic=True), (),
+                     ift.RGSpace((8, 8), harmonic=True, distances=0.123),
+                     ift.RGSpace((2, 3, 7))]))
+    def test_norm(self, space):
+        f = ift.Field.from_random("normal", domain=space, dtype=np.complex128)
+        gd = f.to_global_data().reshape(-1)
         assert_allclose(f.norm(), np.linalg.norm(gd))
         assert_allclose(f.norm(1), np.linalg.norm(gd, ord=1))
         assert_allclose(f.norm(2), np.linalg.norm(gd, ord=2))