diff --git a/nifty5/data_objects/distributed_do.py b/nifty5/data_objects/distributed_do.py
index da44fac7983b1574f1a03d3b9119a396b6ef447b..ac4bc2327205c7ab426a093e9a119047ca28248e 100644
--- a/nifty5/data_objects/distributed_do.py
+++ b/nifty5/data_objects/distributed_do.py
@@ -32,7 +32,7 @@ __all__ = ["ntask", "rank", "master", "local_shape", "data_object", "full",
"local_data", "ibegin", "ibegin_from_shape", "np_allreduce_sum",
"np_allreduce_min", "np_allreduce_max",
"distaxis", "from_local_data", "from_global_data", "to_global_data",
- "redistribute", "default_distaxis", "is_numpy",
+ "redistribute", "default_distaxis", "is_numpy", "absmax", "norm",
"lock", "locked", "uniform_full", "transpose", "to_global_data_rw",
"ensure_not_distributed", "ensure_default_distributed"]
@@ -553,3 +553,22 @@ def ensure_default_distributed(arr):
if arr._distaxis != 0:
arr = redistribute(arr, dist=0)
return arr
+
+
+def absmax(arr):
+ if arr._data.size == 0:
+ tmp = np.array(0, dtype=arr._data.dtype)
+ else:
+ tmp = np.linalg.norm(arr._data, ord=np.inf)
+ res = np.empty_like(tmp)
+ _comm.Allreduce(tmp, res, MPI.MAX)
+ return res[()]
+
+
+def norm(arr, ord=2):
+ if ord == np.inf:
+ return absmax(arr)
+ tmp = np.linalg.norm(np.atleast_1d(arr._data), ord=ord) ** ord
+ res = np.empty_like(tmp)
+ _comm.Allreduce(tmp, res, MPI.SUM)
+ return res[()] ** (1./ord)
diff --git a/nifty5/data_objects/numpy_do.py b/nifty5/data_objects/numpy_do.py
index 68cfa99db3fa001d1960d6ca704f2ce6b73ed4f8..9cde82533273cd58eb357e9f3feb83cc0eaf8183 100644
--- a/nifty5/data_objects/numpy_do.py
+++ b/nifty5/data_objects/numpy_do.py
@@ -31,7 +31,7 @@ __all__ = ["ntask", "rank", "master", "local_shape", "data_object", "full",
"local_data", "ibegin", "ibegin_from_shape", "np_allreduce_sum",
"np_allreduce_min", "np_allreduce_max",
"distaxis", "from_local_data", "from_global_data", "to_global_data",
- "redistribute", "default_distaxis", "is_numpy",
+ "redistribute", "default_distaxis", "is_numpy", "absmax", "norm",
"lock", "locked", "uniform_full", "to_global_data_rw",
"ensure_not_distributed", "ensure_default_distributed"]
@@ -141,3 +141,11 @@ def ensure_not_distributed(arr, axes):
def ensure_default_distributed(arr):
return arr
+
+
+def absmax(arr):
+ return np.linalg.norm(arr, ord=np.inf)
+
+
+def norm(arr, ord=2):
+ return np.linalg.norm(np.atleast_1d(arr), ord=ord)
diff --git a/nifty5/field.py b/nifty5/field.py
index 0785512d50b98469d57fbfbed19caeab8d1967e8..0c418cc7f47c98182bd0ffec3f2f96bdfee6eb69 100644
--- a/nifty5/field.py
+++ b/nifty5/field.py
@@ -360,25 +360,20 @@ class Field(object):
# For the moment, do this the explicit, non-optimized way
return (self.conjugate()*x).sum(spaces=spaces)
- def norm(self):
+ def norm(self, ord=2):
""" Computes the L2-norm of the field values.
- Returns
- -------
- float
- The L2-norm of the field values.
- """
- return np.sqrt(abs(self.vdot(x=self)))
-
- def squared_norm(self):
- """ Computes the square of the L2-norm of the field values.
+ Parameters
+ ----------
+ ord : int, default=2
+ accepted values: 1, 2, ..., np.inf
Returns
-------
float
- The square of the L2-norm of the field values.
+ The L2-norm of the field values.
"""
- return abs(self.vdot(x=self))
+ return dobj.norm(self._val, ord)
def conjugate(self):
""" Returns the complex conjugate of the field.
diff --git a/nifty5/multi_field.py b/nifty5/multi_field.py
index 5f6aa304a56b31a192295cc8118ba804e9ebaffe..10795f819a834a36687be3eec466f2d926c4dd43 100644
--- a/nifty5/multi_field.py
+++ b/nifty5/multi_field.py
@@ -137,15 +137,24 @@ class MultiField(object):
domain, tuple(Field.from_global_data(domain[key], arr[key], sum_up)
for key in domain.keys()))
- def norm(self):
- """ Computes the L2-norm of the field values.
+ def norm(self, ord=2):
+ """ Computes the norm of the field values.
+
+ Parameters
+ ----------
+ ord : int, default=2
+ accepted values: 1, 2, ..., np.inf
Returns
-------
norm : float
- The L2-norm of the field values.
+ The norm of the field values.
"""
- return np.sqrt(np.abs(self.vdot(x=self)))
+ nrm = np.asarray([f.norm(ord) for f in self._val])
+ if ord == np.inf:
+ return nrm.max()
+ return (nrm ** ord).sum() ** (1./ord)
+# return np.sqrt(np.abs(self.vdot(x=self)))
def sum(self):
""" Computes the sum all field values.
@@ -168,16 +177,6 @@ class MultiField(object):
"""
return utilities.my_sum(map(lambda d: d.size, self._domain.domains()))
- def squared_norm(self):
- """ Computes the square of the L2-norm of the field values.
-
- Returns
- -------
- float
- The square of the L2-norm of the field values.
- """
- return abs(self.vdot(x=self))
-
def __neg__(self):
return self._transform(lambda x: -x)
diff --git a/test/test_field.py b/test/test_field.py
index cfc6f7d0e3ddd7553962f30bfbc2e2545fccce03..fce50f8aeae20bc7d402205b9f22a04c0a49d3f3 100644
--- a/test/test_field.py
+++ b/test/test_field.py
@@ -110,6 +110,16 @@ 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()
+ 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))
+ assert_allclose(f.norm(3), np.linalg.norm(gd, ord=3))
+ assert_allclose(f.norm(np.inf), np.linalg.norm(gd, ord=np.inf))
+
def test_vdot(self):
s = ift.RGSpace((10,))
f1 = ift.Field.from_random("normal", domain=s, dtype=np.complex128)