From 67bed0b7f781fae2d7ec2b0c42317e7f3fed1b54 Mon Sep 17 00:00:00 2001
From: Martin Reinecke <martin@mpa-garching.mpg.de>
Date: Wed, 8 Nov 2017 11:57:18 +0100
Subject: [PATCH] tweaks
---
nifty/data_objects/my_own_do.py | 40 +++++++----------
nifty/data_objects/numpy_do.py | 8 ----
nifty/field.py | 2 +-
nifty/operators/power_projection_operator.py | 44 ++++++++++---------
nifty/spaces/power_space.py | 4 +-
nifty/spaces/rg_space.py | 1 +
.../test_operators/test_smoothing_operator.py | 5 ++-
7 files changed, 46 insertions(+), 58 deletions(-)
diff --git a/nifty/data_objects/my_own_do.py b/nifty/data_objects/my_own_do.py
index 8e9d86bf6..ecca5b12b 100644
--- a/nifty/data_objects/my_own_do.py
+++ b/nifty/data_objects/my_own_do.py
@@ -6,6 +6,7 @@ class data_object(object):
def __init__(self, npdata):
self._data = np.asarray(npdata)
+ # FIXME: subscripting support will most likely go away
def __getitem__(self, key):
res = self._data[key]
return res if np.isscalar(res) else data_object(res)
@@ -37,6 +38,9 @@ class data_object(object):
return data_object(self._data.imag)
def _contraction_helper(self, op, axis):
+ if axis is not None:
+ if len(axis)==len(self._data.shape):
+ axis = None
if axis is None:
return getattr(self._data, op)()
@@ -164,32 +168,28 @@ def vdot(a, b):
return np.vdot(a._data, b._data)
+def _math_helper(x, function, out):
+ if out is not None:
+ function(x._data, out=out._data)
+ return out
+ else:
+ return data_object(function(x._data))
+
+
def abs(a, out=None):
- if out is None:
- out = empty_like(a)
- np.abs(a._data, out=out._data)
- return out
+ return _math_helper(a, np.abs, out)
def exp(a, out=None):
- if out is None:
- out = empty_like(a)
- np.exp(a._data, out=out._data)
- return out
+ return _math_helper(a, np.exp, out)
def log(a, out=None):
- if out is None:
- out = empty_like(a)
- np.log(a._data, out=out._data)
- return out
+ return _math_helper(a, np.log, out)
def sqrt(a, out=None):
- if out is None:
- out = empty_like(a)
- np.sqrt(a._data, out=out._data)
- return out
+ return _math_helper(a, np.sqrt, out)
def bincount(x, weights=None, minlength=None):
@@ -224,12 +224,6 @@ def ibegin(arr):
return (0,)*arr._data.ndim
-def create_from_template(tmpl, local_data, dtype):
- res = np.ndarray(tmpl.shape, dtype=dtype)
- res[()] = local_data
- return data_object(res)
-
-
def np_allreduce_sum(arr):
return arr
@@ -249,8 +243,6 @@ def from_local_data (shape, arr, dist_axis):
def from_global_data (arr, dist_axis):
if dist_axis!=-1:
raise NotImplementedError
- if shape!=arr.shape:
- raise ValueError
return data_object(arr)
diff --git a/nifty/data_objects/numpy_do.py b/nifty/data_objects/numpy_do.py
index de2eb0ccd..40a30adaf 100644
--- a/nifty/data_objects/numpy_do.py
+++ b/nifty/data_objects/numpy_do.py
@@ -31,12 +31,6 @@ def ibegin(arr):
return (0,)*arr.ndim
-def create_from_template(tmpl, local_data, dtype):
- res = np.ndarray(tmpl.shape, dtype=dtype)
- res[()] = local_data
- return res
-
-
def np_allreduce_sum(arr):
return arr
@@ -56,8 +50,6 @@ def from_local_data (shape, arr, dist_axis):
def from_global_data (arr, dist_axis):
if dist_axis!=-1:
raise NotImplementedError
- if shape!=arr.shape:
- raise ValueError
return arr
diff --git a/nifty/field.py b/nifty/field.py
index 0edb52d07..75c78cf60 100644
--- a/nifty/field.py
+++ b/nifty/field.py
@@ -455,7 +455,7 @@ class Field(object):
raise TypeError("argument must be a Field")
if other.domain != self.domain:
raise ValueError("domains are incompatible.")
- self.val[()] = other.val[()]
+ dobj.local_data(self.val)[()] = dobj.local_data(other.val)[()]
# ---General binary methods---
diff --git a/nifty/operators/power_projection_operator.py b/nifty/operators/power_projection_operator.py
index 8a817538b..33ca402df 100644
--- a/nifty/operators/power_projection_operator.py
+++ b/nifty/operators/power_projection_operator.py
@@ -55,34 +55,36 @@ class PowerProjectionOperator(LinearOperator):
res = Field.zeros(self._target, dtype=x.dtype)
if dobj.dist_axis(x.val) in x.domain.axes[self._space]: # the distributed axis is part of the projected space
pindex = dobj.local_data(pindex)
- pindex.reshape((1, pindex.size, 1))
- arr = dobj.local_data(x.weight(1).val)
- firstaxis = x.domain.axes[self._space][0]
- lastaxis = x.domain.axes[self._space][-1]
- presize = np.prod(arr.shape[0:firstaxis], dtype=np.int)
- postsize = np.prod(arr.shape[lastaxis+1:], dtype=np.int)
- arr = arr.reshape((presize,pindex.size,postsize))
- oarr = dobj.local_data(res.val).reshape((presize,-1,postsize))
- np.add.at(oarr, (slice(None), pindex.ravel(), slice(None)), arr)
else:
pindex = dobj.to_ndarray(pindex)
- pindex.reshape((1, pindex.size, 1))
- arr = dobj.local_data(x.weight(1).val)
- firstaxis = x.domain.axes[self._space][0]
- lastaxis = x.domain.axes[self._space][-1]
- presize = np.prod(arr.shape[0:firstaxis], dtype=np.int)
- postsize = np.prod(arr.shape[lastaxis+1:], dtype=np.int)
- arr = arr.reshape((presize,pindex.size,postsize))
- oarr = dobj.local_data(res.val).reshape((presize,-1,postsize))
- np.add.at(oarr, (slice(None), pindex.ravel(), slice(None)), arr)
+ pindex.reshape((1, pindex.size, 1))
+ arr = dobj.local_data(x.weight(1).val)
+ firstaxis = x.domain.axes[self._space][0]
+ lastaxis = x.domain.axes[self._space][-1]
+ presize = np.prod(arr.shape[0:firstaxis], dtype=np.int)
+ postsize = np.prod(arr.shape[lastaxis+1:], dtype=np.int)
+ arr = arr.reshape((presize,pindex.size,postsize))
+ oarr = dobj.local_data(res.val).reshape((presize,-1,postsize))
+ np.add.at(oarr, (slice(None), pindex.ravel(), slice(None)), arr)
return res.weight(-1, spaces=self._space)
def _adjoint_times(self, x):
pindex = self._target[self._space].pindex
+ res = Field.empty(self._domain, dtype=x.dtype)
+ if dobj.dist_axis(x.val) in x.domain.axes[self._space]: # the distributed axis is part of the projected space
+ pindex = dobj.local_data(pindex)
+ else:
+ pindex = dobj.to_ndarray(pindex)
pindex = pindex.reshape((1, pindex.size, 1))
- arr = x.val.reshape(x.domain.collapsed_shape_for_domain(self._space))
- out = arr[(slice(None), dobj.to_ndarray(pindex.ravel()), slice(None))]
- return Field(self._domain, out.reshape(self._domain.shape))
+ arr = dobj.local_data(x.val)
+ firstaxis = x.domain.axes[self._space][0]
+ lastaxis = x.domain.axes[self._space][-1]
+ presize = np.prod(arr.shape[0:firstaxis], dtype=np.int)
+ postsize = np.prod(arr.shape[lastaxis+1:], dtype=np.int)
+ arr = arr.reshape((presize,-1,postsize))
+ oarr = dobj.local_data(res.val).reshape((presize,-1,postsize))
+ oarr[()] = arr[(slice(None), pindex.ravel(), slice(None))]
+ return res
@property
def domain(self):
diff --git a/nifty/spaces/power_space.py b/nifty/spaces/power_space.py
index 05fe9ce6a..a75795226 100644
--- a/nifty/spaces/power_space.py
+++ b/nifty/spaces/power_space.py
@@ -143,8 +143,8 @@ class PowerSpace(Space):
else:
tbb = binbounds
locdat = np.searchsorted(tbb, dobj.local_data(k_length_array.val))
- temp_pindex = dobj.create_from_template(
- k_length_array.val, local_data=locdat, dtype=locdat.dtype)
+ temp_pindex = dobj.from_local_data(
+ k_length_array.val.shape, locdat, dobj.dist_axis(k_length_array.val))
nbin = len(tbb)
temp_rho = np.bincount(dobj.local_data(temp_pindex).ravel(),
minlength=nbin)
diff --git a/nifty/spaces/rg_space.py b/nifty/spaces/rg_space.py
index 947e98e87..42b35affc 100644
--- a/nifty/spaces/rg_space.py
+++ b/nifty/spaces/rg_space.py
@@ -115,6 +115,7 @@ class RGSpace(Space):
tmp[t2] = True
return np.sqrt(np.nonzero(tmp)[0])*self.distances[0]
else: # do it the hard way
+ # FIXME: this needs to improve for MPI. Maybe unique()/gather()?
tmp = np.unique(dobj.to_ndarray(self.get_k_length_array().val)) # expensive!
tol = 1e-12*tmp[-1]
# remove all points that are closer than tol to their right
diff --git a/test/test_operators/test_smoothing_operator.py b/test/test_operators/test_smoothing_operator.py
index 9180fabf6..2e6cd345f 100644
--- a/test/test_operators/test_smoothing_operator.py
+++ b/test/test_operators/test_smoothing_operator.py
@@ -56,8 +56,9 @@ class SmoothingOperator_Tests(unittest.TestCase):
@expand(product(spaces, [0., .5, 5.]))
def test_times(self, space, sigma):
op = ift.FFTSmoothingOperator(space, sigma=sigma)
- rand1 = ift.Field.zeros(space)
- rand1.val[0] = 1.
+ fld = np.zeros(space.shape, dtype=np.float64)
+ fld[0] = 1.
+ rand1 = ift.Field(space, ift.dobj.from_global_data(fld, dist_axis=-1))
tt1 = op.times(rand1)
assert_allclose(1, tt1.sum())
--
GitLab