diff --git a/demos/wiener_filter_via_curvature.py b/demos/wiener_filter_via_curvature.py
index 51bcbefc6f4cb1322d45a4e30a9acd4e108ce0a8..ee9b173e32e86efe87a0ea33d651b52db26784fb 100644
--- a/demos/wiener_filter_via_curvature.py
+++ b/demos/wiener_filter_via_curvature.py
@@ -44,7 +44,7 @@ if __name__ == "__main__":
np.random.seed(43)
mock_power = ift.Field(power_space,
- val=power_spectrum(power_space.k_lengths))
+ val=ift.dobj.from_global_data(power_spectrum(power_space.k_lengths)))
mock_harmonic = ift.power_synthesize(mock_power, real_signal=True)
mock_harmonic = mock_harmonic.real
mock_signal = fft(mock_harmonic)
diff --git a/demos/wiener_filter_via_hamiltonian.py b/demos/wiener_filter_via_hamiltonian.py
index 94b4c20a27201aa60ec5f8fc1335da04fd080287..7142ccf4b1272f8ef3b4e7651934255ce14067d4 100644
--- a/demos/wiener_filter_via_hamiltonian.py
+++ b/demos/wiener_filter_via_hamiltonian.py
@@ -50,14 +50,15 @@ if __name__ == "__main__":
S = ift.create_power_operator(h_space, power_spectrum=p_spec)
# Drawing a sample sh from the prior distribution in harmonic space
- sp = ift.Field(p_space, val=p_spec(p_space.k_lengths))
+ sp = ift.Field(p_space, ift.dobj.from_global_data(p_spec(p_space.k_lengths)))
sh = ift.power_synthesize(sp, real_signal=True)
ss = fft.adjoint_times(sh)
# Choosing the measurement instrument
# Instrument = ift.FFTSmoothingOperator(s_space, sigma=0.05)
- diag = ift.Field.ones(s_space)
- diag.val[20:80, 20:80] = 0
+ diag = np.ones(s_space.shape)
+ diag[20:80, 20:80] = 0
+ diag = ift.Field(s_space,ift.dobj.from_global_data(diag))
Instrument = ift.DiagonalOperator(diag)
# Adding a harmonic transformation to the instrument
diff --git a/nifty/data_objects/distributed_do.py b/nifty/data_objects/distributed_do.py
index 53825b1eceeb4e8d5e29050757795a8d9b172814..ed388eff394e1d17b1a3c82f31405d91879ecc0b 100644
--- a/nifty/data_objects/distributed_do.py
+++ b/nifty/data_objects/distributed_do.py
@@ -17,7 +17,7 @@ def shareRange(nwork, nshares, myshare):
hi = lo+nbase+ (1 if myshare<additional else 0)
return lo,hi
-def get_locshape(shape, distaxis):
+def local_shape(shape, distaxis):
if len(shape)==0:
distaxis = -1
if distaxis==-1:
@@ -25,8 +25,6 @@ def get_locshape(shape, distaxis):
shape2=list(shape)
shape2[distaxis]=shareSize(shape[distaxis],ntask,rank)
return tuple(shape2)
-def local_shape(shape, distaxis):
- return get_locshape(shape,distaxis)
class data_object(object):
def __init__(self, shape, data, distaxis):
@@ -35,7 +33,7 @@ class data_object(object):
if len(self._shape)==0:
distaxis = -1
self._distaxis = distaxis
- lshape = get_locshape(self._shape, self._distaxis)
+ lshape = local_shape(self._shape, self._distaxis)
self._data = data
def sanity_checks(self):
@@ -123,6 +121,14 @@ class data_object(object):
def sum(self, axis=None):
return self._contraction_helper("sum", MPI.SUM, axis)
+ # FIXME: to be improved!
+ def mean(self):
+ return self.sum()/self.size
+ def std(self):
+ return np.sqrt(self.var())
+ def var(self):
+ return (abs(self-self.mean())**2).mean()
+
def _binary_helper(self, other, op):
a = self
if isinstance(other, data_object):
@@ -173,6 +179,9 @@ class data_object(object):
def __rdiv__(self, other):
return self._binary_helper(other, op='__rdiv__')
+ def __idiv__(self, other):
+ return self._binary_helper(other, op='__idiv__')
+
def __truediv__(self, other):
return self._binary_helper(other, op='__truediv__')
@@ -214,19 +223,19 @@ class data_object(object):
def full(shape, fill_value, dtype=None, distaxis=0):
- return data_object(shape, np.full(get_locshape(shape, distaxis), fill_value, dtype), distaxis)
+ return data_object(shape, np.full(local_shape(shape, distaxis), fill_value, dtype), distaxis)
def empty(shape, dtype=None, distaxis=0):
- return data_object(shape, np.empty(get_locshape(shape, distaxis), dtype), distaxis)
+ return data_object(shape, np.empty(local_shape(shape, distaxis), dtype), distaxis)
def zeros(shape, dtype=None, distaxis=0):
- return data_object(shape, np.zeros(get_locshape(shape, distaxis), dtype), distaxis)
+ return data_object(shape, np.zeros(local_shape(shape, distaxis), dtype), distaxis)
def ones(shape, dtype=None, distaxis=0):
- return data_object(shape, np.ones(get_locshape(shape, distaxis), dtype), distaxis)
+ return data_object(shape, np.ones(local_shape(shape, distaxis), dtype), distaxis)
def empty_like(a, dtype=None):
@@ -277,9 +286,9 @@ def from_object(object, dtype=None, copy=True):
def from_random(random_type, shape, dtype=np.float64, distaxis=0, **kwargs):
generator_function = getattr(Random, random_type)
- lshape = get_locshape(shape, distaxis)
- return data_object(shape, generator_function(dtype=dtype, shape=lshape, **kwargs), distaxis=distaxis)
-
+ #lshape = local_shape(shape, distaxis)
+ #return data_object(shape, generator_function(dtype=dtype, shape=lshape, **kwargs), distaxis=distaxis)
+ return from_global_data(generator_function(dtype=dtype, shape=shape, **kwargs), distaxis=distaxis)
def local_data(arr):
return arr._data
@@ -368,8 +377,8 @@ def redistribute (arr, dist=None, nodist=None):
ssz=np.empty(ntask,dtype=np.int)
rsz=np.empty(ntask,dtype=np.int)
for i in range(ntask):
- ssz[i]=slabsize*tmp.shape[1]*shareSize(arr.shape[dist],ntask,i)
- rsz[i]=slabsize*shareSize(arr.shape[dist],ntask,rank)*shareSize(arr.shape[arr._distaxis],ntask,i)
+ ssz[i]=shareSize(arr.shape[dist],ntask,i)*tmp.shape[1]*slabsize
+ rsz[i]=shareSize(arr.shape[dist],ntask,rank)*shareSize(arr.shape[arr._distaxis],ntask,i)*slabsize
sdisp=np.empty(ntask,dtype=np.int)
rdisp=np.empty(ntask,dtype=np.int)
sdisp[0]=0
@@ -377,7 +386,7 @@ def redistribute (arr, dist=None, nodist=None):
sdisp[1:]=np.cumsum(ssz[:-1])
rdisp[1:]=np.cumsum(rsz[:-1])
tmp=tmp.flatten()
- out = np.empty(np.prod(get_locshape(arr.shape,dist)),dtype=arr.dtype)
+ out = np.empty(np.prod(local_shape(arr.shape,dist)),dtype=arr.dtype)
s_msg = [tmp, (ssz, sdisp), MPI.BYTE]
r_msg = [out, (rsz, rdisp), MPI.BYTE]
comm.Alltoallv(s_msg, r_msg)
diff --git a/nifty/operators/power_projection_operator.py b/nifty/operators/power_projection_operator.py
index 3448af1e57e3f4f5f535a64ef6bf0814efeea0b8..dff28f6d0b7a481a39b19df7b94ddd3ea56916a5 100644
--- a/nifty/operators/power_projection_operator.py
+++ b/nifty/operators/power_projection_operator.py
@@ -77,7 +77,7 @@ class PowerProjectionOperator(LinearOperator):
oarr = oarr.reshape(self._target.shape)
res = Field(self._target, dobj.from_global_data(oarr))
else:
- oarr = oarr.reshape(dobj.get_locshape(self._target.shape, dobj.distaxis(x.val)))
+ oarr = oarr.reshape(dobj.local_shape(self._target.shape, dobj.distaxis(x.val)))
res = Field(self._target, dobj.from_local_data(self._target.shape,oarr,dobj.default_distaxis()))
return res.weight(-1, spaces=self._space)
diff --git a/nifty/plotting/plot.py b/nifty/plotting/plot.py
index f5599ec98d1a3b5b2e1bf0fe18f9c596020c5ccf..65c2ce45e0a8c3c693360b59dc9459129cd250f4 100644
--- a/nifty/plotting/plot.py
+++ b/nifty/plotting/plot.py
@@ -1,6 +1,6 @@
from __future__ import division
import numpy as np
-from ..import Field, RGSpace, HPSpace, GLSpace, PowerSpace
+from ..import Field, RGSpace, HPSpace, GLSpace, PowerSpace, dobj
import os
# relevant properties:
@@ -45,6 +45,8 @@ def _find_closest(A, target):
def _makeplot(name):
import matplotlib.pyplot as plt
+ if dobj.rank!=0:
+ return
if name is None:
plt.show()
return
@@ -185,7 +187,7 @@ def plot(f, **kwargs):
dy = dom.distances[1]
xc = np.arange(nx, dtype=np.float64)*dx
yc = np.arange(ny, dtype=np.float64)*dy
- im = ax.imshow(f.val, extent=[xc[0], xc[-1], yc[0], yc[-1]],
+ im = ax.imshow(dobj.to_global_data(f.val), extent=[xc[0], xc[-1], yc[0], yc[-1]],
vmin=kwargs.get("zmin"),
vmax=kwargs.get("zmax"), cmap=cmap, origin="lower")
# from mpl_toolkits.axes_grid1 import make_axes_locatable