diff --git a/nifty/operators/smoothing_operator/smooth_util.pyx b/nifty/operators/smoothing_operator/smooth_util.pyx
index db1676543516362c864f911b0497769747de42c2..f1e714562c9c907774315bb2ee87cd39d93b1d18 100644
--- a/nifty/operators/smoothing_operator/smooth_util.pyx
+++ b/nifty/operators/smoothing_operator/smooth_util.pyx
@@ -1,17 +1,38 @@
+#cython: nonecheck=False
+#cython: boundscheck=True
+#cython: wraparound=False
+#cython: cdivision=True
+
import numpy as np
cimport numpy as np
#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
-cpdef np.float32_t gaussianKernel_f(np.ndarray[np.float32_t, ndim=1] mpower, np.ndarray[np.float32_t, ndim=1] mk, np.float32_t mu, np.float32_t smooth_length):
- cdef np.ndarray[np.float32_t, ndim=1] C = np.exp(-(mk - mu) ** 2 / (2. * smooth_length ** 2))
+cdef int SIGMA_RANGE = 3
+
+cpdef np.float32_t gaussianKernel_f(np.ndarray[np.float32_t, ndim=1] mpower,
+ np.ndarray[np.float32_t, ndim=1] mk,
+ np.float32_t mu,
+ np.float32_t smooth_length):
+ cdef np.ndarray[np.float32_t, ndim=1] C = \
+ np.exp(-(mk - mu) ** 2 / (2. * smooth_length ** 2))
return np.sum(C * mpower) / np.sum(C)
-cpdef np.float64_t gaussianKernel(np.ndarray[np.float64_t, ndim=1] mpower, np.ndarray[np.float64_t, ndim=1] mk, np.float64_t mu, np.float64_t smooth_length):
- cdef np.ndarray[np.float64_t, ndim=1] C = np.exp(-(mk - mu) ** 2 / (2. * smooth_length ** 2))
+cpdef np.float64_t gaussianKernel(np.ndarray[np.float64_t, ndim=1] mpower,
+ np.ndarray[np.float64_t, ndim=1] mk,
+ np.float64_t mu,
+ np.float64_t smooth_length):
+ cdef np.ndarray[np.float64_t, ndim=1] C = \
+ np.exp(-(mk - mu) ** 2 / (2. * smooth_length ** 2))
return np.sum(C * mpower) / np.sum(C)
-cpdef np.ndarray[np.float64_t, ndim=1] apply_kernel_along_array(np.ndarray[np.float64_t, ndim=1] power, np.int_t startindex, np.int_t endindex, np.ndarray[np.float64_t, ndim=1] distances, np.float64_t smooth_length):
+cpdef np.ndarray[np.float64_t, ndim=1] apply_kernel_along_array(
+ np.ndarray[np.float64_t, ndim=1] power,
+ np.int_t startindex,
+ np.int_t endindex,
+ np.ndarray[np.float64_t, ndim=1] distances,
+ np.float64_t smooth_length,
+ np.float64_t smoothing_width):
if smooth_length == 0.0:
return power[startindex:endindex]
@@ -19,16 +40,29 @@ cpdef np.ndarray[np.float64_t, ndim=1] apply_kernel_along_array(np.ndarray[np.fl
if (smooth_length is None) or (smooth_length < 0):
smooth_length = distances[1]-distances[0]
- cdef np.ndarray[np.float64_t, ndim=1] p_smooth = np.empty(endindex-startindex, dtype=np.float64)
- cdef np.int i
+ cdef np.ndarray[np.float64_t, ndim=1] p_smooth = \
+ np.empty(endindex-startindex, dtype=np.float64)
+ cdef np.int64_t i, l, u
for i in xrange(startindex, endindex):
- l = max(i-int(2*smooth_length)-1,0)
- u = min(i+int(2*smooth_length)+2,len(p_smooth))
- p_smooth[i-startindex] = gaussianKernel(power[l:u], distances[l:u], distances[i], smooth_length)
+ current_location = distances[i]
+ l = np.searchsorted(distances,
+ current_location - smoothing_width*smooth_length)
+ u = np.searchsorted(distances,
+ current_location + smoothing_width*smooth_length)
+ p_smooth[i-startindex] = gaussianKernel(power[l:u],
+ distances[l:u],
+ distances[i],
+ smooth_length)
return p_smooth
-cpdef np.ndarray[np.float32_t, ndim=1] apply_kernel_along_array_f(np.ndarray[np.float32_t, ndim=1] power, np.int_t startindex, np.int_t endindex, np.ndarray[np.float32_t, ndim=1] distances, np.float32_t smooth_length):
+cpdef np.ndarray[np.float32_t, ndim=1] apply_kernel_along_array_f(
+ np.ndarray[np.float32_t, ndim=1] power,
+ np.int_t startindex,
+ np.int_t endindex,
+ np.ndarray[np.float32_t, ndim=1] distances,
+ np.float64_t smooth_length,
+ np.float64_t smoothing_width):
if smooth_length == 0.0:
return power[startindex:endindex]
@@ -36,20 +70,33 @@ cpdef np.ndarray[np.float32_t, ndim=1] apply_kernel_along_array_f(np.ndarray[np.
if (smooth_length is None) or (smooth_length < 0):
smooth_length = distances[1]-distances[0]
- cdef np.ndarray[np.float32_t, ndim=1] p_smooth = np.empty(endindex-startindex, dtype=np.float32_t)
- cdef np.int i
+ cdef np.ndarray[np.float32_t, ndim=1] p_smooth = \
+ np.empty(endindex-startindex, dtype=np.float64)
+ cdef np.int64_t i, l, u
for i in xrange(startindex, endindex):
- l = max(i-int(2*smooth_length)-1,0)
- u = min(i+int(2*smooth_length)+2,len(p_smooth))
- p_smooth[i-startindex] = gaussianKernel_f(power[l:u], distances[l:u], distances[i], smooth_length)
+ current_location = distances[i]
+ l = np.searchsorted(distances,
+ current_location - smoothing_width*smooth_length)
+ u = np.searchsorted(distances,
+ current_location + smoothing_width*smooth_length)
+ p_smooth[i-startindex] = gaussianKernel(power[l:u],
+ distances[l:u],
+ distances[i],
+ smooth_length)
return p_smooth
-
def getShape(a):
return tuple(a.shape)
-cpdef np.ndarray[np.float64_t, ndim=1] apply_along_axis(np.int_t axis, np.ndarray arr, np.int_t startindex, np.int_t endindex, np.ndarray[np.float64_t, ndim=1] distances, np.float64_t smooth_length):
+cpdef np.ndarray[np.float64_t, ndim=1] apply_along_axis(
+ np.int_t axis,
+ np.ndarray arr,
+ np.int_t startindex,
+ np.int_t endindex,
+ np.ndarray[np.float64_t, ndim=1] distances,
+ np.float64_t smooth_length,
+ np.float64_t smoothing_width):
cdef np.int_t nd = arr.ndim
if axis < 0:
axis += nd
@@ -62,7 +109,8 @@ cpdef np.ndarray[np.float64_t, ndim=1] apply_along_axis(np.int_t axis, np.ndarra
cdef np.ndarray[np.int_t, ndim=1] indlist = np.asarray(range(nd))
indlist = np.delete(indlist,axis)
i[axis] = slice(None, None)
- cdef np.ndarray[np.int_t, ndim=1] outshape = np.asarray(shape).take(indlist)
+ cdef np.ndarray[np.int_t, ndim=1] outshape = \
+ np.asarray(shape).take(indlist)
i.put(indlist, ind)
@@ -73,7 +121,12 @@ cpdef np.ndarray[np.float64_t, ndim=1] apply_along_axis(np.int_t axis, np.ndarra
cdef np.ndarray[np.int_t, ndim=1] holdshape = outshape
slicedArr = arr[tuple(i.tolist())]
- res = apply_kernel_along_array(slicedArr, startindex, endindex, distances, smooth_length)
+ res = apply_kernel_along_array(slicedArr,
+ startindex,
+ endindex,
+ distances,
+ smooth_length,
+ smoothing_width)
outshape = np.asarray(getShape(arr))
outshape[axis] = endindex - startindex
@@ -82,7 +135,7 @@ cpdef np.ndarray[np.float64_t, ndim=1] apply_along_axis(np.int_t axis, np.ndarra
cdef np.int_t n, k = 1
while k < Ntot:
# increment the index
- ind[-1] += 1
+ ind[nd-1] += 1
n = -1
while (ind[n] >= holdshape[n]) and (n > (1-nd)):
ind[n-1] += 1
@@ -90,14 +143,26 @@ cpdef np.ndarray[np.float64_t, ndim=1] apply_along_axis(np.int_t axis, np.ndarra
n -= 1
i.put(indlist, ind)
slicedArr = arr[tuple(i.tolist())]
- res = apply_kernel_along_array(slicedArr, startindex, endindex, distances, smooth_length)
+ res = apply_kernel_along_array(slicedArr,
+ startindex,
+ endindex,
+ distances,
+ smooth_length,
+ smoothing_width)
outarr[tuple(i.tolist())] = res
k += 1
return outarr
-cpdef np.ndarray[np.float32_t, ndim=1] apply_along_axis_f(np.int_t axis, np.ndarray arr, np.int_t startindex, np.int_t endindex, np.ndarray[np.float32_t, ndim=1] distances, np.float32_t smooth_length):
+cpdef np.ndarray[np.float32_t, ndim=1] apply_along_axis_f(
+ np.int_t axis,
+ np.ndarray arr,
+ np.int_t startindex,
+ np.int_t endindex,
+ np.ndarray[np.float32_t, ndim=1] distances,
+ np.float64_t smooth_length,
+ np.float64_t smoothing_width):
cdef np.int_t nd = arr.ndim
if axis < 0:
axis += nd
@@ -110,7 +175,8 @@ cpdef np.ndarray[np.float32_t, ndim=1] apply_along_axis_f(np.int_t axis, np.ndar
cdef np.ndarray[np.int_t, ndim=1] indlist = np.asarray(range(nd))
indlist = np.delete(indlist,axis)
i[axis] = slice(None, None)
- cdef np.ndarray[np.int_t, ndim=1] outshape = np.asarray(shape).take(indlist)
+ cdef np.ndarray[np.int_t, ndim=1] outshape = \
+ np.asarray(shape).take(indlist)
i.put(indlist, ind)
@@ -121,16 +187,21 @@ cpdef np.ndarray[np.float32_t, ndim=1] apply_along_axis_f(np.int_t axis, np.ndar
cdef np.ndarray[np.int_t, ndim=1] holdshape = outshape
slicedArr = arr[tuple(i.tolist())]
- res = apply_kernel_along_array_f(slicedArr, startindex, endindex, distances, smooth_length)
+ res = apply_kernel_along_array_f(slicedArr,
+ startindex,
+ endindex,
+ distances,
+ smooth_length,
+ smoothing_width)
outshape = np.asarray(getShape(arr))
outshape[axis] = endindex - startindex
- outarr = np.zeros(outshape, dtype=np.float32_t)
+ outarr = np.zeros(outshape, dtype=np.float32)
outarr[tuple(i.tolist())] = res
cdef np.int_t n, k = 1
while k < Ntot:
# increment the index
- ind[-1] += 1
+ ind[nd-1] += 1
n = -1
while (ind[n] >= holdshape[n]) and (n > (1-nd)):
ind[n-1] += 1
@@ -138,7 +209,12 @@ cpdef np.ndarray[np.float32_t, ndim=1] apply_along_axis_f(np.int_t axis, np.ndar
n -= 1
i.put(indlist, ind)
slicedArr = arr[tuple(i.tolist())]
- res = apply_kernel_along_array(slicedArr, startindex, endindex, distances, smooth_length)
+ res = apply_kernel_along_array(slicedArr,
+ startindex,
+ endindex,
+ distances,
+ smooth_length,
+ smoothing_width)
outarr[tuple(i.tolist())] = res
k += 1
diff --git a/nifty/operators/smoothing_operator/smoothing_operator.py b/nifty/operators/smoothing_operator/smoothing_operator.py
index b7f3451cd730f5d923c1f812506b186c52d0f8f6..f8cac64e28491d94b03def89b2c78fc77f87f5e4 100644
--- a/nifty/operators/smoothing_operator/smoothing_operator.py
+++ b/nifty/operators/smoothing_operator/smoothing_operator.py
@@ -31,7 +31,7 @@ class SmoothingOperator(EndomorphicOperator):
self.sigma = sigma
self.log_distances = log_distances
- self._direct_smoothing_width = 2.
+ self._direct_smoothing_width = 3.
def _inverse_times(self, x, spaces, types):
return self._smoothing_helper(x, spaces, inverse=True)
@@ -147,14 +147,18 @@ class SmoothingOperator(EndomorphicOperator):
# we rely on the knowledge, that `spaces` is a tuple with length 1.
affected_axes = x.domain_axes[spaces[0]]
- axes_local_distribution_strategy = \
- x.val.get_axes_local_distribution_strategy(axes=affected_axes)
+ if len(affected_axes) > 1:
+ raise ValueError("By this implementation only one-dimensional "
+ "spaces can be smoothed directly.")
+
+ affected_axis = affected_axes[0]
distance_array = x.domain[spaces[0]].get_distance_array(
- distribution_strategy=axes_local_distribution_strategy)
+ distribution_strategy='not')
+ distance_array = distance_array.get_local_data(copy=False)
if self.log_distances:
- distance_array.apply_scalar_function(np.log, inplace=True)
+ np.log(distance_array, out=distance_array)
# collect the local data + ghost cells
local_data_Q = False
@@ -164,88 +168,100 @@ class SmoothingOperator(EndomorphicOperator):
elif x.distribution_strategy in STRATEGIES['slicing']:
# infer the local start/end based on the slicing information of
# x's d2o. Only gets non-trivial for axis==0.
- if 0 not in affected_axes:
+ if 0 != affected_axis:
local_data_Q = True
else:
- # we rely on the fact, that the content of x.domain_axes is
- # sorted
- true_starts = [x.val.distributor.local_start]
- true_starts += [0] * (len(affected_axes) - 1)
- true_ends = [x.val.distributor.local_end]
- true_ends += [x.shape[i] for i in affected_axes[1:]]
-
- augmented_start = max(0,
- true_starts[0] -
- self._direct_smoothing_width * self.sigma)
- augmented_end = min(x.shape[affected_axes[0]],
- true_ends[0] +
- self._direct_smoothing_width * self.sigma)
- augmented_slice = slice(augmented_start, augmented_end)
+ start_index = x.val.distributor.local_start
+ start_distance = distance_array[start_index]
+ augmented_start_distance = \
+ (start_distance - self._direct_smoothing_width*self.sigma)
+ augmented_start_index = \
+ np.searchsorted(distance_array, augmented_start_distance)
+ true_start = start_index - augmented_start_index
+ end_index = x.val.distributor.local_end
+ end_distance = distance_array[end_index-1]
+ augmented_end_distance = \
+ (end_distance + self._direct_smoothing_width*self.sigma)
+ augmented_end_index = \
+ np.searchsorted(distance_array, augmented_end_distance)
+ true_end = true_start + x.val.distributor.local_length
+ augmented_slice = slice(augmented_start_index,
+ augmented_end_index)
+
augmented_data = x.val.get_data(augmented_slice,
local_keys=True,
copy=False)
augmented_data = augmented_data.get_local_data(copy=False)
- augmented_distance_array = distance_array.get_data(
- augmented_slice,
- local_keys=True,
- copy=False)
- augmented_distance_array = \
- augmented_distance_array.get_local_data(copy=False)
+ augmented_distance_array = distance_array[augmented_slice]
else:
- raise ValueError(about._errors.cstring(
- "ERROR: Direct smoothing not implemented for given"
- "distribution strategy."))
+ raise ValueError("Direct smoothing not implemented for given"
+ "distribution strategy.")
if local_data_Q:
# if the needed data resides on the nodes already, the necessary
# are the same; no matter what the distribution strategy was.
augmented_data = x.val.get_local_data(copy=False)
- augmented_distance_array = \
- distance_array.get_local_data(copy=False)
- true_starts = [0] * len(affected_axes)
- true_ends = [x.shape[i] for i in affected_axes]
+ augmented_distance_array = distance_array
+ true_start = 0
+ true_end = x.shape[affected_axis]
# perform the convolution along the affected axes
- local_result = augmented_data
- for index in range(len(affected_axes)):
- data_axis = affected_axes[index]
- distances_axis = index
- true_start = true_starts[index]
- true_end = true_ends[index]
-
- local_result = self._direct_smoothing_single_axis(
- local_result,
- data_axis,
- augmented_distance_array,
- distances_axis,
- true_start,
- true_end,
- inverse)
-
+ # currently only one axis is supported
+ data_axis = affected_axes[0]
+ local_result = self._direct_smoothing_single_axis(
+ augmented_data,
+ data_axis,
+ augmented_distance_array,
+ true_start,
+ true_end,
+ inverse)
result = x.copy_empty()
result.val.set_local_data(local_result, copy=False)
return result
def _direct_smoothing_single_axis(self, data, data_axis, distances,
- distances_axis, true_start, true_end,
- inverse):
+ true_start, true_end, inverse):
if inverse:
- true_sigma = 1 / self.sigma
+ true_sigma = 1. / self.sigma
else:
true_sigma = self.sigma
- if (data.dtype == np.dtype('float32')):
- smoothed_data = su.apply_along_axis_f(data_axis, data,
- startindex=true_start,
- endindex=true_end,
- distances=distances,
- smooth_length=true_sigma)
+ if data.dtype is np.dtype('float32'):
+ distances = distances.astype(np.float32, copy=False)
+ smoothed_data = su.apply_along_axis_f(
+ data_axis, data,
+ startindex=true_start,
+ endindex=true_end,
+ distances=distances,
+ smooth_length=true_sigma,
+ smoothing_width=self._direct_smoothing_width)
+ elif data.dtype is np.dtype('float64'):
+ distances = distances.astype(np.float64, copy=False)
+ smoothed_data = su.apply_along_axis(
+ data_axis, data,
+ startindex=true_start,
+ endindex=true_end,
+ distances=distances,
+ smooth_length=true_sigma,
+ smoothing_width=self._direct_smoothing_width)
+
+ elif np.issubdtype(data.dtype, np.complexfloating):
+ real = self._direct_smoothing_single_axis(data.real,
+ data_axis,
+ distances,
+ true_start,
+ true_end, inverse)
+ imag = self._direct_smoothing_single_axis(data.imag,
+ data_axis,
+ distances,
+ true_start,
+ true_end, inverse)
+
+ return real + 1j*imag
+
else:
- smoothed_data = su.apply_along_axis(data_axis, data,
- startindex=true_start,
- endindex=true_end,
- distances=distances,
- smooth_length=true_sigma)
+ raise TypeError("Dtype %s not supported" % str(data.dtype))
+
return smoothed_data
diff --git a/nifty/spaces/gl_space/gl_space.py b/nifty/spaces/gl_space/gl_space.py
index 81cfeff6b47c2379b62cf6b74d8ab961a0485b40..381fff54daeb1082ad045eb1da6a55c170279230 100644
--- a/nifty/spaces/gl_space/gl_space.py
+++ b/nifty/spaces/gl_space/gl_space.py
@@ -3,7 +3,8 @@ from __future__ import division
import itertools
import numpy as np
-from d2o import arange, STRATEGIES as DISTRIBUTION_STRATEGIES
+import d2o
+from d2o import STRATEGIES as DISTRIBUTION_STRATEGIES
from nifty.spaces.space import Space
from nifty.config import nifty_configuration as gc,\
@@ -154,8 +155,8 @@ class GLSpace(Space):
return result_x
def get_distance_array(self, distribution_strategy):
- dists = arange(start=0, stop=self.shape[0],
- distribution_strategy=distribution_strategy)
+ dists = d2o.arange(start=0, stop=self.shape[0],
+ distribution_strategy=distribution_strategy)
dists = dists.apply_scalar_function(
lambda x: self._distance_array_helper(divmod(x, self.nlon)),
@@ -172,7 +173,7 @@ class GLSpace(Space):
return np.arctan(numerator / denominator)
- def get_smoothing_kernel_function(self, sigma):
+ def get_fft_smoothing_kernel_function(self, sigma):
if sigma is None:
sigma = np.sqrt(2) * np.pi
diff --git a/nifty/spaces/hp_space/hp_space.py b/nifty/spaces/hp_space/hp_space.py
index c8d1fffcca87418ba662bb04d4ad0558eb4449cb..ae74476f29f5abe4d4ba30decbd0ec303d68ad39 100644
--- a/nifty/spaces/hp_space/hp_space.py
+++ b/nifty/spaces/hp_space/hp_space.py
@@ -35,6 +35,8 @@ from __future__ import division
import numpy as np
+import d2o
+
from nifty.spaces.space import Space
from nifty.config import nifty_configuration as gc, \
dependency_injector as gdi
@@ -169,7 +171,7 @@ class HPSpace(Space):
-------
dists: distributed_data_object
"""
- dists = arange(
+ dists = d2o.arange(
start=0, stop=self.shape[0],
distribution_strategy=distribution_strategy
)
@@ -183,7 +185,7 @@ class HPSpace(Space):
return dists
- def get_smoothing_kernel_function(self, sigma):
+ def get_fft_smoothing_kernel_function(self, sigma):
if sigma is None:
sigma = np.sqrt(2) * np.pi
diff --git a/nifty/spaces/lm_space/lm_space.py b/nifty/spaces/lm_space/lm_space.py
index 0a34c939eccd6a895ca68c81cd85eb69385bd9d7..4a33a354e892ad7d8b74c2566c559ad5206909be 100644
--- a/nifty/spaces/lm_space/lm_space.py
+++ b/nifty/spaces/lm_space/lm_space.py
@@ -156,7 +156,7 @@ class LMSpace(Space):
return dists
- def get_smoothing_kernel_function(self, sigma):
+ def get_fft_smoothing_kernel_function(self, sigma):
if sigma is None:
sigma = np.sqrt(2) * np.pi / (self.lmax + 1)
diff --git a/nifty/spaces/power_space/power_space.py b/nifty/spaces/power_space/power_space.py
index 54ed22824e48cca65df6affed858f7fdf91313fa..6da672751e918d646e63913a3cff4712a18b5af6 100644
--- a/nifty/spaces/power_space/power_space.py
+++ b/nifty/spaces/power_space/power_space.py
@@ -2,6 +2,8 @@
import numpy as np
+import d2o
+
from power_index_factory import PowerIndexFactory
from nifty.spaces.space import Space
@@ -106,13 +108,14 @@ class PowerSpace(Space):
return result_x
def get_distance_array(self, distribution_strategy):
- raise NotImplementedError(
- "There is no get_distance_array implementation for "
- "PowerSpace.")
+ result = d2o.distributed_data_object(
+ self.kindex,
+ distribution_strategy=distribution_strategy)
+ return result
- def get_smoothing_kernel_function(self, sigma):
+ def get_fft_smoothing_kernel_function(self, sigma):
raise NotImplementedError(
- "There is no smoothing function for PowerSpace.")
+ "There is no fft smoothing function for PowerSpace.")
# ---Added properties and methods---
diff --git a/nifty/spaces/rg_space/rg_space.py b/nifty/spaces/rg_space/rg_space.py
index 1bd9bfae949965e1ff9e663532f89e4433b0527d..9c99617dfead8e429d80d78ec34b3044a1e64c66 100644
--- a/nifty/spaces/rg_space/rg_space.py
+++ b/nifty/spaces/rg_space/rg_space.py
@@ -282,7 +282,7 @@ class RGSpace(Space):
dists = np.sqrt(dists)
return dists
- def get_smoothing_kernel_function(self, sigma):
+ def get_fft_smoothing_kernel_function(self, sigma):
if sigma is None:
sigma = np.sqrt(2) * np.max(self.distances)
diff --git a/nifty/spaces/space/space.py b/nifty/spaces/space/space.py
index 802ae2f0c7ba997f670c8d95420edabd8bf528f5..eaa9286c2ec72d5f0b823ce4fcb4f1ce9c63b04d 100644
--- a/nifty/spaces/space/space.py
+++ b/nifty/spaces/space/space.py
@@ -278,7 +278,7 @@ class Space(object, Loggable):
raise NotImplementedError(
"There is no generic distance structure for Space base class.")
- def get_smoothing_kernel_function(self, sigma):
+ def get_fft_smoothing_kernel_function(self, sigma):
raise NotImplementedError(
"There is no generic co-smoothing kernel for Space base class.")
diff --git a/setup.py b/setup.py
index cb20468e86ffaacbc906c8ebf5875a369d8b308c..83e64ae9b8d2f7e35b8fb03df51d7da2b79fd188 100644
--- a/setup.py
+++ b/setup.py
@@ -39,8 +39,9 @@ setup(name="ift_nifty",
zip_safe=False,
ext_modules=cythonize([
"nifty/operators/fft_operator/transformations/"
- "lm_transformation_factory.pyx",
- "nifty/spaces/lm_space/lm_helper.pyx"
+ "lm_transformation_factory.pyx",
+ "nifty/spaces/lm_space/lm_helper.pyx",
+ "nifty/operators/smoothing_operator/smooth_util.pyx"
]),
include_dirs=[numpy.get_include()],
dependency_links=[