remove DirectSmoothingOperator

differences

@@ -93,11 +93,3 @@ Significant differences between NIFTy3 and nifty4
    its adjoint and inverse, respectively.
 
 18) Handling of volume factors has been changed completely.
-
-
-Obsolescent functionality:
-
-- DirectSmoothingOperator
-- LaplaceOperator(?)
-- FFTSmoothingOperator(?)
-- most of the probing machinery(?)

nifty4/__init__.py

@@ -21,7 +21,6 @@ from .operators.diagonal_operator import DiagonalOperator
 from .operators.harmonic_transform_operator import HarmonicTransformOperator
 from .operators.fft_operator import FFTOperator
 from .operators.fft_smoothing_operator import FFTSmoothingOperator
-from .operators.direct_smoothing_operator import DirectSmoothingOperator
 from .operators.geometry_remover import GeometryRemover
 from .operators.laplace_operator import LaplaceOperator
 from .operators.power_projection_operator import PowerProjectionOperator
@@ -57,6 +56,6 @@ __all__ = ["Domain", "UnstructuredDomain", "StructuredDomain",
            "PowerSpace", "DomainTuple",
            "LinearOperator", "EndomorphicOperator", "ScalingOperator",
            "DiagonalOperator", "FFTOperator", "FFTSmoothingOperator",
-           "DirectSmoothingOperator", "LaplaceOperator",
+           "LaplaceOperator",
            "PowerProjectionOperator", "InversionEnabler",
            "Field", "sqrt", "exp", "log"]

nifty4/operators/direct_smoothing_operator.py

-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program.  If not, see <http://www.gnu.org/licenses/>.
-#
-# Copyright(C) 2013-2018 Max-Planck-Society
-#
-# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
-# and financially supported by the Studienstiftung des deutschen Volkes.
-
-from __future__ import division
-from builtins import range
-import numpy as np
-from .endomorphic_operator import EndomorphicOperator
-from .. import dobj
-from .. import utilities
-from ..field import Field
-from ..domain_tuple import DomainTuple
-from ..spaces.power_space import PowerSpace
-
-
-class DirectSmoothingOperator(EndomorphicOperator):
-    def __init__(self, domain, sigma, log_distances=False,
-                 space=None):
-        super(DirectSmoothingOperator, self).__init__()
-
-        self._domain = DomainTuple.make(domain)
-        self._space = utilities.infer_space(self._domain, space)
-        if not isinstance(self._domain[self._space], PowerSpace):
-            raise TypeError("PowerSpace needed")
-
-        self._sigma = float(sigma)
-        self._log_distances = log_distances
-        self._effective_smoothing_width = 3.01
-
-        self._axis = self._domain.axes[self._space][0]
-        if self._sigma != 0:
-            distances = self._domain[self._space].k_lengths
-            if self._log_distances:
-                distances = np.log(np.maximum(distances, 1e-15))
-
-            self._ibegin, self._nval, self._wgt = self._precompute(distances)
-
-    def apply(self, x, mode):
-        self._check_input(x, mode)
-        if self._sigma == 0:
-            return x.copy()
-
-        return self._smooth(x)
-
-    @property
-    def domain(self):
-        return self._domain
-
-    @property
-    def capability(self):
-        return self.TIMES | self.ADJOINT_TIMES
-
-    def _precompute(self, x):
-        """ Does precomputations for Gaussian smoothing on a 1D irregular grid.
-
-        Parameters
-        ----------
-        x: 1D floating point array or list containing the individual grid
-            positions. Points must be given in ascending order.
-
-
-        Returns
-        -------
-        ibegin: integer array of the same size as x
-            ibegin[i] is the minimum grid index to consider when computing the
-            smoothed value at grid index i
-        nval: integer array of the same size as x
-            nval[i] is the number of indices to consider when computing the
-            smoothed value at grid index i.
-        wgt: list with the same number of entries as x
-            wgt[i] is an array with nval[i] entries containing the
-            normalized smoothing weights.
-        """
-        dxmax = self._effective_smoothing_width*self._sigma
-
-        x = np.asarray(x)
-
-        ibegin = np.searchsorted(x, x-dxmax)
-        nval = np.searchsorted(x, x+dxmax) - ibegin
-
-        wgt = []
-        expfac = 1. / (2. * self._sigma*self._sigma)
-        for i in range(x.size):
-            if nval[i] > 0:
-                t = x[ibegin[i]:ibegin[i]+nval[i]]-x[i]
-                t = np.exp(-t*t*expfac)
-                t *= 1./np.sum(t)
-                wgt.append(t)
-            else:
-                wgt.append(np.array([]))
-
-        return ibegin, nval, wgt
-
-    def _smooth(self, x):
-        if dobj.distaxis(x.val) == self._axis:
-            val = dobj.redistribute(x.val, nodist=(self._axis,))
-        else:
-            val = x.val.copy()
-        lval = dobj.local_data(val)
-
-        for sl in utilities.get_slice_list(lval.shape, (self._axis,)):
-            inp = lval[sl]
-            out = np.zeros(inp.shape[0], dtype=inp.dtype)
-            for i in range(inp.shape[0]):
-                out[self._ibegin[i]:self._ibegin[i]+self._nval[i]] += \
-                    inp[i] * self._wgt[i][:]
-            lval[sl] = out
-        val = dobj.redistribute(val, dobj.distaxis(x.val))
-        return Field(x.domain, val)

test/test_operators/test_smoothing_operator.py

@@ -79,29 +79,3 @@ class SmoothingOperator_Tests(unittest.TestCase):
                                     mean=4, dtype=tp)
         out = smo(inp)
         assert_allclose(inp.sum(), out.sum(), rtol=tol, atol=tol)
-
-    @expand(product([100, 200], [False, True], [0., 1.,  3.7],
-                    [np.float64, np.complex128]))
-    def test_smooth_irregular1(self, sz, log, sigma, tp):
-        tol = _get_rtol(tp)
-        sp = ift.RGSpace(sz, harmonic=True)
-        bb = ift.PowerSpace.useful_binbounds(sp, logarithmic=log)
-        ps = ift.PowerSpace(sp, binbounds=bb)
-        smo = ift.DirectSmoothingOperator(ps, sigma=sigma)
-        inp = ift.Field.from_random(domain=ps, random_type='normal', std=1,
-                                    mean=4, dtype=tp)
-        out = smo(inp)
-        assert_allclose(inp.sum(), out.sum(), rtol=tol, atol=tol)
-
-    @expand(product([10, 15], [7, 10], [False, True], [0., 1.,  3.7],
-                    [np.float64, np.complex128]))
-    def test_smooth_irregular2(self, sz1, sz2, log, sigma, tp):
-        tol = _get_rtol(tp)
-        sp = ift.RGSpace([sz1, sz2], harmonic=True)
-        bb = ift.PowerSpace.useful_binbounds(sp, logarithmic=log)
-        ps = ift.PowerSpace(sp, binbounds=bb)
-        smo = ift.DirectSmoothingOperator(ps, sigma=sigma)
-        inp = ift.Field.from_random(domain=ps, random_type='normal', std=1,
-                                    mean=4, dtype=tp)
-        out = smo(inp)
-        assert_allclose(inp.sum(), out.sum(), rtol=tol, atol=tol)