remove DirectSmoothingOperator

nifty/operators/__init__.py

@@ -7,7 +7,6 @@ from .diagonal_operator import DiagonalOperator
 from .endomorphic_operator import EndomorphicOperator
 
 from .fft_smoothing_operator import FFTSmoothingOperator
-from .direct_smoothing_operator import DirectSmoothingOperator
 
 from .fft_operator import FFTOperator
 

nifty/operators/direct_smoothing_operator.py

-from __future__ import division
-from builtins import range
-import numpy as np
-
-from .endomorphic_operator import EndomorphicOperator
-from ..spaces import PowerSpace
-from .. import nifty_utilities as utilities
-from .. import Field, DomainTuple
-
-
-class DirectSmoothingOperator(EndomorphicOperator):
-    def __init__(self, domain, sigma, log_distances=False,
-                 space=None):
-        super(DirectSmoothingOperator, self).__init__()
-
-        self._domain = DomainTuple.make(domain)
-        if space is None:
-            if len(self._domain.domains) != 1:
-                raise ValueError("need a Field with exactly one domain")
-            space = 0
-        space = int(space)
-        if (space<0) or space>=len(self._domain.domains):
-            raise ValueError("space index out of range")
-        if not isinstance(self._domain[space],PowerSpace):
-            raise TypeError("PowerSpace needed")
-        self._space = space
-
-        self._sigma = float(sigma)
-        self._log_distances = log_distances
-        self._effective_smoothing_width = 3.01
-
-    def _times(self, x):
-        if self._sigma == 0:
-            return x.copy()
-
-        return self._smooth(x)
-
-    # ---Mandatory properties and methods---
-    @property
-    def domain(self):
-        return self._domain
-
-    @property
-    def self_adjoint(self):
-        return True
-
-    @property
-    def unitary(self):
-        return False
-
-    # ---Added properties and methods---
-
-    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):
-        # infer affected axes
-        affected_axes = x.domain.axes[self._space]
-        axis = affected_axes[0]
-
-        distances = x.domain[self._space].k_lengths
-        if self._log_distances:
-            distances = np.log(np.maximum(distances, 1e-15))
-
-        ibegin, nval, wgt = self._precompute(distances)
-        res = Field.empty_like(x)
-        for sl in utilities.get_slice_list(x.val.shape, (axis,)):
-            inp = x.val[sl]
-            out = np.zeros(inp.shape[0], dtype=inp.dtype)
-            for i in range(inp.shape[0]):
-                out[ibegin[i]:ibegin[i]+nval[i]] += inp[i] * wgt[i][:]
-            res.val[sl] = out
-        return res

test/test_operators/test_smoothing_operator.py

@@ -23,9 +23,7 @@ from numpy.testing import assert_allclose
 from nifty2go import Field,\
     RGSpace,\
     PowerSpace,\
-    FFTSmoothingOperator,\
-    DirectSmoothingOperator
-
+    FFTSmoothingOperator
 from itertools import product
 from test.common import expand
 
@@ -88,29 +86,3 @@ class SmoothingOperator_Tests(unittest.TestCase):
                                 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 = RGSpace(sz, harmonic=True)
-        bb = PowerSpace.useful_binbounds(sp, logarithmic=log)
-        ps = PowerSpace(sp, binbounds=bb)
-        smo = DirectSmoothingOperator(ps, sigma=sigma)
-        inp = 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 = RGSpace([sz1, sz2], harmonic=True)
-        bb = PowerSpace.useful_binbounds(sp, logarithmic=log)
-        ps = PowerSpace(sp, binbounds=bb)
-        smo = DirectSmoothingOperator(ps, sigma=sigma)
-        inp = 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)