Commit 05e24a79 authored by Martin Reinecke's avatar Martin Reinecke

remove DirectSmoothingOperator

parent 900e4084
Pipeline #21181 passed with stage
in 4 minutes and 6 seconds
......@@ -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
......
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
......@@ -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)
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