los_response.py 9.17 KB
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# 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/>.
#
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# Copyright(C) 2013-2019 Max-Planck-Society
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#
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# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.
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import numpy as np
from scipy.sparse import coo_matrix
from scipy.sparse.linalg import aslinearoperator
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from scipy.special import erfc

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from ..domain_tuple import DomainTuple
from ..domains.rg_space import RGSpace
from ..domains.unstructured_domain import UnstructuredDomain
from ..field import Field
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from ..operators.linear_operator import LinearOperator
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def _gaussian_sf(x):
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    return 0.5*erfc(x/np.sqrt(2.))
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def _comp_traverse(start, end, shp, dist, lo, mid, hi, sig, erf):
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    ndim = start.shape[0]
    nlos = start.shape[1]
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    inc = np.full(len(shp), 1, dtype=np.int64)
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    for i in range(-2, -len(shp)-1, -1):
        inc[i] = inc[i+1]*shp[i+1]

    pmax = np.array(shp)

    out = [None]*nlos
    for i in range(nlos):
        dir = end[:, i]-start[:, i]
        dirx = np.where(dir == 0., 1e-12, dir)
        d0 = np.where(dir == 0., ((start[:, i] > 0)-0.5)*1e12,
                      -start[:, i]/dirx)
        d1 = np.where(dir == 0., ((start[:, i] < pmax)-0.5)*-1e12,
                      (pmax-start[:, i])/dirx)
        (dmin, dmax) = (np.minimum(d0, d1), np.maximum(d0, d1))
        dmin = dmin.max()
        dmax = dmax.min()
        dmin = np.maximum(0., dmin)
        dmax = np.minimum(1., dmax)
        dmax = np.maximum(dmin, dmax)
        # hack: move away from potential grid crossings
        dmin += 1e-7
        dmax -= 1e-7
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        if dmin >= dmax:  # no intersection
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            out[i] = (np.full(0, 0, dtype=np.int64), np.full(0, 0.))
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            continue
        # determine coordinates of first cell crossing
        c_first = np.ceil(start[:, i]+dir*dmin)
        c_first = np.where(dir > 0., c_first, c_first-1.)
        c_first = (c_first-start[:, i])/dirx
        pos1 = np.asarray((start[:, i]+dmin*dir), dtype=np.int)
        pos1 = np.sum(pos1*inc)
        cdist = np.empty(0, dtype=np.float64)
        add = np.empty(0, dtype=np.int)
        for j in range(ndim):
            if dir[j] != 0:
                step = inc[j] if dir[j] > 0 else -inc[j]
                tmp = np.arange(start=c_first[j], stop=dmax,
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                                step=abs(1./dir[j]))
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                cdist = np.append(cdist, tmp)
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                add = np.append(add, np.full(len(tmp), step, dtype=np.int64))
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        idx = np.argsort(cdist)
        cdist = cdist[idx]
        add = add[idx]
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        cdist = np.append(np.full(1, dmin), cdist)
        cdist = np.append(cdist, np.full(1, dmax))
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        corfac = np.linalg.norm(dir*dist)
        cdist *= corfac
        wgt = np.diff(cdist)
        mdist = 0.5*(cdist[:-1]+cdist[1:])
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        wgt = apply_erf(wgt, mdist, lo[i], mid[i], hi[i], sig[i], erf)
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        add = np.append(pos1, add)
        add = np.cumsum(add)
        out[i] = (add, wgt)
    return out


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def apply_erf(wgt, dist, lo, mid, hi, sig, erf):
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    wgt = wgt.copy()
    mask = dist > hi
    wgt[mask] = 0.
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    mask = (dist > lo) & (dist <= hi)
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    wgt[mask] *= erf((-1/dist[mask]+1/mid)/sig)
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    return wgt


class LOSResponse(LinearOperator):
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    """Line-of-sight response operator

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    This operator transforms from a single RGSpace to an UnstructuredDomain
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    with as many entries as there were lines of sight passed to the
    constructor. Adjoint application is also provided.

    Parameters
    ----------
    domain : RGSpace or DomainTuple
        The operator's input domain. This must be a single RGSpace.
    starts, ends : numpy.ndarray(float) with two dimensions
        Arrays containing the start and end points of the individual lines
        of sight. The first dimension must have as many entries as `domain`
        has dimensions. The second dimensions must be identical for both arrays
        and indicated the total number of lines of sight.
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    sigmas: numpy.ndarray(float) (optional)
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        If this is not None, the inverse of the lengths of the LOSs are assumed
        to be Gaussian distributed with these sigmas. The start point will
        remain the same, but the endpoint is assumed to be unknown.
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        This is a typical statistical model for astrophysical parallaxes.
        The LOS response then returns the expected integral
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        over the input given that the length of the LOS is unknown and
        therefore the result is averaged over different endpoints.
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        Default: None.
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    truncation: float (optional)
        Use only if the sigmas keyword argument is used!
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        This truncates the probability of the endpoint lying more sigmas away
        than the truncation. Used to speed up computation and to avoid negative
        distances. It should hold that `1./(1./length-sigma*truncation)>0`
        for all lengths of the LOSs and all corresponding sigma of sigmas.
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        If unsure, leave blank.
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        Default: 3.
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    Notes
    -----
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    `starts, `ends`, `sigmas`, and `truncation` have to be identical on
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    every calling MPI task (i.e. the full LOS information has to be provided on
    every task).
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    """
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    def __init__(self, domain, starts, ends, sigmas=None, truncation=3.):
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        self._domain = DomainTuple.make(domain)
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        self._capability = self.TIMES | self.ADJOINT_TIMES
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        if ((not isinstance(self.domain[0], RGSpace)) or
                (len(self._domain) != 1)):
            raise TypeError("The domain must be exactly one RGSpace instance.")

        ndim = len(self.domain[0].shape)
        starts = np.array(starts)
        nlos = starts.shape[1]
        ends = np.array(ends)
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        if sigmas is None:
            sigmas = np.zeros(nlos, dtype=np.float32)
        sigmas = np.array(sigmas)
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        if starts.shape[0] != ndim:
            raise TypeError("dimension mismatch")
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        if nlos != sigmas.shape[0]:
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            raise TypeError("dimension mismatch")
        if starts.shape != ends.shape:
            raise TypeError("dimension mismatch")
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        diffs = ends-starts
        difflen = np.linalg.norm(diffs, axis=0)
        diffs /= difflen
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        real_distances = 1./(1./difflen - truncation*sigmas)
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        if np.any(real_distances < 0):
            raise ValueError("parallax error truncation to high: "
                             "getting negative distances")
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        real_ends = starts + diffs*real_distances
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        dist = np.array(self.domain[0].distances).reshape((-1, 1))
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        localized_pixel_starts = starts/dist + 0.5
        localized_pixel_ends = real_ends/dist + 0.5
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        # get the shape of the local data slice
        w_i = _comp_traverse(localized_pixel_starts,
                             localized_pixel_ends,
                             self._local_shape,
                             np.array(self.domain[0].distances),
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                             1./(1./difflen+truncation*sigmas),
                             difflen,
                             1./(1./difflen-truncation*sigmas),
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                             sigmas,
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                             _gaussian_sf)
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        boxsz = 16
        nlos = len(w_i)
        npix = np.prod(self._local_shape)
        ntot = 0
        for i in w_i:
            ntot += len(i[1])
        pri = np.empty(ntot, dtype=np.float64)
        ilos = np.empty(ntot, dtype=np.int32)
        iarr = np.empty(ntot, dtype=np.int32)
        xwgt = np.empty(ntot, dtype=np.float32)
        ofs = 0
        cnt = 0
        for i in w_i:
            nval = len(i[1])
            ilos[ofs:ofs+nval] = cnt
            iarr[ofs:ofs+nval] = i[0]
            xwgt[ofs:ofs+nval] = i[1]
            fullidx = np.unravel_index(i[0], self._local_shape)
            tmp = np.zeros(nval, dtype=np.float64)
            fct = 1.
            for j in range(ndim):
                tmp += (fullidx[j]//boxsz)*fct
                fct *= self._local_shape[j]
            tmp += cnt/float(nlos)
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            tmp += iarr[ofs:ofs+nval]/(float(nlos)*float(npix))
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            pri[ofs:ofs+nval] = tmp
            ofs += nval
            cnt += 1
        xtmp = np.argsort(pri)
        ilos = ilos[xtmp]
        iarr = iarr[xtmp]
        xwgt = xwgt[xtmp]
        self._smat = aslinearoperator(
            coo_matrix((xwgt, (ilos, iarr)),
                       shape=(nlos, np.prod(self._local_shape))))

        self._target = DomainTuple.make(UnstructuredDomain(nlos))

    def apply(self, x, mode):
        self._check_input(x, mode)
        if mode == self.TIMES:
            result_arr = self._smat.matvec(x.local_data.reshape(-1))
            return Field.from_global_data(self._target, result_arr,
                                          sum_up=True)
        local_input_data = x.to_global_data().reshape(-1)
        res = self._smat.rmatvec(local_input_data).reshape(self._local_shape)
        return Field.from_local_data(self._domain, res)