lm_space.py 7.39 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) 2013-2017 Max-Planck-Society
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
# and financially supported by the Studienstiftung des deutschen Volkes.
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from __future__ import division

import numpy as np

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from nifty.spaces.space import Space
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from d2o import arange

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class LMSpace(Space):
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    """
        ..       __
        ..     /  /
        ..    /  /    __ ____ ___
        ..   /  /   /   _    _   |
        ..  /  /_  /  / /  / /  /
        ..  \___/ /__/ /__/ /__/  space class

        NIFTY subclass for spherical harmonics components, for representations
        of fields on the two-sphere.

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        Parameters
        ----------
        lmax : int
            The maximal :math:`l` value of any spherical harmonics
            :math:`Y_{lm}` that is represented in this Space.

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        Attributes
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        ----------
        lmax : int
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            The maximal :math:`l` value of any spherical harmonics
            :math:`Y_{lm}` that is represented in this Space.
        mmax : int
            The maximal :math:`m` value of any spherical harmonic
            :math:`Y_{lm}` that is represented in this Space.
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        dim : np.int
            Total number of dimensionality, i.e. the number of pixels.
        harmonic : bool
            Specifies whether the space is a signal or harmonic space.
        total_volume : np.float
            The total volume of the space.
        shape : tuple of np.ints
            The shape of the space's data array.
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        See Also
        --------
        hp_space : A class for the HEALPix discretization of the sphere [#]_.
        gl_space : A class for the Gauss-Legendre discretization of the
            sphere [#]_.

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        Raises
        ------
        ValueError
            If given lmax is negative.

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        Notes
        -----
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            This implementation implicitly sets the mmax parameter to lmax.
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        References
        ----------
        .. [#] K.M. Gorski et al., 2005, "HEALPix: A Framework for
               High-Resolution Discretization and Fast Analysis of Data
               Distributed on the Sphere", *ApJ* 622..759G.
        .. [#] M. Reinecke and D. Sverre Seljebotn, 2013, "Libsharp - spherical
               harmonic transforms revisited";
               `arXiv:1303.4945 <http://www.arxiv.org/abs/1303.4945>`_
    """

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    def __init__(self, lmax):
        super(LMSpace, self).__init__()
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        self._lmax = self._parse_lmax(lmax)
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    def hermitian_decomposition(self, x, axes=None):
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        if issubclass(x.dtype.type, np.complexfloating):
            hermitian_part = x.copy_empty()
            anti_hermitian_part = x.copy_empty()
            hermitian_part[:] = x.real
            anti_hermitian_part[:] = x.imag * 1j
        else:
            hermitian_part = x.copy()
            anti_hermitian_part = x.copy_empty()
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            anti_hermitian_part[:] = 0
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        return (hermitian_part, anti_hermitian_part)

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    def hermitian_fixed_points(self):
        return None
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    # ---Mandatory properties and methods---
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    def __repr__(self):
        return ("LMSpace(lmax=%r)" % self.lmax)

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    @property
    def harmonic(self):
        return True
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    @property
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    def shape(self):
        return (self.dim, )
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    @property
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    def dim(self):
        l = self.lmax
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        # the LMSpace consists of the full triangle (including -m's!),
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        # minus two little triangles if mmax < lmax
        # dim = (((2*(l+1)-1)+1)**2/4 - 2 * (l-m)(l-m+1)/2
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        # dim = np.int((l+1)**2 - (l-m)*(l-m+1.))
        # We fix l == m
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        return np.int((l+1)*(l+1))
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    @property
    def total_volume(self):
        # the individual pixels have a fixed volume of 1.
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        return np.float64(self.dim)
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    def copy(self):
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        return self.__class__(lmax=self.lmax)
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    def weight(self, x, power=1, axes=None, inplace=False):
        if inplace:
            return x
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        else:
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            return x.copy()
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    def get_distance_array(self, distribution_strategy):
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        if distribution_strategy == 'not':  # short cut
            lmax = self.lmax
            ldist = np.empty((self.dim,), dtype=np.float64)
            ldist[0:lmax+1] = np.arange(lmax+1, dtype=np.float64)
            tmp = np.empty((2*lmax+2), dtype=np.float64)
            tmp[0::2] = np.arange(lmax+1)
            tmp[1::2] = np.arange(lmax+1)
            idx = lmax+1
            for l in range(1, lmax+1):
                ldist[idx:idx+2*(lmax+1-l)] = tmp[2*l:]
                idx += 2*(lmax+1-l)
            dists = arange(start=0, stop=self.shape[0],
                           distribution_strategy=distribution_strategy)
            dists.set_local_data(ldist)
            return dists

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        dists = arange(start=0, stop=self.shape[0],
                       distribution_strategy=distribution_strategy)

        dists = dists.apply_scalar_function(
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            lambda x: self._distance_array_helper(x, self.lmax),
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            dtype=np.float64)
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        return dists

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    def get_unique_distances(self):
        return np.arange(self.lmax+1, dtype=np.float64)

    def get_natural_binbounds(self):
        return np.arange(self.lmax, dtype=np.float64) + 0.5

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    @staticmethod
    def _distance_array_helper(index_array, lmax):
        u = 2*lmax + 1
        index_half = (index_array+np.minimum(lmax, index_array)+1)//2
        m = (np.ceil((u - np.sqrt(u*u - 8*(index_half - lmax)))/2)).astype(int)
        res = (index_half - m*(u - m)//2).astype(np.float64)
        return res

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    def get_fft_smoothing_kernel_function(self, sigma):
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        # cf. "All-sky convolution for polarimetry experiments"
        # by Challinor et al.
        # http://arxiv.org/abs/astro-ph/0008228
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        return lambda x: np.exp(-0.5 * x * (x + 1) * sigma*sigma)
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    # ---Added properties and methods---

    @property
    def lmax(self):
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        """ Returns the maximal :math:`l` value of any spherical harmonics
        :math:`Y_{lm}` that is represented in this Space.
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        """
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        return self._lmax

    @property
    def mmax(self):
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        """ Returns the maximal :math:`m` value of any spherical harmonic
        :math:`Y_{lm}` that is represented in this Space. As :math:`m` goes
        from :math:`-l` to :math:`l` for every :math:`l` this just returns the
        same as lmax.

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        See Also
        --------
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        lmax : Returns the maximal :math:`l`-value of the spherical harmonics
            being used.

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        """
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        return self._lmax
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    def _parse_lmax(self, lmax):
        lmax = np.int(lmax)
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        if lmax < 0:
            raise ValueError("lmax must be >=0.")
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        return lmax
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    # ---Serialization---

    def _to_hdf5(self, hdf5_group):
        hdf5_group['lmax'] = self.lmax
        return None

    @classmethod
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    def _from_hdf5(cls, hdf5_group, repository):
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        result = cls(
            lmax=hdf5_group['lmax'][()],
            )
        return result