hp_space.py 6.35 KB
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# NIFTY (Numerical Information Field Theory) has been developed at the
# Max-Planck-Institute for Astrophysics.
#
# Copyright (C) 2015 Max-Planck-Society
#
# Author: Marco Selig
# Project homepage: <http://www.mpa-garching.mpg.de/ift/nifty/>
#
# 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/>.

"""
    ..                  __   ____   __
    ..                /__/ /   _/ /  /_
    ..      __ ___    __  /  /_  /   _/  __   __
    ..    /   _   | /  / /   _/ /  /   /  / /  /
    ..   /  / /  / /  / /  /   /  /_  /  /_/  /
    ..  /__/ /__/ /__/ /__/    \___/  \___   /  lm
    ..                               /______/

    NIFTY submodule for grids on the two-sphere.

"""
from __future__ import division

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import pickle

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import numpy as np
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import d2o
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from keepers import Versionable
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from nifty.spaces.space import Space
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from nifty.config import nifty_configuration as gc, \
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                         dependency_injector as gdi

hp = gdi.get('healpy')

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class HPSpace(Versionable, Space):
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    """
        ..        __
        ..      /  /
        ..     /  /___    ______
        ..    /   _   | /   _   |
        ..   /  / /  / /  /_/  /
        ..  /__/ /__/ /   ____/  space class
        ..           /__/

        NIFTY subclass for HEALPix discretizations of the two-sphere [#]_.

        Parameters
        ----------
        nside : int
            Resolution parameter for the HEALPix discretization, resulting in
            ``12*nside**2`` pixels.

        See Also
        --------
        gl_space : A class for the Gauss-Legendre discretization of the
            sphere [#]_.
        lm_space : A class for spherical harmonic components.

        Notes
        -----
        Only powers of two are allowed for `nside`.

        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>`_

        Attributes
        ----------
        para : numpy.ndarray
            Array containing the number `nside`.
        dtype : numpy.dtype
            Data type of the field values, which is always numpy.float64.
        discrete : bool
            Whether or not the underlying space is discrete, always ``False``
            for spherical spaces.
        vol : numpy.ndarray
            An array with one element containing the pixel size.
    """

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    # ---Overwritten properties and methods---

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    def __init__(self, nside=2, dtype=np.dtype('float')):
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        """
            Sets the attributes for a hp_space class instance.

            Parameters
            ----------
            nside : int
                Resolution parameter for the HEALPix discretization, resulting
                in ``12*nside**2`` pixels.

            Returns
            -------
            None

            Raises
            ------
            ImportError
                If the healpy module is not available.
            ValueError
                If input `nside` is invaild.

        """
        # check imports
        if not gc['use_healpy']:
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            raise ImportError("healpy not available or not loaded.")
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        super(HPSpace, self).__init__(dtype)
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        self._nside = self._parse_nside(nside)
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    # ---Mandatory properties and methods---

    @property
    def harmonic(self):
        return False
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    @property
    def shape(self):
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        return (np.int(12 * self.nside ** 2),)
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    @property
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    def dim(self):
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        return np.int(12 * self.nside ** 2)
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    @property
    def total_volume(self):
        return 4 * np.pi
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    def copy(self):
        return self.__class__(nside=self.nside,
                              dtype=self.dtype)

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    def weight(self, x, power=1, axes=None, inplace=False):
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        weight = ((4*np.pi) / (12 * self.nside**2)) ** power
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        if inplace:
            x *= weight
            result_x = x
        else:
            result_x = x * weight

        return result_x
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    def get_distance_array(self, distribution_strategy):
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        """
        Calculates distance from center to all the points on the sphere

        Parameters
        ----------
        distribution_strategy: Result d2o's distribution strategy

        Returns
        -------
        dists: distributed_data_object
        """
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        dists = d2o.arange(
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            start=0, stop=self.shape[0],
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            distribution_strategy=distribution_strategy
        )

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        # translate distances to 3D unit vectors on a sphere,
        # extract the first entry (simulates the scalar product with (1,0,0))
        # and apply arccos
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        dists = dists.apply_scalar_function(
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                    lambda z: np.arccos(hp.pix2vec(self.nside, z)[0]),
                    dtype=np.float)
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        return dists

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    def get_fft_smoothing_kernel_function(self, sigma):
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        if sigma is None:
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            sigma = np.sqrt(2) * np.pi
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        return lambda x: np.exp((-0.5 * x**2) / sigma**2)
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    # ---Added properties and methods---

    @property
    def nside(self):
        return self._nside

    def _parse_nside(self, nside):
        nside = int(nside)
        if nside & (nside - 1) != 0 or nside < 2:
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            raise ValueError(
                "nside must be positive and a multiple of 2.")
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        return nside
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    # ---Serialization---

    def _to_hdf5(self, hdf5_group):
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        hdf5_group['nside'] = self.nside
        hdf5_group['dtype'] = pickle.dumps(self.dtype)
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        return None

    @classmethod
    def _from_hdf5(cls, hdf5_group, loopback_get):
        result = cls(
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            nside=hdf5_group['nside'][()],
            dtype=pickle.loads(hdf5_group['dtype'][()])
            )
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        return result