hp_space.py 4.2 KB
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# NIFTy
# Copyright (C) 2017  Theo Steininger
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#
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# Author: Theo Steininger
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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
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
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#
# You should have received a copy of the GNU General Public License
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# along with this program.  If not, see <http://www.gnu.org/licenses/>.
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import numpy as np
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from nifty.spaces.space import Space
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class HPSpace(Space):
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    """
        ..        __
        ..      /  /
        ..     /  /___    ______
        ..    /   _   | /   _   |
        ..   /  / /  / /  /_/  /
        ..  /__/ /__/ /   ____/  space class
        ..           /__/

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

        Parameters
        ----------
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        nside :
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            Resolution parameter for the HEALPix discretization, resulting in
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            ``12*nside**2`` pixels. Must be positive.
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        See Also
        --------
        gl_space : A class for the Gauss-Legendre discretization of the
            sphere [#]_.
        lm_space : A class for spherical harmonic components.

        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
        ----------
        dtype : numpy.dtype
            Data type of the field values, which is always numpy.float64.
    """

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    # ---Overwritten properties and methods---

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    def __init__(self, nside, dtype=None):
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        """
            Sets the attributes for a hp_space class instance.

            Parameters
            ----------
            nside : int
                Resolution parameter for the HEALPix discretization, resulting
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                in ``12*nside**2`` pixels. Must be positive.
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            Returns
            -------
            None

            Raises
            ------
            ValueError
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                If input `nside` is invalid.
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        """

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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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        raise NotImplementedError
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    def get_fft_smoothing_kernel_function(self, sigma):
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        raise NotImplementedError
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    # ---Added properties and methods---

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

    def _parse_nside(self, nside):
        nside = int(nside)
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        if nside < 1:
            raise ValueError("nside must be >=1.")
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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
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        hdf5_group.attrs['dtype'] = self.dtype.name
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        return None

    @classmethod
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    def _from_hdf5(cls, hdf5_group, repository):
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        result = cls(
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            nside=hdf5_group['nside'][()],
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            dtype=np.dtype(hdf5_group.attrs['dtype'])
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            )
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        return result
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    def plot(self):
        pass