rg_space.py 10.6 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.
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#
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# 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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#
# 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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"""
    ..                  __   ____   __
    ..                /__/ /   _/ /  /_
    ..      __ ___    __  /  /_  /   _/  __   __
    ..    /   _   | /  / /   _/ /  /   /  / /  /
    ..   /  / /  / /  / /  /   /  /_  /  /_/  /
    ..  /__/ /__/ /__/ /__/    \___/  \___   /  rg
    ..                               /______/

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    NIFTY submodule for regular Cartesian grids.
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"""
from __future__ import division
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import numpy as np
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from d2o import distributed_data_object,\
                STRATEGIES as DISTRIBUTION_STRATEGIES
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from nifty.spaces.space import Space
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class RGSpace(Space):
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    """
        ..      _____   _______
        ..    /   __/ /   _   /
        ..   /  /    /  /_/  /
        ..  /__/     \____  /  space class
        ..          /______/

        NIFTY subclass for spaces of regular Cartesian grids.

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        Parameters
        ----------
        shape : {int, numpy.ndarray}
            Number of grid points or numbers of gridpoints along each axis.
        distances : {float, numpy.ndarray}, *optional*
            Distance between two grid points along each axis
            (default: None).
            If distances==None:
                if harmonic==True, all distances will be set to 1
                if harmonic==False, the distance along each axis will be
                  set to the inverse of the number of points along that
                  axis.
        harmonic : bool, *optional*
        Whether the space represents a grid in position or harmonic space.
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            (default: False).
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        Attributes
        ----------
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        harmonic : bool
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            Whether or not the grid represents a position or harmonic space.
        distances : tuple of floats
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            Distance between two grid points along the correponding axis.
        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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    """

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    # ---Overwritten properties and methods---

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    def __init__(self, shape, distances=None, harmonic=False):
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        self._harmonic = bool(harmonic)

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        super(RGSpace, self).__init__()
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        self._shape = self._parse_shape(shape)
        self._distances = self._parse_distances(distances)
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# This code is unused but may be useful to keep around if it is ever needed
# again in the future ...

#    def hermitian_fixed_points(self):
#        dimensions = len(self.shape)
#        mid_index = np.array(self.shape)//2
#        ndlist = [1]*dimensions
#        for k in range(dimensions):
#            if self.shape[k] % 2 == 0:
#                ndlist[k] = 2
#        ndlist = tuple(ndlist)
#        fixed_points = []
#        for index in np.ndindex(ndlist):
#            for k in range(dimensions):
#                if self.shape[k] % 2 != 0 and self.zerocenter[k]:
#                    index = list(index)
#                    index[k] = 1
#                    index = tuple(index)
#            fixed_points += [tuple(index * mid_index)]
#        return fixed_points
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    def hermitianize_inverter(self, x, axes):
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        # calculate the number of dimensions the input array has
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        dimensions = len(x.shape)
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        # prepare the slicing object which will be used for mirroring
        slice_primitive = [slice(None), ] * dimensions
        # copy the input data
        y = x.copy()

        # flip in the desired directions
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        for k in range(len(axes)):
            i = axes[k]
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            slice_picker = slice_primitive[:]
            slice_inverter = slice_primitive[:]
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            slice_picker[i] = slice(1, None, None)
            slice_inverter[i] = slice(None, 0, -1)
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            slice_picker = tuple(slice_picker)
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            slice_inverter = tuple(slice_inverter)

            try:
                y.set_data(to_key=slice_picker, data=y,
                           from_key=slice_inverter)
            except(AttributeError):
                y[slice_picker] = y[slice_inverter]
        return y

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    # ---Mandatory properties and methods---

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    def __repr__(self):
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        return ("RGSpace(shape=%r, distances=%r, harmonic=%r)"
                % (self.shape, self.distances, self.harmonic))
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    @property
    def harmonic(self):
        return self._harmonic

    @property
    def shape(self):
        return self._shape

    @property
    def dim(self):
        return reduce(lambda x, y: x*y, self.shape)

    @property
    def total_volume(self):
        return self.dim * reduce(lambda x, y: x*y, self.distances)

    def copy(self):
        return self.__class__(shape=self.shape,
                              distances=self.distances,
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                              harmonic=self.harmonic)
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    def weight(self, x, power=1, axes=None, inplace=False):
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        weight = reduce(lambda x, y: x*y, self.distances) ** np.float(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 an n-dimensional array with its entries being the
        lengths of the vectors from the zero point of the grid.
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        Parameters
        ----------
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        distribution_strategy : str
            The distribution_strategy which shall be used the returned
            distributed_data_object.
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        Returns
        -------
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        distributed_data_object
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            A d2o containing the distances.

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        """
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        shape = self.shape
        # prepare the distributed_data_object
        nkdict = distributed_data_object(
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                        global_shape=shape, dtype=np.float64,
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                        distribution_strategy=distribution_strategy)

        if distribution_strategy in DISTRIBUTION_STRATEGIES['slicing']:
            # get the node's individual slice of the first dimension
            slice_of_first_dimension = slice(
                                    *nkdict.distributor.local_slice[0:2])
        elif distribution_strategy in DISTRIBUTION_STRATEGIES['not']:
            slice_of_first_dimension = slice(0, shape[0])
        else:
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            raise ValueError(
                "Unsupported distribution strategy")
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        dists = self._distance_array_helper(slice_of_first_dimension)
        nkdict.set_local_data(dists)

        return nkdict

    def _distance_array_helper(self, slice_of_first_dimension):
        dk = self.distances
        shape = self.shape

        inds = []
        for a in shape:
            inds += [slice(0, a)]

        cords = np.ogrid[inds]

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        dists = (cords[0] - shape[0]//2)*dk[0]
        dists *= dists
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        dists = np.fft.ifftshift(dists)
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        # only save the individual slice
        dists = dists[slice_of_first_dimension]
        for ii in range(1, len(shape)):
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            temp = (cords[ii] - shape[ii] // 2) * dk[ii]
            temp *= temp
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            temp = np.fft.ifftshift(temp)
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            dists = dists + temp
        dists = np.sqrt(dists)
        return dists

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    def get_unique_distances(self):
        dimensions = len(self.shape)
        if dimensions == 1:  # extra easy
            maxdist = self.shape[0]//2
            return np.arange(maxdist+1, dtype=np.float64) * self.distances[0]
        if np.all(self.distances == self.distances[0]):  # shortcut
            maxdist = np.asarray(self.shape)//2
            tmp = np.sum(maxdist*maxdist)
            tmp = np.zeros(tmp+1, dtype=np.bool)
            t2 = np.arange(maxdist[0]+1, dtype=np.int64)
            t2 *= t2
            for i in range(1, dimensions):
                t3 = np.arange(maxdist[i]+1, dtype=np.int64)
                t3 *= t3
                t2 = np.add.outer(t2, t3)
            tmp[t2] = True
            return np.sqrt(np.nonzero(tmp)[0])*self.distances[0]
        else:  # do it the hard way
            tmp = self.get_distance_array('not').unique()  # expensive!
            tol = 1e-12*tmp[-1]
            # remove all points that are closer than tol to their right
            # neighbors.
            # I'm appending the last value*2 to the array to treat the
            # rightmost point correctly.
            return tmp[np.diff(np.r_[tmp, 2*tmp[-1]]) > tol]

    def get_natural_binbounds(self):
        tmp = self.get_unique_distances()
        return 0.5*(tmp[:-1]+tmp[1:])

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    def get_fft_smoothing_kernel_function(self, sigma):
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        return lambda x: np.exp(-2. * np.pi*np.pi * x*x * sigma*sigma)
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    # ---Added properties and methods---

    @property
    def distances(self):
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        """Distance between two grid points along each axis. It is a tuple
        of positive floating point numbers with the n-th entry giving the
        distances of grid points along the n-th dimension.
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        """
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        return self._distances

    def _parse_shape(self, shape):
        if np.isscalar(shape):
            shape = (shape,)
        temp = np.empty(len(shape), dtype=np.int)
        temp[:] = shape
        return tuple(temp)

    def _parse_distances(self, distances):
        if distances is None:
            if self.harmonic:
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                temp = np.ones_like(self.shape, dtype=np.float64)
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            else:
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                temp = 1 / np.array(self.shape, dtype=np.float64)
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        else:
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            temp = np.empty(len(self.shape), dtype=np.float64)
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            temp[:] = distances
        return tuple(temp)

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    # ---Serialization---

    def _to_hdf5(self, hdf5_group):
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        hdf5_group['shape'] = self.shape
        hdf5_group['distances'] = self.distances
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        hdf5_group['harmonic'] = self.harmonic
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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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            shape=hdf5_group['shape'][:],
            distances=hdf5_group['distances'][:],
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            harmonic=hdf5_group['harmonic'][()],
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            )
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        return result