# 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 . # # 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. import abc from nifty.domain_object import DomainObject class Space(DomainObject): """ The abstract base class for all NIFTy spaces. An instance of a space contains information about the manifolds geometry and enhances the functionality of DomainObject by methods that are needed for powerspectrum analysis and smoothing. Parameters ---------- None Attributes ---------- 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. Raises ------ TypeError Raised if instantiated directly. Notes ----- `Space` is an abstract base class. In order to allow for instantiation the methods `get_distance_array`, `total_volume` and `copy` must be implemented as well as the abstract methods inherited from `DomainObject`. """ def __init__(self): super(Space, self).__init__() @abc.abstractproperty def harmonic(self): """ Returns True if this space is a harmonic space. """ raise NotImplementedError @abc.abstractproperty def total_volume(self): """ Returns the total volume of the space. Returns ------- float A real number representing the sum of all pixel volumes. """ raise NotImplementedError( "There is no generic volume for the Space base class.") @abc.abstractmethod def copy(self): """ Returns a copy of this Space instance. Returns ------- Space A copy of this instance. """ return self.__class__() def get_distance_array(self, distribution_strategy): """ The distances of the pixel to zero. This returns an array that gives for each pixel its distance to the center of the manifolds grid. Parameters ---------- distribution_strategy : str The distribution_strategy which shall be used the returned distributed_data_object. Returns ------- distributed_data_object A d2o containing the distances """ raise NotImplementedError( "There is no generic distance structure for Space base class.") def get_unique_distances(self): raise NotImplementedError def get_natural_binbounds(self): """ The boundaries for natural power spectrum binning. Returns ------- distributed_data_object A numpy array containing the binbounds """ raise NotImplementedError def get_fft_smoothing_kernel_function(self, sigma): """ This method returns a smoothing kernel function. This method, which is only implemented for harmonic spaces, helps smoothing fields that live in a position space that has this space as its harmonic space. The returned function multiplies field values of a field with a zero centered Gaussian which corresponds to a convolution with a Gaussian kernel and sigma standard deviation in position space. Parameters ---------- sigma : float A real number representing a physical scale on which the smoothing takes place. The smoothing is defined with respect to the real physical field and points that are closer together than one sigma are blurred together. Mathematically sigma is the standard deviation of a convolution with a normalized, zero-centered Gaussian that takes place in position space. Returns ------- function (array-like -> array-like) A smoothing operation that multiplies values with a Gaussian kernel. """ raise NotImplementedError( "There is no generic co-smoothing kernel for Space base class.") def hermitianize_inverter(self, x, axes): """ Inverts/flips x in the context of Hermitian decomposition. This method is mainly used for power-synthesizing and -analyzing Fields. Parameters ---------- axes : tuple of ints Specifies the axes of x which correspond to this space. Returns ------- distributed_data_object The Hermitian-flipped of x. """ return x