# 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) 2013-2019 Max-Planck-Society # # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik. import numpy as np from .. import dobj from ..compat import * from ..field import Field from ..sugar import exp from .structured_domain import StructuredDomain class LogRGSpace(StructuredDomain): """NIFTy subclass for logarithmic Cartesian grids. Parameters ---------- shape : int or tuple of int Number of grid points or numbers of gridpoints along each axis. bindistances : float or tuple of float Distance between two grid points along each axis. These are measured on logarithmic scale and are constant therfore. t_0 : float or tuple of float FIXME harmonic : bool, optional Whether the space represents a grid in position or harmonic space. (default: False). """ _needed_for_hash = ['_shape', '_bindistances', '_t_0', '_harmonic'] def __init__(self, shape, bindistances, t_0, harmonic=False): self._harmonic = bool(harmonic) if np.isscalar(shape): shape = (shape,) self._shape = tuple(int(i) for i in shape) self._bindistances = tuple(bindistances) self._t_0 = tuple(t_0) self._dim = int(reduce(lambda x, y: x*y, self._shape)) self._dvol = float(reduce(lambda x, y: x*y, self._bindistances)) @property def harmonic(self): return self._harmonic @property def shape(self): return self._shape @property def scalar_dvol(self): return self._dvol @property def bindistances(self): return np.array(self._bindistances) @property def size(self): return np.prod(self._shape) @property def t_0(self): return np.array(self._t_0) def __repr__(self): return ("LogRGSpace(shape={}, harmonic={})".format( self.shape, self.harmonic)) def get_default_codomain(self): codomain_bindistances = 1./(self.bindistances*self.shape) return LogRGSpace(self.shape, codomain_bindistances, self._t_0, True) def get_k_length_array(self): if not self.harmonic: raise NotImplementedError ks = self.get_k_array() return Field.from_global_data(self, np.linalg.norm(ks, axis=0)) def get_k_array(self): ndim = len(self.shape) k_array = np.zeros((ndim,) + self.shape) dist = self.bindistances for i in range(ndim): ks = np.zeros(self.shape[i]) ks[1:] = np.minimum(self.shape[i] - 1 - np.arange(self.shape[i]-1), np.arange(self.shape[i]-1)) * dist[i] if self.harmonic: ks[0] = np.nan else: ks[0] = -np.inf ks[1:] += self.t_0[i] k_array[i] += ks.reshape((1,)*i + (self.shape[i],) + (1,)*(ndim-i-1)) return k_array