consolidation

nifty5/__init__.py

@@ -25,11 +25,10 @@ from .operators.dof_distributor import DOFDistributor
 from .operators.domain_distributor import DomainDistributor
 from .operators.endomorphic_operator import EndomorphicOperator
 from .operators.exp_transform import ExpTransform
-from .operators.fft_operator import FFTOperator
+from .operators.harmonic_operators import (
+    FFTOperator, HartleyOperator, SHTOperator, HarmonicTransformOperator,
+    HarmonicSmoothingOperator)
 from .operators.field_zero_padder import FieldZeroPadder
-from .operators.hartley_operator import HartleyOperator
-from .operators.harmonic_smoothing_operator import HarmonicSmoothingOperator
-from .operators.harmonic_transform_operator import HarmonicTransformOperator
 from .operators.inversion_enabler import InversionEnabler
 from .operators.laplace_operator import LaplaceOperator
 from .operators.linear_operator import LinearOperator

nifty5/field.py

@@ -608,17 +608,6 @@ class Field(object):
                self._domain.__str__() + \
                "\n- val         = " + repr(self._val)
 
-    def isEquivalentTo(self, other):
-        """Determines (as quickly as possible) whether `self`'s content is
-        identical to `other`'s content."""
-        if self is other:
-            return True
-        if not isinstance(other, Field):
-            return False
-        if self._domain is not other._domain:
-            return False
-        return (self._val == other._val).all()
-
     def extract(self, dom):
         if dom is not self._domain:
             raise ValueError("domain mismatch")

nifty5/library/correlated_fields.py

@@ -23,7 +23,7 @@ from ..domain_tuple import DomainTuple
 from ..multi_field import MultiField
 from ..multi_domain import MultiDomain
 from ..operators.domain_distributor import DomainDistributor
-from ..operators.harmonic_transform_operator import HarmonicTransformOperator
+from ..operators.harmonic_operators import HarmonicTransformOperator
 from ..operators.power_distributor import PowerDistributor
 from ..operators.operator import Operator
 

nifty5/multi_field.py

@@ -199,22 +199,10 @@ class MultiField(object):
                 return True
         return False
 
-    def isEquivalentTo(self, other):
-        """Determines (as quickly as possible) whether `self`'s content is
-        identical to `other`'s content."""
-        if self is other:
-            return True
-        if not isinstance(other, MultiField):
-            return False
-        if self._domain is not other._domain:
-            return False
-        for v1, v2 in zip(self._val, other._val):
-            if not v1.isEquivalentTo(v2):
-                return False
-        return True
-
     def extract(self, subset):
         if isinstance(subset, MultiDomain):
+            if subset is self._domain:
+                return self
             return MultiField(subset,
                               tuple(self[key] for key in subset.keys()))
         else:

nifty5/operators/fft_operator.py

-# 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/>.
-#
-# Copyright(C) 2013-2018 Max-Planck-Society
-#
-# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
-# and financially supported by the Studienstiftung des deutschen Volkes.
-
-from __future__ import absolute_import, division, print_function
-
-import numpy as np
-
-from .. import dobj, utilities
-from ..compat import *
-from ..domain_tuple import DomainTuple
-from ..domains.rg_space import RGSpace
-from ..field import Field
-from .linear_operator import LinearOperator
-
-
-class FFTOperator(LinearOperator):
-    """Transforms between a pair of position and harmonic RGSpaces.
-
-    Parameters
-    ----------
-    domain: Domain, tuple of Domain or DomainTuple
-        The domain of the data that is input by "times" and output by
-        "adjoint_times".
-    target: Domain, optional
-        The target (sub-)domain of the transform operation.
-        If omitted, a domain will be chosen automatically.
-    space: int, optional
-        The index of the subdomain on which the operator should act
-        If None, it is set to 0 if `domain` contains exactly one space.
-        `domain[space]` must be an RGSpace.
-
-    Notes
-    -----
-    This operator performs full FFTs, which implies that its output field will
-    always have complex type, regardless of the type of the input field.
-    If a real field is desired after a forward/backward transform couple, it
-    must be manually cast to real.
-    """
-
-    def __init__(self, domain, target=None, space=None):
-        super(FFTOperator, self).__init__()
-
-        # Initialize domain and target
-        self._domain = DomainTuple.make(domain)
-        self._space = utilities.infer_space(self._domain, space)
-
-        adom = self._domain[self._space]
-        if not isinstance(adom, RGSpace):
-            raise TypeError("FFTOperator only works on RGSpaces")
-        if target is None:
-            target = adom.get_default_codomain()
-
-        self._target = [dom for dom in self._domain]
-        self._target[self._space] = target
-        self._target = DomainTuple.make(self._target)
-        adom.check_codomain(target)
-        target.check_codomain(adom)
-
-        utilities.fft_prep()
-
-    def apply(self, x, mode):
-        from pyfftw.interfaces.numpy_fft import fftn, ifftn
-        self._check_input(x, mode)
-        ncells = x.domain[self._space].size
-        if x.domain[self._space].harmonic:  # harmonic -> position
-            func = fftn
-            fct = 1.
-        else:
-            func = ifftn
-            fct = ncells
-        axes = x.domain.axes[self._space]
-        tdom = self._tgt(mode)
-        oldax = dobj.distaxis(x.val)
-        if oldax not in axes:  # straightforward, no redistribution needed
-            ldat = x.local_data
-            ldat = func(ldat, axes=axes)
-            tmp = dobj.from_local_data(x.val.shape, ldat, distaxis=oldax)
-        elif len(axes) < len(x.shape) or len(axes) == 1:
-            # we can use one FFT pass in between the redistributions
-            tmp = dobj.redistribute(x.val, nodist=axes)
-            newax = dobj.distaxis(tmp)
-            ldat = dobj.local_data(tmp)
-            ldat = func(ldat, axes=axes)
-            tmp = dobj.from_local_data(tmp.shape, ldat, distaxis=newax)
-            tmp = dobj.redistribute(tmp, dist=oldax)
-        else:  # two separate FFTs needed
-            rem_axes = tuple(i for i in axes if i != oldax)
-            tmp = x.val
-            ldat = dobj.local_data(tmp)
-            ldat = func(ldat, axes=rem_axes)
-            if oldax != 0:
-                raise ValueError("bad distribution")
-            ldat2 = ldat.reshape((ldat.shape[0],
-                                  np.prod(ldat.shape[1:])))
-            shp2d = (x.val.shape[0], np.prod(x.val.shape[1:]))
-            tmp = dobj.from_local_data(shp2d, ldat2, distaxis=0)
-            tmp = dobj.transpose(tmp)
-            ldat2 = dobj.local_data(tmp)
-            ldat2 = func(ldat2, axes=(1,))
-            tmp = dobj.from_local_data(tmp.shape, ldat2, distaxis=0)
-            tmp = dobj.transpose(tmp)
-            ldat2 = dobj.local_data(tmp).reshape(ldat.shape)
-            tmp = dobj.from_local_data(x.val.shape, ldat2, distaxis=0)
-        Tval = Field(tdom, tmp)
-        if mode & (LinearOperator.TIMES | LinearOperator.ADJOINT_TIMES):
-            fct *= self._domain[self._space].scalar_dvol
-        else:
-            fct *= self._target[self._space].scalar_dvol
-        return Tval if fct == 1 else Tval*fct
-
-    @property
-    def domain(self):
-        return self._domain
-
-    @property
-    def target(self):
-        return self._target
-
-    @property
-    def capability(self):
-        return self._all_ops

nifty5/operators/hartley_operator.py → nifty5/operators/harmonic_operators.py

@@ -24,8 +24,120 @@ from .. import dobj, utilities
 from ..compat import *
 from ..domain_tuple import DomainTuple
 from ..domains.rg_space import RGSpace
+from ..domains.lm_space import LMSpace
+from ..domains.gl_space import GLSpace
 from ..field import Field
 from .linear_operator import LinearOperator
+from .diagonal_operator import DiagonalOperator
+from .scaling_operator import ScalingOperator
+
+
+class FFTOperator(LinearOperator):
+    """Transforms between a pair of position and harmonic RGSpaces.
+
+    Parameters
+    ----------
+    domain: Domain, tuple of Domain or DomainTuple
+        The domain of the data that is input by "times" and output by
+        "adjoint_times".
+    target: Domain, optional
+        The target (sub-)domain of the transform operation.
+        If omitted, a domain will be chosen automatically.
+    space: int, optional
+        The index of the subdomain on which the operator should act
+        If None, it is set to 0 if `domain` contains exactly one space.
+        `domain[space]` must be an RGSpace.
+
+    Notes
+    -----
+    This operator performs full FFTs, which implies that its output field will
+    always have complex type, regardless of the type of the input field.
+    If a real field is desired after a forward/backward transform couple, it
+    must be manually cast to real.
+    """
+
+    def __init__(self, domain, target=None, space=None):
+        super(FFTOperator, self).__init__()
+
+        # Initialize domain and target
+        self._domain = DomainTuple.make(domain)
+        self._space = utilities.infer_space(self._domain, space)
+
+        adom = self._domain[self._space]
+        if not isinstance(adom, RGSpace):
+            raise TypeError("FFTOperator only works on RGSpaces")
+        if target is None:
+            target = adom.get_default_codomain()
+
+        self._target = [dom for dom in self._domain]
+        self._target[self._space] = target
+        self._target = DomainTuple.make(self._target)
+        adom.check_codomain(target)
+        target.check_codomain(adom)
+
+        utilities.fft_prep()
+
+    def apply(self, x, mode):
+        from pyfftw.interfaces.numpy_fft import fftn, ifftn
+        self._check_input(x, mode)
+        ncells = x.domain[self._space].size
+        if x.domain[self._space].harmonic:  # harmonic -> position
+            func = fftn
+            fct = 1.
+        else:
+            func = ifftn
+            fct = ncells
+        axes = x.domain.axes[self._space]
+        tdom = self._tgt(mode)
+        oldax = dobj.distaxis(x.val)
+        if oldax not in axes:  # straightforward, no redistribution needed
+            ldat = x.local_data
+            ldat = func(ldat, axes=axes)
+            tmp = dobj.from_local_data(x.val.shape, ldat, distaxis=oldax)
+        elif len(axes) < len(x.shape) or len(axes) == 1:
+            # we can use one FFT pass in between the redistributions
+            tmp = dobj.redistribute(x.val, nodist=axes)
+            newax = dobj.distaxis(tmp)
+            ldat = dobj.local_data(tmp)
+            ldat = func(ldat, axes=axes)
+            tmp = dobj.from_local_data(tmp.shape, ldat, distaxis=newax)
+            tmp = dobj.redistribute(tmp, dist=oldax)
+        else:  # two separate FFTs needed
+            rem_axes = tuple(i for i in axes if i != oldax)
+            tmp = x.val
+            ldat = dobj.local_data(tmp)
+            ldat = func(ldat, axes=rem_axes)
+            if oldax != 0:
+                raise ValueError("bad distribution")
+            ldat2 = ldat.reshape((ldat.shape[0],
+                                  np.prod(ldat.shape[1:])))
+            shp2d = (x.val.shape[0], np.prod(x.val.shape[1:]))
+            tmp = dobj.from_local_data(shp2d, ldat2, distaxis=0)
+            tmp = dobj.transpose(tmp)
+            ldat2 = dobj.local_data(tmp)
+            ldat2 = func(ldat2, axes=(1,))
+            tmp = dobj.from_local_data(tmp.shape, ldat2, distaxis=0)
+            tmp = dobj.transpose(tmp)
+            ldat2 = dobj.local_data(tmp).reshape(ldat.shape)
+            tmp = dobj.from_local_data(x.val.shape, ldat2, distaxis=0)
+        Tval = Field(tdom, tmp)
+        if mode & (LinearOperator.TIMES | LinearOperator.ADJOINT_TIMES):
+            fct *= self._domain[self._space].scalar_dvol
+        else:
+            fct *= self._target[self._space].scalar_dvol
+        return Tval if fct == 1 else Tval*fct
+
+    @property
+    def domain(self):
+        return self._domain
+
+    @property
+    def target(self):
+        return self._target
+
+    @property
+    def capability(self):
+        return self._all_ops
 
 
 class HartleyOperator(LinearOperator):
@@ -146,3 +258,226 @@ class HartleyOperator(LinearOperator):
     @property
     def capability(self):
         return self._all_ops
+
+
+class SHTOperator(LinearOperator):
+    """Transforms between a harmonic domain on the sphere and a position
+    domain counterpart.
+
+    Built-in domain pairs are
+      - an LMSpace and a HPSpace
+      - an LMSpace and a GLSpace
+
+    The supported operations are times() and adjoint_times().
+
+    Parameters
+    ----------
+    domain : Domain, tuple of Domain or DomainTuple
+        The domain of the data that is input by "times" and output by
+        "adjoint_times".
+    target : Domain, optional
+        The target domain of the transform operation.
+        If omitted, a domain will be chosen automatically.
+        Whenever the input domain of the transform is an RGSpace, the codomain
+        (and its parameters) are uniquely determined.
+        For LMSpace, a GLSpace of sufficient resolution is chosen.
+    space : int, optional
+        The index of the domain on which the operator should act
+        If None, it is set to 0 if domain contains exactly one subdomain.
+        domain[space] must be a LMSpace.
+    """
+
+    def __init__(self, domain, target=None, space=None):
+        super(SHTOperator, self).__init__()
+
+        # Initialize domain and target
+        self._domain = DomainTuple.make(domain)
+        self._space = utilities.infer_space(self._domain, space)
+
+        hspc = self._domain[self._space]
+        if not isinstance(hspc, LMSpace):
+            raise TypeError("SHTOperator only works on a LMSpace domain")
+        if target is None:
+            target = hspc.get_default_codomain()
+
+        self._target = [dom for dom in self._domain]
+        self._target[self._space] = target
+        self._target = DomainTuple.make(self._target)
+        hspc.check_codomain(target)
+        target.check_codomain(hspc)
+
+        from pyHealpix import sharpjob_d
+        self.lmax = hspc.lmax
+        self.mmax = hspc.mmax
+        self.sjob = sharpjob_d()
+        self.sjob.set_triangular_alm_info(self.lmax, self.mmax)
+        if isinstance(target, GLSpace):
+            self.sjob.set_Gauss_geometry(target.nlat, target.nlon)
+        else:
+            self.sjob.set_Healpix_geometry(target.nside)
+
+    def apply(self, x, mode):
+        self._check_input(x, mode)
+        if utilities.iscomplextype(x.dtype):
+            return (self._apply_spherical(x.real, mode) +
+                    1j*self._apply_spherical(x.imag, mode))
+        else:
+            return self._apply_spherical(x, mode)
+
+    def _slice_p2h(self, inp):
+        rr = self.sjob.alm2map_adjoint(inp)
+        if len(rr) != ((self.mmax+1)*(self.mmax+2))//2 + \
+                      (self.mmax+1)*(self.lmax-self.mmax):
+            raise ValueError("array length mismatch")
+        res = np.empty(2*len(rr)-self.lmax-1, dtype=rr[0].real.dtype)
+        res[0:self.lmax+1] = rr[0:self.lmax+1].real
+        res[self.lmax+1::2] = np.sqrt(2)*rr[self.lmax+1:].real
+        res[self.lmax+2::2] = np.sqrt(2)*rr[self.lmax+1:].imag
+        return res/np.sqrt(np.pi*4)
+
+    def _slice_h2p(self, inp):
+        res = np.empty((len(inp)+self.lmax+1)//2, dtype=(inp[0]*1j).dtype)
+        if len(res) != ((self.mmax+1)*(self.mmax+2))//2 + \
+                       (self.mmax+1)*(self.lmax-self.mmax):
+            raise ValueError("array length mismatch")
+        res[0:self.lmax+1] = inp[0:self.lmax+1]
+        res[self.lmax+1:] = np.sqrt(0.5)*(inp[self.lmax+1::2] +
+                                          1j*inp[self.lmax+2::2])
+        res = self.sjob.alm2map(res)
+        return res/np.sqrt(np.pi*4)
+
+    def _apply_spherical(self, x, mode):
+        axes = x.domain.axes[self._space]
+        axis = axes[0]
+        tval = x.val
+        if dobj.distaxis(tval) == axis:
+            tval = dobj.redistribute(tval, nodist=(axis,))
+        distaxis = dobj.distaxis(tval)
+
+        p2h = not x.domain[self._space].harmonic
+        tdom = self._tgt(mode)
+        func = self._slice_p2h if p2h else self._slice_h2p
+        idat = dobj.local_data(tval)
+        odat = np.empty(dobj.local_shape(tdom.shape, distaxis=distaxis),
+                        dtype=x.dtype)
+        for slice in utilities.get_slice_list(idat.shape, axes):
+            odat[slice] = func(idat[slice])
+        odat = dobj.from_local_data(tdom.shape, odat, distaxis)
+        if distaxis != dobj.distaxis(x.val):
+            odat = dobj.redistribute(odat, dist=dobj.distaxis(x.val))
+        return Field(tdom, odat)
+
+    @property
+    def domain(self):
+        return self._domain
+
+    @property
+    def target(self):
+        return self._target
+
+    @property
+    def capability(self):
+        return self.TIMES | self.ADJOINT_TIMES
+
+
+class HarmonicTransformOperator(LinearOperator):
+    """Transforms between a harmonic domain and a position domain counterpart.
+
+    Built-in domain pairs are
+      - a harmonic and a non-harmonic RGSpace (with matching distances)
+      - an LMSpace and a HPSpace
+      - an LMSpace and a GLSpace
+
+    The supported operations are times() and adjoint_times().
+
+    Parameters
+    ----------
+    domain : Domain, tuple of Domain or DomainTuple
+        The domain of the data that is input by "times" and output by
+        "adjoint_times".
+    target : Domain, optional
+        The target domain of the transform operation.
+        If omitted, a domain will be chosen automatically.
+        Whenever the input domain of the transform is an RGSpace, the codomain
+        (and its parameters) are uniquely determined.
+        For LMSpace, a GLSpace of sufficient resolution is chosen.
+    space : int, optional
+        The index of the domain on which the operator should act
+        If None, it is set to 0 if domain contains exactly one subdomain.
+        domain[space] must be a harmonic domain.
+    """
+
+    def __init__(self, domain, target=None, space=None):
+        super(HarmonicTransformOperator, self).__init__()
+
+        domain = DomainTuple.make(domain)
+        space = utilities.infer_space(domain, space)
+
+        hspc = domain[space]
+        if not hspc.harmonic:
+            raise TypeError(
+                "HarmonicTransformOperator only works on a harmonic space")
+        if isinstance(hspc, RGSpace):
+            self._op = HartleyOperator(domain, target, space)
+        else:
+            self._op = SHTOperator(domain, target, space)
+
+    def apply(self, x, mode):
+        self._check_input(x, mode)
+        return self._op.apply(x, mode)
+
+    @property
+    def domain(self):
+        return self._op.domain
+
+    @property
+    def target(self):
+        return self._op.target
+
+    @property
+    def capability(self):
+        return self.TIMES | self.ADJOINT_TIMES
+
+
+def HarmonicSmoothingOperator(domain, sigma, space=None):
+    """ This function returns an operator that carries out a smoothing with
+    a Gaussian kernel of width `sigma` on the part of `domain` given by
+    `space`.
+
+    Parameters
+    ----------
+    domain : Domain, tuple of Domain, or DomainTuple
+       The total domain of the operator's input and output fields
+    sigma : float>=0
+       The sigma of the Gaussian used for smoothing. It has the same units as
+       the RGSpace the operator is working on.
+       If `sigma==0`, an identity operator will be returned.
+    space : int, optional
+       The index of the sub-domain on which the smoothing is performed.
+       Can be omitted if `domain` only has one sub-domain.
+
+    Notes
+    -----
+       The sub-domain on which the smoothing is carried out *must* be a
+       non-harmonic `RGSpace`.
+    """
+
+    sigma = float(sigma)
+    if sigma < 0.:
+        raise ValueError("sigma must be nonnegative")
+    if sigma == 0.:
+        return ScalingOperator(1., domain)
+
+    domain = DomainTuple.make(domain)
+    space = utilities.infer_space(domain, space)
+    if domain[space].harmonic:
+        raise TypeError("domain must not be harmonic")
+    Hartley = HartleyOperator(domain, space=space)
+    codomain = Hartley.domain[space].get_default_codomain()
+    kernel = codomain.get_k_length_array()
+    smoother = codomain.get_fft_smoothing_kernel_function(sigma)
+    kernel = smoother(kernel)
+    ddom = list(domain)
+    ddom[space] = codomain
+    diag = DiagonalOperator(kernel, ddom, space)
+    return Hartley.inverse(diag(Hartley))

nifty5/operators/harmonic_smoothing_operator.py

-# 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/>.
-#
-# Copyright(C) 2013-2018 Max-Planck-Society
-#
-# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
-# and financially supported by the Studienstiftung des deutschen Volkes.