Merge branch 'simplify_htops' into 'NIFTy_4'

put SHTs into their own operator class

See merge request ift/NIFTy!234

nifty4/operators/harmonic_transform_operator.py

@@ -16,23 +16,20 @@
 # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
 # and financially supported by the Studienstiftung des deutschen Volkes.
 
-import numpy as np
 from ..domain_tuple import DomainTuple
 from ..domains.rg_space import RGSpace
 from .linear_operator import LinearOperator
-from .. import dobj
+from .fft_operator import FFTOperator
+from .sht_operator import SHTOperator
 from .. import utilities
-from ..field import Field
-from ..domains.gl_space import GLSpace
 
 
 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 LMSpace
+      - an LMSpace and a HPSpace
       - an LMSpace and a GLSpace
 
     The supported operations are times() and adjoint_times().
@@ -57,147 +54,29 @@ class HarmonicTransformOperator(LinearOperator):
     def __init__(self, domain, target=None, space=None):
         super(HarmonicTransformOperator, self).__init__()
 
-        # Initialize domain and target
-        self._domain = DomainTuple.make(domain)
-        self._space = utilities.infer_space(self._domain, space)
+        domain = DomainTuple.make(domain)
+        space = utilities.infer_space(domain, space)
 
-        hspc = self._domain[self._space]
+        hspc = domain[space]
         if not hspc.harmonic:
             raise TypeError(
                 "HarmonicTransformOperator only works on a harmonic space")
-        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)
-
         if isinstance(hspc, RGSpace):
-            self._applyfunc = self._apply_cartesian
-            import pyfftw
-            pyfftw.interfaces.cache.enable()
+            self._op = FFTOperator(domain, target, space)
         else:
-            from pyHealpix import sharpjob_d
-            self._applyfunc = self._apply_spherical
-            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)
+            self._op = SHTOperator(domain, target, space)
 
     def apply(self, x, mode):
         self._check_input(x, mode)
-        if np.issubdtype(x.dtype, np.complexfloating):
-            return (self._applyfunc(x.real, mode) +
-                    1j*self._applyfunc(x.imag, mode))
-        else:
-            return self._applyfunc(x, mode)
-
-    def _apply_cartesian(self, x, mode):
-        from pyfftw.interfaces.numpy_fft import fftn
-        axes = x.domain.axes[self._space]
-        tdom = self._target if x.domain == self._domain else self._domain
-        oldax = dobj.distaxis(x.val)
-        if oldax not in axes:  # straightforward, no redistribution needed
-            ldat = x.local_data
-            ldat = utilities.hartley(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 Hartley pass in between the redistributions
-            tmp = dobj.redistribute(x.val, nodist=axes)
-            newax = dobj.distaxis(tmp)
-            ldat = dobj.local_data(tmp)
-            ldat = utilities.hartley(ldat, axes=axes)
-            tmp = dobj.from_local_data(tmp.shape, ldat, distaxis=newax)
-            tmp = dobj.redistribute(tmp, dist=oldax)
-        else:  # two separate, full FFTs needed
-            # ideal strategy for the moment would be:
-            # - do real-to-complex FFT on all local axes
-            # - fill up array
-            # - redistribute array
-            # - do complex-to-complex FFT on remaining axis
-            # - add re+im
-            # - redistribute back
-            rem_axes = tuple(i for i in axes if i != oldax)
-            tmp = x.val
-            ldat = dobj.local_data(tmp)
-            ldat = utilities.my_fftn_r2c(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 = fftn(ldat2, axes=(1,))
-            ldat2 = ldat2.real+ldat2.imag
-            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)
-        fct = self._domain[self._space].scalar_dvol
-        if fct != 1:
-            Tval *= fct
-
-        return Tval
-
-    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._target if x.domain == self._domain else self._domain
-        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)
+        return self._op.apply(x, mode)
 
     @property
     def domain(self):
-        return self._domain
+        return self._op.domain
 
     @property
     def target(self):
-        return self._target
+        return self._op.target
 
     @property
     def capability(self):

nifty4/operators/sht_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.
+
+import numpy as np
+from ..domain_tuple import DomainTuple
+from .linear_operator import LinearOperator
+from .. import dobj
+from .. import utilities
+from ..field import Field
+from ..domains.gl_space import GLSpace
+from ..domains.lm_space import LMSpace
+
+
+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 np.issubdtype(x.dtype, np.complexfloating):
+            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._target if x.domain == self._domain else self._domain
+        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