switch

ChangeLog.md

 Changes since NIFTy 6
 =====================
 
-*None.*
+Change in external dependencies
+-------------------------------
+
+Instead of the optional external packages `pypocketfft` and `pyHealpix`, NIFTy
+now uses the DUCC package (<https://gitlab.mpcdf.mpg.de/mtr/ducc)>,
+which is their successor.
 
 
 Changes since NIFTy 5

Dockerfile

@@ -12,7 +12,7 @@ RUN apt-get update && apt-get install -y \
     # Optional NIFTy dependencies
     python3-mpi4py python3-matplotlib \
   # more optional NIFTy dependencies
-  && pip3 install git+https://gitlab.mpcdf.mpg.de/ift/pyHealpix.git \
+  && pip3 install git+https://gitlab.mpcdf.mpg.de/mtr/ducc.git@ducc_0_1 \
   && pip3 install git+https://gitlab.mpcdf.mpg.de/ift/nifty_gridder.git \
   && pip3 install jupyter \
   && rm -rf /var/lib/apt/lists/*

README.md

@@ -49,13 +49,12 @@ Installation
 - [SciPy](https://www.scipy.org/)
 
 Optional dependencies:
-- [pyHealpix](https://gitlab.mpcdf.mpg.de/ift/pyHealpix) (for harmonic
-    transforms involving domains on the sphere)
+- [DUCC](https://gitlab.mpcdf.mpg.de/mtr/ducc) for faster FFTs, spherical
+    harmonic transforms, and non-uniform Fourier transforms
 - [nifty_gridder](https://gitlab.mpcdf.mpg.de/ift/nifty_gridder) (for radio
     interferometry responses)
 - [mpi4py](https://mpi4py.scipy.org) (for MPI-parallel execution)
 - [matplotlib](https://matplotlib.org/) (for field plotting)
-- [pypocketfft](https://gitlab.mpcdf.mpg.de/mtr/pypocketfft) (for faster FFTs)
 
 ### Sources
 
@@ -78,9 +77,14 @@ Plotting support is added via:
 
     sudo apt-get install python3-matplotlib
 
-Support for spherical harmonic transforms is added via:
+The DUCC 0.1 package is installed via:
 
-    pip3 install --user git+https://gitlab.mpcdf.mpg.de/ift/pyHealpix.git
+    pip3 install --user git+https://gitlab.mpcdf.mpg.de/mtr/ducc.git@ducc_0_1
+
+If this library is present, NIFTy will detect it automatically and prefer
+`ducc_0_1.fft` over SciPy's FFT. The underlying code is actually the same, but
+DUCC's FFT is compiled with optimizations for the host CPU and can provide
+significantly faster transforms.
 
 Support for the radio interferometry gridder is added via:
 
@@ -90,15 +94,6 @@ MPI support is added via:
 
     sudo apt-get install python3-mpi4py
 
-Pypocketfft is added via:
-
-    pip3 install --user git+https://gitlab.mpcdf.mpg.de/mtr/pypocketfft
-
-If this library is present, NIFTy will detect it automatically and prefer
-it over SciPy's FFT. The underlying code is actually the same, but
-pypocketfft is compiled with optimizations for the host CPU and can provide
-significantly faster transforms.
-
 ### Running the tests
 
 To run the tests, additional packages are required:

docs/source/installation.rst

@@ -14,9 +14,14 @@ Plotting support is added via::
 
     sudo apt-get install python3-matplotlib
 
-Support for spherical harmonic transforms is added via::
+The DUCC 0.1 package is installed via::
 
-    pip3 install --user git+https://gitlab.mpcdf.mpg.de/ift/pyHealpix.git
+    pip3 install --user git+https://gitlab.mpcdf.mpg.de/mtr/ducc.git@ducc_0_1
+
+If this library is present, NIFTy will detect it automatically and prefer
+`ducc_0_1.fft` over SciPy's FFT. The underlying code is actually the same, but
+DUCC's FFT is compiled with optimizations for the host CPU and can provide
+significantly faster transforms.
 
 Support for the radio interferometry gridder is added via::
 
@@ -26,14 +31,6 @@ MPI support is added via::
 
     sudo apt-get install python3-mpi4py
 
-Pypocketfft is added via:
-    pip3 install --user git+https://gitlab.mpcdf.mpg.de/mtr/pypocketfft
-
-If this library is present, NIFTy will detect it automatically and prefer
-it over SciPy's FFT. The underlying code is actually the same, but
-pypocketfft is compiled with optimizations for the host CPU and can provide
-significantly faster transforms.
-
 NIFTy documentation is provided by Sphinx. To build the documentation::
 
     sudo apt-get install python3-sphinx-rtd-theme dvipng

src/domains/gl_space.py

@@ -73,7 +73,7 @@ class GLSpace(StructuredDomain):
     #           blown up by a factor of self.nlon
     @property
     def dvol(self):
-        from pyHealpix import GL_weights
+        from ducc_0_1.misc import GL_weights
         if self._dvol is None:
             self._dvol = GL_weights(self.nlat, self.nlon)
         return np.repeat(self._dvol, self.nlon)

src/domains/lm_space.py

@@ -117,11 +117,11 @@ class LMSpace(StructuredDomain):
         e.g. only dependant on theta in radians"""
         from .gl_space import GLSpace
         from ..operators.harmonic_operators import HarmonicTransformOperator
-        import pyHealpix
+        from ducc_0_1.misc import GL_thetas
         # define azimuthally symmetric spaces for kernel transform
         gl = GLSpace(self.lmax + 1, 1)
         lm0 = gl.get_default_codomain()
-        theta = pyHealpix.GL_thetas(gl.nlat)
+        theta = GL_thetas(gl.nlat)
         # evaluate the kernel function at the required thetas
         kernel_sphere = Field.from_raw(gl, func(theta))
         # normalize the kernel such that the integral over the sphere is 4pi

src/fft.py

@@ -29,21 +29,20 @@ def set_nthreads(nthr):
 
 
 try:
-    import pypocketfft
+    import ducc_0_1.fft as my_fft
 
 
     def fftn(a, axes=None):
-        return pypocketfft.c2c(a, axes=axes, nthreads=max(_nthreads, 0))
+        return my_fft.c2c(a, axes=axes, nthreads=max(_nthreads, 0))
 
 
     def ifftn(a, axes=None):
-        return pypocketfft.c2c(a, axes=axes, inorm=2, forward=False,
-                               nthreads=max(_nthreads, 0))
+        return my_fft.c2c(a, axes=axes, inorm=2, forward=False,
+                          nthreads=max(_nthreads, 0))
 
 
     def hartley(a, axes=None):
-        return pypocketfft.genuine_hartley(a, axes=axes,
-                                           nthreads=max(_nthreads, 0))
+        return my_fft.genuine_hartley(a, axes=axes, nthreads=max(_nthreads, 0))
 
 except ImportError:
     import scipy.fft

src/operators/harmonic_operators.py

@@ -202,15 +202,15 @@ class SHTOperator(LinearOperator):
         hspc.check_codomain(target)
         target.check_codomain(hspc)
 
-        from pyHealpix import sharpjob_d
+        from ducc_0_1.sht 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)
+            self.sjob.set_gauss_geometry(target.nlat, target.nlon)
         else:
-            self.sjob.set_Healpix_geometry(target.nside)
+            self.sjob.set_healpix_geometry(target.nside)
 
     def __reduce__(self):
         return (_unpickleSHTOperator,

src/plot.py

@@ -423,7 +423,7 @@ def _plot2D(f, ax, **kwargs):
         _limit_xy(**kwargs)
         return
     elif isinstance(dom, (HPSpace, GLSpace)):
-        import pyHealpix
+        from ducc_0_1.healpix import Healpix_Base
         xsize = 800
         res, mask, theta, phi = _mollweide_helper(xsize)
         if have_rgb:
@@ -434,14 +434,15 @@ def _plot2D(f, ax, **kwargs):
             ptg = np.empty((phi.size, 2), dtype=np.float64)
             ptg[:, 0] = theta
             ptg[:, 1] = phi
-            base = pyHealpix.Healpix_Base(int(np.sqrt(dom.size//12)), "RING")
+            base = Healpix_Base(int(np.sqrt(dom.size//12)), "RING")
             if have_rgb:
                 res[mask] = rgb[base.ang2pix(ptg)]
             else:
                 res[mask] = f.val[base.ang2pix(ptg)]
         else:
+            from ducc_0_1.misc import GL_thetas
             ra = np.linspace(0, 2*np.pi, dom.nlon+1)
-            dec = pyHealpix.GL_thetas(dom.nlat)
+            dec = GL_thetas(dom.nlat)
             ilat = _find_closest(dec, theta)
             ilon = _find_closest(ra, phi)
             ilon = np.where(ilon == dom.nlon, 0, ilon)