update to new naming scheme, use package from PyPI

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/mtr/ducc.git@ducc_0_1 \
+  && pip3 install ducc0 \
   && pip3 install git+https://gitlab.mpcdf.mpg.de/ift/nifty_gridder.git \
   && pip3 install jupyter \
   && rm -rf /var/lib/apt/lists/*

README.md

@@ -49,7 +49,7 @@ Installation
 - [SciPy](https://www.scipy.org/)
 
 Optional dependencies:
-- [DUCC](https://gitlab.mpcdf.mpg.de/mtr/ducc) for faster FFTs, spherical
+- [DUCC0](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)
@@ -77,12 +77,12 @@ Plotting support is added via:
 
     sudo apt-get install python3-matplotlib
 
-The DUCC 0.1 package is installed via:
+The DUCC0 package is installed via:
 
-    pip3 install --user git+https://gitlab.mpcdf.mpg.de/mtr/ducc.git@ducc_0_1
+    pip3 install ducc0
 
 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
+`ducc0.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.
 

docs/source/installation.rst

@@ -14,12 +14,12 @@ Plotting support is added via::
 
     sudo apt-get install python3-matplotlib
 
-The DUCC 0.1 package is installed via::
+The DUCC0 package is installed via::
 
-    pip3 install --user git+https://gitlab.mpcdf.mpg.de/mtr/ducc.git@ducc_0_1
+    pip3 install --user ducc0
 
 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
+`ducc0.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.
 

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 ducc_0_1.misc import GL_weights
+        from ducc0.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

@@ -117,7 +117,7 @@ class LMSpace(StructuredDomain):
         e.g. only dependant on theta in radians"""
         from .gl_space import GLSpace
         from ..operators.harmonic_operators import HarmonicTransformOperator
-        from ducc_0_1.misc import GL_thetas
+        from ducc0.misc import GL_thetas
         # define azimuthally symmetric spaces for kernel transform
         gl = GLSpace(self.lmax + 1, 1)
         lm0 = gl.get_default_codomain()

src/fft.py

@@ -29,7 +29,7 @@ def set_nthreads(nthr):
 
 
 try:
-    import ducc_0_1.fft as my_fft
+    import ducc0.fft as my_fft
 
 
     def fftn(a, axes=None):

src/operators/harmonic_operators.py

@@ -202,7 +202,7 @@ class SHTOperator(LinearOperator):
         hspc.check_codomain(target)
         target.check_codomain(hspc)
 
-        from ducc_0_1.sht import sharpjob_d
+        from ducc0.sht import sharpjob_d
         self.lmax = hspc.lmax
         self.mmax = hspc.mmax
         self.sjob = sharpjob_d()

src/plot.py

@@ -423,7 +423,7 @@ def _plot2D(f, ax, **kwargs):
         _limit_xy(**kwargs)
         return
     elif isinstance(dom, (HPSpace, GLSpace)):
-        from ducc_0_1.healpix import Healpix_Base
+        from ducc0.healpix import Healpix_Base
         xsize = 800
         res, mask, theta, phi = _mollweide_helper(xsize)
         if have_rgb:
@@ -440,7 +440,7 @@ def _plot2D(f, ax, **kwargs):
             else:
                 res[mask] = f.val[base.ang2pix(ptg)]
         else:
-            from ducc_0_1.misc import GL_thetas
+            from ducc0.misc import GL_thetas
             ra = np.linspace(0, 2*np.pi, dom.nlon+1)
             dec = GL_thetas(dom.nlat)
             ilat = _find_closest(dec, theta)