Commit 98b214c2 authored by Martin Reinecke's avatar Martin Reinecke

update to new naming scheme, use package from PyPI

parent c28c31c2
......@@ -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/*
......
......@@ -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.
......
......@@ -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.
......
......@@ -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)
......
......@@ -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()
......
......@@ -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):
......
......@@ -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()
......
......@@ -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)
......
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