Metadata-Version: 2.1
Name: ducc0
Version: 0.18.0
Summary: Distinctly useful code collection: contains efficient
Requires-Dist: numpy (>=1.17.0)

algorithms for Fast Fourier (and related) transforms, spherical harmonic
transforms involving very general spherical grids, gridding/degridding tools
for radio interferometry, 4pi spherical convolution operators and much more.
Home-page: https://gitlab.mpcdf.mpg.de/mtr/ducc
Author: Martin Reinecke
Author-email: martin@mpa-garching.mpg.de
License: GPLv2+
Project-URL: Bug Tracker, https://gitlab.mpcdf.mpg.de/mtr/ducc/issues
Project-URL: Bug Tracker (Github), https://github.com/mreineck/ducc/issues
Project-URL: Documentation, https://mtr.pages.mpcdf.de/ducc
Project-URL: Source Code, https://gitlab.mpcdf.mpg.de/mtr/ducc
Project-URL: Source Code (Github), https://github.com/mreineck/ducc
Project-URL: README, https://gitlab.mpcdf.mpg.de/mtr/ducc/-/blob/ducc0/README.md
Project-URL: Changelog, https://gitlab.mpcdf.mpg.de/mtr/ducc/-/blob/ducc0/ChangeLog
Description: Distinctly Useful Code Collection (DUCC)
        ========================================
        
        This is a collection of basic programming tools for numerical computation,
        including Fast Fourier Transforms, Spherical Harmonic Transforms, non-equispaced
        Fourier transforms, as well as some concrete applications like 4pi convolution
        on the sphere and gridding/degridding of radio interferometry data.
        
        The code is written in C++17, but provides a simple and comprehensive Python
        interface.
        
        ### Requirements
        
        - [Python >= 3.6](https://www.python.org/)
        - [pybind11](https://github.com/pybind/pybind11)
          (only during compiling/installation)
        - a C++17-capable compiler (tested with g++ version 7 or newer, clang++,
          MSVC 2019 and Intel icpx 2021.1.2)
        
        ### Sources
        
        The latest version of DUCC can be obtained by cloning the repository via
        
            git clone https://gitlab.mpcdf.mpg.de/mtr/ducc.git
        
        ### Installation
        
        DUCC can be installed using a simple `pip` invocation:
        
            pip3 install --user ducc0
        
        This will download the source package and compile it to a binary that is
        optimized for the host CPU, but will probably not run on other computers
        with slightly different CPUs.
        
        At the cost of performance (typically factors of 1.5 to 2) it is possible to
        generate a binary which will work on all CPUs of a given architecture (e.g.
        x86_64). This can be done via
        
            DUCC0_OPTIMIZATION=portable pip3 install --user ducc0
        
        NOTE: compilation of the code can take a significant amount of time
        (several minutes).
        
        
        Installing multiple versions simultaneously
        -------------------------------------------
        
        The interfaces of the DUCC components are expected to evolve over time; whenever
        an interface changes in a manner that is not backwards compatible, the DUCC
        version number will increase. As a consequence it might happen that one part of
        a Python code may use an older version of DUCC while at the same time another
        part requires a newer version. Since DUCC's version number is included in the
        module name itself (the module is not called `ducc`, but rather `ducc<X>`),
        this is not a problem, as multiple DUCC versions can be installed
        simultaneously.
        The latest patch levels of a given DUCC version will always be available at the
        HEAD of the git branch with the respective name. In other words, if you need
        the latest incarnation of DUCC 0, this will be on branch "ducc0" of the
        git repository, and it will be installed as the package "ducc0".
        Later versions will be maintained on new branches and will be installed as
        "ducc1" and "ducc2", so that there will be no conflict with potentially
        installed older versions.
        
        
        DUCC components
        ===============
        
        ducc.fft
        --------
        
        This package provides Fast Fourier, trigonometric and Hartley transforms with a
        simple Python interface. It is an evolution of `pocketfft` and `pypocketfft`
        which are currently used by `numpy` and `scipy`.
        
        The central algorithms are derived from Paul Swarztrauber's
        [FFTPACK](http://www.netlib.org/fftpack) code.
        
        ### Features
        - supports fully complex and half-complex (i.e. complex-to-real and
          real-to-complex) FFTs, discrete sine/cosine transforms and Hartley transforms
        - achieves very high accuracy for all transforms
        - supports multidimensional arrays and selection of the axes to be transformed
        - supports single, double, and long double precision
        - makes use of CPU vector instructions when performing 2D and higher-dimensional
          transforms
        - supports prime-length transforms without degrading to O(N**2) performance
        - has optional multi-threading support for multidimensional transforms
        
        ### Design decisions and performance characteristics
        - there is no internal caching of plans and twiddle factors, making the
          interface as simple as possible
        - 1D transforms are significantly slower than those provided by FFTW (if FFTW's
          plan generation overhead is ignored)
        - multi-D transforms in double precision perform fairly similar to FFTW with
          FFTW_MEASURE; in single precision `ducc.fft` can be significantly faster.
        
        ducc.sht
        --------
        
        This package provides efficient spherical harmonic trasforms (SHTs). Its code
        is derived from [libsharp](https://arxiv.org/abs/1303.4945), but has been
        significantly enhanced.
        
        ### Noteworthy features
        - support for any grid based on iso-latitude rings with equidistant pixels in
          each of the rings
        - support for accurate spherical harmonic analyis on certain sub-classes of
          grids (Clenshaw-Curtis, Fejer-1 and McEwen-Wiaux) at band limits beyond those
          for which quadrature weights exist. For details see
          [this note](https://wwwmpa.mpa-garching.mpg.de/~martin/shtnote.pdf).
        - substantially improved transformation speed (up to a factor of 2) on the
          above mentioned grid geometries for high band limits
        - accelerated recurrences as presented in
          [Ishioka (2018)](https://www.jstage.jst.go.jp/article/jmsj/96/2/96_2018-019/_pdf)
        - vector instruction support
        - multi-threading support
        
        The code for rotating spherical harmonic coefficients was taken (with some
        modifications) from Mikael Slevinsky's
        [FastTransforms package](https://github.com/MikaelSlevinsky/FastTransforms).
        
        
        ducc.healpix
        ------------
        
        This library provides Python bindings for the most important functionality
        related to the [HEALPix](https://arxiv.org/abs/astro-ph/0409513) tesselation,
        except for spherical harmonic transforms, which are covered by `ducc.sht`.
        
        The design goals are
        - similarity to the interface of the HEALPix C++ library
          (while respecting some Python peculiarities)
        - simplicity (no optional function parameters)
        - low function calling overhead
        
        
        ducc.totalconvolve
        ------------------
        
        Library for high-accuracy 4pi convolution on the sphere, which generates a
        total convolution data cube from a set of sky and beam `a_lm` and computes
        interpolated values for a given list of detector pointings.
        This code has evolved from the original
        [totalconvolver](https://arxiv.org/abs/astro-ph/0008227) algorithm
        via the [conviqt](https://arxiv.org/abs/1002.1050) code.
        
        
        ### Algorithmic details:
        - the code uses `ducc.sht` SHTs and `ducc.fft` FFTs to compute the data cube
        - shared-memory parallelization is provided via standard C++ threads.
        - for interpolation, the algorithm and kernel described in
          <https://arxiv.org/abs/1808.06736> are used. This allows very efficient
          interpolation with user-adjustable accuracy.
        
        
        ducc.wgridder
        -------------
        
        Library for high-accuracy gridding/degridding of radio interferometry datasets
        (code paper available at <https://arxiv.org/abs/2010.10122>).
        This code has also been integrated into
        [wsclean](https://gitlab.com/aroffringa/wsclean)
        (<https://arxiv.org/abs/1407.1943>)
        as the `wgridder` component.
        
        ### Programming aspects
        - shared-memory parallelization via standard C++ threads.
        - kernel computation is performed on the fly, avoiding inaccuracies
          due to table lookup and reducing overall memory bandwidth
        
        ### Numerical aspects
        - uses the analytical gridding kernel presented in
          <https://arxiv.org/abs/1808.06736>
        - uses the "improved W-stacking method" described in
          <https://arxiv.org/abs/2101.11172>
        - in combination these two aspects allow extremely accurate gridding/degridding
          operations (L2 error compared to explicit DFTs can go below 1e-12) with
          reasonable resource consumption
        
        
        ducc.misc
        ---------
        
        Various unsorted functionality which will hopefully be categorized in the
        future.
        
        This module contains an efficient algorithm for the computation of abscissas and
        weights for Gauss-Legendre quadrature. For degrees up to 100, the solutions are
        computed in the standard iterative fashion; for higher degrees Ignace Bogaert's
        [FastGL algorithm](https://epubs.siam.org/doi/pdf/10.1137/140954969)
        is used.
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: License :: OSI Approved :: GNU General Public License v2 or later (GPLv2+)
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: C++
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3.6
Description-Content-Type: text/markdown
