pixell 0.26.0

A rectangular pixel map manipulation and harmonic analysis library derived from Sigurd Naess' enlib.

pixell is a library for loading, manipulating and analyzing maps stored in rectangular pixelization. It is mainly targeted for use with maps of the sky (e.g. CMB intensity and polarization maps, stacks of 21 cm intensity maps, binned galaxy positions or shear) in cylindrical projection, but its core functionality is more general. It extends numpy’s ndarray to an ndmap class that associates a World Coordinate System (WCS) with a numpy array. It includes tools for Fourier transforms (through numpy or pyfft) and spherical harmonic transforms (through ducc0) of such maps and tools for visualization (through the Python Image Library).

• Free software: BSD license

• Documentation: https://pixell.readthedocs.io.

• Tutorials

Dependencies

• Python>=3.9.

• gcc/gfortran or Intel compilers (clang might not work out of the box), if compiling from source

• ducc0, healpy, Cython, astropy, numpy, scipy, matplotlib, pyyaml, h5py, Pillow (Python Image Library)

On MacOS, and other systems with non-traditional environments, you should specify the following standard environment variables:

• CC: C compiler (example: gcc)

• FC: Fortran compiler (example: gfortran)

We recommend using gcc installed from Homebrew to access these compilers on MacOS, and you should make sure to point e.g. $CC to the full path of your gcc installation, as the gcc name usually points to the Apple clang install by default.

Runtime threading behaviour

Certain parts of pixell are parallelized using OpenMP, with the underlying ducc0 library using pthreads. By default, these libraries use the number of cores on your system to determine the number of threads to use. If you wish to override this behaviour, you can use two environment variables:

• OMP_NUM_THREADS will set both the number of pixell threads and ducc0 threads.

• DUCC0_NUM_THREADS will set the number of threads for the ducc0 library to use, overwriting OMP_NUM_THREADS if both are set. pixell behaviour is not affected.

If you are using a modern chip (e.g. Apple M series chips, Intel 12th Gen or newer) that have both efficiency and performance cores, you may wish to set OMP_NUM_THREADS to the number of performance cores in your system. This will ensure that the efficiency cores are not used for the parallelized parts of pixell and ducc0.

You can check the threading behaviour (and the installation of pixell) by running the benchmark script:

$ benchmark-pixell-runner

Installing

Make sure your pip tool is up-to-date. To install pixell, run:

$ pip install pixell --user

This will install a pre-compiled binary suitable for your system (only Linux and Mac OS X with Python>=3.9 are supported).

If you require more control over your installation, e.g. using Intel compilers, please see the section below on compiling from source.

Compiling from source (advanced / development workflow)

The easiest way to install from source is to use the pip tool, with the --no-binary flag. This will download the source distribution and compile it for you. Don’t forget to make sure you have CC and FC set if you have any problems.

For all other cases, below are general instructions.

First, download the source distribution or git clone this repository. You can work from master or checkout one of the released version tags (see the Releases section on Github). Then change into the cloned/source directory.

Once downloaded, you can install using pip install . inside the project directory. We use the meson build system, which should be understood by pip (it will build in an isolated environment).

We suggest you then test the installation by running the unit tests. You can do this by running pytest.

To run an editable install, you will need to do so in a way that does not have build isolation (as the backend build system, meson and ninja, actually perform micro-builds on usage in this case):

$ pip install --upgrade pip meson ninja meson-python cython numpy
$ pip install  --no-build-isolation --editable .

Contributions

If you have write access to this repository, please:

1. create a new branch

2. push your changes to that branch

3. merge or rebase to get in sync with master

4. submit a pull request on github

If you do not have write access, create a fork of this repository and proceed as described above. For more details, see Contributing.