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
- Python>=3.7
- 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
)CXX
: C++ compiler (example:g++
)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.
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 ofpixell
threads andducc0
threads.DUCC0_NUM_THREADS
will set the number of threads for theducc0
library to use, overwritingOMP_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
.
Make sure your pip
tool is up-to-date. To install pixell
, run:
$ pip install pixell --user
$ test-pixell
This will install a pre-compiled binary suitable for your system (only Linux and Mac OS X with Python>=3.7 are supported). Note that you need ~/.local/bin
to be in your PATH
for the latter test-pixell
to work.
If you require more control over your installation, e.g. using Intel compilers, please see the section below on compiling from source. The test-pixell
command will run a suite of unit tests.
For compilation instructions specific to NERSC/cori, see NERSC.
For all other, 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.
If not using Intel compilers (see below), build the package using
$ python setup.py build_ext -i
You may now test the installation:
$ py.test pixell/tests/
If the tests pass, you can install the package (optionally with -e
if you would like to edit the files after installation)
$ python setup.py install --user
Intel compilers require you to modify the build step above as follows
$ python setup.py build_ext -i --fcompiler=intelem --compiler=intelem
On some systems, further specification might be required (make sure to get a fresh copy of the repository before trying out a new install method), e.g.:
$ LDSHARED="icc -shared" LD=icc LINKCC=icc CC=icc python setup.py build_ext -i --fcompiler=intelem --compiler=intelem
If you have write access to this repository, please:
- create a new branch
- push your changes to that branch
- merge or rebase to get in sync with master
- 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.