The easiest way to install 21cmFAST is to use conda. Simply use
conda install -c conda-forge 21cmFAST. With this method, all dependencies are taken
care of, and it should work on either Linux or MacOS. If for some reason this is not
possible for you, read on.
We try to have as many of the dependencies automatically installed as possible.
However, since 21cmFAST relies on some C libraries, this is not always possible.
The C libraries required are:
gslfftw(compiled with floating-point enabled, and--enable-shared)openmp- A C-compiler with compatibility with the
-fopenmpflag. Note: it seems that on OSX, if usinggcc, you will needv4.9.4+.
As it turns out, though these are fairly common libraries, getting them installed in a
way that 21cmFAST understands on various operating systems can be slightly non-trivial.
These libraries will often be available on a HPC environment by using the
module load gsl and similar commands. Note that just because they are loaded
doesn't mean that 21cmFAST will be able to find them. You may have to point to the
relevant lib/ and include/ folders for both gsl and fftw (these should
be available using module show gsl etc.)
Note also that while fftw may be available to load, it may not have the correct
compilation options (i.e. float-enabled and multiprocessing-enabled). In this case,
see below.
Most linux distros come with packages for the requirements, and also gcc by default,
which supports -fopenmp. As long as these packages install into the standard location,
a standard installation of 21cmFAST will be automatically possible (see below).
If they are installed to a place not on the LD_LIBRARY/INCLUDE paths, then you
must use the compilation options (see below) to specify where they are.
Note
there exists the option of installing gsl, fftw and gcc using conda.
This is discussed below in the context of MacOSX, where it is often the
easiest way to get the dependencies, but it is equally applicable to linux.
On MacOSX, obtaining gsl and fftw is typically more difficult, and in addition,
the newer native clang does not offer -fopenmp support.
For conda users (which we recommend using), the easiest way to get gsl and fftw
is by doing conda install -c conda-forge gsl fftw in your environment.
Note
if you use conda to install gsl and fftw, then you will need to point at
their location when installing 21cmFAST (see compiler options below for details).
In this case, the installation command should simply be prepended with:
LIB=/path/to/conda/env/lib INC=/path/to/conda/env/include
To get gcc, either use homebrew, or again, conda: conda install -c anaconda gcc.
If you get the conda version, you still need to install the headers:
xcode-select --install
On older versions then you need to do:
open /Library/Developer/CommandLineTools/Packages/macOS_SDK_headers_for_macOS_<input version>.pkg
Note
some versions of MacOS will also require you to point to the correct gcc
compiler using the CC environment variable. Overall, the point is to NOT
use clang. If gcc --version shows that it is actually GCC, then you
can set CC=gcc. If you use homebrew to install gcc, it is likely that
you'll have to set CC=gcc-11.
For newer versions, you may need to prepend the following command to your pip install command
when installing 21cmFAST (see later instructions):
CFLAGS="-isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX<input version>.sdk"
See faqs/installation_faq for more detailed questions on installation. If you are on MacOSX and are having trouble with installation (or would like to share a successful installation strategy!) please see the open issue.
With the dependencies installed, follow the instructions below, depending on whether you are a user or a developer.
Note
conda users may want to pre-install the following packages before running
the below installation commands:
conda install numpy scipy click pyyaml cffi astropy h5py
Then, at the command line:
pip install git+https://github.com/21cmFAST/21cmFAST.git
If developing, from the top-level directory do:
pip install -e .
Note the compile options discussed below!
If you are developing 21cmFAST, we highly recommend using conda to manage your
environment, and setting up an isolated environment. If this is the case, setting up
a full environment (with all testing and documentation dependencies) should be as easy
as (from top-level dir):
conda env create -f environment_dev.yml
Otherwise, if you are using pip:
pip install -e .[dev]
The [dev] "extra" here installs all development dependencies. You can instead use
[tests] if you only want dependencies for testing, or [docs] to be able to
compile the documentation.
Various options exist to manage compilation via environment variables. Basically,
any variable with "INC" in its name will add to the includes directories, while
any variable with "lib" in its name will add to the directories searched for
libraries. To change the C compiler, use CC. Finally, if you want to compile
the C-library in dev mode (so you can do stuff like valgrid and gdb with it),
install with DEBUG=True. So for example:
CC=/usr/bin/gcc DEBUG=True GSL_LIB=/opt/local/lib FFTW_INC=/usr/local/include pip install -e .
Note
For MacOS a typical installation command will look like
CC=gcc CFLAGS="-isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX<input version>.sdk" pip install .
(using either gcc or gcc-11 depending on how you installed gcc), with
other compile options possible as well.
In addition, the BOXDIR variable specifies the default directory that any
data produced by 21cmFAST will be cached. This value can be updated at any time by
changing it in the $CFGDIR/config.yml file, and can be overwritten on a
per-call basis.
While the -e option will keep your library up-to-date with any (Python)
changes, this will not work when changing the C extension. If the C code
changes, you need to manually run rm -rf build/* then re-install as above.
By default, the C-code will only print to stderr when it encounters warnings or critical errors. However, there exist several levels of logging output that can be switched on, but only at compilation time. To enable these, use the following:
LOG_LEVEL=<log_level> pip install -e .
The <log_level> can be any non-negative integer, or one of the following
(case-insensitive) identifiers:
NONE, ERROR, WARNING, INFO, DEBUG, SUPER_DEBUG, ULTRA_DEBUG
If an integer is passed, it corresponds to the above levels in order (starting from zero). Be careful if the level is set to 0 (or NONE), as useful error and warning messages will not be printed. By default, the log level is 2 (or WARNING), unless the DEBUG=1 environment variable is set, in which case the default is 4 (or DEBUG). Using very high levels (eg. ULTRA_DEBUG) can print out a lot of information and make the run time much longer, but may be useful in some specific cases.