The usual instructions for pycbc are
here.
They are long, and nested. This is (hopefully) an easier option.
lalsuite, libframe, and libmetaio are copywrite the LIGO
Scientific Collaboration.
These recipes were originally created by Mike Boyle.
Anybody can use the docker image maintained here. This is an alternative.
64 bit Linux users,
conda config --add channels conda-forge
conda update -y --all
conda install -c libframe metaio lalsuite
pip install pycbcAll other users, see below
Your system python is old and crusty, but it needs to be that way or your printer might break. Your package manager needs its own version of python to support that weird OCR package you want, or whatever it is this week. But you---when running straight python on its own---you want a separate up-to-date version of python to run fancy new code with. And most of all you want to minimize the headaches that usually go along with managing python.
The answer is anaconda, which is a python manager developed by the
guy behind numpy (and a lot of scipy). He decided that it was
just too much hassle maintaining those big, complex packages with
existing python tools like pip and setuptools, so he created
anaconda. Scientific python developers have now almost uniformly
adopted this as the way to use python, because there's no better way
to manage python packages.
Anaconda installs in your user directory, so you can use it on
clusters just as easily as on your laptop, and it doesn't get mixed up
with your OS or package-manager versions of python. Outside of its
own directory, the only change it (optionally!) makes is to prepend
its bin directory to your PATH in your ~/.bashrc file.
Note that you should not use PYTHONPATH or similar environment
variables. Python can still get confused by that occasionally, even
with anaconda, so make sure it is not set.
Since pycbc apparently doesn't work with newer versions of python,
you need to do this step if you installed the python 3.5 version of
anaconda. This step is optional if you installed the python 2.7
version, but it might still be nice to contain LAL's dependencies in
one environment. An analogous thing is also recommended (using the
outdated virtualenv) in the pycbc installation instructions. To
create and activate the environment, do this:
conda create -y --name lal python=2.7
source activate lal
conda config --add channels conda-forge
conda update -y --allNow, your current shell will always use python-y things from this
environment only. Other shells will not be affected. And you can get
out of this environment in the current shell just by running
source deactivate at any time.
Be sure to run source activate lal in any new shell before you try
using or installing in this environment.
Whether or not you created the lal environment above, you should run
these commands:
conda install -y numpy h5py cython conda-build fftw gsl
pip install python-cjson
conda build libframe
conda install --use-local libframe
conda build metaio
conda install --use-local metaio
conda build lalsuite
conda install --use-local lalsuite
pip install pycbc