Installation¶

Start off by downloading ompy:

git clone --recurse https://github.com/oslocyclotronlab/ompy/

where the --recurse flag specifies, that all submodules shall be downloaded as well.

Dependencies¶

Get and compile MultiNest (use the cmake version from github.com/JohannesBuchner/MultiNest). The goal is to create lib/libmultinest.so
```
git clone https://github.com/JohannesBuchner/MultiNest
cd MultiNest/build
cmake ..
make
sudo make install
```
Multinest had following hard dependencies: lapack and blas. To use MPI, additionally openmp has to be installed (probably does not work for MAC users, see below.). With apt-get you may fix the dependencies by:
```
sudo apt-get install liblapack-dev libblas-dev libomp-dev
```
If you still get an error like:
```
OSError: libmultinest.so: cannot open shared object file: No such file or directory
```
visit http://johannesbuchner.github.io/PyMultiNest/install .
We require python>=3.7. Make sure you use the correct python version and the correct pip. You may need to replace python by python3 and pip by pip3 in the examples below. Run python --version and pip --version to check whether you have a sufficient python version.
All other dependencies can be installed automatically by pip (see below). Alternatively, make sure to install all requirements listed in requirements.txt, eg. using conda or apt-get. You may try following in conda (untested)
```
conda install --file requirements.txt
```
For openMP support (optional), install libomp. Easiest on linux/ubuntu: sudo apt-get install libomp-dev or MAC brew install libomp.
Many examples are written with jupyter notebooks, so you probably want to install this, too.

OMpy package¶

There are two main options on how to install OMpy. We will start off with our recommendation, that is with the -e flag is a local project in “editable” mode. This way, you will in principal not have to reinstall ompy if you pull a new version from git or create any local changes yourself.

Note: If you change any of the cython modules (*.pyx files), you will have to reinstall/recompile anyways. As they may have changed upstream, the easiest is probably if you install again every time you pull.

pip install -e .

If you want to install at the system specific path instead, use

pip install .

For debugging, you might want to compile the cython modules “manually”. The first line here is just to delete any existing cython modules in order to make sure that they will be recompiled.

rm ompy/*.so
rm ompy/*.c
python setup.py build_ext --inplace

Troubleshooting¶

Docker container¶

If you don’t succeed with the above, we also provide a Docker container via dockerhub, see https://hub.docker.com/r/oslocyclotronlab/ompy. However, for everyday usage, we think it’s easier to install the package normally on your machine

Python version¶

If you had some failed attempts, you might try to uninstall ompy before retrying the stepts above:

pip uninstall ompy

Note that we require python 3.7 or higher. If your standard python and pip link to python 2, you may have to use python3 and pip3.

Try to reinstall¶

If you changed / if after a git pull there have been any changes to one of the cython modules, you will have to reinstall/recompile anyways: pip install -e ..

OpenMP / MAC¶

If you don’t have OpenMP / have problems installing it (see above), you can install without OpenMP. Type export ompy_OpenMP=False in the terminal before the setup above.

Cloned the repo before September 2019¶

NB: Read this (only) if you have cloned the repo before October 2019: We cleaned the repository from old comits clogging the repo (big data files that should never have been there). Unfortunetely, this has the sideeffect that the history had to be rewritten: Previous commits now have a different SHA1 (git version keys). If you need anything from the previous repo, see ompy_Archive_Sept2019. This will unfortunately also destroy references in issues. The simplest way to get the new repo is to rerun the installation instructions below.

General usage¶

All the functions and classes in the package are available in the main module. You get everything by importing the package

import ompy

The overarching philosophy is that the package shall be flexible and transparent to use and modify. All of the “steps” in the Oslo method are implemented as classes with a common structure and call signature. If you understand one class, you’ll understand them all, making extending the code easy.

As the Oslo method is a complex method involving dozen of variables which can be daunting for the uninitiated, many class attributes have default values that should give satisfying results. Attributes that should be modified even though it is not strictly necessary to do so will give annoying warnings. The documentation and docstrings give in-depth explanation of each variable and its usage.