The PyNE project aims to provide a common set of tools for nuclear science and engineering needs.
If you are interested in the package itself, or would like to help and contribute, please let us know either on the mailing list (https://groups.google.com/forum/#!forum/pyne-dev, [email protected]) or github.
Examples, documentation, and more can be found at http://pyne.io/, the official PyNE project site.
PyNE has the following dependencies:
- Fortran compiler
- C++ compiler
- CMake (>= 2.8.5)
- NumPy (>= 1.8.0)
- SciPy
- Cython (>= 0.19.1)
- HDF5
- PyTables
- Python 2.7
- LAPACK
- BLAS
- Jinja2
Additionally, building the documentation requires the following:
Most of the dependencies are readily available through package managers.
Binary distributions of the latest release for mac and linux (64-bit) using the conda package manager can be installed by running the command:
conda install -c conda-forge pyne
where VERSION should be replaced with the version number to be installed.
A windows 32-bit binary is also available on conda via the same command but it is highly experimental and likely broken. Conda binaries do not have moab/pytaps/mesh support (yet).
Installing PyNE from source is a two-step process. First, download and unzip the source (zip, tar). Then run the following commands from the unzipped directory:
cd pyne/ python setup.py install --user scripts/nuc_data_make
The setup.py command compiles and installs the PyNE source code.
The nuc_data_make builds and installs a database of nuclear data.
Unfortunately, this must be done as a second step because most nuclear
data is under some form of license restriction or export control which
prevents the developers from distributing it with PyNE. However, the
nuc_data_make program (which is installed by setup.py) will
do its best to find relevant nuclear data elsewhere on your machine
or from public sources on the internet.
PyNE is currently built and tested daily on the following platforms on Python 2.7
- Ubuntu 12.04 - x86_64
- OSX 10.8 - x86_64
PyNE has pre-built binaries for the lastest release (0.4) on windows mac and linux
- Mac (Python 2.7 and Python 3.3) - x86_64
- Linux (Python 2.7 and Python 3.3) - x86_64
- Windows (Python 2.7) - x86
PyNE has known issues on the following platforms
- Windows (64-bit build currently not feasible)
- 32-bit platforms (all variants) have known problems - see #315
After installing anaconda or miniconda from the downloads page, in a new terminal run the following conda install command:
conda install -c conda-forge pyne
where VERSION should be replaced with the version number to be installed.
If you have any issues, please let us know.
On mac and linux PyNE can be installed via the package manager conda. After installing anaconda or miniconda from the downloads page add conda's binary directory to your bash profile by adding:
export PATH=/path/to/anaconda/bin:$PATH
to your .bashrc or .bash_profile. Then in a new shell:
conda install conda-build jinja2 nose setuptools pytables hdf5 scipy
on linux you may also need to run:
conda install patchelf
Then dowload the latest conda-recipes here
cd to the conda-recipes directory and run:
conda build pyne conda install $(conda build --output pyne) nuc_data_make
The simplest method of installing PyNE on mac is via macports. Version 0.4 can be installed using the following commands(assuming you are using python 2.7):
sudo port install py27-pyne nuc_data_make --fetch-prebuilt False
The latest development version of PyNE can also be installed from source. The instructions below outline how it can be installed using the homebrew http://brew.sh/ package manager.
Before starting install the command line tools from https://developer.apple.com/downloads/ you will need to create an account in order to download them. After installing brew and the command line tools run the following commands:
ruby -e "$(curl -fsSL https://raw.github.com/mxcl/homebrew/go/install)" brew doctor brew tap homebrew/science brew install hdf5 brew install cmake brew install python
Add:
export PATH=/usr/local/bin:$PATH export PATH=/usr/local/share/python:$PATH
to ~/.bash_profile, then:
source ~/.bash_profile sudo pip install numpy sudo chown -R $(whoami) /usr/local brew install gfortran pip install scipy pip install cython pip install numexpr pip install tables
download pyne-staging cd to that directory:
cd Downloads/pyne-staging python setup.py install
Once those lines have been added, run the following command before running
nuc_data_make:
source ~/.bashrc
Scripts for installing PyNE and all its dependencies from scratch on Ubuntu 14.04 - 15.04 are found here.
We highly encourage contributions to PyNE! If you would like to contribute, it is as easy as forking the repository on GitHub, making your changes, and issuing a pull request. If you have any questions about this process don't hesitate to ask the mailing list (https://groups.google.com/forum/#!forum/pyne-dev, [email protected]).