.. | ||
pytrexio | ||
test | ||
tools | ||
.gitignore | ||
install_pytrexio.sh | ||
MANIFEST.in | ||
pyproject.toml | ||
README.md | ||
requirements.txt | ||
setup.cfg | ||
setup.py |
TREXIO Python API
TREXIO provides a Python API, which enables interactive calls to the library. It facilitates the development of interfaces between different codes and can be used to convert data from one input/output file format into another.
Requirements
- python3 (>= 3.6)
- numpy
- C compiler (gcc/icc)
Installation from PyPI
Run pip3 install trexio
Note: we highly recommend to use virtual environments to avoid compatibility issues. For more details, see the corresponding part of the Python documentation.
Additional requirements (for installation from source)
- HDF5 library (>= 1.8)
- pkgconfig (Python package)
Installation from source
- Download the latest source code distribution (in
.tar.gz
format) of the TREXIO Python API - Unpack and
cd
in the output directory - Run
pip3 install -r requirements.txt
(this installs all required python dependencies) - Export custom environment variables needed for the installation
following the procedure below and replacing
/path/to/hdf5/
with your paths. Steps (i) and (ii) can be skipped if HDF5 is properly configured forpkg-config
(i.e. if executingpkg-config --libs hdf5
returns a list of options).export H5_CFLAGS=-I/path/to/hdf5/include
export H5_LDFLAGS=-L/path/to/hdf5/lib
source tools/set_NUMPY_INCLUDEDIR.sh
- Run
pip3 install .
(this installstrexio
in your environment) - Run
cd test && python3 test_api.py
(this executes several tests that verify the installation)
You are ready to go!
Note: installation based on pip
compiles its own C extension (shared library) called
pytrexio
. This extension is built from the TREXIO source
files coupled to the wrapper code generated by SWIG. The compiler options during such
installation may differ from the ones used to compile the primary TREXIO
API in C. Furthermore, custom compiler flags provided to
./configure
or make
are not applicable to the
Python API.
Examples
An interactive Jupyter notebook called
tutorial_benzene.ipynb
is provided in the
examples
directory. It demonstrates some basic use cases of
the TREXIO library in general and of the Python API in particular.
Jupyter can be installed using pip install jupyter
. If
you are not familiar with it, feel free to consult the Jupyter
documentation.
Running the notebook
The example notebook can be launched using the following command:
jupyter notebook tutorial_benzene.ipynb
Additional steps needed to run a custom virtual environment in Jupyter notebooks
In some cases, it may happen that the Jupyter kernels in the
activated virtual environment (e.g. myvenv
) still point to
the system-wide python binaries and not to the environment ones. This
will result in ImportError
when importing
trexio
in the notebook cell. In order to avoid this, the
myvenv
has to be installed as an additional kernel. This
requires ipykernel
python package, which usually comes
together with the Jupyter installation. If this is not the case, run
pip install ipykernel
. You can install myvenv
as a kernel by executing the following command:
python3 -m ipykernel install --user --name=myvenv
Now you can launch a Jupyter notebook. Once it is open, make sure that your virtual environment is selected as the current kernel. If this is not the case, try this:
- Press the
Kernel
button in the navigation panel - In the output list of options select
Change kernel
- Find the name of your virtual environment (e.g.
myvenv
) in the list and select it
That’s it, you have activated the custom virtual environment called
myvenv
in your notebook.
To uninstall the kernel named myvenv
, execute the
following command:
jupyter kernelspec uninstall myvenv