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 for pkg-config (i.e. if executing pkg-config --libs hdf5 returns a list of options).
1. export H5_CFLAGS=-I/path/to/hdf5/include
2. export H5_LDFLAGS=-L/path/to/hdf5/lib
3. source tools/set_NUMPY_INCLUDEDIR.sh
Run pip3 install . (this installs trexio 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