trexio/python

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 (>= 1.17.3)

Installation from PyPI

In short, you can run the following command:

pip install trexio

However, it is good practice to first check for updates of the build-system packages. This can be achieved by running

python -m pip install --upgrade pip setuptools build wheel

Note: we highly recommend to use virtual environments to avoid compatibility issues and to improve reproducibility. For more details, see the corresponding part of the Python documentation.

Note: our build farm (GitHub Actions) does not support ARM64 architectures (including the Mac M1/M2 chips). Therefore, pip install trexio does not work on an ARM64-based machine. Thus, we recommend to install TREXIO from source on an ARM64-based machine. If one uses a Mac where HDF5 is installed with brew (i.e., brew install hdf5), a workaround is to execute the following 2 lines before doing pip install trexio:

• export H5_CFLAGS="-I$(brew --prefix hdf5)/include"
• export H5_LDFLAGS="-L$(brew --prefix hdf5)/lib"

Additional requirements (for installation from source)

• C compiler (gcc/icc/clang)
• HDF5 library (>= 1.8)
• pkgconfig (Python package)
• build (Python package)
• pytest (Python package)

Installation from source

1. Download the trexio-<version>.tar.gz file with the latest Python API
2. gzip -cd trexio-<version>.tar.gz | tar xvf -
3. cd trexio-<version>
4. pip install -r requirements.txt (this installs all required python dependencies)
5. Export custom environment variables needed for the installation following the procedure below and replacing /path/to/hdf5/ with your paths. The following two steps can be skipped if HDF5 is properly configured for pkg-config (i.e. if executing pkg-config --libs hdf5 returns a list of options).
   • export H5_CFLAGS=-I/path/to/hdf5/include
   • export H5_LDFLAGS=-L/path/to/hdf5/lib
6. pip install . (this installs trexio in your environment)
7. cd test && python -m pytest -v 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 this 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 applied to the Python API.

Examples

An interactive Jupyter notebook called tutorial_benzene.ipynb is provided in the examples directory. The notebook can be lauched either locally (see next section for details) or using pre-built environment on Binder.

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:

1. Press the Kernel button in the navigation panel
2. In the output list of options select Change kernel
3. 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