User guide

Welcome to the TRIQS project!

This guide presents the main classes of TRIQS through ready-to-use examples in Python. These small demo codes should help you understand the logic behind the general design and organization of the library.

Note

We strongly recommend first-time users to go through the extensive set of Jupyter Notebook tutorials provided on GitHub. You can run the notebooks either in an interactive Binder Session on the web or run them locally after setting up TRIQS on your computer.

Note

This guide does not aim to replace the full API Documentation. The C++ library together with its examples is documented separately in the C++ API.

Why use TRIQS?

TRIQS is a powerful library that allows you to easily write code to study strongly correlated problems. It is designed as a toolkit containing all the essential ingredients to tackle condensed matter physics. Before presenting the tools available both in C++ and Python, we want you to have a look at a small script where a full Dynamical Mean-Field Theory calculation using the Continuous-Time Hybridization Expansion (CT-HYB) solver is realized in… one page!

• Dynamical mean-field theory on a Bethe lattice

Python Tutorials

We provide here different Tutorial series, with increasing difficulty, that introduce you to various aspects of the TRIQS Python interface. These Tutorials are also provided as Jupyter Notebooks in our TRIQS/tutorials repository.

• Basics
  • Introducing Python and the IPython Notebook
  • A Very Short Introduction to Python
  • Matplotlib Examples
  • TRIQS Green’s functions
  • HDF5 Archives
  • Writing to an archive
  • Manipulating fermionic operators
  • Introduction to multivariable Green’s functions
• Model DMFT
  • A first DMFT calculation
  • General reminder: Anderson impurity model and CTHYB solver
  • The TRIQS/CTHYB impurity solver
  • Single-orbital Hubbard model
  • Two-orbital Hubbard model
  • Valence-Bond DMFT solution of the Hubbard model
• Two-particle Response
  • Fermions on the square lattice & perfect nesting
  • Susceptibility \(\chi_0\) of non-interacting fermions
  • The random phase approximation (RPA)
  • Two-particle self consistency (TPSC)
  • Finite temperature antiferromagnetism in two dimensional systems
• A tight-binding model on a square lattice

If you want to learn more about Python, iPython and the notebook, you can check our external resources:

• Learn more about Python, ipython and the notebook

For the documentation of individual classes and functions — together with ready-to-use code examples — see the Python API.