.. image:: _static/tiebenn_logo.svg
     :alt: TieBeNN
     :width: 500px
     :align: center

TieBeNN documentation
=====================

.. raw:: html

   <p style="margin-bottom: 1.5em; font-size: 1.05em; font-family: 'Lato', 'Open Sans', sans-serif;">
     TieBeNN (<b>TIE</b>fen<b>BE</b>stimmung mittels <b>N</b>euronaler <b>N</b>etze,
     <em>Depth Estimation using Neural Networks</em>) is an event-based Python-wrapper that leverages several tools—some machine-learning-based, some traditional—to automatically generate phase picks for probabilistic hypocenter estimation of local events using <a href="http://alomax.free.fr/nlloc/" target="_blank" style="color: #ae8974; font-weight: bold;">NonLinLoc</a>.
   </p>

.. toctree::
   :maxdepth: 3
   :caption: Getting started:

   pages/workflow.rst
   pages/requirements.rst
   pages/installation.rst
   pages/usage.rst
   pages/velocity_models.rst
   tiebenn
   pages/about.rst

Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`