Lagrangian Trajectories Toolbox
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The Lagrangian Trajectories Toolbox is a Python library dedicated to the post-processing, visualisation and analysis of Lagrangian particle trajectories. 

The toolbox introduces the Trajectory Frames (TrajFrames) data structure to work with Lagrangian particle trajectories. TrajFrames make use of the blazingly fast `polars <https://pola-rs.github.io/polars-book/user-guide/index.html>`_ DataFrame library to store attribute variables in a tabular format. Traditionally, the data structure used to store trajectories has been determined by the Lagrangian trajectory code used to evaluate particle trajectories. However, TrajFrames are well suited for working with both large .csv files generated from `TRACMASS <https://www.tracmass.org>`_ and .zarr files output from `OceanParcels <https://oceanparcels.org>`_.

Beyond simply storing Lagrangian data, TrajFrames contain a wide range of built-in methods to enable the efficient post-processing and visualisation of particle trajectories with `plotly <https://plotly.com/python-api-reference/index.html>`_, `matplotlib <https://matplotlib.org>`_ and `Cartopy <https://scitools.org.uk/cartopy/docs/latest/>`_.

Current Features of the LT Toolbox:
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* **Store** the attribute variables of Lagrangian trajectories in a TrajFrame object.

* **Add** new variables, such as transit times and seeding levels, to your TrajFrame.

* **Filter** trajectories using any attribute variable contained in your TrajFrame.

* **Get** existing features, including trajectory start/end times, start/end locations and durations.

* **Compute** metrics, such as distance travelled, binned-statistics and Lagrangian probabilities from trajectories.

* **Plot** trajectories, properties and probability distributions in the form of maps, time series, temperature-salinity diagrams and more.

.. toctree::
   :maxdepth: 2
   :hidden:

   Home <self>
   Installation <installation>
   Tutorials <tutorials>
   API Reference <reference>
   Contributing <contributing>