Metadata-Version: 2.1
Name: traja
Version: 0.2.6
Summary: Traja is a trajectory analysis and visualization tool
Home-page: https://github.com/traja-team/traja
Author: Justin Shenk
Author-email: shenkjustin@gmail.com
License: MIT
Project-URL: Bug Tracker, https://github.com/traja-team/traja/issues
Project-URL: Documentation, https://traja.rtfd.io/en/latest/
Project-URL: Source Code, https://github.com/traja-team/traja
Description: Traja |Python-ver| |Travis| |PyPI| |RTD| |Gitter| |Black| |License| |Binder| |Codecov| |DOI|
        ============================================================================================
        
        |Colab|
        
        .. |Python-ver| image:: https://img.shields.io/badge/python-3.6+-blue.svg
            :target: https://www.python.org/downloads/release/python-360/
            :alt: Python 3.6+
        
        .. |Travis| image:: https://travis-ci.org/travis-team/traja.svg?branch=master
            :target: https://travis-ci.org/travis-team/traja
        
        .. |PyPI| image:: https://badge.fury.io/py/traja.svg
            :target: https://badge.fury.io/py/traja
        
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            :target: https://gitter.im/traja-chat/community
        
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            :target: https://traja.readthedocs.io/en/latest/?badge=latest
            :alt: Documentation Status
        
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            :target: https://github.com/ambv/black
        
        .. |License| image:: https://img.shields.io/badge/License-MIT-blue.svg
            :target: https://opensource.org/licenses/MIT
            :alt: License: MIT
        
        .. |Binder| image:: https://mybinder.org/badge_logo.svg
            :target: https://mybinder.org/v2/gh/justinshenk/traja/master?filepath=demo.ipynb
        
        .. |Codecov| image:: https://codecov.io/gh/traja-team/traja/branch/master/graph/badge.svg
          :target: https://codecov.io/gh/traja-team/traja
        
        .. |DOI| image:: https://zenodo.org/badge/166056696.svg
           :target: https://zenodo.org/badge/latestdoi/166056696
        
        
        .. |Colab| image:: https://colab.research.google.com/assets/colab-badge.svg
           :target: https://colab.research.google.com/github/justinshenk/traja/blob/master/demo.ipynb
        
        Traja is a Python library for trajectory analysis. It extends the capability of
        pandas DataFrame specific for animal trajectory analysis in 2D, and provides
        convenient interfaces to other geometric analysis packages (eg, R and shapely).
        
        Introduction
        ------------
        
        The traja Python package is a toolkit for the numerical characterization
        and analysis of the trajectories of moving animals. Trajectory analysis
        is applicable in fields as diverse as optimal foraging theory,
        migration, and behavioral mimicry (e.g. for verifying similarities in
        locomotion). A trajectory is simply a record of the path followed by a
        moving animal. Traja operates on trajectories in the form of a series of
        locations (as x, y coordinates) with times. Trajectories may be obtained
        by any method which provides this information, including manual
        tracking, radio telemetry, GPS tracking, and motion tracking from
        videos.
        
        The goal of this package (and this document) is to aid biological
        researchers, who may not have extensive experience with Python, to
        analyze trajectories without being handicapped by a limited knowledge of
        Python or programming. However, a basic understanding of Python is
        useful.
        
        If you use traja in your publications, please cite the repo 
        
        .. code-block::
        
            @misc{justin_shenk_2019_3237827,
              author       = {Justin Shenk and
                              Rüdiger Busche},
              title        = {justinshenk/traja: v0.1.1},
              month        = jun,
              year         = 2019,
              doi          = {10.5281/zenodo.3237827},
              url          = {https://doi.org/10.5281/zenodo.3237827}
            }
        
        
        Installation and setup
        ----------------------
        
        To install traja with conda, run
        
        ``conda install -c conda-forge traja``
        
        or with pip
        
        ``pip install traja``.
        
        Import traja into your Python script or via the Python command-line with
        ``import traja``.
        
        Trajectories with traja
        -----------------------
        
        Traja stores trajectories in pandas DataFrames, allowing any pandas
        functions to be used.
        
        Load trajectory with x, y and time coordinates:
        
        .. code-block:: python
        
            import traja
        
            df = traja.read_file('coords.csv')
        
        Once a DataFrame is loaded, use the ``.traja`` accessor to access the
        visualization and analysis methods:
        
        .. code-block:: python
        
            df.traja.plot(title='Cage trajectory')
        
        
        Analyze Trajectory
        ------------------
        
        .. csv-table:: The following functions are available via ``traja.trajectory.[method]``
           :header: "Function", "Description"
           :widths: 30, 80
           
           "``calc_derivatives``", "Calculate derivatives of x, y values "
           "``calc_turn_angles``", "Calculate turn angles w.r.t. x-axis "
           "``transitions``", "Calculate first-order Markov model for transitions between grid bins"
           "``generate``", "Generate random walk"
           "``resample_time``", "Resample to consistent step_time intervals"
           "``rediscretize_points``", "Rediscretize points to given step length"
           
        For up-to-date documentation, see `https://traja.readthedocs.io <https://traja.readthedocs.io>`_.
        
        Random walk
        -----------
        
        Generate random walks with
        
        .. code-block:: python
        
            df = traja.generate(n=1000, step_length=2)
            df.traja.plot()
        
        .. image:: https://raw.githubusercontent.com/justinshenk/traja/master/docs/source/_static/walk_screenshot.png
           :alt: walk\_screenshot.png
        
        
        Resample time
        -------------
        ``traja.trajectory.resample_time`` allows resampling trajectories by a ``step_time``.
        
        
        Flow Plotting
        -------------
        
        .. code-block:: python
        
            df = traja.generate()
            traja.plot_surface(df)
        
        .. image:: https://traja.readthedocs.io/en/latest/_images/sphx_glr_plot_average_direction_001.png
           :alt: 3D plot
        
        .. code-block:: python
        
            traja.plot_quiver(df, bins=32)
        
        .. image:: https://traja.readthedocs.io/en/latest/_images/sphx_glr_plot_average_direction_002.png
           :alt: quiver plot
        
        .. code-block:: python
        
            traja.plot_contour(df, filled=False, quiver=False, bins=32)
        
        .. image:: https://traja.readthedocs.io/en/latest/_images/sphx_glr_plot_average_direction_003.png
           :alt: contour plot
        
        .. code-block:: python
        
            traja.plot_contour(df, filled=False, quiver=False, bins=32)
        
        .. image:: https://traja.readthedocs.io/en/latest/_images/sphx_glr_plot_average_direction_004.png
           :alt: contour plot filled
        
        .. code-block:: python
        
            traja.plot_contour(df, bins=32, contourfplot_kws={'cmap':'coolwarm'})
        
        .. image:: https://traja.readthedocs.io/en/latest/_images/sphx_glr_plot_average_direction_005.png
           :alt: streamplot
        
        Acknowledgements
        ----------------
        
        traja code implementation and analytical methods (particularly
        ``rediscretize_points``) are heavily inspired by Jim McLean's R package
        `trajr <https://github.com/JimMcL/trajr>`__. Many thanks to Jim for his
        feedback.
        
Keywords: trajectory analysis
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Software Development :: Libraries
Requires-Python: >= 3.6
Description-Content-Type: text/x-rst
Provides-Extra: all
