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        "author": "Justin Shenk",
        "author_email": "shenkjustin@gmail.com",
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            "Development Status :: 5 - Production/Stable",
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            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Mathematics",
            "Topic :: Software Development :: Libraries",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "Traja |Python-ver| |Travis| |PyPI| |RTD| |Gitter| |Black| |License| |Binder| |Codecov| |DOI|\n============================================================================================\n\n|Colab|\n\n.. |Python-ver| image:: https://img.shields.io/badge/python-3.6+-blue.svg\n    :target: https://www.python.org/downloads/release/python-360/\n    :alt: Python 3.6+\n\n.. |Travis| image:: https://travis-ci.org/travis-team/traja.svg?branch=master\n    :target: https://travis-ci.org/travis-team/traja\n\n.. |PyPI| image:: https://badge.fury.io/py/traja.svg\n    :target: https://badge.fury.io/py/traja\n\n.. |Gitter| image:: https://badges.gitter.im/traja-chat/community.svg\n    :target: https://gitter.im/traja-chat/community\n\n.. |RTD| image:: https://readthedocs.org/projects/traja/badge/?version=latest\n    :target: https://traja.readthedocs.io/en/latest/?badge=latest\n    :alt: Documentation Status\n\n.. |Black| image:: https://img.shields.io/badge/code%20style-black-000000.svg\n    :target: https://github.com/ambv/black\n\n.. |License| image:: https://img.shields.io/badge/License-MIT-blue.svg\n    :target: https://opensource.org/licenses/MIT\n    :alt: License: MIT\n\n.. |Binder| image:: https://mybinder.org/badge_logo.svg\n    :target: https://mybinder.org/v2/gh/justinshenk/traja/master?filepath=demo.ipynb\n\n.. |Codecov| image:: https://codecov.io/gh/traja-team/traja/branch/master/graph/badge.svg\n  :target: https://codecov.io/gh/traja-team/traja\n\n.. |DOI| image:: https://zenodo.org/badge/166056696.svg\n   :target: https://zenodo.org/badge/latestdoi/166056696\n\n\n.. |Colab| image:: https://colab.research.google.com/assets/colab-badge.svg\n   :target: https://colab.research.google.com/github/justinshenk/traja/blob/master/demo.ipynb\n\nTraja is a Python library for trajectory analysis. It extends the capability of\npandas DataFrame specific for animal trajectory analysis in 2D, and provides\nconvenient interfaces to other geometric analysis packages (eg, R and shapely).\n\nIntroduction\n------------\n\nThe traja Python package is a toolkit for the numerical characterization\nand analysis of the trajectories of moving animals. Trajectory analysis\nis applicable in fields as diverse as optimal foraging theory,\nmigration, and behavioral mimicry (e.g. for verifying similarities in\nlocomotion). A trajectory is simply a record of the path followed by a\nmoving animal. Traja operates on trajectories in the form of a series of\nlocations (as x, y coordinates) with times. Trajectories may be obtained\nby any method which provides this information, including manual\ntracking, radio telemetry, GPS tracking, and motion tracking from\nvideos.\n\nThe goal of this package (and this document) is to aid biological\nresearchers, who may not have extensive experience with Python, to\nanalyze trajectories without being handicapped by a limited knowledge of\nPython or programming. However, a basic understanding of Python is\nuseful.\n\nIf you use traja in your publications, please cite the repo \n\n.. code-block::\n\n    @misc{justin_shenk_2019_3237827,\n      author       = {Justin Shenk and\n                      R\u00fcdiger Busche},\n      title        = {justinshenk/traja: v0.1.1},\n      month        = jun,\n      year         = 2019,\n      doi          = {10.5281/zenodo.3237827},\n      url          = {https://doi.org/10.5281/zenodo.3237827}\n    }\n\n\nInstallation and setup\n----------------------\n\nTo install traja with conda, run\n\n``conda install -c conda-forge traja``\n\nor with pip\n\n``pip install traja``.\n\nImport traja into your Python script or via the Python command-line with\n``import traja``.\n\nTrajectories with traja\n-----------------------\n\nTraja stores trajectories in pandas DataFrames, allowing any pandas\nfunctions to be used.\n\nLoad trajectory with x, y and time coordinates:\n\n.. code-block:: python\n\n    import traja\n\n    df = traja.read_file('coords.csv')\n\nOnce a DataFrame is loaded, use the ``.traja`` accessor to access the\nvisualization and analysis methods:\n\n.. code-block:: python\n\n    df.traja.plot(title='Cage trajectory')\n\n\nAnalyze Trajectory\n------------------\n\n.. csv-table:: The following functions are available via ``traja.trajectory.[method]``\n   :header: \"Function\", \"Description\"\n   :widths: 30, 80\n\n   \"``calc_derivatives``\", \"Calculate derivatives of x, y values \"\n   \"``calc_turn_angles``\", \"Calculate turn angles w.r.t. x-axis \"\n   \"``transitions``\", \"Calculate first-order Markov model for transitions between grid bins\"\n   \"``generate``\", \"Generate random walk\"\n   \"``resample_time``\", \"Resample to consistent step_time intervals\"\n   \"``rediscretize_points``\", \"Rediscretize points to given step length\"\n\nFor up-to-date documentation, see `https://traja.readthedocs.io <https://traja.readthedocs.io>`_.\n\nRandom walk\n-----------\n\nGenerate random walks with\n\n.. code-block:: python\n\n    df = traja.generate(n=1000, step_length=2)\n    df.traja.plot()\n\n.. image:: https://raw.githubusercontent.com/justinshenk/traja/master/docs/source/_static/walk_screenshot.png\n   :alt: walk\\_screenshot.png\n\n\nResample time\n-------------\n``traja.trajectory.resample_time`` allows resampling trajectories by a ``step_time``.\n\n\nFlow Plotting\n-------------\n\n.. code-block:: python\n\n    df = traja.generate()\n    traja.plot_surface(df)\n\n.. image:: https://traja.readthedocs.io/en/latest/_images/sphx_glr_plot_average_direction_001.png\n   :alt: 3D plot\n\n.. code-block:: python\n\n    traja.plot_quiver(df, bins=32)\n\n.. image:: https://traja.readthedocs.io/en/latest/_images/sphx_glr_plot_average_direction_002.png\n   :alt: quiver plot\n\n.. code-block:: python\n\n    traja.plot_contour(df, filled=False, quiver=False, bins=32)\n\n.. image:: https://traja.readthedocs.io/en/latest/_images/sphx_glr_plot_average_direction_003.png\n   :alt: contour plot\n\n.. code-block:: python\n\n    traja.plot_contour(df, filled=False, quiver=False, bins=32)\n\n.. image:: https://traja.readthedocs.io/en/latest/_images/sphx_glr_plot_average_direction_004.png\n   :alt: contour plot filled\n\n.. code-block:: python\n\n    traja.plot_contour(df, bins=32, contourfplot_kws={'cmap':'coolwarm'})\n\n.. image:: https://traja.readthedocs.io/en/latest/_images/sphx_glr_plot_average_direction_005.png\n   :alt: streamplot\n\nAcknowledgements\n----------------\n\ntraja code implementation and analytical methods (particularly\n``rediscretize_points``) are heavily inspired by Jim McLean's R package\n`trajr <https://github.com/JimMcL/trajr>`__. Many thanks to Jim for his\nfeedback.\n\n\n",
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