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Distance Sampling automation through Distance sofware

Project description

Python module for AUtomated DIstance SAMpling analyses

This is module interfaces distance sampling analysis engines from Distance software, and possibly others in the future.

It is intended for making it easier :

  • to run numerous analysis with many (many) parameter variants on many field observation samples (possibly using some optimisation techniques for automated computation of right and left distance truncations),
  • to select the best analysis variant results through a mostly automated process, based on customisable statistical quality indicators,
  • to produce partly customisable reports in spreadsheet format (numerical results only), and in HTML format (more complete, with full-featured plots like in Distance, and more).

As for now, only the Windows MCDS.exe engine and Point Transect analyses are supported.

Warning !

While now fully operational, this is still work-in-progress,

  • not yet usable without good python skills and Distance Sampling through Distance software knowledge,
  • not yet documented (even if the code is), no real example available.

Version 1.0.0 will come soon with documentation and some real life examples :-)

Requirements

The module itself:

  • python 3.8+
  • pandas 0.25+
  • matplotlib 3.1+
  • jinja2 2.10+
  • zoopt 0.4+

Tests:

  • pytest
  • plotly (sometimes)

Installation

You can install pyaudisam from PyPI in your current python environment (conda or venv, whatever):

$ pip install pyaudisam

TODO: Publish also on Conda Forge, probably following this recipe.

Usage

As a python package, pyaudisam can be used through its API : for the moment, you can have an idea of how to use it by playing with the fully functional jupyter notebook 'tests/valtests.ipynb' (see below Running tests for how to obtain and run it).

But there's also a command-line interface: run it with the -h/--help option ...

$ python -m pyaudisam --help

Documentation

TODO:

  • a concrete quick-start guide with a real life use case and relevant data to play with,
  • a guide for building the module API documentation (sphinx should work out of the box as reStructured text has been used in docstrings),

Running tests

You first need to clone the source tree or download and install a source package: once done, look in the tests sub-folder, everything's in :

  • some tests are fully automated : after installing pytest, simply run it:

    $ pytest
  • some other tests not: they are implemented as jupyter notebooks (see 'tests/unintests.ipynb' and 'tests/valtests.ipynb') that you must run step by step (as long as no one has fully automated them :-).

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