{
    "info": {
        "author": "Charles-David Hevert",
        "author_email": "charles-david.hebert@usherbrooke.ca",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.6",
            "Topic :: Software Development :: Build Tools"
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        "description": "\n[![Build Status](https://travis-ci.org/ZGCDDoo/statsfiles.svg?branch=master)](https://travis-ci.org/ZGCDDoo/statsfiles)\n[![codecov](https://codecov.io/gh/ZGCDDoo/statsfiles/branch/master/graph/badge.svg)](https://codecov.io/gh/ZGCDDoo/statsfiles)\n[![Documentation Status](https://readthedocs.org/projects/statsfiles/badge/?version=latest)](https://statsfiles.readthedocs.io/en/latest/?badge=latest)\n\n\n# Statsfiles\nPlease consult the bare bones documentation by clicking on the docs badge above.\n\n\nStatsfiles is a small python module to compute simple statistics (average and means) of similarly named files. This can be usefull for simulations where a program outputs many files with the same name, but with a number appended. \n\nFor exemple, a program that calculated the temperature on a given number of points in a city for each hour would ouput a file called: temperature1.dat, temperature2.dat , etc for each day starting from day 1.\nThe file would be composed of 24 rows (each hour of the day) and say 10 columns (10 places in the city). To get the average temperature for the year, one needs to do some statistics on these files. This is the usefullness of statsfiles. The previous files which have an array (matrix, i.e numpy) form are called **array_files**. Now if we are interseted in the number of coffees taken by the employeess of this temperature program (for the city broadcasting station),  would be logged in a file called cafe1.dat, cafe2.dat, etc. This file would contain the hour on the first column and the amount of coffee taken of the second column. These type of files are called **obs_files**. The first coulmn is ignored for the statistics, and the statistics for this type of files are saved in json format in the file called **statsobs.json**. \n\nThe output of this module for this previous example is the folder **Stats-${date-time}** with temperature_moy.dat, temperature_et.dat for respectively the average value and the standard deviation, as well as statsobs.json.\n\n\n\n## Installation\nSimple as :\n```bash\npip install statsfiles\n```\n\n### Dependencies\nThis module can run on python >= 3.6. The dependencies will be installed automatically by pip.\n\n\n## Usage\nI recommend taking a look and trying the example. Once this is done, it is quite easy to use it for your own sake. To run the exemple (after Installation):\n\n```bash\n    cd example\n    python -m statsfiles 1 --file example.yml\n```\n\n\nThe example is composed of a simple yaml file (see [pyyaml](https://pyyaml.org/)), that describes the files that will be used for the statistics. Let us explain in detail this yaml file.\n\n### Yaml config file\n\n#### obs_files\nThe name of the files which have a structure given by k.dat\n\n#### array_files\nThe files which have a structure of matrices (the temperature files described above).\n\n#### param_files\n\n\n## README and example under construction.\n\n",
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