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        "author": "Mauro Cavalcanti",
        "author_email": "maurobio@gmail.com",
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            "License :: OSI Approved :: GNU General Public License (GPL)",
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            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Bio-Informatics"
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        "description": "CornPy: Cornell Ecology Programs in Python\n******************************************\n\n**CornPy** provides Python wrappers for two classic programs for ecological data analyses, DECORANA (DEtrended COrrespondence ANAlysis) and TWINSPAN (Two-Way SPecies INdicator ANalysis). Both programs were written by M. O. Hill in FORTRAN for mainframe computers, and modified for the IBM PC. \n\nThese modified versions use the \"strict\" convergence criteria of Oksanen & Minchin (1997) for eigenanalysis, with a tolerance of 0.000005 and a maximum iteration limit of 999. In DECORANA, the bug in non-linear scaling has been corrected.\n\nBesides binary executables compiled with GNU gfortran to run under MS-Windows or GNU/Linux, FORTRAN source files, decorana.f and twinspan.f, are also provided for those who wish to change the maximum dimensions (by simply changing the numbers in the first PARAMETER statement in each program). \n\nBoth programs require an input data file in Cornell Condensed format, containing the community data to be analysed. The layout of this file should follow the same rules as for the original versions of DECORANA and TWINSPAN. The write_cep() function included in this package will automatically convert a pandas dataframe into a the required format for input to each program.\n\nInstall via 'pip install cornpy'\n\nLicense\n=====\n**CornPy** is distributed under the GNU General Public License\n\nVersion\n=====\n0.1.0\n\nExamples\n======\nDetrended correspondence analysis:\n\n\timport pandas as pd\n    import cornpy as cp\n    df = pd.read_csv(\"data.csv\")\n    site_scores, species_scores = cp.decorana(df)\n\nTwo-way species indicator analysis:\n\n    import pandas as pd\n    import cornpy as cp\n    df = pd.read_csv(\"data.csv\")\n    output = twinspan(df)\n\n",
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        "name": "cornpy",
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