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    "info": {
        "author": "Boris Goncharov",
        "author_email": "goncharov.boris@physics.msu.ru",
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            "License :: OSI Approved :: MIT License",
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        "description": "# normal_corner\n\nThis code produces a corner plot for analytical multi-dimensional Gaussian distribution, using covariance matrix and mean matrix. It also allows us to plot another distribution, with reduced dimensionality, on top. I.e. a distribution for a case where we fixed one variable.\n\nSee demo.py for examples.\n\n## Installation\n\nOption 1:\n - `pip install normal_corner`\n\nOption 2:\n - `python setup.py install` in cloned directory\n\n## Documentation\n\nThe main component is function normal\\_corner inside a normal\\_corner package.\nBelow is a description of inputs and outputs.\n\n#### Output:\n - Matplotlib figure object with a corner plot\n\n#### Main input:\n - `covm` : covariance matrix, _numpy array_, NxN.\n - `mean` : mean matrix, _numpy array_, 1xN.\n - `varlabels` : labels for plotting, 1xN, _list of str_ in LaTex format, between ($$).\n\n#### Input for a second distribution on top:\n - `fixedvarindex` : index of variable that we do not use (fix), _int_, starting from 0. If not _None_, covm2 and mean2 must not be _None_.\n - `fixedvarvalue` : value of fixed variable, _float_. Leave None not to plot fixed value.\n - `newcov` and `newmean` : new covariance and mean matrices, same format, as above.\n\n#### Optional input:\n - `scale_factor` : scale factor for plotting area, _float_, in sigma.\n - `diagnostic` : an option to print out some diagnostic messages, _bool_.\n - `color` : color for a main Normal distribution, _str_.\n - `color2` : color for a secondary Normal distribution, with reduced dimensionality, _str_.\n\n\n",
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        "name": "normal-corner",
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        "summary": "Analytical corner plot for Gaussian distributions",
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