Metadata-Version: 1.1
Name: corner
Version: 1.0.2
Summary: Make some beautiful corner plots of samples.
Home-page: https://github.com/dfm/corner.py
Author: Daniel Foreman-Mackey
Author-email: danfm@nyu.edu
License: UNKNOWN
Description: corner.py
        =========
        
        Make some beautiful corner plots.
        
        Corner plot /ˈkôrnər plät/ (noun):
            An illustrative representation of different projections of samples in
            high dimensional spaces. It is awesome. I promise.
        
        Built by `Dan Foreman-Mackey <http://dan.iel.fm>`_ and collaborators (see
        ``corner.__contributors__`` for the most up to date list). Licensed under
        the 2-clause BSD license (see ``LICENSE``).
        
        
        Installation
        ------------
        
        Just run
        
        ::
        
            pip install corner
        
        to get the most recent stable version.
        
        
        Usage
        -----
        
        The main entry point is the ``corner.corner`` function. You'll just use it
        like this:
        
        ::
        
            import numpy as np
            import corner
        
            ndim, nsamples = 5, 10000
            samples = np.random.randn(ndim * nsamples).reshape([nsamples, ndim])
            figure = corner.corner(samples)
            figure.savefig("corner.png")
        
        With some other tweaks (see `demo.py
        <https://github.com/dfm/corner.py/blob/master/demo.py>`_) you can get
        something that looks awesome like:
        
        .. image:: https://raw.github.com/dfm/corner.py/master/corner.png
        
        By default, data points are shown as grayscale points with contours.
        Contours are shown at 0.5, 1, 1.5, and 2 sigma.
        
        For more usage examples, take a look at `tests.py
        <https://github.com/dfm/corner.py/blob/master/tests.py>`_.
        
        
        Documentation
        -------------
        
        All the options are documented in the docstrings for the ``corner`` and
        ``hist2d`` functions. These can be viewed in a Python shell using:
        
        ::
        
            import corner
            print(corner.corner.__doc__)
        
        or, in IPython using:
        
        ::
        
            import corner
            corner.corner?
        
        
        A note about "sigmas"
        +++++++++++++++++++++
        
        We are regularly asked about the "sigma" levels in the 2D histograms. These
        are not the 68%, *etc.* values that we're used to for 1D distributions. In two
        dimensions, a Gaussian density is given by:
        
        ::
        
            pdf(r) = exp(-(r/s)^2/2) / (2*pi*s^2)
        
        The integral under this density is:
        
        ::
        
            cdf(x) = Integral(r * exp(-(r/s)^2/2) / s^2, {r, 0, x})
                   = 1 - exp(-(x/s)^2/2)
        
        This means that within "1-sigma", the Gaussian contains ``1-exp(-0.5) ~ 0.393``
        or 39.3% of the volume. Therefore the relevant 1-sigma levels for a 2D
        histogram of samples is 39% not 68%. If you must use 68% of the mass, use the
        ``levels`` keyword argument.
        
        The `"sigma-demo" notebook
        <https://github.com/dfm/corner.py/blob/master/sigma-demo.ipynb>`_ visually
        demonstrates the difference between these choices of levels.
        
        
        Attribution
        -----------
        
        .. image:: https://zenodo.org/badge/doi/10.5281/zenodo.11020.png
           :target: http://dx.doi.org/10.5281/zenodo.11020
        
        If you make use of this code, please `cite it
        <http://dx.doi.org/10.5281/zenodo.11020>`_.
        
        
        License
        -------
        
        Copyright 2013, 2014 Dan Foreman-Mackey
        
        corner.py is free software made available under the BSD License.
        For details see the LICENSE file.
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: BSD License
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
