Metadata-Version: 2.1
Name: anesthetic
Version: 0.5.3
Summary: anesthetic: nested sampling post-processing
Home-page: https://github.com/williamjameshandley/anesthetic
Author: Will Handley
Author-email: wh260@cam.ac.uk
License: MIT
Description: ===========================================
        aNESThetic: nested sampling post-processing 
        ===========================================
        :aNESThetic: nested sampling post-processing
        :Author: Will Handley
        :Version: 0.5.3
        :Homepage: https://github.com/williamjameshandley/anesthetic
        
        .. image:: https://badge.fury.io/py/anesthetic.svg
           :target: https://badge.fury.io/py/anesthetic
           :alt: PyPi location
        .. image:: https://zenodo.org/badge/175663535.svg
           :target: https://zenodo.org/badge/latestdoi/175663535
           :alt: Permanent DOI for this release
        
        
        This project is still in alpha stage. It aims to bring together tools for processing nested sampling chains, leveraging the standard python libraries:
        
        - numpy
        - scipy
        - pandas
        
        As well as the state-of-the-art KDE tool:
        
        - fastKDE
        
        You can see it in action in the `plot gallery <http://htmlpreview.github.io/?https://github.com/williamjameshandley/cosmo_example/blob/master/demos/demo.html>`__.
        
        Current functionality includes:
        - Computation of Bayesian evidences, Kullback-Liebler divergences and Bayesian model dimensionalities.
        - Marginalised 1d and 2d plots.
        
        This tool was designed primarily for use with nested sampling, although it can be used for normal MCMC chains.
        
        Installation
        ------------
        
        This can be installed via pip
        
        .. code:: bash
        
            pip install anesthetic
        
        Or via the setup.py
        
        .. code:: bash
        
            git clone https://github.com/williamjameshandley/anesthetic 
            cd anesthetic
            python setup.py install --user
        
        Another posterior plotting tool?
        --------------------------------
        
        ::
            
            This is my posterior plotter. There are many like it, but this one is mine.
        
        There are several excellent tools for plotting marginalised posteriors:
        
        - `getdist <http://getdist.readthedocs.io/en/latest/intro.html>`__ 
        - `corner <https://corner.readthedocs.io/en/latest/>`__
        - `MontePython <http://baudren.github.io/montepython.html>`__
        - `pygtc <https://pygtc.readthedocs.io/en/latest/>`__
        
        Why create another one? In general, any dedicated user of software will find that there is some functionality that in their use case is lacking, and the designs of previous codes make such an extensions challenging. In my case this was:
        
        1. For large numbers of samples, kernel density estimation is slow, or inaccurate. There are now better state-of-the-art kernel density estimators, such as `fastKDE <https://pypi.org/project/fastkde/>`__, which ameliorate many of these difficulties.
        
        2. Existing tools can make it painfully difficult to define new parameters. Take for example the default cosmomc chain, which defines ``omegabh2``, but not ``omegab``. The transformation is easy, since ``omegab = omegabh2/ (H0/100)**2``, but writing this simple transformation in code is not so trivial. anesthetic solves this issue by storing the samples as a pandas array, for which the relevant code for defining the above new parameter would be
        
        .. code:: python
        
            from anesthetic.anesthetic import MCMCSamples
        
            samples = MCMCSamples.read(file_root)          # Load the samples
        
            h = samples['H0']/100                          # Define h
            samples['omegab'] = samples.omegabh2/h**2      # Define omegab
            samples.tex['omegab'] = '$\Omega_b$'           # Label omegab
        
            samples.plot_1d('omegab')                      # Simple 1D plot
            
        3. Many KDE plotting tools have conventions that don't play well with uniformly distributed parameters, which is a pain if you are trying to plot priors along with your posteriors. anesthetic a sensible mechanism, by defining the contours by the amount of iso-probability mass they contain, but colouring the fill in relation to the probability density of the contour.
        
        To Do
        -----
        - tests
        - CI
        - docstrings
        - better interfaces for plotting multiple samples
        - better interfaces for prior + posterior
        - multiple nested sampler input formats (MultiNest, Dynesty, NeuralNest)
        - Read multiple MCMC chains
        - Automatic coloring
        - Legends
        - resurrect rhinestone
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Astronomy
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Mathematics
Provides-Extra: docs
