:py:mod:`nessai.flowsampler`
============================

.. py:module:: nessai.flowsampler

.. autoapi-nested-parse::

   Main code that handles running and checkpoiting the sampler.

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Module Contents
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Classes
~~~~~~~

.. autoapisummary::

   nessai.flowsampler.FlowSampler




Attributes
~~~~~~~~~~

.. autoapisummary::

   nessai.flowsampler.logger


.. py:data:: logger
   

   
















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.. py:class:: FlowSampler(model, output='./', resume=True, resume_file='nested_sampler_resume.pkl', weights_file=None, signal_handling=True, exit_code=130, max_threads=1, **kwargs)

   
   Main class to handle running the nested sampler.


   :Parameters:

       **model** : :obj:`nessai.model.Model`
           User-defined model.

       **output** : str, optional
           Output directory

       **resume** : bool, optional
           If True try to resume the sampler is the resume file exists.

       **resume_file** : str, optional
           File to resume sampler from.

       **weights_files** : str, optional
           Weights files used to resume sampler that replaces the weights file
           saved internally.

       **max_threads** : int, optional
           Maximum number of threads to use. If ``None`` torch uses all available
           threads.

       **signal_handling** : bool
           Enable or disable signal handling.

       **exit_code** : int, optional
           Exit code to use when forceably exiting the sampler.

       **kwargs**
           Keyword arguments passed to :obj:`~nessai.nestedsampler.NestedSampler`.














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   .. py:method:: run(self, plot=True, save=True)

      
      Run the nested samper


      :Parameters:

          **plot** : bool, optional
              Toggle plots produced once the sampler has converged

          **save** : bool, optional
              Toggle automatic saving of results














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   .. py:method:: save_kwargs(self, kwargs)

      
      Save the dictionary of keyword arguments used.

      Uses an encoder class to handle numpy arrays.

      :Parameters:

          **kwargs** : dict
              Dictionary of kwargs to save.














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   .. py:method:: save_results(self, filename)

      
      Save the results from sampling to a specific JSON file.


      :Parameters:

          **filename** : str
              Name of file to save results to.














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   .. py:method:: safe_exit(self, signum=None, frame=None)

      
      Safely exit. This includes closing the multiprocessing pool.
















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