:py:mod:`flatspin.runner`
=========================

.. py:module:: flatspin.runner

.. autoapi-nested-parse::

   Dipole model runner



Module Contents
---------------


Functions
~~~~~~~~~

.. autoapisummary::

   flatspin.runner.run
   flatspin.runner.run_and_save
   flatspin.runner.run_local
   flatspin.runner.generate_script
   flatspin.runner.run_dist



Attributes
~~~~~~~~~~

.. autoapisummary::

   flatspin.runner.deprecated_params
   flatspin.runner.job_script_template


.. py:data:: deprecated_params
   :annotation: = ['sw_a', 'sw_alpha', 'temp_interp']

   

.. py:exception:: DeprecationError

   Bases: :py:obj:`Exception`

   Common base class for all non-exit exceptions.


.. py:function:: run(model, encoder='sin', input=1, input_key=None, periods=100, spp=100, temp=0, temp_func='interp', **params)

   Run a flatspin simulation

   :param model: Model instance
   :type model: SpinIce
   :param encoder: Name of input encoder to use.
                   See flatspin.encoder for a list of available encoders
   :type encoder: string or Encoder
   :param input: Input to be encoded
                 float: constant input repeated periods times
                 array: array of input to encode
                 string: filename of table input data
   :type input: float, array or string
   :param input_key: Index into input (table column)
   :type input_key: int or str
   :param periods: Number of periods of the external field to run
                   Only used if input is a float
   :type periods: int
   :param spp: Number of samples to save per input value
   :type spp: int
   :param temp: Temperature to be used
                float: constant temperature for whole run
                array: array of temperatures stretched to fit run
   :type temp: float, array
   :param params: Params to pass to the encoder
   :type params: dict


.. py:function:: run_and_save(model_class, params, outdir, data_format)


.. py:function:: run_local(dataset)

   Run on localhost


.. py:function:: generate_script(template, outfile, **params)


.. py:data:: job_script_template
   

   

.. py:function:: run_dist(dataset, wait=True, max_jobs=1000)

   Run distributed on a cluster