:py:mod:`orcanet_contrib.parser_orcapred`
=========================================

.. py:module:: orcanet_contrib.parser_orcapred

.. autoapi-nested-parse::

   Use orga.predict with a parser.

   Usage:
       parser_orcapred.py FOLDER LIST CONFIG MODEL [--epoch EPOCH] [--fileno FILENO]
       parser_orcapred.py (-h | --help)

   Arguments:
       FOLDER  Path to the folder where everything gets saved to, e.g. the
               summary.txt, the plots, the trained models, etc.
       LIST    A .toml file which contains the pathes of the training and
               validation files. An example can be found in
               examples/list_file.toml
       CONFIG  A .toml file which sets up the training. An example can be
               found in examples/config_file.toml. The possible parameters
               are listed in core.py in the class Configuration.
       MODEL   Path to a .toml file with infos about a model.
               An example can be found in examples/explanation.toml.

   Options:
       -h --help        Show this screen.
       --epoch=EPOCH    Use model of given epoch. [default: None]
       --fileno=FILENO  Use model of given fileno. [default: None]

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


Functions
~~~~~~~~~

.. autoapisummary::

   orcanet_contrib.parser_orcapred.orca_pred
   orcanet_contrib.parser_orcapred.main



.. py:function:: orca_pred(output_folder, list_file, config_file, model_file, epoch=None, fileno=None)

   
   Run orga.predict with predefined ModelBuilder networks using a parser.

   Per default, the most recent saved model will be loaded.

   :Parameters:

       **output_folder** : str
           Path to the folder where everything gets saved to, e.g. the summary
           log file, the plots, the trained models, etc.

       **list_file** : str
           Path to a list file which contains pathes to all the h5 files that
           should be used for training and validation.

       **config_file** : str
           Path to a .toml file which overwrites some of the default settings
           for training and validating a model.

       **model_file** : str
           Path to a file with parameters to build a model of a predefined
           architecture with OrcaNet.

       **epoch** : int, optional
           The epoch of the saved model to predict with.

       **fileno** : int, optional
           The filenumber of the saved model to predict with.














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.. py:function:: main()

   
   Run the orca_pred function with a parser. 
















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