:py:mod:`orcanet_contrib.orca_handler_util`
===========================================

.. py:module:: orcanet_contrib.orca_handler_util

.. autoapi-nested-parse::

   Michael's orcanet utility stuff.

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


Functions
~~~~~~~~~

.. autoapisummary::

   orcanet_contrib.orca_handler_util.update_objects
   orcanet_contrib.orca_handler_util.orca_sample_modifiers
   orcanet_contrib.orca_handler_util.orca_label_modifiers
   orcanet_contrib.orca_handler_util.orca_dataset_modifiers
   orcanet_contrib.orca_handler_util.dict_to_recarray
   orcanet_contrib.orca_handler_util.orca_learning_rates



.. py:function:: update_objects(orga, model_file)

   
   Update the organizer for using the model.

   Look up and load in the respective sample-, label-, and dataset-
   modifiers, as well as the custom objects.
   Will assert that the respective objects have not already been set
   to a non-default value (nothing is overwritten).

   :Parameters:

       **orga** : object Organizer
           Contains all the configurable options in the OrcaNet scripts.

       **model_file** : str
           Path to a toml file which has the infos about which modifiers
           to use.














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.. py:function:: orca_sample_modifiers(name)

   
   Returns one of the sample modifiers used for Orca networks.

   They will permute columns, and/or add permuted columns to xs.

   The input to the functions is:
       xs_files : dict
           Dict that contains the input samples from the file(s).
           The keys are the names of the inputs in the toml list file.
           The values are a single batch of data from each corresponding file.

   The output is:
       xs_layer : dict
           Dict that contains the input samples for a Keras NN.
           The keys are the names of the input layers of the network.
           The values are a single batch of data for each input layer.

   :Parameters:

       **name** : None/str
           Name of the sample modifier to return.

   :Returns:

       **sample_modifier** : function
           The sample modifier function.













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.. py:function:: orca_label_modifiers(name)

   
   Returns one of the label modifiers used for Orca networks.

   CAREFUL: y_values is a structured numpy array! if you use advanced
   numpy indexing, this may lead to errors. Let's suppose you want to
   assign a particular value to one or multiple elements of the
   y_values array.

   E.g.
   y_values[1]['bjorkeny'] = 5
   This works, since it is basic indexing.

   Likewise,
   y_values[1:3]['bjorkeny'] = 5
   works as well, because basic indexing gives you a view (!).

   Advanced indexing though, gives you a copy.
   So this
   y_values[[1,2,4]]['bjorkeny'] = 5
   will NOT work! Same with boolean indexing, like

   bool_idx = np.array([True,False,False,True,False]) # if len(y_values) = 5
   y_values[bool_idx]['bjorkeny'] = 10
   This will NOT work as well!!

   Instead, use
   np.place(y_values['bjorkeny'], bool_idx, 10)
   This works.

   :Parameters:

       **name** : str
           Name of the label modifier that should be used.

   :Returns:

       **label_modifier** : function
           The label modifier function.













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.. py:function:: orca_dataset_modifiers(name)

   
   Returns one of the dataset modifiers used for predicting with OrcaNet.


   :Parameters:

       **name** : str
           Name of the dataset modifier that should be used.














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.. py:function:: dict_to_recarray(data_dict)

   
   Convert a dict with 2d np arrays to a 2d struc array, with column
   names derived from the dict keys.


   :Parameters:

       **data_dict** : dict
           Keys: name of the output layer.
           Values: 2d arrays, first dimension matches

   :Returns:

       **recarray** : ndarray
           ..













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.. py:function:: orca_learning_rates(name, total_file_no)

   
   Returns one of the learning rate schedules used for Orca networks.


   :Parameters:

       **name** : str
           Name of the schedule.

       **total_file_no** : int
           How many files there are to train on.

   :Returns:

       **learning_rate** : function
           The learning rate schedule.













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