:py:mod:`orcanet.utilities.visualization`
=========================================

.. py:module:: orcanet.utilities.visualization

.. autoapi-nested-parse::

   Visualization tools used without Keras.
   Makes performance graphs for training and validating.

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

Classes
~~~~~~~

.. autoapisummary::

   orcanet.utilities.visualization.TrainValPlotter



Functions
~~~~~~~~~

.. autoapisummary::

   orcanet.utilities.visualization.gaussian_smooth
   orcanet.utilities.visualization.plot_history
   orcanet.utilities.visualization.skip_nans
   orcanet.utilities.visualization.get_ylims
   orcanet.utilities.visualization.get_epoch_xticks
   orcanet.utilities.visualization.update_summary_plot
   orcanet.utilities.visualization.sort_metrics



.. py:class:: TrainValPlotter

   
   Class for plotting train/val curves.
















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   .. py:method:: plot_curves(train_data, val_data=None, train_label='training', val_label='validation', color=None, smooth_sigma=None, tlw=0.5, vlw=0.5, vms=3)

      
      Plot a training and optionally a validation line.

      The data can contain nan's.

      :Parameters:

          **train_data** : List
              X data [0] and y data [1] of the train curve. Will be plotted as
              connected dots.

          **val_data** : List, optional
              Optional X data [0] and y data [1] of the validation curve.
              Will be plotted as a faint solid line of the same color as train.

          **train_label** : str, optional
              Label for the train line in the legend.

          **val_label** : str, optional
              Label for the validation line in the legend.

          **color** : str, optional
              Color used for the train/val line.

          **smooth_sigma** : int, optional
              Apply gaussian blur to the train curve with given sigma.

          **tlw** : float
              Linewidth of train curve.

          **vlw** : float
              Linewidth of val curve.

          **vms** : float
              Markersize of the val curve.














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   .. py:method:: apply_layout(title=None, x_label='Epoch', y_label=None, grid=True, legend=True, x_lims=None, y_lims='auto', x_ticks='auto', logy=False)

      
      Apply given layout.
      Can calculate good y_lims and x_ticks automatically.


      :Parameters:

          **title** : str
              Title of the plot.

          **x_label** : str
              X label of the plot.

          **y_label** : str
              Y label of the plot.

          **grid** : bool
              If true, show a grid.

          **legend** : bool
              If true, show a legend.

          **x_lims** : List
              X limits of the data.

          **y_lims** : List or str
              Y limits of the data. "auto" for auto-calculation.

          **x_ticks** : List
              Positions of the major x ticks.

          **logy** : bool
              If true, make y axis log.














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.. py:function:: gaussian_smooth(y, sigma, truncate=4)

   
   Smooth a 1d ndarray with a gaussian filter.
















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.. py:function:: plot_history(train_data, val_data=None, train_label='training', val_label='validation', color=None, **kwargs)

   
   Plot the train/val curves in a single plot.

   For backward compat. Functionality moved to TrainValPlotter















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.. py:function:: skip_nans(data)

   
   Skip over nan values, so that all dots are connected.


   :Parameters:

       **data** : List
           Contains x and y data as ndarrays. The y values may contain nans.

   :Returns:

       **data_clean** : List
           Contains x and y data as ndarrays. Points with y=nan are skipped.













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.. py:function:: get_ylims(y_points_train, y_points_val=None, fraction=0.25)

   
   Get the y limits for the summary plot.

   For the training data, limits are calculated while ignoring data points
   which are far from the median (in terms of the median distance
   from the median).
   This is because there are outliers sometimes in the training data,
   especially early on in the training.

   :Parameters:

       **y_points_train** : List
           y data of the train curve.

       **y_points_val** : List or None
           Y data of the validation curve.

       **fraction** : float or List
           How much whitespace of the total y range is added below and above
           the lines.

   :Returns:

       **y_lims** : tuple
           Minimum, maximum of the data.













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.. py:function:: get_epoch_xticks(x_points)

   
   Calculates the xticks for the train and validation summary plot.

   One tick per epoch. Less the larger #epochs is.

   :Parameters:

       **x_points** : List
           A list of the x coordinates of all points.

   :Returns:

       **x_ticks_major** : numpy.ndarray
           Array containing the ticks.













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.. py:function:: update_summary_plot(orga)

   
   Plot and save all metrics of the given validation- and train-data
   into a pdf file, each metric in its own plot.

   If metric pairs of a variable and its error are found (e.g. e_loss
   and e_err_loss), they will have the same color and appear back to
   back in the plot.

   :Parameters:

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














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.. py:function:: sort_metrics(metric_names)

   
   Sort a list of metrics, so that errors are right after their variable.
   The format of the metric names have to be e.g. e_loss and e_err_loss
   for this to work.


   :Parameters:

       **metric_names** : List
           List of metric names.

   :Returns:

       **sorted_metrics** : List
           List of sorted metric names with the same length as the input.













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