leaspy.io.logs.visualization.plotter
====================================

.. py:module:: leaspy.io.logs.visualization.plotter


Classes
-------

.. autoapisummary::

   leaspy.io.logs.visualization.plotter.Plotter


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

.. py:class:: Plotter(output_path = None)

   
   Class defining some plotting tools.


   :Parameters:

       **output_path** : :obj:`str`, (optional)
           Folder where plots will be saved.
           If None, default to current working directory.














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   .. py:attribute:: output_path
      :value: None



   .. py:method:: plt_show()

      
      Display the current matplotlib figure if `self._show` is True.

      This method wraps `matplotlib.pyplot.show` using the internal
      flags `_show` and `_block` to control interactive plotting.















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   .. py:method:: plot_mean_trajectory(model, *, n_pts = 100, n_std_left = 3, n_std_right = 6, **kwargs)

      
      Plot the mean model trajectory.


      :Parameters:

          **model** : :class:`McmcSaemCompatibleModel` or iterable of such
              Model(s) to compute the mean trajectory for.

          **n_pts** : :obj:`int`, optional
              Number of timepoints to evaluate between the start and end.
              Default=100.

          **n_std_left** : :obj:`int`, optional
              How many standard deviations before the mean to plot.
              Default=3.

          **n_std_right** : :obj:`int`, optional
              How many standard deviations after the mean to plot.
              Default=6.

          **\*\*kwargs**
              - ``color`` : iterable of colors
              - ``title`` : :obj:`str`, plot title
              - ``save_as`` : :obj:`str`, filename to save the plot







      :Raises:

          LeaspyInputError
              If the model(s) is/are not initialized.







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   .. py:method:: plot_mean_validity(model, results, **kwargs)

      
      Plot histogram of reparametrized times for all individuals.


      :Parameters:

          **model** : :class:`McmcSaemCompatibleModel`
              Fitted model providing `tau_mean` and `tau_std`.

          **results** : object
              Results containing `data` and `individual_parameters`
              with keys ``xi`` and ``tau``.

          **\*\*kwargs**
              - ``save_as`` : :obj:`str`, filename to save the plot.














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   .. py:method:: plot_patient_trajectory(model, results, indices, **kwargs)

      
      Plot observed data and reconstructed trajectory for one or more patients.


      :Parameters:

          **model** : :class:`McmcSaemCompatibleModel`
              Model to use for computing individual trajectories.

          **results** : object
              Results containing data and individual parameters.

          **indices** : :obj:`str` or list of :obj:`int`
              Patient index/indices to plot.

          **\*\*kwargs**
              - ``ax`` : matplotlib axis to plot on.
              - ``color`` : iterable of colors.
              - ``title`` : :obj:`str`, plot title.
              - ``save_as`` : :obj:`str`, filename to save the plot.














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   .. py:method:: plot_from_individual_parameters(model, indiv_parameters, timepoints, **kwargs)

      
      Plot a trajectory computed from given individual parameters.


      :Parameters:

          **model** : :class:`McmcSaemCompatibleModel`
              Model used to compute the trajectory.

          **indiv_parameters** : DictParamsTorch
              Individual parameter dictionary.

          **timepoints** : :obj:`torch.Tensor`
              Timepoints at which to compute the trajectory.

          **\*\*kwargs**
              - ``color`` : iterable of colors.
              - ``save_as`` : :obj:`str`, filename to save the plot.














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   .. py:method:: plot_distribution(results, parameter, cofactor=None, **kwargs)

      
      Plot histogram(s) of an estimated parameter distribution.


      :Parameters:

          **results** : object
              Results containing a `get_parameter_distribution` method.

          **parameter** : :obj:`str`
              Parameter name to plot.

          **cofactor** : optional
              Grouping variable; if given, plot separate histograms per group.

          **\*\*kwargs**
              - ``save_as`` : :obj:`str`, filename to save the plot.














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   .. py:method:: plot_correlation(results, parameter_1, parameter_2, cofactor=None, **kwargs)

      
      Plot scatter correlation between two parameters.


      :Parameters:

          **results** : object
              Results containing a `get_parameter_distribution` method.

          **parameter_1** : :obj:`str`
              First parameter name.

          **parameter_2** : :obj:`str`
              Second parameter name.

          **cofactor** : optional
              Grouping variable; if given, scatter different colors per group.

          **\*\*kwargs**
              - ``save_as`` : :obj:`str`, filename to save the plot.














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   .. py:method:: plot_patients_mapped_on_mean_trajectory(model, results, *, n_std_left = 2, n_std_right = 4, n_pts = 100)

      
      Plot observed patient values mapped onto the mean trajectory.


      :Parameters:

          **model** : :class:`McmcSaemCompatibleModel`
              Model used for computing mean and individual trajectories.

          **results** : object
              Results containing data and individual parameters.

          **n_std_left** : :obj:`int`, optional
              How many standard deviations before the mean to plot.
              Default=2.

          **n_std_right** : :obj:`int`, optional
              How many standard deviations after the mean to plot.
              Default=4.

          **n_pts** : :obj:`int`, optional
              Number of timepoints to evaluate. Default=100.














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   .. py:method:: plot_error(path, dataset, model, param_ind, colors=None, labels=None)
      :classmethod:



   .. py:method:: plot_patient_reconstructions(path, dataset, model, param_ind, *, max_patient_number = 5, attribute_type=None)
      :classmethod:



   .. py:method:: plot_param_ind(path, param_ind)
      :staticmethod:



   .. py:method:: plot_convergence_model_parameters(path, path_saveplot_1, path_saveplot_2, model)
      :staticmethod: