leaspy.io.logs.visualization.plotting
=====================================

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


Classes
-------

.. autoapisummary::

   leaspy.io.logs.visualization.plotting.Plotting


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

.. py:class:: Plotting(model, output_path='.', palette='tab10', max_colors=10)

   
   .. deprecated:: 1.2

   Class defining some plotting tools.

   :Parameters:

       **model** : :class:`~leaspy.models.BaseModel`
           The used model.

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

       **palette** : :obj:`str` (palette name) or :class:`matplotlib.colors.Colormap` (`ListedColormap` or `LinearSegmentedColormap`)
           The palette to use.

       **max_colors** : :obj:`int` > 0, optional (default, corresponding to model nb of features)
           Only used if palette is a string














   ..
       !! processed by numpydoc !!

   .. py:attribute:: model


   .. py:attribute:: color_palette
      :value: None



   .. py:attribute:: standard_size
      :value: (8, 4)



   .. py:attribute:: linestyle


   .. py:attribute:: linewidth


   .. py:attribute:: alpha


   .. py:attribute:: output_path
      :value: '.'



   .. py:method:: set_palette(palette, max_colors=None)

      
      Set palette of plots


      :Parameters:

          **palette** : :obj:`str` (palette name) or :class:`matplotlib.colors.Colormap` (`ListedColormap` or `LinearSegmentedColormap`)
              The palette to use.

          **max_colors** : :obj:`int` > 0, optional (default, corresponding to model nb of features)
              Only used if palette is a string














      ..
          !! processed by numpydoc !!


   .. py:method:: colors(at=None)

      
      Wrapper over color_palette iterator to get colors


      :Parameters:

          **at** : any legit color_palette arg (int, float or iterable of any of these) or None (default)
              if None returns all colors of palette upto model dimension



      :Returns:

          **colors** : single color tuple (RGBA) or np.array of RGBA colors (number of colors x 4)
              ..











      ..
          !! processed by numpydoc !!


   .. py:method:: average_trajectory(**kwargs)

      
      Plot the population average trajectories. They are parametrized by the population parameters derived
      during the calibration.


      :Parameters:

          **\*\*kwargs**
              * alpha: :obj:`float`, default 0.6
                  Matplotlib's transparency option. Must be in [0, 1].
              * linestyle: {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}
                  Matplotlib's linestyle option.
              * linewidth: :obj:`float`
                  Matplotlib's linewidth option.
              * features: list[:obj:`str`]
                  Name of features (if set it must be a subset of model features)
                  Default: all model features.
              * colors: list[:obj:`str`]
                  Contains matplotlib compatible colors.
                  At least as many as number of features.
              * labels: list[:obj:`str`]
                  Used to rename features in the plot.
                  Exactly as many as number of features.
                  Default: raw variable name of each feature
              * ax: matplotlib.axes.Axes
                  Axes object to modify, instead of creating a new one.
              * figsize: tuple of int
                  The figure's size.
              * save_as: :obj:`str`, default None
                  Path to save the figure.
              * title: :obj:`str`
              * n_tpts: :obj:`int`
                  Number of timepoints in plot (default: 100)
              * n_std_left, n_std_right: :obj:`float` (default: 3 and 6 resp.)
                  Time window around `tau_mean`, expressed as times of max(`tau_std`, 4)



      :Returns:

          :class:`matplotlib.axes.Axes`
              ..











      ..
          !! processed by numpydoc !!


   .. py:method:: patient_observations(data, patients_idx='all', individual_parameters=None, **kwargs)

      
      Plot patient observations


      :Parameters:

          **data** : :class:`.Data`
              ..

          **patients_idx** : 'all' (default), :obj:`str` or list[:obj:`str`]
              Patients to display (by their ID).

          **individual_parameters** : :class:`.IndividualParameters` or :class:`pandas.DataFrame` (as may be output by ip.to_dataframe()) or dict (Pytorch ip format), optional
              If not None, observations are plotted with respect to reparametrized ages.














      ..
          !! processed by numpydoc !!


   .. py:method:: patient_observations_reparametrized(data, individual_parameters, patients_idx='all', **kwargs)

      
      Plot patient observations (reparametrized ages)
















      ..
          !! processed by numpydoc !!


   .. py:method:: patient_trajectories(data, individual_parameters, patients_idx='all', reparametrized_ages=False, **kwargs)

      
      Plot patient observations together with model individual reconstruction


      :Parameters:

          **data** : :class:`.Data`
              ..

          **individual_parameters** : :class:`.IndividualParameters` or :class:`pandas.DataFrame` (as may be output by ip.to_dataframe()) or dict (Pytorch ip format)
              ..

          **patients_idx** : 'all' (default), :obj:`str` or list[:obj:`str`]
              Patients to display (by their ID).

          **reparametrized_ages** : :obj:`bool` (default False)
              Should we plot trajectories in reparam age or not? to study source impact essentially

          **\*\*kwargs**
              cf. :meth:`._plot_model_trajectories`
              In particular, pass marker=None if you don't want observations besides model














      ..
          !! processed by numpydoc !!