pastas.plotting.plots.TrackSolve
================================

.. toctree::
   :hidden:

   /api/pastas/plotting/plots/TrackSolve.track_solve
   /api/pastas/plotting/plots/TrackSolve.initialize_figure
   /api/pastas/plotting/plots/TrackSolve.plot_track_solve
   /api/pastas/plotting/plots/TrackSolve.plot_track_solve_history

.. py:class:: pastas.plotting.plots.TrackSolve(ml: pastas.typing.Model, tmin: pandas.Timestamp | str | None = None, tmax: pandas.Timestamp | str | None = None, update_iter: int | None = None)

   
   Track and/or visualize optimization progress for Pastas models.

   :param ml: pastas Model to track
   :type ml: pastas.model.Model
   :param tmin: start time for simulation, by default None which defaults to first index in
                ml.oseries.series
   :type tmin: pandas.Timestamp or str, optional
   :param tmax: end time for simulation, by default None which defaults to last index in
                ml.oseries.series
   :type tmax: pandas.Timestamp or str, optional
   :param update_iter: if visualizing optimization progress, update plot every update_iter
                       iterations, by default nparam
   :type update_iter: int, optional

   .. rubric:: Notes

   Interactive plotting of optimization progress requires a matplotlib backend that
   supports interactive plotting, e.g. `mpl.use("TkAgg")` and `mpl.interactive(
   True)`. Some possible speedups on the matplotlib side include:
   - mpl.style.use("fast")
   - mpl.rcParams['path.simplify_threshold'] = 1.0

   .. rubric:: Examples

   Set matplotlib backend and interactive mode (put this at the top of your script)::

       import matplotlib as mpl
       mpl.use("TkAgg")
       import matplotlib.pyplot as plt
       plt.ion()

   Create a TrackSolve object for your model::

       track = TrackSolve(ml)

   Solve model and store intermediate optimization results::

       ml.solve(callback=track.track_solve)

   Calculated parameters per iteration are stored in a pandas.DataFrame::

       track.parameters

   Other stored statistics include `track.evp` (explained variance percentage),
   `track.rmse_res` (root-mean-squared error of the residuals), `track.rmse_noise` (
   root mean squared error of the noise, only if noise=True).

   To interactively plot model optimization progress while solving pass
   `track.plot_track_solve` as callback function::

       ml.solve(callback=track.plot_track_solve)

   Access the resulting figure through `track.fig`.















   ..
       !! processed by numpydoc !!
Methods
-------

.. autoapisummary::

   pastas.plotting.plots.TrackSolve.track_solve
   pastas.plotting.plots.TrackSolve.initialize_figure
   pastas.plotting.plots.TrackSolve.plot_track_solve
   pastas.plotting.plots.TrackSolve.plot_track_solve_history