pastas.solver.least_squares.LeastSquaresBase.fit_report
=======================================================

.. py:method:: pastas.solver.least_squares.LeastSquaresBase.fit_report(full_output: bool = False, corr: bool = False, stderr: bool = False, warnings: bool = True, obj_func: float = np.nan) -> str

   
   Report on the fit after a model is optimized.

   :param full_output: If True, all options are shown in the fit report. This is a shortcut for
                       `corr=True`, `stderr=True`, and `warnings=True`.
   :type full_output: bool, optional
   :param corr: If True the parameter correlations are shown.
   :type corr: bool, optional
   :param stderr: If True the standard error of the parameter values are shown. Please be
                  aware of the conditions for reliable uncertainty estimates, more information
                  here:
                  https://pastas.readthedocs.io/stable/examples/diagnostic_checking.html
   :type stderr: bool, optional
   :param warnings: print warnings in case of optimization failure, parameters hitting
                    bounds, or length of responses exceeding calibration period.
   :type warnings: bool, optional
   :param obj_func: Value of the found minimal loss function value from the
                    optimization algorithm. Generally obtained from the result attribute
                    which is not present when loading the solver, thus by default nan.
   :type obj_func: float, optional

   :returns: **report** -- String with the report.
   :rtype: str

   .. rubric:: Examples

   This method is called by the solve method if report=True, but can also be
   called on its own::

   >>> print(ml.fit_report)

   .. rubric:: Notes

   The reported values for the fit use the residuals time series where possible.
   If interpolation is used this means that the result may slightly differ
   compared to using ml.simulate() and ml.observations().















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