pastas.modelstats.Statistics.diagnostics
========================================

.. py:method:: pastas.modelstats.Statistics.diagnostics(tmin: pandas.Timestamp | str | None = None, tmax: pandas.Timestamp | str | None = None, alpha: float = 0.05, stats: tuple = (), float_fmt: str = '{0:.2f}') -> pandas.DataFrame

   
   Methods to compute various diagnostics checks for the noise time series. If
   no NoiseModel is used, the diagnostics are computed on the model residuals.

   :param tmin: A string or pandas.Timestamp with the start date (E.g. '1980-01-01
                00:00:00') of the residual / noise time series to compute the diagnostics
                checks for.  Strings are converted to pandas.Timestamp internally.
   :type tmin: pandas.Timestamp or str, optional
   :param tmax: A string or pandas.Timestamp with the end date (E.g. '1980-01-01
                00:00:00') of the residuals / noise time series to compute the diagnostics
                checks for. Strings are converted to pandas.Timestamp internally.
   :type tmax: pandas.Timestamp or str, optional
   :param alpha: significance level to use for the hypothesis testing.
   :type alpha: float, optional
   :param stats: Tuple with the diagnostic checks to perform. Not implemented yet.
   :type stats: tuple, optional
   :param float_fmt: String to use for formatting the floats in the returned DataFrame.
   :type float_fmt: str

   :returns: **df** -- DataFrame with the information for the diagnostics checks. The final column
             in this DataFrame report if the Null-Hypothesis is rejected. If H0 is not
             rejected (=False) the data is in agreement with one of the properties of
             white noise (e.g., normally distributed).
   :rtype: Pandas.DataFrame

   .. seealso:: :py:obj:`pastas.stats.diagnostics`















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