pastas.stats.tests.diagnostics
==============================

.. py:function:: pastas.stats.tests.diagnostics(series: pandas.Series, alpha: float = 0.05, nparam: int = 0, lags: int = 15, stats: tuple = (), float_fmt: str = '{0:.2f}') -> pandas.DataFrame

   
   Methods to compute various diagnostics checks for a time series.

   :param series: Time series to compute the diagnostics for.
   :type series: pandas.Series
   :param alpha: significance level to use for the hypothesis testing.
   :type alpha: float, optional
   :param nparam: Number of parameters of the noisemodel.
   :type nparam: int, optional
   :param lags: Maximum number of lags (in days) to compute the autocorrelation tests for.
   :type lags: int, 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

   .. rubric:: Notes

   Different tests are computed depending on the regularity of the time step of the
   provided time series. pd.infer_freq is used to determined if the time steps are
   regular.

   .. rubric:: Examples

   >>> data = pd.Series(index=pd.date_range(start=0, periods=1000, freq="D"),
   >>>                 data=np.random.rand(1000))
   >>> ps.stats.diagnostics(data)
   Out[0]:
                     Checks Statistic P-value  Reject H0
   Shapiroo       Normality      1.00    0.86      False
   D'Agostino     Normality      1.18    0.56      False
   Runs test      Autocorr.     -0.76    0.45      False
   Durbin-Watson  Autocorr.      2.02     nan      False
   Ljung-Box      Autocorr.      5.67    1.00      False

   In this example, the Null-hypothesis is not rejected and the data may be assumed
   to be white noise.















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