:mod:`km3pipe.stats`
====================

.. py:module:: km3pipe.stats

.. autoapi-nested-parse::

   Statistics.

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Module Contents
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Classes
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.. autoapisummary::

   km3pipe.stats.loguniform
   km3pipe.stats.rv_kde
   km3pipe.stats.hist2d





Functions
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.. autoapisummary::


   km3pipe.stats.mad
   km3pipe.stats.mad_std
   km3pipe.stats.drop_zero_variance
   km3pipe.stats.param_names
   km3pipe.stats.perc
   km3pipe.stats.resample_1d
   km3pipe.stats.bootstrap_params
   km3pipe.stats.param_describe
   km3pipe.stats.bootstrap_fit
   km3pipe.stats.bincenters
















































.. data:: log
   

   









.. py:class:: loguniform(low=0.1, high=1, base=10, *args, **kwargs)

   Bases::class:`scipy.stats.rv_continuous`

   

   Loguniform Distributon















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.. py:class:: rv_kde(data, bw=None, bw_method=None, bw_statsmodels=False, **kde_args)

   Bases::class:`scipy.stats.rv_continuous`

   

   Create a `scipy.stats.rv_continuous` instance from a (gaussian) KDE.

   Uses the KDE implementation from sklearn.

   Automatic bandwidth,  either from the statsmodels or scipy implementation.














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   .. method:: pdf(self, x)

      



   

   .. method:: rvs(self, *args, **kwargs)

      
      Draw Random Variates.


      :Parameters:

          **size: int, optional (default=1)**
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          **random_state_: optional (default=None)**
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.. function:: mad(v)

   
   MAD -- Median absolute deviation. More robust than standard deviation.















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.. function:: mad_std(v)

   
   Robust estimate of standard deviation using the MAD.

   Lifted from astropy.stats.














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.. function:: drop_zero_variance(df)

   
   Remove columns from dataframe with zero variance.















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.. function:: param_names(scipy_dist)

   
   Get names of fit parameters from a ``scipy.rv_*`` distribution.















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.. function:: perc(arr, p=95, **kwargs)

   
   Create symmetric percentiles, with ``p`` coverage.















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.. function:: resample_1d(arr, n_out=None, random_state=None)

   
   Resample an array, with replacement.


   :Parameters:

       **arr: np.ndarray**
           The array is resampled along the first axis.

       **n_out: int, optional**
           Number of samples to return. If not specified,
           return ``len(arr)`` samples.













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.. function:: bootstrap_params(rv_cont, data, n_iter=5, **kwargs)

   
   Bootstrap the fit params of a distribution.


   :Parameters:

       **rv_cont: scipy.stats.rv_continuous instance**
           The distribution which to fit.

       **data: array-like, 1d**
           The data on which to fit.

       **n_iter: int [default=10]**
           Number of bootstrap iterations.













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.. function:: param_describe(params, quant=95, axis=0)

   
   Get mean + quantile range from bootstrapped params.















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.. function:: bootstrap_fit(rv_cont, data, n_iter=10, quant=95, print_params=True, **kwargs)

   
   Bootstrap a distribution fit + get confidence intervals for the params.


   :Parameters:

       **rv_cont: scipy.stats.rv_continuous instance**
           The distribution which to fit.

       **data: array-like, 1d**
           The data on which to fit.

       **n_iter: int [default=10]**
           Number of bootstrap iterations.

       **quant: int [default=95]**
           percentile of the confidence limits (default is 95, i.e. 2.5%-97.5%)

       **print_params: bool [default=True]**
           Print a fit summary.













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.. py:class:: hist2d(H2D, *args, **kwargs)

   Bases::class:`scipy.stats.rv_continuous`

   

   Simple implementation of a 2d histogram.















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   .. attribute:: H_pad
      

      



   

   

   .. attribute:: integral
      

      



   

   

   .. attribute:: area
      

      



   

   

   









.. function:: bincenters(bins)

   
   Bincenters, assuming they are all equally spaced.















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