lstid_processing.smoothing.fill_rout
====================================

.. py:module:: lstid_processing.smoothing.fill_rout

.. autoapi-nested-parse::

   Data filtering routines, specifically designed to support TID analysis.

   ..
       !! processed by numpydoc !!


Functions
---------

.. autoapisummary::

   lstid_processing.smoothing.fill_rout.fill_data
   lstid_processing.smoothing.fill_rout.fill_time_series


Module Contents
---------------

.. py:function:: fill_data(data, data_time=None, data_loc=None, min_val=np.nan, max_val=np.nan, fill_val=np.nan, method='linear')

   
   Pad instances of unspecified or bad data using grid interpolation.


   :Parameters:

       **data** : array-like
           ND data array with potential bad values

       **data_time** : array-like or NoneType
           Temporal data as datetime objects, must be along axis 0 of data array
           (default=None)

       **data_loc** : list of array-like or None
           Location coordinate data, contained as a list, in axis order
           corresponding to their order in the data array.  For example, in a data
           array with time along the first axis and altitude along the second
           axis, this would be a list with the first and only element containing
           an array of the altitude data. Alternatively, for a data array with
           longitude along the first axis and altitude along the second axis,
           time_data would be none, and this would be a list with the longitude
           array as the first element and the altitude array as the second
           element. (default=None)

       **min_val** : float
           Minimum allowed value for data, applied if a number (default=NaN)

       **max_val** : float
           Maximum allowed value for data, applied if a number (default=NaN)

       **fill_val** : float
           Value used to fill in for requested points outside of the convex hull
           of the input points. This option has no effect for the ‘nearest’
           method. (default=np.nan)

       **method** : str
           Interpolation method, see scipy.interpolate.griddata (default='linear')



   :Returns:

       **good_data** : array-like
           ND array with no bad values







   .. seealso::

       
       :obj:`scipy.interpolate.griddata`
           ..
       



   ..
       !! processed by numpydoc !!

.. py:function:: fill_time_series(data_time, data, samp_period, min_val=np.nan, max_val=np.nan, method='linear', fill_val=np.nan)

   
   Pad instances of unspecified or bad data using 1D interpolation.


   :Parameters:

       **data_time** : array-like
           Temporal data as datetime objects, must be along axis 0 of data array

       **data** : array-like
           1D data array with potential bad values

       **samp_period** : float
           Sample period in minutes at which the data should be observed

       **min_val** : float
           Minimum allowed value for data, applied if a number (default=NaN)

       **max_val** : float
           Maximum allowed value for data, applied if a number (default=NaN)

       **method** : str
           Interpolation method, see `kind` in scipy.interpolate.interp1d
           (default='linear')

       **fill_val** : array-like or 'extrapolate'
           Fill value if no interpolation possible, see scipy.interpolate.interp1d
           (default=np.nan)



   :Returns:

       **good_time** : array-like
           1D array of time data without gaps

       **good_data** : array-like
           1D array with no bad values







   .. seealso::

       
       :obj:`scipy.interpolate.interp1d`
           ..
       



   ..
       !! processed by numpydoc !!