lstid_processing.smoothing.fill_rout

Data filtering routines, specifically designed to support TID analysis.

Functions

`fill_data`(data[, data_time, data_loc, min_val, ...])	Pad instances of unspecified or bad data using grid interpolation.
`fill_time_series`(data_time, data, samp_period[, ...])	Pad instances of unspecified or bad data using 1D interpolation.

Module Contents

lstid_processing.smoothing.fill_rout.fill_data(data, data_time=None, data_loc=None, min_val=np.nan, max_val=np.nan, fill_val=np.nan, method='linear')[source]

Pad instances of unspecified or bad data using grid interpolation.

Parameters:

dataarray-like: ND data array with potential bad values
data_timearray-like or NoneType: Temporal data as datetime objects, must be along axis 0 of data array (default=None)
data_loclist 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_valfloat: Minimum allowed value for data, applied if a number (default=NaN)
max_valfloat: Maximum allowed value for data, applied if a number (default=NaN)
fill_valfloat: 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)
methodstr: Interpolation method, see scipy.interpolate.griddata (default=’linear’)

Returns:

good_dataarray-like: ND array with no bad values