# Copyright 2018 Regents of the University of Colorado. All Rights Reserved.
# Released under the MIT license.
# This software was developed at the University of Colorado's Laboratory for Atmospheric and Space Physics.
# Verify current version before use at: https://github.com/MAVENSDC/Pytplot
import pytplot
import copy
[docs]def interp_nan(tvar, new_tvar=None, s_limit=None):
"""
Interpolates the tplot variable through NaNs in the data. This is basically just a wrapper for xarray's interpolate_na function.
.. note::
This analysis routine assumes the data is no more than 2 dimensions. If there are more, they may become flattened!
Parameters:
tvar : str
Name of tplot variable.
s_limit : int or float, optional
The maximum size of the gap in seconds to not interpolate over. I.e. if there are too many NaNs in a row, leave them there.
new_tvar : str
Name of new tvar for added data. If not set, then the original tvar is replaced.
Returns:
None
Examples:
>>> # Interpolate through the np.NaN values
>>> pytplot.store_data('e', data={'x':[2,5,8,11,14,17,21], 'y':[[np.nan,1,1],[np.nan,2,3],[4,np.nan,47],[4,np.nan,5],[5,5,99],[6,6,25],[7,np.nan,-5]]})
>>> pytplot.interp_nan('e','e_nonan',s_limit=5)
>>> print(pytplot.data_quants['e_nonan'].values)
"""
x = pytplot.data_quants[tvar].interpolate_na(dim='time', limit=s_limit)
x.attrs = copy.deepcopy(pytplot.data_quants[tvar].attrs)
if new_tvar is None:
pytplot.data_quants[tvar] = x
x.name = tvar
else:
pytplot.data_quants[new_tvar] = x
pytplot.data_quants[new_tvar].name = new_tvar