Interpolates data feature

        :param data: Array in a shape of t x nobs, where nobs = h x w x n
        :type data: numpy.ndarray
        :param times: Array of reference times in second relative to the first timestamp
        :type times: numpy.array
        :param resampled_times: Array of reference times in second relative to the first timestamp in initial timestamp
                                array.
        :type resampled_times: numpy.array
        :return: Array of interpolated values
        :rtype: numpy.ndarray

def interpolate_data(self, data, times, resampled_times):
        """ Interpolates data feature

        :param data: Array in a shape of t x nobs, where nobs = h x w x n
        :type data: numpy.ndarray
        :param times: Array of reference times in second relative to the first timestamp
        :type times: numpy.array
        :param resampled_times: Array of reference times in second relative to the first timestamp in initial timestamp
                                array.
        :type resampled_times: numpy.array
        :return: Array of interpolated values
        :rtype: numpy.ndarray
        """
        if True in np.unique(np.isnan(data)):
            raise ValueError('Data must not contain any masked/invalid pixels or NaN values')

        interp_func = self.get_interpolation_function(times, data)

        time_mask = (resampled_times >= np.min(times)) & (resampled_times <= np.max(times))
        new_data = np.full((resampled_times.size,) + data.shape[1:], np.nan, dtype=data.dtype)
        new_data[time_mask] = interp_func(resampled_times[time_mask])
        return new_data