imap_processing.spice.repoint.interpolate_repoint_data#

imap_processing.spice.repoint.interpolate_repoint_data(query_met_times: float | ndarray[tuple[int, ...], dtype[_ScalarType_co]]) DataFrame#

Interpolate repointing data to the queried MET times.

In addition to the repoint start, end, and id values that come directly from the universal repointing table, a column is added to the output dataframe which indicates whether each query met time occurs during a repoint maneuver i.e. between the repoint start and end times of a row in the repointing table.

Query times that are more than 24-hours after that last repoint start time in the repoint table will cause an error to be raised. The assumption here is that we shouldn’t be processing data that occurs that close to the next expected repoint start time before getting an updated repoint table.

Parameters:

query_met_times (float or np.ndarray) – Query times in Mission Elapsed Time (MET).

Returns:

repoint_df – Repoint table data interpolated such that there is one row for each of the queried MET times. Output columns are:

  • repoint_start_sec_sclk

  • repoint_start_subsec_sclk

  • repoint_start_met

  • repoint_end_sec_sclk

  • repoint_end_subsec_sclk

  • repoint_end_met

  • repoint_id

  • repoint_in_progress

Return type:

pandas.DataFrame

:raises ValueError : If any of the query_met_times are before the first repoint: start time or after the last repoint start time plus 24-hours.