EddyFinalTracking

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Track eddy with Identification file produce with EddyIdentification
"""
from py_eddy_tracker import EddyParser
from py_eddy_tracker.tracking import Correspondances
from os.path import exists
from os import mkdir
import logging
import datetime as dt


def usage():
    """Usage
    """
    # Run using:
    parser = EddyParser(
        "Tool to use identification step to compute tracking")
    parser.add_argument('nc_file',
                        help='File of correspondances to reload link '
                             'without tracking computation')
    parser.add_argument('--path_out',
                        default='./',
                        help='Path, where to write file')
                        
    parser.add_argument('--eddies_long_model', default=None)
    parser.add_argument('--eddies_short_model', default=None)
    parser.add_argument('--eddies_untracked_model', default=None)

    parser.add_argument('--nb_obs_min',
                        type=int,
                        default=28,
                        help='Minimal length of tracks')
    return parser.parse_args()


if __name__ == '__main__':
    CONFIG = usage()

    # Create output directory
    if not exists(CONFIG.path_out):
        mkdir(CONFIG.path_out)
    SAVE_DIR = CONFIG.path_out
    NB_OBS_MIN = CONFIG.nb_obs_min
    START_TIME = dt.datetime.now()

    CORRESPONDANCES = Correspondances.load(CONFIG.nc_file)

    logging.info('Start merging')

    CORRESPONDANCES.prepare_merging()

    logging.info('The longest tracks have %d observations', CORRESPONDANCES.nb_obs_by_tracks.max())
    logging.info('The mean length is %d observations before filtering', CORRESPONDANCES.nb_obs_by_tracks.mean())

    if CONFIG.eddies_untracked_model is None:
        CONFIG.eddies_untracked_model = '%(path)s/%(sign_type)s_%(prod_time)s_untracked.nc'
    CORRESPONDANCES.get_unused_data().write_netcdf(path=SAVE_DIR, filename=CONFIG.eddies_untracked_model)

    SHORT_CORRESPONDANCES = CORRESPONDANCES._copy()
    SHORT_CORRESPONDANCES.shorter_than(size_max=NB_OBS_MIN)

    CORRESPONDANCES.longer_than(size_min=NB_OBS_MIN)

    FINAL_EDDIES = CORRESPONDANCES.merge()
    SHORT_TRACK = SHORT_CORRESPONDANCES.merge()

    # We flag obs
    if CORRESPONDANCES.virtual:
        FINAL_EDDIES['virtual'][:] = FINAL_EDDIES['time'] == 0
        FINAL_EDDIES.filled_by_interpolation(FINAL_EDDIES['virtual'] == 1)
        SHORT_TRACK['virtual'][:] = SHORT_TRACK['time'] == 0
        SHORT_TRACK.filled_by_interpolation(SHORT_TRACK['virtual'] == 1)

    # Total running time
    FULL_TIME = dt.datetime.now() - START_TIME
    logging.info('Duration : %s', FULL_TIME)

    logging.info('Longer track saved have %d obs', CORRESPONDANCES.nb_obs_by_tracks.max())
    logging.info('The mean length is %d observations after filtering', CORRESPONDANCES.nb_obs_by_tracks.mean())

    if CONFIG.eddies_long_model is None:
        CONFIG.eddies_long_model = '%(path)s/%(sign_type)s_%(prod_time)s.nc'
    if CONFIG.eddies_short_model is None:
        CONFIG.eddies_short_model = '%(path)s/%(sign_type)s_%(prod_time)s_track_too_short.nc'
    FINAL_EDDIES.write_netcdf(filename=CONFIG.eddies_long_model,path=SAVE_DIR)
    SHORT_TRACK.write_netcdf(filename=CONFIG.eddies_short_model, path=SAVE_DIR)