generate_temporal_sampling

museopheno.time_series.generate_temporal_sampling(start_date, last_date, day_interval=5, save_csv=False, fmt='%Y%m%d')[source]

Generate a custom temporal sampling for Satellite Image Time Series.

Parameters
  • start_date (int, default False.) – If specified, format (YYYYMMDD).

  • last_date (int, default False.) – If specified, format (YYYYMMDD).

  • day_interval (int, default 5) – Integer, days delta to between each date.

  • save_csv (False or str.) – If str, path to save the csv.

  • fmt (str, default '%Y%m%d') – Format type of the input dates. Default: ‘%Y%m%d’ (e.g. 20181230)

Example

>>> generateTemporalSampling(20181203,20190326,day_interval=5)
array([20181203, 20181208, 20181213, 20181218, 20181223, 20181228,
   20190102, 20190107, 20190112, 20190117, 20190122, 20190127,
   20190201, 20190206, 20190211, 20190216, 20190221, 20190226,
   20190303, 20190308, 20190313, 20190318, 20190323, 20190328])
>>> generateTemporalSampling('2018-03-12','2019-26-03',day_interval=15,fmt='%Y-%d-%m')
array([20181203, 20181218, 20190102, 20190117, 20190201, 20190216,
   20190303, 20190318, 20190402])
>>> generateTemporalSampling('2018-03-12','2019-26-03',day_interval=15,fmt='%Y-%d-%m',save_csv='/tmp/AcquisitionDates.csv')
>>> np.loadtxt('/tmp/AcquisitionDates.csv',dtype=int)
array([20181203, 20181218, 20190102, 20190117, 20190201, 20190216,
   20190303, 20190318, 20190402])