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])