def generate_temporal_sampling(S2_dir, start_date=False, last_date=False, day_interval=5, save_csv=False):
"""
Generate sample time for gap-filling of Time Series
Parameters
-----------
S2_dir : str
path of the folder where zip or unzipped S2 tiles are.
start_date : False str or int, default false.
If str or int, use %Y%m%d format, e.g. '20180130'.
end_date : False, str or int, default False.
If str or int, use %Y%m%d format, e.g. '20181230'.
day_interval : int, default 5
The delta days interval from the first acquistion to the last
save_csv : False or str, default False
If str, path where the csv file will be saved.
"""
# =============================================================================
# List all subfolders which begins with SENTINEL2
# if no last_date and start_date given
# =============================================================================
AcquisitionDates = [start_date, last_date]
if last_date is False or start_date is False:
S2 = glob.glob(glob.os.path.join(S2_dir, 'SENTINEL2*/'))
# if no folder, looking for zip files
if S2 == []:
S2 = glob.glob(glob.os.path.join(S2_dir, 'SENTINEL2*.zip'))
else:
S2 = [glob.os.path.basename(glob.os.path.dirname(S2Folder))
for S2Folder in S2]
# ==========================================================================
# Detecting YYYYMMDD date format
# ==========================================================================
import re
regexYYYYMMDD = r"(?<!\d)(?:(?:20\d{2})(?:(?:(?:0[13578]|1[02])31)|(?:(?:0[1,3-9]|1[0-2])(?:29|30)))|(?:(?:20(?:0[48]|[2468][048]|[13579][26]))0229)|(?:20\d{2})(?:(?:0?[1-9])|(?:1[0-2]))(?:0?[1-9]|1\d|2[0-8]))(?!\d)"
p = re.compile(regexYYYYMMDD)
AcquisitionDates = sorted(
[p.findall(S2folder)[0] for S2folder in S2])
if start_date is False:
start_date = AcquisitionDates[0]
if last_date is False:
last_date = AcquisitionDates[-1]
from museopheno.time_series import generate_temporal_sampling
return generate_temporal_sampling(AcquisitionDates[0], AcquisitionDates[-1], day_interval=day_interval, save_csv=save_csv)
######################
# import library
# ---------------
from museopheno.time_series import generate_temporal_sampling
###############################
# Generate acquistion date file
# ----------------------------------
start_date = '20171120'
last_date = '20180505'
delta = 5
acquisition_dates = generate_temporal_sampling(start_date='20171120',
last_date='20180505',
day_interval=delta)
print(
'There are {} dates between {} and {} using an interval of {} days'.format(
len(acquisition_dates), start_date, last_date, delta))
delta = 10
acquisition_dates = generate_temporal_sampling(start_date='20171120',
last_date='20180505',
day_interval=delta)
print(
'There are {} dates between {} and {} using an interval of {} days'.format(
len(acquisition_dates), start_date, last_date, delta))
from museopheno import time_series
###############################
# Create example values
# ----------------------------------------------------
#######################################
# Initially these values came from the LCHloC spectral index
x = np.asarray([3.4825737, 4.27786 , 5.0373, 4.7196426, 4.1233397, 4.0338645,2.7735472])
dates = [20180429, 20180513, 20180708, 20180815, 20180915, 20181015, 20181115]
#########################################
# Resample to every 5 days
# ----------------------------
dates_5days = time_series.generate_temporal_sampling(dates[0],dates[-1],5)
ts = time_series.SmoothSignal(dates=dates,output_dates = dates_5days)
#######################
# savitzski golay
# ----------------------------
# Savitzski golay can use several interpolation type before smoothing the trend
#######################
# Savitzski golay from linear interpolation
x_savgol_linear = ts.savitzski_golay(x,window_length=9,polyorder=1,interpolation_params=dict(kind='linear'))
#######################