def extract(my_path_folder,file, my_start_month, my_start_day, my_end_month, my_end_day):
my_extraction=DaisyDlf(my_path_folder+'\\'+'%s.dlf' %file)
my_extraction=my_extraction.Data
years=np.unique([my_extraction.index[i].year for i in range(len(my_extraction.index))])
my_extraction['Date']=my_extraction.index
my_extraction.index=np.arange(0,len(my_extraction))
my_start, my_end = [], []
for i in range(len(years)-1):
my_start.append(my_extraction['Date'][my_extraction['Date'] == datetime.strptime('%0d-%02d-%02d 00:00:00'%(years[i+1], my_start_month, my_start_day), "%Y-%m-%d %H:%M:%S")].index[0])
my_end.append(my_extraction['Date'][my_extraction['Date'] == datetime.strptime('%0d-%02d-%02d 00:00:00'%(years[i+1], my_end_month, my_end_day), "%Y-%m-%d %H:%M:%S")].index[0])
return my_extraction, my_start, my_end
# find simulated data in corresponding folder
for my_start_year, folder in enumerate(
sensitivity_analysis['%d' % threshold + '_' +
'%d' % space]):
print('extraction from folder nb:', my_start_year)
# store the pesticide outputs from Daisy
field_chemicals = defaultdict()
for chem in chemicals:
field_chemicals[chem] = DaisyDlf(
path_folder + '\\' + folder + '\\' +
'%s-%s_%s_field_%s.dlf' %
(prefix, depth, col, chem)).Data
field_chemicals[chem]['Date'] = field_chemicals[
chem].index
field_chemicals[chem].index = np.arange(
0, len(field_chemicals[chem]))
# use the harvest file to select find starting and ending
# index of 3-month monitoring period each year
harvest = DaisyDlf(path_folder + '\\' + folder + '\\' +
'%s-harvest.dlf' % prefix)
harvest = harvest.Data
harvest['Date'] = harvest.index
harvest.index = np.arange(0, len(harvest))
start_harvest = (np.where(
harvest['Date'] == datetime.strptime(
'%d-%02d-%02d 00:00:00' %
(my_start_dates[my_start_year], 9,
25), "%Y-%m-%d %H:%M:%S"))[0][0])
# store the starting and ending index every year,
# and corresponding water outputs