def get_months_years(path, alert):
""" From a given path of images, the function will return a list
of the months/years present in the folder
"""
tifs = glob(f'{path}/*.tif')
if tifs:
try:
years = list(
set([ps.parse_other_files(image)[4].year for image in tifs]))
months = list(
set([ps.parse_other_files(image)[4].month for image in tifs]))
years.sort()
months.sort()
return years, months
except Exception as e:
alert.add_msg("ERROR: Check the folder and make sure that \
all the images follow the format: 'YYYY_mm_dd'",
type_='error')
return None
else:
alert.add_msg("ERROR: The folder is empty. Please make sure that you \
have run the closing filter before run the statistics.",
type_='error')
def filter_dates(tifs, months=None, years=None, ini_date=None, end_date=None):
""" Return a list of images filtered by months and
years.
"""
# Get a list of tuples (date, image_name)
list_ = [(pd.Timestamp(ps.parse_other_files(image)[4]), image)
for image in tifs]
x = pd.DataFrame(list_, columns=['date', 'image_name'])
x = x.sort_values(['date'])
# Create a indexdate
x = x.reset_index(drop=True).set_index('date')
# If months is empty
if years and not months:
months = list(range(1, 13))
elif months and not years:
years = list(set(x.index.year))
if ini_date:
df2 = x[ini_date:end_date]
else:
# Filter by months and years
df2 = x[x.index.month.isin(months) & (x.index.year.isin(years))]
filtered = list(df2['image_name'])
return filtered
def images_summary(tifs):
list_ = [(pd.Timestamp(ps.parse_other_files(image)[4]), image) for image in tifs]
x = pd.DataFrame(list_, columns=['date','Image name'])
x = x.sort_values(['date'])
x = x.reset_index(drop=True).set_index('date')
# Transform the index in string, because json can't decode
# de datetimeindex
x.index = x.index.strftime("%Y-%m-%d")
return x