def filter_months(data_array, month_list):
# To define in GeoDataArray !!!!and GeoDS!!!!
if month_list is not None:
condition = xr.zeros_like(data_array.t)
for i in range(len(data_array.t)):
condition[i] = data_array.t[i].values[()].month in util.months_to_number(month_list)
data_array = data_array.where(condition, drop=True)
return data_array
def crop_months(self, new_month_list):
condition = xr.zeros_like(self.data.t)
for i in range(len(self.data.t)):
condition[i] = self.data.t[i].values[()].month in util.months_to_number(new_month_list)
self.data = self.data.where(condition, drop=True)
self.months = new_month_list
print("____ Data cropped to the new month list.")
return self
def __init__(self, experiment, start_year, end_year, month_list, verbose,
logger):
super(NorESMDS, self).__init__(verbose, logger)
self.t = None
self.lsm = None
self.start_year = start_year
self.end_year = end_year
self.experiment = experiment
if month_list is None:
self.months = None
elif month_list == "full":
self.months = util.months_to_number(self.MONTHS)
else:
self.months = util.months_to_number(month_list)
self.import_data()
self.import_coordinates()
def import_coordinates(self):
self.lon, self.lat = sort_coordinates(self.grid.plon.values,
self.grid.plat.values)
self.lon_p, self.lat_p = cycle_coordinates(self.lon, self.lat)
self.transform_matrix = get_transform_matrix(self.grid.plon.values)
self.z = np.sort(self.sample_data.depth.values)
self.z_p = self.z
self.t = [
cftime.Datetime360Day(year, month, 1)
for year in np.arange(int(self.start_year),
int(self.end_year) + 1)
for month in util.months_to_number(self.months)
]
super(OCNMDS, self).import_coordinates()
def get(self,
data,
zone=zones.NoZone(),
mode_lon=None,
value_lon=None,
mode_lat=None,
value_lat=None,
mode_z=None,
value_z=None,
mode_t=None,
value_t=None,
new_start_year=None,
new_end_year=None,
new_month_list=None):
"""
TO FACTORISE
"""
geo_da = proc.GeoDataArray(
data, ds=self,
process=self.process) # add the GeoDataArray wrapper
geo_da = zone.compact(geo_da)
if any([
new_start_year is not None, new_end_year is not None,
new_month_list is not None
]):
print("____ Truncation to new time coordinates.")
try:
if new_start_year is not None and new_start_year < self.start_year:
raise ValueError(
"**** The new start year is smaller than the imported one."
)
elif new_end_year is not None and new_end_year > self.end_year:
raise ValueError(
"**** The new end year is larger than the imported one.")
elif new_start_year is None and new_end_year is not None:
geo_da.crop_years(self.start_year, new_end_year)
elif new_start_year is not None and new_end_year is None:
geo_da.crop_years(new_start_year, self.end_year)
elif new_start_year is not None and new_end_year is not None:
geo_da.crop_years(new_start_year, new_end_year)
else:
pass
if new_month_list is not None and self.months is None:
raise ValueError(
f"**** The month cropping is not available with {type(self)}."
)
elif new_month_list is not None and \
not all(
month in util.months_to_number(self.months) for month in util.months_to_number(new_month_list)):
raise ValueError(
"**** The new month list includes months not yet imported."
)
elif new_month_list is not None:
geo_da.crop_months(new_month_list)
else:
pass
except ValueError as error:
print(error)
print("____ The crop was not performed.")
geo_da.get_lon(mode_lon, value_lon)
geo_da.get_lat(mode_lat, value_lat)
geo_da.get_z(mode_z, value_z)
geo_da.get_t(mode_t, value_t)
return geo_da