def get_monthly_normals(data_folder_path, start_date = None, end_date = None):
"""
returns numpy array of the monthly normals of the shape (12, nx, ny)
"""
result = [0.0 for i in xrange(12)]
record_counts = [0.0 for i in xrange(12)]
for file_name in os.listdir(data_folder_path):
date_month = ccc_util.get_month_date_from_name(file_name)
#focus only on the interval of interest
if start_date is not None:
if date_month < start_date:
continue
if end_date is not None:
if date_month > end_date:
continue
data = champ_ccc(os.path.join(data_folder_path, file_name)).charge_champs()
m = date_month.month - 1
for record in data:
field = record["field"]
result[m] += field
record_counts[m] += 1.0
result = np.array(result)
record_counts = [ count * np.ones(result.shape[1:]) for count in record_counts ]
record_counts = np.array(record_counts)
result /= np.array(record_counts)
return result
pass
def get_seasonal_mean(data_folder_path, start_date = None, end_date = None, months = None):
"""
returns seasonal mean
"""
result = 0.0
record_count = 0.0
for file_name in os.listdir(data_folder_path):
date_month = ccc_util.get_month_date_from_name(file_name)
#take only months of interest
if date_month.month not in months:
continue
#focus only on the interval of interest
if start_date is not None:
if date_month < start_date:
continue
if end_date is not None:
if date_month > end_date:
continue
data = champ_ccc(os.path.join(data_folder_path, file_name)).charge_champs()
for record in data:
field = record["field"]
result += field
record_count += 1.0
return result / record_count
