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import netCDF4 as nc
import numpy as np
import os
import time
import multiprocessing as mp
def Gumble_func(data,N,length):
if np.isnan(data).any():
return np.nan
datasort = np.sort(data, axis=0)[::-1]
data_select_sorts = datasort[0:length - 1]
mu = np.mean(data_select_sorts)
sum_mm = 0
for data_select_sort in data_select_sorts:
sum_mm = sum_mm + (data_select_sort - mu) ** 2
sigma = np.sqrt(sum_mm / (len(data_select_sorts) - 1))
alpha = np.pi / (np.sqrt(6) * sigma)
u = mu - 0.57721 / alpha
for i in np.arange(1, 99999, 0.1):
if 1 / N >= 1 - np.exp(-np.exp(-alpha * (i - u))):
Recurrence_period_value = i
break
return Recurrence_period_value
def cal_N_year_appear_single(YEARLY_RAINFALL, i, j, N=10):
rainfall_history = YEARLY_RAINFALL[:,i,j].flatten()
if np.isnan(rainfall_history).any():
return (i,j,np.nan)
return (i,j,Gumble_func(rainfall_history, N, rainfall_history.shape[0]))
def cal_N_year_appear(YEARLY_RAINFALL, N=10):
yearly_rainfall_N = np.empty(YEARLY_RAINFALL.shape[1:])
idx = np.where(~np.isnan(YEARLY_RAINFALL[0,:,:]))
print('Number of non-nan elements: ', len(idx[0]))
with mp.Pool(processes=6) as pool:
results = pool.starmap(cal_N_year_appear_single, [(YEARLY_RAINFALL, i, j, N) for i, j in zip(idx[0], idx[1])])
pool.close()
for i, j, result in results:
yearly_rainfall_N[i,j] = result
return yearly_rainfall_N
def main():
dir = 'nc_crop/'
fileList = os.listdir(dir)
print('Files in directory: ', len(fileList))
yearly_rainfall_sum = []
src = None
for file in fileList[:3]:
print('Processing file: ', file)
data = nc.Dataset(dir + file)
print('Variables in file: ', data.variables)
print('Dimensions in file: ', data.dimensions)
print('Shape of variable: ', data.variables['time'].shape)
print('Data type of variable: ', data.variables['time'].dtype)
print('Values of variable: ', data.variables['time'][:])
pre = np.array(data.variables['pre'][:])
print('Shape of pre: ', pre.shape)
year = int(file.split('_')[-1].split('.')[0])
print('Year: ', year)
monthly_days = [31, 28 if year % 4 != 0 else 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
pre_sum = np.dot(pre.transpose((1,2,0)), monthly_days)
print('Shape of pre_sum: ', pre_sum.shape)
yearly_rainfall_sum.append(pre_sum)
print('-----------------------------------')
src = data
yearly_rainfall_sum = np.array(yearly_rainfall_sum)
print('Shape of yearly_rainfall_sum: ', yearly_rainfall_sum.shape)
yearly_rainfall_sum_mean = np.mean(yearly_rainfall_sum, axis=0)
print('Shape of yearly_rainfall_sum_mean: ', yearly_rainfall_sum_mean.shape)
print('开始计算10年一遇降雨量...')
yearly_rainfall_10 = cal_N_year_appear(yearly_rainfall_sum, 10)
print('重现期为10年的平均降水量: ', yearly_rainfall_10.mean())
print('开始计算20年一遇降雨量...')
yearly_rainfall_20 = cal_N_year_appear(yearly_rainfall_sum, 20)
print('重现期为20年的平均降水量: ', yearly_rainfall_20.mean())
print('开始计算50年一遇降雨量...')
yearly_rainfall_50 = cal_N_year_appear(yearly_rainfall_sum, 50)
print('重现期为50年的平均降水量: ', yearly_rainfall_50.mean())
print('开始计算100年一遇降雨量...')
yearly_rainfall_100 = cal_N_year_appear(yearly_rainfall_sum, 100)
print('重现期为100年的平均降水量: ', yearly_rainfall_100.mean())
out = nc.Dataset('yearly_rainfall_%s.nc'%time.time(), 'w', format='NETCDF4')
out.createDimension('latitude', src.dimensions['latitude'].size, )
out.createDimension('longitude', src.dimensions['longitude'].size)
lat=out.createVariable('latitude', 'f4', ('latitude',))
out.variables['latitude'][:] = src.variables['latitude'][:]
long=out.createVariable('longitude', 'f4', ('longitude',))
out.variables['longitude'][:] = src.variables['longitude'][:]
lat.units = 'degreesN'
long.units = 'degreesE'
out.createVariable('yearly_rainfall_sum', 'f8', ('longitude','latitude'))
out.variables['yearly_rainfall_sum'][:] = yearly_rainfall_sum_mean
out.createVariable('yearly_rainfall_10', 'f8', ('longitude','latitude'))
out.variables['yearly_rainfall_10'][:] = yearly_rainfall_10
out.createVariable('yearly_rainfall_20', 'f8', ('longitude','latitude'))
out.variables['yearly_rainfall_20'][:] = yearly_rainfall_20
out.createVariable('yearly_rainfall_50', 'f8', ('longitude','latitude'))
out.variables['yearly_rainfall_50'][:] = yearly_rainfall_50
out.createVariable('yearly_rainfall_100', 'f8', ('longitude','latitude'))
out.variables['yearly_rainfall_100'][:] = yearly_rainfall_100
out.close()
print('File written successfully.')
if __name__ == '__main__':
print('Start processing...')
main()
print('Processing finished.')
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