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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.')