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pro_cli_to_dssat.py
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pro_cli_to_dssat.py
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# coding=utf-8
import os, time, re, calendar
from netCDF4 import Dataset
import numpy as np
import pandas as pd
from datetime import datetime
from date_cal import daysBetweenDates, isLeapYear
from pro_co2 import read_co2
def datelist(beginDate, endDate):
# beginDate, endDate is '20160601', must add '', is string
# ('%y'): last two string of year ('%j'): day number in the year
date_l=[datetime.strftime(x,'%y%j') for x in list(pd.date_range(start=beginDate, end=endDate))]
return date_l
def clm_list(_year, _climate_path = './'):
_clm_list = []
for _root,_dirs,_files in os.walk(_climate_path, topdown=False):
for name in _files:
path = os.path.join(_root, name)
_clm_list.append(path)
_clm_list.sort()
#print _clm_list
# Find file names of selected year, 1951-2099
_clm_year_list = []
for _clmi in xrange(len(_clm_list)):
_m_year = re.match('.*_(\d{4})-(\d{4})\.nc4$', _clm_list[_clmi])
if _m_year:
_clm_year_bg = _m_year.group(1)
_clm_year_ed = _m_year.group(2)
_clm_year_list.append([_clm_year_bg, _clm_year_ed])
#print _clm_year_list
_clm_fine_year_list = []
for _clm_year_group in xrange(len(_clm_year_list)):
if _year in xrange(int(_clm_year_list[_clm_year_group][0]), int(_clm_year_list[_clm_year_group][1]) +1):
_clm_fine_year_list.append(_clm_list[_clm_year_group])
if len(_clm_fine_year_list) == 0:
print 'Attention! Year %s not in years of climate data!' % (_year)
os.exit()
#print _clm_fine_year_list
return _clm_fine_year_list
# _datelist is one year, col 0: doy, col 1-5 is climate data
def read_clm(_lat_in, _lon_in, _year, _wea_list, _climate_path='./'):
# create time series, Gregorian date -> Julian date, note:_datelist is 'list', need to convert to array then can print .shape
_begin_day = str(_year) + '0101'
_end_day = str(_year) + '1231'
_datelist = datelist(_begin_day, _end_day)
_nctime_bg = daysBetweenDates(1860,1,1,_year,1,1)
if isLeapYear(_year):
_nctime_ed = _nctime_bg + 366
else:
_nctime_ed = _nctime_bg + 365
#print _nctime_bg, _nctime_ed
# read vars from climate data
_var_list = ['pr', 'rsds', 'tasmax', 'tasmin', 'wind']
_var_trans = {'pr': 'RAIN',
'tasmax': 'TMAX',
'tasmin': 'TMIN',
'rsds': 'SRAD',
'wind': 'WIND'}
_clm_dic = {}
_clm_dic['DATE'] = _datelist
for _wea in xrange(len(_wea_list)):
f = Dataset(_wea_list[_wea], 'r')
_time = f.variables['time'][:]
_time = _time.astype(np.int64)
_nctime_bg_index = int(_time.tolist().index(_nctime_bg))
_nctime_ed_index = int(_time.tolist().index(_nctime_ed-1))
# find nearest lat and lon, note: numpy array did not have 'index' command, must convert to list
_lat = f.variables['lat'][:]
_lon = f.variables['lon'][:]
_latabs = abs(_lat - _lat_in)
_latindex = _latabs.tolist().index(min(abs(_latabs)))
_lonabs = abs(_lon - _lon_in)
_lonindex = _lonabs.tolist().index(min(abs(_lonabs)))
#exact var name
for i in _var_trans.keys():
#print _var_trans.keys(),i
if i in _wea_list[_wea]:
_var = i
#print _wea_list[_wea], _var, _nctime_bg_index, _nctime_ed_index
_clm_var = f.variables[_var][_nctime_bg_index:_nctime_ed_index+1, _latindex, _lonindex]
_clm_var = _clm_var.astype('float64')
# Climate data UNIT transfer
# DSSAT UNIT CLIMATE DATA UNIT
# RAIN: mm day-1 kg m-2 s-1
# SRAD: MJ m-2 day-1 W m-2 1 w m-2 = 0.0864 MJ m-2 day-1
# TMAX: C TMIN: C K
# WIND: km d-1 m s-1
if _var == 'pr':
_clm_var *= 3600 * 24
elif _var == 'rsds':
_clm_var *= 0.0864
elif _var == 'tasmax':
_clm_var -= 273.15
elif _var == 'tasmin':
_clm_var -= 273.15
elif _var == 'wind':
_clm_var *= 3.6 * 24
#print _clm_var.shape, np.array(datelist(_begin_day, _end_day)).shape
if _clm_var.shape == np.array(datelist(_begin_day, _end_day)).shape:
_clm_dic[_var_trans[_var]] = _clm_var.tolist()
#_datelist = np.column_stack((_datelist,_clm_var))
#print 'Now processing', _var, _wea_list[_wea], _clm_var.shape
else:
print "Attention! climate data days number not the same as date number"
os.exit()
f.close()
print 'Finished reading climate data in %s' % _climate_path
#print _clm_dic.keys()
return _clm_dic
def write_clm(_lat_in, _lon_in, _year, co2, _clm_dic, _site_name, _site_path, _co2_path):
# generally weather data contain
# hur, hurtmax, pr, rsds, tas, tasmax, tasmin, wind
# RAIN, SRAD, TMAX, TMIN, WIND
co2_value = read_co2(_year, _co2_path, co2)
ls = []
ls.append('*WEATHER : %s, CHINA, QING' % _site_name)
ls.append(' ')
ls.append('@ INSI LAT LONG ELEV TAV AMP REFHT WNDHT CO2')
if co2 == 'FIX':
ls.append(' %s %6.3f %7.3f 9 16.5 10.3 2.00 2.00 %6.2f' % (_site_name[0:4], _lat_in, _lon_in, 380) )
else:
ls.append(' %s %6.3f %7.3f 9 16.5 10.3 2.00 2.00 %6.2f' % (_site_name[0:4], _lat_in, _lon_in, co2_value) )
ls.append(' ')
_sorted_var= '@'
for i in sorted(_clm_dic.keys()):
_sorted_var += '%s ' % i
ls.append(_sorted_var)
# Create JDZ 2013 CK Tmax 30 C, Tmin 25 C from 13180 to 13220.
# Generally, JDZ 13198 was grain filling begin Maturity date: 13211
#_modify_clim_index_bg = _datelist[:, 0].tolist().index(13193)
#_modify_clim_index_ed = _datelist[:, 0].tolist().index(13211)
##print 'Now generate CK climate data. index from: ', _modify_clim_index_bg, 'to', _modify_clim_index_ed
#for i in xrange(_modify_clim_index_bg, _modify_clim_index_ed):
# _datelist[i, 3] = 30.0
# _datelist[i, 4] = 25.0
# write to ascii file
for i in xrange(len(_clm_dic['DATE'])):
if _clm_dic['RAIN'][i] is None:
_clm_dic['RAIN'][i] = _clm_dic['RAIN'][i-1]
if _clm_dic['SRAD'][i] is None:
_clm_dic['SRAD'][i] = _clm_dic['SRAD'][i-1]
if _clm_dic['TMAX'][i] is None:
_clm_dic['TMAX'][i] = _clm_dic['TMAX'][i-1]
if _clm_dic['TMIN'][i] is None:
_clm_dic['TMIN'][i] = _clm_dic['TMIN'][i-1]
if _clm_dic['WIND'][i] is None:
_clm_dic['WIND'][i] = _clm_dic['WIND'][i-1]
ls.append('%s%6.1f%6.1f%6.1f%6.1f%6.1f' % (_clm_dic['DATE'][i], _clm_dic['RAIN'][i], _clm_dic['SRAD'][i], _clm_dic['TMAX'][i], _clm_dic['TMIN'][i], _clm_dic['WIND'][i]))
fname = _site_path + _site_name + '.WTH'
with open(fname,'w') as fo:
fo.write('\r\n'.join(ls))
print 'Finished writing %s%s.WTH' % (_site_path, _site_name)
# active accumulated temperature,
# input: _clm_dic dictionary number for one year; output number for one year
def act_temp(_clm_dic, _year, _site_path = './'):
_tmean = []
for i in xrange(len(_clm_dic['TMAX'])):
_tmean.append((_clm_dic['TMAX'][i] + _clm_dic['TMIN'][i]) / 2)
# find index where tmean is lower than 10C
_index_list = []
_index_list.append(0)
for i in xrange(len(_tmean)):
if _tmean[i] < 10.0:
_index_list.append(i)
if isLeapYear(_year):
_index_list.append(366)
else:
_index_list.append(365)
# cal every act acc temp and find the max one, the max one which is act acc temp
_act_temp_list = []
act_acc_temp_begin_list = []
act_acc_temp_end_list = []
for i in xrange(len(_index_list) - 1):
if _index_list[i+1] - _index_list[i] > 30:
_act_temp_list.append(sum(_tmean[_index_list[i]:_index_list[i+1]]))
act_acc_temp_begin_list.append(_index_list[i])
act_acc_temp_end_list.append(_index_list[i+1])
act_acc_temp = max(_act_temp_list)
act_acc_temp_begin = act_acc_temp_begin_list[_act_temp_list.index(max(_act_temp_list))]
act_acc_temp_end = act_acc_temp_end_list[_act_temp_list.index(max(_act_temp_list))]
# write act acc temp to another file
with open (_site_path + 'act_temp.csv', 'a') as fo:
fo.write('%d, %s, %s, %s \r\n' % (_year, act_acc_temp, act_acc_temp_begin, act_acc_temp_end))
return act_acc_temp, act_acc_temp_begin, act_acc_temp_end
'''
# JDZ lat lon
_lat_jdz = 29.180
_lon_jdz = 117.12
_lat_nj = 32.00
_lon_nj = 118.29
_wea_list = ['pr_bced_1960_1999_gfdl-esm2m_rcp2p6_2011-2020.nc4', \
'rsds_bced_1960_1999_gfdl-esm2m_rcp2p6_2011-2020.nc4', \
'tasmax_bced_1960_1999_gfdl-esm2m_rcp2p6_2011-2020.nc4', \
'tasmin_bced_1960_1999_gfdl-esm2m_rcp2p6_2011-2020.nc4', \
'wind_bced_1960_1999_gfdl-esm2m_rcp2p6_2011-2020.nc4']
read_clm('/Users/qingsun/GGCM/run_dssat/GFDL_RCP2.6/', _datelist)
write_clm(_lat_jdz, _lon_jdz, _datelist, 'CNJD1201')
'''