def DMI(fy, ly, product_n):    # 時系列データを得る練習として、DMIを計算してみる。
	npz_dir = subroutine.get_npz_dir(product_n)
	fname = npz_dir + 'DMI-' + str(fy) + str(ly) + '.npz'
	print fname

	if os.path.exists(fname) == False:
		etseries = np.ones([ly - fy + 1, 12])
		wtseries = np.ones([ly - fy + 1, 12])
		for iye in range(fy, ly + 1):
			for itime in range(1, 13):
				etseries[iye - fy, itime - 1] = get_area_mean_data(0, iye, itime, 't', 1, 'hoge', 'eDMI', product_n)
				wtseries[iye - fy, itime - 1] = get_area_mean_data(0, iye, itime, 't', 1, 'hoge', 'wDMI', product_n)

		ano_et = subroutine.array_avestd(etseries, 'ano')
		ano_et = to_oneline(ano_et)
		ano_et = running_mean(ano_et, 5)
		ano_wt = subroutine.array_avestd(wtseries, 'ano')
		ano_wt = to_oneline(ano_wt)
		ano_wt = running_mean(ano_wt, 5)
		dmi = ano_wt - ano_et
		# dmi_oneline=to_oneline(dmi)
		ym = ts.ym_timeseries02(fy, ly)
		np.savez(fname, ym = ym, dmi = dmi)

	else:
		pass

	npload = np.load(fname)
	ym = npload['ym']
	dmi = npload['dmi']

	return dmi, ym
def call_productDMI(product_n):
	# fortranで作成したDMIを呼び出す
	pro_name, title_name, bin_dirname = subroutine.product_n_to_name(product_n)
	dtd = subroutine.dat_dir()
	f = open(dtd + '/' + pro_name + '/' + bin_dirname + '/DMI/fy.dat', 'r')
	fy = int(f.read())
	f.close()
	f = open(dtd + '/' + pro_name + '/' + bin_dirname + '/DMI/ly.dat', 'r')
	ly = int(f.read())
	f.close()
	fd = open(dtd + '/' + pro_name + '/' + bin_dirname + '/DMI/dmi.monthly_ESTOC.out', 'r')
	chunk = np.fromfile(fd, dtype = np.dtype([("data", "<" + str((ly - fy + 1)*12) + "f")]), count = 4)
	ym_convert = chunk[0]['data']
	fy = ym_convert[0]
	ly = int(ym_convert[ym_convert.size - 1])
	ym = ts.ym_timeseries02(fy, ly)
	data = chunk[1]['data']
	data_rm = chunk[2]['data']
	data_rm_tr = chunk[3]['data']

	return ym, data, data_rm, data_rm_tr
示例#3
0
def get_data(var,lon,lat,time_res):
	import subroutine
	import timeseries as ts
	import netCDF4
	import numpy as np
	import scipy as sp
	dir=subroutine.dat_dir()+'RAMA/NetCDF/'
	# ファイル名を指定
	if lat>=0.0:
		strlat=str(lat)+'n'
	else:
		strlat=str(-lat)+'s'

	strlon=str(lon)+'e'
	if var=='t':
		filevar='t'
		floatvar='T_20'
	elif var=='s':
		filevar='s'
		floatvar='S_41'
	elif var=='u':
		filevar='adcp'
		floatvar='u_1205'
	elif var=='v':
		filevar='adcp'
		floatvar='v_1206'
	elif var=='ssu':
		filevar='cur'
		floatvar='U_320'
	elif var=='ssv':
		filevar='cur'
		floatvar='V_321'
	else:
		raise Exception('your var is not valid!')

	if time_res=='D':			# Dailyのデータ
		f_name_tr='dy'
	elif time_res=="M":	# Monthlyのデータ
		f_name_tr='mon'
	else:
		raise Exception('your time_res in not valid!')


	ncfile=dir+filevar+strlat+strlon+'_'+f_name_tr+'.cdf'
	print ncfile
	# ファイル名を指定


	nc=netCDF4.Dataset(ncfile,'r')
	time=nc.variables['time'][:]
	units=nc.variables['time'].units
	dtime=netCDF4.num2date(time,units=units)
	lon=nc.variables['lon'][:]
	lat=nc.variables['lat'][:]
	depth=nc.variables['depth'][:]
	data=nc.variables[floatvar][:,:,0,0]
	data[np.where(data>=10000)]=sp.nan # 欠損値のところはnanに。

	if time_res=="M":				   # 期間を2001年1月から2016年12月まで
		fy=2001
		ly=2016
		all_data=np.zeros([(ly-fy+1)*12,depth.size])
		all_dtime=ts.ym_timeseries02(fy,ly)
		nmin=max(max(np.where(all_dtime==dtime[0]))) # データの開始する番号
		nmax=max(max(np.where(all_dtime==dtime[dtime.size-1]))) # データが終了する番号
		all_data[nmin:nmax+1,:]=data[:,:]
		all_data[:nmin,:]=sp.nan
		all_data[nmax+1:,:]=sp.nan
		dtime=all_dtime
		data=all_data

	if var=='u' or var=='v' or var=='ssu' or var=='ssv':data=data*0.01 # 単位をm/sに
	return dtime,depth,data