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
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