def compare_40_Interim(month,var):
import netCDF4
import numpy as np
import subroutine
import datetime as dt
a=subroutine.read_meta_data('var')
filename=subroutine.celldata(a,'var',var,'ORAS4_fn')
ncfile40=subroutine.dat_dir()+'ORAS4/NetCDF/ERA-40_1989.nc'
ncfileInterim=subroutine.dat_dir()+'ORAS4/NetCDF/ERA-Interim_1989.nc'
nc40=netCDF4.Dataset(ncfile40,'r')
ncInterim=netCDF4.Dataset(ncfileInterim,'r')
if filename=='tp':
# 1988年以前の降水量のデータは、convectiveとlargescaleの和で算出する
data40=nc40.variables['lsp'][month-1,:,:]+nc40.variables['cp'][month-1,:,:]
dataInterim=ncInterim.variables['lsp'][month-1,:,:]+ncInterim.variables['cp'][month-1,:,:]
else:
data40=nc40.variables[filename][month-1,:,:]
dataInterim=ncInterim.variables[filename][month-1,:,:]
# add_offset=nc.variables[filename].add_offset
# scale_factor=nc.variables[filename].scale_factor
# data=data*scale_factor+add_offset
# print 'scale_factor=',scale_factor
# print 'add_offset=',add_offset
nc40.close()
ncInterim.close()
return data40[::-1,:],dataInterim[::-1,:]
def __init__(self): ncfile=subroutine.dat_dir()+'ESTOCver02/NetCDF/k7oda_tmp_20010100_02b.nc' nc=netCDF4.Dataset(ncfile,'r') xgrid=nc.variables['LONGITUDE'][:] ygrid=nc.variables['LATITUDE'][:] zgrid=nc.variables['DEPTH'][:] self.nx=xgrid.size self.ny=ygrid.size self.nz=zgrid.size self.fy=1957 self.ncdir=subroutine.dat_dir()+'ESTOCver02/NetCDF/'
def get_data(year,month,var):
import netCDF4
import numpy as np
import subroutine
import datetime as dt
a=subroutine.read_meta_data('var')
filename=subroutine.celldata(a,'var',var,'ERA_fn')
if year <= 1967:
ncfile=subroutine.dat_dir()+'ERA/NetCDF/ERA-40_1958-1967.nc'
elif year >= 1968 and year <= 1977:
ncfile=subroutine.dat_dir()+'ERA/NetCDF/ERA-40_1968-1977.nc'
elif year >= 1978 and year<=1988:
ncfile=subroutine.dat_dir()+'ERA/NetCDF/ERA-40_1978-1988.nc'
else:
ncfile=subroutine.dat_dir()+'ERA/NetCDF/ERA-Interim.nc'
nc=netCDF4.Dataset(ncfile,'r')
time=nc.variables['time'][:]
units=nc.variables['time'].units
dtime=netCDF4.num2date(time,units=units)
for i in range(0,dtime.size):
if getattr(dtime[i],'year')==year and getattr(dtime[i],'month')==month:
if year <=1988 and filename=='tp':
# 1988年以前の降水量のデータは、convectiveとlargescaleの和で算出する
data=nc.variables['lsp'][i,:,:]+nc.variables['cp'][i,:,:]
elif year<=1988 and filename=='sff':
data=(nc.variables['e'][i,:,:]+nc.variables['ro'][i,:,:]+\
nc.variables['lsp'][i,:,:]+nc.variables['cp'][i,:,:])*2 # 何故か２倍することで丁度いい
elif filename=='sff':
# 淡水フラックス
data=nc.variables['e'][i,:,:]+nc.variables['ro'][i,:,:]+\
nc.variables['tp'][i,:,:]
elif filename=='q':
# 熱フラックス
data=nc.variables['sshf'][i,:,:]+nc.variables['slhf'][i,:,:]+\
nc.variables['ssr'][i,:,:]+nc.variables['str'][i,:,:]
else:
data=nc.variables[filename][i,:,:]
# print i,dtime[i]
# add_offset=nc.variables[filename].add_offset
# scale_factor=nc.variables[filename].scale_factor
# data=data*scale_factor+add_offset
# print 'scale_factor=',scale_factor
# print 'add_offset=',add_offset
if filename=='sff': # 何故か100倍することでオーダーが合う
data=data*100
break
nc.close()
return data[::-1,:]
def nc_read(year,month,var,depth):
# １地点につき１つのデータしかないものに関してはdepthに何を入れても構わない。
# ４６層あるデータに関しては、depth=1とすると、５メートル水深でのデータが得られるようにする。
import netCDF4
import numpy as np
import subroutine
import scipy as sp
a=subroutine.read_meta_data('var')
filename=subroutine.celldata(a,'var',var,'ORAS4_fn')
ncfile=subroutine.dat_dir()+'ORAS4/NetCDF/'+filename+'_oras4_1m_'+\
str(year)+'_grid_1x1.nc'
# print ncfile
nc=netCDF4.Dataset(ncfile,'r')
if var=='ht': # 海面高度の時だけ、データが2次元になる。
data=nc.variables[filename][month-1,:,:]
else: # それ以外のデータは3次元。
if depth != 0:
data=nc.variables[filename][month-1,depth-1,:,:]
else:
data=np.ones((ny,nx,nz))
for k in range(0,nz):
data[:,:,k]=nc.variables[filename][month-1,k,:,:]
nc.close()
data[np.where(data>=10000)]=sp.nan # 欠損値のところはnanに。
return data
def get_grid_value(var): import netCDF4 import subroutine import numpy as np ncfile=subroutine.dat_dir()+'ERA/NetCDF/ERA-40_1958-1967.nc' nc=netCDF4.Dataset(ncfile,'r') xgrid=nc.variables['longitude'][:] ygrid=nc.variables['latitude'][:] nc.close() return [xgrid,ygrid[::-1],np.array([0])]
def get_grid_value(var): import netCDF4 import subroutine ncfile=subroutine.dat_dir()+'MOAA_GPV/NetCDF/TS_200101_GLB.nc' nc=netCDF4.Dataset(ncfile,'r') zgrid=nc.variables['PRES'][:] xgrid=nc.variables['LONGITUDE'][:] ygrid=nc.variables['LATITUDE'][:] nc.close() return [xgrid,ygrid,zgrid]
def get_grid_value(var):
import netCDF4
import subroutine
import numpy as np
ncfile = subroutine.dat_dir() + "COBE-SST/sst.mon.mean.nc"
nc = netCDF4.Dataset(ncfile, "r")
xgrid = nc.variables["lon"][:]
ygrid = nc.variables["lat"][:]
nc.close()
return [xgrid, ygrid, np.array([0])]
def get_grid_value(var): import netCDF4 import subroutine import numpy as np stryear,strmonth=subroutine.strym(2012,1) ncfile=subroutine.dat_dir()+'Aquarius/NetCDF/sss'+stryear+strmonth+'.v4.0cap.nc' nc=netCDF4.Dataset(ncfile,'r') xgrid=nc.variables['lon'][:] ygrid=nc.variables['lat'][:] nc.close() return [xgrid,ygrid,np.array([0])]
def get_grid_value(var): import netCDF4 import subroutine import numpy as np ncfile=subroutine.dat_dir()+'HadISST/HadISST_sst.nc' nc=netCDF4.Dataset(ncfile,'r') xgrid=nc.variables['longitude'][:] xgrid=np.r_[xgrid[xgrid.size/2:],360+xgrid[:xgrid.size/2]] ygrid=nc.variables['latitude'][:] nc.close() return [xgrid,ygrid[::-1],np.array([0])]
def get_grid_value(var):
import netCDF4
import subroutine
a=subroutine.read_meta_data('var')
fn=subroutine.celldata(a,'var',var,'WOA01_fn')
vn=subroutine.celldata(a,'var',var,'WOA01_vn')
ncfile=subroutine.dat_dir()+'WOA01/'+filen+'.nc'
nc=netCDF4.Dataset(ncfile,'r')
var_p=nc.variables['Z'][:]
xgrid=nc.variables['X'][:]
ygrid=nc.variables['Y'][:]
nc.close()
return [xgrid,ygrid,var_p]
def nc_read(year,month): import netCDF4 import subroutine import scipy as sp import numpy as np Threshold=-998.0 stryear,strmonth=subroutine.strym(year,month) ncfile=subroutine.dat_dir()+'ERSST/ersst.'+stryear+strmonth+'.nc' nc=netCDF4.Dataset(ncfile,'r') the_sst=nc.variables['sst'][0,0,:,:] the_sst[np.where(the_sst<=Threshold)]=sp.nan # 欠損値のところはnanに。 nc.close() return the_sst
def get_grid_value(var): # 水温とか塩分であれば0を、流速であれば1を与えてやる file_name='vel' if var=='u' or var=='v' or var=='ustar' or var=='vstar' else 'tmp' ncfile=subroutine.dat_dir()+'ESTOCver02/NetCDF/k7oda_'+file_name+'_20010100_02b.nc' nc=netCDF4.Dataset(ncfile,'r') var_p=nc.variables['DEPTH'][:] xgrid=nc.variables['LONGITUDE'][:] ygrid=nc.variables['LATITUDE'][:] nc.close() if var=='w': var_h=np.zeros(var_p.size) for i in range(var_p.size): var_h[i]=2*var_p[i] if i==0 else 2*var_p[i]-var_h[i-1] var_p=var_h return [xgrid,ygrid,var_p]
def nc_read(year,month): import netCDF4 import subroutine import scipy as sp import numpy as np Threshold=-998.0 time_n=(year-1870)*12+(month-1) ncfile=subroutine.dat_dir()+'HadISST/HadISST_sst.nc' nc=netCDF4.Dataset(ncfile,'r') the_sst=nc.variables['sst'][time_n,:,:] the_sst=np.c_[the_sst[:,the_sst.shape[1]/2:],the_sst[:,:the_sst.shape[1]/2]] nc.close() the_sst[np.where(the_sst<=Threshold)]=sp.nan # 欠損値のところはnanに。 return the_sst[::-1,:]
def get_grid_value(var):
import netCDF4
import subroutine
import numpy as np
a=subroutine.read_meta_data('var')
fn=subroutine.celldata(a,'var',var,'RG_fn')
ncfile=subroutine.dat_dir()+'Roemmich_Gilson/NetCDF/RG_ArgoClim_'+fn+'_2015.nc'
nc=netCDF4.Dataset(ncfile,'r')
zgrid=nc.variables['PRESSURE'][:]
xgrid=nc.variables['LONGITUDE'][:]
xgrid=np.r_[-360+xgrid[340:],xgrid[:340]]
ygrid=nc.variables['LATITUDE'][:]
nc.close()
return [xgrid,ygrid,zgrid]
def nc_read(year, month):
import netCDF4
import subroutine
import scipy as sp
import numpy as np
Threshold = 998.0
time_n = (year - 1891) * 12 + (month - 1)
ncfile = subroutine.dat_dir() + "COBE-SST/sst.mon.mean.nc"
nc = netCDF4.Dataset(ncfile, "r")
the_sst = nc.variables["sst"][time_n, :, :]
nc.close()
the_sst[np.where(the_sst >= Threshold)] = sp.nan # 欠損値のところはnanに。
return the_sst
def call_jamstecDMI(tres): # tres=0でweekly,tres=1でmonthly。
dtd = subroutine.dat_dir()
fd = open(dtd + 'ESTOCver03/binary/DMI/dmi.monthly_JAMSTEC.out', 'r')
chunk = np.fromfile(fd, dtype = np.dtype([("data", "<" + str(1585) + "f")]), count = 2)
ym_convert = chunk[0]['data']
data = chunk[1]['data']
if tres == 0:
return ym_convert, data
elif tres == 1:
ym = ts.ym_timeseries(1982, 2011)
tt = interpolate.interp1d(ym_convert, data)
Interpolated = tt(ym)
return ym, Interpolated
else:
raise Exception('error! your tres is not valid!')
def get_data(year,month): import subroutine import netCDF4 dir=subroutine.dat_dir()+'AQC/NetCDF/' stryear,strmonth=subroutine.strym(year,month) ncfile=dir+'AQC_Profile_Data_'+stryear+strmonth+'.nc' nc=netCDF4.Dataset(ncfile,'r') time=nc.variables['TIME'][:,:] N_PROF=time.shape[0] STRING16=time.shape[1] pres=nc.variables['PRES'][:,:] N_LEVELS=pres.shape[1] temp=nc.variables['TEMP'][:] salt=nc.variables['PSAL'][:] lon=nc.variables['LONGITUDE'][:] lat=nc.variables['LATITUDE'][:] return temp,salt,pres,lon,lat
def get_data(year,month): import subroutine import netCDF4 import numpy as np import scipy as sp # ファイル名を指定 stryear,strmonth=subroutine.strym(year,month) ncfile=subroutine.dat_dir()+'Aquarius/NetCDF/sss'+stryear+strmonth+'.v4.0cap.nc' print ncfile # ファイル名を指定 nc=netCDF4.Dataset(ncfile,'r') lon=nc.variables['lon'][:] lon=nc.variables['lon'][:] data=nc.variables['sss_cap'][:,:] data[np.where(data<=-999)]=sp.nan # 欠損値のところはnanに。 return data
def masuda_data_read(year,month,var,depth):
Mt=60*60*24*30 # 1ヶ月の秒数
Threshold=-1e+30 # Threshold of Missing value
data_dir=subroutine.dat_dir()+'ESTOCver03/binary/each_data/'
ID = Var.var_to_id(var)
dt=np.dtype([("data","<"+str(nx*ny)+"f")])
stryear,strmonth=subroutine.strym(year,month)
fd=open(data_dir+"/"+stryear+strmonth+"/"+var+".out","r")
dim = Var.VAR[ID].Get_dim()
if dim == '3D':
count=ik
else:
count=1
if count == 1 and depth != 1:
raise ValueError('if you choose 2D data, depth must be 1!')
chunk=np.fromfile(fd,dtype=dt,count=count)
if depth != 0:
data=chunk[depth-1]['data']
data=np.reshape(data,(ny,nx))
else:
data=np.ones((ny,nx,ik))
for k in range(0,ik):
data[:,:,k]=np.reshape(chunk[k]['data'],(ny,nx))
data[np.where(data<=Threshold)]=np.nan # 欠損値のところはnanに。
# 単位に関してはすでに修正済み。
# taux,tauy:N/m^2
# u,v,w,sff:m/s
# q:cal/(m^2 s)
if var=='sff':# 単位をm/Monthに
data=data*Mt
elif var=='ustar' or var=='vstar' or var=='wstar':
data=data / 100.0 # 単位をm/sに
elif var=='eq': # 淡水フラックスと同じ次元で扱えるように
data=data*-Mt*0.01
return data
def nc_read(month,var,depth):
import numpy as np
import netCDF4
import subroutine
a=subroutine.read_meta_data('var')
fn=subroutine.celldata(a,'var',var,'WOA01_fn')
vn=subroutine.celldata(a,'var',var,'WOA01_vn')
ncfile=subroutine.dat_dir()+'WOA01/'+filen+'.nc'
nc=netCDF4.Dataset(ncfile,'r')
if depth != 0:
data=nc.variables[vn][month-1,depth-1,:,:]
else:
x,y,z=get_grid_value()
nz,nx,ny=z.size,x.size,y.size
data=np.ones((ny,nx,nz))
for k in range(0,nz):
data_2D=nc.variables[vn][month-1,k,:,:]
data[:,:,k]=np.reshape(data_2D,(ny,nx))
nc.close()
return data
def nc_read(year,month,var,depth):
import numpy as np
import scipy as sp
import netCDF4
import subroutine
a=subroutine.read_meta_data('var')
vn=subroutine.celldata(a,'var',var,'MOAA_GPV_vn')
stryear,strmonth=subroutine.strym(year,month)
ncfile=subroutine.dat_dir()+'MOAA_GPV/NetCDF/TS_'+stryear+strmonth+'_GLB.nc'
nc=netCDF4.Dataset(ncfile,'r')
if depth != 0:
data=nc.variables[vn][depth-1,:,:]
else:
data=np.ones((ny,nx,nz))
for k in range(0,nz):
data[:,:,k]=nc.variables[vn][k,:,:]
nc.close()
data[np.where(data>=10000)]=sp.nan # 欠損値のところはnanに。
return data
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_grid_value(var):
import netCDF4
import subroutine
import numpy as np
if var=='w':
filename='thetao'
else:
a=subroutine.read_meta_data('var')
filename=subroutine.celldata(a,'var',var,'ORAS4_fn')
ncfile=subroutine.dat_dir()+'ORAS4/NetCDF/'+filename+'_oras4_1m_1958_grid_1x1.nc'
nc=netCDF4.Dataset(ncfile,'r')
var_p=nc.variables['depth'][:]
xgrid=nc.variables['lon'][:]
ygrid=nc.variables['lat'][:]
nc.close()
if var=='w':
var_h=np.zeros(var_p.size)
for i in range(var_p.size):
var_h[i]=2*var_p[i] if i==0 else 2*var_p[i]-var_h[i-1]
var_p=var_h
return [xgrid,ygrid,var_p]
def nc_read(year,month,var,depth):
import numpy as np
import scipy as sp
import netCDF4
import subroutine
Threshold=-999
a=subroutine.read_meta_data('var')
fn=subroutine.celldata(a,'var',var,'RG_fn')
ncfile=subroutine.dat_dir()+'Roemmich_Gilson/NetCDF/RG_ArgoClim_'+fn+'_2015.nc'
nc=netCDF4.Dataset(ncfile,'r')
vn1=subroutine.celldata(a,'var',var,'RG_vn1')
vn2=subroutine.celldata(a,'var',var,'RG_vn2')
time=(year-2004)*12+month-1 # 2004年1月であれば、time=0、2014年12月であれば、time=131
if depth != 0:
meandata=nc.variables[vn1][depth-1,:,:]
meandata[np.where(meandata<=Threshold)]=sp.nan # 欠損値のところはnanに。
anomdata=nc.variables[vn2][time,depth-1,:,:]
anomdata[np.where(anomdata<=Threshold)]=sp.nan # 欠損値のところはnanに。
data=meandata+anomdata
data=np.c_[data[:,340:],data[:,:340]]
else:
data=np.ones((ny,nx,nz))
meandata=nc.variables[vn1][:,:,:]
meandata[np.where(meandata<=Threshold)]=sp.nan # 欠損値のところはnanに。
anomdata=nc.variables[vn2][time,:,:,:]
anomdata[np.where(anomdata<=Threshold)]=sp.nan # 欠損値のところはnanに。
ndata=meandata+anomdata
ndata=np.c_[ndata[:,:,340:],ndata[:,:,:340]]
for k in range(0,nz):
data[:,:,k]=ndata[k,:,:]
nc.close()
return data
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
