def save_Anomaly_as_npz(fy, ly, var, product_n = 3):
a = subroutine.read_meta_data('var')
formal_name = subroutine.celldata(a, 'var', var, 'formal_name')
dim = subroutine.celldata(a, 'var', var, 'dim')
_,title_name,_=subroutine.product_n_to_name(product_n)
tmpyear = 2000
for month in range(1,13):
_,strmonth=subroutine.strym(2000,month)
Clim=subroutine.load_npz('Ave_of_' + formal_name + '_m'+strmonth+'_' + str(fy) + '-' + str(ly) + '_'+title_name)
print 'month = ', month
start = time.time()
for year in range(fy,ly+1):
stryear,strmonth=subroutine.strym(year,month)
j=year-fy
if dim == '3D':
data=subroutine.get_data(year,month,var,0,title_name)
elif dim == '2D':
data=subroutine.get_data(year,month,var,1,title_name)
else:
raise Exception('your dim is not valid!')
Anomaly=data-Clim
DIR = subroutine.get_DIR(year, month, formal_name, product_n)
subroutine.check_and_make_DIR(DIR)
subroutine.save_npz(Anomaly,DIR+'Anomaly_from_Mean_Annual_Cycle_Year'+str(fy)+'-'+str(ly),data_dir_flg='NO')
print 'elapsed time:', time.time() - start
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 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 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 save_climatology_or_interannual_variation_as_npz(data_allmonth, fy, ly, var, Ave_or_Std, product_n = 3):
a=subroutine.read_meta_data('var')
formal_name = subroutine.celldata(a, 'var', var, 'formal_name')
_, title_name, _ = subroutine.product_n_to_name(product_n)
for month in range(1, 13):
_, strmonth = subroutine.strym(2000, month)
subroutine.save_npz(data_allmonth[:, :, :, month - 1], Ave_or_Std + '_of_' + \
formal_name + '_m' + strmonth + '_' + str(fy) + '-' + str(ly) + \
'_' + title_name)
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 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 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,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 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 make_climatology_or_interannual_variation_of_all_month_and_depth(fy, ly, var, Ave_or_Std, product_n = 3):
xgrid, ygrid, zgrid = subroutine.product_grid_info(var, 'data', product_n)
a=subroutine.read_meta_data('var')
d=subroutine.celldata(a,'var',var,'dim')
if d == '2D':
data_allmonth = np.zeros((ygrid.size, xgrid.size, 12))
depth_d = 1
elif d == '3D':
data_allmonth = np.zeros((ygrid.size, xgrid.size, zgrid.size, 12))
depth_d = 0
else:
raise Exception('data dimension is not valid!')
for month in range(1, 13):
data = make_climatology_or_interannual_variation_of_a_month(fy, ly, month, var, depth_d, Ave_or_Std, product_n)
if data.ndim == 3:
data_allmonth[:, :, :, month - 1] = data[:, :, :]
elif data.ndim == 2:
data_allmonth[:, :, month - 1] = data[:, :]
return data_allmonth
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 load_Anomaly_of_npz(year, month, var, fy = 1990, ly = 2011, product_n = 3):
a = subroutine.read_meta_data('var')
formal_name = subroutine.celldata(a, 'var', var, 'formal_name')
DIR = subroutine.get_DIR(year, month, formal_name, product_n)
data = subroutine.load_npz(DIR + 'Anomaly_from_Mean_Annual_Cycle_Year' + str(fy) + '-' + str(ly), data_dir_flg = 'NO')
return data