def get_SUMsalinity(year, month, product_n = 3):
# 全球での塩分の総和を求める。
# 単位はkg。
rho0 = 1024.0 # 海水の密度はこれで統一。
_, title_name, _ = product_n_to_name(product_n)
s = get_data(year, month, 's', 0, title_name)
xgrid, ygrid, zgrid = product_grid_info('w', 'data', product_n)
xn = xgrid.size
yn = ygrid.size
zn = zgrid.size
ew_dist = zeros([yn, xn])
ns_dist = zeros([yn, xn])
S = zeros([yn, xn])
for i in range(0, xn):
for j in range(0, yn):
ew_dist[j, i] = dist_on_sphere([0, ygrid[j]], [1, ygrid[j]])
ns_dist[j, i] = dist_on_sphere([0, ygrid[j] - 0.5], [0, ygrid[j] + 0.5])
if isnan(s[j, i, 0]) == True:
S[j, i] = 0.0
else:
for k in range(0, zn):
if k == 0:
dH = zgrid[k]
else:
dH = zgrid[k] - zgrid[k - 1]
if isnan(s[j, i, k]) == False:
S[j, i] = S[j, i] + dH * ew_dist[j, i] * ns_dist[j, i] * rho0 * (s[j, i, k]/1000)
else:
break
return sum(S)
def get_average_of_some_month(year, fm, lm, var, depth, product_n):
import numpy as np
if fm <= 0 or fm >= 13 or lm <= 0 or lm >= 0:
raise Exception('your month value is not valid!')
if lm < fm:
raise Exception('lm must be greater equal than fm!')
xgrid, ygrid, zgrid = subroutine.product_grid_info(var, 'data', product_n)
xn, yn, zn = xgrid.size, ygrid.size, zgrid.size
pro_name, title_name, bin_dirname = subroutine.product_n_to_name(product_n)
nmonth = lm - fm + 1
if depth == 0:
each_data = np.zeros((nmonth, yn, xn, zn))
else:
each_data = np.zeros((nmonth, yn, xn))
for month in range(fm, lm + 1):
i = month - fm
if depth == 0:
each_data[i, :, :, :] = subroutine.get_data(year, month, var, depth, title_name)
else:
each_data[i, :, :] = subroutine.get_data(year, month, var, depth, title_name)
average_data = np.average(each_data, axis = 0)
return average_data
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 make_data_from_ts(t, s, product_n = 3, dt = 0.2):
_,title_name,_=subroutine.product_n_to_name(product_n)
xgrid,ygrid,zgrid=subroutine.product_grid_info('t','data',product_n)
# MOAA_GPVだけ、格納されているのが（ポテンシャル水温でなく）通常の水温なので、まずポテンシャル水温への変換が必要
if title_name=='MOAA_GPV':
theta=np.zeros((t.shape[0],t.shape[1],t.shape[2]))
for m in range(0,zgrid.size):
theta[:,:,m]=density.poT(t[:,:,m],s[:,:,m],zgrid[m])
t=theta
t[np.where(abs(t)== inf)]=nan
rho=density.rho(t,s,0)
rho[np.where(abs(rho)== inf)]=nan
xn=xgrid.size
yn=ygrid.size
ILD=np.zeros([yn,xn])
MLD=np.zeros([yn,xn])
BLT=np.zeros([yn,xn])
for i in range(0,xn):
for j in range(0,yn):
if np.isnan(t[j,i,0]) == False:
tpro=t[j,i,:]
rhopro=rho[j,i,:]
spro = s[j, i, :]
ILD[j, i], MLD[j, i], BLT[j, i] = make_data_from_profile(tpro, spro, rhopro, zgrid, dt)
else:
ILD[j,i]=nan
MLD[j,i]=nan
BLT[j, i] = nan
return ILD,MLD,BLT
def make_climatology_or_interannual_variation_of_a_month(fy, ly, month, var, depth, Ave_or_Std, product_n = 3):
if ly < fy:
raise Exception('ly must be greater equal than fy!')
xgrid, ygrid, zgrid = subroutine.product_grid_info(var, 'data', product_n)
xn, yn, zn = xgrid.size, ygrid.size, zgrid.size
pro_name, title_name, bin_dirname = subroutine.product_n_to_name(product_n)
nyear = ly - fy + 1
if depth == 0:
each_data = np.zeros((nyear, yn, xn, zn))
else:
each_data = np.zeros((nyear, yn, xn))
for year in range(fy, ly + 1):
i = year - fy
if depth == 0:
each_data[i, :, :, :] = subroutine.get_data(year, month, var, depth, title_name)
else:
each_data[i, :, :] = subroutine.get_data(year, month, var, depth, title_name)
if Ave_or_Std == 'Ave':
climatology = np.average(each_data, axis = 0)
return climatology
elif Ave_or_Std == 'Std':
Interannual_variation = np.std(each_data, axis = 0)
return Interannual_variation
else:
raise Exception('your Ave_or_Std argument is not valid!')
def make_data(year,month,product_n=3,dt=0.2): # ESTOC、ORAS4などで、層厚の計算を行うための関数。 # MOAA_GPVだけ、格納されているのが（ポテンシャル水温でなく）通常の水温なので、まずポテンシャル水温への変換が必要 _,title_name,_=subroutine.product_n_to_name(product_n) t=subroutine.get_data(year,month,'t',0,title_name) s=subroutine.get_data(year,month,'s',0,title_name) ILD, MLD, BLT = make_data_from_ts(t, s, product_n, dt) return ILD, MLD, BLT
def make_data_of_24sigma_t_depth(year, month, var, product_n = 3): _,title_name,_=subroutine.product_n_to_name(product_n) sgmt_d = make_24sigma_t_depth(year, month, product_n) data = subroutine.get_data(year, month, var, 0, title_name) if var == 'u' or var == 'v': data = convert.convert_UVgrid_value_to_Sgrid_value_3D(data) data_of_sgm_t = make_data_of_24sigma_t_depth_from_data(sgmt_d, data, product_n = product_n) return data_of_sgm_t
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 make_rho_from_ts(t,s, product_n = 3):
xgrid,ygrid,zgrid=subroutine.product_grid_info('t','data',product_n)
_, title_name, _ = subroutine.product_n_to_name(product_n)
# MOAA_GPVだけ、格納されているのが（ポテンシャル水温でなく）通常の水温なので、まずポテンシャル水温への変換が必要
if title_name=='MOAA_GPV':
theta=zeros((t.shape[0],t.shape[1],t.shape[2]))
for m in range(0,zgrid.size):
theta[:,:,m]=density.poT(t[:,:,m],s[:,:,m],zgrid[m])
t=theta
t[where(abs(t)== inf)]=nan
rho = density.rho(t, s, 0)
rho[np.where(np.abs(rho) == sp.inf)] = np.nan
return rho
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 draw_12month_cycle(cb_min, cb_max, depthn1 = 1, depthn2 = 4, product_n = 3, interval = 15):
_,title_name,_=subroutine.product_n_to_name(product_n)
xgrid, ygrid, zgrid = subroutine.product_grid_info('u', 'data', product_n)
xlim = [40, 110]
ylim = [-20, 30]
fy = 1990
ly = 2011
Data = np.zeros((12, ygrid.size, xgrid.size))
for i in range(0, 12):
month = i + 1
# データの取得
_, strmonth = subroutine.strym(2000, month)
u = CI.load_climatology_or_interannual_variation_as_npz(fy, ly, month, 'u', 'Ave', product_n = product_n)
v = CI.load_climatology_or_interannual_variation_as_npz(fy, ly, month, 'v', 'Ave', product_n = product_n)
data1 = cal_Length_of_Current_Vector(u[:, :, depthn1 - 1], v[:, :, depthn1 - 1])
data2 = cal_Length_of_Current_Vector(u[:, :, depthn2 - 1], v[:, :, depthn2 - 1])
Data[i, :, :] = data1 - data2
plta = quick.draw_12month_cycle_with_map(Data, ygrid, xgrid, cb_min, cb_max, xlim = xlim, ylim = ylim, \
clabel = "Vertical Shear of Current(m/s) " + str(zgrid[depthn1 - 1]) + 'm - ' + str(zgrid[depthn2 - 1]) + 'm',\
interval = interval, my_color = 0)
def cal_ew_ns_dist(year,month,product_n=3):
_,title_name,_=product_n_to_name(product_n)
xgrid,ygrid,zgrid=product_grid_info('u','data',product_n)
u=get_data(year,month,'u',0,title_name)
v=get_data(year,month,'v',0,title_name)
xn=xgrid.size
yn=ygrid.size
zn=zgrid.size
ew_dist=zeros([yn-1,xn])
ns_dist=zeros([yn-1,xn])
xgrid_for_manual_w=zeros(xn)
ygrid_for_manual_w=zeros(yn-1)
zgrid_for_manual_w=zeros(zn)
for i in range(0,xn):
x0=i
lon0=xgrid[x0]
if i==xn-1:
x1=0
lon1=xgrid[x1]
xgrid_for_manual_w[i]=average([lon0,lon1+360])
else:
x1=i+1
lon1=xgrid[x1]
xgrid_for_manual_w[i]=average([lon0,lon1])
for j in range(0,yn-1):
y0=j
y1=j+1
lat0=ygrid[y0]
lat1=ygrid[y1]
ygrid_for_manual_w[j]=average([lat0,lat1])
ew_dist[j,i]=dist_on_sphere([lon0,ygrid_for_manual_w[j]],[lon1,ygrid_for_manual_w[j]])
ns_dist[j,i]=dist_on_sphere([xgrid_for_manual_w[i],lat0],[xgrid_for_manual_w[i],lat1])
return ew_dist,ns_dist
def get_SUMsff(year, month, product_n = 3):
# 全球での淡水フラックスの総和を求める
# 単位はm^3/month。
_, title_name, _ = product_n_to_name(product_n)
sff = get_data(year, month, 'sff', 1, title_name)
xgrid, ygrid, _ = product_grid_info('t', 'data', product_n)
xn = xgrid.size
yn = ygrid.size
ew_dist = zeros([yn, xn])
ns_dist = zeros([yn, xn])
SFF = zeros([yn, xn])
for i in range(0, xn):
for j in range(0, yn):
ew_dist[j, i] = dist_on_sphere([0, ygrid[j]], [1, ygrid[j]])
ns_dist[j, i] = dist_on_sphere([0, ygrid[j] - 0.5], [0, ygrid[j] + 0.5])
if isnan(sff[j, i]) == True:
SFF[j, i] = 0.0
else:
SFF[j, i] = sff[j, i] * ew_dist[j, i] * ns_dist[j, i]
return sum(SFF)
def get_water_mass(product_n = 3):
_, title_name, _ = product_n_to_name(product_n)
s = get_data(2001, 1, 's', 0, title_name)
xgrid, ygrid, zgrid = product_grid_info('w', 'data', product_n)
xn = xgrid.size
yn = ygrid.size
zn = zgrid.size
ew_dist = zeros([yn, xn])
ns_dist = zeros([yn, xn])
Mass = zeros([yn, xn])
for i in range(0, xn):
for j in range(0, yn):
ew_dist[j, i] = dist_on_sphere([0, ygrid[j]], [1, ygrid[j]])
ns_dist[j, i] = dist_on_sphere([0, ygrid[j] - 0.5], [0, ygrid[j] + 0.5])
if isnan(s[j, i, 0]) == True:
Mass[j, i] = 0.0
else:
L = max(where(isnan(s[j, i, :]) == False)[0])
Mass[j, i] = zgrid[L] * ew_dist[j, i] * ns_dist[j, i]
return sum(Mass)
def make_20degree_depth(year, month, product_n = 3): _, title_name, _ = subroutine.product_n_to_name(product_n) t = subroutine.get_data(year, month, 't', 0, title_name) data = make_20degree_depth_from_t(t, product_n) return data
def cal_manual_w(year,month,product_n=3, GM = 'N'):
_,title_name,_=product_n_to_name(product_n)
if GM == 'N':
u=get_data(year,month,'u',0,title_name)
v=get_data(year,month,'v',0,title_name)
elif GM == 'Y':
u = get_data(year, month, 'ustar', 0, title_name)
v = get_data(year, month, 'vstar', 0, title_name)
else:
raise Exception('your GM argument is not valid!')
xgrid,ygrid,zgrid=product_grid_info('u','data',product_n)
xn=xgrid.size
yn=ygrid.size
zn=zgrid.size
manual_w=zeros([yn,xn,zn])
xgrid_for_manual_w=zeros(xn)
ygrid_for_manual_w=zeros(yn)
zgrid_for_manual_w=zeros(zn)
dH=zeros(zn)
for k in range(0,zn):
if k==0:
zgrid_for_manual_w[k]=2*zgrid[k]
dH[k]=zgrid_for_manual_w[k]
else:
zgrid_for_manual_w[k]=2*zgrid[k]-zgrid_for_manual_w[k-1]
dH[k]=zgrid_for_manual_w[k]-zgrid_for_manual_w[k-1]
for i in range(0,xn):
x0=i
lon0=xgrid[x0]
if i==xn-1:
x1=0
lon1=xgrid[x1]
xgrid_for_manual_w[i]=average([lon0,lon1+360])
else:
x1=i+1
lon1=xgrid[x1]
xgrid_for_manual_w[i]=average([lon0,lon1])
for j in range(0,yn):
if j == yn - 1:
manual_w[j,i,:]=nan
ygrid_for_manual_w[j]=average([ygrid[yn - 1],ygrid[yn - 1] + 1])
else:
y0=j
y1=j + 1
lat0=ygrid[y0]
lat1=ygrid[y1]
ygrid_for_manual_w[j]=average([lat0,lat1])
ew_dist=dist_on_sphere([lon0,ygrid_for_manual_w[j]],[lon1,ygrid_for_manual_w[j]])
ns_dist=dist_on_sphere([xgrid_for_manual_w[i],lat0],[xgrid_for_manual_w[i],lat1])
for k in range(0,zn):
if k==0:
manual_w0=0.0
else:
manual_w0=manual_w[j,i,k-1]
u00=u[y0,x0,k]
v00=v[y0,x0,k]
u01=u[y1,x0,k]
v01=v[y1,x0,k]
u10=u[y0,x1,k]
v10=v[y0,x1,k]
u11=u[y1,x1,k]
v11=v[y1,x1,k]
if any(isnan(u00, u01, u10, u11)) == False:
manual_w[j,i,k]=dH[k]*(((v01+v11)/2.0-(v00+v10)/2.0)/ns_dist
+((u10+u11)/2.0-(u00+u01)/2.0)/ew_dist)+manual_w0
else:
manual_w[j,i,k]=nan
return manual_w,xgrid_for_manual_w,ygrid_for_manual_w,zgrid_for_manual_w
def make_rho(year, month, product_n = 3): _, title_name, _ = subroutine.product_n_to_name(product_n) t = subroutine.get_data(year, month, 't', 0, title_name) s = subroutine.get_data(year, month, 's', 0, title_name) rho = make_rho_from_ts(t, s, product_n) return rho
def get_Length_of_Current_Vector(year, month, depthn = 1, product_n = 3): _,title_name,_=subroutine.product_n_to_name(product_n) u = subroutine.get_data(year, month, 'u', depthn, title_name) v = subroutine.get_data(year, month, 'v', depthn, title_name) L = cal_Length_of_Current_Vector(u, v) return L