def get_depth_of_minimum_of_vertical_gradient_from_data(data, var, product_n = 3): xgrid, ygrid, zgrid = D.get_grid_value(var, product_n) xn = xgrid.size yn = ygrid.size zn = zgrid.size Grad = np.zeros((yn, xn, zn)) for k in range(1, zn - 1): dz = 0.5 * (zgrid[k + 1] - zgrid[k - 1]) Grad[:, :, k] = (data[:, :, k + 1] - data[:, :, k - 1]) / dz Grad[np.where(np.isnan(Grad) == True)] = np.inf GradMin = np.min(Grad, axis = 2) Depth_of_GradMin = np.zeros((yn, xn)) for j in range(0, yn): for i in range(0, xn): if GradMin[j, i] == 0.0: Depth_of_GradMin[j, i] = np.nan else: K1 = np.where(Grad[j, i, :] == GradMin[j, i])[0][0] Depth_of_GradMin[j, i] = zgrid[K1] Depth_of_GradMin[np.where(np.abs(Depth_of_GradMin) == np.inf)] = np.nan Depth_of_GradMin[np.where(Depth_of_GradMin == 0.0)] = np.nan return Depth_of_GradMin
def get_depth_of_maximum_of_vertical_gradient_from_profile(profile, product_n = 3):
_, _, zgrid = D.get_grid_value('s', product_n)
zn = zgrid.size
Grad = np.zeros(zn)
for k in range(1, zn - 1):
dz = 0.5 * (zgrid[k + 1] - zgrid[k - 1])
Grad[k] = (profile[k + 1] - profile[k - 1]) / dz
Grad[np.where(np.isnan(Grad) == True)] = -np.inf
GradMax = np.max(Grad)
Grad[np.where(Grad == -np.inf)] = np.inf
GradMin = np.min(Grad)
if GradMax == 0.0 and GradMin == 0.0:
Depth_of_GradMax = np.nan
else:
K1 = np.where(Grad == GradMax)[0][0]
K2 = np.where(Grad == GradMin)[0][0]
if GradMax >= abs(GradMin):
Depth_of_GradMax = zgrid[K1]
else:
Depth_of_GradMax = zgrid[K2]
return Depth_of_GradMax
def Get_VerticalSection_from_data(self, data, var, product_n):
import D
import numpy as np
import convert
xgrid, ygrid, zgrid = D.get_grid_value('t', product_n)
if var == 'u' or var == 'v':
data = convert.convert_UVgrid_value_to_Sgrid_value_3D(data)
Ln = self.Pn
if self.direction == 'EW':
Data = np.zeros((self.band, Ln, zgrid.size))
for i in range(0, Ln):
xn = np.where(xgrid == self.hgrid[i])[0][0]
for j in range(0, self.band):
yn = np.where(ygrid == self.latp[i] + j)[0][0]
Data[j, i, :] = data[yn, xn, :]
elif self.direction == 'NS':
Data = np.zeros((Ln, self.band, zgrid.size))
for j in range(0, Ln):
yn = np.where(ygrid == self.hgrid[j])[0][0]
for i in range(0, self.band):
xn = np.where(xgrid == self.lonp[j] + i)[0][0]
Data[j, i, :] = data[yn, xn, :]
else:
raise ValueError("your direction is not valid!")
return np.average(Data, axis = self.average_axis), self.hgrid, zgrid
def Get_Current_of_VerticalSection_from_UV(self, u, v, product_n):
import D
import numpy as np
xgrid, ygrid, zgrid = D.get_grid_value('u', product_n)
Ln = self.Pn
if self.direction == 'EW':
Data = np.zeros((self.band - 1, Ln - 1, zgrid.size))
for i in range(0, Ln - 1):
xn = np.where(xgrid == self.hgrid[i] + 0.5)[0][0]
x = self.lonp[i + 1] - self.lonp[i]
y = self.latp[i + 1] - self.latp[i]
cos_alpha = x / np.sqrt(x ** 2 + y ** 2)
sin_alpha = y / np.sqrt(x ** 2 + y ** 2)
for j in range(0, self.band - 1):
yn = np.where(ygrid == self.latp[i] + j + 0.5)[0][0]
Data[j, i, :] = cos_alpha * u[yn, xn, :] + sin_alpha * v[yn, xn, :]
elif self.direction == 'NS':
Data = np.zeros((Ln - 1, self.band - 1, zgrid.size))
for j in range(0, Ln - 1):
yn = np.where(ygrid == self.hgrid[j] + 0.5)[0][0]
x = self.lonp[j + 1] - self.lonp[j]
y = self.latp[j + 1] - self.latp[j]
cos_alpha = x / np.sqrt(x ** 2 + y ** 2)
sin_alpha = y / np.sqrt(x ** 2 + y ** 2)
for i in range(0, self.band - 1):
xn = np.where(xgrid == self.lonp[j] + i + 0.5)[0][0]
Data[j, i, :] = cos_alpha * u[yn, xn, :] + sin_alpha * v[yn, xn, :]
else:
raise ValueError("your direction is not valid!")
return np.average(Data, axis = self.average_axis), self.hgrid[:self.Pn - 1] + 0.5, zgrid
def Get_VerticalSection_from_2Ddata(self, data, var, product_n):
import D
import numpy as np
xgrid, ygrid, zgrid = D.get_grid_value(var, product_n)
Ln = self.Pn
if self.direction == 'EW':
Data = np.zeros((self.band, Ln))
for i in range(0, Ln):
xn = np.where(xgrid == self.hgrid[i])[0][0]
for j in range(0, self.band):
yn = np.where(ygrid == self.latp[i] + j)[0][0]
Data[j, i] = data[yn, xn]
elif self.direction == 'NS':
Data = np.zeros((Ln, self.band))
for j in range(0, Ln):
yn = np.where(ygrid == self.hgrid[j])[0][0]
for i in range(0, self.band):
xn = np.where(xgrid == self.lonp[j] + i)[0][0]
Data[j, i] = data[yn, xn]
else:
raise ValueError("your direction is not valid!")
return np.average(Data, axis = self.average_axis), self.hgrid
def get_Gradient_of_variable_from_data(data, var, product_n = 3): xgrid, ygrid, zgrid = D.get_grid_value(var, product_n) xn = xgrid.size yn = ygrid.size zn = zgrid.size Data_ns = np.zeros((yn, xn, zn)) Data_ew = np.zeros((yn, xn, zn)) ns_dist = subroutine.dist_on_sphere([0.0, - 0.5], [0.0, 0.5]) for j in range(0, yn): if j == 0 or j == yn - 1: Data_ns[j, :, :] = np.nan Data_ew[j, :, :] = np.nan else: y0 = j - 1 y1 = j y2 = j + 1 lat1 = ygrid[y1] ew_dist = subroutine.dist_on_sphere([0.0, lat1], [1.0, lat1]) for i in range(0, xn): x1 = i lon1 = xgrid[x1] if i == 0: x0 = xn - 1 x2 = i + 1 elif i == xn - 1: x0 = i - 1 x2 = xn - 1 else: x0 = i - 1 x2 = i + 1 data01 = data[y1, x0, :] data10 = data[y0, x1, :] data11 = data[y1, x1, :] data21 = data[y1, x2, :] data12 = data[y2, x1, :] if np.isnan(data01[0]) == True and np.isnan(data21[0]) == False: Data_ew[j, i, :] = (data21 - data11) / ew_dist elif np.isnan(data21[0]) == True and np.isnan(data01[0]) == False: Data_ew[j, i, :] = (data11 - data01) / ew_dist else: Data_ew[j, i, :] = (data21 - data01) / (2.0 * ew_dist) if np.isnan(data10[0]) == True and np.isnan(data12[0]) == False: Data_ns[j, i, :] = (data12 - data11) / ns_dist elif np.isnan(data12[0]) == True and np.isnan(data10[0]) == False: Data_ns[j, i, :] = (data11 - data10) / ns_dist else: Data_ns[j, i, :] = (data12 - data10) / (2.0 * ns_dist) return Data_ew, Data_ns
def save_data_as_npz(self, Timeseries, fy, ly, var, depth = 1, product_n = 3): import D import Var import subroutine vid = Var.var_to_id(var) formal_name = Var.VAR[vid].Get_formal_name() title_name = D.Data[product_n].title_name xgrid, ygrid, zgrid = D.get_grid_value(var, product_n) subroutine.save_npz(Timeseries, self.AreaName + '_Area-Averaged-' + \ formal_name + '_at_' + str(zgrid[depth - 1]) + \ 'm_Year' + str(fy) + '-' + str(ly) + '_' + title_name)
def product_grid_info(var, data_or_size, product_n = 3): # グリッドに関するデータを獲得する
import D
if type(product_n) != int: # product_nが整数でない時はエラーを吐き出す。
raise Exception('product_n is not integer!')
xgrid, ygrid, zgrid = D.get_grid_value(var, product_n)
if data_or_size == 'data':
return xgrid, ygrid, zgrid
elif data_or_size == 'size':
return xgrid.size, ygrid.size, zgrid.size
else:
raise Exception('error! your character is not valid!')
def cal_GeostrophicCurrent_from_SSH(ssh, product_n = 3):
ns_dist = subroutine.dist_on_sphere([0.0, - 0.5], [0.0, 0.5])
xgrid, ygrid, zgrid = D.get_grid_value('ht', product_n)
yn = ygrid.size
xn = xgrid.size
Ug = np.zeros((yn, xn))
Vg = np.zeros((yn, xn))
xgrid_new = np.zeros(xn)
ygrid_new = np.zeros(yn)
for j in range(0, yn):
if j == yn - 1:
ygrid_new[j] = 0.5 * (ygrid[j] + ygrid[j] + 1)
Ug[j, :] = np.nan
Vg[j, :] = np.nan
else:
y0 = j
y1 = j + 1
ygrid_new[j] = 0.5 * (ygrid[y0] + ygrid[y1])
if abs(ygrid_new[j]) <= 2.0: # 赤道付近においては地衡流は計算しない
Ug[j, :] = np.nan
Vg[j, :] = np.nan
else:
ew_dist = subroutine.dist_on_sphere([0.0, ygrid_new[j]], [1.0, ygrid_new[j]])
f = subroutine.f0(ygrid_new[j])
for i in range(0, xn):
x0 = i
if i == xn - 1:
x1 = 0
lon = 0.5 * (xgrid[i] + xgrid[i] + 1.0)
else:
x1 = i + 1
lon = 0.5 * (xgrid[x0] + xgrid[x1])
if j == 1:
xgrid_new[i] = lon
ssh00 = ssh[y0, x0]
ssh01 = ssh[y1, x0]
ssh10 = ssh[y0, x1]
ssh11 = ssh[y1, x1]
a = Using_jit.average_of_2data(ssh01, ssh11)
b = Using_jit.average_of_2data(ssh00, ssh10)
c = Using_jit.average_of_2data(ssh10, ssh11)
d = Using_jit.average_of_2data(ssh00, ssh01)
Ug[j, i] = -g / f * (a - b) / ns_dist
Vg[j, i] = g / f * (c - d) / ew_dist
return Ug, Vg, xgrid_new, ygrid_new
def cal_curl(year, month, product_n = 3):
taux = D.get_data(year, month, 'taux', 1, product_n)
tauy = D.get_data(year, month, 'tauy', 1, product_n)
xgrid, ygrid, zgrid = D.get_grid_value('taux', product_n)
xn = xgrid.size
yn = ygrid.size
curl = np.zeros([yn, xn])
ns_dist = subroutine.dist_on_sphere([0.0, - 0.5], [0.0, 0.5])
for j in range(0, yn):
if j == yn - 1:
curl[j, :] = np.nan
else:
y0 = j
y1 = j + 1
lat0 = ygrid[y0]
lat1 = ygrid[y1]
tmpdist = np.average(np.array([lat0, lat1]))
ew_dist = subroutine.dist_on_sphere([0.0, tmpdist], [1.0, tmpdist])
for i in range(0, xn):
x0 = i
lon0 = xgrid[x0]
if i == xn - 1:
x1 = 0
lon1 = xgrid[x1]
else:
x1 = i + 1
lon1 = xgrid[x1]
taux00 = taux[y0, x0]
tauy00 = tauy[y0, x0]
taux01 = taux[y1, x0]
tauy01 = tauy[y1, x0]
taux10 = taux[y0, x1]
tauy10 = tauy[y0, x1]
taux11 = taux[y1, x1]
tauy11 = tauy[y1, x1]
a = Using_jit.average_of_2data(tauy10, tauy11)
b = Using_jit.average_of_2data(tauy00, tauy01)
c = Using_jit.average_of_2data(taux01, taux11)
d = Using_jit.average_of_2data(taux00, taux10)
if np.isnan(a - b) == False and np.isnan(c - d) == False:
curl[j, i] = (a - b) / ew_dist - (c - d) / ns_dist
elif np.isnan(a - b) == False:
curl[j, i] = (a - b) / ew_dist
elif np.isnan(c - d) == False:
curl[j, i] = - (c - d) / ns_dist
else:
curl[j, i] = np.nan
return curl
def load_data_of_npz(self, fy, ly, var, depth = 1, product_n = 3): import D import Var import subroutine vid = Var.var_to_id(var) formal_name = Var.VAR[vid].Get_formal_name() title_name = D.Data[product_n].title_name xgrid, ygrid, zgrid = D.get_grid_value(var, product_n) Timeseries = subroutine.load_npz(self.AreaName + '_Area-Averaged-' + \ formal_name + '_at_' + str(zgrid[depth - 1]) + \ 'm_Year' + str(fy) + '-' + str(ly) + '_' + title_name) months, label = subroutine.get_months_and_label(fy, ly) return Timeseries, months, label
def cal_Ue_or_Ve(year, month, product_n, Ue_or_Ve, nu):
taux = D.get_data(year, month, 'taux', 1, product_n)
tauy = D.get_data(year, month, 'tauy', 1, product_n)
xgrid, ygrid, zgrid = D.get_grid_value('taux', product_n)
if Ue_or_Ve == 'Ue':
Ue, _ = cal_EkmanCurrent_from_tau(taux, tauy, ygrid, nu)
elif Ue_or_Ve == 'Ve':
_, Ue = cal_EkmanCurrent_from_tau(taux, tauy, ygrid, nu)
else:
raise ValueError('your Ue_or_Ve argument is not valid!')
Ue = convert.convert_Sgrid_value_to_UVgrid_value_3D(Ue)
a = np.zeros((ygrid.size, xgrid.size, zgrid.size - M)) * np.nan # 鉛直にuやvと同じだけ層を作ってやる
Ue = np.c_[Ue, a]
return Ue
def cal_Salinity_Transport_Budget(self, year, month, depth = 10, product_n = 3):
# その領域における塩分輸送量収支を計算する。
# 注意！ x,y,z軸方向のグリッドの大きさはすべて等しいと仮定して計算しております。
# さらに、領域は南北に長くなく、EWdistで南北両側面の断面積を計算していいこととしております。
import D
import D2
import Using_jit
import numpy as np
import Budget_at_Global_Ocean
rho0 = 1024.0
dS = Budget_at_Global_Ocean.cal_dSdt(year, month, product_n)[:, :, :depth]
sff = D.get_data(year, month, 'sff', 1, product_n)
s = D.get_data(year, month, 's', 0, product_n)[:, :, :depth]
u = D.get_data(year, month, 'u', 0, product_n)[:, :, :depth]
v = D.get_data(year, month, 'v', 0, product_n)[:, :, :depth]
ssh = D2.D2Data[33].load_data_of_npz(year, month, product_n)
ssh_Sec = self.Get_data_of_AreaSection_from_data(ssh, 'ht', product_n)
ssh_Area, _, _ = self.Get_data_of_area(ssh, 'ht', product_n)
s_bottom = s[:, :, depth - 1]
s_surface = s[:, :, 0]
w = D.get_data(year, month, 'w', depth, product_n)
_, _, zgrid = D.get_grid_value('w', product_n)
ZonTsp = Using_jit.cal_Salt_Transport(s, u, 0, product_n)
MerTsp = Using_jit.cal_Salt_Transport(s, v, 1, product_n)
ZonTsp = self.Get_data_of_AreaSection_from_data(ZonTsp, 's', product_n)
MerTsp = self.Get_data_of_AreaSection_from_data(MerTsp, 's', product_n)
# 海面力学高度の分だけ、各断面の高さに下駄を履かせてやる
West = np.sum(ZonTsp.West) * (zgrid[depth - 1] + np.average(ssh_Sec.West)) / zgrid[depth - 1]
East = np.sum(ZonTsp.East) * (zgrid[depth - 1] + np.average(ssh_Sec.East)) / zgrid[depth - 1]
North = np.sum(MerTsp.North) * (zgrid[depth - 1] + np.average(ssh_Sec.North)) / zgrid[depth - 1]
South = np.sum(MerTsp.South) * (zgrid[depth - 1] + np.average(ssh_Sec.South)) / zgrid[depth - 1]
w, _, _ = self.Get_data_of_area(w, 'w', product_n)
s_bottom, _, _ = self.Get_data_of_area(s_bottom, 's', product_n)
Bottom = self.Square * np.average(1e-3 * rho0 * w * s_bottom)
sff, _, _ = self.Get_data_of_area(sff, 'sff', product_n)
s_surface, _, _ = self.Get_data_of_area(s_surface, 's', product_n)
Surface = self.Square * np.average(1e-3 * rho0 * sff * s_surface) / (60 * 60 * 24 * 30.0)
# 海面力学高度の分だけ、水柱の高さに下駄を履かせてやる
dS, _, _ = self.Get_data_of_area(dS, 's', product_n)
Change = 1e-3 * rho0 * np.average(dS) * (zgrid[depth - 1] + np.average(ssh_Area)) * self.Square / (60 * 60 * 24 * 30.0)
return South, North, West, East, Bottom, Surface, Change
def draw_line_with_data_map(self, plt, year, month, var, depth, cb_min, cb_max, product_n = 3, xlim = [40, 110], ylim = [ - 20, 30], \ fsizex = 10, fsizey = 6, div = 20.0, interval = 10, color = 'red', linetype = '--'): import D import Var import quick import subroutine xgrid, ygrid, zgrid = D.get_grid_value(var, product_n) data = D.get_data(year, month, var, depth, product_n) stryear, strmonth = subroutine.strym(year, month) vid = Var.var_to_id(var) formal_name = Var.VAR[vid].Get_formal_name() title_name = D.Data[product_n].title_name clabel = stryear + '/' + strmonth + ' ' + formal_name + ' ' + ' at ' + str(zgrid[depth - 1]) + 'm '+ title_name plta = quick.draw_with_axis_and_map(data, ygrid, xgrid, cb_min, cb_max, xlim = xlim, ylim = ylim, \ interval = interval, clabel = clabel, fsizex = fsizex, fsizey = fsizey) plta = self.draw_line(plta, xlim, ylim, interval = interval, color = color, linetype = linetype) return plta
def cal_Salt_Transport(s, u, Zonal_or_Meridional, product_n):
# 東西輸送量も鉛直輸送量もどっちも求められます。
# Zonal : Zonal_or_Meridional = 0
# Meridional : Zonal_or_Meridional = 1
rho0 = 1024.0 # 面倒くさいので密度は一定値。
yn = s.shape[0]
xn = s.shape[1]
zn = s.shape[2]
ns_dist = subroutine.dist_on_sphere([0.0, - 0.5], [0.0, 0.5])
xgrid, ygrid, zgrid = D.get_grid_value('s', product_n)
Data = np.zeros((yn, xn, zn))
for j in range(0, yn):
if j == 0:
Data[j, :, :] = np.nan
else:
y0 = j - 1
y1 = j
lat1 = ygrid[y1]
if Zonal_or_Meridional == 0:
L = ns_dist # 東西輸送量であれば、南北に積分
elif Zonal_or_Meridional == 1:
L = subroutine.dist_on_sphere([0.0, lat1], [1.0, lat1]) # 南北輸送量であれば、東西に積分
else:
raise ValueError('your Zonal_or_Meridional argument is not valid!')
for i in range(0, xn):
x1 = i
if i == 0:
x0 = xn - 1
else:
x0 = i - 1
if np.isnan(s[y1, x1, 0]) == True:
Data[y1, x1, :] = np.nan
else:
for k in range(0, zn):
# U = np.array([u[y0, x0, k], u[y1, x0, k], u[y0, x1, k], u[y1, x1, k]])
# Data[y1, x1, k] = L * lz[k] * 1e-3 * rho0 * s[y1, x1, k] * \
# np.average(U[np.where(np.isnan(U) == False)])
Data[y1, x1, k] = L * lz[k] * 1e-3 * rho0 * s[y1, x1, k] * \
average_of_4data(u[y0, x0, k], u[y1, x0, k], u[y0, x1, k], u[y1, x1, k])
return Data
def get_data_for_draw_12month_cycle(depthn = 1, product_n = 3, Ekman_or_Geost = 'Ekman', Vector_or_Scalar = 'Scalar'):
xgrid, ygrid, zgrid = D.get_grid_value('u', product_n)
if Ekman_or_Geost == 'Ekman':
fname = 'Ekman'
elif Ekman_or_Geost == 'Geost':
fname = 'Geostrophic'
else:
raise ValueError("your Ekman_or_Geost argument is not vald!")
if Vector_or_Scalar == 'Scalar':
DataDrawN = 0
Data = np.zeros((12, ygrid.size, xgrid.size))
elif Vector_or_Scalar == 'Vector':
DataDrawN = 1
Data = np.zeros((12, 2, ygrid.size, xgrid.size))
else:
raise ValueError("your Vector_or_Scalar argument is not vald!")
fy = 1990
ly = 2011
for i in range(0, 12):
month = i + 1
print 'month=', month
# データの取得
each_u = np.zeros((ygrid.size, xgrid.size, ly - fy + 1))
each_v = np.zeros((ygrid.size, xgrid.size, ly - fy + 1))
for year in range(fy, ly + 1):
k = year - fy
tmpu, tmpv = load_data_of_npz(year, month, product_n = product_n, Ekman_or_Geost = Ekman_or_Geost)
each_u[:, :, k] = tmpu[:, :, depthn - 1]
each_v[:, :, k] = tmpv[:, :, depthn - 1]
if Vector_or_Scalar == 'Scalar':
Data[i, :, :] = np.sqrt(np.average(each_u, axis = 2)**2, np.average(each_v, axis = 2)**2)
elif Vector_or_Scalar == 'Vector':
Data_d1 = np.average(each_u, axis = 2)
Data_d2 = np.average(each_v, axis = 2)
Data[i, :, :, :] = np.array([Data_d1, Data_d2])
else:
raise ValueError("your Vector_or_Scalar argument is not vald!")
return Data
def save_data_as_npz(self, Timeseries, fy, ly, var = 's', depthn = 1, product_n = 3, Rawdata_or_Anomaly = 'Rawdata', fy_of_Anomalydata = 1990, ly_of_Anomalydata = 2011):
import D
import subroutine
import Var
title_name = D.Data[product_n].title_name
xgrid, ygrid, zgrid = D.get_grid_value('s', product_n)
vid = Var.var_to_id(var)
formal_name = Var.VAR[vid].Get_formal_name()
if Rawdata_or_Anomaly == 'Rawdata':
fname_RA = formal_name
elif Rawdata_or_Anomaly == 'Anomaly':
fname_RA = '[Anomaly_of_' + formal_name + str(fy_of_Anomalydata) + '-' + str(ly_of_Anomalydata) + ']'
else:
raise ValueError('your Rawdata_or_Anomaly argument is not valid!')
subroutine.save_npz(Timeseries, self.AreaName + '_of_' + fname_RA + '_at_' + str(zgrid[depthn - 1]) + \
'm_Year' + str(fy) + '-' + str(ly) + '_' + title_name)
def get_depth_of_minimum_of_vertical_gradient_from_profile(profile, product_n = 3):
# プロファイルの鉛直勾配が負に最大になる深さを求める。
# 塩分に関してはアラビア海向け。
_, _, zgrid = D.get_grid_value('s', product_n)
zn = zgrid.size
Grad = np.zeros(zn)
for k in range(1, zn - 1):
dz = 0.5 * (zgrid[k + 1] - zgrid[k - 1])
Grad[k] = (profile[k + 1] - profile[k - 1]) / dz
Grad[np.where(np.isnan(Grad) == True)] = np.inf
GradMin = np.min(Grad)
if GradMin == 0.0:
Depth_of_GradMin = np.nan
else:
K1 = np.where(Grad == GradMin)[0][0]
Depth_of_GradMin = zgrid[K1]
return Depth_of_GradMin
def Get_data_of_area(self, data, var, product_n):
# 2次元のデータは勿論、3次元のデータもこの関数を使ってtrimmingできます。
import D
import numpy as np
import convert
xgrid, ygrid, zgrid = D.get_grid_value('t', product_n)
if var == 'u' or var == 'v':
if data.ndim == 3:
data = convert.convert_UVgrid_value_to_Sgrid_value_3D(data)
elif data.ndim == 2:
data = convert.convert_UVgrid_value_to_Sgrid_value_2D(data)
mx = np.where(xgrid == self.wlon)[0][0]
nx = np.where(xgrid == self.elon)[0][0] + 1
my = np.where(ygrid == self.slat)[0][0]
ny = np.where(ygrid == self.nlat)[0][0] + 1
data = data[my:ny, mx:nx]
xgrid = xgrid[mx:nx]
ygrid = ygrid[my:ny]
return data, xgrid, ygrid
def get_smoothed_ssh(year, month, product_n = 3):
ssh = D.get_data(year, month, 'ht', 1, product_n)
xgrid, ygrid, zgrid = D.get_grid_value('ht', product_n)
xn = xgrid.size
yn = ygrid.size
ssh_new = np.zeros((yn, xn))
for j in range(0, yn):
if j == 0 or j == yn - 1:
ssh_new[j, :] = np.nan
else:
y0 = j - 1
y1 = j
y2 = j + 1
for i in range(0, xn):
x1 = i
lon1 = xgrid[x1]
if i == 0:
x0 = xn - 1
x2 = i + 1
elif i == xn - 1:
x0 = i - 1
x2 = xn - 1
else:
x0 = i - 1
x2 = i + 1
ssh01 = ssh[y1, x0]
ssh10 = ssh[y0, x1]
ssh11 = ssh[y1, x1]
ssh21 = ssh[y1, x2]
ssh12 = ssh[y2, x1]
if np.isnan(ssh11) == True:
ssh_new[j, i] = np.nan
else:
ssh_around = np.array([ssh01, ssh10, ssh21, ssh12])
ssh_new[j, i] = 0.5 * (ssh11 + np.average(ssh_around[np.where(np.isnan(ssh_around) == False)]))
return ssh_new / 100.0
def make_Ave_or_Std_of_Data(self, fy, ly, product_n, Ave_or_Std = 'Ave'):
import D
import numpy as np
title_name = D.Data[product_n].title_name
xgrid, ygrid, zgrid = D.get_grid_value('t', product_n) # tでもuでもよい
if self.dimension == 2:
AvSt_of_Data = np.zeros((ygrid.size, xgrid.size, 12))
for month in range(1, 13):
each_Data = np.zeros((ygrid.size, xgrid.size, ly - fy + 1))
for year in range(fy, ly + 1):
k = year - fy
each_Data[:, :, k] = self.load_data_of_npz(year, month, product_n)
if Ave_or_Std == 'Ave':
AvSt_of_Data[:, :, month - 1] = np.average(each_Data, axis = 2)
elif Ave_or_Std == 'Std':
AvSt_of_Data[:, :, month - 1] = np.std(each_Data, axis = 2)
else:
raise ValueError('your Ave_or_Std argument is not valid!')
elif self.dimension == 3:
AvSt_of_Data = np.zeros((ygrid.size, xgrid.size, zgrid.size, 12))
for month in range(1, 13):
each_Data = np.zeros((ygrid.size, xgrid.size, zgrid.size, ly - fy + 1))
for year in range(fy, ly + 1):
k = year - fy
each_Data[:, :, :, k] = self.load_data_of_npz(year, month, product_n)
if Ave_or_Std == 'Ave':
AvSt_of_Data[:, :, :, month - 1] = np.average(each_Data, axis = 2)
elif Ave_or_Std == 'Std':
AvSt_of_Data[:, :, :, month - 1] = np.std(each_Data, axis = 2)
else:
raise ValueError('your Ave_or_Std argument is not valid!')
else:
raise ValueError('your dimension is not valid!')
return AvSt_of_Data
def Get_data_of_AreaSection_from_data(self, data, var, product_n):
# 例えばwlon=10.5, elon=19.5だったとしたら、
# Data_wは経度10.0度、Data_e=は経度20度に於けるデータを取得することとします。
import numpy as np
import D
import convert
xgrid, ygrid, zgrid = D.get_grid_value('t', product_n)
if var == 'u' or var == 'v':
if data.ndim == 3:
data = convert.convert_UVgrid_value_to_Sgrid_value_3D(data)
elif data.ndim == 2:
data = convert.convert_UVgrid_value_to_Sgrid_value_2D(data)
mx = np.where(xgrid == self.wlon)[0][0]
nx = np.where(xgrid == self.elon)[0][0] + 1
my = np.where(ygrid == self.slat)[0][0]
ny = np.where(ygrid == self.nlat)[0][0] + 1
Data_S = 0.5 * (data[my - 1, mx:nx] + data[my, mx:nx])
Data_N = 0.5 * (data[ny - 1, mx:nx] + data[ny, mx:nx])
Data_W = 0.5 * (data[my:ny, mx - 1] + data[my:ny, mx])
Data_E = 0.5 * (data[my:ny, nx - 1] + data[my:ny, nx])
Data = EACH_SECTION(Data_S, Data_N, Data_W, Data_E)
return Data
def cal_Mass_Budget(self, year, month, depth = 10, product_n = 3):
# その領域における体積収支を計算する。
# 注意！ x,y,z軸方向のグリッドの大きさはすべて等しいと仮定して計算しております。
# さらに、領域は南北に長くなく、EWdistで南北両側面の断面積を計算していいこととしております。
import D
import D2
import numpy as np
u = D.get_data(year, month, 'u', 0, product_n)[:, :, :depth]
v = D.get_data(year, month, 'v', 0, product_n)[:, :, :depth]
w = D.get_data(year, month, 'w', depth, product_n)
ssh = D2.D2Data[33].load_data_of_npz(year, month, product_n)
ssh_Sec = self.Get_data_of_AreaSection_from_data(ssh, 'ht', product_n)
_, _, zgrid = D.get_grid_value('w', product_n)
u = self.Get_data_of_AreaSection_from_data(u, 'u', product_n)
v = self.Get_data_of_AreaSection_from_data(v, 'v', product_n)
w, _, _ = self.Get_data_of_area(w, 'w', product_n)
# 海面力学高度の分だけ、各断面の高さに下駄を履かせてやる
West = np.average(u.West) * (zgrid[depth - 1] + np.average(ssh_Sec.West)) * self.NSdist
East = np.average(u.East) * (zgrid[depth - 1] + np.average(ssh_Sec.East)) * self.NSdist
North = np.average(v.North) * (zgrid[depth - 1] + np.average(ssh_Sec.North)) * self.EWdist
South = np.average(v.South) * (zgrid[depth - 1] + np.average(ssh_Sec.South)) * self.EWdist
Bottom = self.Square * np.average(w)
return South, North, West, East, Bottom