yn = 641
zn = 51
time = np.asarray(range(dayi,dayf,days))*1440
S_avg = []
S_time = []
for tt in range(len(time)):
print tt
tlabel = str(tt)
while len(tlabel) < 3: tlabel = '0'+tlabel
#
file1 = label+'_' + tlabel
#
U = fio.read_Scalar('./Velocity_CG/Velocity_CG_0_'+label+'_'+str(tt)+'.csv',xn,yn,zn)
V = fio.read_Scalar('./Velocity_CG/Velocity_CG_1_'+label+'_'+str(tt)+'.csv',xn,yn,zn)
#
S_avg.append(np.mean(np.sqrt(U**2+V**2)))
S_time.append(time[tt])
import csv
f = open('./Savg_'+label+'.csv','w')
writer = csv.writer(f)
for tt in range(len(time)):
writer.writerow((S_time[tt],S_avg[tt]))
f.close()
Xlist = np.linspace(0,8000,xn)# x co-ordinates of the desired array shape
Zlist = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
Zlist = np.linspace(0,-50,zn)
Ylist = np.linspace(0,8000,yn)# y co-ordinates of the desired array shape
[X,Y] = np.meshgrid(Xlist,Ylist)
time = range(dayi,dayf,days)
t = 0
for tt in time:
tlabel = str(tt)
while len(tlabel) < 3: tlabel = '0'+tlabel
#
file1 = label+'_' + tlabel
#
Temp = fio.read_Scalar('./Temperature_CG/Temperature_CG_'+label+'_'+str(tt)+'.csv',xn,yn,zn)
#
for z in [0]:
# determine speed direction
#
plt.figure()
plt.contourf(Xlist,Ylist,Temp[:,:,0],50,extend='both',cmap=plt.cm.PiYG)
plt.colorbar()
plt.ylabel('Y [Km]',fontsize=18)
plt.xlabel('X [Km]',fontsize=18)
plt.savefig('./plot/'+label+'/T_'+str(z)+'_'+file1+'.png')
plt.close()
print './plot/'+label+'/T_'+str(z)+'_'+file1+'.png'
fd = open(filename, "wb")
for p in range(pt):
fd.write(str(x0[p]) + ", " + str(y0[p]) + ", " + str(-1.0 * z0[p]) + ", " + str(time[0]) + "\n")
import random
for t in range(len(time) - 1):
print "time:", time[t]
file0 = path + "Velocity_CG_0_" + label + "_" + str(time[t]) + ".csv"
file1 = path + "Velocity_CG_1_" + label + "_" + str(time[t]) + ".csv"
file2 = path + "Velocity_CG_2_" + label + "_" + str(time[t]) + ".csv"
Ut0 = fio.read_Scalar(file0, xn, yn, zn)
Vt0 = fio.read_Scalar(file1, xn, yn, zn)
Wt0 = -1.0 * fio.read_Scalar(file2, xn, yn, zn) # 0*Ut0
file0 = path + "Velocity_CG_0_" + label + "_" + str(time[t + 1]) + ".csv"
file1 = path + "Velocity_CG_1_" + label + "_" + str(time[t + 1]) + ".csv"
file2 = path + "Velocity_CG_2_" + label + "_" + str(time[t + 1]) + ".csv"
Ut1 = fio.read_Scalar(file0, xn, yn, zn)
Vt1 = fio.read_Scalar(file1, xn, yn, zn)
Wt1 = -1.0 * fio.read_Scalar(file2, xn, yn, zn) # 0*Ut0
# subcycling
nt = 20
ds = 1.0 * dt / nt
# for st in range(nt+1):
dx, dy = np.meshgrid(np.gradient(Xlist),np.gradient(Ylist))
for time in range(dayi,dayf,days):
print 'time:', time
tlabel = str(time)
while len(tlabel) < 3: tlabel = '0'+tlabel
#Temperature_CG_m_50_6e_9.csv
fileU = path+'Velocity_CG_0_'+label+'_'+str(time+480)+'.csv'
fileV = path+'Velocity_CG_1_'+label+'_'+str(time+480)+'.csv'
fileT = '../../Detectors/Tracer_CG/Tracer_1_CG_'+label+'_'+str(time-dayi)+'.csv'
print fileT
file1 = 'DivTr_'+label+'_'+str(time)
#
U = fio.read_Scalar(fileU,xn,yn,zn)
V = fio.read_Scalar(fileV,xn,yn,zn)
T = fio.read_Scalar(fileT,xn,yn,zn)
for k in range(1):
dU = np.asarray(np.gradient(U[:,:,k]))
dV = np.asarray(np.gradient(V[:,:,k]))
Div = dU[0,:,:]/dx + dV[1,:,:]/dy
fig = plt.figure(figsize=(8,8))
plt.contourf(Xlist/1000,Ylist/1000,Div,np.linspace(-0.0008,0.0008,30),extend='both',cmap=plt.cm.PiYG)
plt.colorbar(ticks=np.linspace(-0.0008,0.0008,5))
plt.contour(Xlist/1000,Ylist/1000,np.mean(T[:,:,:],2),10,colors='k')
plt.xlabel('X [km]',fontsize=18)
plt.ylabel('Y [km]',fontsize=18)
plt.axes().set_aspect('equal')
plt.xlim(0,2)
fd = open(filename,'wb') for p in range(pt): fd.write(str(x0[p])+', '+str(y0[p])+', '+str(-1.*z0[p])+', '+str(time[0])+'\n') import random for t in range(len(time)-1): print 'time:', time[t] file0 = path+'Velocity_CG_0_'+label+'_'+str(time[t])+'.csv' file1 = path+'Velocity_CG_1_'+label+'_'+str(time[t])+'.csv' file2 = path+'Velocity_CG_2_'+label+'_'+str(time[t])+'.csv' Ut0 = fio.read_Scalar(file0,xn,yn,zn) Vt0 = fio.read_Scalar(file1,xn,yn,zn) Wt0 = 0*Ut0 #-1.*fio.read_Scalar(file2,xn,yn,zn) #0*Ut0 file0 = path+'Velocity_CG_0_'+label+'_'+str(time[t+1])+'.csv' file1 = path+'Velocity_CG_1_'+label+'_'+str(time[t+1])+'.csv' file2 = path+'Velocity_CG_2_'+label+'_'+str(time[t+1])+'.csv' Ut1 = fio.read_Scalar(file0,xn,yn,zn) Vt1 = fio.read_Scalar(file1,xn,yn,zn) Wt1 = 0*Ut0 #-1.*fio.read_Scalar(file2,xn,yn,zn) #0*Ut0 # subcycling nt = 20 ds = 1.*dt / nt # for st in range(nt+1):
zn = len(Zlist)
time = range(dayi,dayf,days)
t = 0
for tt in time:
tlabel = str(tt)
while len(tlabel) < 3: tlabel = '0'+tlabel
#
file1 = './vts/'+basename+'_'+label+'_' + tlabel
print file1
var = np.zeros((xn,yn,zn))
x = np.zeros((xn,yn,zn))
y = np.zeros((xn,yn,zn))
z = np.zeros((xn,yn,zn))
#
Temp = fio.read_Scalar(path+'/'+basename+'_'+label+'_'+str(tt)+'.csv',xn,yn,zn)
#
for k in range(len(Zlist)):
for i in range(len(Xlist)):
for j in range(len(Ylist)):
var[i,j,k] = Temp[i,j,k]
x[i,j,k] = Xlist[i]
y[i,j,k] = Ylist[j]
z[i,j,k] = Zlist[k]
gridToVTK(file1, x,y,z, pointData = {basename : var})
print 'time:', time tlabel = str(timeT[time]) while len(tlabel) < 3: tlabel = '0'+tlabel #Temperature_CG_m_50_6e_9.csv fileU_B = path+'Velocity_CG_0_'+label_BV+'_'+str(timeT[time])+'.csv' #+480 fileV_B = path+'Velocity_CG_1_'+label_BV+'_'+str(timeT[time])+'.csv' #+480 fileT_B = '../RST/Tracer_CG/Tracer_1_CG_'+label_B+'_'+str(timeT[time])+'.csv' #-960 fileU_BW = path+'Velocity_CG_0_'+label_BWV+'_'+str(timeT[time])+'.csv' #+480 fileV_BW = path+'Velocity_CG_1_'+label_BWV+'_'+str(timeT[time])+'.csv' #+480 fileT_BW = '../RST/Tracer_CG/Tracer_1_CG_'+label_BW+'_'+str(timeT[time])+'.csv' #-960 print fileT_BW, fileT_B file1_B = 'DivTr_'+label_B+'_'+str(timeT[time]) file1_BW = 'DivTr_'+label_BW+'_'+str(timeT[time]) # U_B = fio.read_Scalar(fileU_B,xn_B,yn_B,zn) V_B = fio.read_Scalar(fileV_B,xn_B,yn_B,zn) T_B = fio.read_Scalar(fileT_B,xn_B,yn_B,zn) U_BW = fio.read_Scalar(fileU_BW,xn_BW,yn_BW,zn) V_BW = fio.read_Scalar(fileV_BW,xn_BW,yn_BW,zn) T_BW = fio.read_Scalar(fileT_BW,xn_BW,yn_BW,zn) # U_BW = U_B # V_BW = V_B # T_BW = T_B # for k in [0]: # print 'depth',Zlist[k] # dU_B = np.asarray(np.gradient(np.mean(U_B[:,:,2:3],2))) # dV_B = np.asarray(np.gradient(np.mean(V_B[:,:,2:3],2))) # Div_B = dU_B[0,:,:]/dx_B + dV_B[1,:,:]/dy_B # T_B = np.mean(T_B[:,:,2:3],2)
for d in days: T = [] W = [] DT= [] for t in day: print 'time:', t tlabel = str(t) while len(tlabel) < 3: tlabel = '0'+tlabel #Temperature_CG_m_50_6e_9.csv #fileT = '../RST/Temperature_CG/Temperature_CG_'+label+'_'+str(t+d*240+481)+'.csv' #fileW = path+'Velocity_CG_2_'+label+'_'+str(t+d*240+481)+'.csv' fileT = '../RST/Temperature_CG/Temperature_CG_'+label+'_'+str(t+d*60)+'.csv' fileW = path+'Velocity_CG_2_'+label+'_'+str(t+d*60)+'.csv' print fileT # Ttemp = fio.read_Scalar(fileT,xn,yn,zn)+274.5 T.append(Ttemp) W.append(fio.read_Scalar(fileW,xn,yn,zn)) DT.append(np.mean(np.mean(Ttemp[:,:,-1]-Ttemp[:,:,0],0),0)) T = np.asarray(T) W = np.asarray(W) DT= np.asarray(DT) T = np.mean(T,3) W = np.mean(W,3) DT= np.mean(DT) H = 50 Nu.append(1 + np.mean(np.mean(np.mean(W[:,:,:]*T[:,:,:],0),0),0)/DT*H/alpha) Ra.append(beta*DT*9.81*H**3/(alpha*nu))
U = np.zeros((xn_25,yn_25,zn,len(time))) V = np.zeros((xn_25,yn_25,zn,len(time))) W = np.zeros((xn_25,yn_25,zn,len(time))) for t in range(len(time)): print 'time:', time[t] tlabel = str(time[t]) while len(tlabel) < 3: tlabel = '0'+tlabel #Velocity_CG_m_50_6e_9.csv file0_U = path+'Velocity_CG_0_'+label_25+'_'+str(time[t])+'.csv' file0_V = path+'Velocity_CG_1_'+label_25+'_'+str(time[t])+'.csv' file0_W = path+'Velocity_CG_2_'+label_25+'_'+str(time[t])+'.csv' file1 = 'drate_'+label+'_'+str(time[t]) file1_25 = 'drate_'+label_25 U[:,:,:,t] = fio.read_Scalar(file0_U,xn_25,yn_25,zn) V[:,:,:,t] = fio.read_Scalar(file0_V,xn_25,yn_25,zn) W[:,:,:,t] = fio.read_Scalar(file0_W,xn_25,yn_25,zn) # remove mean mU = mU + U[:,:,:,t]*time1 mV = mV + V[:,:,:,t]*time1 mW = mW + W[:,:,:,t]*time1 for t in range(len(time)): print 'time:', time[t] Ua = U[:,:,:,t] - mU Va = V[:,:,:,t] - mV Wa = W[:,:,:,t] - mW
Q = Q_0 / (1027.0 * 4000.0) - 245.64 / (1027.0 * 4000.0)
return Q
for t in time:
if t * 360.0 / 3600.0 % 24 / 6 >= 3:
print "time:", t
tlabel = str(t)
while len(tlabel) < 3:
tlabel = "0" + tlabel
# Temperature_CG_m_50_6e_9.csv
fileT = "../RST/Temperature_CG/Temperature_CG_" + label + "_" + str(t) + ".csv"
fileW = path + "Velocity_CG_2_" + label + "_" + str(t) + ".csv"
print fileT
#
T = fio.read_Scalar(fileT, xn, yn, zn) + 274.5
# W = fio.read_Scalar(fileW,xn,yn,zn)
DT = -np.mean(np.mean(T[:, :, 0] - T[:, :, -1], 0), 0)
# Q0.append(-np.mean(np.mean(T[:,:,0]))*np.mean(np.mean(W[:,:,0])))
H = 50.0
Q1.append(-Q_surf(t)) # np.mean(np.mean(W[:,:,0]*T[:,:,0],0),0) # -1*Q_surf(t)
Nu.append(1 + Q1[-1] / DT * H / alpha)
Ra.append(beta * DT * 9.81 * H ** 3 / (alpha * nu))
# print 'Q0:', Q0[-1], 'Q1:', Q1[-1]
# for k in range(zn):
# DT = np.mean(np.mean(T[:,:,k]-T[:,:,0],0),0)
# H = Zlist[k]
# Q = -1*Q_surf(t)
# print Q
# Nu.append(1 + Q/DT*H/alpha)
Ylist = np.linspace(0,2000,161)
dl = [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]
Zlist = 1.*np.cumsum(dl)
xn = len(Xlist)
yn = len(Ylist)
zn = len(Zlist)
for t in range(0,len(time),10):
tlabel = str(t)
while len(tlabel) < 3: tlabel = '0'+tlabel
#
for z in [1]:
file0 = path+'Tracer_'+str(z)+'_CG_'+label+'_'+str(time[t])+'.csv'
#print file0
T = fio.read_Scalar(file0,xn,yn,zn)
#
v = np.linspace(0, 1, 30, endpoint=True)
vl = np.linspace(0, 1, 5, endpoint=True)
#
fig = plt.figure(figsize=(6,5))
plt.contourf(Ylist/1000,-1*Zlist,np.transpose(np.mean(T,0)),30,extend='both',cmap=plt.cm.PiYG)
plt.colorbar()
# plt.colorbar(ticks=vl)
plt.title(str(np.round(10*(time[t]*1200/3600.0+24*6))/10.0)+'h')
plt.ylabel('Z [m]',fontsize=16)
plt.xlabel('X [km]',fontsize=16)
plt.savefig('./plot/'+label+'/Tracer_'+str(z)+'_CG_'+label+'_'+str(time[t])+'_v.eps')
plt.close()
print './plot/'+label+'/Tracer_'+str(z)+'_CG_'+label+'_'+str(time[t])+'_v.eps'
yn_b = len(Ylist_b) zn = len(Zlist) for time in range(dayi,dayf,days): print 'time:', time tlabel = str(time) while len(tlabel) < 3: tlabel = '0'+tlabel #Temperature_CG_m_50_6e_9.csv fileU_w = path+'Velocity_CG_0_'+label_w+'_'+str(time)+'.csv' fileV_w = path+'Velocity_CG_1_'+label_w+'_'+str(time)+'.csv' fileU_b = path+'Velocity_CG_0_'+label_b+'_'+str(time)+'.csv' fileV_b = path+'Velocity_CG_1_'+label_b+'_'+str(time)+'.csv' file1 = 'Shear_v_'+label+'_'+str(time) # U_w = fio.read_Scalar(fileU_w,xn_w,yn_w,zn) V_w = fio.read_Scalar(fileV_w,xn_w,yn_w,zn) U_b = fio.read_Scalar(fileU_b,xn_b,yn_b,zn) V_b = fio.read_Scalar(fileV_b,xn_b,yn_b,zn) S_w = [] S_b = [] for i in range(xn_w): for j in range(yn_w): S_w.append((np.gradient(U_w[i,j,:])/np.gradient(Zlist))**2+(np.gradient(V_w[i,j,:])/np.gradient(Zlist))**2) for i in range(xn_b): for j in range(yn_b): S_b.append((np.gradient(U_b[i,j,:])/np.gradient(Zlist))**2+(np.gradient(V_b[i,j,:])/np.gradient(Zlist))**2)
#mld_25 = np.zeros(len(range(dayi,dayf,days))) nu_h = 0.05 nu_v = 0.0005 for t in range(len(time)): print 'time:', time[t] tlabel = str(time[t]) while len(tlabel) < 3: tlabel = '0'+tlabel #Velocity_CG_m_50_6e_9.csv file0_U = path+'Velocity_CG_0_'+label_25+'_'+str(time[t])+'.csv' file0_V = path+'Velocity_CG_1_'+label_25+'_'+str(time[t])+'.csv' file0_W = path+'Velocity_CG_2_'+label_25+'_'+str(time[t])+'.csv' file1 = 'drate_'+label+'_'+str(time[t]) file1_25 = 'drate_'+label_25 U_25 = fio.read_Scalar(file0_U,xn_25,yn_25,zn) V_25 = fio.read_Scalar(file0_V,xn_25,yn_25,zn) W_25 = fio.read_Scalar(file0_W,xn_25,yn_25,zn) for i in range(0,len(Xlist_25),50): for j in range(0,len(Ylist_25),50): #FW_25[j,i,:,t] = 0.5*nu_h*((np.gradient(U_25[i,j,:]-np.mean(U_25[i,j,:]))/dz_25)**2 + (np.gradient(V_25[i,j,:]-np.mean(V_25[i,j,:]))/dz_25)**2) + 0.5*nu_v*((np.gradient(W_25[i,j,:]-np.mean(W_25[i,j,:]))/dz_25)**2) FW_25[j,i,:,t] = 0.5*nu_h*((np.gradient(U_25[i,j,:])/dz_25)**2 + (np.gradient(V_25[i,j,:])/dz_25)**2) + 0.5*nu_v*(np.gradient(W_25[i,j,:])/dz_25)**2 FW_t25 = np.mean(np.mean(FW_25,0),0) # plt.figure(figsize=(4,8)) # p25, = plt.semilogx(7.5*0.05*FW_t25[:,t],Zlist,'k--',linewidth=2) FW_m = -11 FW_M = -7
xn_b = len(Xlist_b) yn_b = len(Ylist_b) zn = len(Zlist) for time in range(dayi,dayf,days): print 'time:', time tlabel = str(time) while len(tlabel) < 3: tlabel = '0'+tlabel #Temperature_CG_m_50_6e_9.csv fileT_w = path+'Temperature_CG_'+label_w+'_'+str(time)+'.csv' fileT_b = path+'Temperature_CG_'+label_b+'_'+str(time)+'.csv' file1 = 'T_v_'+label+'_'+str(time) file2 = 'T_vgrad_'+label+'_'+str(time) # T_w = fio.read_Scalar(fileT_w,xn_w,yn_w,zn) T_b = fio.read_Scalar(fileT_b,xn_b,yn_b,zn) Tm_w = [] Tm_b = [] Tm_w = np.mean(np.mean(T_w,0),0) Tm_b = np.mean(np.mean(T_b,0),0) Tg_b = np.gradient(Tm_b)/np.gradient(Zlist) Tg_w = np.gradient(Tm_w)/np.gradient(Zlist) Tmin = 19.2 Tmax = 20.2
dayf = 241
days = 1
basename = 'Velocity_CG_2_m_25_2b_particles'
#basename = 'drate_m_25_1b_particles'
#Xlist = np.linspace(0,10000,801)
#Ylist = np.linspace(0,4000,321)
Xlist = np.linspace(0,8000,641)
Ylist = np.linspace(0,8000,641)
dl = [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]
Zlist = 1.*np.cumsum(dl)
xn = yn = 641
zn = 36
timeD = range(dayi,dayf,days)
for t in timeD:
print 'read drate', t
# read
file_W = './Velocity_CG/'+basename+'_'+str(t)+'.csv'
W = fio.read_Scalar(file_W,xn,yn,zn)
Wz = np.mean(np.mean(W,0),0)
print Wz
f = open('./Velocity_CG/z/'+basename+'_'+str(t)+'_z.csv','w')
writer = csv.writer(f)
writer.writerow((list(Wz)))
f.close()
for time in range(dayi,dayf,days):
tlabel = str(time)
while len(tlabel) < 3: tlabel = '0'+tlabel
#
#
file0u = path+'Velocity_CG_0_'+label+'_'+str(time)+'.csv'
file0v = path+'Velocity_CG_1_'+label+'_'+str(time)+'.csv'
file0w = path+'Velocity_CG_2_'+label+'_'+str(time)+'.csv'
file1 = 'Velocity_CG_'+label+'_'+str(time)
print file1
#
u = fio.read_Scalar(file0u,xn,yn,zn)
v = fio.read_Scalar(file0v,xn,yn,zn)
w = fio.read_Scalar(file0w,xn,yn,zn)
#
dt = 1 # s
mu = np.mean(np.mean(u,0),0)
mv = np.mean(np.mean(v,0),0)
mw = np.mean(np.mean(w,0),0)
dx = mu*dt
dy = mv*dt
dz = mw*dt
fd = open('./UVW_'+file1+'.csv','w')
fd.write('x, y, z, u, v, w \n')
for z in range(len(Zlist)):
fd.write('0, 0, '+str(Zlist[z])+', '+str(mu[z])+', '+str(mv[z])+', '+str(mw[z])+'\n')
